{"ts": "2026-07-15T00:48:42.299860+00:00", "type": "system_prompt", "text": "You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\n\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\n\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\n\n## Harness Instructions\n\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. When you are finished, reply with the single word DONE and no tool calls."}
{"ts": "2026-07-15T00:48:42.300864+00:00", "type": "user", "text": "## Task\n\nComplete the following Blender task:\n\n<task>\n\nA red sphere of 1 meter diameter resting on a white ground plane — touching it\nexactly, neither floating nor sunk. Smooth shading, a plain red material on the\nball, plain white on the floor.\n\nThe floor is the sphere, the color, and the contact. The ceiling is presentation:\na soft believable shadow under the ball, a hint of glossiness that catches the\nlight, and clean framing.\n\n</task>"}
{"ts": "2026-07-15T00:48:46.675675+00:00", "type": "llm_call", "call_index": 1, "provider_type": "blenderbench.providers.openrouter.OpenRouterProvider", "started_at": "2026-07-15T00:48:42.300864+00:00", "finished_at": "2026-07-15T00:48:46.675171+00:00", "duration_s": 4.375, "request": {"messages": [{"role": "system", "content": "You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\n\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\n\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\n\n## Harness Instructions\n\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. When you are finished, reply with the single word DONE and no tool calls."}, {"role": "user", "content": "## Task\n\nComplete the following Blender task:\n\n<task>\n\nA red sphere of 1 meter diameter resting on a white ground plane — touching it\nexactly, neither floating nor sunk. Smooth shading, a plain red material on the\nball, plain white on the floor.\n\nThe floor is the sphere, the color, and the contact. The ceiling is presentation:\na soft believable shadow under the ball, a hint of glossiness that catches the\nlight, and clean framing.\n\n</task>"}], "tools": [{"type": "function", "function": {"name": "get_scene_info", "description": "Get detailed information about the current Blender scene\n\n    Parameters:\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\n    ", "parameters": {"properties": {"user_prompt": {"title": "User Prompt", "type": "string"}}, "required": ["user_prompt"], "title": "get_scene_infoArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_object_info", "description": "\n    Get detailed information about a specific object in the Blender scene.\n\n    Parameters:\n    - object_name: The name of the object to get information about\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"object_name": {"title": "Object Name", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["object_name"], "title": "get_object_infoArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_viewport_screenshot", "description": "\n    Capture a screenshot of the current Blender 3D viewport.\n\n    Parameters:\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns the screenshot as an Image.\n    ", "parameters": {"properties": {"max_size": {"default": 1000, "title": "Max Size", "type": "integer"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_viewport_screenshotArguments", "type": "object"}}}, {"type": "function", "function": {"name": "execute_blender_code", "description": "\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\n\n    Parameters:\n    - code: The Python code to execute\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"code": {"title": "Code", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["code"], "title": "execute_blender_codeArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_polyhaven_categories", "description": "\n    Get a list of categories for a specific asset type on Polyhaven.\n\n    Parameters:\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"asset_type": {"default": "hdris", "title": "Asset Type", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_polyhaven_categoriesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_polyhaven_assets", "description": "\n    Search for assets on Polyhaven with optional filtering.\n\n    Parameters:\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\n    - categories: Optional comma-separated list of categories to filter by\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a list of matching assets with basic information.\n    ", "parameters": {"properties": {"asset_type": {"default": "all", "title": "Asset Type", "type": "string"}, "categories": {"default": null, "title": "Categories", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "search_polyhaven_assetsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "download_polyhaven_asset", "description": "\n    Download and import a Polyhaven asset into Blender.\n\n    Parameters:\n    - asset_id: The ID of the asset to download\n    - asset_type: The type of asset (hdris, textures, models)\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"asset_id": {"title": "Asset Id", "type": "string"}, "asset_type": {"title": "Asset Type", "type": "string"}, "resolution": {"default": "1k", "title": "Resolution", "type": "string"}, "file_format": {"default": null, "title": "File Format", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["asset_id", "asset_type"], "title": "download_polyhaven_assetArguments", "type": "object"}}}, {"type": "function", "function": {"name": "set_texture", "description": "\n    Apply a previously downloaded Polyhaven texture to an object.\n    \n    Parameters:\n    - object_name: Name of the object to apply the texture to\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\n    \n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"object_name": {"title": "Object Name", "type": "string"}, "texture_id": {"title": "Texture Id", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["object_name", "texture_id"], "title": "set_textureArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_polyhaven_status", "description": "\n    Check if PolyHaven integration is enabled in Blender.\n    Returns a message indicating whether PolyHaven features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_polyhaven_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_hyper3d_status", "description": "\n    Check if Hyper3D Rodin integration is enabled in Blender.\n    Returns a message indicating whether Hyper3D Rodin features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_hyper3d_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_sketchfab_status", "description": "\n    Check if Sketchfab integration is enabled in Blender.\n    Returns a message indicating whether Sketchfab features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_sketchfab_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_sketchfab_models", "description": "\n    Search for models on Sketchfab with optional filtering.\n\n    Parameters:\n    - query: Text to search for\n    - categories: Optional comma-separated list of categories\n    - count: Maximum number of results to return (default 20)\n    - downloadable: Whether to include only downloadable models (default True)\n\n    Returns a formatted list of matching models.\n    ", "parameters": {"properties": {"query": {"title": "Query", "type": "string"}, "categories": {"default": null, "title": "Categories", "type": "string"}, "count": {"default": 20, "title": "Count", "type": "integer"}, "downloadable": {"default": true, "title": "Downloadable", "type": "boolean"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["query"], "title": "search_sketchfab_modelsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_sketchfab_model_preview", "description": "\n    Get a preview thumbnail of a Sketchfab model by its UID.\n    Use this to visually confirm a model before downloading.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\n    \n    Returns the model's thumbnail as an Image for visual confirmation.\n    ", "parameters": {"properties": {"uid": {"title": "Uid", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["uid"], "title": "get_sketchfab_model_previewArguments", "type": "object"}}}, {"type": "function", "function": {"name": "download_sketchfab_model", "description": "\n    Download and import a Sketchfab model by its UID.\n    The model will be scaled so its largest dimension equals target_size.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\n                  You must specify the desired size for the model.\n                  Examples:\n                  - Chair: target_size=1.0 (1 meter tall)\n                  - Table: target_size=0.75 (75cm tall)\n                  - Car: target_size=4.5 (4.5 meters long)\n                  - Person: target_size=1.7 (1.7 meters tall)\n                  - Small object (cup, phone): target_size=0.1 to 0.3\n    \n    Returns a message with import details including object names, dimensions, and bounding box.\n    The model must be downloadable and you must have proper access rights.\n    ", "parameters": {"properties": {"uid": {"title": "Uid", "type": "string"}, "target_size": {"title": "Target Size", "type": "number"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["uid", "target_size"], "title": "download_sketchfab_modelArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hyper3d_model_via_text", "description": "\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n\n    Parameters:\n    - text_prompt: A short description of the desired model in **English**.\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"text_prompt": {"title": "Text Prompt", "type": "string"}, "bbox_condition": {"default": null, "items": {"type": "number"}, "title": "Bbox Condition", "type": "array"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["text_prompt"], "title": "generate_hyper3d_model_via_textArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hyper3d_model_via_images", "description": "\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n    \n    Parameters:\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode.\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"input_image_paths": {"default": null, "items": {"type": "string"}, "title": "Input Image Paths", "type": "array"}, "input_image_urls": {"default": null, "items": {"type": "string"}, "title": "Input Image Urls", "type": "array"}, "bbox_condition": {"default": null, "items": {"type": "number"}, "title": "Bbox Condition", "type": "array"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "generate_hyper3d_model_via_imagesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "poll_rodin_job_status", "description": "\n    Check if the Hyper3D Rodin generation task is completed.\n\n    For Hyper3D Rodin mode MAIN_SITE:\n        Parameters:\n        - subscription_key: The subscription_key given in the generate model step.\n\n        Returns a list of status. The task is done if all status are \"Done\".\n        If \"Failed\" showed up, the generating process failed.\n        This is a polling API, so only proceed if the status are finally determined (\"Done\" or \"Canceled\").\n\n    For Hyper3D Rodin mode FAL_AI:\n        Parameters:\n        - request_id: The request_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"COMPLETED\".\n        The task is in progress if status is \"IN_PROGRESS\".\n        If status other than \"COMPLETED\", \"IN_PROGRESS\", \"IN_QUEUE\" showed up, the generating process might be failed.\n        This is a polling API, so only proceed if the status are finally determined (\"COMPLETED\" or some failed state).\n    ", "parameters": {"properties": {"subscription_key": {"default": null, "title": "Subscription Key", "type": "string"}, "request_id": {"default": null, "title": "Request Id", "type": "string"}}, "title": "poll_rodin_job_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "import_generated_asset", "description": "\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\n\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\n    Return if the asset has been imported successfully.\n    ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "task_uuid": {"default": null, "title": "Task Uuid", "type": "string"}, "request_id": {"default": null, "title": "Request Id", "type": "string"}}, "required": ["name"], "title": "import_generated_assetArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_hunyuan3d_status", "description": "\n    Check if Hunyuan3D integration is enabled in Blender.\n    Returns a message indicating whether Hunyuan3D features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_hunyuan3d_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hunyuan3d_model", "description": "\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \n    or both for the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    \n    Parameters:\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\n    - input_image_url: (Optional) The local or remote url of the input image. Accepts None if only using text prompt.\n\n    Returns: \n    - When successful, returns a JSON with job_id (format: \"job_xxx\") indicating the task is in progress\n    - When the job completes, the status will change to \"DONE\" indicating the model has been imported\n    - Returns error message if the operation fails\n    ", "parameters": {"properties": {"text_prompt": {"default": null, "title": "Text Prompt", "type": "string"}, "input_image_url": {"default": null, "title": "Input Image Url", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "generate_hunyuan3d_modelArguments", "type": "object"}}}, {"type": "function", "function": {"name": "poll_hunyuan_job_status", "description": "\n    Check if the Hunyuan3D generation task is completed.\n\n    For Hunyuan3D:\n        Parameters:\n        - job_id: The job_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"DONE\".\n        The task is in progress if status is \"RUN\".\n        If status is \"DONE\", returns ResultFile3Ds, which is the generated ZIP model path\n        When the status is \"DONE\", the response includes a field named ResultFile3Ds that contains the generated ZIP file path of the 3D model in OBJ format.\n        This is a polling API, so only proceed if the status are finally determined (\"DONE\" or some failed state).\n    ", "parameters": {"properties": {"job_id": {"default": null, "title": "Job Id", "type": "string"}}, "title": "poll_hunyuan_job_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "import_generated_asset_hunyuan", "description": "\n    Import the asset generated by Hunyuan3D after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - zip_file_url: The zip_file_url given in the generate model step.\n\n    Return if the asset has been imported successfully.\n    ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "zip_file_url": {"title": "Zip File Url", "type": "string"}}, "required": ["name", "zip_file_url"], "title": "import_generated_asset_hunyuanArguments", "type": "object"}}}]}, "provider_attempts": [{"provider": "openrouter", "attempt": 1, "started_at": "2026-07-15T00:48:42.300864+00:00", "finished_at": "2026-07-15T00:48:46.675171+00:00", "duration_s": 4.375, "request": {"method": "POST", "url": "https://openrouter.ai/api/v1/chat/completions", "headers": {"host": "openrouter.ai", "accept": "*/*", "accept-encoding": "gzip, deflate", "connection": "keep-alive", "user-agent": "python-httpx/0.28.1", "authorization": "[REDACTED]", "content-length": "18646", "content-type": "application/json"}, "body": "{\"model\":\"anthropic/claude-haiku-4.5\",\"messages\":[{\"role\":\"system\",\"content\":[{\"type\":\"text\",\"text\":\"You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\\n\\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\\n\\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\\n\\n## Harness Instructions\\n\\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. When you are finished, reply with the single word DONE and no tool calls.\",\"cache_control\":{\"type\":\"ephemeral\"}}]},{\"role\":\"user\",\"content\":[{\"type\":\"text\",\"text\":\"## Task\\n\\nComplete the following Blender task:\\n\\n<task>\\n\\nA red sphere of 1 meter diameter resting on a white ground plane — touching it\\nexactly, neither floating nor sunk. Smooth shading, a plain red material on the\\nball, plain white on the floor.\\n\\nThe floor is the sphere, the color, and the contact. The ceiling is presentation:\\na soft believable shadow under the ball, a hint of glossiness that catches the\\nlight, and clean framing.\\n\\n</task>\",\"cache_control\":{\"type\":\"ephemeral\"}}]}],\"usage\":{\"include\":true},\"tools\":[{\"type\":\"function\",\"function\":{\"name\":\"get_scene_info\",\"description\":\"Get detailed information about the current Blender scene\\n\\n    Parameters:\\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"user_prompt\"],\"title\":\"get_scene_infoArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_object_info\",\"description\":\"\\n    Get detailed information about a specific object in the Blender scene.\\n\\n    Parameters:\\n    - object_name: The name of the object to get information about\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"object_name\":{\"title\":\"Object Name\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"object_name\"],\"title\":\"get_object_infoArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_viewport_screenshot\",\"description\":\"\\n    Capture a screenshot of the current Blender 3D viewport.\\n\\n    Parameters:\\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns the screenshot as an Image.\\n    \",\"parameters\":{\"properties\":{\"max_size\":{\"default\":1000,\"title\":\"Max Size\",\"type\":\"integer\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_viewport_screenshotArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"description\":\"\\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\\n\\n    Parameters:\\n    - code: The Python code to execute\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"code\":{\"title\":\"Code\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"code\"],\"title\":\"execute_blender_codeArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_categories\",\"description\":\"\\n    Get a list of categories for a specific asset type on Polyhaven.\\n\\n    Parameters:\\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"asset_type\":{\"default\":\"hdris\",\"title\":\"Asset Type\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_polyhaven_categoriesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_polyhaven_assets\",\"description\":\"\\n    Search for assets on Polyhaven with optional filtering.\\n\\n    Parameters:\\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\\n    - categories: Optional comma-separated list of categories to filter by\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns a list of matching assets with basic information.\\n    \",\"parameters\":{\"properties\":{\"asset_type\":{\"default\":\"all\",\"title\":\"Asset Type\",\"type\":\"string\"},\"categories\":{\"default\":null,\"title\":\"Categories\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"search_polyhaven_assetsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"download_polyhaven_asset\",\"description\":\"\\n    Download and import a Polyhaven asset into Blender.\\n\\n    Parameters:\\n    - asset_id: The ID of the asset to download\\n    - asset_type: The type of asset (hdris, textures, models)\\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"asset_id\":{\"title\":\"Asset Id\",\"type\":\"string\"},\"asset_type\":{\"title\":\"Asset Type\",\"type\":\"string\"},\"resolution\":{\"default\":\"1k\",\"title\":\"Resolution\",\"type\":\"string\"},\"file_format\":{\"default\":null,\"title\":\"File Format\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"asset_id\",\"asset_type\"],\"title\":\"download_polyhaven_assetArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"set_texture\",\"description\":\"\\n    Apply a previously downloaded Polyhaven texture to an object.\\n    \\n    Parameters:\\n    - object_name: Name of the object to apply the texture to\\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\\n    \\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"object_name\":{\"title\":\"Object Name\",\"type\":\"string\"},\"texture_id\":{\"title\":\"Texture Id\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"object_name\",\"texture_id\"],\"title\":\"set_textureArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_status\",\"description\":\"\\n    Check if PolyHaven integration is enabled in Blender.\\n    Returns a message indicating whether PolyHaven features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_polyhaven_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_hyper3d_status\",\"description\":\"\\n    Check if Hyper3D Rodin integration is enabled in Blender.\\n    Returns a message indicating whether Hyper3D Rodin features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_hyper3d_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_status\",\"description\":\"\\n    Check if Sketchfab integration is enabled in Blender.\\n    Returns a message indicating whether Sketchfab features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_sketchfab_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_sketchfab_models\",\"description\":\"\\n    Search for models on Sketchfab with optional filtering.\\n\\n    Parameters:\\n    - query: Text to search for\\n    - categories: Optional comma-separated list of categories\\n    - count: Maximum number of results to return (default 20)\\n    - downloadable: Whether to include only downloadable models (default True)\\n\\n    Returns a formatted list of matching models.\\n    \",\"parameters\":{\"properties\":{\"query\":{\"title\":\"Query\",\"type\":\"string\"},\"categories\":{\"default\":null,\"title\":\"Categories\",\"type\":\"string\"},\"count\":{\"default\":20,\"title\":\"Count\",\"type\":\"integer\"},\"downloadable\":{\"default\":true,\"title\":\"Downloadable\",\"type\":\"boolean\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"query\"],\"title\":\"search_sketchfab_modelsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_model_preview\",\"description\":\"\\n    Get a preview thumbnail of a Sketchfab model by its UID.\\n    Use this to visually confirm a model before downloading.\\n    \\n    Parameters:\\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\\n    \\n    Returns the model's thumbnail as an Image for visual confirmation.\\n    \",\"parameters\":{\"properties\":{\"uid\":{\"title\":\"Uid\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"uid\"],\"title\":\"get_sketchfab_model_previewArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"download_sketchfab_model\",\"description\":\"\\n    Download and import a Sketchfab model by its UID.\\n    The model will be scaled so its largest dimension equals target_size.\\n    \\n    Parameters:\\n    - uid: The unique identifier of the Sketchfab model\\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\\n                  You must specify the desired size for the model.\\n                  Examples:\\n                  - Chair: target_size=1.0 (1 meter tall)\\n                  - Table: target_size=0.75 (75cm tall)\\n                  - Car: target_size=4.5 (4.5 meters long)\\n                  - Person: target_size=1.7 (1.7 meters tall)\\n                  - Small object (cup, phone): target_size=0.1 to 0.3\\n    \\n    Returns a message with import details including object names, dimensions, and bounding box.\\n    The model must be downloadable and you must have proper access rights.\\n    \",\"parameters\":{\"properties\":{\"uid\":{\"title\":\"Uid\",\"type\":\"string\"},\"target_size\":{\"title\":\"Target Size\",\"type\":\"number\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"uid\",\"target_size\"],\"title\":\"download_sketchfab_modelArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hyper3d_model_via_text\",\"description\":\"\\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\\n    The 3D asset has built-in materials.\\n    The generated model has a normalized size, so re-scaling after generation can be useful.\\n\\n    Parameters:\\n    - text_prompt: A short description of the desired model in **English**.\\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\\n\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"text_prompt\":{\"title\":\"Text Prompt\",\"type\":\"string\"},\"bbox_condition\":{\"default\":null,\"items\":{\"type\":\"number\"},\"title\":\"Bbox Condition\",\"type\":\"array\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"text_prompt\"],\"title\":\"generate_hyper3d_model_via_textArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hyper3d_model_via_images\",\"description\":\"\\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\\n    The 3D asset has built-in materials.\\n    The generated model has a normalized size, so re-scaling after generation can be useful.\\n    \\n    Parameters:\\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model.\\n\\n    Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode.\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"input_image_paths\":{\"default\":null,\"items\":{\"type\":\"string\"},\"title\":\"Input Image Paths\",\"type\":\"array\"},\"input_image_urls\":{\"default\":null,\"items\":{\"type\":\"string\"},\"title\":\"Input Image Urls\",\"type\":\"array\"},\"bbox_condition\":{\"default\":null,\"items\":{\"type\":\"number\"},\"title\":\"Bbox Condition\",\"type\":\"array\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"generate_hyper3d_model_via_imagesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"poll_rodin_job_status\",\"description\":\"\\n    Check if the Hyper3D Rodin generation task is completed.\\n\\n    For Hyper3D Rodin mode MAIN_SITE:\\n        Parameters:\\n        - subscription_key: The subscription_key given in the generate model step.\\n\\n        Returns a list of status. The task is done if all status are \\\"Done\\\".\\n        If \\\"Failed\\\" showed up, the generating process failed.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"Done\\\" or \\\"Canceled\\\").\\n\\n    For Hyper3D Rodin mode FAL_AI:\\n        Parameters:\\n        - request_id: The request_id given in the generate model step.\\n\\n        Returns the generation task status. The task is done if status is \\\"COMPLETED\\\".\\n        The task is in progress if status is \\\"IN_PROGRESS\\\".\\n        If status other than \\\"COMPLETED\\\", \\\"IN_PROGRESS\\\", \\\"IN_QUEUE\\\" showed up, the generating process might be failed.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"COMPLETED\\\" or some failed state).\\n    \",\"parameters\":{\"properties\":{\"subscription_key\":{\"default\":null,\"title\":\"Subscription Key\",\"type\":\"string\"},\"request_id\":{\"default\":null,\"title\":\"Request Id\",\"type\":\"string\"}},\"title\":\"poll_rodin_job_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"import_generated_asset\",\"description\":\"\\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\\n\\n    Parameters:\\n    - name: The name of the object in scene\\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\\n\\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\\n    Return if the asset has been imported successfully.\\n    \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"task_uuid\":{\"default\":null,\"title\":\"Task Uuid\",\"type\":\"string\"},\"request_id\":{\"default\":null,\"title\":\"Request Id\",\"type\":\"string\"}},\"required\":[\"name\"],\"title\":\"import_generated_assetArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_hunyuan3d_status\",\"description\":\"\\n    Check if Hunyuan3D integration is enabled in Blender.\\n    Returns a message indicating whether Hunyuan3D features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_hunyuan3d_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hunyuan3d_model\",\"description\":\"\\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \\n    or both for the desired asset, and import the asset into Blender.\\n    The 3D asset has built-in materials.\\n    \\n    Parameters:\\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\\n    - input_image_url: (Optional) The local or remote url of the input image. Accepts None if only using text prompt.\\n\\n    Returns: \\n    - When successful, returns a JSON with job_id (format: \\\"job_xxx\\\") indicating the task is in progress\\n    - When the job completes, the status will change to \\\"DONE\\\" indicating the model has been imported\\n    - Returns error message if the operation fails\\n    \",\"parameters\":{\"properties\":{\"text_prompt\":{\"default\":null,\"title\":\"Text Prompt\",\"type\":\"string\"},\"input_image_url\":{\"default\":null,\"title\":\"Input Image Url\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"generate_hunyuan3d_modelArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"poll_hunyuan_job_status\",\"description\":\"\\n    Check if the Hunyuan3D generation task is completed.\\n\\n    For Hunyuan3D:\\n        Parameters:\\n        - job_id: The job_id given in the generate model step.\\n\\n        Returns the generation task status. The task is done if status is \\\"DONE\\\".\\n        The task is in progress if status is \\\"RUN\\\".\\n        If status is \\\"DONE\\\", returns ResultFile3Ds, which is the generated ZIP model path\\n        When the status is \\\"DONE\\\", the response includes a field named ResultFile3Ds that contains the generated ZIP file path of the 3D model in OBJ format.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"DONE\\\" or some failed state).\\n    \",\"parameters\":{\"properties\":{\"job_id\":{\"default\":null,\"title\":\"Job Id\",\"type\":\"string\"}},\"title\":\"poll_hunyuan_job_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"import_generated_asset_hunyuan\",\"description\":\"\\n    Import the asset generated by Hunyuan3D after the generation task is completed.\\n\\n    Parameters:\\n    - name: The name of the object in scene\\n    - zip_file_url: The zip_file_url given in the generate model step.\\n\\n    Return if the asset has been imported successfully.\\n    \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"zip_file_url\":{\"title\":\"Zip File Url\",\"type\":\"string\"}},\"required\":[\"name\",\"zip_file_url\"],\"title\":\"import_generated_asset_hunyuanArguments\",\"type\":\"object\"}}}],\"reasoning\":{\"effort\":\"xhigh\"}}", "body_base64": "{"model":"anthropic/claude-haiku-4.5","messages":[{"role":"system","content":[{"type":"text","text":"You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\n\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\n\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\n\n## Harness Instructions\n\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. When you are finished, reply with the single word DONE and no tool calls.","cache_control":{"type":"ephemeral"}}]},{"role":"user","content":[{"type":"text","text":"## Task\n\nComplete the following Blender task:\n\n<task>\n\nA red sphere of 1 meter diameter resting on a white ground plane — touching it\nexactly, neither floating nor sunk. Smooth shading, a plain red material on the\nball, plain white on the floor.\n\nThe floor is the sphere, the color, and the contact. The ceiling is presentation:\na soft believable shadow under the ball, a hint of glossiness that catches the\nlight, and clean framing.\n\n</task>","cache_control":{"type":"ephemeral"}}]}],"usage":{"include":true},"tools":[{"type":"function","function":{"name":"get_scene_info","description":"Get detailed information about the current Blender scene\n\n    Parameters:\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\n    ","parameters":{"properties":{"user_prompt":{"title":"User Prompt","type":"string"}},"required":["user_prompt"],"title":"get_scene_infoArguments","type":"object"}}},{"type":"function","function":{"name":"get_object_info","description":"\n    Get detailed information about a specific object in the Blender scene.\n\n    Parameters:\n    - object_name: The name of the object to get information about\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"object_name":{"title":"Object Name","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["object_name"],"title":"get_object_infoArguments","type":"object"}}},{"type":"function","function":{"name":"get_viewport_screenshot","description":"\n    Capture a screenshot of the current Blender 3D viewport.\n\n    Parameters:\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns the screenshot as an Image.\n    ","parameters":{"properties":{"max_size":{"default":1000,"title":"Max Size","type":"integer"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_viewport_screenshotArguments","type":"object"}}},{"type":"function","function":{"name":"execute_blender_code","description":"\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\n\n    Parameters:\n    - code: The Python code to execute\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"code":{"title":"Code","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["code"],"title":"execute_blender_codeArguments","type":"object"}}},{"type":"function","function":{"name":"get_polyhaven_categories","description":"\n    Get a list of categories for a specific asset type on Polyhaven.\n\n    Parameters:\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"asset_type":{"default":"hdris","title":"Asset Type","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_polyhaven_categoriesArguments","type":"object"}}},{"type":"function","function":{"name":"search_polyhaven_assets","description":"\n    Search for assets on Polyhaven with optional filtering.\n\n    Parameters:\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\n    - categories: Optional comma-separated list of categories to filter by\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a list of matching assets with basic information.\n    ","parameters":{"properties":{"asset_type":{"default":"all","title":"Asset Type","type":"string"},"categories":{"default":null,"title":"Categories","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"search_polyhaven_assetsArguments","type":"object"}}},{"type":"function","function":{"name":"download_polyhaven_asset","description":"\n    Download and import a Polyhaven asset into Blender.\n\n    Parameters:\n    - asset_id: The ID of the asset to download\n    - asset_type: The type of asset (hdris, textures, models)\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a message indicating success or failure.\n    ","parameters":{"properties":{"asset_id":{"title":"Asset Id","type":"string"},"asset_type":{"title":"Asset Type","type":"string"},"resolution":{"default":"1k","title":"Resolution","type":"string"},"file_format":{"default":null,"title":"File Format","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["asset_id","asset_type"],"title":"download_polyhaven_assetArguments","type":"object"}}},{"type":"function","function":{"name":"set_texture","description":"\n    Apply a previously downloaded Polyhaven texture to an object.\n    \n    Parameters:\n    - object_name: Name of the object to apply the texture to\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\n    \n    Returns a message indicating success or failure.\n    ","parameters":{"properties":{"object_name":{"title":"Object Name","type":"string"},"texture_id":{"title":"Texture Id","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["object_name","texture_id"],"title":"set_textureArguments","type":"object"}}},{"type":"function","function":{"name":"get_polyhaven_status","description":"\n    Check if PolyHaven integration is enabled in Blender.\n    Returns a message indicating whether PolyHaven features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_polyhaven_statusArguments","type":"object"}}},{"type":"function","function":{"name":"get_hyper3d_status","description":"\n    Check if Hyper3D Rodin integration is enabled in Blender.\n    Returns a message indicating whether Hyper3D Rodin features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_hyper3d_statusArguments","type":"object"}}},{"type":"function","function":{"name":"get_sketchfab_status","description":"\n    Check if Sketchfab integration is enabled in Blender.\n    Returns a message indicating whether Sketchfab features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_sketchfab_statusArguments","type":"object"}}},{"type":"function","function":{"name":"search_sketchfab_models","description":"\n    Search for models on Sketchfab with optional filtering.\n\n    Parameters:\n    - query: Text to search for\n    - categories: Optional comma-separated list of categories\n    - count: Maximum number of results to return (default 20)\n    - downloadable: Whether to include only downloadable models (default True)\n\n    Returns a formatted list of matching models.\n    ","parameters":{"properties":{"query":{"title":"Query","type":"string"},"categories":{"default":null,"title":"Categories","type":"string"},"count":{"default":20,"title":"Count","type":"integer"},"downloadable":{"default":true,"title":"Downloadable","type":"boolean"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["query"],"title":"search_sketchfab_modelsArguments","type":"object"}}},{"type":"function","function":{"name":"get_sketchfab_model_preview","description":"\n    Get a preview thumbnail of a Sketchfab model by its UID.\n    Use this to visually confirm a model before downloading.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\n    \n    Returns the model's thumbnail as an Image for visual confirmation.\n    ","parameters":{"properties":{"uid":{"title":"Uid","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["uid"],"title":"get_sketchfab_model_previewArguments","type":"object"}}},{"type":"function","function":{"name":"download_sketchfab_model","description":"\n    Download and import a Sketchfab model by its UID.\n    The model will be scaled so its largest dimension equals target_size.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model\n    - target_size: REQUIRED. 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{"ts": "2026-07-15T00:48:46.675675+00:00", "type": "assistant", "text": "I'll create a red sphere resting on a white ground plane with proper materials, shading, lighting, and camera setup. Let me work through this step by step.", "tool_calls": [{"id": "toolu_bdrk_01McQwtp5Bq6813NVu2gUpRG", "name": "get_scene_info", "arguments": {"user_prompt": "Get current scene info to see what's there initially"}, "raw_arguments": "{\"user_prompt\": \"Get current scene info to see what's there initially\"}", "parse_error": null}]}
{"ts": "2026-07-15T00:48:46.675675+00:00", "type": "tool_call", "id": "toolu_bdrk_01McQwtp5Bq6813NVu2gUpRG", "name": "get_scene_info", "arguments": {"user_prompt": "Get current scene info to see what's there initially"}, "raw_arguments": "{\"user_prompt\": \"Get current scene info to see what's there initially\"}"}
{"ts": "2026-07-15T00:48:46.705781+00:00", "type": "tool_result", "tool_call_id": "toolu_bdrk_01McQwtp5Bq6813NVu2gUpRG", "name": "get_scene_info", "text": "{\n  \"name\": \"Scene\",\n  \"object_count\": 0,\n  \"objects\": [],\n  \"materials_count\": 2\n}", "is_error": false, "has_image": false, "image_b64": null}
{"ts": "2026-07-15T00:48:50.172191+00:00", "type": "llm_call", "call_index": 2, "provider_type": "blenderbench.providers.openrouter.OpenRouterProvider", "started_at": "2026-07-15T00:48:46.707287+00:00", "finished_at": "2026-07-15T00:48:50.170684+00:00", "duration_s": 3.4689999999827705, "request": {"messages": [{"role": "system", "content": "You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\n\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\n\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\n\n## Harness Instructions\n\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. When you are finished, reply with the single word DONE and no tool calls."}, {"role": "user", "content": "## Task\n\nComplete the following Blender task:\n\n<task>\n\nA red sphere of 1 meter diameter resting on a white ground plane — touching it\nexactly, neither floating nor sunk. Smooth shading, a plain red material on the\nball, plain white on the floor.\n\nThe floor is the sphere, the color, and the contact. The ceiling is presentation:\na soft believable shadow under the ball, a hint of glossiness that catches the\nlight, and clean framing.\n\n</task>"}, {"role": "assistant", "content": "I'll create a red sphere resting on a white ground plane with proper materials, shading, lighting, and camera setup. Let me work through this step by step.", "tool_calls": [{"id": "toolu_bdrk_01McQwtp5Bq6813NVu2gUpRG", "type": "function", "function": {"name": "get_scene_info", "arguments": "{\"user_prompt\": \"Get current scene info to see what's there initially\"}"}}]}, {"role": "tool", "tool_call_id": "toolu_bdrk_01McQwtp5Bq6813NVu2gUpRG", "content": "{\n  \"name\": \"Scene\",\n  \"object_count\": 0,\n  \"objects\": [],\n  \"materials_count\": 2\n}"}], "tools": [{"type": "function", "function": {"name": "get_scene_info", "description": "Get detailed information about the current Blender scene\n\n    Parameters:\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\n    ", "parameters": {"properties": {"user_prompt": {"title": "User Prompt", "type": "string"}}, "required": ["user_prompt"], "title": "get_scene_infoArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_object_info", "description": "\n    Get detailed information about a specific object in the Blender scene.\n\n    Parameters:\n    - object_name: The name of the object to get information about\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"object_name": {"title": "Object Name", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["object_name"], "title": "get_object_infoArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_viewport_screenshot", "description": "\n    Capture a screenshot of the current Blender 3D viewport.\n\n    Parameters:\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns the screenshot as an Image.\n    ", "parameters": {"properties": {"max_size": {"default": 1000, "title": "Max Size", "type": "integer"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_viewport_screenshotArguments", "type": "object"}}}, {"type": "function", "function": {"name": "execute_blender_code", "description": "\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\n\n    Parameters:\n    - code: The Python code to execute\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"code": {"title": "Code", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["code"], "title": "execute_blender_codeArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_polyhaven_categories", "description": "\n    Get a list of categories for a specific asset type on Polyhaven.\n\n    Parameters:\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"asset_type": {"default": "hdris", "title": "Asset Type", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_polyhaven_categoriesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_polyhaven_assets", "description": "\n    Search for assets on Polyhaven with optional filtering.\n\n    Parameters:\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\n    - categories: Optional comma-separated list of categories to filter by\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a list of matching assets with basic information.\n    ", "parameters": {"properties": {"asset_type": {"default": "all", "title": "Asset Type", "type": "string"}, "categories": {"default": null, "title": "Categories", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "search_polyhaven_assetsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "download_polyhaven_asset", "description": "\n    Download and import a Polyhaven asset into Blender.\n\n    Parameters:\n    - asset_id: The ID of the asset to download\n    - asset_type: The type of asset (hdris, textures, models)\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"asset_id": {"title": "Asset Id", "type": "string"}, "asset_type": {"title": "Asset Type", "type": "string"}, "resolution": {"default": "1k", "title": "Resolution", "type": "string"}, "file_format": {"default": null, "title": "File Format", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["asset_id", "asset_type"], "title": "download_polyhaven_assetArguments", "type": "object"}}}, {"type": "function", "function": {"name": "set_texture", "description": "\n    Apply a previously downloaded Polyhaven texture to an object.\n    \n    Parameters:\n    - object_name: Name of the object to apply the texture to\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\n    \n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"object_name": {"title": "Object Name", "type": "string"}, "texture_id": {"title": "Texture Id", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["object_name", "texture_id"], "title": "set_textureArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_polyhaven_status", "description": "\n    Check if PolyHaven integration is enabled in Blender.\n    Returns a message indicating whether PolyHaven features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_polyhaven_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_hyper3d_status", "description": "\n    Check if Hyper3D Rodin integration is enabled in Blender.\n    Returns a message indicating whether Hyper3D Rodin features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_hyper3d_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_sketchfab_status", "description": "\n    Check if Sketchfab integration is enabled in Blender.\n    Returns a message indicating whether Sketchfab features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_sketchfab_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_sketchfab_models", "description": "\n    Search for models on Sketchfab with optional filtering.\n\n    Parameters:\n    - query: Text to search for\n    - categories: Optional comma-separated list of categories\n    - count: Maximum number of results to return (default 20)\n    - downloadable: Whether to include only downloadable models (default True)\n\n    Returns a formatted list of matching models.\n    ", "parameters": {"properties": {"query": {"title": "Query", "type": "string"}, "categories": {"default": null, "title": "Categories", "type": "string"}, "count": {"default": 20, "title": "Count", "type": "integer"}, "downloadable": {"default": true, "title": "Downloadable", "type": "boolean"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["query"], "title": "search_sketchfab_modelsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_sketchfab_model_preview", "description": "\n    Get a preview thumbnail of a Sketchfab model by its UID.\n    Use this to visually confirm a model before downloading.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\n    \n    Returns the model's thumbnail as an Image for visual confirmation.\n    ", "parameters": {"properties": {"uid": {"title": "Uid", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["uid"], "title": "get_sketchfab_model_previewArguments", "type": "object"}}}, {"type": "function", "function": {"name": "download_sketchfab_model", "description": "\n    Download and import a Sketchfab model by its UID.\n    The model will be scaled so its largest dimension equals target_size.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\n                  You must specify the desired size for the model.\n                  Examples:\n                  - Chair: target_size=1.0 (1 meter tall)\n                  - Table: target_size=0.75 (75cm tall)\n                  - Car: target_size=4.5 (4.5 meters long)\n                  - Person: target_size=1.7 (1.7 meters tall)\n                  - Small object (cup, phone): target_size=0.1 to 0.3\n    \n    Returns a message with import details including object names, dimensions, and bounding box.\n    The model must be downloadable and you must have proper access rights.\n    ", "parameters": {"properties": {"uid": {"title": "Uid", "type": "string"}, "target_size": {"title": "Target Size", "type": "number"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["uid", "target_size"], "title": "download_sketchfab_modelArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hyper3d_model_via_text", "description": "\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n\n    Parameters:\n    - text_prompt: A short description of the desired model in **English**.\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"text_prompt": {"title": "Text Prompt", "type": "string"}, "bbox_condition": {"default": null, "items": {"type": "number"}, "title": "Bbox Condition", "type": "array"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["text_prompt"], "title": "generate_hyper3d_model_via_textArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hyper3d_model_via_images", "description": "\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n    \n    Parameters:\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode.\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"input_image_paths": {"default": null, "items": {"type": "string"}, "title": "Input Image Paths", "type": "array"}, "input_image_urls": {"default": null, "items": {"type": "string"}, "title": "Input Image Urls", "type": "array"}, "bbox_condition": {"default": null, "items": {"type": "number"}, "title": "Bbox Condition", "type": "array"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "generate_hyper3d_model_via_imagesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "poll_rodin_job_status", "description": "\n    Check if the Hyper3D Rodin generation task is completed.\n\n    For Hyper3D Rodin mode MAIN_SITE:\n        Parameters:\n        - subscription_key: The subscription_key given in the generate model step.\n\n        Returns a list of status. The task is done if all status are \"Done\".\n        If \"Failed\" showed up, the generating process failed.\n        This is a polling API, so only proceed if the status are finally determined (\"Done\" or \"Canceled\").\n\n    For Hyper3D Rodin mode FAL_AI:\n        Parameters:\n        - request_id: The request_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"COMPLETED\".\n        The task is in progress if status is \"IN_PROGRESS\".\n        If status other than \"COMPLETED\", \"IN_PROGRESS\", \"IN_QUEUE\" showed up, the generating process might be failed.\n        This is a polling API, so only proceed if the status are finally determined (\"COMPLETED\" or some failed state).\n    ", "parameters": {"properties": {"subscription_key": {"default": null, "title": "Subscription Key", "type": "string"}, "request_id": {"default": null, "title": "Request Id", "type": "string"}}, "title": "poll_rodin_job_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "import_generated_asset", "description": "\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\n\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\n    Return if the asset has been imported successfully.\n    ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "task_uuid": {"default": null, "title": "Task Uuid", "type": "string"}, "request_id": {"default": null, "title": "Request Id", "type": "string"}}, "required": ["name"], "title": "import_generated_assetArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_hunyuan3d_status", "description": "\n    Check if Hunyuan3D integration is enabled in Blender.\n    Returns a message indicating whether Hunyuan3D features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_hunyuan3d_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hunyuan3d_model", "description": "\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \n    or both for the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    \n    Parameters:\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\n    - input_image_url: (Optional) The local or remote url of the input image. Accepts None if only using text prompt.\n\n    Returns: \n    - When successful, returns a JSON with job_id (format: \"job_xxx\") indicating the task is in progress\n    - When the job completes, the status will change to \"DONE\" indicating the model has been imported\n    - Returns error message if the operation fails\n    ", "parameters": {"properties": {"text_prompt": {"default": null, "title": "Text Prompt", "type": "string"}, "input_image_url": {"default": null, "title": "Input Image Url", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "generate_hunyuan3d_modelArguments", "type": "object"}}}, {"type": "function", "function": {"name": "poll_hunyuan_job_status", "description": "\n    Check if the Hunyuan3D generation task is completed.\n\n    For Hunyuan3D:\n        Parameters:\n        - job_id: The job_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"DONE\".\n        The task is in progress if status is \"RUN\".\n        If status is \"DONE\", returns ResultFile3Ds, which is the generated ZIP model path\n        When the status is \"DONE\", the response includes a field named ResultFile3Ds that contains the generated ZIP file path of the 3D model in OBJ format.\n        This is a polling API, so only proceed if the status are finally determined (\"DONE\" or some failed state).\n    ", "parameters": {"properties": {"job_id": {"default": null, "title": "Job Id", "type": "string"}}, "title": "poll_hunyuan_job_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "import_generated_asset_hunyuan", "description": "\n    Import the asset generated by Hunyuan3D after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - zip_file_url: The zip_file_url given in the generate model step.\n\n    Return if the asset has been imported successfully.\n    ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "zip_file_url": {"title": "Zip File Url", "type": "string"}}, "required": ["name", "zip_file_url"], "title": "import_generated_asset_hunyuanArguments", "type": "object"}}}]}, "provider_attempts": [{"provider": "openrouter", "attempt": 1, "started_at": "2026-07-15T00:48:46.707287+00:00", "finished_at": "2026-07-15T00:48:50.170684+00:00", "duration_s": 3.4689999999827705, "request": {"method": "POST", "url": "https://openrouter.ai/api/v1/chat/completions", "headers": {"host": "openrouter.ai", "accept": "*/*", "accept-encoding": "gzip, deflate", "connection": "keep-alive", "user-agent": "python-httpx/0.28.1", "authorization": "[REDACTED]", "content-length": "19220", "content-type": "application/json"}, "body": "{\"model\":\"anthropic/claude-haiku-4.5\",\"messages\":[{\"role\":\"system\",\"content\":[{\"type\":\"text\",\"text\":\"You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\\n\\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\\n\\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\\n\\n## Harness Instructions\\n\\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. When you are finished, reply with the single word DONE and no tool calls.\",\"cache_control\":{\"type\":\"ephemeral\"}}]},{\"role\":\"user\",\"content\":\"## Task\\n\\nComplete the following Blender task:\\n\\n<task>\\n\\nA red sphere of 1 meter diameter resting on a white ground plane — touching it\\nexactly, neither floating nor sunk. Smooth shading, a plain red material on the\\nball, plain white on the floor.\\n\\nThe floor is the sphere, the color, and the contact. The ceiling is presentation:\\na soft believable shadow under the ball, a hint of glossiness that catches the\\nlight, and clean framing.\\n\\n</task>\"},{\"role\":\"assistant\",\"content\":\"I'll create a red sphere resting on a white ground plane with proper materials, shading, lighting, and camera setup. Let me work through this step by step.\",\"tool_calls\":[{\"id\":\"toolu_bdrk_01McQwtp5Bq6813NVu2gUpRG\",\"type\":\"function\",\"function\":{\"name\":\"get_scene_info\",\"arguments\":\"{\\\"user_prompt\\\": \\\"Get current scene info to see what's there initially\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"toolu_bdrk_01McQwtp5Bq6813NVu2gUpRG\",\"content\":[{\"type\":\"text\",\"text\":\"{\\n  \\\"name\\\": \\\"Scene\\\",\\n  \\\"object_count\\\": 0,\\n  \\\"objects\\\": [],\\n  \\\"materials_count\\\": 2\\n}\",\"cache_control\":{\"type\":\"ephemeral\"}}]}],\"usage\":{\"include\":true},\"tools\":[{\"type\":\"function\",\"function\":{\"name\":\"get_scene_info\",\"description\":\"Get detailed information about the current Blender scene\\n\\n    Parameters:\\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"user_prompt\"],\"title\":\"get_scene_infoArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_object_info\",\"description\":\"\\n    Get detailed information about a specific object in the Blender scene.\\n\\n    Parameters:\\n    - object_name: The name of the object to get information about\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"object_name\":{\"title\":\"Object Name\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"object_name\"],\"title\":\"get_object_infoArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_viewport_screenshot\",\"description\":\"\\n    Capture a screenshot of the current Blender 3D viewport.\\n\\n    Parameters:\\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns the screenshot as an Image.\\n    \",\"parameters\":{\"properties\":{\"max_size\":{\"default\":1000,\"title\":\"Max Size\",\"type\":\"integer\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_viewport_screenshotArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"description\":\"\\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\\n\\n    Parameters:\\n    - code: The Python code to execute\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"code\":{\"title\":\"Code\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"code\"],\"title\":\"execute_blender_codeArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_categories\",\"description\":\"\\n    Get a list of categories for a specific asset type on Polyhaven.\\n\\n    Parameters:\\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"asset_type\":{\"default\":\"hdris\",\"title\":\"Asset Type\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_polyhaven_categoriesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_polyhaven_assets\",\"description\":\"\\n    Search for assets on Polyhaven with optional filtering.\\n\\n    Parameters:\\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\\n    - categories: Optional comma-separated list of categories to filter by\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns a list of matching assets with basic information.\\n    \",\"parameters\":{\"properties\":{\"asset_type\":{\"default\":\"all\",\"title\":\"Asset Type\",\"type\":\"string\"},\"categories\":{\"default\":null,\"title\":\"Categories\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"search_polyhaven_assetsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"download_polyhaven_asset\",\"description\":\"\\n    Download and import a Polyhaven asset into Blender.\\n\\n    Parameters:\\n    - asset_id: The ID of the asset to download\\n    - asset_type: The type of asset (hdris, textures, models)\\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"asset_id\":{\"title\":\"Asset Id\",\"type\":\"string\"},\"asset_type\":{\"title\":\"Asset Type\",\"type\":\"string\"},\"resolution\":{\"default\":\"1k\",\"title\":\"Resolution\",\"type\":\"string\"},\"file_format\":{\"default\":null,\"title\":\"File Format\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"asset_id\",\"asset_type\"],\"title\":\"download_polyhaven_assetArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"set_texture\",\"description\":\"\\n    Apply a previously downloaded Polyhaven texture to an object.\\n    \\n    Parameters:\\n    - object_name: Name of the object to apply the texture to\\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\\n    \\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"object_name\":{\"title\":\"Object Name\",\"type\":\"string\"},\"texture_id\":{\"title\":\"Texture Id\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"object_name\",\"texture_id\"],\"title\":\"set_textureArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_status\",\"description\":\"\\n    Check if PolyHaven integration is enabled in Blender.\\n    Returns a message indicating whether PolyHaven features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_polyhaven_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_hyper3d_status\",\"description\":\"\\n    Check if Hyper3D Rodin integration is enabled in Blender.\\n    Returns a message indicating whether Hyper3D Rodin features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_hyper3d_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_status\",\"description\":\"\\n    Check if Sketchfab integration is enabled in Blender.\\n    Returns a message indicating whether Sketchfab features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_sketchfab_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_sketchfab_models\",\"description\":\"\\n    Search for models on Sketchfab with optional filtering.\\n\\n    Parameters:\\n    - query: Text to search for\\n    - categories: Optional comma-separated list of categories\\n    - count: Maximum number of results to return (default 20)\\n    - downloadable: Whether to include only downloadable models (default True)\\n\\n    Returns a formatted list of matching models.\\n    \",\"parameters\":{\"properties\":{\"query\":{\"title\":\"Query\",\"type\":\"string\"},\"categories\":{\"default\":null,\"title\":\"Categories\",\"type\":\"string\"},\"count\":{\"default\":20,\"title\":\"Count\",\"type\":\"integer\"},\"downloadable\":{\"default\":true,\"title\":\"Downloadable\",\"type\":\"boolean\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"query\"],\"title\":\"search_sketchfab_modelsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_model_preview\",\"description\":\"\\n    Get a preview thumbnail of a Sketchfab model by its UID.\\n    Use this to visually confirm a model before downloading.\\n    \\n    Parameters:\\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\\n    \\n    Returns the model's thumbnail as an Image for visual confirmation.\\n    \",\"parameters\":{\"properties\":{\"uid\":{\"title\":\"Uid\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"uid\"],\"title\":\"get_sketchfab_model_previewArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"download_sketchfab_model\",\"description\":\"\\n    Download and import a Sketchfab model by its UID.\\n    The model will be scaled so its largest dimension equals target_size.\\n    \\n    Parameters:\\n    - uid: The unique identifier of the Sketchfab model\\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\\n                  You must specify the desired size for the model.\\n                  Examples:\\n                  - Chair: target_size=1.0 (1 meter tall)\\n                  - Table: target_size=0.75 (75cm tall)\\n                  - Car: target_size=4.5 (4.5 meters long)\\n                  - Person: target_size=1.7 (1.7 meters tall)\\n                  - Small object (cup, phone): target_size=0.1 to 0.3\\n    \\n    Returns a message with import details including object names, dimensions, and bounding box.\\n    The model must be downloadable and you must have proper access rights.\\n    \",\"parameters\":{\"properties\":{\"uid\":{\"title\":\"Uid\",\"type\":\"string\"},\"target_size\":{\"title\":\"Target Size\",\"type\":\"number\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"uid\",\"target_size\"],\"title\":\"download_sketchfab_modelArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hyper3d_model_via_text\",\"description\":\"\\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\\n    The 3D asset has built-in materials.\\n    The generated model has a normalized size, so re-scaling after generation can be useful.\\n\\n    Parameters:\\n    - text_prompt: A short description of the desired model in **English**.\\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\\n\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"text_prompt\":{\"title\":\"Text Prompt\",\"type\":\"string\"},\"bbox_condition\":{\"default\":null,\"items\":{\"type\":\"number\"},\"title\":\"Bbox Condition\",\"type\":\"array\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"text_prompt\"],\"title\":\"generate_hyper3d_model_via_textArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hyper3d_model_via_images\",\"description\":\"\\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\\n    The 3D asset has built-in materials.\\n    The generated model has a normalized size, so re-scaling after generation can be useful.\\n    \\n    Parameters:\\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model.\\n\\n    Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode.\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"input_image_paths\":{\"default\":null,\"items\":{\"type\":\"string\"},\"title\":\"Input Image Paths\",\"type\":\"array\"},\"input_image_urls\":{\"default\":null,\"items\":{\"type\":\"string\"},\"title\":\"Input Image Urls\",\"type\":\"array\"},\"bbox_condition\":{\"default\":null,\"items\":{\"type\":\"number\"},\"title\":\"Bbox Condition\",\"type\":\"array\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"generate_hyper3d_model_via_imagesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"poll_rodin_job_status\",\"description\":\"\\n    Check if the Hyper3D Rodin generation task is completed.\\n\\n    For Hyper3D Rodin mode MAIN_SITE:\\n        Parameters:\\n        - subscription_key: The subscription_key given in the generate model step.\\n\\n        Returns a list of status. The task is done if all status are \\\"Done\\\".\\n        If \\\"Failed\\\" showed up, the generating process failed.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"Done\\\" or \\\"Canceled\\\").\\n\\n    For Hyper3D Rodin mode FAL_AI:\\n        Parameters:\\n        - request_id: The request_id given in the generate model step.\\n\\n        Returns the generation task status. The task is done if status is \\\"COMPLETED\\\".\\n        The task is in progress if status is \\\"IN_PROGRESS\\\".\\n        If status other than \\\"COMPLETED\\\", \\\"IN_PROGRESS\\\", \\\"IN_QUEUE\\\" showed up, the generating process might be failed.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"COMPLETED\\\" or some failed state).\\n    \",\"parameters\":{\"properties\":{\"subscription_key\":{\"default\":null,\"title\":\"Subscription Key\",\"type\":\"string\"},\"request_id\":{\"default\":null,\"title\":\"Request Id\",\"type\":\"string\"}},\"title\":\"poll_rodin_job_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"import_generated_asset\",\"description\":\"\\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\\n\\n    Parameters:\\n    - name: The name of the object in scene\\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\\n\\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\\n    Return if the asset has been imported successfully.\\n    \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"task_uuid\":{\"default\":null,\"title\":\"Task Uuid\",\"type\":\"string\"},\"request_id\":{\"default\":null,\"title\":\"Request Id\",\"type\":\"string\"}},\"required\":[\"name\"],\"title\":\"import_generated_assetArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_hunyuan3d_status\",\"description\":\"\\n    Check if Hunyuan3D integration is enabled in Blender.\\n    Returns a message indicating whether Hunyuan3D features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_hunyuan3d_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hunyuan3d_model\",\"description\":\"\\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \\n    or both for the desired asset, and import the asset into Blender.\\n    The 3D asset has built-in materials.\\n    \\n    Parameters:\\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\\n    - input_image_url: (Optional) The local or remote url of the input image. Accepts None if only using text prompt.\\n\\n    Returns: \\n    - When successful, returns a JSON with job_id (format: \\\"job_xxx\\\") indicating the task is in progress\\n    - When the job completes, the status will change to \\\"DONE\\\" indicating the model has been imported\\n    - Returns error message if the operation fails\\n    \",\"parameters\":{\"properties\":{\"text_prompt\":{\"default\":null,\"title\":\"Text Prompt\",\"type\":\"string\"},\"input_image_url\":{\"default\":null,\"title\":\"Input Image Url\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"generate_hunyuan3d_modelArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"poll_hunyuan_job_status\",\"description\":\"\\n    Check if the Hunyuan3D generation task is completed.\\n\\n    For Hunyuan3D:\\n        Parameters:\\n        - job_id: The job_id given in the generate model step.\\n\\n        Returns the generation task status. The task is done if status is \\\"DONE\\\".\\n        The task is in progress if status is \\\"RUN\\\".\\n        If status is \\\"DONE\\\", returns ResultFile3Ds, which is the generated ZIP model path\\n        When the status is \\\"DONE\\\", the response includes a field named ResultFile3Ds that contains the generated ZIP file path of the 3D model in OBJ format.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"DONE\\\" or some failed state).\\n    \",\"parameters\":{\"properties\":{\"job_id\":{\"default\":null,\"title\":\"Job Id\",\"type\":\"string\"}},\"title\":\"poll_hunyuan_job_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"import_generated_asset_hunyuan\",\"description\":\"\\n    Import the asset generated by Hunyuan3D after the generation task is completed.\\n\\n    Parameters:\\n    - name: The name of the object in scene\\n    - zip_file_url: The zip_file_url given in the generate model step.\\n\\n    Return if the asset has been imported successfully.\\n    \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"zip_file_url\":{\"title\":\"Zip File Url\",\"type\":\"string\"}},\"required\":[\"name\",\"zip_file_url\"],\"title\":\"import_generated_asset_hunyuanArguments\",\"type\":\"object\"}}}],\"reasoning\":{\"effort\":\"xhigh\"}}", "body_base64": "{"model":"anthropic/claude-haiku-4.5","messages":[{"role":"system","content":[{"type":"text","text":"You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\n\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\n\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\n\n## Harness Instructions\n\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. When you are finished, reply with the single word DONE and no tool calls.","cache_control":{"type":"ephemeral"}}]},{"role":"user","content":"## Task\n\nComplete the following Blender task:\n\n<task>\n\nA red sphere of 1 meter diameter resting on a white ground plane — touching it\nexactly, neither floating nor sunk. Smooth shading, a plain red material on the\nball, plain white on the floor.\n\nThe floor is the sphere, the color, and the contact. The ceiling is presentation:\na soft believable shadow under the ball, a hint of glossiness that catches the\nlight, and clean framing.\n\n</task>"},{"role":"assistant","content":"I'll create a red sphere resting on a white ground plane with proper materials, shading, lighting, and camera setup. Let me work through this step by step.","tool_calls":[{"id":"toolu_bdrk_01McQwtp5Bq6813NVu2gUpRG","type":"function","function":{"name":"get_scene_info","arguments":"{\"user_prompt\": \"Get current scene info to see what's there initially\"}"}}]},{"role":"tool","tool_call_id":"toolu_bdrk_01McQwtp5Bq6813NVu2gUpRG","content":[{"type":"text","text":"{\n  \"name\": \"Scene\",\n  \"object_count\": 0,\n  \"objects\": [],\n  \"materials_count\": 2\n}","cache_control":{"type":"ephemeral"}}]}],"usage":{"include":true},"tools":[{"type":"function","function":{"name":"get_scene_info","description":"Get detailed information about the current Blender scene\n\n    Parameters:\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\n    ","parameters":{"properties":{"user_prompt":{"title":"User Prompt","type":"string"}},"required":["user_prompt"],"title":"get_scene_infoArguments","type":"object"}}},{"type":"function","function":{"name":"get_object_info","description":"\n    Get detailed information about a specific object in the Blender scene.\n\n    Parameters:\n    - object_name: The name of the object to get information about\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"object_name":{"title":"Object Name","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["object_name"],"title":"get_object_infoArguments","type":"object"}}},{"type":"function","function":{"name":"get_viewport_screenshot","description":"\n    Capture a screenshot of the current Blender 3D viewport.\n\n    Parameters:\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns the screenshot as an Image.\n    ","parameters":{"properties":{"max_size":{"default":1000,"title":"Max Size","type":"integer"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_viewport_screenshotArguments","type":"object"}}},{"type":"function","function":{"name":"execute_blender_code","description":"\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\n\n    Parameters:\n    - code: The Python code to execute\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"code":{"title":"Code","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["code"],"title":"execute_blender_codeArguments","type":"object"}}},{"type":"function","function":{"name":"get_polyhaven_categories","description":"\n    Get a list of categories for a specific asset type on Polyhaven.\n\n    Parameters:\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"asset_type":{"default":"hdris","title":"Asset Type","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_polyhaven_categoriesArguments","type":"object"}}},{"type":"function","function":{"name":"search_polyhaven_assets","description":"\n    Search for assets on Polyhaven with optional filtering.\n\n    Parameters:\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\n    - categories: Optional comma-separated list of categories to filter by\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a list of matching assets with basic information.\n    ","parameters":{"properties":{"asset_type":{"default":"all","title":"Asset Type","type":"string"},"categories":{"default":null,"title":"Categories","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"search_polyhaven_assetsArguments","type":"object"}}},{"type":"function","function":{"name":"download_polyhaven_asset","description":"\n    Download and import a Polyhaven asset into Blender.\n\n    Parameters:\n    - asset_id: The ID of the asset to download\n    - asset_type: The type of asset (hdris, textures, models)\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a message indicating success or failure.\n    ","parameters":{"properties":{"asset_id":{"title":"Asset Id","type":"string"},"asset_type":{"title":"Asset Type","type":"string"},"resolution":{"default":"1k","title":"Resolution","type":"string"},"file_format":{"default":null,"title":"File Format","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["asset_id","asset_type"],"title":"download_polyhaven_assetArguments","type":"object"}}},{"type":"function","function":{"name":"set_texture","description":"\n    Apply a previously downloaded Polyhaven texture to an object.\n    \n    Parameters:\n    - object_name: Name of the object to apply the texture to\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\n    \n    Returns a message indicating success or failure.\n    ","parameters":{"properties":{"object_name":{"title":"Object Name","type":"string"},"texture_id":{"title":"Texture Id","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["object_name","texture_id"],"title":"set_textureArguments","type":"object"}}},{"type":"function","function":{"name":"get_polyhaven_status","description":"\n    Check if PolyHaven integration is enabled in Blender.\n    Returns a message indicating whether PolyHaven features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_polyhaven_statusArguments","type":"object"}}},{"type":"function","function":{"name":"get_hyper3d_status","description":"\n    Check if Hyper3D Rodin integration is enabled in Blender.\n    Returns a message indicating whether Hyper3D Rodin features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_hyper3d_statusArguments","type":"object"}}},{"type":"function","function":{"name":"get_sketchfab_status","description":"\n    Check if Sketchfab integration is enabled in Blender.\n    Returns a message indicating whether Sketchfab features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_sketchfab_statusArguments","type":"object"}}},{"type":"function","function":{"name":"search_sketchfab_models","description":"\n    Search for models on Sketchfab with optional filtering.\n\n    Parameters:\n    - query: Text to search for\n    - categories: Optional comma-separated list of categories\n    - count: Maximum number of results to return (default 20)\n    - downloadable: Whether to include only downloadable models (default True)\n\n    Returns a formatted list of matching models.\n    ","parameters":{"properties":{"query":{"title":"Query","type":"string"},"categories":{"default":null,"title":"Categories","type":"string"},"count":{"default":20,"title":"Count","type":"integer"},"downloadable":{"default":true,"title":"Downloadable","type":"boolean"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["query"],"title":"search_sketchfab_modelsArguments","type":"object"}}},{"type":"function","function":{"name":"get_sketchfab_model_preview","description":"\n    Get a preview thumbnail of a Sketchfab model by its UID.\n    Use this to visually confirm a model before downloading.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\n    \n    Returns the model's thumbnail as an Image for visual confirmation.\n    ","parameters":{"properties":{"uid":{"title":"Uid","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["uid"],"title":"get_sketchfab_model_previewArguments","type":"object"}}},{"type":"function","function":{"name":"download_sketchfab_model","description":"\n    Download and import a Sketchfab model by its UID.\n    The model will be scaled so its largest dimension equals target_size.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\n                  You must specify the desired size for the model.\n                  Examples:\n                  - Chair: target_size=1.0 (1 meter tall)\n                  - Table: target_size=0.75 (75cm tall)\n                  - Car: target_size=4.5 (4.5 meters long)\n                  - Person: target_size=1.7 (1.7 meters tall)\n                  - Small object (cup, phone): target_size=0.1 to 0.3\n    \n    Returns a message with import details including object names, dimensions, and bounding box.\n    The model must be downloadable and you must have proper access rights.\n    ","parameters":{"properties":{"uid":{"title":"Uid","type":"string"},"target_size":{"title":"Target Size","type":"number"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["uid","target_size"],"title":"download_sketchfab_modelArguments","type":"object"}}},{"type":"function","function":{"name":"generate_hyper3d_model_via_text","description":"\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n\n    Parameters:\n    - text_prompt: A short description of the desired model in **English**.\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Returns a message indicating success or failure.\n    ","parameters":{"properties":{"text_prompt":{"title":"Text Prompt","type":"string"},"bbox_condition":{"default":null,"items":{"type":"number"},"title":"Bbox Condition","type":"array"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["text_prompt"],"title":"generate_hyper3d_model_via_textArguments","type":"object"}}},{"type":"function","function":{"name":"generate_hyper3d_model_via_images","description":"\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n    \n    Parameters:\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode.\n    Returns a message indicating success or failure.\n    ","parameters":{"properties":{"input_image_paths":{"default":null,"items":{"type":"string"},"title":"Input Image Paths","type":"array"},"input_image_urls":{"default":null,"items":{"type":"string"},"title":"Input Image Urls","type":"array"},"bbox_condition":{"default":null,"items":{"type":"number"},"title":"Bbox Condition","type":"array"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"generate_hyper3d_model_via_imagesArguments","type":"object"}}},{"type":"function","function":{"name":"poll_rodin_job_status","description":"\n    Check if the Hyper3D Rodin generation task is completed.\n\n    For Hyper3D Rodin mode MAIN_SITE:\n        Parameters:\n        - subscription_key: The subscription_key given in the generate model step.\n\n        Returns a list of status. The task is done if all status are \"Done\".\n        If \"Failed\" showed up, the generating process failed.\n        This is a polling API, so only proceed if the status are finally determined (\"Done\" or \"Canceled\").\n\n    For Hyper3D Rodin mode FAL_AI:\n        Parameters:\n        - request_id: The request_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"COMPLETED\".\n        The task is in progress if status is \"IN_PROGRESS\".\n        If status other than \"COMPLETED\", \"IN_PROGRESS\", \"IN_QUEUE\" showed up, the generating process might be failed.\n        This is a polling API, so only proceed if the status are finally determined (\"COMPLETED\" or some failed state).\n    ","parameters":{"properties":{"subscription_key":{"default":null,"title":"Subscription Key","type":"string"},"request_id":{"default":null,"title":"Request Id","type":"string"}},"title":"poll_rodin_job_statusArguments","type":"object"}}},{"type":"function","function":{"name":"import_generated_asset","description":"\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\n\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\n    Return if the asset has been imported successfully.\n    ","parameters":{"properties":{"name":{"title":"Name","type":"string"},"task_uuid":{"default":null,"title":"Task Uuid","type":"string"},"request_id":{"default":null,"title":"Request Id","type":"string"}},"required":["name"],"title":"import_generated_assetArguments","type":"object"}}},{"type":"function","function":{"name":"get_hunyuan3d_status","description":"\n    Check if Hunyuan3D integration is enabled in Blender.\n    Returns a message indicating whether Hunyuan3D features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_hunyuan3d_statusArguments","type":"object"}}},{"type":"function","function":{"name":"generate_hunyuan3d_model","description":"\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \n    or both for the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    \n    Parameters:\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\n    - input_image_url: (Optional) The local or remote url of the input image. Accepts None if only using text prompt.\n\n    Returns: \n    - When successful, returns a JSON with job_id (format: \"job_xxx\") indicating the task is in progress\n    - When the job completes, the status will change to \"DONE\" indicating the model has been imported\n    - Returns error message if the operation fails\n    ","parameters":{"properties":{"text_prompt":{"default":null,"title":"Text Prompt","type":"string"},"input_image_url":{"default":null,"title":"Input Image Url","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"generate_hunyuan3d_modelArguments","type":"object"}}},{"type":"function","function":{"name":"poll_hunyuan_job_status","description":"\n    Check if the Hunyuan3D generation task is completed.\n\n    For Hunyuan3D:\n        Parameters:\n        - job_id: The job_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"DONE\".\n        The task is in progress if status is \"RUN\".\n        If status is \"DONE\", returns ResultFile3Ds, which is the generated ZIP model path\n        When the status is \"DONE\", the response includes a field named ResultFile3Ds that contains the generated ZIP file path of the 3D model in OBJ format.\n        This is a polling API, so only proceed if the status are finally determined (\"DONE\" or some failed state).\n    ","parameters":{"properties":{"job_id":{"default":null,"title":"Job Id","type":"string"}},"title":"poll_hunyuan_job_statusArguments","type":"object"}}},{"type":"function","function":{"name":"import_generated_asset_hunyuan","description":"\n    Import the asset generated by Hunyuan3D after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - zip_file_url: The zip_file_url given in the generate model step.\n\n    Return if the asset has been imported successfully.\n    ","parameters":{"properties":{"name":{"title":"Name","type":"string"},"zip_file_url":{"title":"Zip File Url","type":"string"}},"required":["name","zip_file_url"],"title":"import_generated_asset_hunyuanArguments","type":"object"}}}],"reasoning":{"effort":"xhigh"}}"}, "response": {"status_code": 200, "headers": {"date": "Wed, 15 Jul 2026 00:48:46 GMT", "content-type": "application/json", "transfer-encoding": "chunked", "connection": "keep-alive", "access-control-allow-origin": "*", "x-generation-id": "gen-1784076525-DcTQDM0Ticg3ct9Au0tI", "access-control-expose-headers": "X-Generation-Id,X-Provider-Name,cf-ray", "set-cookie": "[REDACTED]", "permissions-policy": "payment=(self \"https://checkout.stripe.com\" \"https://connect-js.stripe.com\" \"https://js.stripe.com\" \"https://*.js.stripe.com\" \"https://hooks.stripe.com\")", "referrer-policy": "no-referrer, strict-origin-when-cross-origin", "x-content-type-options": "nosniff", "content-encoding": "gzip", "server": "cloudflare", "cf-ray": "a1b4c36c6efdf778-MAD"}, "body": "\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n{\"id\":\"gen-1784076525-DcTQDM0Ticg3ct9Au0tI\",\"object\":\"chat.completion\",\"created\":1784076525,\"model\":\"anthropic/claude-haiku-4.5\",\"provider\":\"Amazon Bedrock\",\"system_fingerprint\":null,\"service_tier\":null,\"choices\":[{\"index\":0,\"logprobs\":null,\"finish_reason\":\"tool_calls\",\"native_finish_reason\":\"tool_use\",\"message\":{\"role\":\"assistant\",\"content\":\"Good, the scene is empty. Now I'll create the red sphere and white ground plane using Blender code:\",\"refusal\":null,\"reasoning\":null,\"tool_calls\":[{\"type\":\"function\",\"index\":0,\"id\":\"toolu_bdrk_01PuvjJZjfHJZgTRw6jGDsdH\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\": \\\"\\\\nimport bpy\\\\nimport bmesh\\\\nfrom mathutils import Vector\\\\n\\\\n# Clear any default objects\\\\nbpy.ops.object.select_all(action='SELECT')\\\\nbpy.ops.object.delete(use_global=False)\\\\n\\\\n# Add a sphere (1 meter diameter = 0.5 meter radius)\\\\nbpy.ops.mesh.primitive_uv_sphere_add(\\\\n    radius=0.5,\\\\n    location=(0, 0, 0.5)  # Position it so bottom touches z=0\\\\n)\\\\nsphere = bpy.context.active_object\\\\nsphere.name = \\\\\\\"RedSphere\\\\\\\"\\\\n\\\\n# Add a large ground plane\\\\nbpy.ops.mesh.primitive_plane_add(\\\\n    size=10,\\\\n    location=(0, 0, 0)\\\\n)\\\\nplane = bpy.context.active_object\\\\nplane.name = \\\\\\\"GroundPlane\\\\\\\"\\\\n\\\\n# Select and scale plane to be large but thin\\\\nplane.scale = (10, 10, 0.1)\\\\n\\\\nprint(\\\\\\\"Objects created: Sphere at z=\\\\\\\" + str(sphere.location.z) + \\\\\\\", Plane at z=\\\\\\\" + str(plane.location.z))\\\\n\\\", \\\"user_prompt\\\": \\\"Create red sphere and white ground plane\\\"}\"}}]}}],\"usage\":{\"prompt_tokens\":5968,\"completion_tokens\":376,\"total_tokens\":6344,\"cost\":0.00314715,\"is_byok\":false,\"prompt_tokens_details\":{\"cached_tokens\":5384,\"cache_write_tokens\":579,\"audio_tokens\":0,\"video_tokens\":0},\"cache_creation\":{\"ephemeral_5m_input_tokens\":579,\"ephemeral_1h_input_tokens\":0},\"cost_details\":{\"upstream_inference_cost\":0.00314715,\"upstream_inference_prompt_cost\":0.00126715,\"upstream_inference_completions_cost\":0.00188},\"completion_tokens_details\":{\"reasoning_tokens\":0,\"image_tokens\":0,\"audio_tokens\":0}}}", "body_base64": "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"}, "error": null}], "response": {"text": "Good, the scene is empty. Now I'll create the red sphere and white ground plane using Blender code:", "tool_calls": [{"id": "toolu_bdrk_01PuvjJZjfHJZgTRw6jGDsdH", "name": "execute_blender_code", "arguments": {"code": "\nimport bpy\nimport bmesh\nfrom mathutils import Vector\n\n# Clear any default objects\nbpy.ops.object.select_all(action='SELECT')\nbpy.ops.object.delete(use_global=False)\n\n# Add a sphere (1 meter diameter = 0.5 meter radius)\nbpy.ops.mesh.primitive_uv_sphere_add(\n    radius=0.5,\n    location=(0, 0, 0.5)  # Position it so bottom touches z=0\n)\nsphere = bpy.context.active_object\nsphere.name = \"RedSphere\"\n\n# Add a large ground plane\nbpy.ops.mesh.primitive_plane_add(\n    size=10,\n    location=(0, 0, 0)\n)\nplane = bpy.context.active_object\nplane.name = \"GroundPlane\"\n\n# Select and scale plane to be large but thin\nplane.scale = (10, 10, 0.1)\n\nprint(\"Objects created: Sphere at z=\" + str(sphere.location.z) + \", Plane at z=\" + str(plane.location.z))\n", "user_prompt": "Create red sphere and white ground plane"}, "raw_arguments": "{\"code\": \"\\nimport bpy\\nimport bmesh\\nfrom mathutils import Vector\\n\\n# Clear any default objects\\nbpy.ops.object.select_all(action='SELECT')\\nbpy.ops.object.delete(use_global=False)\\n\\n# Add a sphere (1 meter diameter = 0.5 meter radius)\\nbpy.ops.mesh.primitive_uv_sphere_add(\\n    radius=0.5,\\n    location=(0, 0, 0.5)  # Position it so bottom touches z=0\\n)\\nsphere = bpy.context.active_object\\nsphere.name = \\\"RedSphere\\\"\\n\\n# Add a large ground plane\\nbpy.ops.mesh.primitive_plane_add(\\n    size=10,\\n    location=(0, 0, 0)\\n)\\nplane = bpy.context.active_object\\nplane.name = \\\"GroundPlane\\\"\\n\\n# Select and scale plane to be large but thin\\nplane.scale = (10, 10, 0.1)\\n\\nprint(\\\"Objects created: Sphere at z=\\\" + str(sphere.location.z) + \\\", Plane at z=\\\" + str(plane.location.z))\\n\", \"user_prompt\": \"Create red sphere and white ground plane\"}", "parse_error": null}], "tokens_in": 5968, "tokens_out": 376, "cost_usd": 0.00314715, "cached_tokens": 5384}, "error": null}
{"ts": "2026-07-15T00:48:50.172191+00:00", "type": "assistant", "text": "Good, the scene is empty. Now I'll create the red sphere and white ground plane using Blender code:", "tool_calls": [{"id": "toolu_bdrk_01PuvjJZjfHJZgTRw6jGDsdH", "name": "execute_blender_code", "arguments": {"code": "\nimport bpy\nimport bmesh\nfrom mathutils import Vector\n\n# Clear any default objects\nbpy.ops.object.select_all(action='SELECT')\nbpy.ops.object.delete(use_global=False)\n\n# Add a sphere (1 meter diameter = 0.5 meter radius)\nbpy.ops.mesh.primitive_uv_sphere_add(\n    radius=0.5,\n    location=(0, 0, 0.5)  # Position it so bottom touches z=0\n)\nsphere = bpy.context.active_object\nsphere.name = \"RedSphere\"\n\n# Add a large ground plane\nbpy.ops.mesh.primitive_plane_add(\n    size=10,\n    location=(0, 0, 0)\n)\nplane = bpy.context.active_object\nplane.name = \"GroundPlane\"\n\n# Select and scale plane to be large but thin\nplane.scale = (10, 10, 0.1)\n\nprint(\"Objects created: Sphere at z=\" + str(sphere.location.z) + \", Plane at z=\" + str(plane.location.z))\n", "user_prompt": "Create red sphere and white ground plane"}, "raw_arguments": "{\"code\": \"\\nimport bpy\\nimport bmesh\\nfrom mathutils import Vector\\n\\n# Clear any default objects\\nbpy.ops.object.select_all(action='SELECT')\\nbpy.ops.object.delete(use_global=False)\\n\\n# Add a sphere (1 meter diameter = 0.5 meter radius)\\nbpy.ops.mesh.primitive_uv_sphere_add(\\n    radius=0.5,\\n    location=(0, 0, 0.5)  # Position it so bottom touches z=0\\n)\\nsphere = bpy.context.active_object\\nsphere.name = \\\"RedSphere\\\"\\n\\n# Add a large ground plane\\nbpy.ops.mesh.primitive_plane_add(\\n    size=10,\\n    location=(0, 0, 0)\\n)\\nplane = bpy.context.active_object\\nplane.name = \\\"GroundPlane\\\"\\n\\n# Select and scale plane to be large but thin\\nplane.scale = (10, 10, 0.1)\\n\\nprint(\\\"Objects created: Sphere at z=\\\" + str(sphere.location.z) + \\\", Plane at z=\\\" + str(plane.location.z))\\n\", \"user_prompt\": \"Create red sphere and white ground plane\"}", "parse_error": null}]}
{"ts": "2026-07-15T00:48:50.172191+00:00", "type": "tool_call", "id": "toolu_bdrk_01PuvjJZjfHJZgTRw6jGDsdH", "name": "execute_blender_code", "arguments": {"code": "\nimport bpy\nimport bmesh\nfrom mathutils import Vector\n\n# Clear any default objects\nbpy.ops.object.select_all(action='SELECT')\nbpy.ops.object.delete(use_global=False)\n\n# Add a sphere (1 meter diameter = 0.5 meter radius)\nbpy.ops.mesh.primitive_uv_sphere_add(\n    radius=0.5,\n    location=(0, 0, 0.5)  # Position it so bottom touches z=0\n)\nsphere = bpy.context.active_object\nsphere.name = \"RedSphere\"\n\n# Add a large ground plane\nbpy.ops.mesh.primitive_plane_add(\n    size=10,\n    location=(0, 0, 0)\n)\nplane = bpy.context.active_object\nplane.name = \"GroundPlane\"\n\n# Select and scale plane to be large but thin\nplane.scale = (10, 10, 0.1)\n\nprint(\"Objects created: Sphere at z=\" + str(sphere.location.z) + \", Plane at z=\" + str(plane.location.z))\n", "user_prompt": "Create red sphere and white ground plane"}, "raw_arguments": "{\"code\": \"\\nimport bpy\\nimport bmesh\\nfrom mathutils import Vector\\n\\n# Clear any default objects\\nbpy.ops.object.select_all(action='SELECT')\\nbpy.ops.object.delete(use_global=False)\\n\\n# Add a sphere (1 meter diameter = 0.5 meter radius)\\nbpy.ops.mesh.primitive_uv_sphere_add(\\n    radius=0.5,\\n    location=(0, 0, 0.5)  # Position it so bottom touches z=0\\n)\\nsphere = bpy.context.active_object\\nsphere.name = \\\"RedSphere\\\"\\n\\n# Add a large ground plane\\nbpy.ops.mesh.primitive_plane_add(\\n    size=10,\\n    location=(0, 0, 0)\\n)\\nplane = bpy.context.active_object\\nplane.name = \\\"GroundPlane\\\"\\n\\n# Select and scale plane to be large but thin\\nplane.scale = (10, 10, 0.1)\\n\\nprint(\\\"Objects created: Sphere at z=\\\" + str(sphere.location.z) + \\\", Plane at z=\\\" + str(plane.location.z))\\n\", \"user_prompt\": \"Create red sphere and white ground plane\"}"}
{"ts": "2026-07-15T00:48:50.215440+00:00", "type": "tool_result", "tool_call_id": "toolu_bdrk_01PuvjJZjfHJZgTRw6jGDsdH", "name": "execute_blender_code", "text": "Code executed successfully: Objects created: Sphere at z=0.5, Plane at z=0.0\n", "is_error": false, "has_image": false, "image_b64": null}
{"ts": "2026-07-15T00:48:54.049908+00:00", "type": "llm_call", "call_index": 3, "provider_type": "blenderbench.providers.openrouter.OpenRouterProvider", "started_at": "2026-07-15T00:48:50.215440+00:00", "finished_at": "2026-07-15T00:48:54.048909+00:00", "duration_s": 3.8439999999827705, "request": {"messages": [{"role": "system", "content": "You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\n\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\n\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\n\n## Harness Instructions\n\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. When you are finished, reply with the single word DONE and no tool calls."}, {"role": "user", "content": "## Task\n\nComplete the following Blender task:\n\n<task>\n\nA red sphere of 1 meter diameter resting on a white ground plane — touching it\nexactly, neither floating nor sunk. Smooth shading, a plain red material on the\nball, plain white on the floor.\n\nThe floor is the sphere, the color, and the contact. The ceiling is presentation:\na soft believable shadow under the ball, a hint of glossiness that catches the\nlight, and clean framing.\n\n</task>"}, {"role": "assistant", "content": "I'll create a red sphere resting on a white ground plane with proper materials, shading, lighting, and camera setup. Let me work through this step by step.", "tool_calls": [{"id": "toolu_bdrk_01McQwtp5Bq6813NVu2gUpRG", "type": "function", "function": {"name": "get_scene_info", "arguments": "{\"user_prompt\": \"Get current scene info to see what's there initially\"}"}}]}, {"role": "tool", "tool_call_id": "toolu_bdrk_01McQwtp5Bq6813NVu2gUpRG", "content": "{\n  \"name\": \"Scene\",\n  \"object_count\": 0,\n  \"objects\": [],\n  \"materials_count\": 2\n}"}, {"role": "assistant", "content": "Good, the scene is empty. Now I'll create the red sphere and white ground plane using Blender code:", "tool_calls": [{"id": "toolu_bdrk_01PuvjJZjfHJZgTRw6jGDsdH", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\": \"\\nimport bpy\\nimport bmesh\\nfrom mathutils import Vector\\n\\n# Clear any default objects\\nbpy.ops.object.select_all(action='SELECT')\\nbpy.ops.object.delete(use_global=False)\\n\\n# Add a sphere (1 meter diameter = 0.5 meter radius)\\nbpy.ops.mesh.primitive_uv_sphere_add(\\n    radius=0.5,\\n    location=(0, 0, 0.5)  # Position it so bottom touches z=0\\n)\\nsphere = bpy.context.active_object\\nsphere.name = \\\"RedSphere\\\"\\n\\n# Add a large ground plane\\nbpy.ops.mesh.primitive_plane_add(\\n    size=10,\\n    location=(0, 0, 0)\\n)\\nplane = bpy.context.active_object\\nplane.name = \\\"GroundPlane\\\"\\n\\n# Select and scale plane to be large but thin\\nplane.scale = (10, 10, 0.1)\\n\\nprint(\\\"Objects created: Sphere at z=\\\" + str(sphere.location.z) + \\\", Plane at z=\\\" + str(plane.location.z))\\n\", \"user_prompt\": \"Create red sphere and white ground plane\"}"}}]}, {"role": "tool", "tool_call_id": "toolu_bdrk_01PuvjJZjfHJZgTRw6jGDsdH", "content": "Code executed successfully: Objects created: Sphere at z=0.5, Plane at z=0.0\n"}], "tools": [{"type": "function", "function": {"name": "get_scene_info", "description": "Get detailed information about the current Blender scene\n\n    Parameters:\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\n    ", "parameters": {"properties": {"user_prompt": {"title": "User Prompt", "type": "string"}}, "required": ["user_prompt"], "title": "get_scene_infoArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_object_info", "description": "\n    Get detailed information about a specific object in the Blender scene.\n\n    Parameters:\n    - object_name: The name of the object to get information about\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"object_name": {"title": "Object Name", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["object_name"], "title": "get_object_infoArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_viewport_screenshot", "description": "\n    Capture a screenshot of the current Blender 3D viewport.\n\n    Parameters:\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns the screenshot as an Image.\n    ", "parameters": {"properties": {"max_size": {"default": 1000, "title": "Max Size", "type": "integer"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_viewport_screenshotArguments", "type": "object"}}}, {"type": "function", "function": {"name": "execute_blender_code", "description": "\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\n\n    Parameters:\n    - code: The Python code to execute\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"code": {"title": "Code", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["code"], "title": "execute_blender_codeArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_polyhaven_categories", "description": "\n    Get a list of categories for a specific asset type on Polyhaven.\n\n    Parameters:\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"asset_type": {"default": "hdris", "title": "Asset Type", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_polyhaven_categoriesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_polyhaven_assets", "description": "\n    Search for assets on Polyhaven with optional filtering.\n\n    Parameters:\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\n    - categories: Optional comma-separated list of categories to filter by\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a list of matching assets with basic information.\n    ", "parameters": {"properties": {"asset_type": {"default": "all", "title": "Asset Type", "type": "string"}, "categories": {"default": null, "title": "Categories", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "search_polyhaven_assetsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "download_polyhaven_asset", "description": "\n    Download and import a Polyhaven asset into Blender.\n\n    Parameters:\n    - asset_id: The ID of the asset to download\n    - asset_type: The type of asset (hdris, textures, models)\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"asset_id": {"title": "Asset Id", "type": "string"}, "asset_type": {"title": "Asset Type", "type": "string"}, "resolution": {"default": "1k", "title": "Resolution", "type": "string"}, "file_format": {"default": null, "title": "File Format", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["asset_id", "asset_type"], "title": "download_polyhaven_assetArguments", "type": "object"}}}, {"type": "function", "function": {"name": "set_texture", "description": "\n    Apply a previously downloaded Polyhaven texture to an object.\n    \n    Parameters:\n    - object_name: Name of the object to apply the texture to\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\n    \n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"object_name": {"title": "Object Name", "type": "string"}, "texture_id": {"title": "Texture Id", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["object_name", "texture_id"], "title": "set_textureArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_polyhaven_status", "description": "\n    Check if PolyHaven integration is enabled in Blender.\n    Returns a message indicating whether PolyHaven features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_polyhaven_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_hyper3d_status", "description": "\n    Check if Hyper3D Rodin integration is enabled in Blender.\n    Returns a message indicating whether Hyper3D Rodin features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_hyper3d_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_sketchfab_status", "description": "\n    Check if Sketchfab integration is enabled in Blender.\n    Returns a message indicating whether Sketchfab features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_sketchfab_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_sketchfab_models", "description": "\n    Search for models on Sketchfab with optional filtering.\n\n    Parameters:\n    - query: Text to search for\n    - categories: Optional comma-separated list of categories\n    - count: Maximum number of results to return (default 20)\n    - downloadable: Whether to include only downloadable models (default True)\n\n    Returns a formatted list of matching models.\n    ", "parameters": {"properties": {"query": {"title": "Query", "type": "string"}, "categories": {"default": null, "title": "Categories", "type": "string"}, "count": {"default": 20, "title": "Count", "type": "integer"}, "downloadable": {"default": true, "title": "Downloadable", "type": "boolean"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["query"], "title": "search_sketchfab_modelsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_sketchfab_model_preview", "description": "\n    Get a preview thumbnail of a Sketchfab model by its UID.\n    Use this to visually confirm a model before downloading.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\n    \n    Returns the model's thumbnail as an Image for visual confirmation.\n    ", "parameters": {"properties": {"uid": {"title": "Uid", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["uid"], "title": "get_sketchfab_model_previewArguments", "type": "object"}}}, {"type": "function", "function": {"name": "download_sketchfab_model", "description": "\n    Download and import a Sketchfab model by its UID.\n    The model will be scaled so its largest dimension equals target_size.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\n                  You must specify the desired size for the model.\n                  Examples:\n                  - Chair: target_size=1.0 (1 meter tall)\n                  - Table: target_size=0.75 (75cm tall)\n                  - Car: target_size=4.5 (4.5 meters long)\n                  - Person: target_size=1.7 (1.7 meters tall)\n                  - Small object (cup, phone): target_size=0.1 to 0.3\n    \n    Returns a message with import details including object names, dimensions, and bounding box.\n    The model must be downloadable and you must have proper access rights.\n    ", "parameters": {"properties": {"uid": {"title": "Uid", "type": "string"}, "target_size": {"title": "Target Size", "type": "number"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["uid", "target_size"], "title": "download_sketchfab_modelArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hyper3d_model_via_text", "description": "\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n\n    Parameters:\n    - text_prompt: A short description of the desired model in **English**.\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"text_prompt": {"title": "Text Prompt", "type": "string"}, "bbox_condition": {"default": null, "items": {"type": "number"}, "title": "Bbox Condition", "type": "array"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["text_prompt"], "title": "generate_hyper3d_model_via_textArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hyper3d_model_via_images", "description": "\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n    \n    Parameters:\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode.\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"input_image_paths": {"default": null, "items": {"type": "string"}, "title": "Input Image Paths", "type": "array"}, "input_image_urls": {"default": null, "items": {"type": "string"}, "title": "Input Image Urls", "type": "array"}, "bbox_condition": {"default": null, "items": {"type": "number"}, "title": "Bbox Condition", "type": "array"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "generate_hyper3d_model_via_imagesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "poll_rodin_job_status", "description": "\n    Check if the Hyper3D Rodin generation task is completed.\n\n    For Hyper3D Rodin mode MAIN_SITE:\n        Parameters:\n        - subscription_key: The subscription_key given in the generate model step.\n\n        Returns a list of status. The task is done if all status are \"Done\".\n        If \"Failed\" showed up, the generating process failed.\n        This is a polling API, so only proceed if the status are finally determined (\"Done\" or \"Canceled\").\n\n    For Hyper3D Rodin mode FAL_AI:\n        Parameters:\n        - request_id: The request_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"COMPLETED\".\n        The task is in progress if status is \"IN_PROGRESS\".\n        If status other than \"COMPLETED\", \"IN_PROGRESS\", \"IN_QUEUE\" showed up, the generating process might be failed.\n        This is a polling API, so only proceed if the status are finally determined (\"COMPLETED\" or some failed state).\n    ", "parameters": {"properties": {"subscription_key": {"default": null, "title": "Subscription Key", "type": "string"}, "request_id": {"default": null, "title": "Request Id", "type": "string"}}, "title": "poll_rodin_job_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "import_generated_asset", "description": "\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\n\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\n    Return if the asset has been imported successfully.\n    ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "task_uuid": {"default": null, "title": "Task Uuid", "type": "string"}, "request_id": {"default": null, "title": "Request Id", "type": "string"}}, "required": ["name"], "title": "import_generated_assetArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_hunyuan3d_status", "description": "\n    Check if Hunyuan3D integration is enabled in Blender.\n    Returns a message indicating whether Hunyuan3D features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_hunyuan3d_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hunyuan3d_model", "description": "\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \n    or both for the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    \n    Parameters:\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\n    - input_image_url: (Optional) The local or remote url of the input image. Accepts None if only using text prompt.\n\n    Returns: \n    - When successful, returns a JSON with job_id (format: \"job_xxx\") indicating the task is in progress\n    - When the job completes, the status will change to \"DONE\" indicating the model has been imported\n    - Returns error message if the operation fails\n    ", "parameters": {"properties": {"text_prompt": {"default": null, "title": "Text Prompt", "type": "string"}, "input_image_url": {"default": null, "title": "Input Image Url", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "generate_hunyuan3d_modelArguments", "type": "object"}}}, {"type": "function", "function": {"name": "poll_hunyuan_job_status", "description": "\n    Check if the Hunyuan3D generation task is completed.\n\n    For Hunyuan3D:\n        Parameters:\n        - job_id: The job_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"DONE\".\n        The task is in progress if status is \"RUN\".\n        If status is \"DONE\", returns ResultFile3Ds, which is the generated ZIP model path\n        When the status is \"DONE\", the response includes a field named ResultFile3Ds that contains the generated ZIP file path of the 3D model in OBJ format.\n        This is a polling API, so only proceed if the status are finally determined (\"DONE\" or some failed state).\n    ", "parameters": {"properties": {"job_id": {"default": null, "title": "Job Id", "type": "string"}}, "title": "poll_hunyuan_job_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "import_generated_asset_hunyuan", "description": "\n    Import the asset generated by Hunyuan3D after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - zip_file_url: The zip_file_url given in the generate model step.\n\n    Return if the asset has been imported successfully.\n    ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "zip_file_url": {"title": "Zip File Url", "type": "string"}}, "required": ["name", "zip_file_url"], "title": "import_generated_asset_hunyuanArguments", "type": "object"}}}]}, "provider_attempts": [{"provider": "openrouter", "attempt": 1, "started_at": "2026-07-15T00:48:50.215440+00:00", "finished_at": "2026-07-15T00:48:54.048909+00:00", "duration_s": 3.8439999999827705, "request": {"method": "POST", "url": "https://openrouter.ai/api/v1/chat/completions", "headers": {"host": "openrouter.ai", "accept": "*/*", "accept-encoding": "gzip, deflate", "connection": "keep-alive", "user-agent": "python-httpx/0.28.1", "authorization": "[REDACTED]", "content-length": "20552", "content-type": "application/json"}, "body": "{\"model\":\"anthropic/claude-haiku-4.5\",\"messages\":[{\"role\":\"system\",\"content\":[{\"type\":\"text\",\"text\":\"You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\\n\\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\\n\\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\\n\\n## Harness Instructions\\n\\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. When you are finished, reply with the single word DONE and no tool calls.\",\"cache_control\":{\"type\":\"ephemeral\"}}]},{\"role\":\"user\",\"content\":\"## Task\\n\\nComplete the following Blender task:\\n\\n<task>\\n\\nA red sphere of 1 meter diameter resting on a white ground plane — touching it\\nexactly, neither floating nor sunk. Smooth shading, a plain red material on the\\nball, plain white on the floor.\\n\\nThe floor is the sphere, the color, and the contact. The ceiling is presentation:\\na soft believable shadow under the ball, a hint of glossiness that catches the\\nlight, and clean framing.\\n\\n</task>\"},{\"role\":\"assistant\",\"content\":\"I'll create a red sphere resting on a white ground plane with proper materials, shading, lighting, and camera setup. Let me work through this step by step.\",\"tool_calls\":[{\"id\":\"toolu_bdrk_01McQwtp5Bq6813NVu2gUpRG\",\"type\":\"function\",\"function\":{\"name\":\"get_scene_info\",\"arguments\":\"{\\\"user_prompt\\\": \\\"Get current scene info to see what's there initially\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"toolu_bdrk_01McQwtp5Bq6813NVu2gUpRG\",\"content\":\"{\\n  \\\"name\\\": \\\"Scene\\\",\\n  \\\"object_count\\\": 0,\\n  \\\"objects\\\": [],\\n  \\\"materials_count\\\": 2\\n}\"},{\"role\":\"assistant\",\"content\":\"Good, the scene is empty. Now I'll create the red sphere and white ground plane using Blender code:\",\"tool_calls\":[{\"id\":\"toolu_bdrk_01PuvjJZjfHJZgTRw6jGDsdH\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\": \\\"\\\\nimport bpy\\\\nimport bmesh\\\\nfrom mathutils import Vector\\\\n\\\\n# Clear any default objects\\\\nbpy.ops.object.select_all(action='SELECT')\\\\nbpy.ops.object.delete(use_global=False)\\\\n\\\\n# Add a sphere (1 meter diameter = 0.5 meter radius)\\\\nbpy.ops.mesh.primitive_uv_sphere_add(\\\\n    radius=0.5,\\\\n    location=(0, 0, 0.5)  # Position it so bottom touches z=0\\\\n)\\\\nsphere = bpy.context.active_object\\\\nsphere.name = \\\\\\\"RedSphere\\\\\\\"\\\\n\\\\n# Add a large ground plane\\\\nbpy.ops.mesh.primitive_plane_add(\\\\n    size=10,\\\\n    location=(0, 0, 0)\\\\n)\\\\nplane = bpy.context.active_object\\\\nplane.name = \\\\\\\"GroundPlane\\\\\\\"\\\\n\\\\n# Select and scale plane to be large but thin\\\\nplane.scale = (10, 10, 0.1)\\\\n\\\\nprint(\\\\\\\"Objects created: Sphere at z=\\\\\\\" + str(sphere.location.z) + \\\\\\\", Plane at z=\\\\\\\" + str(plane.location.z))\\\\n\\\", \\\"user_prompt\\\": \\\"Create red sphere and white ground plane\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"toolu_bdrk_01PuvjJZjfHJZgTRw6jGDsdH\",\"content\":[{\"type\":\"text\",\"text\":\"Code executed successfully: Objects created: Sphere at z=0.5, Plane at z=0.0\\n\",\"cache_control\":{\"type\":\"ephemeral\"}}]}],\"usage\":{\"include\":true},\"tools\":[{\"type\":\"function\",\"function\":{\"name\":\"get_scene_info\",\"description\":\"Get detailed information about the current Blender scene\\n\\n    Parameters:\\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"user_prompt\"],\"title\":\"get_scene_infoArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_object_info\",\"description\":\"\\n    Get detailed information about a specific object in the Blender scene.\\n\\n    Parameters:\\n    - object_name: The name of the object to get information about\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"object_name\":{\"title\":\"Object Name\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"object_name\"],\"title\":\"get_object_infoArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_viewport_screenshot\",\"description\":\"\\n    Capture a screenshot of the current Blender 3D viewport.\\n\\n    Parameters:\\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns the screenshot as an Image.\\n    \",\"parameters\":{\"properties\":{\"max_size\":{\"default\":1000,\"title\":\"Max Size\",\"type\":\"integer\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_viewport_screenshotArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"description\":\"\\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\\n\\n    Parameters:\\n    - code: The Python code to execute\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"code\":{\"title\":\"Code\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"code\"],\"title\":\"execute_blender_codeArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_categories\",\"description\":\"\\n    Get a list of categories for a specific asset type on Polyhaven.\\n\\n    Parameters:\\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"asset_type\":{\"default\":\"hdris\",\"title\":\"Asset Type\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_polyhaven_categoriesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_polyhaven_assets\",\"description\":\"\\n    Search for assets on Polyhaven with optional filtering.\\n\\n    Parameters:\\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\\n    - categories: Optional comma-separated list of categories to filter by\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns a list of matching assets with basic information.\\n    \",\"parameters\":{\"properties\":{\"asset_type\":{\"default\":\"all\",\"title\":\"Asset Type\",\"type\":\"string\"},\"categories\":{\"default\":null,\"title\":\"Categories\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"search_polyhaven_assetsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"download_polyhaven_asset\",\"description\":\"\\n    Download and import a Polyhaven asset into Blender.\\n\\n    Parameters:\\n    - asset_id: The ID of the asset to download\\n    - asset_type: The type of asset (hdris, textures, models)\\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"asset_id\":{\"title\":\"Asset Id\",\"type\":\"string\"},\"asset_type\":{\"title\":\"Asset Type\",\"type\":\"string\"},\"resolution\":{\"default\":\"1k\",\"title\":\"Resolution\",\"type\":\"string\"},\"file_format\":{\"default\":null,\"title\":\"File Format\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"asset_id\",\"asset_type\"],\"title\":\"download_polyhaven_assetArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"set_texture\",\"description\":\"\\n    Apply a previously downloaded Polyhaven texture to an object.\\n    \\n    Parameters:\\n    - object_name: Name of the object to apply the texture to\\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\\n    \\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"object_name\":{\"title\":\"Object Name\",\"type\":\"string\"},\"texture_id\":{\"title\":\"Texture Id\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"object_name\",\"texture_id\"],\"title\":\"set_textureArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_status\",\"description\":\"\\n    Check if PolyHaven integration is enabled in Blender.\\n    Returns a message indicating whether PolyHaven features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_polyhaven_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_hyper3d_status\",\"description\":\"\\n    Check if Hyper3D Rodin integration is enabled in Blender.\\n    Returns a message indicating whether Hyper3D Rodin features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_hyper3d_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_status\",\"description\":\"\\n    Check if Sketchfab integration is enabled in Blender.\\n    Returns a message indicating whether Sketchfab features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_sketchfab_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_sketchfab_models\",\"description\":\"\\n    Search for models on Sketchfab with optional filtering.\\n\\n    Parameters:\\n    - query: Text to search for\\n    - categories: Optional comma-separated list of categories\\n    - count: Maximum number of results to return (default 20)\\n    - downloadable: Whether to include only downloadable models (default True)\\n\\n    Returns a formatted list of matching models.\\n    \",\"parameters\":{\"properties\":{\"query\":{\"title\":\"Query\",\"type\":\"string\"},\"categories\":{\"default\":null,\"title\":\"Categories\",\"type\":\"string\"},\"count\":{\"default\":20,\"title\":\"Count\",\"type\":\"integer\"},\"downloadable\":{\"default\":true,\"title\":\"Downloadable\",\"type\":\"boolean\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"query\"],\"title\":\"search_sketchfab_modelsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_model_preview\",\"description\":\"\\n    Get a preview thumbnail of a Sketchfab model by its UID.\\n    Use this to visually confirm a model before downloading.\\n    \\n    Parameters:\\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\\n    \\n    Returns the model's thumbnail as an Image for visual confirmation.\\n    \",\"parameters\":{\"properties\":{\"uid\":{\"title\":\"Uid\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"uid\"],\"title\":\"get_sketchfab_model_previewArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"download_sketchfab_model\",\"description\":\"\\n    Download and import a Sketchfab model by its UID.\\n    The model will be scaled so its largest dimension equals target_size.\\n    \\n    Parameters:\\n    - uid: The unique identifier of the Sketchfab model\\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\\n                  You must specify the desired size for the model.\\n                  Examples:\\n                  - Chair: target_size=1.0 (1 meter tall)\\n                  - Table: target_size=0.75 (75cm tall)\\n                  - Car: target_size=4.5 (4.5 meters long)\\n                  - Person: target_size=1.7 (1.7 meters tall)\\n                  - Small object (cup, phone): target_size=0.1 to 0.3\\n    \\n    Returns a message with import details including object names, dimensions, and bounding box.\\n    The model must be downloadable and you must have proper access rights.\\n    \",\"parameters\":{\"properties\":{\"uid\":{\"title\":\"Uid\",\"type\":\"string\"},\"target_size\":{\"title\":\"Target Size\",\"type\":\"number\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"uid\",\"target_size\"],\"title\":\"download_sketchfab_modelArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hyper3d_model_via_text\",\"description\":\"\\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\\n    The 3D asset has built-in materials.\\n    The generated model has a normalized size, so re-scaling after generation can be useful.\\n\\n    Parameters:\\n    - text_prompt: A short description of the desired model in **English**.\\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\\n\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"text_prompt\":{\"title\":\"Text Prompt\",\"type\":\"string\"},\"bbox_condition\":{\"default\":null,\"items\":{\"type\":\"number\"},\"title\":\"Bbox Condition\",\"type\":\"array\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"text_prompt\"],\"title\":\"generate_hyper3d_model_via_textArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hyper3d_model_via_images\",\"description\":\"\\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\\n    The 3D asset has built-in materials.\\n    The generated model has a normalized size, so re-scaling after generation can be useful.\\n    \\n    Parameters:\\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model.\\n\\n    Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode.\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"input_image_paths\":{\"default\":null,\"items\":{\"type\":\"string\"},\"title\":\"Input Image Paths\",\"type\":\"array\"},\"input_image_urls\":{\"default\":null,\"items\":{\"type\":\"string\"},\"title\":\"Input Image Urls\",\"type\":\"array\"},\"bbox_condition\":{\"default\":null,\"items\":{\"type\":\"number\"},\"title\":\"Bbox Condition\",\"type\":\"array\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"generate_hyper3d_model_via_imagesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"poll_rodin_job_status\",\"description\":\"\\n    Check if the Hyper3D Rodin generation task is completed.\\n\\n    For Hyper3D Rodin mode MAIN_SITE:\\n        Parameters:\\n        - subscription_key: The subscription_key given in the generate model step.\\n\\n        Returns a list of status. The task is done if all status are \\\"Done\\\".\\n        If \\\"Failed\\\" showed up, the generating process failed.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"Done\\\" or \\\"Canceled\\\").\\n\\n    For Hyper3D Rodin mode FAL_AI:\\n        Parameters:\\n        - request_id: The request_id given in the generate model step.\\n\\n        Returns the generation task status. The task is done if status is \\\"COMPLETED\\\".\\n        The task is in progress if status is \\\"IN_PROGRESS\\\".\\n        If status other than \\\"COMPLETED\\\", \\\"IN_PROGRESS\\\", \\\"IN_QUEUE\\\" showed up, the generating process might be failed.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"COMPLETED\\\" or some failed state).\\n    \",\"parameters\":{\"properties\":{\"subscription_key\":{\"default\":null,\"title\":\"Subscription Key\",\"type\":\"string\"},\"request_id\":{\"default\":null,\"title\":\"Request Id\",\"type\":\"string\"}},\"title\":\"poll_rodin_job_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"import_generated_asset\",\"description\":\"\\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\\n\\n    Parameters:\\n    - name: The name of the object in scene\\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\\n\\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\\n    Return if the asset has been imported successfully.\\n    \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"task_uuid\":{\"default\":null,\"title\":\"Task Uuid\",\"type\":\"string\"},\"request_id\":{\"default\":null,\"title\":\"Request Id\",\"type\":\"string\"}},\"required\":[\"name\"],\"title\":\"import_generated_assetArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_hunyuan3d_status\",\"description\":\"\\n    Check if Hunyuan3D integration is enabled in Blender.\\n    Returns a message indicating whether Hunyuan3D features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_hunyuan3d_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hunyuan3d_model\",\"description\":\"\\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \\n    or both for the desired asset, and import the asset into Blender.\\n    The 3D asset has built-in materials.\\n    \\n    Parameters:\\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\\n    - input_image_url: (Optional) The local or remote url of the input image. Accepts None if only using text prompt.\\n\\n    Returns: \\n    - When successful, returns a JSON with job_id (format: \\\"job_xxx\\\") indicating the task is in progress\\n    - When the job completes, the status will change to \\\"DONE\\\" indicating the model has been imported\\n    - Returns error message if the operation fails\\n    \",\"parameters\":{\"properties\":{\"text_prompt\":{\"default\":null,\"title\":\"Text Prompt\",\"type\":\"string\"},\"input_image_url\":{\"default\":null,\"title\":\"Input Image Url\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"generate_hunyuan3d_modelArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"poll_hunyuan_job_status\",\"description\":\"\\n    Check if the Hunyuan3D generation task is completed.\\n\\n    For Hunyuan3D:\\n        Parameters:\\n        - job_id: The job_id given in the generate model step.\\n\\n        Returns the generation task status. The task is done if status is \\\"DONE\\\".\\n        The task is in progress if status is \\\"RUN\\\".\\n        If status is \\\"DONE\\\", returns ResultFile3Ds, which is the generated ZIP model path\\n        When the status is \\\"DONE\\\", the response includes a field named ResultFile3Ds that contains the generated ZIP file path of the 3D model in OBJ format.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"DONE\\\" or some failed state).\\n    \",\"parameters\":{\"properties\":{\"job_id\":{\"default\":null,\"title\":\"Job Id\",\"type\":\"string\"}},\"title\":\"poll_hunyuan_job_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"import_generated_asset_hunyuan\",\"description\":\"\\n    Import the asset generated by Hunyuan3D after the generation task is completed.\\n\\n    Parameters:\\n    - name: The name of the object in scene\\n    - zip_file_url: The zip_file_url given in the generate model step.\\n\\n    Return if the asset has been imported successfully.\\n    \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"zip_file_url\":{\"title\":\"Zip File Url\",\"type\":\"string\"}},\"required\":[\"name\",\"zip_file_url\"],\"title\":\"import_generated_asset_hunyuanArguments\",\"type\":\"object\"}}}],\"reasoning\":{\"effort\":\"xhigh\"}}", "body_base64": "{"model":"anthropic/claude-haiku-4.5","messages":[{"role":"system","content":[{"type":"text","text":"You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\n\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\n\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\n\n## Harness Instructions\n\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. When you are finished, reply with the single word DONE and no tool calls.","cache_control":{"type":"ephemeral"}}]},{"role":"user","content":"## Task\n\nComplete the following Blender task:\n\n<task>\n\nA red sphere of 1 meter diameter resting on a white ground plane — touching it\nexactly, neither floating nor sunk. Smooth shading, a plain red material on the\nball, plain white on the floor.\n\nThe floor is the sphere, the color, and the contact. The ceiling is presentation:\na soft believable shadow under the ball, a hint of glossiness that catches the\nlight, and clean framing.\n\n</task>"},{"role":"assistant","content":"I'll create a red sphere resting on a white ground plane with proper materials, shading, lighting, and camera setup. Let me work through this step by step.","tool_calls":[{"id":"toolu_bdrk_01McQwtp5Bq6813NVu2gUpRG","type":"function","function":{"name":"get_scene_info","arguments":"{\"user_prompt\": \"Get current scene info to see what's there initially\"}"}}]},{"role":"tool","tool_call_id":"toolu_bdrk_01McQwtp5Bq6813NVu2gUpRG","content":"{\n  \"name\": \"Scene\",\n  \"object_count\": 0,\n  \"objects\": [],\n  \"materials_count\": 2\n}"},{"role":"assistant","content":"Good, the scene is empty. Now I'll create the red sphere and white ground plane using Blender code:","tool_calls":[{"id":"toolu_bdrk_01PuvjJZjfHJZgTRw6jGDsdH","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\": \"\\nimport bpy\\nimport bmesh\\nfrom mathutils import Vector\\n\\n# Clear any default objects\\nbpy.ops.object.select_all(action='SELECT')\\nbpy.ops.object.delete(use_global=False)\\n\\n# Add a sphere (1 meter diameter = 0.5 meter radius)\\nbpy.ops.mesh.primitive_uv_sphere_add(\\n    radius=0.5,\\n    location=(0, 0, 0.5)  # Position it so bottom touches z=0\\n)\\nsphere = bpy.context.active_object\\nsphere.name = \\\"RedSphere\\\"\\n\\n# Add a large ground plane\\nbpy.ops.mesh.primitive_plane_add(\\n    size=10,\\n    location=(0, 0, 0)\\n)\\nplane = bpy.context.active_object\\nplane.name = \\\"GroundPlane\\\"\\n\\n# Select and scale plane to be large but thin\\nplane.scale = (10, 10, 0.1)\\n\\nprint(\\\"Objects created: Sphere at z=\\\" + str(sphere.location.z) + \\\", Plane at z=\\\" + str(plane.location.z))\\n\", \"user_prompt\": \"Create red sphere and white ground plane\"}"}}]},{"role":"tool","tool_call_id":"toolu_bdrk_01PuvjJZjfHJZgTRw6jGDsdH","content":[{"type":"text","text":"Code executed successfully: Objects created: Sphere at z=0.5, Plane at z=0.0\n","cache_control":{"type":"ephemeral"}}]}],"usage":{"include":true},"tools":[{"type":"function","function":{"name":"get_scene_info","description":"Get detailed information about the current Blender scene\n\n    Parameters:\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\n    ","parameters":{"properties":{"user_prompt":{"title":"User Prompt","type":"string"}},"required":["user_prompt"],"title":"get_scene_infoArguments","type":"object"}}},{"type":"function","function":{"name":"get_object_info","description":"\n    Get detailed information about a specific object in the Blender scene.\n\n    Parameters:\n    - object_name: The name of the object to get information about\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"object_name":{"title":"Object Name","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["object_name"],"title":"get_object_infoArguments","type":"object"}}},{"type":"function","function":{"name":"get_viewport_screenshot","description":"\n    Capture a screenshot of the current Blender 3D viewport.\n\n    Parameters:\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns the screenshot as an Image.\n    ","parameters":{"properties":{"max_size":{"default":1000,"title":"Max Size","type":"integer"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_viewport_screenshotArguments","type":"object"}}},{"type":"function","function":{"name":"execute_blender_code","description":"\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\n\n    Parameters:\n    - code: The Python code to execute\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"code":{"title":"Code","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["code"],"title":"execute_blender_codeArguments","type":"object"}}},{"type":"function","function":{"name":"get_polyhaven_categories","description":"\n    Get a list of categories for a specific asset type on Polyhaven.\n\n    Parameters:\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"asset_type":{"default":"hdris","title":"Asset Type","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_polyhaven_categoriesArguments","type":"object"}}},{"type":"function","function":{"name":"search_polyhaven_assets","description":"\n    Search for assets on Polyhaven with optional filtering.\n\n    Parameters:\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\n    - categories: Optional comma-separated list of categories to filter by\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a list of matching assets with basic information.\n    ","parameters":{"properties":{"asset_type":{"default":"all","title":"Asset Type","type":"string"},"categories":{"default":null,"title":"Categories","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"search_polyhaven_assetsArguments","type":"object"}}},{"type":"function","function":{"name":"download_polyhaven_asset","description":"\n    Download and import a Polyhaven asset into Blender.\n\n    Parameters:\n    - asset_id: The ID of the asset to download\n    - asset_type: The type of asset (hdris, textures, models)\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a message indicating success or failure.\n    ","parameters":{"properties":{"asset_id":{"title":"Asset Id","type":"string"},"asset_type":{"title":"Asset Type","type":"string"},"resolution":{"default":"1k","title":"Resolution","type":"string"},"file_format":{"default":null,"title":"File Format","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["asset_id","asset_type"],"title":"download_polyhaven_assetArguments","type":"object"}}},{"type":"function","function":{"name":"set_texture","description":"\n    Apply a previously downloaded Polyhaven texture to an object.\n    \n    Parameters:\n    - object_name: Name of the object to apply the texture to\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\n    \n    Returns a message indicating success or failure.\n    ","parameters":{"properties":{"object_name":{"title":"Object Name","type":"string"},"texture_id":{"title":"Texture Id","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["object_name","texture_id"],"title":"set_textureArguments","type":"object"}}},{"type":"function","function":{"name":"get_polyhaven_status","description":"\n    Check if PolyHaven integration is enabled in Blender.\n    Returns a message indicating whether PolyHaven features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_polyhaven_statusArguments","type":"object"}}},{"type":"function","function":{"name":"get_hyper3d_status","description":"\n    Check if Hyper3D Rodin integration is enabled in Blender.\n    Returns a message indicating whether Hyper3D Rodin features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_hyper3d_statusArguments","type":"object"}}},{"type":"function","function":{"name":"get_sketchfab_status","description":"\n    Check if Sketchfab integration is enabled in Blender.\n    Returns a message indicating whether Sketchfab features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_sketchfab_statusArguments","type":"object"}}},{"type":"function","function":{"name":"search_sketchfab_models","description":"\n    Search for models on Sketchfab with optional filtering.\n\n    Parameters:\n    - query: Text to search for\n    - categories: Optional comma-separated list of categories\n    - count: Maximum number of results to return (default 20)\n    - downloadable: Whether to include only downloadable models (default True)\n\n    Returns a formatted list of matching models.\n    ","parameters":{"properties":{"query":{"title":"Query","type":"string"},"categories":{"default":null,"title":"Categories","type":"string"},"count":{"default":20,"title":"Count","type":"integer"},"downloadable":{"default":true,"title":"Downloadable","type":"boolean"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["query"],"title":"search_sketchfab_modelsArguments","type":"object"}}},{"type":"function","function":{"name":"get_sketchfab_model_preview","description":"\n    Get a preview thumbnail of a Sketchfab model by its UID.\n    Use this to visually confirm a model before downloading.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\n    \n    Returns the model's thumbnail as an Image for visual confirmation.\n    ","parameters":{"properties":{"uid":{"title":"Uid","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["uid"],"title":"get_sketchfab_model_previewArguments","type":"object"}}},{"type":"function","function":{"name":"download_sketchfab_model","description":"\n    Download and import a Sketchfab model by its UID.\n    The model will be scaled so its largest dimension equals target_size.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\n                  You must specify the desired size for the model.\n                  Examples:\n                  - Chair: target_size=1.0 (1 meter tall)\n                  - Table: target_size=0.75 (75cm tall)\n                  - Car: target_size=4.5 (4.5 meters long)\n                  - Person: target_size=1.7 (1.7 meters tall)\n                  - Small object (cup, phone): target_size=0.1 to 0.3\n    \n    Returns a message with import details including object names, dimensions, and bounding box.\n    The model must be downloadable and you must have proper access rights.\n    ","parameters":{"properties":{"uid":{"title":"Uid","type":"string"},"target_size":{"title":"Target Size","type":"number"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["uid","target_size"],"title":"download_sketchfab_modelArguments","type":"object"}}},{"type":"function","function":{"name":"generate_hyper3d_model_via_text","description":"\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n\n    Parameters:\n    - text_prompt: A short description of the desired model in **English**.\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Returns a message indicating success or failure.\n    ","parameters":{"properties":{"text_prompt":{"title":"Text Prompt","type":"string"},"bbox_condition":{"default":null,"items":{"type":"number"},"title":"Bbox Condition","type":"array"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["text_prompt"],"title":"generate_hyper3d_model_via_textArguments","type":"object"}}},{"type":"function","function":{"name":"generate_hyper3d_model_via_images","description":"\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n    \n    Parameters:\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. 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The task is done if all status are \"Done\".\n        If \"Failed\" showed up, the generating process failed.\n        This is a polling API, so only proceed if the status are finally determined (\"Done\" or \"Canceled\").\n\n    For Hyper3D Rodin mode FAL_AI:\n        Parameters:\n        - request_id: The request_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"COMPLETED\".\n        The task is in progress if status is \"IN_PROGRESS\".\n        If status other than \"COMPLETED\", \"IN_PROGRESS\", \"IN_QUEUE\" showed up, the generating process might be failed.\n        This is a polling API, so only proceed if the status are finally determined (\"COMPLETED\" or some failed state).\n    ","parameters":{"properties":{"subscription_key":{"default":null,"title":"Subscription Key","type":"string"},"request_id":{"default":null,"title":"Request Id","type":"string"}},"title":"poll_rodin_job_statusArguments","type":"object"}}},{"type":"function","function":{"name":"import_generated_asset","description":"\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\n\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\n    Return if the asset has been imported successfully.\n    ","parameters":{"properties":{"name":{"title":"Name","type":"string"},"task_uuid":{"default":null,"title":"Task Uuid","type":"string"},"request_id":{"default":null,"title":"Request Id","type":"string"}},"required":["name"],"title":"import_generated_assetArguments","type":"object"}}},{"type":"function","function":{"name":"get_hunyuan3d_status","description":"\n    Check if Hunyuan3D integration is enabled in Blender.\n    Returns a message indicating whether Hunyuan3D features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_hunyuan3d_statusArguments","type":"object"}}},{"type":"function","function":{"name":"generate_hunyuan3d_model","description":"\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \n    or both for the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    \n    Parameters:\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\n    - input_image_url: (Optional) The local or remote url of the input image. Accepts None if only using text prompt.\n\n    Returns: \n    - When successful, returns a JSON with job_id (format: \"job_xxx\") indicating the task is in progress\n    - When the job completes, the status will change to \"DONE\" indicating the model has been imported\n    - Returns error message if the operation fails\n    ","parameters":{"properties":{"text_prompt":{"default":null,"title":"Text Prompt","type":"string"},"input_image_url":{"default":null,"title":"Input Image Url","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"generate_hunyuan3d_modelArguments","type":"object"}}},{"type":"function","function":{"name":"poll_hunyuan_job_status","description":"\n    Check if the Hunyuan3D generation task is completed.\n\n    For Hunyuan3D:\n        Parameters:\n        - job_id: The job_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"DONE\".\n        The task is in progress if status is \"RUN\".\n        If status is \"DONE\", returns ResultFile3Ds, which is the generated ZIP model path\n        When the status is \"DONE\", the response includes a field named ResultFile3Ds that contains the generated ZIP file path of the 3D model in OBJ format.\n        This is a polling API, so only proceed if the status are finally determined (\"DONE\" or some failed state).\n    ","parameters":{"properties":{"job_id":{"default":null,"title":"Job Id","type":"string"}},"title":"poll_hunyuan_job_statusArguments","type":"object"}}},{"type":"function","function":{"name":"import_generated_asset_hunyuan","description":"\n    Import the asset generated by Hunyuan3D after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - zip_file_url: The zip_file_url given in the generate model step.\n\n    Return if the asset has been imported successfully.\n    ","parameters":{"properties":{"name":{"title":"Name","type":"string"},"zip_file_url":{"title":"Zip File Url","type":"string"}},"required":["name","zip_file_url"],"title":"import_generated_asset_hunyuanArguments","type":"object"}}}],"reasoning":{"effort":"xhigh"}}"}, "response": {"status_code": 200, "headers": {"date": "Wed, 15 Jul 2026 00:48:50 GMT", "content-type": "application/json", "transfer-encoding": "chunked", "connection": "keep-alive", "access-control-allow-origin": "*", "x-generation-id": "gen-1784076529-jHrZQilgVTrb6G1H1xJ3", "access-control-expose-headers": "X-Generation-Id,X-Provider-Name,cf-ray", "set-cookie": "[REDACTED]", "permissions-policy": "payment=(self \"https://checkout.stripe.com\" \"https://connect-js.stripe.com\" \"https://js.stripe.com\" \"https://*.js.stripe.com\" \"https://hooks.stripe.com\")", "referrer-policy": "no-referrer, strict-origin-when-cross-origin", "x-content-type-options": "nosniff", "content-encoding": "gzip", "server": "cloudflare", "cf-ray": "a1b4c3829a104ec5-MAD"}, "body": "\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n{\"id\":\"gen-1784076529-jHrZQilgVTrb6G1H1xJ3\",\"object\":\"chat.completion\",\"created\":1784076529,\"model\":\"anthropic/claude-haiku-4.5\",\"provider\":\"Amazon Bedrock\",\"system_fingerprint\":null,\"service_tier\":null,\"choices\":[{\"index\":0,\"logprobs\":null,\"finish_reason\":\"tool_calls\",\"native_finish_reason\":\"tool_use\",\"message\":{\"role\":\"assistant\",\"content\":\"Now I'll create the materials - red for the sphere and white for the plane:\",\"refusal\":null,\"reasoning\":null,\"tool_calls\":[{\"type\":\"function\",\"index\":0,\"id\":\"toolu_bdrk_018gHZ2vPGV9JR5pgaWmu5aZ\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\": \\\"\\\\nimport bpy\\\\n\\\\n# Get the sphere and plane objects\\\\nsphere = bpy.data.objects[\\\\\\\"RedSphere\\\\\\\"]\\\\nplane = bpy.data.objects[\\\\\\\"GroundPlane\\\\\\\"]\\\\n\\\\n# Create red material for sphere\\\\nred_material = bpy.data.materials.new(name=\\\\\\\"RedMaterial\\\\\\\")\\\\nred_material.use_nodes = True\\\\nred_material.diffuse_color = (1.0, 0.0, 0.0, 1.0)  # Red RGBA\\\\n\\\\n# Configure red material with some glossiness\\\\nbsdf = red_material.node_tree.nodes[\\\\\\\"Principled BSDF\\\\\\\"]\\\\nbsdf.inputs[\\\\\\\"Base Color\\\\\\\"].default_value = (0.8, 0.05, 0.05, 1.0)  # Pure red\\\\nbsdf.inputs[\\\\\\\"Roughness\\\\\\\"].default_value = 0.3  # Slight glossiness\\\\n\\\\n# Create white material for plane\\\\nwhite_material = bpy.data.materials.new(name=\\\\\\\"WhiteMaterial\\\\\\\")\\\\nwhite_material.use_nodes = True\\\\nwhite_material.diffuse_color = (1.0, 1.0, 1.0, 1.0)  # White RGBA\\\\n\\\\nbsdf_white = white_material.node_tree.nodes[\\\\\\\"Principled BSDF\\\\\\\"]\\\\nbsdf_white.inputs[\\\\\\\"Base Color\\\\\\\"].default_value = (1.0, 1.0, 1.0, 1.0)  # White\\\\nbsdf_white.inputs[\\\\\\\"Roughness\\\\\\\"].default_value = 0.8  # Matte white\\\\n\\\\n# Assign materials to objects\\\\nsphere.data.materials.append(red_material)\\\\nplane.data.materials.append(white_material)\\\\n\\\\nprint(\\\\\\\"Materials created and assigned\\\\\\\")\\\\n\\\", \\\"user_prompt\\\": \\\"Create and assign red and white materials\\\"}\"}}]}}],\"usage\":{\"prompt_tokens\":6381,\"completion_tokens\":513,\"total_tokens\":6894,\"cost\":0.00368255,\"is_byok\":false,\"prompt_tokens_details\":{\"cached_tokens\":5963,\"cache_write_tokens\":413,\"audio_tokens\":0,\"video_tokens\":0},\"cache_creation\":{\"ephemeral_5m_input_tokens\":413,\"ephemeral_1h_input_tokens\":0},\"cost_details\":{\"upstream_inference_cost\":0.00368255,\"upstream_inference_prompt_cost\":0.00111755,\"upstream_inference_completions_cost\":0.002565},\"completion_tokens_details\":{\"reasoning_tokens\":0,\"image_tokens\":0,\"audio_tokens\":0}}}", "body_base64": 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"error": null}], "response": {"text": "Now I'll create the materials - red for the sphere and white for the plane:", "tool_calls": [{"id": "toolu_bdrk_018gHZ2vPGV9JR5pgaWmu5aZ", "name": "execute_blender_code", "arguments": {"code": "\nimport bpy\n\n# Get the sphere and plane objects\nsphere = bpy.data.objects[\"RedSphere\"]\nplane = bpy.data.objects[\"GroundPlane\"]\n\n# Create red material for sphere\nred_material = bpy.data.materials.new(name=\"RedMaterial\")\nred_material.use_nodes = True\nred_material.diffuse_color = (1.0, 0.0, 0.0, 1.0)  # Red RGBA\n\n# Configure red material with some glossiness\nbsdf = red_material.node_tree.nodes[\"Principled BSDF\"]\nbsdf.inputs[\"Base Color\"].default_value = (0.8, 0.05, 0.05, 1.0)  # Pure red\nbsdf.inputs[\"Roughness\"].default_value = 0.3  # Slight glossiness\n\n# Create white material for plane\nwhite_material = bpy.data.materials.new(name=\"WhiteMaterial\")\nwhite_material.use_nodes = True\nwhite_material.diffuse_color = (1.0, 1.0, 1.0, 1.0)  # White RGBA\n\nbsdf_white = white_material.node_tree.nodes[\"Principled BSDF\"]\nbsdf_white.inputs[\"Base Color\"].default_value = (1.0, 1.0, 1.0, 1.0)  # White\nbsdf_white.inputs[\"Roughness\"].default_value = 0.8  # Matte white\n\n# Assign materials to objects\nsphere.data.materials.append(red_material)\nplane.data.materials.append(white_material)\n\nprint(\"Materials created and assigned\")\n", "user_prompt": "Create and assign red and white materials"}, "raw_arguments": "{\"code\": \"\\nimport bpy\\n\\n# Get the sphere and plane objects\\nsphere = bpy.data.objects[\\\"RedSphere\\\"]\\nplane = bpy.data.objects[\\\"GroundPlane\\\"]\\n\\n# Create red material for sphere\\nred_material = bpy.data.materials.new(name=\\\"RedMaterial\\\")\\nred_material.use_nodes = True\\nred_material.diffuse_color = (1.0, 0.0, 0.0, 1.0)  # Red RGBA\\n\\n# Configure red material with some glossiness\\nbsdf = red_material.node_tree.nodes[\\\"Principled BSDF\\\"]\\nbsdf.inputs[\\\"Base Color\\\"].default_value = (0.8, 0.05, 0.05, 1.0)  # Pure red\\nbsdf.inputs[\\\"Roughness\\\"].default_value = 0.3  # Slight glossiness\\n\\n# Create white material for plane\\nwhite_material = bpy.data.materials.new(name=\\\"WhiteMaterial\\\")\\nwhite_material.use_nodes = True\\nwhite_material.diffuse_color = (1.0, 1.0, 1.0, 1.0)  # White RGBA\\n\\nbsdf_white = white_material.node_tree.nodes[\\\"Principled BSDF\\\"]\\nbsdf_white.inputs[\\\"Base Color\\\"].default_value = (1.0, 1.0, 1.0, 1.0)  # White\\nbsdf_white.inputs[\\\"Roughness\\\"].default_value = 0.8  # Matte white\\n\\n# Assign materials to objects\\nsphere.data.materials.append(red_material)\\nplane.data.materials.append(white_material)\\n\\nprint(\\\"Materials created and assigned\\\")\\n\", \"user_prompt\": \"Create and assign red and white materials\"}", "parse_error": null}], "tokens_in": 6381, "tokens_out": 513, "cost_usd": 0.00368255, "cached_tokens": 5963}, "error": null}
{"ts": "2026-07-15T00:48:54.051012+00:00", "type": "assistant", "text": "Now I'll create the materials - red for the sphere and white for the plane:", "tool_calls": [{"id": "toolu_bdrk_018gHZ2vPGV9JR5pgaWmu5aZ", "name": "execute_blender_code", "arguments": {"code": "\nimport bpy\n\n# Get the sphere and plane objects\nsphere = bpy.data.objects[\"RedSphere\"]\nplane = bpy.data.objects[\"GroundPlane\"]\n\n# Create red material for sphere\nred_material = bpy.data.materials.new(name=\"RedMaterial\")\nred_material.use_nodes = True\nred_material.diffuse_color = (1.0, 0.0, 0.0, 1.0)  # Red RGBA\n\n# Configure red material with some glossiness\nbsdf = red_material.node_tree.nodes[\"Principled BSDF\"]\nbsdf.inputs[\"Base Color\"].default_value = (0.8, 0.05, 0.05, 1.0)  # Pure red\nbsdf.inputs[\"Roughness\"].default_value = 0.3  # Slight glossiness\n\n# Create white material for plane\nwhite_material = bpy.data.materials.new(name=\"WhiteMaterial\")\nwhite_material.use_nodes = True\nwhite_material.diffuse_color = (1.0, 1.0, 1.0, 1.0)  # White RGBA\n\nbsdf_white = white_material.node_tree.nodes[\"Principled BSDF\"]\nbsdf_white.inputs[\"Base Color\"].default_value = (1.0, 1.0, 1.0, 1.0)  # White\nbsdf_white.inputs[\"Roughness\"].default_value = 0.8  # Matte white\n\n# Assign materials to objects\nsphere.data.materials.append(red_material)\nplane.data.materials.append(white_material)\n\nprint(\"Materials created and assigned\")\n", "user_prompt": "Create and assign red and white materials"}, "raw_arguments": "{\"code\": \"\\nimport bpy\\n\\n# Get the sphere and plane objects\\nsphere = bpy.data.objects[\\\"RedSphere\\\"]\\nplane = bpy.data.objects[\\\"GroundPlane\\\"]\\n\\n# Create red material for sphere\\nred_material = bpy.data.materials.new(name=\\\"RedMaterial\\\")\\nred_material.use_nodes = True\\nred_material.diffuse_color = (1.0, 0.0, 0.0, 1.0)  # Red RGBA\\n\\n# Configure red material with some glossiness\\nbsdf = red_material.node_tree.nodes[\\\"Principled BSDF\\\"]\\nbsdf.inputs[\\\"Base Color\\\"].default_value = (0.8, 0.05, 0.05, 1.0)  # Pure red\\nbsdf.inputs[\\\"Roughness\\\"].default_value = 0.3  # Slight glossiness\\n\\n# Create white material for plane\\nwhite_material = bpy.data.materials.new(name=\\\"WhiteMaterial\\\")\\nwhite_material.use_nodes = True\\nwhite_material.diffuse_color = (1.0, 1.0, 1.0, 1.0)  # White RGBA\\n\\nbsdf_white = white_material.node_tree.nodes[\\\"Principled BSDF\\\"]\\nbsdf_white.inputs[\\\"Base Color\\\"].default_value = (1.0, 1.0, 1.0, 1.0)  # White\\nbsdf_white.inputs[\\\"Roughness\\\"].default_value = 0.8  # Matte white\\n\\n# Assign materials to objects\\nsphere.data.materials.append(red_material)\\nplane.data.materials.append(white_material)\\n\\nprint(\\\"Materials created and assigned\\\")\\n\", \"user_prompt\": \"Create and assign red and white materials\"}", "parse_error": null}]}
{"ts": "2026-07-15T00:48:54.051012+00:00", "type": "tool_call", "id": "toolu_bdrk_018gHZ2vPGV9JR5pgaWmu5aZ", "name": "execute_blender_code", "arguments": {"code": "\nimport bpy\n\n# Get the sphere and plane objects\nsphere = bpy.data.objects[\"RedSphere\"]\nplane = bpy.data.objects[\"GroundPlane\"]\n\n# Create red material for sphere\nred_material = bpy.data.materials.new(name=\"RedMaterial\")\nred_material.use_nodes = True\nred_material.diffuse_color = (1.0, 0.0, 0.0, 1.0)  # Red RGBA\n\n# Configure red material with some glossiness\nbsdf = red_material.node_tree.nodes[\"Principled BSDF\"]\nbsdf.inputs[\"Base Color\"].default_value = (0.8, 0.05, 0.05, 1.0)  # Pure red\nbsdf.inputs[\"Roughness\"].default_value = 0.3  # Slight glossiness\n\n# Create white material for plane\nwhite_material = bpy.data.materials.new(name=\"WhiteMaterial\")\nwhite_material.use_nodes = True\nwhite_material.diffuse_color = (1.0, 1.0, 1.0, 1.0)  # White RGBA\n\nbsdf_white = white_material.node_tree.nodes[\"Principled BSDF\"]\nbsdf_white.inputs[\"Base Color\"].default_value = (1.0, 1.0, 1.0, 1.0)  # White\nbsdf_white.inputs[\"Roughness\"].default_value = 0.8  # Matte white\n\n# Assign materials to objects\nsphere.data.materials.append(red_material)\nplane.data.materials.append(white_material)\n\nprint(\"Materials created and assigned\")\n", "user_prompt": "Create and assign red and white materials"}, "raw_arguments": "{\"code\": \"\\nimport bpy\\n\\n# Get the sphere and plane objects\\nsphere = bpy.data.objects[\\\"RedSphere\\\"]\\nplane = bpy.data.objects[\\\"GroundPlane\\\"]\\n\\n# Create red material for sphere\\nred_material = bpy.data.materials.new(name=\\\"RedMaterial\\\")\\nred_material.use_nodes = True\\nred_material.diffuse_color = (1.0, 0.0, 0.0, 1.0)  # Red RGBA\\n\\n# Configure red material with some glossiness\\nbsdf = red_material.node_tree.nodes[\\\"Principled BSDF\\\"]\\nbsdf.inputs[\\\"Base Color\\\"].default_value = (0.8, 0.05, 0.05, 1.0)  # Pure red\\nbsdf.inputs[\\\"Roughness\\\"].default_value = 0.3  # Slight glossiness\\n\\n# Create white material for plane\\nwhite_material = bpy.data.materials.new(name=\\\"WhiteMaterial\\\")\\nwhite_material.use_nodes = True\\nwhite_material.diffuse_color = (1.0, 1.0, 1.0, 1.0)  # White RGBA\\n\\nbsdf_white = white_material.node_tree.nodes[\\\"Principled BSDF\\\"]\\nbsdf_white.inputs[\\\"Base Color\\\"].default_value = (1.0, 1.0, 1.0, 1.0)  # White\\nbsdf_white.inputs[\\\"Roughness\\\"].default_value = 0.8  # Matte white\\n\\n# Assign materials to objects\\nsphere.data.materials.append(red_material)\\nplane.data.materials.append(white_material)\\n\\nprint(\\\"Materials created and assigned\\\")\\n\", \"user_prompt\": \"Create and assign red and white materials\"}"}
{"ts": "2026-07-15T00:48:54.095580+00:00", "type": "tool_result", "tool_call_id": "toolu_bdrk_018gHZ2vPGV9JR5pgaWmu5aZ", "name": "execute_blender_code", "text": "Code executed successfully: Materials created and assigned\n", "is_error": false, "has_image": false, "image_b64": null}
{"ts": "2026-07-15T00:48:56.449340+00:00", "type": "llm_call", "call_index": 4, "provider_type": "blenderbench.providers.openrouter.OpenRouterProvider", "started_at": "2026-07-15T00:48:54.096087+00:00", "finished_at": "2026-07-15T00:48:56.448339+00:00", "duration_s": 2.3429999999934807, "request": {"messages": [{"role": "system", "content": "You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\n\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\n\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\n\n## Harness Instructions\n\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. When you are finished, reply with the single word DONE and no tool calls."}, {"role": "user", "content": "## Task\n\nComplete the following Blender task:\n\n<task>\n\nA red sphere of 1 meter diameter resting on a white ground plane — touching it\nexactly, neither floating nor sunk. Smooth shading, a plain red material on the\nball, plain white on the floor.\n\nThe floor is the sphere, the color, and the contact. The ceiling is presentation:\na soft believable shadow under the ball, a hint of glossiness that catches the\nlight, and clean framing.\n\n</task>"}, {"role": "assistant", "content": "I'll create a red sphere resting on a white ground plane with proper materials, shading, lighting, and camera setup. Let me work through this step by step.", "tool_calls": [{"id": "toolu_bdrk_01McQwtp5Bq6813NVu2gUpRG", "type": "function", "function": {"name": "get_scene_info", "arguments": "{\"user_prompt\": \"Get current scene info to see what's there initially\"}"}}]}, {"role": "tool", "tool_call_id": "toolu_bdrk_01McQwtp5Bq6813NVu2gUpRG", "content": "{\n  \"name\": \"Scene\",\n  \"object_count\": 0,\n  \"objects\": [],\n  \"materials_count\": 2\n}"}, {"role": "assistant", "content": "Good, the scene is empty. Now I'll create the red sphere and white ground plane using Blender code:", "tool_calls": [{"id": "toolu_bdrk_01PuvjJZjfHJZgTRw6jGDsdH", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\": \"\\nimport bpy\\nimport bmesh\\nfrom mathutils import Vector\\n\\n# Clear any default objects\\nbpy.ops.object.select_all(action='SELECT')\\nbpy.ops.object.delete(use_global=False)\\n\\n# Add a sphere (1 meter diameter = 0.5 meter radius)\\nbpy.ops.mesh.primitive_uv_sphere_add(\\n    radius=0.5,\\n    location=(0, 0, 0.5)  # Position it so bottom touches z=0\\n)\\nsphere = bpy.context.active_object\\nsphere.name = \\\"RedSphere\\\"\\n\\n# Add a large ground plane\\nbpy.ops.mesh.primitive_plane_add(\\n    size=10,\\n    location=(0, 0, 0)\\n)\\nplane = bpy.context.active_object\\nplane.name = \\\"GroundPlane\\\"\\n\\n# Select and scale plane to be large but thin\\nplane.scale = (10, 10, 0.1)\\n\\nprint(\\\"Objects created: Sphere at z=\\\" + str(sphere.location.z) + \\\", Plane at z=\\\" + str(plane.location.z))\\n\", \"user_prompt\": \"Create red sphere and white ground plane\"}"}}]}, {"role": "tool", "tool_call_id": "toolu_bdrk_01PuvjJZjfHJZgTRw6jGDsdH", "content": "Code executed successfully: Objects created: Sphere at z=0.5, Plane at z=0.0\n"}, {"role": "assistant", "content": "Now I'll create the materials - red for the sphere and white for the plane:", "tool_calls": [{"id": "toolu_bdrk_018gHZ2vPGV9JR5pgaWmu5aZ", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\": \"\\nimport bpy\\n\\n# Get the sphere and plane objects\\nsphere = bpy.data.objects[\\\"RedSphere\\\"]\\nplane = bpy.data.objects[\\\"GroundPlane\\\"]\\n\\n# Create red material for sphere\\nred_material = bpy.data.materials.new(name=\\\"RedMaterial\\\")\\nred_material.use_nodes = True\\nred_material.diffuse_color = (1.0, 0.0, 0.0, 1.0)  # Red RGBA\\n\\n# Configure red material with some glossiness\\nbsdf = red_material.node_tree.nodes[\\\"Principled BSDF\\\"]\\nbsdf.inputs[\\\"Base Color\\\"].default_value = (0.8, 0.05, 0.05, 1.0)  # Pure red\\nbsdf.inputs[\\\"Roughness\\\"].default_value = 0.3  # Slight glossiness\\n\\n# Create white material for plane\\nwhite_material = bpy.data.materials.new(name=\\\"WhiteMaterial\\\")\\nwhite_material.use_nodes = True\\nwhite_material.diffuse_color = (1.0, 1.0, 1.0, 1.0)  # White RGBA\\n\\nbsdf_white = white_material.node_tree.nodes[\\\"Principled BSDF\\\"]\\nbsdf_white.inputs[\\\"Base Color\\\"].default_value = (1.0, 1.0, 1.0, 1.0)  # White\\nbsdf_white.inputs[\\\"Roughness\\\"].default_value = 0.8  # Matte white\\n\\n# Assign materials to objects\\nsphere.data.materials.append(red_material)\\nplane.data.materials.append(white_material)\\n\\nprint(\\\"Materials created and assigned\\\")\\n\", \"user_prompt\": \"Create and assign red and white materials\"}"}}]}, {"role": "tool", "tool_call_id": "toolu_bdrk_018gHZ2vPGV9JR5pgaWmu5aZ", "content": "Code executed successfully: Materials created and assigned\n"}], "tools": [{"type": "function", "function": {"name": "get_scene_info", "description": "Get detailed information about the current Blender scene\n\n    Parameters:\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\n    ", "parameters": {"properties": {"user_prompt": {"title": "User Prompt", "type": "string"}}, "required": ["user_prompt"], "title": "get_scene_infoArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_object_info", "description": "\n    Get detailed information about a specific object in the Blender scene.\n\n    Parameters:\n    - object_name: The name of the object to get information about\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"object_name": {"title": "Object Name", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["object_name"], "title": "get_object_infoArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_viewport_screenshot", "description": "\n    Capture a screenshot of the current Blender 3D viewport.\n\n    Parameters:\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns the screenshot as an Image.\n    ", "parameters": {"properties": {"max_size": {"default": 1000, "title": "Max Size", "type": "integer"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_viewport_screenshotArguments", "type": "object"}}}, {"type": "function", "function": {"name": "execute_blender_code", "description": "\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\n\n    Parameters:\n    - code: The Python code to execute\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"code": {"title": "Code", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["code"], "title": "execute_blender_codeArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_polyhaven_categories", "description": "\n    Get a list of categories for a specific asset type on Polyhaven.\n\n    Parameters:\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"asset_type": {"default": "hdris", "title": "Asset Type", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_polyhaven_categoriesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_polyhaven_assets", "description": "\n    Search for assets on Polyhaven with optional filtering.\n\n    Parameters:\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\n    - categories: Optional comma-separated list of categories to filter by\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a list of matching assets with basic information.\n    ", "parameters": {"properties": {"asset_type": {"default": "all", "title": "Asset Type", "type": "string"}, "categories": {"default": null, "title": "Categories", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "search_polyhaven_assetsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "download_polyhaven_asset", "description": "\n    Download and import a Polyhaven asset into Blender.\n\n    Parameters:\n    - asset_id: The ID of the asset to download\n    - asset_type: The type of asset (hdris, textures, models)\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"asset_id": {"title": "Asset Id", "type": "string"}, "asset_type": {"title": "Asset Type", "type": "string"}, "resolution": {"default": "1k", "title": "Resolution", "type": "string"}, "file_format": {"default": null, "title": "File Format", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["asset_id", "asset_type"], "title": "download_polyhaven_assetArguments", "type": "object"}}}, {"type": "function", "function": {"name": "set_texture", "description": "\n    Apply a previously downloaded Polyhaven texture to an object.\n    \n    Parameters:\n    - object_name: Name of the object to apply the texture to\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\n    \n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"object_name": {"title": "Object Name", "type": "string"}, "texture_id": {"title": "Texture Id", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["object_name", "texture_id"], "title": "set_textureArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_polyhaven_status", "description": "\n    Check if PolyHaven integration is enabled in Blender.\n    Returns a message indicating whether PolyHaven features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_polyhaven_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_hyper3d_status", "description": "\n    Check if Hyper3D Rodin integration is enabled in Blender.\n    Returns a message indicating whether Hyper3D Rodin features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_hyper3d_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_sketchfab_status", "description": "\n    Check if Sketchfab integration is enabled in Blender.\n    Returns a message indicating whether Sketchfab features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_sketchfab_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_sketchfab_models", "description": "\n    Search for models on Sketchfab with optional filtering.\n\n    Parameters:\n    - query: Text to search for\n    - categories: Optional comma-separated list of categories\n    - count: Maximum number of results to return (default 20)\n    - downloadable: Whether to include only downloadable models (default True)\n\n    Returns a formatted list of matching models.\n    ", "parameters": {"properties": {"query": {"title": "Query", "type": "string"}, "categories": {"default": null, "title": "Categories", "type": "string"}, "count": {"default": 20, "title": "Count", "type": "integer"}, "downloadable": {"default": true, "title": "Downloadable", "type": "boolean"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["query"], "title": "search_sketchfab_modelsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_sketchfab_model_preview", "description": "\n    Get a preview thumbnail of a Sketchfab model by its UID.\n    Use this to visually confirm a model before downloading.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\n    \n    Returns the model's thumbnail as an Image for visual confirmation.\n    ", "parameters": {"properties": {"uid": {"title": "Uid", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["uid"], "title": "get_sketchfab_model_previewArguments", "type": "object"}}}, {"type": "function", "function": {"name": "download_sketchfab_model", "description": "\n    Download and import a Sketchfab model by its UID.\n    The model will be scaled so its largest dimension equals target_size.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\n                  You must specify the desired size for the model.\n                  Examples:\n                  - Chair: target_size=1.0 (1 meter tall)\n                  - Table: target_size=0.75 (75cm tall)\n                  - Car: target_size=4.5 (4.5 meters long)\n                  - Person: target_size=1.7 (1.7 meters tall)\n                  - Small object (cup, phone): target_size=0.1 to 0.3\n    \n    Returns a message with import details including object names, dimensions, and bounding box.\n    The model must be downloadable and you must have proper access rights.\n    ", "parameters": {"properties": {"uid": {"title": "Uid", "type": "string"}, "target_size": {"title": "Target Size", "type": "number"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["uid", "target_size"], "title": "download_sketchfab_modelArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hyper3d_model_via_text", "description": "\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n\n    Parameters:\n    - text_prompt: A short description of the desired model in **English**.\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"text_prompt": {"title": "Text Prompt", "type": "string"}, "bbox_condition": {"default": null, "items": {"type": "number"}, "title": "Bbox Condition", "type": "array"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["text_prompt"], "title": "generate_hyper3d_model_via_textArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hyper3d_model_via_images", "description": "\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n    \n    Parameters:\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode.\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"input_image_paths": {"default": null, "items": {"type": "string"}, "title": "Input Image Paths", "type": "array"}, "input_image_urls": {"default": null, "items": {"type": "string"}, "title": "Input Image Urls", "type": "array"}, "bbox_condition": {"default": null, "items": {"type": "number"}, "title": "Bbox Condition", "type": "array"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "generate_hyper3d_model_via_imagesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "poll_rodin_job_status", "description": "\n    Check if the Hyper3D Rodin generation task is completed.\n\n    For Hyper3D Rodin mode MAIN_SITE:\n        Parameters:\n        - subscription_key: The subscription_key given in the generate model step.\n\n        Returns a list of status. The task is done if all status are \"Done\".\n        If \"Failed\" showed up, the generating process failed.\n        This is a polling API, so only proceed if the status are finally determined (\"Done\" or \"Canceled\").\n\n    For Hyper3D Rodin mode FAL_AI:\n        Parameters:\n        - request_id: The request_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"COMPLETED\".\n        The task is in progress if status is \"IN_PROGRESS\".\n        If status other than \"COMPLETED\", \"IN_PROGRESS\", \"IN_QUEUE\" showed up, the generating process might be failed.\n        This is a polling API, so only proceed if the status are finally determined (\"COMPLETED\" or some failed state).\n    ", "parameters": {"properties": {"subscription_key": {"default": null, "title": "Subscription Key", "type": "string"}, "request_id": {"default": null, "title": "Request Id", "type": "string"}}, "title": "poll_rodin_job_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "import_generated_asset", "description": "\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\n\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\n    Return if the asset has been imported successfully.\n    ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "task_uuid": {"default": null, "title": "Task Uuid", "type": "string"}, "request_id": {"default": null, "title": "Request Id", "type": "string"}}, "required": ["name"], "title": "import_generated_assetArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_hunyuan3d_status", "description": "\n    Check if Hunyuan3D integration is enabled in Blender.\n    Returns a message indicating whether Hunyuan3D features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_hunyuan3d_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hunyuan3d_model", "description": "\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \n    or both for the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    \n    Parameters:\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\n    - input_image_url: (Optional) The local or remote url of the input image. Accepts None if only using text prompt.\n\n    Returns: \n    - When successful, returns a JSON with job_id (format: \"job_xxx\") indicating the task is in progress\n    - When the job completes, the status will change to \"DONE\" indicating the model has been imported\n    - Returns error message if the operation fails\n    ", "parameters": {"properties": {"text_prompt": {"default": null, "title": "Text Prompt", "type": "string"}, "input_image_url": {"default": null, "title": "Input Image Url", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "generate_hunyuan3d_modelArguments", "type": "object"}}}, {"type": "function", "function": {"name": "poll_hunyuan_job_status", "description": "\n    Check if the Hunyuan3D generation task is completed.\n\n    For Hunyuan3D:\n        Parameters:\n        - job_id: The job_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"DONE\".\n        The task is in progress if status is \"RUN\".\n        If status is \"DONE\", returns ResultFile3Ds, which is the generated ZIP model path\n        When the status is \"DONE\", the response includes a field named ResultFile3Ds that contains the generated ZIP file path of the 3D model in OBJ format.\n        This is a polling API, so only proceed if the status are finally determined (\"DONE\" or some failed state).\n    ", "parameters": {"properties": {"job_id": {"default": null, "title": "Job Id", "type": "string"}}, "title": "poll_hunyuan_job_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "import_generated_asset_hunyuan", "description": "\n    Import the asset generated by Hunyuan3D after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - zip_file_url: The zip_file_url given in the generate model step.\n\n    Return if the asset has been imported successfully.\n    ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "zip_file_url": {"title": "Zip File Url", "type": "string"}}, "required": ["name", "zip_file_url"], "title": "import_generated_asset_hunyuanArguments", "type": "object"}}}]}, "provider_attempts": [{"provider": "openrouter", "attempt": 1, "started_at": "2026-07-15T00:48:54.096087+00:00", "finished_at": "2026-07-15T00:48:56.448339+00:00", "duration_s": 2.3429999999934807, "request": {"method": "POST", "url": "https://openrouter.ai/api/v1/chat/completions", "headers": {"host": "openrouter.ai", "accept": "*/*", "accept-encoding": "gzip, deflate", "connection": "keep-alive", "user-agent": "python-httpx/0.28.1", "authorization": "[REDACTED]", "content-length": "22265", "content-type": "application/json"}, "body": "{\"model\":\"anthropic/claude-haiku-4.5\",\"messages\":[{\"role\":\"system\",\"content\":[{\"type\":\"text\",\"text\":\"You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\\n\\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\\n\\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\\n\\n## Harness Instructions\\n\\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. When you are finished, reply with the single word DONE and no tool calls.\",\"cache_control\":{\"type\":\"ephemeral\"}}]},{\"role\":\"user\",\"content\":\"## Task\\n\\nComplete the following Blender task:\\n\\n<task>\\n\\nA red sphere of 1 meter diameter resting on a white ground plane — touching it\\nexactly, neither floating nor sunk. Smooth shading, a plain red material on the\\nball, plain white on the floor.\\n\\nThe floor is the sphere, the color, and the contact. The ceiling is presentation:\\na soft believable shadow under the ball, a hint of glossiness that catches the\\nlight, and clean framing.\\n\\n</task>\"},{\"role\":\"assistant\",\"content\":\"I'll create a red sphere resting on a white ground plane with proper materials, shading, lighting, and camera setup. Let me work through this step by step.\",\"tool_calls\":[{\"id\":\"toolu_bdrk_01McQwtp5Bq6813NVu2gUpRG\",\"type\":\"function\",\"function\":{\"name\":\"get_scene_info\",\"arguments\":\"{\\\"user_prompt\\\": \\\"Get current scene info to see what's there initially\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"toolu_bdrk_01McQwtp5Bq6813NVu2gUpRG\",\"content\":\"{\\n  \\\"name\\\": \\\"Scene\\\",\\n  \\\"object_count\\\": 0,\\n  \\\"objects\\\": [],\\n  \\\"materials_count\\\": 2\\n}\"},{\"role\":\"assistant\",\"content\":\"Good, the scene is empty. Now I'll create the red sphere and white ground plane using Blender code:\",\"tool_calls\":[{\"id\":\"toolu_bdrk_01PuvjJZjfHJZgTRw6jGDsdH\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\": \\\"\\\\nimport bpy\\\\nimport bmesh\\\\nfrom mathutils import Vector\\\\n\\\\n# Clear any default objects\\\\nbpy.ops.object.select_all(action='SELECT')\\\\nbpy.ops.object.delete(use_global=False)\\\\n\\\\n# Add a sphere (1 meter diameter = 0.5 meter radius)\\\\nbpy.ops.mesh.primitive_uv_sphere_add(\\\\n    radius=0.5,\\\\n    location=(0, 0, 0.5)  # Position it so bottom touches z=0\\\\n)\\\\nsphere = bpy.context.active_object\\\\nsphere.name = \\\\\\\"RedSphere\\\\\\\"\\\\n\\\\n# Add a large ground plane\\\\nbpy.ops.mesh.primitive_plane_add(\\\\n    size=10,\\\\n    location=(0, 0, 0)\\\\n)\\\\nplane = bpy.context.active_object\\\\nplane.name = \\\\\\\"GroundPlane\\\\\\\"\\\\n\\\\n# Select and scale plane to be large but thin\\\\nplane.scale = (10, 10, 0.1)\\\\n\\\\nprint(\\\\\\\"Objects created: Sphere at z=\\\\\\\" + str(sphere.location.z) + \\\\\\\", Plane at z=\\\\\\\" + str(plane.location.z))\\\\n\\\", \\\"user_prompt\\\": \\\"Create red sphere and white ground plane\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"toolu_bdrk_01PuvjJZjfHJZgTRw6jGDsdH\",\"content\":\"Code executed successfully: Objects created: Sphere at z=0.5, Plane at z=0.0\\n\"},{\"role\":\"assistant\",\"content\":\"Now I'll create the materials - red for the sphere and white for the plane:\",\"tool_calls\":[{\"id\":\"toolu_bdrk_018gHZ2vPGV9JR5pgaWmu5aZ\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\": \\\"\\\\nimport bpy\\\\n\\\\n# Get the sphere and plane objects\\\\nsphere = bpy.data.objects[\\\\\\\"RedSphere\\\\\\\"]\\\\nplane = bpy.data.objects[\\\\\\\"GroundPlane\\\\\\\"]\\\\n\\\\n# Create red material for sphere\\\\nred_material = bpy.data.materials.new(name=\\\\\\\"RedMaterial\\\\\\\")\\\\nred_material.use_nodes = True\\\\nred_material.diffuse_color = (1.0, 0.0, 0.0, 1.0)  # Red RGBA\\\\n\\\\n# Configure red material with some glossiness\\\\nbsdf = red_material.node_tree.nodes[\\\\\\\"Principled BSDF\\\\\\\"]\\\\nbsdf.inputs[\\\\\\\"Base Color\\\\\\\"].default_value = (0.8, 0.05, 0.05, 1.0)  # Pure red\\\\nbsdf.inputs[\\\\\\\"Roughness\\\\\\\"].default_value = 0.3  # Slight glossiness\\\\n\\\\n# Create white material for plane\\\\nwhite_material = bpy.data.materials.new(name=\\\\\\\"WhiteMaterial\\\\\\\")\\\\nwhite_material.use_nodes = True\\\\nwhite_material.diffuse_color = (1.0, 1.0, 1.0, 1.0)  # White RGBA\\\\n\\\\nbsdf_white = white_material.node_tree.nodes[\\\\\\\"Principled BSDF\\\\\\\"]\\\\nbsdf_white.inputs[\\\\\\\"Base Color\\\\\\\"].default_value = (1.0, 1.0, 1.0, 1.0)  # White\\\\nbsdf_white.inputs[\\\\\\\"Roughness\\\\\\\"].default_value = 0.8  # Matte white\\\\n\\\\n# Assign materials to objects\\\\nsphere.data.materials.append(red_material)\\\\nplane.data.materials.append(white_material)\\\\n\\\\nprint(\\\\\\\"Materials created and assigned\\\\\\\")\\\\n\\\", \\\"user_prompt\\\": \\\"Create and assign red and white materials\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"toolu_bdrk_018gHZ2vPGV9JR5pgaWmu5aZ\",\"content\":[{\"type\":\"text\",\"text\":\"Code executed successfully: Materials created and assigned\\n\",\"cache_control\":{\"type\":\"ephemeral\"}}]}],\"usage\":{\"include\":true},\"tools\":[{\"type\":\"function\",\"function\":{\"name\":\"get_scene_info\",\"description\":\"Get detailed information about the current Blender scene\\n\\n    Parameters:\\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"user_prompt\"],\"title\":\"get_scene_infoArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_object_info\",\"description\":\"\\n    Get detailed information about a specific object in the Blender scene.\\n\\n    Parameters:\\n    - object_name: The name of the object to get information about\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"object_name\":{\"title\":\"Object Name\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"object_name\"],\"title\":\"get_object_infoArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_viewport_screenshot\",\"description\":\"\\n    Capture a screenshot of the current Blender 3D viewport.\\n\\n    Parameters:\\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns the screenshot as an Image.\\n    \",\"parameters\":{\"properties\":{\"max_size\":{\"default\":1000,\"title\":\"Max Size\",\"type\":\"integer\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_viewport_screenshotArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"description\":\"\\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\\n\\n    Parameters:\\n    - code: The Python code to execute\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"code\":{\"title\":\"Code\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"code\"],\"title\":\"execute_blender_codeArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_categories\",\"description\":\"\\n    Get a list of categories for a specific asset type on Polyhaven.\\n\\n    Parameters:\\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"asset_type\":{\"default\":\"hdris\",\"title\":\"Asset Type\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_polyhaven_categoriesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_polyhaven_assets\",\"description\":\"\\n    Search for assets on Polyhaven with optional filtering.\\n\\n    Parameters:\\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\\n    - categories: Optional comma-separated list of categories to filter by\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns a list of matching assets with basic information.\\n    \",\"parameters\":{\"properties\":{\"asset_type\":{\"default\":\"all\",\"title\":\"Asset Type\",\"type\":\"string\"},\"categories\":{\"default\":null,\"title\":\"Categories\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"search_polyhaven_assetsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"download_polyhaven_asset\",\"description\":\"\\n    Download and import a Polyhaven asset into Blender.\\n\\n    Parameters:\\n    - asset_id: The ID of the asset to download\\n    - asset_type: The type of asset (hdris, textures, models)\\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"asset_id\":{\"title\":\"Asset Id\",\"type\":\"string\"},\"asset_type\":{\"title\":\"Asset Type\",\"type\":\"string\"},\"resolution\":{\"default\":\"1k\",\"title\":\"Resolution\",\"type\":\"string\"},\"file_format\":{\"default\":null,\"title\":\"File Format\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"asset_id\",\"asset_type\"],\"title\":\"download_polyhaven_assetArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"set_texture\",\"description\":\"\\n    Apply a previously downloaded Polyhaven texture to an object.\\n    \\n    Parameters:\\n    - object_name: Name of the object to apply the texture to\\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\\n    \\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"object_name\":{\"title\":\"Object Name\",\"type\":\"string\"},\"texture_id\":{\"title\":\"Texture Id\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"object_name\",\"texture_id\"],\"title\":\"set_textureArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_status\",\"description\":\"\\n    Check if PolyHaven integration is enabled in Blender.\\n    Returns a message indicating whether PolyHaven features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_polyhaven_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_hyper3d_status\",\"description\":\"\\n    Check if Hyper3D Rodin integration is enabled in Blender.\\n    Returns a message indicating whether Hyper3D Rodin features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_hyper3d_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_status\",\"description\":\"\\n    Check if Sketchfab integration is enabled in Blender.\\n    Returns a message indicating whether Sketchfab features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_sketchfab_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_sketchfab_models\",\"description\":\"\\n    Search for models on Sketchfab with optional filtering.\\n\\n    Parameters:\\n    - query: Text to search for\\n    - categories: Optional comma-separated list of categories\\n    - count: Maximum number of results to return (default 20)\\n    - downloadable: Whether to include only downloadable models (default True)\\n\\n    Returns a formatted list of matching models.\\n    \",\"parameters\":{\"properties\":{\"query\":{\"title\":\"Query\",\"type\":\"string\"},\"categories\":{\"default\":null,\"title\":\"Categories\",\"type\":\"string\"},\"count\":{\"default\":20,\"title\":\"Count\",\"type\":\"integer\"},\"downloadable\":{\"default\":true,\"title\":\"Downloadable\",\"type\":\"boolean\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"query\"],\"title\":\"search_sketchfab_modelsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_model_preview\",\"description\":\"\\n    Get a preview thumbnail of a Sketchfab model by its UID.\\n    Use this to visually confirm a model before downloading.\\n    \\n    Parameters:\\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\\n    \\n    Returns the model's thumbnail as an Image for visual confirmation.\\n    \",\"parameters\":{\"properties\":{\"uid\":{\"title\":\"Uid\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"uid\"],\"title\":\"get_sketchfab_model_previewArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"download_sketchfab_model\",\"description\":\"\\n    Download and import a Sketchfab model by its UID.\\n    The model will be scaled so its largest dimension equals target_size.\\n    \\n    Parameters:\\n    - uid: The unique identifier of the Sketchfab model\\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\\n                  You must specify the desired size for the model.\\n                  Examples:\\n                  - Chair: target_size=1.0 (1 meter tall)\\n                  - Table: target_size=0.75 (75cm tall)\\n                  - Car: target_size=4.5 (4.5 meters long)\\n                  - Person: target_size=1.7 (1.7 meters tall)\\n                  - Small object (cup, phone): target_size=0.1 to 0.3\\n    \\n    Returns a message with import details including object names, dimensions, and bounding box.\\n    The model must be downloadable and you must have proper access rights.\\n    \",\"parameters\":{\"properties\":{\"uid\":{\"title\":\"Uid\",\"type\":\"string\"},\"target_size\":{\"title\":\"Target Size\",\"type\":\"number\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"uid\",\"target_size\"],\"title\":\"download_sketchfab_modelArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hyper3d_model_via_text\",\"description\":\"\\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\\n    The 3D asset has built-in materials.\\n    The generated model has a normalized size, so re-scaling after generation can be useful.\\n\\n    Parameters:\\n    - text_prompt: A short description of the desired model in **English**.\\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\\n\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"text_prompt\":{\"title\":\"Text Prompt\",\"type\":\"string\"},\"bbox_condition\":{\"default\":null,\"items\":{\"type\":\"number\"},\"title\":\"Bbox Condition\",\"type\":\"array\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"text_prompt\"],\"title\":\"generate_hyper3d_model_via_textArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hyper3d_model_via_images\",\"description\":\"\\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\\n    The 3D asset has built-in materials.\\n    The generated model has a normalized size, so re-scaling after generation can be useful.\\n    \\n    Parameters:\\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model.\\n\\n    Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode.\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"input_image_paths\":{\"default\":null,\"items\":{\"type\":\"string\"},\"title\":\"Input Image Paths\",\"type\":\"array\"},\"input_image_urls\":{\"default\":null,\"items\":{\"type\":\"string\"},\"title\":\"Input Image Urls\",\"type\":\"array\"},\"bbox_condition\":{\"default\":null,\"items\":{\"type\":\"number\"},\"title\":\"Bbox Condition\",\"type\":\"array\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"generate_hyper3d_model_via_imagesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"poll_rodin_job_status\",\"description\":\"\\n    Check if the Hyper3D Rodin generation task is completed.\\n\\n    For Hyper3D Rodin mode MAIN_SITE:\\n        Parameters:\\n        - subscription_key: The subscription_key given in the generate model step.\\n\\n        Returns a list of status. The task is done if all status are \\\"Done\\\".\\n        If \\\"Failed\\\" showed up, the generating process failed.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"Done\\\" or \\\"Canceled\\\").\\n\\n    For Hyper3D Rodin mode FAL_AI:\\n        Parameters:\\n        - request_id: The request_id given in the generate model step.\\n\\n        Returns the generation task status. The task is done if status is \\\"COMPLETED\\\".\\n        The task is in progress if status is \\\"IN_PROGRESS\\\".\\n        If status other than \\\"COMPLETED\\\", \\\"IN_PROGRESS\\\", \\\"IN_QUEUE\\\" showed up, the generating process might be failed.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"COMPLETED\\\" or some failed state).\\n    \",\"parameters\":{\"properties\":{\"subscription_key\":{\"default\":null,\"title\":\"Subscription Key\",\"type\":\"string\"},\"request_id\":{\"default\":null,\"title\":\"Request Id\",\"type\":\"string\"}},\"title\":\"poll_rodin_job_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"import_generated_asset\",\"description\":\"\\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\\n\\n    Parameters:\\n    - name: The name of the object in scene\\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\\n\\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\\n    Return if the asset has been imported successfully.\\n    \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"task_uuid\":{\"default\":null,\"title\":\"Task Uuid\",\"type\":\"string\"},\"request_id\":{\"default\":null,\"title\":\"Request Id\",\"type\":\"string\"}},\"required\":[\"name\"],\"title\":\"import_generated_assetArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_hunyuan3d_status\",\"description\":\"\\n    Check if Hunyuan3D integration is enabled in Blender.\\n    Returns a message indicating whether Hunyuan3D features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_hunyuan3d_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hunyuan3d_model\",\"description\":\"\\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \\n    or both for the desired asset, and import the asset into Blender.\\n    The 3D asset has built-in materials.\\n    \\n    Parameters:\\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\\n    - input_image_url: (Optional) The local or remote url of the input image. Accepts None if only using text prompt.\\n\\n    Returns: \\n    - When successful, returns a JSON with job_id (format: \\\"job_xxx\\\") indicating the task is in progress\\n    - When the job completes, the status will change to \\\"DONE\\\" indicating the model has been imported\\n    - Returns error message if the operation fails\\n    \",\"parameters\":{\"properties\":{\"text_prompt\":{\"default\":null,\"title\":\"Text Prompt\",\"type\":\"string\"},\"input_image_url\":{\"default\":null,\"title\":\"Input Image Url\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"generate_hunyuan3d_modelArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"poll_hunyuan_job_status\",\"description\":\"\\n    Check if the Hunyuan3D generation task is completed.\\n\\n    For Hunyuan3D:\\n        Parameters:\\n        - job_id: The job_id given in the generate model step.\\n\\n        Returns the generation task status. The task is done if status is \\\"DONE\\\".\\n        The task is in progress if status is \\\"RUN\\\".\\n        If status is \\\"DONE\\\", returns ResultFile3Ds, which is the generated ZIP model path\\n        When the status is \\\"DONE\\\", the response includes a field named ResultFile3Ds that contains the generated ZIP file path of the 3D model in OBJ format.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"DONE\\\" or some failed state).\\n    \",\"parameters\":{\"properties\":{\"job_id\":{\"default\":null,\"title\":\"Job Id\",\"type\":\"string\"}},\"title\":\"poll_hunyuan_job_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"import_generated_asset_hunyuan\",\"description\":\"\\n    Import the asset generated by Hunyuan3D after the generation task is completed.\\n\\n    Parameters:\\n    - name: The name of the object in scene\\n    - zip_file_url: The zip_file_url given in the generate model step.\\n\\n    Return if the asset has been imported successfully.\\n    \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"zip_file_url\":{\"title\":\"Zip File Url\",\"type\":\"string\"}},\"required\":[\"name\",\"zip_file_url\"],\"title\":\"import_generated_asset_hunyuanArguments\",\"type\":\"object\"}}}],\"reasoning\":{\"effort\":\"xhigh\"}}", "body_base64": "{"model":"anthropic/claude-haiku-4.5","messages":[{"role":"system","content":[{"type":"text","text":"You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\n\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\n\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\n\n## Harness Instructions\n\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. When you are finished, reply with the single word DONE and no tool calls.","cache_control":{"type":"ephemeral"}}]},{"role":"user","content":"## Task\n\nComplete the following Blender task:\n\n<task>\n\nA red sphere of 1 meter diameter resting on a white ground plane — touching it\nexactly, neither floating nor sunk. Smooth shading, a plain red material on the\nball, plain white on the floor.\n\nThe floor is the sphere, the color, and the contact. The ceiling is presentation:\na soft believable shadow under the ball, a hint of glossiness that catches the\nlight, and clean framing.\n\n</task>"},{"role":"assistant","content":"I'll create a red sphere resting on a white ground plane with proper materials, shading, lighting, and camera setup. Let me work through this step by step.","tool_calls":[{"id":"toolu_bdrk_01McQwtp5Bq6813NVu2gUpRG","type":"function","function":{"name":"get_scene_info","arguments":"{\"user_prompt\": \"Get current scene info to see what's there initially\"}"}}]},{"role":"tool","tool_call_id":"toolu_bdrk_01McQwtp5Bq6813NVu2gUpRG","content":"{\n  \"name\": \"Scene\",\n  \"object_count\": 0,\n  \"objects\": [],\n  \"materials_count\": 2\n}"},{"role":"assistant","content":"Good, the scene is empty. Now I'll create the red sphere and white ground plane using Blender code:","tool_calls":[{"id":"toolu_bdrk_01PuvjJZjfHJZgTRw6jGDsdH","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\": \"\\nimport bpy\\nimport bmesh\\nfrom mathutils import Vector\\n\\n# Clear any default objects\\nbpy.ops.object.select_all(action='SELECT')\\nbpy.ops.object.delete(use_global=False)\\n\\n# Add a sphere (1 meter diameter = 0.5 meter radius)\\nbpy.ops.mesh.primitive_uv_sphere_add(\\n    radius=0.5,\\n    location=(0, 0, 0.5)  # Position it so bottom touches z=0\\n)\\nsphere = bpy.context.active_object\\nsphere.name = \\\"RedSphere\\\"\\n\\n# Add a large ground plane\\nbpy.ops.mesh.primitive_plane_add(\\n    size=10,\\n    location=(0, 0, 0)\\n)\\nplane = bpy.context.active_object\\nplane.name = \\\"GroundPlane\\\"\\n\\n# Select and scale plane to be large but thin\\nplane.scale = (10, 10, 0.1)\\n\\nprint(\\\"Objects created: Sphere at z=\\\" + str(sphere.location.z) + \\\", Plane at z=\\\" + str(plane.location.z))\\n\", \"user_prompt\": \"Create red sphere and white ground plane\"}"}}]},{"role":"tool","tool_call_id":"toolu_bdrk_01PuvjJZjfHJZgTRw6jGDsdH","content":"Code executed successfully: Objects created: Sphere at z=0.5, Plane at z=0.0\n"},{"role":"assistant","content":"Now I'll create the materials - red for the sphere and white for the plane:","tool_calls":[{"id":"toolu_bdrk_018gHZ2vPGV9JR5pgaWmu5aZ","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\": \"\\nimport bpy\\n\\n# Get the sphere and plane objects\\nsphere = bpy.data.objects[\\\"RedSphere\\\"]\\nplane = bpy.data.objects[\\\"GroundPlane\\\"]\\n\\n# Create red material for sphere\\nred_material = bpy.data.materials.new(name=\\\"RedMaterial\\\")\\nred_material.use_nodes = True\\nred_material.diffuse_color = (1.0, 0.0, 0.0, 1.0)  # Red RGBA\\n\\n# Configure red material with some glossiness\\nbsdf = red_material.node_tree.nodes[\\\"Principled BSDF\\\"]\\nbsdf.inputs[\\\"Base Color\\\"].default_value = (0.8, 0.05, 0.05, 1.0)  # Pure red\\nbsdf.inputs[\\\"Roughness\\\"].default_value = 0.3  # Slight glossiness\\n\\n# Create white material for plane\\nwhite_material = bpy.data.materials.new(name=\\\"WhiteMaterial\\\")\\nwhite_material.use_nodes = True\\nwhite_material.diffuse_color = (1.0, 1.0, 1.0, 1.0)  # White RGBA\\n\\nbsdf_white = white_material.node_tree.nodes[\\\"Principled BSDF\\\"]\\nbsdf_white.inputs[\\\"Base Color\\\"].default_value = (1.0, 1.0, 1.0, 1.0)  # White\\nbsdf_white.inputs[\\\"Roughness\\\"].default_value = 0.8  # Matte white\\n\\n# Assign materials to objects\\nsphere.data.materials.append(red_material)\\nplane.data.materials.append(white_material)\\n\\nprint(\\\"Materials created and assigned\\\")\\n\", \"user_prompt\": \"Create and assign red and white materials\"}"}}]},{"role":"tool","tool_call_id":"toolu_bdrk_018gHZ2vPGV9JR5pgaWmu5aZ","content":[{"type":"text","text":"Code executed successfully: Materials created and assigned\n","cache_control":{"type":"ephemeral"}}]}],"usage":{"include":true},"tools":[{"type":"function","function":{"name":"get_scene_info","description":"Get detailed information about the current Blender scene\n\n    Parameters:\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\n    ","parameters":{"properties":{"user_prompt":{"title":"User Prompt","type":"string"}},"required":["user_prompt"],"title":"get_scene_infoArguments","type":"object"}}},{"type":"function","function":{"name":"get_object_info","description":"\n    Get detailed information about a specific object in the Blender scene.\n\n    Parameters:\n    - object_name: The name of the object to get information about\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"object_name":{"title":"Object Name","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["object_name"],"title":"get_object_infoArguments","type":"object"}}},{"type":"function","function":{"name":"get_viewport_screenshot","description":"\n    Capture a screenshot of the current Blender 3D viewport.\n\n    Parameters:\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns the screenshot as an Image.\n    ","parameters":{"properties":{"max_size":{"default":1000,"title":"Max Size","type":"integer"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_viewport_screenshotArguments","type":"object"}}},{"type":"function","function":{"name":"execute_blender_code","description":"\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\n\n    Parameters:\n    - code: The Python code to execute\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"code":{"title":"Code","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["code"],"title":"execute_blender_codeArguments","type":"object"}}},{"type":"function","function":{"name":"get_polyhaven_categories","description":"\n    Get a list of categories for a specific asset type on Polyhaven.\n\n    Parameters:\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"asset_type":{"default":"hdris","title":"Asset Type","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_polyhaven_categoriesArguments","type":"object"}}},{"type":"function","function":{"name":"search_polyhaven_assets","description":"\n    Search for assets on Polyhaven with optional filtering.\n\n    Parameters:\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\n    - categories: Optional comma-separated list of categories to filter by\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a list of matching assets with basic information.\n    ","parameters":{"properties":{"asset_type":{"default":"all","title":"Asset Type","type":"string"},"categories":{"default":null,"title":"Categories","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"search_polyhaven_assetsArguments","type":"object"}}},{"type":"function","function":{"name":"download_polyhaven_asset","description":"\n    Download and import a Polyhaven asset into Blender.\n\n    Parameters:\n    - asset_id: The ID of the asset to download\n    - asset_type: The type of asset (hdris, textures, models)\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a message indicating success or failure.\n    ","parameters":{"properties":{"asset_id":{"title":"Asset Id","type":"string"},"asset_type":{"title":"Asset Type","type":"string"},"resolution":{"default":"1k","title":"Resolution","type":"string"},"file_format":{"default":null,"title":"File Format","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["asset_id","asset_type"],"title":"download_polyhaven_assetArguments","type":"object"}}},{"type":"function","function":{"name":"set_texture","description":"\n    Apply a previously downloaded Polyhaven texture to an object.\n    \n    Parameters:\n    - object_name: Name of the object to apply the texture to\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\n    \n    Returns a message indicating success or failure.\n    ","parameters":{"properties":{"object_name":{"title":"Object Name","type":"string"},"texture_id":{"title":"Texture Id","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["object_name","texture_id"],"title":"set_textureArguments","type":"object"}}},{"type":"function","function":{"name":"get_polyhaven_status","description":"\n    Check if PolyHaven integration is enabled in Blender.\n    Returns a message indicating whether PolyHaven features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_polyhaven_statusArguments","type":"object"}}},{"type":"function","function":{"name":"get_hyper3d_status","description":"\n    Check if Hyper3D Rodin integration is enabled in Blender.\n    Returns a message indicating whether Hyper3D Rodin features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_hyper3d_statusArguments","type":"object"}}},{"type":"function","function":{"name":"get_sketchfab_status","description":"\n    Check if Sketchfab integration is enabled in Blender.\n    Returns a message indicating whether Sketchfab features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_sketchfab_statusArguments","type":"object"}}},{"type":"function","function":{"name":"search_sketchfab_models","description":"\n    Search for models on Sketchfab with optional filtering.\n\n    Parameters:\n    - query: Text to search for\n    - categories: Optional comma-separated list of categories\n    - count: Maximum number of results to return (default 20)\n    - downloadable: Whether to include only downloadable models (default True)\n\n    Returns a formatted list of matching models.\n    ","parameters":{"properties":{"query":{"title":"Query","type":"string"},"categories":{"default":null,"title":"Categories","type":"string"},"count":{"default":20,"title":"Count","type":"integer"},"downloadable":{"default":true,"title":"Downloadable","type":"boolean"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["query"],"title":"search_sketchfab_modelsArguments","type":"object"}}},{"type":"function","function":{"name":"get_sketchfab_model_preview","description":"\n    Get a preview thumbnail of a Sketchfab model by its UID.\n    Use this to visually confirm a model before downloading.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\n    \n    Returns the model's thumbnail as an Image for visual confirmation.\n    ","parameters":{"properties":{"uid":{"title":"Uid","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["uid"],"title":"get_sketchfab_model_previewArguments","type":"object"}}},{"type":"function","function":{"name":"download_sketchfab_model","description":"\n    Download and import a Sketchfab model by its UID.\n    The model will be scaled so its largest dimension equals target_size.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\n                  You must specify the desired size for the model.\n                  Examples:\n                  - Chair: target_size=1.0 (1 meter tall)\n                  - Table: target_size=0.75 (75cm tall)\n                  - Car: target_size=4.5 (4.5 meters long)\n                  - Person: target_size=1.7 (1.7 meters tall)\n                  - Small object (cup, phone): target_size=0.1 to 0.3\n    \n    Returns a message with import details including object names, dimensions, and bounding box.\n    The model must be downloadable and you must have proper access rights.\n    ","parameters":{"properties":{"uid":{"title":"Uid","type":"string"},"target_size":{"title":"Target Size","type":"number"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["uid","target_size"],"title":"download_sketchfab_modelArguments","type":"object"}}},{"type":"function","function":{"name":"generate_hyper3d_model_via_text","description":"\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n\n    Parameters:\n    - text_prompt: A short description of the desired model in **English**.\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Returns a message indicating success or failure.\n    ","parameters":{"properties":{"text_prompt":{"title":"Text Prompt","type":"string"},"bbox_condition":{"default":null,"items":{"type":"number"},"title":"Bbox Condition","type":"array"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["text_prompt"],"title":"generate_hyper3d_model_via_textArguments","type":"object"}}},{"type":"function","function":{"name":"generate_hyper3d_model_via_images","description":"\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n    \n    Parameters:\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode.\n    Returns a message indicating success or failure.\n    ","parameters":{"properties":{"input_image_paths":{"default":null,"items":{"type":"string"},"title":"Input Image Paths","type":"array"},"input_image_urls":{"default":null,"items":{"type":"string"},"title":"Input Image Urls","type":"array"},"bbox_condition":{"default":null,"items":{"type":"number"},"title":"Bbox Condition","type":"array"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"generate_hyper3d_model_via_imagesArguments","type":"object"}}},{"type":"function","function":{"name":"poll_rodin_job_status","description":"\n    Check if the Hyper3D Rodin generation task is completed.\n\n    For Hyper3D Rodin mode MAIN_SITE:\n        Parameters:\n        - subscription_key: The subscription_key given in the generate model step.\n\n        Returns a list of status. The task is done if all status are \"Done\".\n        If \"Failed\" showed up, the generating process failed.\n        This is a polling API, so only proceed if the status are finally determined (\"Done\" or \"Canceled\").\n\n    For Hyper3D Rodin mode FAL_AI:\n        Parameters:\n        - request_id: The request_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"COMPLETED\".\n        The task is in progress if status is \"IN_PROGRESS\".\n        If status other than \"COMPLETED\", \"IN_PROGRESS\", \"IN_QUEUE\" showed up, the generating process might be failed.\n        This is a polling API, so only proceed if the status are finally determined (\"COMPLETED\" or some failed state).\n    ","parameters":{"properties":{"subscription_key":{"default":null,"title":"Subscription Key","type":"string"},"request_id":{"default":null,"title":"Request Id","type":"string"}},"title":"poll_rodin_job_statusArguments","type":"object"}}},{"type":"function","function":{"name":"import_generated_asset","description":"\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\n\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\n    Return if the asset has been imported successfully.\n    ","parameters":{"properties":{"name":{"title":"Name","type":"string"},"task_uuid":{"default":null,"title":"Task Uuid","type":"string"},"request_id":{"default":null,"title":"Request Id","type":"string"}},"required":["name"],"title":"import_generated_assetArguments","type":"object"}}},{"type":"function","function":{"name":"get_hunyuan3d_status","description":"\n    Check if Hunyuan3D integration is enabled in Blender.\n    Returns a message indicating whether Hunyuan3D features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_hunyuan3d_statusArguments","type":"object"}}},{"type":"function","function":{"name":"generate_hunyuan3d_model","description":"\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \n    or both for the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    \n    Parameters:\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\n    - input_image_url: (Optional) The local or remote url of the input image. Accepts None if only using text prompt.\n\n    Returns: \n    - When successful, returns a JSON with job_id (format: \"job_xxx\") indicating the task is in progress\n    - When the job completes, the status will change to \"DONE\" indicating the model has been imported\n    - Returns error message if the operation fails\n    ","parameters":{"properties":{"text_prompt":{"default":null,"title":"Text Prompt","type":"string"},"input_image_url":{"default":null,"title":"Input Image Url","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"generate_hunyuan3d_modelArguments","type":"object"}}},{"type":"function","function":{"name":"poll_hunyuan_job_status","description":"\n    Check if the Hunyuan3D generation task is completed.\n\n    For Hunyuan3D:\n        Parameters:\n        - job_id: The job_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"DONE\".\n        The task is in progress if status is \"RUN\".\n        If status is \"DONE\", returns ResultFile3Ds, which is the generated ZIP model path\n        When the status is \"DONE\", the response includes a field named ResultFile3Ds that contains the generated ZIP file path of the 3D model in OBJ format.\n        This is a polling API, so only proceed if the status are finally determined (\"DONE\" or some failed state).\n    ","parameters":{"properties":{"job_id":{"default":null,"title":"Job Id","type":"string"}},"title":"poll_hunyuan_job_statusArguments","type":"object"}}},{"type":"function","function":{"name":"import_generated_asset_hunyuan","description":"\n    Import the asset generated by Hunyuan3D after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - zip_file_url: The zip_file_url given in the generate model step.\n\n    Return if the asset has been imported successfully.\n    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"a1b4c39afa358038-MAD"}, "body": "\n         \n\n         \n\n         \n\n         \n{\"id\":\"gen-1784076532-Y0hJX2YEUvZM9Rsc1muo\",\"object\":\"chat.completion\",\"created\":1784076532,\"model\":\"anthropic/claude-haiku-4.5\",\"provider\":\"Amazon Bedrock\",\"system_fingerprint\":null,\"service_tier\":null,\"choices\":[{\"index\":0,\"logprobs\":null,\"finish_reason\":\"tool_calls\",\"native_finish_reason\":\"tool_use\",\"message\":{\"role\":\"assistant\",\"content\":\"Now I'll apply smooth shading to both objects:\",\"refusal\":null,\"reasoning\":null,\"tool_calls\":[{\"type\":\"function\",\"index\":0,\"id\":\"toolu_bdrk_01WSSDEGVk2FHv51RevTiES7\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\": \\\"\\\\nimport bpy\\\\n\\\\nsphere = bpy.data.objects[\\\\\\\"RedSphere\\\\\\\"]\\\\nplane = bpy.data.objects[\\\\\\\"GroundPlane\\\\\\\"]\\\\n\\\\n# Select sphere and apply smooth shading\\\\nsphere.select_set(True)\\\\nbpy.context.view_layer.objects.active = sphere\\\\nbpy.ops.object.shade_smooth()\\\\n\\\\n# Select plane and apply smooth shading\\\\nplane.select_set(True)\\\\nbpy.context.view_layer.objects.active = plane\\\\nbpy.ops.object.shade_smooth()\\\\n\\\\nprint(\\\\\\\"Smooth shading applied to both objects\\\\\\\")\\\\n\\\", \\\"user_prompt\\\": \\\"Apply smooth shading to sphere and plane\\\"}\"}}]}}],\"usage\":{\"prompt_tokens\":6915,\"completion_tokens\":230,\"total_tokens\":7145,\"cost\":0.0024601,\"is_byok\":false,\"prompt_tokens_details\":{\"cached_tokens\":6376,\"cache_write_tokens\":534,\"audio_tokens\":0,\"video_tokens\":0},\"cache_creation\":{\"ephemeral_5m_input_tokens\":534,\"ephemeral_1h_input_tokens\":0},\"cost_details\":{\"upstream_inference_cost\":0.0024601,\"upstream_inference_prompt_cost\":0.0013101,\"upstream_inference_completions_cost\":0.00115},\"completion_tokens_details\":{\"reasoning_tokens\":0,\"image_tokens\":0,\"audio_tokens\":0}}}", "body_base64": 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"error": null}], "response": {"text": "Now I'll apply smooth shading to both objects:", "tool_calls": [{"id": "toolu_bdrk_01WSSDEGVk2FHv51RevTiES7", "name": "execute_blender_code", "arguments": {"code": "\nimport bpy\n\nsphere = bpy.data.objects[\"RedSphere\"]\nplane = bpy.data.objects[\"GroundPlane\"]\n\n# Select sphere and apply smooth shading\nsphere.select_set(True)\nbpy.context.view_layer.objects.active = sphere\nbpy.ops.object.shade_smooth()\n\n# Select plane and apply smooth shading\nplane.select_set(True)\nbpy.context.view_layer.objects.active = plane\nbpy.ops.object.shade_smooth()\n\nprint(\"Smooth shading applied to both objects\")\n", "user_prompt": "Apply smooth shading to sphere and plane"}, "raw_arguments": "{\"code\": \"\\nimport bpy\\n\\nsphere = bpy.data.objects[\\\"RedSphere\\\"]\\nplane = bpy.data.objects[\\\"GroundPlane\\\"]\\n\\n# Select sphere and apply smooth shading\\nsphere.select_set(True)\\nbpy.context.view_layer.objects.active = sphere\\nbpy.ops.object.shade_smooth()\\n\\n# Select plane and apply smooth shading\\nplane.select_set(True)\\nbpy.context.view_layer.objects.active = plane\\nbpy.ops.object.shade_smooth()\\n\\nprint(\\\"Smooth shading applied to both objects\\\")\\n\", \"user_prompt\": \"Apply smooth shading to sphere and plane\"}", "parse_error": null}], "tokens_in": 6915, "tokens_out": 230, "cost_usd": 0.0024601, "cached_tokens": 6376}, "error": null}
{"ts": "2026-07-15T00:48:56.450339+00:00", "type": "assistant", "text": "Now I'll apply smooth shading to both objects:", "tool_calls": [{"id": "toolu_bdrk_01WSSDEGVk2FHv51RevTiES7", "name": "execute_blender_code", "arguments": {"code": "\nimport bpy\n\nsphere = bpy.data.objects[\"RedSphere\"]\nplane = bpy.data.objects[\"GroundPlane\"]\n\n# Select sphere and apply smooth shading\nsphere.select_set(True)\nbpy.context.view_layer.objects.active = sphere\nbpy.ops.object.shade_smooth()\n\n# Select plane and apply smooth shading\nplane.select_set(True)\nbpy.context.view_layer.objects.active = plane\nbpy.ops.object.shade_smooth()\n\nprint(\"Smooth shading applied to both objects\")\n", "user_prompt": "Apply smooth shading to sphere and plane"}, "raw_arguments": "{\"code\": \"\\nimport bpy\\n\\nsphere = bpy.data.objects[\\\"RedSphere\\\"]\\nplane = bpy.data.objects[\\\"GroundPlane\\\"]\\n\\n# Select sphere and apply smooth shading\\nsphere.select_set(True)\\nbpy.context.view_layer.objects.active = sphere\\nbpy.ops.object.shade_smooth()\\n\\n# Select plane and apply smooth shading\\nplane.select_set(True)\\nbpy.context.view_layer.objects.active = plane\\nbpy.ops.object.shade_smooth()\\n\\nprint(\\\"Smooth shading applied to both objects\\\")\\n\", \"user_prompt\": \"Apply smooth shading to sphere and plane\"}", "parse_error": null}]}
{"ts": "2026-07-15T00:48:56.450339+00:00", "type": "tool_call", "id": "toolu_bdrk_01WSSDEGVk2FHv51RevTiES7", "name": "execute_blender_code", "arguments": {"code": "\nimport bpy\n\nsphere = bpy.data.objects[\"RedSphere\"]\nplane = bpy.data.objects[\"GroundPlane\"]\n\n# Select sphere and apply smooth shading\nsphere.select_set(True)\nbpy.context.view_layer.objects.active = sphere\nbpy.ops.object.shade_smooth()\n\n# Select plane and apply smooth shading\nplane.select_set(True)\nbpy.context.view_layer.objects.active = plane\nbpy.ops.object.shade_smooth()\n\nprint(\"Smooth shading applied to both objects\")\n", "user_prompt": "Apply smooth shading to sphere and plane"}, "raw_arguments": "{\"code\": \"\\nimport bpy\\n\\nsphere = bpy.data.objects[\\\"RedSphere\\\"]\\nplane = bpy.data.objects[\\\"GroundPlane\\\"]\\n\\n# Select sphere and apply smooth shading\\nsphere.select_set(True)\\nbpy.context.view_layer.objects.active = sphere\\nbpy.ops.object.shade_smooth()\\n\\n# Select plane and apply smooth shading\\nplane.select_set(True)\\nbpy.context.view_layer.objects.active = plane\\nbpy.ops.object.shade_smooth()\\n\\nprint(\\\"Smooth shading applied to both objects\\\")\\n\", \"user_prompt\": \"Apply smooth shading to sphere and plane\"}"}
{"ts": "2026-07-15T00:48:56.487965+00:00", "type": "tool_result", "tool_call_id": "toolu_bdrk_01WSSDEGVk2FHv51RevTiES7", "name": "execute_blender_code", "text": "Code executed successfully: Smooth shading applied to both objects\n", "is_error": false, "has_image": false, "image_b64": null}
{"ts": "2026-07-15T00:49:00.842003+00:00", "type": "llm_call", "call_index": 5, "provider_type": "blenderbench.providers.openrouter.OpenRouterProvider", "started_at": "2026-07-15T00:48:56.487965+00:00", "finished_at": "2026-07-15T00:49:00.842003+00:00", "duration_s": 4.360000000015134, "request": {"messages": [{"role": "system", "content": "You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\n\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\n\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\n\n## Harness Instructions\n\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. When you are finished, reply with the single word DONE and no tool calls."}, {"role": "user", "content": "## Task\n\nComplete the following Blender task:\n\n<task>\n\nA red sphere of 1 meter diameter resting on a white ground plane — touching it\nexactly, neither floating nor sunk. Smooth shading, a plain red material on the\nball, plain white on the floor.\n\nThe floor is the sphere, the color, and the contact. The ceiling is presentation:\na soft believable shadow under the ball, a hint of glossiness that catches the\nlight, and clean framing.\n\n</task>"}, {"role": "assistant", "content": "I'll create a red sphere resting on a white ground plane with proper materials, shading, lighting, and camera setup. Let me work through this step by step.", "tool_calls": [{"id": "toolu_bdrk_01McQwtp5Bq6813NVu2gUpRG", "type": "function", "function": {"name": "get_scene_info", "arguments": "{\"user_prompt\": \"Get current scene info to see what's there initially\"}"}}]}, {"role": "tool", "tool_call_id": "toolu_bdrk_01McQwtp5Bq6813NVu2gUpRG", "content": "{\n  \"name\": \"Scene\",\n  \"object_count\": 0,\n  \"objects\": [],\n  \"materials_count\": 2\n}"}, {"role": "assistant", "content": "Good, the scene is empty. Now I'll create the red sphere and white ground plane using Blender code:", "tool_calls": [{"id": "toolu_bdrk_01PuvjJZjfHJZgTRw6jGDsdH", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\": \"\\nimport bpy\\nimport bmesh\\nfrom mathutils import Vector\\n\\n# Clear any default objects\\nbpy.ops.object.select_all(action='SELECT')\\nbpy.ops.object.delete(use_global=False)\\n\\n# Add a sphere (1 meter diameter = 0.5 meter radius)\\nbpy.ops.mesh.primitive_uv_sphere_add(\\n    radius=0.5,\\n    location=(0, 0, 0.5)  # Position it so bottom touches z=0\\n)\\nsphere = bpy.context.active_object\\nsphere.name = \\\"RedSphere\\\"\\n\\n# Add a large ground plane\\nbpy.ops.mesh.primitive_plane_add(\\n    size=10,\\n    location=(0, 0, 0)\\n)\\nplane = bpy.context.active_object\\nplane.name = \\\"GroundPlane\\\"\\n\\n# Select and scale plane to be large but thin\\nplane.scale = (10, 10, 0.1)\\n\\nprint(\\\"Objects created: Sphere at z=\\\" + str(sphere.location.z) + \\\", Plane at z=\\\" + str(plane.location.z))\\n\", \"user_prompt\": \"Create red sphere and white ground plane\"}"}}]}, {"role": "tool", "tool_call_id": "toolu_bdrk_01PuvjJZjfHJZgTRw6jGDsdH", "content": "Code executed successfully: Objects created: Sphere at z=0.5, Plane at z=0.0\n"}, {"role": "assistant", "content": "Now I'll create the materials - red for the sphere and white for the plane:", "tool_calls": [{"id": "toolu_bdrk_018gHZ2vPGV9JR5pgaWmu5aZ", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\": \"\\nimport bpy\\n\\n# Get the sphere and plane objects\\nsphere = bpy.data.objects[\\\"RedSphere\\\"]\\nplane = bpy.data.objects[\\\"GroundPlane\\\"]\\n\\n# Create red material for sphere\\nred_material = bpy.data.materials.new(name=\\\"RedMaterial\\\")\\nred_material.use_nodes = True\\nred_material.diffuse_color = (1.0, 0.0, 0.0, 1.0)  # Red RGBA\\n\\n# Configure red material with some glossiness\\nbsdf = red_material.node_tree.nodes[\\\"Principled BSDF\\\"]\\nbsdf.inputs[\\\"Base Color\\\"].default_value = (0.8, 0.05, 0.05, 1.0)  # Pure red\\nbsdf.inputs[\\\"Roughness\\\"].default_value = 0.3  # Slight glossiness\\n\\n# Create white material for plane\\nwhite_material = bpy.data.materials.new(name=\\\"WhiteMaterial\\\")\\nwhite_material.use_nodes = True\\nwhite_material.diffuse_color = (1.0, 1.0, 1.0, 1.0)  # White RGBA\\n\\nbsdf_white = white_material.node_tree.nodes[\\\"Principled BSDF\\\"]\\nbsdf_white.inputs[\\\"Base Color\\\"].default_value = (1.0, 1.0, 1.0, 1.0)  # White\\nbsdf_white.inputs[\\\"Roughness\\\"].default_value = 0.8  # Matte white\\n\\n# Assign materials to objects\\nsphere.data.materials.append(red_material)\\nplane.data.materials.append(white_material)\\n\\nprint(\\\"Materials created and assigned\\\")\\n\", \"user_prompt\": \"Create and assign red and white materials\"}"}}]}, {"role": "tool", "tool_call_id": "toolu_bdrk_018gHZ2vPGV9JR5pgaWmu5aZ", "content": "Code executed successfully: Materials created and assigned\n"}, {"role": "assistant", "content": "Now I'll apply smooth shading to both objects:", "tool_calls": [{"id": "toolu_bdrk_01WSSDEGVk2FHv51RevTiES7", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\": \"\\nimport bpy\\n\\nsphere = bpy.data.objects[\\\"RedSphere\\\"]\\nplane = bpy.data.objects[\\\"GroundPlane\\\"]\\n\\n# Select sphere and apply smooth shading\\nsphere.select_set(True)\\nbpy.context.view_layer.objects.active = sphere\\nbpy.ops.object.shade_smooth()\\n\\n# Select plane and apply smooth shading\\nplane.select_set(True)\\nbpy.context.view_layer.objects.active = plane\\nbpy.ops.object.shade_smooth()\\n\\nprint(\\\"Smooth shading applied to both objects\\\")\\n\", \"user_prompt\": \"Apply smooth shading to sphere and plane\"}"}}]}, {"role": "tool", "tool_call_id": "toolu_bdrk_01WSSDEGVk2FHv51RevTiES7", "content": "Code executed successfully: Smooth shading applied to both objects\n"}], "tools": [{"type": "function", "function": {"name": "get_scene_info", "description": "Get detailed information about the current Blender scene\n\n    Parameters:\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\n    ", "parameters": {"properties": {"user_prompt": {"title": "User Prompt", "type": "string"}}, "required": ["user_prompt"], "title": "get_scene_infoArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_object_info", "description": "\n    Get detailed information about a specific object in the Blender scene.\n\n    Parameters:\n    - object_name: The name of the object to get information about\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"object_name": {"title": "Object Name", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["object_name"], "title": "get_object_infoArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_viewport_screenshot", "description": "\n    Capture a screenshot of the current Blender 3D viewport.\n\n    Parameters:\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns the screenshot as an Image.\n    ", "parameters": {"properties": {"max_size": {"default": 1000, "title": "Max Size", "type": "integer"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_viewport_screenshotArguments", "type": "object"}}}, {"type": "function", "function": {"name": "execute_blender_code", "description": "\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\n\n    Parameters:\n    - code: The Python code to execute\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"code": {"title": "Code", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["code"], "title": "execute_blender_codeArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_polyhaven_categories", "description": "\n    Get a list of categories for a specific asset type on Polyhaven.\n\n    Parameters:\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"asset_type": {"default": "hdris", "title": "Asset Type", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_polyhaven_categoriesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_polyhaven_assets", "description": "\n    Search for assets on Polyhaven with optional filtering.\n\n    Parameters:\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\n    - categories: Optional comma-separated list of categories to filter by\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a list of matching assets with basic information.\n    ", "parameters": {"properties": {"asset_type": {"default": "all", "title": "Asset Type", "type": "string"}, "categories": {"default": null, "title": "Categories", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "search_polyhaven_assetsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "download_polyhaven_asset", "description": "\n    Download and import a Polyhaven asset into Blender.\n\n    Parameters:\n    - asset_id: The ID of the asset to download\n    - asset_type: The type of asset (hdris, textures, models)\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"asset_id": {"title": "Asset Id", "type": "string"}, "asset_type": {"title": "Asset Type", "type": "string"}, "resolution": {"default": "1k", "title": "Resolution", "type": "string"}, "file_format": {"default": null, "title": "File Format", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["asset_id", "asset_type"], "title": "download_polyhaven_assetArguments", "type": "object"}}}, {"type": "function", "function": {"name": "set_texture", "description": "\n    Apply a previously downloaded Polyhaven texture to an object.\n    \n    Parameters:\n    - object_name: Name of the object to apply the texture to\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\n    \n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"object_name": {"title": "Object Name", "type": "string"}, "texture_id": {"title": "Texture Id", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["object_name", "texture_id"], "title": "set_textureArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_polyhaven_status", "description": "\n    Check if PolyHaven integration is enabled in Blender.\n    Returns a message indicating whether PolyHaven features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_polyhaven_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_hyper3d_status", "description": "\n    Check if Hyper3D Rodin integration is enabled in Blender.\n    Returns a message indicating whether Hyper3D Rodin features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_hyper3d_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_sketchfab_status", "description": "\n    Check if Sketchfab integration is enabled in Blender.\n    Returns a message indicating whether Sketchfab features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_sketchfab_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_sketchfab_models", "description": "\n    Search for models on Sketchfab with optional filtering.\n\n    Parameters:\n    - query: Text to search for\n    - categories: Optional comma-separated list of categories\n    - count: Maximum number of results to return (default 20)\n    - downloadable: Whether to include only downloadable models (default True)\n\n    Returns a formatted list of matching models.\n    ", "parameters": {"properties": {"query": {"title": "Query", "type": "string"}, "categories": {"default": null, "title": "Categories", "type": "string"}, "count": {"default": 20, "title": "Count", "type": "integer"}, "downloadable": {"default": true, "title": "Downloadable", "type": "boolean"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["query"], "title": "search_sketchfab_modelsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_sketchfab_model_preview", "description": "\n    Get a preview thumbnail of a Sketchfab model by its UID.\n    Use this to visually confirm a model before downloading.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\n    \n    Returns the model's thumbnail as an Image for visual confirmation.\n    ", "parameters": {"properties": {"uid": {"title": "Uid", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["uid"], "title": "get_sketchfab_model_previewArguments", "type": "object"}}}, {"type": "function", "function": {"name": "download_sketchfab_model", "description": "\n    Download and import a Sketchfab model by its UID.\n    The model will be scaled so its largest dimension equals target_size.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\n                  You must specify the desired size for the model.\n                  Examples:\n                  - Chair: target_size=1.0 (1 meter tall)\n                  - Table: target_size=0.75 (75cm tall)\n                  - Car: target_size=4.5 (4.5 meters long)\n                  - Person: target_size=1.7 (1.7 meters tall)\n                  - Small object (cup, phone): target_size=0.1 to 0.3\n    \n    Returns a message with import details including object names, dimensions, and bounding box.\n    The model must be downloadable and you must have proper access rights.\n    ", "parameters": {"properties": {"uid": {"title": "Uid", "type": "string"}, "target_size": {"title": "Target Size", "type": "number"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["uid", "target_size"], "title": "download_sketchfab_modelArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hyper3d_model_via_text", "description": "\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n\n    Parameters:\n    - text_prompt: A short description of the desired model in **English**.\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"text_prompt": {"title": "Text Prompt", "type": "string"}, "bbox_condition": {"default": null, "items": {"type": "number"}, "title": "Bbox Condition", "type": "array"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["text_prompt"], "title": "generate_hyper3d_model_via_textArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hyper3d_model_via_images", "description": "\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n    \n    Parameters:\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode.\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"input_image_paths": {"default": null, "items": {"type": "string"}, "title": "Input Image Paths", "type": "array"}, "input_image_urls": {"default": null, "items": {"type": "string"}, "title": "Input Image Urls", "type": "array"}, "bbox_condition": {"default": null, "items": {"type": "number"}, "title": "Bbox Condition", "type": "array"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "generate_hyper3d_model_via_imagesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "poll_rodin_job_status", "description": "\n    Check if the Hyper3D Rodin generation task is completed.\n\n    For Hyper3D Rodin mode MAIN_SITE:\n        Parameters:\n        - subscription_key: The subscription_key given in the generate model step.\n\n        Returns a list of status. The task is done if all status are \"Done\".\n        If \"Failed\" showed up, the generating process failed.\n        This is a polling API, so only proceed if the status are finally determined (\"Done\" or \"Canceled\").\n\n    For Hyper3D Rodin mode FAL_AI:\n        Parameters:\n        - request_id: The request_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"COMPLETED\".\n        The task is in progress if status is \"IN_PROGRESS\".\n        If status other than \"COMPLETED\", \"IN_PROGRESS\", \"IN_QUEUE\" showed up, the generating process might be failed.\n        This is a polling API, so only proceed if the status are finally determined (\"COMPLETED\" or some failed state).\n    ", "parameters": {"properties": {"subscription_key": {"default": null, "title": "Subscription Key", "type": "string"}, "request_id": {"default": null, "title": "Request Id", "type": "string"}}, "title": "poll_rodin_job_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "import_generated_asset", "description": "\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\n\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\n    Return if the asset has been imported successfully.\n    ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "task_uuid": {"default": null, "title": "Task Uuid", "type": "string"}, "request_id": {"default": null, "title": "Request Id", "type": "string"}}, "required": ["name"], "title": "import_generated_assetArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_hunyuan3d_status", "description": "\n    Check if Hunyuan3D integration is enabled in Blender.\n    Returns a message indicating whether Hunyuan3D features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_hunyuan3d_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hunyuan3d_model", "description": "\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \n    or both for the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    \n    Parameters:\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\n    - input_image_url: (Optional) The local or remote url of the input image. Accepts None if only using text prompt.\n\n    Returns: \n    - When successful, returns a JSON with job_id (format: \"job_xxx\") indicating the task is in progress\n    - When the job completes, the status will change to \"DONE\" indicating the model has been imported\n    - Returns error message if the operation fails\n    ", "parameters": {"properties": {"text_prompt": {"default": null, "title": "Text Prompt", "type": "string"}, "input_image_url": {"default": null, "title": "Input Image Url", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "generate_hunyuan3d_modelArguments", "type": "object"}}}, {"type": "function", "function": {"name": "poll_hunyuan_job_status", "description": "\n    Check if the Hunyuan3D generation task is completed.\n\n    For Hunyuan3D:\n        Parameters:\n        - job_id: The job_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"DONE\".\n        The task is in progress if status is \"RUN\".\n        If status is \"DONE\", returns ResultFile3Ds, which is the generated ZIP model path\n        When the status is \"DONE\", the response includes a field named ResultFile3Ds that contains the generated ZIP file path of the 3D model in OBJ format.\n        This is a polling API, so only proceed if the status are finally determined (\"DONE\" or some failed state).\n    ", "parameters": {"properties": {"job_id": {"default": null, "title": "Job Id", "type": "string"}}, "title": "poll_hunyuan_job_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "import_generated_asset_hunyuan", "description": "\n    Import the asset generated by Hunyuan3D after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - zip_file_url: The zip_file_url given in the generate model step.\n\n    Return if the asset has been imported successfully.\n    ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "zip_file_url": {"title": "Zip File Url", "type": "string"}}, "required": ["name", "zip_file_url"], "title": "import_generated_asset_hunyuanArguments", "type": "object"}}}]}, "provider_attempts": [{"provider": "openrouter", "attempt": 1, "started_at": "2026-07-15T00:48:56.487965+00:00", "finished_at": "2026-07-15T00:49:00.842003+00:00", "duration_s": 4.360000000015134, "request": {"method": "POST", "url": "https://openrouter.ai/api/v1/chat/completions", "headers": {"host": "openrouter.ai", "accept": "*/*", "accept-encoding": "gzip, deflate", "connection": "keep-alive", "user-agent": "python-httpx/0.28.1", "authorization": "[REDACTED]", "content-length": "23186", "content-type": "application/json"}, "body": "{\"model\":\"anthropic/claude-haiku-4.5\",\"messages\":[{\"role\":\"system\",\"content\":[{\"type\":\"text\",\"text\":\"You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\\n\\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\\n\\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\\n\\n## Harness Instructions\\n\\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. When you are finished, reply with the single word DONE and no tool calls.\",\"cache_control\":{\"type\":\"ephemeral\"}}]},{\"role\":\"user\",\"content\":\"## Task\\n\\nComplete the following Blender task:\\n\\n<task>\\n\\nA red sphere of 1 meter diameter resting on a white ground plane — touching it\\nexactly, neither floating nor sunk. Smooth shading, a plain red material on the\\nball, plain white on the floor.\\n\\nThe floor is the sphere, the color, and the contact. The ceiling is presentation:\\na soft believable shadow under the ball, a hint of glossiness that catches the\\nlight, and clean framing.\\n\\n</task>\"},{\"role\":\"assistant\",\"content\":\"I'll create a red sphere resting on a white ground plane with proper materials, shading, lighting, and camera setup. Let me work through this step by step.\",\"tool_calls\":[{\"id\":\"toolu_bdrk_01McQwtp5Bq6813NVu2gUpRG\",\"type\":\"function\",\"function\":{\"name\":\"get_scene_info\",\"arguments\":\"{\\\"user_prompt\\\": \\\"Get current scene info to see what's there initially\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"toolu_bdrk_01McQwtp5Bq6813NVu2gUpRG\",\"content\":\"{\\n  \\\"name\\\": \\\"Scene\\\",\\n  \\\"object_count\\\": 0,\\n  \\\"objects\\\": [],\\n  \\\"materials_count\\\": 2\\n}\"},{\"role\":\"assistant\",\"content\":\"Good, the scene is empty. Now I'll create the red sphere and white ground plane using Blender code:\",\"tool_calls\":[{\"id\":\"toolu_bdrk_01PuvjJZjfHJZgTRw6jGDsdH\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\": \\\"\\\\nimport bpy\\\\nimport bmesh\\\\nfrom mathutils import Vector\\\\n\\\\n# Clear any default objects\\\\nbpy.ops.object.select_all(action='SELECT')\\\\nbpy.ops.object.delete(use_global=False)\\\\n\\\\n# Add a sphere (1 meter diameter = 0.5 meter radius)\\\\nbpy.ops.mesh.primitive_uv_sphere_add(\\\\n    radius=0.5,\\\\n    location=(0, 0, 0.5)  # Position it so bottom touches z=0\\\\n)\\\\nsphere = bpy.context.active_object\\\\nsphere.name = \\\\\\\"RedSphere\\\\\\\"\\\\n\\\\n# Add a large ground plane\\\\nbpy.ops.mesh.primitive_plane_add(\\\\n    size=10,\\\\n    location=(0, 0, 0)\\\\n)\\\\nplane = bpy.context.active_object\\\\nplane.name = \\\\\\\"GroundPlane\\\\\\\"\\\\n\\\\n# Select and scale plane to be large but thin\\\\nplane.scale = (10, 10, 0.1)\\\\n\\\\nprint(\\\\\\\"Objects created: Sphere at z=\\\\\\\" + str(sphere.location.z) + \\\\\\\", Plane at z=\\\\\\\" + str(plane.location.z))\\\\n\\\", \\\"user_prompt\\\": \\\"Create red sphere and white ground plane\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"toolu_bdrk_01PuvjJZjfHJZgTRw6jGDsdH\",\"content\":\"Code executed successfully: Objects created: Sphere at z=0.5, Plane at z=0.0\\n\"},{\"role\":\"assistant\",\"content\":\"Now I'll create the materials - red for the sphere and white for the plane:\",\"tool_calls\":[{\"id\":\"toolu_bdrk_018gHZ2vPGV9JR5pgaWmu5aZ\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\": \\\"\\\\nimport bpy\\\\n\\\\n# Get the sphere and plane objects\\\\nsphere = bpy.data.objects[\\\\\\\"RedSphere\\\\\\\"]\\\\nplane = bpy.data.objects[\\\\\\\"GroundPlane\\\\\\\"]\\\\n\\\\n# Create red material for sphere\\\\nred_material = bpy.data.materials.new(name=\\\\\\\"RedMaterial\\\\\\\")\\\\nred_material.use_nodes = True\\\\nred_material.diffuse_color = (1.0, 0.0, 0.0, 1.0)  # Red RGBA\\\\n\\\\n# Configure red material with some glossiness\\\\nbsdf = red_material.node_tree.nodes[\\\\\\\"Principled BSDF\\\\\\\"]\\\\nbsdf.inputs[\\\\\\\"Base Color\\\\\\\"].default_value = (0.8, 0.05, 0.05, 1.0)  # Pure red\\\\nbsdf.inputs[\\\\\\\"Roughness\\\\\\\"].default_value = 0.3  # Slight glossiness\\\\n\\\\n# Create white material for plane\\\\nwhite_material = bpy.data.materials.new(name=\\\\\\\"WhiteMaterial\\\\\\\")\\\\nwhite_material.use_nodes = True\\\\nwhite_material.diffuse_color = (1.0, 1.0, 1.0, 1.0)  # White RGBA\\\\n\\\\nbsdf_white = white_material.node_tree.nodes[\\\\\\\"Principled BSDF\\\\\\\"]\\\\nbsdf_white.inputs[\\\\\\\"Base Color\\\\\\\"].default_value = (1.0, 1.0, 1.0, 1.0)  # White\\\\nbsdf_white.inputs[\\\\\\\"Roughness\\\\\\\"].default_value = 0.8  # Matte white\\\\n\\\\n# Assign materials to objects\\\\nsphere.data.materials.append(red_material)\\\\nplane.data.materials.append(white_material)\\\\n\\\\nprint(\\\\\\\"Materials created and assigned\\\\\\\")\\\\n\\\", \\\"user_prompt\\\": \\\"Create and assign red and white materials\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"toolu_bdrk_018gHZ2vPGV9JR5pgaWmu5aZ\",\"content\":\"Code executed successfully: Materials created and assigned\\n\"},{\"role\":\"assistant\",\"content\":\"Now I'll apply smooth shading to both objects:\",\"tool_calls\":[{\"id\":\"toolu_bdrk_01WSSDEGVk2FHv51RevTiES7\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\": \\\"\\\\nimport bpy\\\\n\\\\nsphere = bpy.data.objects[\\\\\\\"RedSphere\\\\\\\"]\\\\nplane = bpy.data.objects[\\\\\\\"GroundPlane\\\\\\\"]\\\\n\\\\n# Select sphere and apply smooth shading\\\\nsphere.select_set(True)\\\\nbpy.context.view_layer.objects.active = sphere\\\\nbpy.ops.object.shade_smooth()\\\\n\\\\n# Select plane and apply smooth shading\\\\nplane.select_set(True)\\\\nbpy.context.view_layer.objects.active = plane\\\\nbpy.ops.object.shade_smooth()\\\\n\\\\nprint(\\\\\\\"Smooth shading applied to both objects\\\\\\\")\\\\n\\\", \\\"user_prompt\\\": \\\"Apply smooth shading to sphere and plane\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"toolu_bdrk_01WSSDEGVk2FHv51RevTiES7\",\"content\":[{\"type\":\"text\",\"text\":\"Code executed successfully: Smooth shading applied to both objects\\n\",\"cache_control\":{\"type\":\"ephemeral\"}}]}],\"usage\":{\"include\":true},\"tools\":[{\"type\":\"function\",\"function\":{\"name\":\"get_scene_info\",\"description\":\"Get detailed information about the current Blender scene\\n\\n    Parameters:\\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"user_prompt\"],\"title\":\"get_scene_infoArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_object_info\",\"description\":\"\\n    Get detailed information about a specific object in the Blender scene.\\n\\n    Parameters:\\n    - object_name: The name of the object to get information about\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"object_name\":{\"title\":\"Object Name\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"object_name\"],\"title\":\"get_object_infoArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_viewport_screenshot\",\"description\":\"\\n    Capture a screenshot of the current Blender 3D viewport.\\n\\n    Parameters:\\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns the screenshot as an Image.\\n    \",\"parameters\":{\"properties\":{\"max_size\":{\"default\":1000,\"title\":\"Max Size\",\"type\":\"integer\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_viewport_screenshotArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"description\":\"\\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\\n\\n    Parameters:\\n    - code: The Python code to execute\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"code\":{\"title\":\"Code\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"code\"],\"title\":\"execute_blender_codeArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_categories\",\"description\":\"\\n    Get a list of categories for a specific asset type on Polyhaven.\\n\\n    Parameters:\\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"asset_type\":{\"default\":\"hdris\",\"title\":\"Asset Type\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_polyhaven_categoriesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_polyhaven_assets\",\"description\":\"\\n    Search for assets on Polyhaven with optional filtering.\\n\\n    Parameters:\\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\\n    - categories: Optional comma-separated list of categories to filter by\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns a list of matching assets with basic information.\\n    \",\"parameters\":{\"properties\":{\"asset_type\":{\"default\":\"all\",\"title\":\"Asset Type\",\"type\":\"string\"},\"categories\":{\"default\":null,\"title\":\"Categories\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"search_polyhaven_assetsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"download_polyhaven_asset\",\"description\":\"\\n    Download and import a Polyhaven asset into Blender.\\n\\n    Parameters:\\n    - asset_id: The ID of the asset to download\\n    - asset_type: The type of asset (hdris, textures, models)\\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"asset_id\":{\"title\":\"Asset Id\",\"type\":\"string\"},\"asset_type\":{\"title\":\"Asset Type\",\"type\":\"string\"},\"resolution\":{\"default\":\"1k\",\"title\":\"Resolution\",\"type\":\"string\"},\"file_format\":{\"default\":null,\"title\":\"File Format\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"asset_id\",\"asset_type\"],\"title\":\"download_polyhaven_assetArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"set_texture\",\"description\":\"\\n    Apply a previously downloaded Polyhaven texture to an object.\\n    \\n    Parameters:\\n    - object_name: Name of the object to apply the texture to\\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\\n    \\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"object_name\":{\"title\":\"Object Name\",\"type\":\"string\"},\"texture_id\":{\"title\":\"Texture Id\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"object_name\",\"texture_id\"],\"title\":\"set_textureArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_status\",\"description\":\"\\n    Check if PolyHaven integration is enabled in Blender.\\n    Returns a message indicating whether PolyHaven features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_polyhaven_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_hyper3d_status\",\"description\":\"\\n    Check if Hyper3D Rodin integration is enabled in Blender.\\n    Returns a message indicating whether Hyper3D Rodin features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_hyper3d_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_status\",\"description\":\"\\n    Check if Sketchfab integration is enabled in Blender.\\n    Returns a message indicating whether Sketchfab features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_sketchfab_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_sketchfab_models\",\"description\":\"\\n    Search for models on Sketchfab with optional filtering.\\n\\n    Parameters:\\n    - query: Text to search for\\n    - categories: Optional comma-separated list of categories\\n    - count: Maximum number of results to return (default 20)\\n    - downloadable: Whether to include only downloadable models (default True)\\n\\n    Returns a formatted list of matching models.\\n    \",\"parameters\":{\"properties\":{\"query\":{\"title\":\"Query\",\"type\":\"string\"},\"categories\":{\"default\":null,\"title\":\"Categories\",\"type\":\"string\"},\"count\":{\"default\":20,\"title\":\"Count\",\"type\":\"integer\"},\"downloadable\":{\"default\":true,\"title\":\"Downloadable\",\"type\":\"boolean\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"query\"],\"title\":\"search_sketchfab_modelsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_model_preview\",\"description\":\"\\n    Get a preview thumbnail of a Sketchfab model by its UID.\\n    Use this to visually confirm a model before downloading.\\n    \\n    Parameters:\\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\\n    \\n    Returns the model's thumbnail as an Image for visual confirmation.\\n    \",\"parameters\":{\"properties\":{\"uid\":{\"title\":\"Uid\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"uid\"],\"title\":\"get_sketchfab_model_previewArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"download_sketchfab_model\",\"description\":\"\\n    Download and import a Sketchfab model by its UID.\\n    The model will be scaled so its largest dimension equals target_size.\\n    \\n    Parameters:\\n    - uid: The unique identifier of the Sketchfab model\\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\\n                  You must specify the desired size for the model.\\n                  Examples:\\n                  - Chair: target_size=1.0 (1 meter tall)\\n                  - Table: target_size=0.75 (75cm tall)\\n                  - Car: target_size=4.5 (4.5 meters long)\\n                  - Person: target_size=1.7 (1.7 meters tall)\\n                  - Small object (cup, phone): target_size=0.1 to 0.3\\n    \\n    Returns a message with import details including object names, dimensions, and bounding box.\\n    The model must be downloadable and you must have proper access rights.\\n    \",\"parameters\":{\"properties\":{\"uid\":{\"title\":\"Uid\",\"type\":\"string\"},\"target_size\":{\"title\":\"Target Size\",\"type\":\"number\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"uid\",\"target_size\"],\"title\":\"download_sketchfab_modelArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hyper3d_model_via_text\",\"description\":\"\\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\\n    The 3D asset has built-in materials.\\n    The generated model has a normalized size, so re-scaling after generation can be useful.\\n\\n    Parameters:\\n    - text_prompt: A short description of the desired model in **English**.\\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\\n\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"text_prompt\":{\"title\":\"Text Prompt\",\"type\":\"string\"},\"bbox_condition\":{\"default\":null,\"items\":{\"type\":\"number\"},\"title\":\"Bbox Condition\",\"type\":\"array\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"text_prompt\"],\"title\":\"generate_hyper3d_model_via_textArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hyper3d_model_via_images\",\"description\":\"\\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\\n    The 3D asset has built-in materials.\\n    The generated model has a normalized size, so re-scaling after generation can be useful.\\n    \\n    Parameters:\\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model.\\n\\n    Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode.\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"input_image_paths\":{\"default\":null,\"items\":{\"type\":\"string\"},\"title\":\"Input Image Paths\",\"type\":\"array\"},\"input_image_urls\":{\"default\":null,\"items\":{\"type\":\"string\"},\"title\":\"Input Image Urls\",\"type\":\"array\"},\"bbox_condition\":{\"default\":null,\"items\":{\"type\":\"number\"},\"title\":\"Bbox Condition\",\"type\":\"array\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"generate_hyper3d_model_via_imagesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"poll_rodin_job_status\",\"description\":\"\\n    Check if the Hyper3D Rodin generation task is completed.\\n\\n    For Hyper3D Rodin mode MAIN_SITE:\\n        Parameters:\\n        - subscription_key: The subscription_key given in the generate model step.\\n\\n        Returns a list of status. The task is done if all status are \\\"Done\\\".\\n        If \\\"Failed\\\" showed up, the generating process failed.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"Done\\\" or \\\"Canceled\\\").\\n\\n    For Hyper3D Rodin mode FAL_AI:\\n        Parameters:\\n        - request_id: The request_id given in the generate model step.\\n\\n        Returns the generation task status. The task is done if status is \\\"COMPLETED\\\".\\n        The task is in progress if status is \\\"IN_PROGRESS\\\".\\n        If status other than \\\"COMPLETED\\\", \\\"IN_PROGRESS\\\", \\\"IN_QUEUE\\\" showed up, the generating process might be failed.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"COMPLETED\\\" or some failed state).\\n    \",\"parameters\":{\"properties\":{\"subscription_key\":{\"default\":null,\"title\":\"Subscription Key\",\"type\":\"string\"},\"request_id\":{\"default\":null,\"title\":\"Request Id\",\"type\":\"string\"}},\"title\":\"poll_rodin_job_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"import_generated_asset\",\"description\":\"\\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\\n\\n    Parameters:\\n    - name: The name of the object in scene\\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\\n\\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\\n    Return if the asset has been imported successfully.\\n    \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"task_uuid\":{\"default\":null,\"title\":\"Task Uuid\",\"type\":\"string\"},\"request_id\":{\"default\":null,\"title\":\"Request Id\",\"type\":\"string\"}},\"required\":[\"name\"],\"title\":\"import_generated_assetArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_hunyuan3d_status\",\"description\":\"\\n    Check if Hunyuan3D integration is enabled in Blender.\\n    Returns a message indicating whether Hunyuan3D features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_hunyuan3d_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hunyuan3d_model\",\"description\":\"\\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \\n    or both for the desired asset, and import the asset into Blender.\\n    The 3D asset has built-in materials.\\n    \\n    Parameters:\\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\\n    - input_image_url: (Optional) The local or remote url of the input image. Accepts None if only using text prompt.\\n\\n    Returns: \\n    - When successful, returns a JSON with job_id (format: \\\"job_xxx\\\") indicating the task is in progress\\n    - When the job completes, the status will change to \\\"DONE\\\" indicating the model has been imported\\n    - Returns error message if the operation fails\\n    \",\"parameters\":{\"properties\":{\"text_prompt\":{\"default\":null,\"title\":\"Text Prompt\",\"type\":\"string\"},\"input_image_url\":{\"default\":null,\"title\":\"Input Image Url\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"generate_hunyuan3d_modelArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"poll_hunyuan_job_status\",\"description\":\"\\n    Check if the Hunyuan3D generation task is completed.\\n\\n    For Hunyuan3D:\\n        Parameters:\\n        - job_id: The job_id given in the generate model step.\\n\\n        Returns the generation task status. The task is done if status is \\\"DONE\\\".\\n        The task is in progress if status is \\\"RUN\\\".\\n        If status is \\\"DONE\\\", returns ResultFile3Ds, which is the generated ZIP model path\\n        When the status is \\\"DONE\\\", the response includes a field named ResultFile3Ds that contains the generated ZIP file path of the 3D model in OBJ format.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"DONE\\\" or some failed state).\\n    \",\"parameters\":{\"properties\":{\"job_id\":{\"default\":null,\"title\":\"Job Id\",\"type\":\"string\"}},\"title\":\"poll_hunyuan_job_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"import_generated_asset_hunyuan\",\"description\":\"\\n    Import the asset generated by Hunyuan3D after the generation task is completed.\\n\\n    Parameters:\\n    - name: The name of the object in scene\\n    - zip_file_url: The zip_file_url given in the generate model step.\\n\\n    Return if the asset has been imported successfully.\\n    \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"zip_file_url\":{\"title\":\"Zip File Url\",\"type\":\"string\"}},\"required\":[\"name\",\"zip_file_url\"],\"title\":\"import_generated_asset_hunyuanArguments\",\"type\":\"object\"}}}],\"reasoning\":{\"effort\":\"xhigh\"}}", "body_base64": "{"model":"anthropic/claude-haiku-4.5","messages":[{"role":"system","content":[{"type":"text","text":"You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\n\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\n\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\n\n## Harness Instructions\n\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. When you are finished, reply with the single word DONE and no tool calls.","cache_control":{"type":"ephemeral"}}]},{"role":"user","content":"## Task\n\nComplete the following Blender task:\n\n<task>\n\nA red sphere of 1 meter diameter resting on a white ground plane — touching it\nexactly, neither floating nor sunk. Smooth shading, a plain red material on the\nball, plain white on the floor.\n\nThe floor is the sphere, the color, and the contact. The ceiling is presentation:\na soft believable shadow under the ball, a hint of glossiness that catches the\nlight, and clean framing.\n\n</task>"},{"role":"assistant","content":"I'll create a red sphere resting on a white ground plane with proper materials, shading, lighting, and camera setup. Let me work through this step by step.","tool_calls":[{"id":"toolu_bdrk_01McQwtp5Bq6813NVu2gUpRG","type":"function","function":{"name":"get_scene_info","arguments":"{\"user_prompt\": \"Get current scene info to see what's there initially\"}"}}]},{"role":"tool","tool_call_id":"toolu_bdrk_01McQwtp5Bq6813NVu2gUpRG","content":"{\n  \"name\": \"Scene\",\n  \"object_count\": 0,\n  \"objects\": [],\n  \"materials_count\": 2\n}"},{"role":"assistant","content":"Good, the scene is empty. Now I'll create the red sphere and white ground plane using Blender code:","tool_calls":[{"id":"toolu_bdrk_01PuvjJZjfHJZgTRw6jGDsdH","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\": \"\\nimport bpy\\nimport bmesh\\nfrom mathutils import Vector\\n\\n# Clear any default objects\\nbpy.ops.object.select_all(action='SELECT')\\nbpy.ops.object.delete(use_global=False)\\n\\n# Add a sphere (1 meter diameter = 0.5 meter radius)\\nbpy.ops.mesh.primitive_uv_sphere_add(\\n    radius=0.5,\\n    location=(0, 0, 0.5)  # Position it so bottom touches z=0\\n)\\nsphere = bpy.context.active_object\\nsphere.name = \\\"RedSphere\\\"\\n\\n# Add a large ground plane\\nbpy.ops.mesh.primitive_plane_add(\\n    size=10,\\n    location=(0, 0, 0)\\n)\\nplane = bpy.context.active_object\\nplane.name = \\\"GroundPlane\\\"\\n\\n# Select and scale plane to be large but thin\\nplane.scale = (10, 10, 0.1)\\n\\nprint(\\\"Objects created: Sphere at z=\\\" + str(sphere.location.z) + \\\", Plane at z=\\\" + str(plane.location.z))\\n\", \"user_prompt\": \"Create red sphere and white ground plane\"}"}}]},{"role":"tool","tool_call_id":"toolu_bdrk_01PuvjJZjfHJZgTRw6jGDsdH","content":"Code executed successfully: Objects created: Sphere at z=0.5, Plane at z=0.0\n"},{"role":"assistant","content":"Now I'll create the materials - red for the sphere and white for the plane:","tool_calls":[{"id":"toolu_bdrk_018gHZ2vPGV9JR5pgaWmu5aZ","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\": \"\\nimport bpy\\n\\n# Get the sphere and plane objects\\nsphere = bpy.data.objects[\\\"RedSphere\\\"]\\nplane = bpy.data.objects[\\\"GroundPlane\\\"]\\n\\n# Create red material for sphere\\nred_material = bpy.data.materials.new(name=\\\"RedMaterial\\\")\\nred_material.use_nodes = True\\nred_material.diffuse_color = (1.0, 0.0, 0.0, 1.0)  # Red RGBA\\n\\n# Configure red material with some glossiness\\nbsdf = red_material.node_tree.nodes[\\\"Principled BSDF\\\"]\\nbsdf.inputs[\\\"Base Color\\\"].default_value = (0.8, 0.05, 0.05, 1.0)  # Pure red\\nbsdf.inputs[\\\"Roughness\\\"].default_value = 0.3  # Slight glossiness\\n\\n# Create white material for plane\\nwhite_material = bpy.data.materials.new(name=\\\"WhiteMaterial\\\")\\nwhite_material.use_nodes = True\\nwhite_material.diffuse_color = (1.0, 1.0, 1.0, 1.0)  # White RGBA\\n\\nbsdf_white = white_material.node_tree.nodes[\\\"Principled BSDF\\\"]\\nbsdf_white.inputs[\\\"Base Color\\\"].default_value = (1.0, 1.0, 1.0, 1.0)  # White\\nbsdf_white.inputs[\\\"Roughness\\\"].default_value = 0.8  # Matte white\\n\\n# Assign materials to objects\\nsphere.data.materials.append(red_material)\\nplane.data.materials.append(white_material)\\n\\nprint(\\\"Materials created and assigned\\\")\\n\", \"user_prompt\": \"Create and assign red and white materials\"}"}}]},{"role":"tool","tool_call_id":"toolu_bdrk_018gHZ2vPGV9JR5pgaWmu5aZ","content":"Code executed successfully: Materials created and assigned\n"},{"role":"assistant","content":"Now I'll apply smooth shading to both objects:","tool_calls":[{"id":"toolu_bdrk_01WSSDEGVk2FHv51RevTiES7","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\": \"\\nimport bpy\\n\\nsphere = bpy.data.objects[\\\"RedSphere\\\"]\\nplane = bpy.data.objects[\\\"GroundPlane\\\"]\\n\\n# Select sphere and apply smooth shading\\nsphere.select_set(True)\\nbpy.context.view_layer.objects.active = sphere\\nbpy.ops.object.shade_smooth()\\n\\n# Select plane and apply smooth shading\\nplane.select_set(True)\\nbpy.context.view_layer.objects.active = plane\\nbpy.ops.object.shade_smooth()\\n\\nprint(\\\"Smooth shading applied to both objects\\\")\\n\", \"user_prompt\": \"Apply smooth shading to sphere and plane\"}"}}]},{"role":"tool","tool_call_id":"toolu_bdrk_01WSSDEGVk2FHv51RevTiES7","content":[{"type":"text","text":"Code executed successfully: Smooth shading applied to both objects\n","cache_control":{"type":"ephemeral"}}]}],"usage":{"include":true},"tools":[{"type":"function","function":{"name":"get_scene_info","description":"Get detailed information about the current Blender scene\n\n    Parameters:\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\n    ","parameters":{"properties":{"user_prompt":{"title":"User Prompt","type":"string"}},"required":["user_prompt"],"title":"get_scene_infoArguments","type":"object"}}},{"type":"function","function":{"name":"get_object_info","description":"\n    Get detailed information about a specific object in the Blender scene.\n\n    Parameters:\n    - object_name: The name of the object to get information about\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"object_name":{"title":"Object Name","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["object_name"],"title":"get_object_infoArguments","type":"object"}}},{"type":"function","function":{"name":"get_viewport_screenshot","description":"\n    Capture a screenshot of the current Blender 3D viewport.\n\n    Parameters:\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns the screenshot as an Image.\n    ","parameters":{"properties":{"max_size":{"default":1000,"title":"Max Size","type":"integer"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_viewport_screenshotArguments","type":"object"}}},{"type":"function","function":{"name":"execute_blender_code","description":"\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\n\n    Parameters:\n    - code: The Python code to execute\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"code":{"title":"Code","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["code"],"title":"execute_blender_codeArguments","type":"object"}}},{"type":"function","function":{"name":"get_polyhaven_categories","description":"\n    Get a list of categories for a specific asset type on Polyhaven.\n\n    Parameters:\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"asset_type":{"default":"hdris","title":"Asset Type","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_polyhaven_categoriesArguments","type":"object"}}},{"type":"function","function":{"name":"search_polyhaven_assets","description":"\n    Search for assets on Polyhaven with optional filtering.\n\n    Parameters:\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\n    - categories: Optional comma-separated list of categories to filter by\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a list of matching assets with basic information.\n    ","parameters":{"properties":{"asset_type":{"default":"all","title":"Asset Type","type":"string"},"categories":{"default":null,"title":"Categories","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"search_polyhaven_assetsArguments","type":"object"}}},{"type":"function","function":{"name":"download_polyhaven_asset","description":"\n    Download and import a Polyhaven asset into Blender.\n\n    Parameters:\n    - asset_id: The ID of the asset to download\n    - asset_type: The type of asset (hdris, textures, models)\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; 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I'll add a key light, fill light, and set up the camera:\",\"refusal\":null,\"reasoning\":null,\"tool_calls\":[{\"type\":\"function\",\"index\":0,\"id\":\"toolu_bdrk_01AMvEaBThonE94HvaJjwfZk\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\": \\\"\\\\nimport bpy\\\\nfrom mathutils import Vector\\\\n\\\\n# Add a key light (three-quarter lighting)\\\\nbpy.ops.object.light_add(\\\\n    type='SUN',\\\\n    location=(3, 4, 5)\\\\n)\\\\nkey_light = bpy.context.active_object\\\\nkey_light.name = \\\\\\\"KeyLight\\\\\\\"\\\\nkey_light.data.energy = 2.0\\\\nkey_light.data.angle = 0.5  # Slight softness\\\\nbpy.context.view_layer.objects.active = key_light\\\\nkey_light.select_set(True)\\\\n\\\\n# Add a fill light (softer light from the other side)\\\\nbpy.ops.object.light_add(\\\\n    type='SUN',\\\\n    location=(-2, -3, 3)\\\\n)\\\\nfill_light = bpy.context.active_object\\\\nfill_light.name = \\\\\\\"FillLight\\\\\\\"\\\\nfill_light.data.energy = 0.6  # Much softer\\\\nfill_light.data.angle = 2.0  # Very soft\\\\nbpy.context.view_layer.objects.active = fill_light\\\\nfill_light.select_set(True)\\\\n\\\\n# Add an area light positioned slightly to the side and above for catch lights\\\\nbpy.ops.object.light_add(\\\\n    type='AREA',\\\\n    location=(2, -3, 4)\\\\n)\\\\ncatch_light = bpy.context.active_object\\\\ncatch_light.name = \\\\\\\"CatchLight\\\\\\\"\\\\ncatch_light.data.energy = 0.8\\\\ncatch_light.data.size = 3\\\\nbpy.context.view_layer.objects.active = catch_light\\\\ncatch_light.select_set(True)\\\\n\\\\nprint(\\\\\\\"Lights created: Key, Fill, and Catch lights\\\\\\\")\\\\n\\\", \\\"user_prompt\\\": \\\"Add lighting setup for presentation\\\"}\"}}]}}],\"usage\":{\"prompt_tokens\":7170,\"completion_tokens\":529,\"total_tokens\":7699,\"cost\":0.00365975,\"is_byok\":false,\"prompt_tokens_details\":{\"cached_tokens\":6910,\"cache_write_tokens\":255,\"audio_tokens\":0,\"video_tokens\":0},\"cache_creation\":{\"ephemeral_5m_input_tokens\":255,\"ephemeral_1h_input_tokens\":0},\"cost_details\":{\"upstream_inference_cost\":0.00365975,\"upstream_inference_prompt_cost\":0.00101475,\"upstream_inference_completions_cost\":0.002645},\"completion_tokens_details\":{\"reasoning_tokens\":0,\"image_tokens\":0,\"audio_tokens\":0}}}", "body_base64": "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"}, "error": null}], "response": {"text": "Now I need to set up the lighting and camera for a good presentation. I'll add a key light, fill light, and set up the camera:", "tool_calls": [{"id": "toolu_bdrk_01AMvEaBThonE94HvaJjwfZk", "name": "execute_blender_code", "arguments": {"code": "\nimport bpy\nfrom mathutils import Vector\n\n# Add a key light (three-quarter lighting)\nbpy.ops.object.light_add(\n    type='SUN',\n    location=(3, 4, 5)\n)\nkey_light = bpy.context.active_object\nkey_light.name = \"KeyLight\"\nkey_light.data.energy = 2.0\nkey_light.data.angle = 0.5  # Slight softness\nbpy.context.view_layer.objects.active = key_light\nkey_light.select_set(True)\n\n# Add a fill light (softer light from the other side)\nbpy.ops.object.light_add(\n    type='SUN',\n    location=(-2, -3, 3)\n)\nfill_light = bpy.context.active_object\nfill_light.name = \"FillLight\"\nfill_light.data.energy = 0.6  # Much softer\nfill_light.data.angle = 2.0  # Very soft\nbpy.context.view_layer.objects.active = fill_light\nfill_light.select_set(True)\n\n# Add an area light positioned slightly to the side and above for catch lights\nbpy.ops.object.light_add(\n    type='AREA',\n    location=(2, -3, 4)\n)\ncatch_light = bpy.context.active_object\ncatch_light.name = \"CatchLight\"\ncatch_light.data.energy = 0.8\ncatch_light.data.size = 3\nbpy.context.view_layer.objects.active = catch_light\ncatch_light.select_set(True)\n\nprint(\"Lights created: Key, Fill, and Catch lights\")\n", "user_prompt": "Add lighting setup for presentation"}, "raw_arguments": "{\"code\": \"\\nimport bpy\\nfrom mathutils import Vector\\n\\n# Add a key light (three-quarter lighting)\\nbpy.ops.object.light_add(\\n    type='SUN',\\n    location=(3, 4, 5)\\n)\\nkey_light = bpy.context.active_object\\nkey_light.name = \\\"KeyLight\\\"\\nkey_light.data.energy = 2.0\\nkey_light.data.angle = 0.5  # Slight softness\\nbpy.context.view_layer.objects.active = key_light\\nkey_light.select_set(True)\\n\\n# Add a fill light (softer light from the other side)\\nbpy.ops.object.light_add(\\n    type='SUN',\\n    location=(-2, -3, 3)\\n)\\nfill_light = bpy.context.active_object\\nfill_light.name = \\\"FillLight\\\"\\nfill_light.data.energy = 0.6  # Much softer\\nfill_light.data.angle = 2.0  # Very soft\\nbpy.context.view_layer.objects.active = fill_light\\nfill_light.select_set(True)\\n\\n# Add an area light positioned slightly to the side and above for catch lights\\nbpy.ops.object.light_add(\\n    type='AREA',\\n    location=(2, -3, 4)\\n)\\ncatch_light = bpy.context.active_object\\ncatch_light.name = \\\"CatchLight\\\"\\ncatch_light.data.energy = 0.8\\ncatch_light.data.size = 3\\nbpy.context.view_layer.objects.active = catch_light\\ncatch_light.select_set(True)\\n\\nprint(\\\"Lights created: Key, Fill, and Catch lights\\\")\\n\", \"user_prompt\": \"Add lighting setup for presentation\"}", "parse_error": null}], "tokens_in": 7170, "tokens_out": 529, "cost_usd": 0.00365975, "cached_tokens": 6910}, "error": null}
{"ts": "2026-07-15T00:49:00.843530+00:00", "type": "assistant", "text": "Now I need to set up the lighting and camera for a good presentation. I'll add a key light, fill light, and set up the camera:", "tool_calls": [{"id": "toolu_bdrk_01AMvEaBThonE94HvaJjwfZk", "name": "execute_blender_code", "arguments": {"code": "\nimport bpy\nfrom mathutils import Vector\n\n# Add a key light (three-quarter lighting)\nbpy.ops.object.light_add(\n    type='SUN',\n    location=(3, 4, 5)\n)\nkey_light = bpy.context.active_object\nkey_light.name = \"KeyLight\"\nkey_light.data.energy = 2.0\nkey_light.data.angle = 0.5  # Slight softness\nbpy.context.view_layer.objects.active = key_light\nkey_light.select_set(True)\n\n# Add a fill light (softer light from the other side)\nbpy.ops.object.light_add(\n    type='SUN',\n    location=(-2, -3, 3)\n)\nfill_light = bpy.context.active_object\nfill_light.name = \"FillLight\"\nfill_light.data.energy = 0.6  # Much softer\nfill_light.data.angle = 2.0  # Very soft\nbpy.context.view_layer.objects.active = fill_light\nfill_light.select_set(True)\n\n# Add an area light positioned slightly to the side and above for catch lights\nbpy.ops.object.light_add(\n    type='AREA',\n    location=(2, -3, 4)\n)\ncatch_light = bpy.context.active_object\ncatch_light.name = \"CatchLight\"\ncatch_light.data.energy = 0.8\ncatch_light.data.size = 3\nbpy.context.view_layer.objects.active = catch_light\ncatch_light.select_set(True)\n\nprint(\"Lights created: Key, Fill, and Catch lights\")\n", "user_prompt": "Add lighting setup for presentation"}, "raw_arguments": "{\"code\": \"\\nimport bpy\\nfrom mathutils import Vector\\n\\n# Add a key light (three-quarter lighting)\\nbpy.ops.object.light_add(\\n    type='SUN',\\n    location=(3, 4, 5)\\n)\\nkey_light = bpy.context.active_object\\nkey_light.name = \\\"KeyLight\\\"\\nkey_light.data.energy = 2.0\\nkey_light.data.angle = 0.5  # Slight softness\\nbpy.context.view_layer.objects.active = key_light\\nkey_light.select_set(True)\\n\\n# Add a fill light (softer light from the other side)\\nbpy.ops.object.light_add(\\n    type='SUN',\\n    location=(-2, -3, 3)\\n)\\nfill_light = bpy.context.active_object\\nfill_light.name = \\\"FillLight\\\"\\nfill_light.data.energy = 0.6  # Much softer\\nfill_light.data.angle = 2.0  # Very soft\\nbpy.context.view_layer.objects.active = fill_light\\nfill_light.select_set(True)\\n\\n# Add an area light positioned slightly to the side and above for catch lights\\nbpy.ops.object.light_add(\\n    type='AREA',\\n    location=(2, -3, 4)\\n)\\ncatch_light = bpy.context.active_object\\ncatch_light.name = \\\"CatchLight\\\"\\ncatch_light.data.energy = 0.8\\ncatch_light.data.size = 3\\nbpy.context.view_layer.objects.active = catch_light\\ncatch_light.select_set(True)\\n\\nprint(\\\"Lights created: Key, Fill, and Catch lights\\\")\\n\", \"user_prompt\": \"Add lighting setup for presentation\"}", "parse_error": null}]}
{"ts": "2026-07-15T00:49:00.844537+00:00", "type": "tool_call", "id": "toolu_bdrk_01AMvEaBThonE94HvaJjwfZk", "name": "execute_blender_code", "arguments": {"code": "\nimport bpy\nfrom mathutils import Vector\n\n# Add a key light (three-quarter lighting)\nbpy.ops.object.light_add(\n    type='SUN',\n    location=(3, 4, 5)\n)\nkey_light = bpy.context.active_object\nkey_light.name = \"KeyLight\"\nkey_light.data.energy = 2.0\nkey_light.data.angle = 0.5  # Slight softness\nbpy.context.view_layer.objects.active = key_light\nkey_light.select_set(True)\n\n# Add a fill light (softer light from the other side)\nbpy.ops.object.light_add(\n    type='SUN',\n    location=(-2, -3, 3)\n)\nfill_light = bpy.context.active_object\nfill_light.name = \"FillLight\"\nfill_light.data.energy = 0.6  # Much softer\nfill_light.data.angle = 2.0  # Very soft\nbpy.context.view_layer.objects.active = fill_light\nfill_light.select_set(True)\n\n# Add an area light positioned slightly to the side and above for catch lights\nbpy.ops.object.light_add(\n    type='AREA',\n    location=(2, -3, 4)\n)\ncatch_light = bpy.context.active_object\ncatch_light.name = \"CatchLight\"\ncatch_light.data.energy = 0.8\ncatch_light.data.size = 3\nbpy.context.view_layer.objects.active = catch_light\ncatch_light.select_set(True)\n\nprint(\"Lights created: Key, Fill, and Catch lights\")\n", "user_prompt": "Add lighting setup for presentation"}, "raw_arguments": "{\"code\": \"\\nimport bpy\\nfrom mathutils import Vector\\n\\n# Add a key light (three-quarter lighting)\\nbpy.ops.object.light_add(\\n    type='SUN',\\n    location=(3, 4, 5)\\n)\\nkey_light = bpy.context.active_object\\nkey_light.name = \\\"KeyLight\\\"\\nkey_light.data.energy = 2.0\\nkey_light.data.angle = 0.5  # Slight softness\\nbpy.context.view_layer.objects.active = key_light\\nkey_light.select_set(True)\\n\\n# Add a fill light (softer light from the other side)\\nbpy.ops.object.light_add(\\n    type='SUN',\\n    location=(-2, -3, 3)\\n)\\nfill_light = bpy.context.active_object\\nfill_light.name = \\\"FillLight\\\"\\nfill_light.data.energy = 0.6  # Much softer\\nfill_light.data.angle = 2.0  # Very soft\\nbpy.context.view_layer.objects.active = fill_light\\nfill_light.select_set(True)\\n\\n# Add an area light positioned slightly to the side and above for catch lights\\nbpy.ops.object.light_add(\\n    type='AREA',\\n    location=(2, -3, 4)\\n)\\ncatch_light = bpy.context.active_object\\ncatch_light.name = \\\"CatchLight\\\"\\ncatch_light.data.energy = 0.8\\ncatch_light.data.size = 3\\nbpy.context.view_layer.objects.active = catch_light\\ncatch_light.select_set(True)\\n\\nprint(\\\"Lights created: Key, Fill, and Catch lights\\\")\\n\", \"user_prompt\": \"Add lighting setup for presentation\"}"}
{"ts": "2026-07-15T00:49:00.882760+00:00", "type": "tool_result", "tool_call_id": "toolu_bdrk_01AMvEaBThonE94HvaJjwfZk", "name": "execute_blender_code", "text": "Code executed successfully: Lights created: Key, Fill, and Catch lights\n", "is_error": false, "has_image": false, "image_b64": null}
{"ts": "2026-07-15T00:49:04.634321+00:00", "type": "llm_call", "call_index": 6, "provider_type": "blenderbench.providers.openrouter.OpenRouterProvider", "started_at": "2026-07-15T00:49:00.883768+00:00", "finished_at": "2026-07-15T00:49:04.634321+00:00", "duration_s": 3.75, "request": {"messages": [{"role": "system", "content": "You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\n\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\n\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\n\n## Harness Instructions\n\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. When you are finished, reply with the single word DONE and no tool calls."}, {"role": "user", "content": "## Task\n\nComplete the following Blender task:\n\n<task>\n\nA red sphere of 1 meter diameter resting on a white ground plane — touching it\nexactly, neither floating nor sunk. Smooth shading, a plain red material on the\nball, plain white on the floor.\n\nThe floor is the sphere, the color, and the contact. The ceiling is presentation:\na soft believable shadow under the ball, a hint of glossiness that catches the\nlight, and clean framing.\n\n</task>"}, {"role": "assistant", "content": "I'll create a red sphere resting on a white ground plane with proper materials, shading, lighting, and camera setup. Let me work through this step by step.", "tool_calls": [{"id": "toolu_bdrk_01McQwtp5Bq6813NVu2gUpRG", "type": "function", "function": {"name": "get_scene_info", "arguments": "{\"user_prompt\": \"Get current scene info to see what's there initially\"}"}}]}, {"role": "tool", "tool_call_id": "toolu_bdrk_01McQwtp5Bq6813NVu2gUpRG", "content": "{\n  \"name\": \"Scene\",\n  \"object_count\": 0,\n  \"objects\": [],\n  \"materials_count\": 2\n}"}, {"role": "assistant", "content": "Good, the scene is empty. Now I'll create the red sphere and white ground plane using Blender code:", "tool_calls": [{"id": "toolu_bdrk_01PuvjJZjfHJZgTRw6jGDsdH", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\": \"\\nimport bpy\\nimport bmesh\\nfrom mathutils import Vector\\n\\n# Clear any default objects\\nbpy.ops.object.select_all(action='SELECT')\\nbpy.ops.object.delete(use_global=False)\\n\\n# Add a sphere (1 meter diameter = 0.5 meter radius)\\nbpy.ops.mesh.primitive_uv_sphere_add(\\n    radius=0.5,\\n    location=(0, 0, 0.5)  # Position it so bottom touches z=0\\n)\\nsphere = bpy.context.active_object\\nsphere.name = \\\"RedSphere\\\"\\n\\n# Add a large ground plane\\nbpy.ops.mesh.primitive_plane_add(\\n    size=10,\\n    location=(0, 0, 0)\\n)\\nplane = bpy.context.active_object\\nplane.name = \\\"GroundPlane\\\"\\n\\n# Select and scale plane to be large but thin\\nplane.scale = (10, 10, 0.1)\\n\\nprint(\\\"Objects created: Sphere at z=\\\" + str(sphere.location.z) + \\\", Plane at z=\\\" + str(plane.location.z))\\n\", \"user_prompt\": \"Create red sphere and white ground plane\"}"}}]}, {"role": "tool", "tool_call_id": "toolu_bdrk_01PuvjJZjfHJZgTRw6jGDsdH", "content": "Code executed successfully: Objects created: Sphere at z=0.5, Plane at z=0.0\n"}, {"role": "assistant", "content": "Now I'll create the materials - red for the sphere and white for the plane:", "tool_calls": [{"id": "toolu_bdrk_018gHZ2vPGV9JR5pgaWmu5aZ", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\": \"\\nimport bpy\\n\\n# Get the sphere and plane objects\\nsphere = bpy.data.objects[\\\"RedSphere\\\"]\\nplane = bpy.data.objects[\\\"GroundPlane\\\"]\\n\\n# Create red material for sphere\\nred_material = bpy.data.materials.new(name=\\\"RedMaterial\\\")\\nred_material.use_nodes = True\\nred_material.diffuse_color = (1.0, 0.0, 0.0, 1.0)  # Red RGBA\\n\\n# Configure red material with some glossiness\\nbsdf = red_material.node_tree.nodes[\\\"Principled BSDF\\\"]\\nbsdf.inputs[\\\"Base Color\\\"].default_value = (0.8, 0.05, 0.05, 1.0)  # Pure red\\nbsdf.inputs[\\\"Roughness\\\"].default_value = 0.3  # Slight glossiness\\n\\n# Create white material for plane\\nwhite_material = bpy.data.materials.new(name=\\\"WhiteMaterial\\\")\\nwhite_material.use_nodes = True\\nwhite_material.diffuse_color = (1.0, 1.0, 1.0, 1.0)  # White RGBA\\n\\nbsdf_white = white_material.node_tree.nodes[\\\"Principled BSDF\\\"]\\nbsdf_white.inputs[\\\"Base Color\\\"].default_value = (1.0, 1.0, 1.0, 1.0)  # White\\nbsdf_white.inputs[\\\"Roughness\\\"].default_value = 0.8  # Matte white\\n\\n# Assign materials to objects\\nsphere.data.materials.append(red_material)\\nplane.data.materials.append(white_material)\\n\\nprint(\\\"Materials created and assigned\\\")\\n\", \"user_prompt\": \"Create and assign red and white materials\"}"}}]}, {"role": "tool", "tool_call_id": "toolu_bdrk_018gHZ2vPGV9JR5pgaWmu5aZ", "content": "Code executed successfully: Materials created and assigned\n"}, {"role": "assistant", "content": "Now I'll apply smooth shading to both objects:", "tool_calls": [{"id": "toolu_bdrk_01WSSDEGVk2FHv51RevTiES7", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\": \"\\nimport bpy\\n\\nsphere = bpy.data.objects[\\\"RedSphere\\\"]\\nplane = bpy.data.objects[\\\"GroundPlane\\\"]\\n\\n# Select sphere and apply smooth shading\\nsphere.select_set(True)\\nbpy.context.view_layer.objects.active = sphere\\nbpy.ops.object.shade_smooth()\\n\\n# Select plane and apply smooth shading\\nplane.select_set(True)\\nbpy.context.view_layer.objects.active = plane\\nbpy.ops.object.shade_smooth()\\n\\nprint(\\\"Smooth shading applied to both objects\\\")\\n\", \"user_prompt\": \"Apply smooth shading to sphere and plane\"}"}}]}, {"role": "tool", "tool_call_id": "toolu_bdrk_01WSSDEGVk2FHv51RevTiES7", "content": "Code executed successfully: Smooth shading applied to both objects\n"}, {"role": "assistant", "content": "Now I need to set up the lighting and camera for a good presentation. I'll add a key light, fill light, and set up the camera:", "tool_calls": [{"id": "toolu_bdrk_01AMvEaBThonE94HvaJjwfZk", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\": \"\\nimport bpy\\nfrom mathutils import Vector\\n\\n# Add a key light (three-quarter lighting)\\nbpy.ops.object.light_add(\\n    type='SUN',\\n    location=(3, 4, 5)\\n)\\nkey_light = bpy.context.active_object\\nkey_light.name = \\\"KeyLight\\\"\\nkey_light.data.energy = 2.0\\nkey_light.data.angle = 0.5  # Slight softness\\nbpy.context.view_layer.objects.active = key_light\\nkey_light.select_set(True)\\n\\n# Add a fill light (softer light from the other side)\\nbpy.ops.object.light_add(\\n    type='SUN',\\n    location=(-2, -3, 3)\\n)\\nfill_light = bpy.context.active_object\\nfill_light.name = \\\"FillLight\\\"\\nfill_light.data.energy = 0.6  # Much softer\\nfill_light.data.angle = 2.0  # Very soft\\nbpy.context.view_layer.objects.active = fill_light\\nfill_light.select_set(True)\\n\\n# Add an area light positioned slightly to the side and above for catch lights\\nbpy.ops.object.light_add(\\n    type='AREA',\\n    location=(2, -3, 4)\\n)\\ncatch_light = bpy.context.active_object\\ncatch_light.name = \\\"CatchLight\\\"\\ncatch_light.data.energy = 0.8\\ncatch_light.data.size = 3\\nbpy.context.view_layer.objects.active = catch_light\\ncatch_light.select_set(True)\\n\\nprint(\\\"Lights created: Key, Fill, and Catch lights\\\")\\n\", \"user_prompt\": \"Add lighting setup for presentation\"}"}}]}, {"role": "tool", "tool_call_id": "toolu_bdrk_01AMvEaBThonE94HvaJjwfZk", "content": "Code executed successfully: Lights created: Key, Fill, and Catch lights\n"}], "tools": [{"type": "function", "function": {"name": "get_scene_info", "description": "Get detailed information about the current Blender scene\n\n    Parameters:\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\n    ", "parameters": {"properties": {"user_prompt": {"title": "User Prompt", "type": "string"}}, "required": ["user_prompt"], "title": "get_scene_infoArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_object_info", "description": "\n    Get detailed information about a specific object in the Blender scene.\n\n    Parameters:\n    - object_name: The name of the object to get information about\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"object_name": {"title": "Object Name", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["object_name"], "title": "get_object_infoArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_viewport_screenshot", "description": "\n    Capture a screenshot of the current Blender 3D viewport.\n\n    Parameters:\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns the screenshot as an Image.\n    ", "parameters": {"properties": {"max_size": {"default": 1000, "title": "Max Size", "type": "integer"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_viewport_screenshotArguments", "type": "object"}}}, {"type": "function", "function": {"name": "execute_blender_code", "description": "\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\n\n    Parameters:\n    - code: The Python code to execute\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"code": {"title": "Code", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["code"], "title": "execute_blender_codeArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_polyhaven_categories", "description": "\n    Get a list of categories for a specific asset type on Polyhaven.\n\n    Parameters:\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"asset_type": {"default": "hdris", "title": "Asset Type", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_polyhaven_categoriesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_polyhaven_assets", "description": "\n    Search for assets on Polyhaven with optional filtering.\n\n    Parameters:\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\n    - categories: Optional comma-separated list of categories to filter by\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a list of matching assets with basic information.\n    ", "parameters": {"properties": {"asset_type": {"default": "all", "title": "Asset Type", "type": "string"}, "categories": {"default": null, "title": "Categories", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "search_polyhaven_assetsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "download_polyhaven_asset", "description": "\n    Download and import a Polyhaven asset into Blender.\n\n    Parameters:\n    - asset_id: The ID of the asset to download\n    - asset_type: The type of asset (hdris, textures, models)\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"asset_id": {"title": "Asset Id", "type": "string"}, "asset_type": {"title": "Asset Type", "type": "string"}, "resolution": {"default": "1k", "title": "Resolution", "type": "string"}, "file_format": {"default": null, "title": "File Format", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["asset_id", "asset_type"], "title": "download_polyhaven_assetArguments", "type": "object"}}}, {"type": "function", "function": {"name": "set_texture", "description": "\n    Apply a previously downloaded Polyhaven texture to an object.\n    \n    Parameters:\n    - object_name: Name of the object to apply the texture to\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\n    \n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"object_name": {"title": "Object Name", "type": "string"}, "texture_id": {"title": "Texture Id", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["object_name", "texture_id"], "title": "set_textureArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_polyhaven_status", "description": "\n    Check if PolyHaven integration is enabled in Blender.\n    Returns a message indicating whether PolyHaven features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_polyhaven_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_hyper3d_status", "description": "\n    Check if Hyper3D Rodin integration is enabled in Blender.\n    Returns a message indicating whether Hyper3D Rodin features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_hyper3d_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_sketchfab_status", "description": "\n    Check if Sketchfab integration is enabled in Blender.\n    Returns a message indicating whether Sketchfab features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_sketchfab_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_sketchfab_models", "description": "\n    Search for models on Sketchfab with optional filtering.\n\n    Parameters:\n    - query: Text to search for\n    - categories: Optional comma-separated list of categories\n    - count: Maximum number of results to return (default 20)\n    - downloadable: Whether to include only downloadable models (default True)\n\n    Returns a formatted list of matching models.\n    ", "parameters": {"properties": {"query": {"title": "Query", "type": "string"}, "categories": {"default": null, "title": "Categories", "type": "string"}, "count": {"default": 20, "title": "Count", "type": "integer"}, "downloadable": {"default": true, "title": "Downloadable", "type": "boolean"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["query"], "title": "search_sketchfab_modelsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_sketchfab_model_preview", "description": "\n    Get a preview thumbnail of a Sketchfab model by its UID.\n    Use this to visually confirm a model before downloading.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\n    \n    Returns the model's thumbnail as an Image for visual confirmation.\n    ", "parameters": {"properties": {"uid": {"title": "Uid", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["uid"], "title": "get_sketchfab_model_previewArguments", "type": "object"}}}, {"type": "function", "function": {"name": "download_sketchfab_model", "description": "\n    Download and import a Sketchfab model by its UID.\n    The model will be scaled so its largest dimension equals target_size.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\n                  You must specify the desired size for the model.\n                  Examples:\n                  - Chair: target_size=1.0 (1 meter tall)\n                  - Table: target_size=0.75 (75cm tall)\n                  - Car: target_size=4.5 (4.5 meters long)\n                  - Person: target_size=1.7 (1.7 meters tall)\n                  - Small object (cup, phone): target_size=0.1 to 0.3\n    \n    Returns a message with import details including object names, dimensions, and bounding box.\n    The model must be downloadable and you must have proper access rights.\n    ", "parameters": {"properties": {"uid": {"title": "Uid", "type": "string"}, "target_size": {"title": "Target Size", "type": "number"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["uid", "target_size"], "title": "download_sketchfab_modelArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hyper3d_model_via_text", "description": "\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n\n    Parameters:\n    - text_prompt: A short description of the desired model in **English**.\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"text_prompt": {"title": "Text Prompt", "type": "string"}, "bbox_condition": {"default": null, "items": {"type": "number"}, "title": "Bbox Condition", "type": "array"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["text_prompt"], "title": "generate_hyper3d_model_via_textArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hyper3d_model_via_images", "description": "\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n    \n    Parameters:\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode.\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"input_image_paths": {"default": null, "items": {"type": "string"}, "title": "Input Image Paths", "type": "array"}, "input_image_urls": {"default": null, "items": {"type": "string"}, "title": "Input Image Urls", "type": "array"}, "bbox_condition": {"default": null, "items": {"type": "number"}, "title": "Bbox Condition", "type": "array"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "generate_hyper3d_model_via_imagesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "poll_rodin_job_status", "description": "\n    Check if the Hyper3D Rodin generation task is completed.\n\n    For Hyper3D Rodin mode MAIN_SITE:\n        Parameters:\n        - subscription_key: The subscription_key given in the generate model step.\n\n        Returns a list of status. The task is done if all status are \"Done\".\n        If \"Failed\" showed up, the generating process failed.\n        This is a polling API, so only proceed if the status are finally determined (\"Done\" or \"Canceled\").\n\n    For Hyper3D Rodin mode FAL_AI:\n        Parameters:\n        - request_id: The request_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"COMPLETED\".\n        The task is in progress if status is \"IN_PROGRESS\".\n        If status other than \"COMPLETED\", \"IN_PROGRESS\", \"IN_QUEUE\" showed up, the generating process might be failed.\n        This is a polling API, so only proceed if the status are finally determined (\"COMPLETED\" or some failed state).\n    ", "parameters": {"properties": {"subscription_key": {"default": null, "title": "Subscription Key", "type": "string"}, "request_id": {"default": null, "title": "Request Id", "type": "string"}}, "title": "poll_rodin_job_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "import_generated_asset", "description": "\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\n\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\n    Return if the asset has been imported successfully.\n    ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "task_uuid": {"default": null, "title": "Task Uuid", "type": "string"}, "request_id": {"default": null, "title": "Request Id", "type": "string"}}, "required": ["name"], "title": "import_generated_assetArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_hunyuan3d_status", "description": "\n    Check if Hunyuan3D integration is enabled in Blender.\n    Returns a message indicating whether Hunyuan3D features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_hunyuan3d_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hunyuan3d_model", "description": "\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \n    or both for the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    \n    Parameters:\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\n    - input_image_url: (Optional) The local or remote url of the input image. Accepts None if only using text prompt.\n\n    Returns: \n    - When successful, returns a JSON with job_id (format: \"job_xxx\") indicating the task is in progress\n    - When the job completes, the status will change to \"DONE\" indicating the model has been imported\n    - Returns error message if the operation fails\n    ", "parameters": {"properties": {"text_prompt": {"default": null, "title": "Text Prompt", "type": "string"}, "input_image_url": {"default": null, "title": "Input Image Url", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "generate_hunyuan3d_modelArguments", "type": "object"}}}, {"type": "function", "function": {"name": "poll_hunyuan_job_status", "description": "\n    Check if the Hunyuan3D generation task is completed.\n\n    For Hunyuan3D:\n        Parameters:\n        - job_id: The job_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"DONE\".\n        The task is in progress if status is \"RUN\".\n        If status is \"DONE\", returns ResultFile3Ds, which is the generated ZIP model path\n        When the status is \"DONE\", the response includes a field named ResultFile3Ds that contains the generated ZIP file path of the 3D model in OBJ format.\n        This is a polling API, so only proceed if the status are finally determined (\"DONE\" or some failed state).\n    ", "parameters": {"properties": {"job_id": {"default": null, "title": "Job Id", "type": "string"}}, "title": "poll_hunyuan_job_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "import_generated_asset_hunyuan", "description": "\n    Import the asset generated by Hunyuan3D after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - zip_file_url: The zip_file_url given in the generate model step.\n\n    Return if the asset has been imported successfully.\n    ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "zip_file_url": {"title": "Zip File Url", "type": "string"}}, "required": ["name", "zip_file_url"], "title": "import_generated_asset_hunyuanArguments", "type": "object"}}}]}, "provider_attempts": [{"provider": "openrouter", "attempt": 1, "started_at": "2026-07-15T00:49:00.883768+00:00", "finished_at": "2026-07-15T00:49:04.634321+00:00", "duration_s": 3.75, "request": {"method": "POST", "url": "https://openrouter.ai/api/v1/chat/completions", "headers": {"host": "openrouter.ai", "accept": "*/*", "accept-encoding": "gzip, deflate", "connection": "keep-alive", "user-agent": "python-httpx/0.28.1", "authorization": "[REDACTED]", "content-length": "24955", "content-type": "application/json"}, "body": "{\"model\":\"anthropic/claude-haiku-4.5\",\"messages\":[{\"role\":\"system\",\"content\":[{\"type\":\"text\",\"text\":\"You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\\n\\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\\n\\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\\n\\n## Harness Instructions\\n\\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. When you are finished, reply with the single word DONE and no tool calls.\",\"cache_control\":{\"type\":\"ephemeral\"}}]},{\"role\":\"user\",\"content\":\"## Task\\n\\nComplete the following Blender task:\\n\\n<task>\\n\\nA red sphere of 1 meter diameter resting on a white ground plane — touching it\\nexactly, neither floating nor sunk. Smooth shading, a plain red material on the\\nball, plain white on the floor.\\n\\nThe floor is the sphere, the color, and the contact. The ceiling is presentation:\\na soft believable shadow under the ball, a hint of glossiness that catches the\\nlight, and clean framing.\\n\\n</task>\"},{\"role\":\"assistant\",\"content\":\"I'll create a red sphere resting on a white ground plane with proper materials, shading, lighting, and camera setup. Let me work through this step by step.\",\"tool_calls\":[{\"id\":\"toolu_bdrk_01McQwtp5Bq6813NVu2gUpRG\",\"type\":\"function\",\"function\":{\"name\":\"get_scene_info\",\"arguments\":\"{\\\"user_prompt\\\": \\\"Get current scene info to see what's there initially\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"toolu_bdrk_01McQwtp5Bq6813NVu2gUpRG\",\"content\":\"{\\n  \\\"name\\\": \\\"Scene\\\",\\n  \\\"object_count\\\": 0,\\n  \\\"objects\\\": [],\\n  \\\"materials_count\\\": 2\\n}\"},{\"role\":\"assistant\",\"content\":\"Good, the scene is empty. Now I'll create the red sphere and white ground plane using Blender code:\",\"tool_calls\":[{\"id\":\"toolu_bdrk_01PuvjJZjfHJZgTRw6jGDsdH\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\": \\\"\\\\nimport bpy\\\\nimport bmesh\\\\nfrom mathutils import Vector\\\\n\\\\n# Clear any default objects\\\\nbpy.ops.object.select_all(action='SELECT')\\\\nbpy.ops.object.delete(use_global=False)\\\\n\\\\n# Add a sphere (1 meter diameter = 0.5 meter radius)\\\\nbpy.ops.mesh.primitive_uv_sphere_add(\\\\n    radius=0.5,\\\\n    location=(0, 0, 0.5)  # Position it so bottom touches z=0\\\\n)\\\\nsphere = bpy.context.active_object\\\\nsphere.name = \\\\\\\"RedSphere\\\\\\\"\\\\n\\\\n# Add a large ground plane\\\\nbpy.ops.mesh.primitive_plane_add(\\\\n    size=10,\\\\n    location=(0, 0, 0)\\\\n)\\\\nplane = bpy.context.active_object\\\\nplane.name = \\\\\\\"GroundPlane\\\\\\\"\\\\n\\\\n# Select and scale plane to be large but thin\\\\nplane.scale = (10, 10, 0.1)\\\\n\\\\nprint(\\\\\\\"Objects created: Sphere at z=\\\\\\\" + str(sphere.location.z) + \\\\\\\", Plane at z=\\\\\\\" + str(plane.location.z))\\\\n\\\", \\\"user_prompt\\\": \\\"Create red sphere and white ground plane\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"toolu_bdrk_01PuvjJZjfHJZgTRw6jGDsdH\",\"content\":\"Code executed successfully: Objects created: Sphere at z=0.5, Plane at z=0.0\\n\"},{\"role\":\"assistant\",\"content\":\"Now I'll create the materials - red for the sphere and white for the plane:\",\"tool_calls\":[{\"id\":\"toolu_bdrk_018gHZ2vPGV9JR5pgaWmu5aZ\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\": \\\"\\\\nimport bpy\\\\n\\\\n# Get the sphere and plane objects\\\\nsphere = bpy.data.objects[\\\\\\\"RedSphere\\\\\\\"]\\\\nplane = bpy.data.objects[\\\\\\\"GroundPlane\\\\\\\"]\\\\n\\\\n# Create red material for sphere\\\\nred_material = bpy.data.materials.new(name=\\\\\\\"RedMaterial\\\\\\\")\\\\nred_material.use_nodes = True\\\\nred_material.diffuse_color = (1.0, 0.0, 0.0, 1.0)  # Red RGBA\\\\n\\\\n# Configure red material with some glossiness\\\\nbsdf = red_material.node_tree.nodes[\\\\\\\"Principled BSDF\\\\\\\"]\\\\nbsdf.inputs[\\\\\\\"Base Color\\\\\\\"].default_value = (0.8, 0.05, 0.05, 1.0)  # Pure red\\\\nbsdf.inputs[\\\\\\\"Roughness\\\\\\\"].default_value = 0.3  # Slight glossiness\\\\n\\\\n# Create white material for plane\\\\nwhite_material = bpy.data.materials.new(name=\\\\\\\"WhiteMaterial\\\\\\\")\\\\nwhite_material.use_nodes = True\\\\nwhite_material.diffuse_color = (1.0, 1.0, 1.0, 1.0)  # White RGBA\\\\n\\\\nbsdf_white = white_material.node_tree.nodes[\\\\\\\"Principled BSDF\\\\\\\"]\\\\nbsdf_white.inputs[\\\\\\\"Base Color\\\\\\\"].default_value = (1.0, 1.0, 1.0, 1.0)  # White\\\\nbsdf_white.inputs[\\\\\\\"Roughness\\\\\\\"].default_value = 0.8  # Matte white\\\\n\\\\n# Assign materials to objects\\\\nsphere.data.materials.append(red_material)\\\\nplane.data.materials.append(white_material)\\\\n\\\\nprint(\\\\\\\"Materials created and assigned\\\\\\\")\\\\n\\\", \\\"user_prompt\\\": \\\"Create and assign red and white materials\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"toolu_bdrk_018gHZ2vPGV9JR5pgaWmu5aZ\",\"content\":\"Code executed successfully: Materials created and assigned\\n\"},{\"role\":\"assistant\",\"content\":\"Now I'll apply smooth shading to both objects:\",\"tool_calls\":[{\"id\":\"toolu_bdrk_01WSSDEGVk2FHv51RevTiES7\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\": \\\"\\\\nimport bpy\\\\n\\\\nsphere = bpy.data.objects[\\\\\\\"RedSphere\\\\\\\"]\\\\nplane = bpy.data.objects[\\\\\\\"GroundPlane\\\\\\\"]\\\\n\\\\n# Select sphere and apply smooth shading\\\\nsphere.select_set(True)\\\\nbpy.context.view_layer.objects.active = sphere\\\\nbpy.ops.object.shade_smooth()\\\\n\\\\n# Select plane and apply smooth shading\\\\nplane.select_set(True)\\\\nbpy.context.view_layer.objects.active = plane\\\\nbpy.ops.object.shade_smooth()\\\\n\\\\nprint(\\\\\\\"Smooth shading applied to both objects\\\\\\\")\\\\n\\\", \\\"user_prompt\\\": \\\"Apply smooth shading to sphere and plane\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"toolu_bdrk_01WSSDEGVk2FHv51RevTiES7\",\"content\":\"Code executed successfully: Smooth shading applied to both objects\\n\"},{\"role\":\"assistant\",\"content\":\"Now I need to set up the lighting and camera for a good presentation. I'll add a key light, fill light, and set up the camera:\",\"tool_calls\":[{\"id\":\"toolu_bdrk_01AMvEaBThonE94HvaJjwfZk\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\": \\\"\\\\nimport bpy\\\\nfrom mathutils import Vector\\\\n\\\\n# Add a key light (three-quarter lighting)\\\\nbpy.ops.object.light_add(\\\\n    type='SUN',\\\\n    location=(3, 4, 5)\\\\n)\\\\nkey_light = bpy.context.active_object\\\\nkey_light.name = \\\\\\\"KeyLight\\\\\\\"\\\\nkey_light.data.energy = 2.0\\\\nkey_light.data.angle = 0.5  # Slight softness\\\\nbpy.context.view_layer.objects.active = key_light\\\\nkey_light.select_set(True)\\\\n\\\\n# Add a fill light (softer light from the other side)\\\\nbpy.ops.object.light_add(\\\\n    type='SUN',\\\\n    location=(-2, -3, 3)\\\\n)\\\\nfill_light = bpy.context.active_object\\\\nfill_light.name = \\\\\\\"FillLight\\\\\\\"\\\\nfill_light.data.energy = 0.6  # Much softer\\\\nfill_light.data.angle = 2.0  # Very soft\\\\nbpy.context.view_layer.objects.active = fill_light\\\\nfill_light.select_set(True)\\\\n\\\\n# Add an area light positioned slightly to the side and above for catch lights\\\\nbpy.ops.object.light_add(\\\\n    type='AREA',\\\\n    location=(2, -3, 4)\\\\n)\\\\ncatch_light = bpy.context.active_object\\\\ncatch_light.name = \\\\\\\"CatchLight\\\\\\\"\\\\ncatch_light.data.energy = 0.8\\\\ncatch_light.data.size = 3\\\\nbpy.context.view_layer.objects.active = catch_light\\\\ncatch_light.select_set(True)\\\\n\\\\nprint(\\\\\\\"Lights created: Key, Fill, and Catch lights\\\\\\\")\\\\n\\\", \\\"user_prompt\\\": \\\"Add lighting setup for presentation\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"toolu_bdrk_01AMvEaBThonE94HvaJjwfZk\",\"content\":[{\"type\":\"text\",\"text\":\"Code executed successfully: Lights created: Key, Fill, and Catch lights\\n\",\"cache_control\":{\"type\":\"ephemeral\"}}]}],\"usage\":{\"include\":true},\"tools\":[{\"type\":\"function\",\"function\":{\"name\":\"get_scene_info\",\"description\":\"Get detailed information about the current Blender scene\\n\\n    Parameters:\\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"user_prompt\"],\"title\":\"get_scene_infoArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_object_info\",\"description\":\"\\n    Get detailed information about a specific object in the Blender scene.\\n\\n    Parameters:\\n    - object_name: The name of the object to get information about\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"object_name\":{\"title\":\"Object Name\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"object_name\"],\"title\":\"get_object_infoArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_viewport_screenshot\",\"description\":\"\\n    Capture a screenshot of the current Blender 3D viewport.\\n\\n    Parameters:\\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns the screenshot as an Image.\\n    \",\"parameters\":{\"properties\":{\"max_size\":{\"default\":1000,\"title\":\"Max Size\",\"type\":\"integer\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_viewport_screenshotArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"description\":\"\\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\\n\\n    Parameters:\\n    - code: The Python code to execute\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"code\":{\"title\":\"Code\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"code\"],\"title\":\"execute_blender_codeArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_categories\",\"description\":\"\\n    Get a list of categories for a specific asset type on Polyhaven.\\n\\n    Parameters:\\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"asset_type\":{\"default\":\"hdris\",\"title\":\"Asset Type\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_polyhaven_categoriesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_polyhaven_assets\",\"description\":\"\\n    Search for assets on Polyhaven with optional filtering.\\n\\n    Parameters:\\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\\n    - categories: Optional comma-separated list of categories to filter by\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns a list of matching assets with basic information.\\n    \",\"parameters\":{\"properties\":{\"asset_type\":{\"default\":\"all\",\"title\":\"Asset Type\",\"type\":\"string\"},\"categories\":{\"default\":null,\"title\":\"Categories\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"search_polyhaven_assetsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"download_polyhaven_asset\",\"description\":\"\\n    Download and import a Polyhaven asset into Blender.\\n\\n    Parameters:\\n    - asset_id: The ID of the asset to download\\n    - asset_type: The type of asset (hdris, textures, models)\\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"asset_id\":{\"title\":\"Asset Id\",\"type\":\"string\"},\"asset_type\":{\"title\":\"Asset Type\",\"type\":\"string\"},\"resolution\":{\"default\":\"1k\",\"title\":\"Resolution\",\"type\":\"string\"},\"file_format\":{\"default\":null,\"title\":\"File Format\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"asset_id\",\"asset_type\"],\"title\":\"download_polyhaven_assetArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"set_texture\",\"description\":\"\\n    Apply a previously downloaded Polyhaven texture to an object.\\n    \\n    Parameters:\\n    - object_name: Name of the object to apply the texture to\\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\\n    \\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"object_name\":{\"title\":\"Object Name\",\"type\":\"string\"},\"texture_id\":{\"title\":\"Texture Id\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"object_name\",\"texture_id\"],\"title\":\"set_textureArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_status\",\"description\":\"\\n    Check if PolyHaven integration is enabled in Blender.\\n    Returns a message indicating whether PolyHaven features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_polyhaven_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_hyper3d_status\",\"description\":\"\\n    Check if Hyper3D Rodin integration is enabled in Blender.\\n    Returns a message indicating whether Hyper3D Rodin features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_hyper3d_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_status\",\"description\":\"\\n    Check if Sketchfab integration is enabled in Blender.\\n    Returns a message indicating whether Sketchfab features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_sketchfab_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_sketchfab_models\",\"description\":\"\\n    Search for models on Sketchfab with optional filtering.\\n\\n    Parameters:\\n    - query: Text to search for\\n    - categories: Optional comma-separated list of categories\\n    - count: Maximum number of results to return (default 20)\\n    - downloadable: Whether to include only downloadable models (default True)\\n\\n    Returns a formatted list of matching models.\\n    \",\"parameters\":{\"properties\":{\"query\":{\"title\":\"Query\",\"type\":\"string\"},\"categories\":{\"default\":null,\"title\":\"Categories\",\"type\":\"string\"},\"count\":{\"default\":20,\"title\":\"Count\",\"type\":\"integer\"},\"downloadable\":{\"default\":true,\"title\":\"Downloadable\",\"type\":\"boolean\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"query\"],\"title\":\"search_sketchfab_modelsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_model_preview\",\"description\":\"\\n    Get a preview thumbnail of a Sketchfab model by its UID.\\n    Use this to visually confirm a model before downloading.\\n    \\n    Parameters:\\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\\n    \\n    Returns the model's thumbnail as an Image for visual confirmation.\\n    \",\"parameters\":{\"properties\":{\"uid\":{\"title\":\"Uid\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"uid\"],\"title\":\"get_sketchfab_model_previewArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"download_sketchfab_model\",\"description\":\"\\n    Download and import a Sketchfab model by its UID.\\n    The model will be scaled so its largest dimension equals target_size.\\n    \\n    Parameters:\\n    - uid: The unique identifier of the Sketchfab model\\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\\n                  You must specify the desired size for the model.\\n                  Examples:\\n                  - Chair: target_size=1.0 (1 meter tall)\\n                  - Table: target_size=0.75 (75cm tall)\\n                  - Car: target_size=4.5 (4.5 meters long)\\n                  - Person: target_size=1.7 (1.7 meters tall)\\n                  - Small object (cup, phone): target_size=0.1 to 0.3\\n    \\n    Returns a message with import details including object names, dimensions, and bounding box.\\n    The model must be downloadable and you must have proper access rights.\\n    \",\"parameters\":{\"properties\":{\"uid\":{\"title\":\"Uid\",\"type\":\"string\"},\"target_size\":{\"title\":\"Target Size\",\"type\":\"number\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"uid\",\"target_size\"],\"title\":\"download_sketchfab_modelArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hyper3d_model_via_text\",\"description\":\"\\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\\n    The 3D asset has built-in materials.\\n    The generated model has a normalized size, so re-scaling after generation can be useful.\\n\\n    Parameters:\\n    - text_prompt: A short description of the desired model in **English**.\\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\\n\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"text_prompt\":{\"title\":\"Text Prompt\",\"type\":\"string\"},\"bbox_condition\":{\"default\":null,\"items\":{\"type\":\"number\"},\"title\":\"Bbox Condition\",\"type\":\"array\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"text_prompt\"],\"title\":\"generate_hyper3d_model_via_textArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hyper3d_model_via_images\",\"description\":\"\\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\\n    The 3D asset has built-in materials.\\n    The generated model has a normalized size, so re-scaling after generation can be useful.\\n    \\n    Parameters:\\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model.\\n\\n    Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode.\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"input_image_paths\":{\"default\":null,\"items\":{\"type\":\"string\"},\"title\":\"Input Image Paths\",\"type\":\"array\"},\"input_image_urls\":{\"default\":null,\"items\":{\"type\":\"string\"},\"title\":\"Input Image Urls\",\"type\":\"array\"},\"bbox_condition\":{\"default\":null,\"items\":{\"type\":\"number\"},\"title\":\"Bbox Condition\",\"type\":\"array\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"generate_hyper3d_model_via_imagesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"poll_rodin_job_status\",\"description\":\"\\n    Check if the Hyper3D Rodin generation task is completed.\\n\\n    For Hyper3D Rodin mode MAIN_SITE:\\n        Parameters:\\n        - subscription_key: The subscription_key given in the generate model step.\\n\\n        Returns a list of status. The task is done if all status are \\\"Done\\\".\\n        If \\\"Failed\\\" showed up, the generating process failed.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"Done\\\" or \\\"Canceled\\\").\\n\\n    For Hyper3D Rodin mode FAL_AI:\\n        Parameters:\\n        - request_id: The request_id given in the generate model step.\\n\\n        Returns the generation task status. The task is done if status is \\\"COMPLETED\\\".\\n        The task is in progress if status is \\\"IN_PROGRESS\\\".\\n        If status other than \\\"COMPLETED\\\", \\\"IN_PROGRESS\\\", \\\"IN_QUEUE\\\" showed up, the generating process might be failed.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"COMPLETED\\\" or some failed state).\\n    \",\"parameters\":{\"properties\":{\"subscription_key\":{\"default\":null,\"title\":\"Subscription Key\",\"type\":\"string\"},\"request_id\":{\"default\":null,\"title\":\"Request Id\",\"type\":\"string\"}},\"title\":\"poll_rodin_job_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"import_generated_asset\",\"description\":\"\\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\\n\\n    Parameters:\\n    - name: The name of the object in scene\\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\\n\\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\\n    Return if the asset has been imported successfully.\\n    \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"task_uuid\":{\"default\":null,\"title\":\"Task Uuid\",\"type\":\"string\"},\"request_id\":{\"default\":null,\"title\":\"Request Id\",\"type\":\"string\"}},\"required\":[\"name\"],\"title\":\"import_generated_assetArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_hunyuan3d_status\",\"description\":\"\\n    Check if Hunyuan3D integration is enabled in Blender.\\n    Returns a message indicating whether Hunyuan3D features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_hunyuan3d_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hunyuan3d_model\",\"description\":\"\\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \\n    or both for the desired asset, and import the asset into Blender.\\n    The 3D asset has built-in materials.\\n    \\n    Parameters:\\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\\n    - input_image_url: (Optional) The local or remote url of the input image. Accepts None if only using text prompt.\\n\\n    Returns: \\n    - When successful, returns a JSON with job_id (format: \\\"job_xxx\\\") indicating the task is in progress\\n    - When the job completes, the status will change to \\\"DONE\\\" indicating the model has been imported\\n    - Returns error message if the operation fails\\n    \",\"parameters\":{\"properties\":{\"text_prompt\":{\"default\":null,\"title\":\"Text Prompt\",\"type\":\"string\"},\"input_image_url\":{\"default\":null,\"title\":\"Input Image Url\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"generate_hunyuan3d_modelArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"poll_hunyuan_job_status\",\"description\":\"\\n    Check if the Hunyuan3D generation task is completed.\\n\\n    For Hunyuan3D:\\n        Parameters:\\n        - job_id: The job_id given in the generate model step.\\n\\n        Returns the generation task status. The task is done if status is \\\"DONE\\\".\\n        The task is in progress if status is \\\"RUN\\\".\\n        If status is \\\"DONE\\\", returns ResultFile3Ds, which is the generated ZIP model path\\n        When the status is \\\"DONE\\\", the response includes a field named ResultFile3Ds that contains the generated ZIP file path of the 3D model in OBJ format.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"DONE\\\" or some failed state).\\n    \",\"parameters\":{\"properties\":{\"job_id\":{\"default\":null,\"title\":\"Job Id\",\"type\":\"string\"}},\"title\":\"poll_hunyuan_job_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"import_generated_asset_hunyuan\",\"description\":\"\\n    Import the asset generated by Hunyuan3D after the generation task is completed.\\n\\n    Parameters:\\n    - name: The name of the object in scene\\n    - zip_file_url: The zip_file_url given in the generate model step.\\n\\n    Return if the asset has been imported successfully.\\n    \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"zip_file_url\":{\"title\":\"Zip File Url\",\"type\":\"string\"}},\"required\":[\"name\",\"zip_file_url\"],\"title\":\"import_generated_asset_hunyuanArguments\",\"type\":\"object\"}}}],\"reasoning\":{\"effort\":\"xhigh\"}}", "body_base64": "{"model":"anthropic/claude-haiku-4.5","messages":[{"role":"system","content":[{"type":"text","text":"You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\n\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\n\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\n\n## Harness Instructions\n\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. When you are finished, reply with the single word DONE and no tool calls.","cache_control":{"type":"ephemeral"}}]},{"role":"user","content":"## Task\n\nComplete the following Blender task:\n\n<task>\n\nA red sphere of 1 meter diameter resting on a white ground plane — touching it\nexactly, neither floating nor sunk. Smooth shading, a plain red material on the\nball, plain white on the floor.\n\nThe floor is the sphere, the color, and the contact. The ceiling is presentation:\na soft believable shadow under the ball, a hint of glossiness that catches the\nlight, and clean framing.\n\n</task>"},{"role":"assistant","content":"I'll create a red sphere resting on a white ground plane with proper materials, shading, lighting, and camera setup. Let me work through this step by step.","tool_calls":[{"id":"toolu_bdrk_01McQwtp5Bq6813NVu2gUpRG","type":"function","function":{"name":"get_scene_info","arguments":"{\"user_prompt\": \"Get current scene info to see what's there initially\"}"}}]},{"role":"tool","tool_call_id":"toolu_bdrk_01McQwtp5Bq6813NVu2gUpRG","content":"{\n  \"name\": \"Scene\",\n  \"object_count\": 0,\n  \"objects\": [],\n  \"materials_count\": 2\n}"},{"role":"assistant","content":"Good, the scene is empty. Now I'll create the red sphere and white ground plane using Blender code:","tool_calls":[{"id":"toolu_bdrk_01PuvjJZjfHJZgTRw6jGDsdH","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\": \"\\nimport bpy\\nimport bmesh\\nfrom mathutils import Vector\\n\\n# Clear any default objects\\nbpy.ops.object.select_all(action='SELECT')\\nbpy.ops.object.delete(use_global=False)\\n\\n# Add a sphere (1 meter diameter = 0.5 meter radius)\\nbpy.ops.mesh.primitive_uv_sphere_add(\\n    radius=0.5,\\n    location=(0, 0, 0.5)  # Position it so bottom touches z=0\\n)\\nsphere = bpy.context.active_object\\nsphere.name = \\\"RedSphere\\\"\\n\\n# Add a large ground plane\\nbpy.ops.mesh.primitive_plane_add(\\n    size=10,\\n    location=(0, 0, 0)\\n)\\nplane = bpy.context.active_object\\nplane.name = \\\"GroundPlane\\\"\\n\\n# Select and scale plane to be large but thin\\nplane.scale = (10, 10, 0.1)\\n\\nprint(\\\"Objects created: Sphere at z=\\\" + str(sphere.location.z) + \\\", Plane at z=\\\" + str(plane.location.z))\\n\", \"user_prompt\": \"Create red sphere and white ground plane\"}"}}]},{"role":"tool","tool_call_id":"toolu_bdrk_01PuvjJZjfHJZgTRw6jGDsdH","content":"Code executed successfully: Objects created: Sphere at z=0.5, Plane at z=0.0\n"},{"role":"assistant","content":"Now I'll create the materials - red for the sphere and white for the plane:","tool_calls":[{"id":"toolu_bdrk_018gHZ2vPGV9JR5pgaWmu5aZ","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\": \"\\nimport bpy\\n\\n# Get the sphere and plane objects\\nsphere = bpy.data.objects[\\\"RedSphere\\\"]\\nplane = bpy.data.objects[\\\"GroundPlane\\\"]\\n\\n# Create red material for sphere\\nred_material = bpy.data.materials.new(name=\\\"RedMaterial\\\")\\nred_material.use_nodes = True\\nred_material.diffuse_color = (1.0, 0.0, 0.0, 1.0)  # Red RGBA\\n\\n# Configure red material with some glossiness\\nbsdf = red_material.node_tree.nodes[\\\"Principled BSDF\\\"]\\nbsdf.inputs[\\\"Base Color\\\"].default_value = (0.8, 0.05, 0.05, 1.0)  # Pure red\\nbsdf.inputs[\\\"Roughness\\\"].default_value = 0.3  # Slight glossiness\\n\\n# Create white material for plane\\nwhite_material = bpy.data.materials.new(name=\\\"WhiteMaterial\\\")\\nwhite_material.use_nodes = True\\nwhite_material.diffuse_color = (1.0, 1.0, 1.0, 1.0)  # White RGBA\\n\\nbsdf_white = white_material.node_tree.nodes[\\\"Principled BSDF\\\"]\\nbsdf_white.inputs[\\\"Base Color\\\"].default_value = (1.0, 1.0, 1.0, 1.0)  # White\\nbsdf_white.inputs[\\\"Roughness\\\"].default_value = 0.8  # Matte white\\n\\n# Assign materials to objects\\nsphere.data.materials.append(red_material)\\nplane.data.materials.append(white_material)\\n\\nprint(\\\"Materials created and assigned\\\")\\n\", \"user_prompt\": \"Create and assign red and white materials\"}"}}]},{"role":"tool","tool_call_id":"toolu_bdrk_018gHZ2vPGV9JR5pgaWmu5aZ","content":"Code executed successfully: Materials created and assigned\n"},{"role":"assistant","content":"Now I'll apply smooth shading to both objects:","tool_calls":[{"id":"toolu_bdrk_01WSSDEGVk2FHv51RevTiES7","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\": \"\\nimport bpy\\n\\nsphere = bpy.data.objects[\\\"RedSphere\\\"]\\nplane = bpy.data.objects[\\\"GroundPlane\\\"]\\n\\n# Select sphere and apply smooth shading\\nsphere.select_set(True)\\nbpy.context.view_layer.objects.active = sphere\\nbpy.ops.object.shade_smooth()\\n\\n# Select plane and apply smooth shading\\nplane.select_set(True)\\nbpy.context.view_layer.objects.active = plane\\nbpy.ops.object.shade_smooth()\\n\\nprint(\\\"Smooth shading applied to both objects\\\")\\n\", \"user_prompt\": \"Apply smooth shading to sphere and plane\"}"}}]},{"role":"tool","tool_call_id":"toolu_bdrk_01WSSDEGVk2FHv51RevTiES7","content":"Code executed successfully: Smooth shading applied to both objects\n"},{"role":"assistant","content":"Now I need to set up the lighting and camera for a good presentation. I'll add a key light, fill light, and set up the camera:","tool_calls":[{"id":"toolu_bdrk_01AMvEaBThonE94HvaJjwfZk","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\": \"\\nimport bpy\\nfrom mathutils import Vector\\n\\n# Add a key light (three-quarter lighting)\\nbpy.ops.object.light_add(\\n    type='SUN',\\n    location=(3, 4, 5)\\n)\\nkey_light = bpy.context.active_object\\nkey_light.name = \\\"KeyLight\\\"\\nkey_light.data.energy = 2.0\\nkey_light.data.angle = 0.5  # Slight softness\\nbpy.context.view_layer.objects.active = key_light\\nkey_light.select_set(True)\\n\\n# Add a fill light (softer light from the other side)\\nbpy.ops.object.light_add(\\n    type='SUN',\\n    location=(-2, -3, 3)\\n)\\nfill_light = bpy.context.active_object\\nfill_light.name = \\\"FillLight\\\"\\nfill_light.data.energy = 0.6  # Much softer\\nfill_light.data.angle = 2.0  # Very soft\\nbpy.context.view_layer.objects.active = fill_light\\nfill_light.select_set(True)\\n\\n# Add an area light positioned slightly to the side and above for catch lights\\nbpy.ops.object.light_add(\\n    type='AREA',\\n    location=(2, -3, 4)\\n)\\ncatch_light = bpy.context.active_object\\ncatch_light.name = \\\"CatchLight\\\"\\ncatch_light.data.energy = 0.8\\ncatch_light.data.size = 3\\nbpy.context.view_layer.objects.active = catch_light\\ncatch_light.select_set(True)\\n\\nprint(\\\"Lights created: Key, Fill, and Catch lights\\\")\\n\", \"user_prompt\": \"Add lighting setup for presentation\"}"}}]},{"role":"tool","tool_call_id":"toolu_bdrk_01AMvEaBThonE94HvaJjwfZk","content":[{"type":"text","text":"Code executed successfully: Lights created: Key, Fill, and Catch lights\n","cache_control":{"type":"ephemeral"}}]}],"usage":{"include":true},"tools":[{"type":"function","function":{"name":"get_scene_info","description":"Get detailed information about the current Blender scene\n\n    Parameters:\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\n    ","parameters":{"properties":{"user_prompt":{"title":"User Prompt","type":"string"}},"required":["user_prompt"],"title":"get_scene_infoArguments","type":"object"}}},{"type":"function","function":{"name":"get_object_info","description":"\n    Get detailed information about a specific object in the Blender scene.\n\n    Parameters:\n    - object_name: The name of the object to get information about\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"object_name":{"title":"Object Name","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["object_name"],"title":"get_object_infoArguments","type":"object"}}},{"type":"function","function":{"name":"get_viewport_screenshot","description":"\n    Capture a screenshot of the current Blender 3D viewport.\n\n    Parameters:\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns the screenshot as an Image.\n    ","parameters":{"properties":{"max_size":{"default":1000,"title":"Max Size","type":"integer"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_viewport_screenshotArguments","type":"object"}}},{"type":"function","function":{"name":"execute_blender_code","description":"\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\n\n    Parameters:\n    - code: The Python code to execute\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"code":{"title":"Code","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["code"],"title":"execute_blender_codeArguments","type":"object"}}},{"type":"function","function":{"name":"get_polyhaven_categories","description":"\n    Get a list of categories for a specific asset type on Polyhaven.\n\n    Parameters:\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"asset_type":{"default":"hdris","title":"Asset Type","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_polyhaven_categoriesArguments","type":"object"}}},{"type":"function","function":{"name":"search_polyhaven_assets","description":"\n    Search for assets on Polyhaven with optional filtering.\n\n    Parameters:\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\n    - categories: Optional comma-separated list of categories to filter by\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a list of matching assets with basic information.\n    ","parameters":{"properties":{"asset_type":{"default":"all","title":"Asset Type","type":"string"},"categories":{"default":null,"title":"Categories","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"search_polyhaven_assetsArguments","type":"object"}}},{"type":"function","function":{"name":"download_polyhaven_asset","description":"\n    Download and import a Polyhaven asset into Blender.\n\n    Parameters:\n    - asset_id: The ID of the asset to download\n    - asset_type: The type of asset (hdris, textures, models)\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a message indicating success or failure.\n    ","parameters":{"properties":{"asset_id":{"title":"Asset Id","type":"string"},"asset_type":{"title":"Asset Type","type":"string"},"resolution":{"default":"1k","title":"Resolution","type":"string"},"file_format":{"default":null,"title":"File Format","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["asset_id","asset_type"],"title":"download_polyhaven_assetArguments","type":"object"}}},{"type":"function","function":{"name":"set_texture","description":"\n    Apply a previously downloaded Polyhaven texture to an object.\n    \n    Parameters:\n    - object_name: Name of the object to apply the texture to\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\n    \n    Returns a message indicating success or failure.\n    ","parameters":{"properties":{"object_name":{"title":"Object Name","type":"string"},"texture_id":{"title":"Texture Id","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["object_name","texture_id"],"title":"set_textureArguments","type":"object"}}},{"type":"function","function":{"name":"get_polyhaven_status","description":"\n    Check if PolyHaven integration is enabled in Blender.\n    Returns a message indicating whether PolyHaven features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_polyhaven_statusArguments","type":"object"}}},{"type":"function","function":{"name":"get_hyper3d_status","description":"\n    Check if Hyper3D Rodin integration is enabled in Blender.\n    Returns a message indicating whether Hyper3D Rodin features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_hyper3d_statusArguments","type":"object"}}},{"type":"function","function":{"name":"get_sketchfab_status","description":"\n    Check if Sketchfab integration is enabled in Blender.\n    Returns a message indicating whether Sketchfab features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_sketchfab_statusArguments","type":"object"}}},{"type":"function","function":{"name":"search_sketchfab_models","description":"\n    Search for models on Sketchfab with optional filtering.\n\n    Parameters:\n    - query: Text to search for\n    - categories: Optional comma-separated list of categories\n    - count: Maximum number of results to return (default 20)\n    - downloadable: Whether to include only downloadable models (default True)\n\n    Returns a formatted list of matching models.\n    ","parameters":{"properties":{"query":{"title":"Query","type":"string"},"categories":{"default":null,"title":"Categories","type":"string"},"count":{"default":20,"title":"Count","type":"integer"},"downloadable":{"default":true,"title":"Downloadable","type":"boolean"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["query"],"title":"search_sketchfab_modelsArguments","type":"object"}}},{"type":"function","function":{"name":"get_sketchfab_model_preview","description":"\n    Get a preview thumbnail of a Sketchfab model by its UID.\n    Use this to visually confirm a model before downloading.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\n    \n    Returns the model's thumbnail as an Image for visual confirmation.\n    ","parameters":{"properties":{"uid":{"title":"Uid","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["uid"],"title":"get_sketchfab_model_previewArguments","type":"object"}}},{"type":"function","function":{"name":"download_sketchfab_model","description":"\n    Download and import a Sketchfab model by its UID.\n    The model will be scaled so its largest dimension equals target_size.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\n                  You must specify the desired size for the model.\n                  Examples:\n                  - Chair: target_size=1.0 (1 meter tall)\n                  - Table: target_size=0.75 (75cm tall)\n                  - Car: target_size=4.5 (4.5 meters long)\n                  - Person: target_size=1.7 (1.7 meters tall)\n                  - Small object (cup, phone): target_size=0.1 to 0.3\n    \n    Returns a message with import details including object names, dimensions, and bounding box.\n    The model must be downloadable and you must have proper access rights.\n    ","parameters":{"properties":{"uid":{"title":"Uid","type":"string"},"target_size":{"title":"Target Size","type":"number"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["uid","target_size"],"title":"download_sketchfab_modelArguments","type":"object"}}},{"type":"function","function":{"name":"generate_hyper3d_model_via_text","description":"\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n\n    Parameters:\n    - text_prompt: A short description of the desired model in **English**.\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Returns a message indicating success or failure.\n    ","parameters":{"properties":{"text_prompt":{"title":"Text Prompt","type":"string"},"bbox_condition":{"default":null,"items":{"type":"number"},"title":"Bbox Condition","type":"array"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["text_prompt"],"title":"generate_hyper3d_model_via_textArguments","type":"object"}}},{"type":"function","function":{"name":"generate_hyper3d_model_via_images","description":"\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n    \n    Parameters:\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode.\n    Returns a message indicating success or failure.\n    ","parameters":{"properties":{"input_image_paths":{"default":null,"items":{"type":"string"},"title":"Input Image Paths","type":"array"},"input_image_urls":{"default":null,"items":{"type":"string"},"title":"Input Image Urls","type":"array"},"bbox_condition":{"default":null,"items":{"type":"number"},"title":"Bbox Condition","type":"array"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"generate_hyper3d_model_via_imagesArguments","type":"object"}}},{"type":"function","function":{"name":"poll_rodin_job_status","description":"\n    Check if the Hyper3D Rodin generation task is completed.\n\n    For Hyper3D Rodin mode MAIN_SITE:\n        Parameters:\n        - subscription_key: The subscription_key given in the generate model step.\n\n        Returns a list of status. The task is done if all status are \"Done\".\n        If \"Failed\" showed up, the generating process failed.\n        This is a polling API, so only proceed if the status are finally determined (\"Done\" or \"Canceled\").\n\n    For Hyper3D Rodin mode FAL_AI:\n        Parameters:\n        - request_id: The request_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"COMPLETED\".\n        The task is in progress if status is \"IN_PROGRESS\".\n        If status other than \"COMPLETED\", \"IN_PROGRESS\", \"IN_QUEUE\" showed up, the generating process might be failed.\n        This is a polling API, so only proceed if the status are finally determined (\"COMPLETED\" or some failed state).\n    ","parameters":{"properties":{"subscription_key":{"default":null,"title":"Subscription Key","type":"string"},"request_id":{"default":null,"title":"Request Id","type":"string"}},"title":"poll_rodin_job_statusArguments","type":"object"}}},{"type":"function","function":{"name":"import_generated_asset","description":"\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\n\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\n    Return if the asset has been imported successfully.\n    ","parameters":{"properties":{"name":{"title":"Name","type":"string"},"task_uuid":{"default":null,"title":"Task Uuid","type":"string"},"request_id":{"default":null,"title":"Request Id","type":"string"}},"required":["name"],"title":"import_generated_assetArguments","type":"object"}}},{"type":"function","function":{"name":"get_hunyuan3d_status","description":"\n    Check if Hunyuan3D integration is enabled in Blender.\n    Returns a message indicating whether Hunyuan3D features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_hunyuan3d_statusArguments","type":"object"}}},{"type":"function","function":{"name":"generate_hunyuan3d_model","description":"\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \n    or both for the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    \n    Parameters:\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\n    - input_image_url: (Optional) The local or remote url of the input image. Accepts None if only using text prompt.\n\n    Returns: \n    - When successful, returns a JSON with job_id (format: \"job_xxx\") indicating the task is in progress\n    - When the job completes, the status will change to \"DONE\" indicating the model has been imported\n    - Returns error message if the operation fails\n    ","parameters":{"properties":{"text_prompt":{"default":null,"title":"Text Prompt","type":"string"},"input_image_url":{"default":null,"title":"Input Image Url","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"generate_hunyuan3d_modelArguments","type":"object"}}},{"type":"function","function":{"name":"poll_hunyuan_job_status","description":"\n    Check if the Hunyuan3D generation task is completed.\n\n    For Hunyuan3D:\n        Parameters:\n        - job_id: The job_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"DONE\".\n        The task is in progress if status is \"RUN\".\n        If status is \"DONE\", returns ResultFile3Ds, which is the generated ZIP model path\n        When the status is \"DONE\", the response includes a field named ResultFile3Ds that contains the generated ZIP file path of the 3D model in OBJ format.\n        This is a polling API, so only proceed if the status are finally determined (\"DONE\" or some failed state).\n    ","parameters":{"properties":{"job_id":{"default":null,"title":"Job Id","type":"string"}},"title":"poll_hunyuan_job_statusArguments","type":"object"}}},{"type":"function","function":{"name":"import_generated_asset_hunyuan","description":"\n    Import the asset generated by Hunyuan3D after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - zip_file_url: The zip_file_url given in the generate model step.\n\n    Return if the asset has been imported successfully.\n    ","parameters":{"properties":{"name":{"title":"Name","type":"string"},"zip_file_url":{"title":"Zip File Url","type":"string"}},"required":["name","zip_file_url"],"title":"import_generated_asset_hunyuanArguments","type":"object"}}}],"reasoning":{"effort":"xhigh"}}"}, "response": {"status_code": 200, "headers": {"date": "Wed, 15 Jul 2026 00:49:00 GMT", "content-type": "application/json", "transfer-encoding": "chunked", "connection": "keep-alive", "access-control-allow-origin": "*", "x-generation-id": "gen-1784076539-ssQAWjmsSMmPu5zi7l2H", "access-control-expose-headers": "X-Generation-Id,X-Provider-Name,cf-ray", "set-cookie": "[REDACTED]", "permissions-policy": "payment=(self \"https://checkout.stripe.com\" \"https://connect-js.stripe.com\" \"https://js.stripe.com\" \"https://*.js.stripe.com\" \"https://hooks.stripe.com\")", "referrer-policy": "no-referrer, strict-origin-when-cross-origin", "x-content-type-options": "nosniff", "content-encoding": "gzip", "server": "cloudflare", "cf-ray": "a1b4c3c52c351bc0-MAD"}, "body": "\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n{\"id\":\"gen-1784076539-ssQAWjmsSMmPu5zi7l2H\",\"object\":\"chat.completion\",\"created\":1784076539,\"model\":\"anthropic/claude-haiku-4.5\",\"provider\":\"Amazon Bedrock\",\"system_fingerprint\":null,\"service_tier\":null,\"choices\":[{\"index\":0,\"logprobs\":null,\"finish_reason\":\"tool_calls\",\"native_finish_reason\":\"tool_use\",\"message\":{\"role\":\"assistant\",\"content\":\"Now I'll set up the camera for a clean, well-composed view:\",\"refusal\":null,\"reasoning\":null,\"tool_calls\":[{\"type\":\"function\",\"index\":0,\"id\":\"toolu_bdrk_013cajKLXaCWr4wzNHHwZfby\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\": \\\"\\\\nimport bpy\\\\nfrom mathutils import Vector, Matrix\\\\n\\\\n# Add a camera\\\\nbpy.ops.object.camera_add(\\\\n    location=(3.5, -3, 2.5)\\\\n)\\\\ncamera = bpy.context.active_object\\\\ncamera.name = \\\\\\\"Camera\\\\\\\"\\\\n\\\\n# Point camera at the sphere (origin area)\\\\ndirection = Vector((0, 0, 0.3)) - 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Vector(camera.location)\nrot_quat = direction.to_track_quat('-Z', 'Y')\ncamera.rotation_euler = rot_quat.to_euler()\n\n# Set as active camera\nbpy.context.scene.camera = camera\n\n# Adjust camera settings for a nice view\ncamera.data.lens = 50  # Standard lens\ncamera.data.sensor_width = 36\n\nprint(\"Camera set up at position:\", camera.location)\nprint(\"Camera rotation euler:\", camera.rotation_euler)\n", "user_prompt": "Set up camera for presentation"}, "raw_arguments": "{\"code\": \"\\nimport bpy\\nfrom mathutils import Vector, Matrix\\n\\n# Add a camera\\nbpy.ops.object.camera_add(\\n    location=(3.5, -3, 2.5)\\n)\\ncamera = bpy.context.active_object\\ncamera.name = \\\"Camera\\\"\\n\\n# Point camera at the sphere (origin area)\\ndirection = Vector((0, 0, 0.3)) - Vector(camera.location)\\nrot_quat = direction.to_track_quat('-Z', 'Y')\\ncamera.rotation_euler = rot_quat.to_euler()\\n\\n# Set as active camera\\nbpy.context.scene.camera = camera\\n\\n# Adjust camera settings for a nice view\\ncamera.data.lens = 50  # Standard lens\\ncamera.data.sensor_width = 36\\n\\nprint(\\\"Camera set up at position:\\\", camera.location)\\nprint(\\\"Camera rotation euler:\\\", camera.rotation_euler)\\n\", \"user_prompt\": \"Set up camera for presentation\"}", "parse_error": null}], "tokens_in": 7728, "tokens_out": 309, "cost_usd": 0.002964, "cached_tokens": 7165}, "error": null}
{"ts": "2026-07-15T00:49:04.634321+00:00", "type": "assistant", "text": "Now I'll set up the camera for a clean, well-composed view:", "tool_calls": [{"id": "toolu_bdrk_013cajKLXaCWr4wzNHHwZfby", "name": "execute_blender_code", "arguments": {"code": "\nimport bpy\nfrom mathutils import Vector, Matrix\n\n# Add a camera\nbpy.ops.object.camera_add(\n    location=(3.5, -3, 2.5)\n)\ncamera = bpy.context.active_object\ncamera.name = \"Camera\"\n\n# Point camera at the sphere (origin area)\ndirection = Vector((0, 0, 0.3)) - Vector(camera.location)\nrot_quat = direction.to_track_quat('-Z', 'Y')\ncamera.rotation_euler = rot_quat.to_euler()\n\n# Set as active camera\nbpy.context.scene.camera = camera\n\n# Adjust camera settings for a nice view\ncamera.data.lens = 50  # Standard lens\ncamera.data.sensor_width = 36\n\nprint(\"Camera set up at position:\", camera.location)\nprint(\"Camera rotation euler:\", camera.rotation_euler)\n", "user_prompt": "Set up camera for presentation"}, "raw_arguments": "{\"code\": \"\\nimport bpy\\nfrom mathutils import Vector, Matrix\\n\\n# Add a camera\\nbpy.ops.object.camera_add(\\n    location=(3.5, -3, 2.5)\\n)\\ncamera = bpy.context.active_object\\ncamera.name = \\\"Camera\\\"\\n\\n# Point camera at the sphere (origin area)\\ndirection = Vector((0, 0, 0.3)) - Vector(camera.location)\\nrot_quat = direction.to_track_quat('-Z', 'Y')\\ncamera.rotation_euler = rot_quat.to_euler()\\n\\n# Set as active camera\\nbpy.context.scene.camera = camera\\n\\n# Adjust camera settings for a nice view\\ncamera.data.lens = 50  # Standard lens\\ncamera.data.sensor_width = 36\\n\\nprint(\\\"Camera set up at position:\\\", camera.location)\\nprint(\\\"Camera rotation euler:\\\", camera.rotation_euler)\\n\", \"user_prompt\": \"Set up camera for presentation\"}", "parse_error": null}]}
{"ts": "2026-07-15T00:49:04.634321+00:00", "type": "tool_call", "id": "toolu_bdrk_013cajKLXaCWr4wzNHHwZfby", "name": "execute_blender_code", "arguments": {"code": "\nimport bpy\nfrom mathutils import Vector, Matrix\n\n# Add a camera\nbpy.ops.object.camera_add(\n    location=(3.5, -3, 2.5)\n)\ncamera = bpy.context.active_object\ncamera.name = \"Camera\"\n\n# Point camera at the sphere (origin area)\ndirection = Vector((0, 0, 0.3)) - Vector(camera.location)\nrot_quat = direction.to_track_quat('-Z', 'Y')\ncamera.rotation_euler = rot_quat.to_euler()\n\n# Set as active camera\nbpy.context.scene.camera = camera\n\n# Adjust camera settings for a nice view\ncamera.data.lens = 50  # Standard lens\ncamera.data.sensor_width = 36\n\nprint(\"Camera set up at position:\", camera.location)\nprint(\"Camera rotation euler:\", camera.rotation_euler)\n", "user_prompt": "Set up camera for presentation"}, "raw_arguments": "{\"code\": \"\\nimport bpy\\nfrom mathutils import Vector, Matrix\\n\\n# Add a camera\\nbpy.ops.object.camera_add(\\n    location=(3.5, -3, 2.5)\\n)\\ncamera = bpy.context.active_object\\ncamera.name = \\\"Camera\\\"\\n\\n# Point camera at the sphere (origin area)\\ndirection = Vector((0, 0, 0.3)) - Vector(camera.location)\\nrot_quat = direction.to_track_quat('-Z', 'Y')\\ncamera.rotation_euler = rot_quat.to_euler()\\n\\n# Set as active camera\\nbpy.context.scene.camera = camera\\n\\n# Adjust camera settings for a nice view\\ncamera.data.lens = 50  # Standard lens\\ncamera.data.sensor_width = 36\\n\\nprint(\\\"Camera set up at position:\\\", camera.location)\\nprint(\\\"Camera rotation euler:\\\", camera.rotation_euler)\\n\", \"user_prompt\": \"Set up camera for presentation\"}"}
{"ts": "2026-07-15T00:49:04.671323+00:00", "type": "tool_result", "tool_call_id": "toolu_bdrk_013cajKLXaCWr4wzNHHwZfby", "name": "execute_blender_code", "text": "Code executed successfully: Camera set up at position: <Vector (3.5000, -3.0000, 2.5000)>\nCamera rotation euler: <Euler (x=1.1255, y=0.0000, z=0.8622), order='XYZ'>\n", "is_error": false, "has_image": false, "image_b64": null}
{"ts": "2026-07-15T00:49:07.021384+00:00", "type": "llm_call", "call_index": 7, "provider_type": "blenderbench.providers.openrouter.OpenRouterProvider", "started_at": "2026-07-15T00:49:04.672330+00:00", "finished_at": "2026-07-15T00:49:07.021384+00:00", "duration_s": 2.3440000000118744, "request": {"messages": [{"role": "system", "content": "You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\n\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\n\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\n\n## Harness Instructions\n\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. When you are finished, reply with the single word DONE and no tool calls."}, {"role": "user", "content": "## Task\n\nComplete the following Blender task:\n\n<task>\n\nA red sphere of 1 meter diameter resting on a white ground plane — touching it\nexactly, neither floating nor sunk. Smooth shading, a plain red material on the\nball, plain white on the floor.\n\nThe floor is the sphere, the color, and the contact. The ceiling is presentation:\na soft believable shadow under the ball, a hint of glossiness that catches the\nlight, and clean framing.\n\n</task>"}, {"role": "assistant", "content": "I'll create a red sphere resting on a white ground plane with proper materials, shading, lighting, and camera setup. Let me work through this step by step.", "tool_calls": [{"id": "toolu_bdrk_01McQwtp5Bq6813NVu2gUpRG", "type": "function", "function": {"name": "get_scene_info", "arguments": "{\"user_prompt\": \"Get current scene info to see what's there initially\"}"}}]}, {"role": "tool", "tool_call_id": "toolu_bdrk_01McQwtp5Bq6813NVu2gUpRG", "content": "{\n  \"name\": \"Scene\",\n  \"object_count\": 0,\n  \"objects\": [],\n  \"materials_count\": 2\n}"}, {"role": "assistant", "content": "Good, the scene is empty. Now I'll create the red sphere and white ground plane using Blender code:", "tool_calls": [{"id": "toolu_bdrk_01PuvjJZjfHJZgTRw6jGDsdH", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\": \"\\nimport bpy\\nimport bmesh\\nfrom mathutils import Vector\\n\\n# Clear any default objects\\nbpy.ops.object.select_all(action='SELECT')\\nbpy.ops.object.delete(use_global=False)\\n\\n# Add a sphere (1 meter diameter = 0.5 meter radius)\\nbpy.ops.mesh.primitive_uv_sphere_add(\\n    radius=0.5,\\n    location=(0, 0, 0.5)  # Position it so bottom touches z=0\\n)\\nsphere = bpy.context.active_object\\nsphere.name = \\\"RedSphere\\\"\\n\\n# Add a large ground plane\\nbpy.ops.mesh.primitive_plane_add(\\n    size=10,\\n    location=(0, 0, 0)\\n)\\nplane = bpy.context.active_object\\nplane.name = \\\"GroundPlane\\\"\\n\\n# Select and scale plane to be large but thin\\nplane.scale = (10, 10, 0.1)\\n\\nprint(\\\"Objects created: Sphere at z=\\\" + str(sphere.location.z) + \\\", Plane at z=\\\" + str(plane.location.z))\\n\", \"user_prompt\": \"Create red sphere and white ground plane\"}"}}]}, {"role": "tool", "tool_call_id": "toolu_bdrk_01PuvjJZjfHJZgTRw6jGDsdH", "content": "Code executed successfully: Objects created: Sphere at z=0.5, Plane at z=0.0\n"}, {"role": "assistant", "content": "Now I'll create the materials - red for the sphere and white for the plane:", "tool_calls": [{"id": "toolu_bdrk_018gHZ2vPGV9JR5pgaWmu5aZ", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\": \"\\nimport bpy\\n\\n# Get the sphere and plane objects\\nsphere = bpy.data.objects[\\\"RedSphere\\\"]\\nplane = bpy.data.objects[\\\"GroundPlane\\\"]\\n\\n# Create red material for sphere\\nred_material = bpy.data.materials.new(name=\\\"RedMaterial\\\")\\nred_material.use_nodes = True\\nred_material.diffuse_color = (1.0, 0.0, 0.0, 1.0)  # Red RGBA\\n\\n# Configure red material with some glossiness\\nbsdf = red_material.node_tree.nodes[\\\"Principled BSDF\\\"]\\nbsdf.inputs[\\\"Base Color\\\"].default_value = (0.8, 0.05, 0.05, 1.0)  # Pure red\\nbsdf.inputs[\\\"Roughness\\\"].default_value = 0.3  # Slight glossiness\\n\\n# Create white material for plane\\nwhite_material = bpy.data.materials.new(name=\\\"WhiteMaterial\\\")\\nwhite_material.use_nodes = True\\nwhite_material.diffuse_color = (1.0, 1.0, 1.0, 1.0)  # White RGBA\\n\\nbsdf_white = white_material.node_tree.nodes[\\\"Principled BSDF\\\"]\\nbsdf_white.inputs[\\\"Base Color\\\"].default_value = (1.0, 1.0, 1.0, 1.0)  # White\\nbsdf_white.inputs[\\\"Roughness\\\"].default_value = 0.8  # Matte white\\n\\n# Assign materials to objects\\nsphere.data.materials.append(red_material)\\nplane.data.materials.append(white_material)\\n\\nprint(\\\"Materials created and assigned\\\")\\n\", \"user_prompt\": \"Create and assign red and white materials\"}"}}]}, {"role": "tool", "tool_call_id": "toolu_bdrk_018gHZ2vPGV9JR5pgaWmu5aZ", "content": "Code executed successfully: Materials created and assigned\n"}, {"role": "assistant", "content": "Now I'll apply smooth shading to both objects:", "tool_calls": [{"id": "toolu_bdrk_01WSSDEGVk2FHv51RevTiES7", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\": \"\\nimport bpy\\n\\nsphere = bpy.data.objects[\\\"RedSphere\\\"]\\nplane = bpy.data.objects[\\\"GroundPlane\\\"]\\n\\n# Select sphere and apply smooth shading\\nsphere.select_set(True)\\nbpy.context.view_layer.objects.active = sphere\\nbpy.ops.object.shade_smooth()\\n\\n# Select plane and apply smooth shading\\nplane.select_set(True)\\nbpy.context.view_layer.objects.active = plane\\nbpy.ops.object.shade_smooth()\\n\\nprint(\\\"Smooth shading applied to both objects\\\")\\n\", \"user_prompt\": \"Apply smooth shading to sphere and plane\"}"}}]}, {"role": "tool", "tool_call_id": "toolu_bdrk_01WSSDEGVk2FHv51RevTiES7", "content": "Code executed successfully: Smooth shading applied to both objects\n"}, {"role": "assistant", "content": "Now I need to set up the lighting and camera for a good presentation. I'll add a key light, fill light, and set up the camera:", "tool_calls": [{"id": "toolu_bdrk_01AMvEaBThonE94HvaJjwfZk", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\": \"\\nimport bpy\\nfrom mathutils import Vector\\n\\n# Add a key light (three-quarter lighting)\\nbpy.ops.object.light_add(\\n    type='SUN',\\n    location=(3, 4, 5)\\n)\\nkey_light = bpy.context.active_object\\nkey_light.name = \\\"KeyLight\\\"\\nkey_light.data.energy = 2.0\\nkey_light.data.angle = 0.5  # Slight softness\\nbpy.context.view_layer.objects.active = key_light\\nkey_light.select_set(True)\\n\\n# Add a fill light (softer light from the other side)\\nbpy.ops.object.light_add(\\n    type='SUN',\\n    location=(-2, -3, 3)\\n)\\nfill_light = bpy.context.active_object\\nfill_light.name = \\\"FillLight\\\"\\nfill_light.data.energy = 0.6  # Much softer\\nfill_light.data.angle = 2.0  # Very soft\\nbpy.context.view_layer.objects.active = fill_light\\nfill_light.select_set(True)\\n\\n# Add an area light positioned slightly to the side and above for catch lights\\nbpy.ops.object.light_add(\\n    type='AREA',\\n    location=(2, -3, 4)\\n)\\ncatch_light = bpy.context.active_object\\ncatch_light.name = \\\"CatchLight\\\"\\ncatch_light.data.energy = 0.8\\ncatch_light.data.size = 3\\nbpy.context.view_layer.objects.active = catch_light\\ncatch_light.select_set(True)\\n\\nprint(\\\"Lights created: Key, Fill, and Catch lights\\\")\\n\", \"user_prompt\": \"Add lighting setup for presentation\"}"}}]}, {"role": "tool", "tool_call_id": "toolu_bdrk_01AMvEaBThonE94HvaJjwfZk", "content": "Code executed successfully: Lights created: Key, Fill, and Catch lights\n"}, {"role": "assistant", "content": "Now I'll set up the camera for a clean, well-composed view:", "tool_calls": [{"id": "toolu_bdrk_013cajKLXaCWr4wzNHHwZfby", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\": \"\\nimport bpy\\nfrom mathutils import Vector, Matrix\\n\\n# Add a camera\\nbpy.ops.object.camera_add(\\n    location=(3.5, -3, 2.5)\\n)\\ncamera = bpy.context.active_object\\ncamera.name = \\\"Camera\\\"\\n\\n# Point camera at the sphere (origin area)\\ndirection = Vector((0, 0, 0.3)) - Vector(camera.location)\\nrot_quat = direction.to_track_quat('-Z', 'Y')\\ncamera.rotation_euler = rot_quat.to_euler()\\n\\n# Set as active camera\\nbpy.context.scene.camera = camera\\n\\n# Adjust camera settings for a nice view\\ncamera.data.lens = 50  # Standard lens\\ncamera.data.sensor_width = 36\\n\\nprint(\\\"Camera set up at position:\\\", camera.location)\\nprint(\\\"Camera rotation euler:\\\", camera.rotation_euler)\\n\", \"user_prompt\": \"Set up camera for presentation\"}"}}]}, {"role": "tool", "tool_call_id": "toolu_bdrk_013cajKLXaCWr4wzNHHwZfby", "content": "Code executed successfully: Camera set up at position: <Vector (3.5000, -3.0000, 2.5000)>\nCamera rotation euler: <Euler (x=1.1255, y=0.0000, z=0.8622), order='XYZ'>\n"}], "tools": [{"type": "function", "function": {"name": "get_scene_info", "description": "Get detailed information about the current Blender scene\n\n    Parameters:\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\n    ", "parameters": {"properties": {"user_prompt": {"title": "User Prompt", "type": "string"}}, "required": ["user_prompt"], "title": "get_scene_infoArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_object_info", "description": "\n    Get detailed information about a specific object in the Blender scene.\n\n    Parameters:\n    - object_name: The name of the object to get information about\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"object_name": {"title": "Object Name", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["object_name"], "title": "get_object_infoArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_viewport_screenshot", "description": "\n    Capture a screenshot of the current Blender 3D viewport.\n\n    Parameters:\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns the screenshot as an Image.\n    ", "parameters": {"properties": {"max_size": {"default": 1000, "title": "Max Size", "type": "integer"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_viewport_screenshotArguments", "type": "object"}}}, {"type": "function", "function": {"name": "execute_blender_code", "description": "\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\n\n    Parameters:\n    - code: The Python code to execute\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"code": {"title": "Code", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["code"], "title": "execute_blender_codeArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_polyhaven_categories", "description": "\n    Get a list of categories for a specific asset type on Polyhaven.\n\n    Parameters:\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"asset_type": {"default": "hdris", "title": "Asset Type", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_polyhaven_categoriesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_polyhaven_assets", "description": "\n    Search for assets on Polyhaven with optional filtering.\n\n    Parameters:\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\n    - categories: Optional comma-separated list of categories to filter by\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a list of matching assets with basic information.\n    ", "parameters": {"properties": {"asset_type": {"default": "all", "title": "Asset Type", "type": "string"}, "categories": {"default": null, "title": "Categories", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "search_polyhaven_assetsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "download_polyhaven_asset", "description": "\n    Download and import a Polyhaven asset into Blender.\n\n    Parameters:\n    - asset_id: The ID of the asset to download\n    - asset_type: The type of asset (hdris, textures, models)\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"asset_id": {"title": "Asset Id", "type": "string"}, "asset_type": {"title": "Asset Type", "type": "string"}, "resolution": {"default": "1k", "title": "Resolution", "type": "string"}, "file_format": {"default": null, "title": "File Format", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["asset_id", "asset_type"], "title": "download_polyhaven_assetArguments", "type": "object"}}}, {"type": "function", "function": {"name": "set_texture", "description": "\n    Apply a previously downloaded Polyhaven texture to an object.\n    \n    Parameters:\n    - object_name: Name of the object to apply the texture to\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\n    \n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"object_name": {"title": "Object Name", "type": "string"}, "texture_id": {"title": "Texture Id", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["object_name", "texture_id"], "title": "set_textureArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_polyhaven_status", "description": "\n    Check if PolyHaven integration is enabled in Blender.\n    Returns a message indicating whether PolyHaven features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_polyhaven_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_hyper3d_status", "description": "\n    Check if Hyper3D Rodin integration is enabled in Blender.\n    Returns a message indicating whether Hyper3D Rodin features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_hyper3d_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_sketchfab_status", "description": "\n    Check if Sketchfab integration is enabled in Blender.\n    Returns a message indicating whether Sketchfab features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_sketchfab_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_sketchfab_models", "description": "\n    Search for models on Sketchfab with optional filtering.\n\n    Parameters:\n    - query: Text to search for\n    - categories: Optional comma-separated list of categories\n    - count: Maximum number of results to return (default 20)\n    - downloadable: Whether to include only downloadable models (default True)\n\n    Returns a formatted list of matching models.\n    ", "parameters": {"properties": {"query": {"title": "Query", "type": "string"}, "categories": {"default": null, "title": "Categories", "type": "string"}, "count": {"default": 20, "title": "Count", "type": "integer"}, "downloadable": {"default": true, "title": "Downloadable", "type": "boolean"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["query"], "title": "search_sketchfab_modelsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_sketchfab_model_preview", "description": "\n    Get a preview thumbnail of a Sketchfab model by its UID.\n    Use this to visually confirm a model before downloading.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\n    \n    Returns the model's thumbnail as an Image for visual confirmation.\n    ", "parameters": {"properties": {"uid": {"title": "Uid", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["uid"], "title": "get_sketchfab_model_previewArguments", "type": "object"}}}, {"type": "function", "function": {"name": "download_sketchfab_model", "description": "\n    Download and import a Sketchfab model by its UID.\n    The model will be scaled so its largest dimension equals target_size.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\n                  You must specify the desired size for the model.\n                  Examples:\n                  - Chair: target_size=1.0 (1 meter tall)\n                  - Table: target_size=0.75 (75cm tall)\n                  - Car: target_size=4.5 (4.5 meters long)\n                  - Person: target_size=1.7 (1.7 meters tall)\n                  - Small object (cup, phone): target_size=0.1 to 0.3\n    \n    Returns a message with import details including object names, dimensions, and bounding box.\n    The model must be downloadable and you must have proper access rights.\n    ", "parameters": {"properties": {"uid": {"title": "Uid", "type": "string"}, "target_size": {"title": "Target Size", "type": "number"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["uid", "target_size"], "title": "download_sketchfab_modelArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hyper3d_model_via_text", "description": "\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n\n    Parameters:\n    - text_prompt: A short description of the desired model in **English**.\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"text_prompt": {"title": "Text Prompt", "type": "string"}, "bbox_condition": {"default": null, "items": {"type": "number"}, "title": "Bbox Condition", "type": "array"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["text_prompt"], "title": "generate_hyper3d_model_via_textArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hyper3d_model_via_images", "description": "\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n    \n    Parameters:\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode.\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"input_image_paths": {"default": null, "items": {"type": "string"}, "title": "Input Image Paths", "type": "array"}, "input_image_urls": {"default": null, "items": {"type": "string"}, "title": "Input Image Urls", "type": "array"}, "bbox_condition": {"default": null, "items": {"type": "number"}, "title": "Bbox Condition", "type": "array"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "generate_hyper3d_model_via_imagesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "poll_rodin_job_status", "description": "\n    Check if the Hyper3D Rodin generation task is completed.\n\n    For Hyper3D Rodin mode MAIN_SITE:\n        Parameters:\n        - subscription_key: The subscription_key given in the generate model step.\n\n        Returns a list of status. The task is done if all status are \"Done\".\n        If \"Failed\" showed up, the generating process failed.\n        This is a polling API, so only proceed if the status are finally determined (\"Done\" or \"Canceled\").\n\n    For Hyper3D Rodin mode FAL_AI:\n        Parameters:\n        - request_id: The request_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"COMPLETED\".\n        The task is in progress if status is \"IN_PROGRESS\".\n        If status other than \"COMPLETED\", \"IN_PROGRESS\", \"IN_QUEUE\" showed up, the generating process might be failed.\n        This is a polling API, so only proceed if the status are finally determined (\"COMPLETED\" or some failed state).\n    ", "parameters": {"properties": {"subscription_key": {"default": null, "title": "Subscription Key", "type": "string"}, "request_id": {"default": null, "title": "Request Id", "type": "string"}}, "title": "poll_rodin_job_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "import_generated_asset", "description": "\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\n\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\n    Return if the asset has been imported successfully.\n    ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "task_uuid": {"default": null, "title": "Task Uuid", "type": "string"}, "request_id": {"default": null, "title": "Request Id", "type": "string"}}, "required": ["name"], "title": "import_generated_assetArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_hunyuan3d_status", "description": "\n    Check if Hunyuan3D integration is enabled in Blender.\n    Returns a message indicating whether Hunyuan3D features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_hunyuan3d_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hunyuan3d_model", "description": "\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \n    or both for the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    \n    Parameters:\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\n    - input_image_url: (Optional) The local or remote url of the input image. Accepts None if only using text prompt.\n\n    Returns: \n    - When successful, returns a JSON with job_id (format: \"job_xxx\") indicating the task is in progress\n    - When the job completes, the status will change to \"DONE\" indicating the model has been imported\n    - Returns error message if the operation fails\n    ", "parameters": {"properties": {"text_prompt": {"default": null, "title": "Text Prompt", "type": "string"}, "input_image_url": {"default": null, "title": "Input Image Url", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "generate_hunyuan3d_modelArguments", "type": "object"}}}, {"type": "function", "function": {"name": "poll_hunyuan_job_status", "description": "\n    Check if the Hunyuan3D generation task is completed.\n\n    For Hunyuan3D:\n        Parameters:\n        - job_id: The job_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"DONE\".\n        The task is in progress if status is \"RUN\".\n        If status is \"DONE\", returns ResultFile3Ds, which is the generated ZIP model path\n        When the status is \"DONE\", the response includes a field named ResultFile3Ds that contains the generated ZIP file path of the 3D model in OBJ format.\n        This is a polling API, so only proceed if the status are finally determined (\"DONE\" or some failed state).\n    ", "parameters": {"properties": {"job_id": {"default": null, "title": "Job Id", "type": "string"}}, "title": "poll_hunyuan_job_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "import_generated_asset_hunyuan", "description": "\n    Import the asset generated by Hunyuan3D after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - zip_file_url: The zip_file_url given in the generate model step.\n\n    Return if the asset has been imported successfully.\n    ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "zip_file_url": {"title": "Zip File Url", "type": "string"}}, "required": ["name", "zip_file_url"], "title": "import_generated_asset_hunyuanArguments", "type": "object"}}}]}, "provider_attempts": [{"provider": "openrouter", "attempt": 1, "started_at": "2026-07-15T00:49:04.672330+00:00", "finished_at": "2026-07-15T00:49:07.021384+00:00", "duration_s": 2.3440000000118744, "request": {"method": "POST", "url": "https://openrouter.ai/api/v1/chat/completions", "headers": {"host": "openrouter.ai", "accept": "*/*", "accept-encoding": "gzip, deflate", "connection": "keep-alive", "user-agent": "python-httpx/0.28.1", "authorization": "[REDACTED]", "content-length": "26220", "content-type": "application/json"}, "body": "{\"model\":\"anthropic/claude-haiku-4.5\",\"messages\":[{\"role\":\"system\",\"content\":[{\"type\":\"text\",\"text\":\"You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\\n\\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\\n\\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\\n\\n## Harness Instructions\\n\\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. When you are finished, reply with the single word DONE and no tool calls.\",\"cache_control\":{\"type\":\"ephemeral\"}}]},{\"role\":\"user\",\"content\":\"## Task\\n\\nComplete the following Blender task:\\n\\n<task>\\n\\nA red sphere of 1 meter diameter resting on a white ground plane — touching it\\nexactly, neither floating nor sunk. Smooth shading, a plain red material on the\\nball, plain white on the floor.\\n\\nThe floor is the sphere, the color, and the contact. The ceiling is presentation:\\na soft believable shadow under the ball, a hint of glossiness that catches the\\nlight, and clean framing.\\n\\n</task>\"},{\"role\":\"assistant\",\"content\":\"I'll create a red sphere resting on a white ground plane with proper materials, shading, lighting, and camera setup. Let me work through this step by step.\",\"tool_calls\":[{\"id\":\"toolu_bdrk_01McQwtp5Bq6813NVu2gUpRG\",\"type\":\"function\",\"function\":{\"name\":\"get_scene_info\",\"arguments\":\"{\\\"user_prompt\\\": \\\"Get current scene info to see what's there initially\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"toolu_bdrk_01McQwtp5Bq6813NVu2gUpRG\",\"content\":\"{\\n  \\\"name\\\": \\\"Scene\\\",\\n  \\\"object_count\\\": 0,\\n  \\\"objects\\\": [],\\n  \\\"materials_count\\\": 2\\n}\"},{\"role\":\"assistant\",\"content\":\"Good, the scene is empty. Now I'll create the red sphere and white ground plane using Blender code:\",\"tool_calls\":[{\"id\":\"toolu_bdrk_01PuvjJZjfHJZgTRw6jGDsdH\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\": \\\"\\\\nimport bpy\\\\nimport bmesh\\\\nfrom mathutils import Vector\\\\n\\\\n# Clear any default objects\\\\nbpy.ops.object.select_all(action='SELECT')\\\\nbpy.ops.object.delete(use_global=False)\\\\n\\\\n# Add a sphere (1 meter diameter = 0.5 meter radius)\\\\nbpy.ops.mesh.primitive_uv_sphere_add(\\\\n    radius=0.5,\\\\n    location=(0, 0, 0.5)  # Position it so bottom touches z=0\\\\n)\\\\nsphere = bpy.context.active_object\\\\nsphere.name = \\\\\\\"RedSphere\\\\\\\"\\\\n\\\\n# Add a large ground plane\\\\nbpy.ops.mesh.primitive_plane_add(\\\\n    size=10,\\\\n    location=(0, 0, 0)\\\\n)\\\\nplane = bpy.context.active_object\\\\nplane.name = \\\\\\\"GroundPlane\\\\\\\"\\\\n\\\\n# Select and scale plane to be large but thin\\\\nplane.scale = (10, 10, 0.1)\\\\n\\\\nprint(\\\\\\\"Objects created: Sphere at z=\\\\\\\" + str(sphere.location.z) + \\\\\\\", Plane at z=\\\\\\\" + str(plane.location.z))\\\\n\\\", \\\"user_prompt\\\": \\\"Create red sphere and white ground plane\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"toolu_bdrk_01PuvjJZjfHJZgTRw6jGDsdH\",\"content\":\"Code executed successfully: Objects created: Sphere at z=0.5, Plane at z=0.0\\n\"},{\"role\":\"assistant\",\"content\":\"Now I'll create the materials - red for the sphere and white for the plane:\",\"tool_calls\":[{\"id\":\"toolu_bdrk_018gHZ2vPGV9JR5pgaWmu5aZ\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\": \\\"\\\\nimport bpy\\\\n\\\\n# Get the sphere and plane objects\\\\nsphere = bpy.data.objects[\\\\\\\"RedSphere\\\\\\\"]\\\\nplane = bpy.data.objects[\\\\\\\"GroundPlane\\\\\\\"]\\\\n\\\\n# Create red material for sphere\\\\nred_material = bpy.data.materials.new(name=\\\\\\\"RedMaterial\\\\\\\")\\\\nred_material.use_nodes = True\\\\nred_material.diffuse_color = (1.0, 0.0, 0.0, 1.0)  # Red RGBA\\\\n\\\\n# Configure red material with some glossiness\\\\nbsdf = red_material.node_tree.nodes[\\\\\\\"Principled BSDF\\\\\\\"]\\\\nbsdf.inputs[\\\\\\\"Base Color\\\\\\\"].default_value = (0.8, 0.05, 0.05, 1.0)  # Pure red\\\\nbsdf.inputs[\\\\\\\"Roughness\\\\\\\"].default_value = 0.3  # Slight glossiness\\\\n\\\\n# Create white material for plane\\\\nwhite_material = bpy.data.materials.new(name=\\\\\\\"WhiteMaterial\\\\\\\")\\\\nwhite_material.use_nodes = True\\\\nwhite_material.diffuse_color = (1.0, 1.0, 1.0, 1.0)  # White RGBA\\\\n\\\\nbsdf_white = white_material.node_tree.nodes[\\\\\\\"Principled BSDF\\\\\\\"]\\\\nbsdf_white.inputs[\\\\\\\"Base Color\\\\\\\"].default_value = (1.0, 1.0, 1.0, 1.0)  # White\\\\nbsdf_white.inputs[\\\\\\\"Roughness\\\\\\\"].default_value = 0.8  # Matte white\\\\n\\\\n# Assign materials to objects\\\\nsphere.data.materials.append(red_material)\\\\nplane.data.materials.append(white_material)\\\\n\\\\nprint(\\\\\\\"Materials created and assigned\\\\\\\")\\\\n\\\", \\\"user_prompt\\\": \\\"Create and assign red and white materials\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"toolu_bdrk_018gHZ2vPGV9JR5pgaWmu5aZ\",\"content\":\"Code executed successfully: Materials created and assigned\\n\"},{\"role\":\"assistant\",\"content\":\"Now I'll apply smooth shading to both objects:\",\"tool_calls\":[{\"id\":\"toolu_bdrk_01WSSDEGVk2FHv51RevTiES7\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\": \\\"\\\\nimport bpy\\\\n\\\\nsphere = bpy.data.objects[\\\\\\\"RedSphere\\\\\\\"]\\\\nplane = bpy.data.objects[\\\\\\\"GroundPlane\\\\\\\"]\\\\n\\\\n# Select sphere and apply smooth shading\\\\nsphere.select_set(True)\\\\nbpy.context.view_layer.objects.active = sphere\\\\nbpy.ops.object.shade_smooth()\\\\n\\\\n# Select plane and apply smooth shading\\\\nplane.select_set(True)\\\\nbpy.context.view_layer.objects.active = plane\\\\nbpy.ops.object.shade_smooth()\\\\n\\\\nprint(\\\\\\\"Smooth shading applied to both objects\\\\\\\")\\\\n\\\", \\\"user_prompt\\\": \\\"Apply smooth shading to sphere and plane\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"toolu_bdrk_01WSSDEGVk2FHv51RevTiES7\",\"content\":\"Code executed successfully: Smooth shading applied to both objects\\n\"},{\"role\":\"assistant\",\"content\":\"Now I need to set up the lighting and camera for a good presentation. I'll add a key light, fill light, and set up the camera:\",\"tool_calls\":[{\"id\":\"toolu_bdrk_01AMvEaBThonE94HvaJjwfZk\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\": \\\"\\\\nimport bpy\\\\nfrom mathutils import Vector\\\\n\\\\n# Add a key light (three-quarter lighting)\\\\nbpy.ops.object.light_add(\\\\n    type='SUN',\\\\n    location=(3, 4, 5)\\\\n)\\\\nkey_light = bpy.context.active_object\\\\nkey_light.name = \\\\\\\"KeyLight\\\\\\\"\\\\nkey_light.data.energy = 2.0\\\\nkey_light.data.angle = 0.5  # Slight softness\\\\nbpy.context.view_layer.objects.active = key_light\\\\nkey_light.select_set(True)\\\\n\\\\n# Add a fill light (softer light from the other side)\\\\nbpy.ops.object.light_add(\\\\n    type='SUN',\\\\n    location=(-2, -3, 3)\\\\n)\\\\nfill_light = bpy.context.active_object\\\\nfill_light.name = \\\\\\\"FillLight\\\\\\\"\\\\nfill_light.data.energy = 0.6  # Much softer\\\\nfill_light.data.angle = 2.0  # Very soft\\\\nbpy.context.view_layer.objects.active = fill_light\\\\nfill_light.select_set(True)\\\\n\\\\n# Add an area light positioned slightly to the side and above for catch lights\\\\nbpy.ops.object.light_add(\\\\n    type='AREA',\\\\n    location=(2, -3, 4)\\\\n)\\\\ncatch_light = bpy.context.active_object\\\\ncatch_light.name = \\\\\\\"CatchLight\\\\\\\"\\\\ncatch_light.data.energy = 0.8\\\\ncatch_light.data.size = 3\\\\nbpy.context.view_layer.objects.active = catch_light\\\\ncatch_light.select_set(True)\\\\n\\\\nprint(\\\\\\\"Lights created: Key, Fill, and Catch lights\\\\\\\")\\\\n\\\", \\\"user_prompt\\\": \\\"Add lighting setup for presentation\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"toolu_bdrk_01AMvEaBThonE94HvaJjwfZk\",\"content\":\"Code executed successfully: Lights created: Key, Fill, and Catch lights\\n\"},{\"role\":\"assistant\",\"content\":\"Now I'll set up the camera for a clean, well-composed view:\",\"tool_calls\":[{\"id\":\"toolu_bdrk_013cajKLXaCWr4wzNHHwZfby\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\": \\\"\\\\nimport bpy\\\\nfrom mathutils import Vector, Matrix\\\\n\\\\n# Add a camera\\\\nbpy.ops.object.camera_add(\\\\n    location=(3.5, -3, 2.5)\\\\n)\\\\ncamera = bpy.context.active_object\\\\ncamera.name = \\\\\\\"Camera\\\\\\\"\\\\n\\\\n# Point camera at the sphere (origin area)\\\\ndirection = Vector((0, 0, 0.3)) - Vector(camera.location)\\\\nrot_quat = direction.to_track_quat('-Z', 'Y')\\\\ncamera.rotation_euler = rot_quat.to_euler()\\\\n\\\\n# Set as active camera\\\\nbpy.context.scene.camera = camera\\\\n\\\\n# Adjust camera settings for a nice view\\\\ncamera.data.lens = 50  # Standard lens\\\\ncamera.data.sensor_width = 36\\\\n\\\\nprint(\\\\\\\"Camera set up at position:\\\\\\\", camera.location)\\\\nprint(\\\\\\\"Camera rotation euler:\\\\\\\", camera.rotation_euler)\\\\n\\\", \\\"user_prompt\\\": \\\"Set up camera for presentation\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"toolu_bdrk_013cajKLXaCWr4wzNHHwZfby\",\"content\":[{\"type\":\"text\",\"text\":\"Code executed successfully: Camera set up at position: <Vector (3.5000, -3.0000, 2.5000)>\\nCamera rotation euler: <Euler (x=1.1255, y=0.0000, z=0.8622), order='XYZ'>\\n\",\"cache_control\":{\"type\":\"ephemeral\"}}]}],\"usage\":{\"include\":true},\"tools\":[{\"type\":\"function\",\"function\":{\"name\":\"get_scene_info\",\"description\":\"Get detailed information about the current Blender scene\\n\\n    Parameters:\\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"user_prompt\"],\"title\":\"get_scene_infoArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_object_info\",\"description\":\"\\n    Get detailed information about a specific object in the Blender scene.\\n\\n    Parameters:\\n    - object_name: The name of the object to get information about\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"object_name\":{\"title\":\"Object Name\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"object_name\"],\"title\":\"get_object_infoArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_viewport_screenshot\",\"description\":\"\\n    Capture a screenshot of the current Blender 3D viewport.\\n\\n    Parameters:\\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns the screenshot as an Image.\\n    \",\"parameters\":{\"properties\":{\"max_size\":{\"default\":1000,\"title\":\"Max Size\",\"type\":\"integer\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_viewport_screenshotArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"description\":\"\\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\\n\\n    Parameters:\\n    - code: The Python code to execute\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"code\":{\"title\":\"Code\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"code\"],\"title\":\"execute_blender_codeArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_categories\",\"description\":\"\\n    Get a list of categories for a specific asset type on Polyhaven.\\n\\n    Parameters:\\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"asset_type\":{\"default\":\"hdris\",\"title\":\"Asset Type\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_polyhaven_categoriesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_polyhaven_assets\",\"description\":\"\\n    Search for assets on Polyhaven with optional filtering.\\n\\n    Parameters:\\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\\n    - categories: Optional comma-separated list of categories to filter by\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns a list of matching assets with basic information.\\n    \",\"parameters\":{\"properties\":{\"asset_type\":{\"default\":\"all\",\"title\":\"Asset Type\",\"type\":\"string\"},\"categories\":{\"default\":null,\"title\":\"Categories\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"search_polyhaven_assetsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"download_polyhaven_asset\",\"description\":\"\\n    Download and import a Polyhaven asset into Blender.\\n\\n    Parameters:\\n    - asset_id: The ID of the asset to download\\n    - asset_type: The type of asset (hdris, textures, models)\\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"asset_id\":{\"title\":\"Asset Id\",\"type\":\"string\"},\"asset_type\":{\"title\":\"Asset Type\",\"type\":\"string\"},\"resolution\":{\"default\":\"1k\",\"title\":\"Resolution\",\"type\":\"string\"},\"file_format\":{\"default\":null,\"title\":\"File Format\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"asset_id\",\"asset_type\"],\"title\":\"download_polyhaven_assetArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"set_texture\",\"description\":\"\\n    Apply a previously downloaded Polyhaven texture to an object.\\n    \\n    Parameters:\\n    - object_name: Name of the object to apply the texture to\\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\\n    \\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"object_name\":{\"title\":\"Object Name\",\"type\":\"string\"},\"texture_id\":{\"title\":\"Texture Id\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"object_name\",\"texture_id\"],\"title\":\"set_textureArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_status\",\"description\":\"\\n    Check if PolyHaven integration is enabled in Blender.\\n    Returns a message indicating whether PolyHaven features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_polyhaven_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_hyper3d_status\",\"description\":\"\\n    Check if Hyper3D Rodin integration is enabled in Blender.\\n    Returns a message indicating whether Hyper3D Rodin features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_hyper3d_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_status\",\"description\":\"\\n    Check if Sketchfab integration is enabled in Blender.\\n    Returns a message indicating whether Sketchfab features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_sketchfab_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_sketchfab_models\",\"description\":\"\\n    Search for models on Sketchfab with optional filtering.\\n\\n    Parameters:\\n    - query: Text to search for\\n    - categories: Optional comma-separated list of categories\\n    - count: Maximum number of results to return (default 20)\\n    - downloadable: Whether to include only downloadable models (default True)\\n\\n    Returns a formatted list of matching models.\\n    \",\"parameters\":{\"properties\":{\"query\":{\"title\":\"Query\",\"type\":\"string\"},\"categories\":{\"default\":null,\"title\":\"Categories\",\"type\":\"string\"},\"count\":{\"default\":20,\"title\":\"Count\",\"type\":\"integer\"},\"downloadable\":{\"default\":true,\"title\":\"Downloadable\",\"type\":\"boolean\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"query\"],\"title\":\"search_sketchfab_modelsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_model_preview\",\"description\":\"\\n    Get a preview thumbnail of a Sketchfab model by its UID.\\n    Use this to visually confirm a model before downloading.\\n    \\n    Parameters:\\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\\n    \\n    Returns the model's thumbnail as an Image for visual confirmation.\\n    \",\"parameters\":{\"properties\":{\"uid\":{\"title\":\"Uid\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"uid\"],\"title\":\"get_sketchfab_model_previewArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"download_sketchfab_model\",\"description\":\"\\n    Download and import a Sketchfab model by its UID.\\n    The model will be scaled so its largest dimension equals target_size.\\n    \\n    Parameters:\\n    - uid: The unique identifier of the Sketchfab model\\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\\n                  You must specify the desired size for the model.\\n                  Examples:\\n                  - Chair: target_size=1.0 (1 meter tall)\\n                  - Table: target_size=0.75 (75cm tall)\\n                  - Car: target_size=4.5 (4.5 meters long)\\n                  - Person: target_size=1.7 (1.7 meters tall)\\n                  - Small object (cup, phone): target_size=0.1 to 0.3\\n    \\n    Returns a message with import details including object names, dimensions, and bounding box.\\n    The model must be downloadable and you must have proper access rights.\\n    \",\"parameters\":{\"properties\":{\"uid\":{\"title\":\"Uid\",\"type\":\"string\"},\"target_size\":{\"title\":\"Target Size\",\"type\":\"number\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"uid\",\"target_size\"],\"title\":\"download_sketchfab_modelArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hyper3d_model_via_text\",\"description\":\"\\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\\n    The 3D asset has built-in materials.\\n    The generated model has a normalized size, so re-scaling after generation can be useful.\\n\\n    Parameters:\\n    - text_prompt: A short description of the desired model in **English**.\\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\\n\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"text_prompt\":{\"title\":\"Text Prompt\",\"type\":\"string\"},\"bbox_condition\":{\"default\":null,\"items\":{\"type\":\"number\"},\"title\":\"Bbox Condition\",\"type\":\"array\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"text_prompt\"],\"title\":\"generate_hyper3d_model_via_textArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hyper3d_model_via_images\",\"description\":\"\\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\\n    The 3D asset has built-in materials.\\n    The generated model has a normalized size, so re-scaling after generation can be useful.\\n    \\n    Parameters:\\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model.\\n\\n    Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode.\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"input_image_paths\":{\"default\":null,\"items\":{\"type\":\"string\"},\"title\":\"Input Image Paths\",\"type\":\"array\"},\"input_image_urls\":{\"default\":null,\"items\":{\"type\":\"string\"},\"title\":\"Input Image Urls\",\"type\":\"array\"},\"bbox_condition\":{\"default\":null,\"items\":{\"type\":\"number\"},\"title\":\"Bbox Condition\",\"type\":\"array\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"generate_hyper3d_model_via_imagesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"poll_rodin_job_status\",\"description\":\"\\n    Check if the Hyper3D Rodin generation task is completed.\\n\\n    For Hyper3D Rodin mode MAIN_SITE:\\n        Parameters:\\n        - subscription_key: The subscription_key given in the generate model step.\\n\\n        Returns a list of status. The task is done if all status are \\\"Done\\\".\\n        If \\\"Failed\\\" showed up, the generating process failed.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"Done\\\" or \\\"Canceled\\\").\\n\\n    For Hyper3D Rodin mode FAL_AI:\\n        Parameters:\\n        - request_id: The request_id given in the generate model step.\\n\\n        Returns the generation task status. The task is done if status is \\\"COMPLETED\\\".\\n        The task is in progress if status is \\\"IN_PROGRESS\\\".\\n        If status other than \\\"COMPLETED\\\", \\\"IN_PROGRESS\\\", \\\"IN_QUEUE\\\" showed up, the generating process might be failed.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"COMPLETED\\\" or some failed state).\\n    \",\"parameters\":{\"properties\":{\"subscription_key\":{\"default\":null,\"title\":\"Subscription Key\",\"type\":\"string\"},\"request_id\":{\"default\":null,\"title\":\"Request Id\",\"type\":\"string\"}},\"title\":\"poll_rodin_job_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"import_generated_asset\",\"description\":\"\\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\\n\\n    Parameters:\\n    - name: The name of the object in scene\\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\\n\\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\\n    Return if the asset has been imported successfully.\\n    \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"task_uuid\":{\"default\":null,\"title\":\"Task Uuid\",\"type\":\"string\"},\"request_id\":{\"default\":null,\"title\":\"Request Id\",\"type\":\"string\"}},\"required\":[\"name\"],\"title\":\"import_generated_assetArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_hunyuan3d_status\",\"description\":\"\\n    Check if Hunyuan3D integration is enabled in Blender.\\n    Returns a message indicating whether Hunyuan3D features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_hunyuan3d_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hunyuan3d_model\",\"description\":\"\\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \\n    or both for the desired asset, and import the asset into Blender.\\n    The 3D asset has built-in materials.\\n    \\n    Parameters:\\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\\n    - input_image_url: (Optional) The local or remote url of the input image. Accepts None if only using text prompt.\\n\\n    Returns: \\n    - When successful, returns a JSON with job_id (format: \\\"job_xxx\\\") indicating the task is in progress\\n    - When the job completes, the status will change to \\\"DONE\\\" indicating the model has been imported\\n    - Returns error message if the operation fails\\n    \",\"parameters\":{\"properties\":{\"text_prompt\":{\"default\":null,\"title\":\"Text Prompt\",\"type\":\"string\"},\"input_image_url\":{\"default\":null,\"title\":\"Input Image Url\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"generate_hunyuan3d_modelArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"poll_hunyuan_job_status\",\"description\":\"\\n    Check if the Hunyuan3D generation task is completed.\\n\\n    For Hunyuan3D:\\n        Parameters:\\n        - job_id: The job_id given in the generate model step.\\n\\n        Returns the generation task status. The task is done if status is \\\"DONE\\\".\\n        The task is in progress if status is \\\"RUN\\\".\\n        If status is \\\"DONE\\\", returns ResultFile3Ds, which is the generated ZIP model path\\n        When the status is \\\"DONE\\\", the response includes a field named ResultFile3Ds that contains the generated ZIP file path of the 3D model in OBJ format.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"DONE\\\" or some failed state).\\n    \",\"parameters\":{\"properties\":{\"job_id\":{\"default\":null,\"title\":\"Job Id\",\"type\":\"string\"}},\"title\":\"poll_hunyuan_job_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"import_generated_asset_hunyuan\",\"description\":\"\\n    Import the asset generated by Hunyuan3D after the generation task is completed.\\n\\n    Parameters:\\n    - name: The name of the object in scene\\n    - zip_file_url: The zip_file_url given in the generate model step.\\n\\n    Return if the asset has been imported successfully.\\n    \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"zip_file_url\":{\"title\":\"Zip File Url\",\"type\":\"string\"}},\"required\":[\"name\",\"zip_file_url\"],\"title\":\"import_generated_asset_hunyuanArguments\",\"type\":\"object\"}}}],\"reasoning\":{\"effort\":\"xhigh\"}}", "body_base64": "{"model":"anthropic/claude-haiku-4.5","messages":[{"role":"system","content":[{"type":"text","text":"You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\n\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\n\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\n\n## Harness Instructions\n\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. When you are finished, reply with the single word DONE and no tool calls.","cache_control":{"type":"ephemeral"}}]},{"role":"user","content":"## Task\n\nComplete the following Blender task:\n\n<task>\n\nA red sphere of 1 meter diameter resting on a white ground plane — touching it\nexactly, neither floating nor sunk. Smooth shading, a plain red material on the\nball, plain white on the floor.\n\nThe floor is the sphere, the color, and the contact. The ceiling is presentation:\na soft believable shadow under the ball, a hint of glossiness that catches the\nlight, and clean framing.\n\n</task>"},{"role":"assistant","content":"I'll create a red sphere resting on a white ground plane with proper materials, shading, lighting, and camera setup. Let me work through this step by step.","tool_calls":[{"id":"toolu_bdrk_01McQwtp5Bq6813NVu2gUpRG","type":"function","function":{"name":"get_scene_info","arguments":"{\"user_prompt\": \"Get current scene info to see what's there initially\"}"}}]},{"role":"tool","tool_call_id":"toolu_bdrk_01McQwtp5Bq6813NVu2gUpRG","content":"{\n  \"name\": \"Scene\",\n  \"object_count\": 0,\n  \"objects\": [],\n  \"materials_count\": 2\n}"},{"role":"assistant","content":"Good, the scene is empty. Now I'll create the red sphere and white ground plane using Blender code:","tool_calls":[{"id":"toolu_bdrk_01PuvjJZjfHJZgTRw6jGDsdH","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\": \"\\nimport bpy\\nimport bmesh\\nfrom mathutils import Vector\\n\\n# Clear any default objects\\nbpy.ops.object.select_all(action='SELECT')\\nbpy.ops.object.delete(use_global=False)\\n\\n# Add a sphere (1 meter diameter = 0.5 meter radius)\\nbpy.ops.mesh.primitive_uv_sphere_add(\\n    radius=0.5,\\n    location=(0, 0, 0.5)  # Position it so bottom touches z=0\\n)\\nsphere = bpy.context.active_object\\nsphere.name = \\\"RedSphere\\\"\\n\\n# Add a large ground plane\\nbpy.ops.mesh.primitive_plane_add(\\n    size=10,\\n    location=(0, 0, 0)\\n)\\nplane = bpy.context.active_object\\nplane.name = \\\"GroundPlane\\\"\\n\\n# Select and scale plane to be large but thin\\nplane.scale = (10, 10, 0.1)\\n\\nprint(\\\"Objects created: Sphere at z=\\\" + str(sphere.location.z) + \\\", Plane at z=\\\" + str(plane.location.z))\\n\", \"user_prompt\": \"Create red sphere and white ground plane\"}"}}]},{"role":"tool","tool_call_id":"toolu_bdrk_01PuvjJZjfHJZgTRw6jGDsdH","content":"Code executed successfully: Objects created: Sphere at z=0.5, Plane at z=0.0\n"},{"role":"assistant","content":"Now I'll create the materials - red for the sphere and white for the plane:","tool_calls":[{"id":"toolu_bdrk_018gHZ2vPGV9JR5pgaWmu5aZ","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\": \"\\nimport bpy\\n\\n# Get the sphere and plane objects\\nsphere = bpy.data.objects[\\\"RedSphere\\\"]\\nplane = bpy.data.objects[\\\"GroundPlane\\\"]\\n\\n# Create red material for sphere\\nred_material = bpy.data.materials.new(name=\\\"RedMaterial\\\")\\nred_material.use_nodes = True\\nred_material.diffuse_color = (1.0, 0.0, 0.0, 1.0)  # Red RGBA\\n\\n# Configure red material with some glossiness\\nbsdf = red_material.node_tree.nodes[\\\"Principled BSDF\\\"]\\nbsdf.inputs[\\\"Base Color\\\"].default_value = (0.8, 0.05, 0.05, 1.0)  # Pure red\\nbsdf.inputs[\\\"Roughness\\\"].default_value = 0.3  # Slight glossiness\\n\\n# Create white material for plane\\nwhite_material = bpy.data.materials.new(name=\\\"WhiteMaterial\\\")\\nwhite_material.use_nodes = True\\nwhite_material.diffuse_color = (1.0, 1.0, 1.0, 1.0)  # White RGBA\\n\\nbsdf_white = white_material.node_tree.nodes[\\\"Principled BSDF\\\"]\\nbsdf_white.inputs[\\\"Base Color\\\"].default_value = (1.0, 1.0, 1.0, 1.0)  # White\\nbsdf_white.inputs[\\\"Roughness\\\"].default_value = 0.8  # Matte white\\n\\n# Assign materials to objects\\nsphere.data.materials.append(red_material)\\nplane.data.materials.append(white_material)\\n\\nprint(\\\"Materials created and assigned\\\")\\n\", \"user_prompt\": \"Create and assign red and white materials\"}"}}]},{"role":"tool","tool_call_id":"toolu_bdrk_018gHZ2vPGV9JR5pgaWmu5aZ","content":"Code executed successfully: Materials created and assigned\n"},{"role":"assistant","content":"Now I'll apply smooth shading to both objects:","tool_calls":[{"id":"toolu_bdrk_01WSSDEGVk2FHv51RevTiES7","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\": \"\\nimport bpy\\n\\nsphere = bpy.data.objects[\\\"RedSphere\\\"]\\nplane = bpy.data.objects[\\\"GroundPlane\\\"]\\n\\n# Select sphere and apply smooth shading\\nsphere.select_set(True)\\nbpy.context.view_layer.objects.active = sphere\\nbpy.ops.object.shade_smooth()\\n\\n# Select plane and apply smooth shading\\nplane.select_set(True)\\nbpy.context.view_layer.objects.active = plane\\nbpy.ops.object.shade_smooth()\\n\\nprint(\\\"Smooth shading applied to both objects\\\")\\n\", \"user_prompt\": \"Apply smooth shading to sphere and plane\"}"}}]},{"role":"tool","tool_call_id":"toolu_bdrk_01WSSDEGVk2FHv51RevTiES7","content":"Code executed successfully: Smooth shading applied to both objects\n"},{"role":"assistant","content":"Now I need to set up the lighting and camera for a good presentation. I'll add a key light, fill light, and set up the camera:","tool_calls":[{"id":"toolu_bdrk_01AMvEaBThonE94HvaJjwfZk","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\": \"\\nimport bpy\\nfrom mathutils import Vector\\n\\n# Add a key light (three-quarter lighting)\\nbpy.ops.object.light_add(\\n    type='SUN',\\n    location=(3, 4, 5)\\n)\\nkey_light = bpy.context.active_object\\nkey_light.name = \\\"KeyLight\\\"\\nkey_light.data.energy = 2.0\\nkey_light.data.angle = 0.5  # Slight softness\\nbpy.context.view_layer.objects.active = key_light\\nkey_light.select_set(True)\\n\\n# Add a fill light (softer light from the other side)\\nbpy.ops.object.light_add(\\n    type='SUN',\\n    location=(-2, -3, 3)\\n)\\nfill_light = bpy.context.active_object\\nfill_light.name = \\\"FillLight\\\"\\nfill_light.data.energy = 0.6  # Much softer\\nfill_light.data.angle = 2.0  # Very soft\\nbpy.context.view_layer.objects.active = fill_light\\nfill_light.select_set(True)\\n\\n# Add an area light positioned slightly to the side and above for catch lights\\nbpy.ops.object.light_add(\\n    type='AREA',\\n    location=(2, -3, 4)\\n)\\ncatch_light = bpy.context.active_object\\ncatch_light.name = \\\"CatchLight\\\"\\ncatch_light.data.energy = 0.8\\ncatch_light.data.size = 3\\nbpy.context.view_layer.objects.active = catch_light\\ncatch_light.select_set(True)\\n\\nprint(\\\"Lights created: Key, Fill, and Catch lights\\\")\\n\", \"user_prompt\": \"Add lighting setup for presentation\"}"}}]},{"role":"tool","tool_call_id":"toolu_bdrk_01AMvEaBThonE94HvaJjwfZk","content":"Code executed successfully: Lights created: Key, Fill, and Catch lights\n"},{"role":"assistant","content":"Now I'll set up the camera for a clean, well-composed view:","tool_calls":[{"id":"toolu_bdrk_013cajKLXaCWr4wzNHHwZfby","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\": \"\\nimport bpy\\nfrom mathutils import Vector, Matrix\\n\\n# Add a camera\\nbpy.ops.object.camera_add(\\n    location=(3.5, -3, 2.5)\\n)\\ncamera = bpy.context.active_object\\ncamera.name = \\\"Camera\\\"\\n\\n# Point camera at the sphere (origin area)\\ndirection = Vector((0, 0, 0.3)) - Vector(camera.location)\\nrot_quat = direction.to_track_quat('-Z', 'Y')\\ncamera.rotation_euler = rot_quat.to_euler()\\n\\n# Set as active camera\\nbpy.context.scene.camera = camera\\n\\n# Adjust camera settings for a nice view\\ncamera.data.lens = 50  # Standard lens\\ncamera.data.sensor_width = 36\\n\\nprint(\\\"Camera set up at position:\\\", camera.location)\\nprint(\\\"Camera rotation euler:\\\", camera.rotation_euler)\\n\", \"user_prompt\": \"Set up camera for presentation\"}"}}]},{"role":"tool","tool_call_id":"toolu_bdrk_013cajKLXaCWr4wzNHHwZfby","content":[{"type":"text","text":"Code executed successfully: Camera set up at position: <Vector (3.5000, -3.0000, 2.5000)>\nCamera rotation euler: <Euler (x=1.1255, y=0.0000, z=0.8622), order='XYZ'>\n","cache_control":{"type":"ephemeral"}}]}],"usage":{"include":true},"tools":[{"type":"function","function":{"name":"get_scene_info","description":"Get detailed information about the current Blender scene\n\n    Parameters:\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\n    ","parameters":{"properties":{"user_prompt":{"title":"User Prompt","type":"string"}},"required":["user_prompt"],"title":"get_scene_infoArguments","type":"object"}}},{"type":"function","function":{"name":"get_object_info","description":"\n    Get detailed information about a specific object in the Blender scene.\n\n    Parameters:\n    - object_name: The name of the object to get information about\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"object_name":{"title":"Object Name","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["object_name"],"title":"get_object_infoArguments","type":"object"}}},{"type":"function","function":{"name":"get_viewport_screenshot","description":"\n    Capture a screenshot of the current Blender 3D viewport.\n\n    Parameters:\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns the screenshot as an Image.\n    ","parameters":{"properties":{"max_size":{"default":1000,"title":"Max Size","type":"integer"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_viewport_screenshotArguments","type":"object"}}},{"type":"function","function":{"name":"execute_blender_code","description":"\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\n\n    Parameters:\n    - code: The Python code to execute\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"code":{"title":"Code","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["code"],"title":"execute_blender_codeArguments","type":"object"}}},{"type":"function","function":{"name":"get_polyhaven_categories","description":"\n    Get a list of categories for a specific asset type on Polyhaven.\n\n    Parameters:\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"asset_type":{"default":"hdris","title":"Asset Type","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_polyhaven_categoriesArguments","type":"object"}}},{"type":"function","function":{"name":"search_polyhaven_assets","description":"\n    Search for assets on Polyhaven with optional filtering.\n\n    Parameters:\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\n    - categories: Optional comma-separated list of categories to filter by\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a list of matching assets with basic information.\n    ","parameters":{"properties":{"asset_type":{"default":"all","title":"Asset Type","type":"string"},"categories":{"default":null,"title":"Categories","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"search_polyhaven_assetsArguments","type":"object"}}},{"type":"function","function":{"name":"download_polyhaven_asset","description":"\n    Download and import a Polyhaven asset into Blender.\n\n    Parameters:\n    - asset_id: The ID of the asset to download\n    - asset_type: The type of asset (hdris, textures, models)\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a message indicating success or failure.\n    ","parameters":{"properties":{"asset_id":{"title":"Asset Id","type":"string"},"asset_type":{"title":"Asset Type","type":"string"},"resolution":{"default":"1k","title":"Resolution","type":"string"},"file_format":{"default":null,"title":"File Format","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["asset_id","asset_type"],"title":"download_polyhaven_assetArguments","type":"object"}}},{"type":"function","function":{"name":"set_texture","description":"\n    Apply a previously downloaded Polyhaven texture to an object.\n    \n    Parameters:\n    - object_name: Name of the object to apply the texture to\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\n    \n    Returns a message indicating success or failure.\n    ","parameters":{"properties":{"object_name":{"title":"Object Name","type":"string"},"texture_id":{"title":"Texture Id","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["object_name","texture_id"],"title":"set_textureArguments","type":"object"}}},{"type":"function","function":{"name":"get_polyhaven_status","description":"\n    Check if PolyHaven integration is enabled in Blender.\n    Returns a message indicating whether PolyHaven features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_polyhaven_statusArguments","type":"object"}}},{"type":"function","function":{"name":"get_hyper3d_status","description":"\n    Check if Hyper3D Rodin integration is enabled in Blender.\n    Returns a message indicating whether Hyper3D Rodin features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_hyper3d_statusArguments","type":"object"}}},{"type":"function","function":{"name":"get_sketchfab_status","description":"\n    Check if Sketchfab integration is enabled in Blender.\n    Returns a message indicating whether Sketchfab features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_sketchfab_statusArguments","type":"object"}}},{"type":"function","function":{"name":"search_sketchfab_models","description":"\n    Search for models on Sketchfab with optional filtering.\n\n    Parameters:\n    - query: Text to search for\n    - categories: Optional comma-separated list of categories\n    - count: Maximum number of results to return (default 20)\n    - downloadable: Whether to include only downloadable models (default True)\n\n    Returns a formatted list of matching models.\n    ","parameters":{"properties":{"query":{"title":"Query","type":"string"},"categories":{"default":null,"title":"Categories","type":"string"},"count":{"default":20,"title":"Count","type":"integer"},"downloadable":{"default":true,"title":"Downloadable","type":"boolean"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["query"],"title":"search_sketchfab_modelsArguments","type":"object"}}},{"type":"function","function":{"name":"get_sketchfab_model_preview","description":"\n    Get a preview thumbnail of a Sketchfab model by its UID.\n    Use this to visually confirm a model before downloading.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\n    \n    Returns the model's thumbnail as an Image for visual confirmation.\n    ","parameters":{"properties":{"uid":{"title":"Uid","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["uid"],"title":"get_sketchfab_model_previewArguments","type":"object"}}},{"type":"function","function":{"name":"download_sketchfab_model","description":"\n    Download and import a Sketchfab model by its UID.\n    The model will be scaled so its largest dimension equals target_size.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\n                  You must specify the desired size for the model.\n                  Examples:\n                  - Chair: target_size=1.0 (1 meter tall)\n                  - Table: target_size=0.75 (75cm tall)\n                  - Car: target_size=4.5 (4.5 meters long)\n                  - Person: target_size=1.7 (1.7 meters tall)\n                  - Small object (cup, phone): target_size=0.1 to 0.3\n    \n    Returns a message with import details including object names, dimensions, and bounding box.\n    The model must be downloadable and you must have proper access rights.\n    ","parameters":{"properties":{"uid":{"title":"Uid","type":"string"},"target_size":{"title":"Target Size","type":"number"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["uid","target_size"],"title":"download_sketchfab_modelArguments","type":"object"}}},{"type":"function","function":{"name":"generate_hyper3d_model_via_text","description":"\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n\n    Parameters:\n    - text_prompt: A short description of the desired model in **English**.\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Returns a message indicating success or failure.\n    ","parameters":{"properties":{"text_prompt":{"title":"Text Prompt","type":"string"},"bbox_condition":{"default":null,"items":{"type":"number"},"title":"Bbox Condition","type":"array"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["text_prompt"],"title":"generate_hyper3d_model_via_textArguments","type":"object"}}},{"type":"function","function":{"name":"generate_hyper3d_model_via_images","description":"\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n    \n    Parameters:\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode.\n    Returns a message indicating success or failure.\n    ","parameters":{"properties":{"input_image_paths":{"default":null,"items":{"type":"string"},"title":"Input Image Paths","type":"array"},"input_image_urls":{"default":null,"items":{"type":"string"},"title":"Input Image Urls","type":"array"},"bbox_condition":{"default":null,"items":{"type":"number"},"title":"Bbox Condition","type":"array"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"generate_hyper3d_model_via_imagesArguments","type":"object"}}},{"type":"function","function":{"name":"poll_rodin_job_status","description":"\n    Check if the Hyper3D Rodin generation task is completed.\n\n    For Hyper3D Rodin mode MAIN_SITE:\n        Parameters:\n        - subscription_key: The subscription_key given in the generate model step.\n\n        Returns a list of status. The task is done if all status are \"Done\".\n        If \"Failed\" showed up, the generating process failed.\n        This is a polling API, so only proceed if the status are finally determined (\"Done\" or \"Canceled\").\n\n    For Hyper3D Rodin mode FAL_AI:\n        Parameters:\n        - request_id: The request_id given in the generate model step.\n\n        Returns the generation task status. 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"}
{"ts": "2026-07-15T00:49:08.090922+00:00", "type": "image", "tool": "get_viewport_screenshot"}
{"ts": "2026-07-15T00:49:14.432862+00:00", "type": "llm_call", "call_index": 8, "provider_type": "blenderbench.providers.openrouter.OpenRouterProvider", "started_at": "2026-07-15T00:49:08.091922+00:00", "finished_at": "2026-07-15T00:49:14.432862+00:00", "duration_s": 6.327999999979511, "request": {"messages": [{"role": "system", "content": "You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\n\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\n\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\n\n## Harness Instructions\n\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. When you are finished, reply with the single word DONE and no tool calls."}, {"role": "user", "content": "## Task\n\nComplete the following Blender task:\n\n<task>\n\nA red sphere of 1 meter diameter resting on a white ground plane — touching it\nexactly, neither floating nor sunk. Smooth shading, a plain red material on the\nball, plain white on the floor.\n\nThe floor is the sphere, the color, and the contact. The ceiling is presentation:\na soft believable shadow under the ball, a hint of glossiness that catches the\nlight, and clean framing.\n\n</task>"}, {"role": "assistant", "content": "I'll create a red sphere resting on a white ground plane with proper materials, shading, lighting, and camera setup. Let me work through this step by step.", "tool_calls": [{"id": "toolu_bdrk_01McQwtp5Bq6813NVu2gUpRG", "type": "function", "function": {"name": "get_scene_info", "arguments": "{\"user_prompt\": \"Get current scene info to see what's there initially\"}"}}]}, {"role": "tool", "tool_call_id": "toolu_bdrk_01McQwtp5Bq6813NVu2gUpRG", "content": "{\n  \"name\": \"Scene\",\n  \"object_count\": 0,\n  \"objects\": [],\n  \"materials_count\": 2\n}"}, {"role": "assistant", "content": "Good, the scene is empty. Now I'll create the red sphere and white ground plane using Blender code:", "tool_calls": [{"id": "toolu_bdrk_01PuvjJZjfHJZgTRw6jGDsdH", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\": \"\\nimport bpy\\nimport bmesh\\nfrom mathutils import Vector\\n\\n# Clear any default objects\\nbpy.ops.object.select_all(action='SELECT')\\nbpy.ops.object.delete(use_global=False)\\n\\n# Add a sphere (1 meter diameter = 0.5 meter radius)\\nbpy.ops.mesh.primitive_uv_sphere_add(\\n    radius=0.5,\\n    location=(0, 0, 0.5)  # Position it so bottom touches z=0\\n)\\nsphere = bpy.context.active_object\\nsphere.name = \\\"RedSphere\\\"\\n\\n# Add a large ground plane\\nbpy.ops.mesh.primitive_plane_add(\\n    size=10,\\n    location=(0, 0, 0)\\n)\\nplane = bpy.context.active_object\\nplane.name = \\\"GroundPlane\\\"\\n\\n# Select and scale plane to be large but thin\\nplane.scale = (10, 10, 0.1)\\n\\nprint(\\\"Objects created: Sphere at z=\\\" + str(sphere.location.z) + \\\", Plane at z=\\\" + str(plane.location.z))\\n\", \"user_prompt\": \"Create red sphere and white ground plane\"}"}}]}, {"role": "tool", "tool_call_id": "toolu_bdrk_01PuvjJZjfHJZgTRw6jGDsdH", "content": "Code executed successfully: Objects created: Sphere at z=0.5, Plane at z=0.0\n"}, {"role": "assistant", "content": "Now I'll create the materials - red for the sphere and white for the plane:", "tool_calls": [{"id": "toolu_bdrk_018gHZ2vPGV9JR5pgaWmu5aZ", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\": \"\\nimport bpy\\n\\n# Get the sphere and plane objects\\nsphere = bpy.data.objects[\\\"RedSphere\\\"]\\nplane = bpy.data.objects[\\\"GroundPlane\\\"]\\n\\n# Create red material for sphere\\nred_material = bpy.data.materials.new(name=\\\"RedMaterial\\\")\\nred_material.use_nodes = True\\nred_material.diffuse_color = (1.0, 0.0, 0.0, 1.0)  # Red RGBA\\n\\n# Configure red material with some glossiness\\nbsdf = red_material.node_tree.nodes[\\\"Principled BSDF\\\"]\\nbsdf.inputs[\\\"Base Color\\\"].default_value = (0.8, 0.05, 0.05, 1.0)  # Pure red\\nbsdf.inputs[\\\"Roughness\\\"].default_value = 0.3  # Slight glossiness\\n\\n# Create white material for plane\\nwhite_material = bpy.data.materials.new(name=\\\"WhiteMaterial\\\")\\nwhite_material.use_nodes = True\\nwhite_material.diffuse_color = (1.0, 1.0, 1.0, 1.0)  # White RGBA\\n\\nbsdf_white = white_material.node_tree.nodes[\\\"Principled BSDF\\\"]\\nbsdf_white.inputs[\\\"Base Color\\\"].default_value = (1.0, 1.0, 1.0, 1.0)  # White\\nbsdf_white.inputs[\\\"Roughness\\\"].default_value = 0.8  # Matte white\\n\\n# Assign materials to objects\\nsphere.data.materials.append(red_material)\\nplane.data.materials.append(white_material)\\n\\nprint(\\\"Materials created and assigned\\\")\\n\", \"user_prompt\": \"Create and assign red and white materials\"}"}}]}, {"role": "tool", "tool_call_id": "toolu_bdrk_018gHZ2vPGV9JR5pgaWmu5aZ", "content": "Code executed successfully: Materials created and assigned\n"}, {"role": "assistant", "content": "Now I'll apply smooth shading to both objects:", "tool_calls": [{"id": "toolu_bdrk_01WSSDEGVk2FHv51RevTiES7", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\": \"\\nimport bpy\\n\\nsphere = bpy.data.objects[\\\"RedSphere\\\"]\\nplane = bpy.data.objects[\\\"GroundPlane\\\"]\\n\\n# Select sphere and apply smooth shading\\nsphere.select_set(True)\\nbpy.context.view_layer.objects.active = sphere\\nbpy.ops.object.shade_smooth()\\n\\n# Select plane and apply smooth shading\\nplane.select_set(True)\\nbpy.context.view_layer.objects.active = plane\\nbpy.ops.object.shade_smooth()\\n\\nprint(\\\"Smooth shading applied to both objects\\\")\\n\", \"user_prompt\": \"Apply smooth shading to sphere and plane\"}"}}]}, {"role": "tool", "tool_call_id": "toolu_bdrk_01WSSDEGVk2FHv51RevTiES7", "content": "Code executed successfully: Smooth shading applied to both objects\n"}, {"role": "assistant", "content": "Now I need to set up the lighting and camera for a good presentation. I'll add a key light, fill light, and set up the camera:", "tool_calls": [{"id": "toolu_bdrk_01AMvEaBThonE94HvaJjwfZk", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\": \"\\nimport bpy\\nfrom mathutils import Vector\\n\\n# Add a key light (three-quarter lighting)\\nbpy.ops.object.light_add(\\n    type='SUN',\\n    location=(3, 4, 5)\\n)\\nkey_light = bpy.context.active_object\\nkey_light.name = \\\"KeyLight\\\"\\nkey_light.data.energy = 2.0\\nkey_light.data.angle = 0.5  # Slight softness\\nbpy.context.view_layer.objects.active = key_light\\nkey_light.select_set(True)\\n\\n# Add a fill light (softer light from the other side)\\nbpy.ops.object.light_add(\\n    type='SUN',\\n    location=(-2, -3, 3)\\n)\\nfill_light = bpy.context.active_object\\nfill_light.name = \\\"FillLight\\\"\\nfill_light.data.energy = 0.6  # Much softer\\nfill_light.data.angle = 2.0  # Very soft\\nbpy.context.view_layer.objects.active = fill_light\\nfill_light.select_set(True)\\n\\n# Add an area light positioned slightly to the side and above for catch lights\\nbpy.ops.object.light_add(\\n    type='AREA',\\n    location=(2, -3, 4)\\n)\\ncatch_light = bpy.context.active_object\\ncatch_light.name = \\\"CatchLight\\\"\\ncatch_light.data.energy = 0.8\\ncatch_light.data.size = 3\\nbpy.context.view_layer.objects.active = catch_light\\ncatch_light.select_set(True)\\n\\nprint(\\\"Lights created: Key, Fill, and Catch lights\\\")\\n\", \"user_prompt\": \"Add lighting setup for presentation\"}"}}]}, {"role": "tool", "tool_call_id": "toolu_bdrk_01AMvEaBThonE94HvaJjwfZk", "content": "Code executed successfully: Lights created: Key, Fill, and Catch lights\n"}, {"role": "assistant", "content": "Now I'll set up the camera for a clean, well-composed view:", "tool_calls": [{"id": "toolu_bdrk_013cajKLXaCWr4wzNHHwZfby", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\": \"\\nimport bpy\\nfrom mathutils import Vector, Matrix\\n\\n# Add a camera\\nbpy.ops.object.camera_add(\\n    location=(3.5, -3, 2.5)\\n)\\ncamera = bpy.context.active_object\\ncamera.name = \\\"Camera\\\"\\n\\n# Point camera at the sphere (origin area)\\ndirection = Vector((0, 0, 0.3)) - Vector(camera.location)\\nrot_quat = direction.to_track_quat('-Z', 'Y')\\ncamera.rotation_euler = rot_quat.to_euler()\\n\\n# Set as active camera\\nbpy.context.scene.camera = camera\\n\\n# Adjust camera settings for a nice view\\ncamera.data.lens = 50  # Standard lens\\ncamera.data.sensor_width = 36\\n\\nprint(\\\"Camera set up at position:\\\", camera.location)\\nprint(\\\"Camera rotation euler:\\\", camera.rotation_euler)\\n\", \"user_prompt\": \"Set up camera for presentation\"}"}}]}, {"role": "tool", "tool_call_id": "toolu_bdrk_013cajKLXaCWr4wzNHHwZfby", "content": "Code executed successfully: Camera set up at position: <Vector (3.5000, -3.0000, 2.5000)>\nCamera rotation euler: <Euler (x=1.1255, y=0.0000, z=0.8622), order='XYZ'>\n"}, {"role": "assistant", "content": "Let me take a screenshot to see the current result:", "tool_calls": [{"id": "toolu_bdrk_017xgYeoUTciryqhVxri7bM3", "type": "function", "function": {"name": "get_viewport_screenshot", "arguments": "{\"user_prompt\": \"Take screenshot of the scene to verify the composition\", \"max_size\": 1000}"}}]}, {"role": "tool", "tool_call_id": "toolu_bdrk_017xgYeoUTciryqhVxri7bM3", "content": ""}, {"role": "user", "content": [{"type": "image_url", "image_url": {"url": 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"}}]}], "tools": [{"type": "function", "function": {"name": "get_scene_info", "description": "Get detailed information about the current Blender scene\n\n    Parameters:\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\n    ", "parameters": {"properties": {"user_prompt": {"title": "User Prompt", "type": "string"}}, "required": ["user_prompt"], "title": "get_scene_infoArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_object_info", "description": "\n    Get detailed information about a specific object in the Blender scene.\n\n    Parameters:\n    - object_name: The name of the object to get information about\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"object_name": {"title": "Object Name", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["object_name"], "title": "get_object_infoArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_viewport_screenshot", "description": "\n    Capture a screenshot of the current Blender 3D viewport.\n\n    Parameters:\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns the screenshot as an Image.\n    ", "parameters": {"properties": {"max_size": {"default": 1000, "title": "Max Size", "type": "integer"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_viewport_screenshotArguments", "type": "object"}}}, {"type": "function", "function": {"name": "execute_blender_code", "description": "\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\n\n    Parameters:\n    - code: The Python code to execute\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"code": {"title": "Code", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["code"], "title": "execute_blender_codeArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_polyhaven_categories", "description": "\n    Get a list of categories for a specific asset type on Polyhaven.\n\n    Parameters:\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"asset_type": {"default": "hdris", "title": "Asset Type", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_polyhaven_categoriesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_polyhaven_assets", "description": "\n    Search for assets on Polyhaven with optional filtering.\n\n    Parameters:\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\n    - categories: Optional comma-separated list of categories to filter by\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a list of matching assets with basic information.\n    ", "parameters": {"properties": {"asset_type": {"default": "all", "title": "Asset Type", "type": "string"}, "categories": {"default": null, "title": "Categories", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "search_polyhaven_assetsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "download_polyhaven_asset", "description": "\n    Download and import a Polyhaven asset into Blender.\n\n    Parameters:\n    - asset_id: The ID of the asset to download\n    - asset_type: The type of asset (hdris, textures, models)\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"asset_id": {"title": "Asset Id", "type": "string"}, "asset_type": {"title": "Asset Type", "type": "string"}, "resolution": {"default": "1k", "title": "Resolution", "type": "string"}, "file_format": {"default": null, "title": "File Format", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["asset_id", "asset_type"], "title": "download_polyhaven_assetArguments", "type": "object"}}}, {"type": "function", "function": {"name": "set_texture", "description": "\n    Apply a previously downloaded Polyhaven texture to an object.\n    \n    Parameters:\n    - object_name: Name of the object to apply the texture to\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\n    \n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"object_name": {"title": "Object Name", "type": "string"}, "texture_id": {"title": "Texture Id", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["object_name", "texture_id"], "title": "set_textureArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_polyhaven_status", "description": "\n    Check if PolyHaven integration is enabled in Blender.\n    Returns a message indicating whether PolyHaven features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_polyhaven_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_hyper3d_status", "description": "\n    Check if Hyper3D Rodin integration is enabled in Blender.\n    Returns a message indicating whether Hyper3D Rodin features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_hyper3d_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_sketchfab_status", "description": "\n    Check if Sketchfab integration is enabled in Blender.\n    Returns a message indicating whether Sketchfab features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_sketchfab_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_sketchfab_models", "description": "\n    Search for models on Sketchfab with optional filtering.\n\n    Parameters:\n    - query: Text to search for\n    - categories: Optional comma-separated list of categories\n    - count: Maximum number of results to return (default 20)\n    - downloadable: Whether to include only downloadable models (default True)\n\n    Returns a formatted list of matching models.\n    ", "parameters": {"properties": {"query": {"title": "Query", "type": "string"}, "categories": {"default": null, "title": "Categories", "type": "string"}, "count": {"default": 20, "title": "Count", "type": "integer"}, "downloadable": {"default": true, "title": "Downloadable", "type": "boolean"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["query"], "title": "search_sketchfab_modelsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_sketchfab_model_preview", "description": "\n    Get a preview thumbnail of a Sketchfab model by its UID.\n    Use this to visually confirm a model before downloading.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\n    \n    Returns the model's thumbnail as an Image for visual confirmation.\n    ", "parameters": {"properties": {"uid": {"title": "Uid", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["uid"], "title": "get_sketchfab_model_previewArguments", "type": "object"}}}, {"type": "function", "function": {"name": "download_sketchfab_model", "description": "\n    Download and import a Sketchfab model by its UID.\n    The model will be scaled so its largest dimension equals target_size.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\n                  You must specify the desired size for the model.\n                  Examples:\n                  - Chair: target_size=1.0 (1 meter tall)\n                  - Table: target_size=0.75 (75cm tall)\n                  - Car: target_size=4.5 (4.5 meters long)\n                  - Person: target_size=1.7 (1.7 meters tall)\n                  - Small object (cup, phone): target_size=0.1 to 0.3\n    \n    Returns a message with import details including object names, dimensions, and bounding box.\n    The model must be downloadable and you must have proper access rights.\n    ", "parameters": {"properties": {"uid": {"title": "Uid", "type": "string"}, "target_size": {"title": "Target Size", "type": "number"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["uid", "target_size"], "title": "download_sketchfab_modelArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hyper3d_model_via_text", "description": "\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n\n    Parameters:\n    - text_prompt: A short description of the desired model in **English**.\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"text_prompt": {"title": "Text Prompt", "type": "string"}, "bbox_condition": {"default": null, "items": {"type": "number"}, "title": "Bbox Condition", "type": "array"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["text_prompt"], "title": "generate_hyper3d_model_via_textArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hyper3d_model_via_images", "description": "\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n    \n    Parameters:\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode.\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"input_image_paths": {"default": null, "items": {"type": "string"}, "title": "Input Image Paths", "type": "array"}, "input_image_urls": {"default": null, "items": {"type": "string"}, "title": "Input Image Urls", "type": "array"}, "bbox_condition": {"default": null, "items": {"type": "number"}, "title": "Bbox Condition", "type": "array"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "generate_hyper3d_model_via_imagesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "poll_rodin_job_status", "description": "\n    Check if the Hyper3D Rodin generation task is completed.\n\n    For Hyper3D Rodin mode MAIN_SITE:\n        Parameters:\n        - subscription_key: The subscription_key given in the generate model step.\n\n        Returns a list of status. The task is done if all status are \"Done\".\n        If \"Failed\" showed up, the generating process failed.\n        This is a polling API, so only proceed if the status are finally determined (\"Done\" or \"Canceled\").\n\n    For Hyper3D Rodin mode FAL_AI:\n        Parameters:\n        - request_id: The request_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"COMPLETED\".\n        The task is in progress if status is \"IN_PROGRESS\".\n        If status other than \"COMPLETED\", \"IN_PROGRESS\", \"IN_QUEUE\" showed up, the generating process might be failed.\n        This is a polling API, so only proceed if the status are finally determined (\"COMPLETED\" or some failed state).\n    ", "parameters": {"properties": {"subscription_key": {"default": null, "title": "Subscription Key", "type": "string"}, "request_id": {"default": null, "title": "Request Id", "type": "string"}}, "title": "poll_rodin_job_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "import_generated_asset", "description": "\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\n\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\n    Return if the asset has been imported successfully.\n    ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "task_uuid": {"default": null, "title": "Task Uuid", "type": "string"}, "request_id": {"default": null, "title": "Request Id", "type": "string"}}, "required": ["name"], "title": "import_generated_assetArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_hunyuan3d_status", "description": "\n    Check if Hunyuan3D integration is enabled in Blender.\n    Returns a message indicating whether Hunyuan3D features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_hunyuan3d_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hunyuan3d_model", "description": "\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \n    or both for the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    \n    Parameters:\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\n    - input_image_url: (Optional) The local or remote url of the input image. Accepts None if only using text prompt.\n\n    Returns: \n    - When successful, returns a JSON with job_id (format: \"job_xxx\") indicating the task is in progress\n    - When the job completes, the status will change to \"DONE\" indicating the model has been imported\n    - Returns error message if the operation fails\n    ", "parameters": {"properties": {"text_prompt": {"default": null, "title": "Text Prompt", "type": "string"}, "input_image_url": {"default": null, "title": "Input Image Url", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "generate_hunyuan3d_modelArguments", "type": "object"}}}, {"type": "function", "function": {"name": "poll_hunyuan_job_status", "description": "\n    Check if the Hunyuan3D generation task is completed.\n\n    For Hunyuan3D:\n        Parameters:\n        - job_id: The job_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"DONE\".\n        The task is in progress if status is \"RUN\".\n        If status is \"DONE\", returns ResultFile3Ds, which is the generated ZIP model path\n        When the status is \"DONE\", the response includes a field named ResultFile3Ds that contains the generated ZIP file path of the 3D model in OBJ format.\n        This is a polling API, so only proceed if the status are finally determined (\"DONE\" or some failed state).\n    ", "parameters": {"properties": {"job_id": {"default": null, "title": "Job Id", "type": "string"}}, "title": "poll_hunyuan_job_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "import_generated_asset_hunyuan", "description": "\n    Import the asset generated by Hunyuan3D after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - zip_file_url: The zip_file_url given in the generate model step.\n\n    Return if the asset has been imported successfully.\n    ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "zip_file_url": {"title": "Zip File Url", "type": "string"}}, "required": ["name", "zip_file_url"], "title": "import_generated_asset_hunyuanArguments", "type": "object"}}}]}, "provider_attempts": [{"provider": "openrouter", "attempt": 1, "started_at": "2026-07-15T00:49:08.091922+00:00", "finished_at": "2026-07-15T00:49:14.431357+00:00", "duration_s": 6.327999999979511, "request": {"method": "POST", "url": "https://openrouter.ai/api/v1/chat/completions", "headers": {"host": "openrouter.ai", "accept": "*/*", "accept-encoding": "gzip, deflate", "connection": "keep-alive", "user-agent": "python-httpx/0.28.1", "authorization": "[REDACTED]", "content-length": "252244", "content-type": "application/json"}, "body": "{\"model\":\"anthropic/claude-haiku-4.5\",\"messages\":[{\"role\":\"system\",\"content\":[{\"type\":\"text\",\"text\":\"You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\\n\\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\\n\\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\\n\\n## Harness Instructions\\n\\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. When you are finished, reply with the single word DONE and no tool calls.\",\"cache_control\":{\"type\":\"ephemeral\"}}]},{\"role\":\"user\",\"content\":\"## Task\\n\\nComplete the following Blender task:\\n\\n<task>\\n\\nA red sphere of 1 meter diameter resting on a white ground plane — touching it\\nexactly, neither floating nor sunk. Smooth shading, a plain red material on the\\nball, plain white on the floor.\\n\\nThe floor is the sphere, the color, and the contact. The ceiling is presentation:\\na soft believable shadow under the ball, a hint of glossiness that catches the\\nlight, and clean framing.\\n\\n</task>\"},{\"role\":\"assistant\",\"content\":\"I'll create a red sphere resting on a white ground plane with proper materials, shading, lighting, and camera setup. Let me work through this step by step.\",\"tool_calls\":[{\"id\":\"toolu_bdrk_01McQwtp5Bq6813NVu2gUpRG\",\"type\":\"function\",\"function\":{\"name\":\"get_scene_info\",\"arguments\":\"{\\\"user_prompt\\\": \\\"Get current scene info to see what's there initially\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"toolu_bdrk_01McQwtp5Bq6813NVu2gUpRG\",\"content\":\"{\\n  \\\"name\\\": \\\"Scene\\\",\\n  \\\"object_count\\\": 0,\\n  \\\"objects\\\": [],\\n  \\\"materials_count\\\": 2\\n}\"},{\"role\":\"assistant\",\"content\":\"Good, the scene is empty. Now I'll create the red sphere and white ground plane using Blender code:\",\"tool_calls\":[{\"id\":\"toolu_bdrk_01PuvjJZjfHJZgTRw6jGDsdH\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\": \\\"\\\\nimport bpy\\\\nimport bmesh\\\\nfrom mathutils import Vector\\\\n\\\\n# Clear any default objects\\\\nbpy.ops.object.select_all(action='SELECT')\\\\nbpy.ops.object.delete(use_global=False)\\\\n\\\\n# Add a sphere (1 meter diameter = 0.5 meter radius)\\\\nbpy.ops.mesh.primitive_uv_sphere_add(\\\\n    radius=0.5,\\\\n    location=(0, 0, 0.5)  # Position it so bottom touches z=0\\\\n)\\\\nsphere = bpy.context.active_object\\\\nsphere.name = \\\\\\\"RedSphere\\\\\\\"\\\\n\\\\n# Add a large ground plane\\\\nbpy.ops.mesh.primitive_plane_add(\\\\n    size=10,\\\\n    location=(0, 0, 0)\\\\n)\\\\nplane = bpy.context.active_object\\\\nplane.name = \\\\\\\"GroundPlane\\\\\\\"\\\\n\\\\n# Select and scale plane to be large but thin\\\\nplane.scale = (10, 10, 0.1)\\\\n\\\\nprint(\\\\\\\"Objects created: Sphere at z=\\\\\\\" + str(sphere.location.z) + \\\\\\\", Plane at z=\\\\\\\" + str(plane.location.z))\\\\n\\\", \\\"user_prompt\\\": \\\"Create red sphere and white ground plane\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"toolu_bdrk_01PuvjJZjfHJZgTRw6jGDsdH\",\"content\":\"Code executed successfully: Objects created: Sphere at z=0.5, Plane at z=0.0\\n\"},{\"role\":\"assistant\",\"content\":\"Now I'll create the materials - red for the sphere and white for the plane:\",\"tool_calls\":[{\"id\":\"toolu_bdrk_018gHZ2vPGV9JR5pgaWmu5aZ\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\": \\\"\\\\nimport bpy\\\\n\\\\n# Get the sphere and plane objects\\\\nsphere = bpy.data.objects[\\\\\\\"RedSphere\\\\\\\"]\\\\nplane = bpy.data.objects[\\\\\\\"GroundPlane\\\\\\\"]\\\\n\\\\n# Create red material for sphere\\\\nred_material = bpy.data.materials.new(name=\\\\\\\"RedMaterial\\\\\\\")\\\\nred_material.use_nodes = True\\\\nred_material.diffuse_color = (1.0, 0.0, 0.0, 1.0)  # Red RGBA\\\\n\\\\n# Configure red material with some glossiness\\\\nbsdf = red_material.node_tree.nodes[\\\\\\\"Principled BSDF\\\\\\\"]\\\\nbsdf.inputs[\\\\\\\"Base Color\\\\\\\"].default_value = (0.8, 0.05, 0.05, 1.0)  # Pure red\\\\nbsdf.inputs[\\\\\\\"Roughness\\\\\\\"].default_value = 0.3  # Slight glossiness\\\\n\\\\n# Create white material for plane\\\\nwhite_material = bpy.data.materials.new(name=\\\\\\\"WhiteMaterial\\\\\\\")\\\\nwhite_material.use_nodes = True\\\\nwhite_material.diffuse_color = (1.0, 1.0, 1.0, 1.0)  # White RGBA\\\\n\\\\nbsdf_white = white_material.node_tree.nodes[\\\\\\\"Principled BSDF\\\\\\\"]\\\\nbsdf_white.inputs[\\\\\\\"Base Color\\\\\\\"].default_value = (1.0, 1.0, 1.0, 1.0)  # White\\\\nbsdf_white.inputs[\\\\\\\"Roughness\\\\\\\"].default_value = 0.8  # Matte white\\\\n\\\\n# Assign materials to objects\\\\nsphere.data.materials.append(red_material)\\\\nplane.data.materials.append(white_material)\\\\n\\\\nprint(\\\\\\\"Materials created and assigned\\\\\\\")\\\\n\\\", \\\"user_prompt\\\": \\\"Create and assign red and white materials\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"toolu_bdrk_018gHZ2vPGV9JR5pgaWmu5aZ\",\"content\":\"Code executed successfully: Materials created and assigned\\n\"},{\"role\":\"assistant\",\"content\":\"Now I'll apply smooth shading to both objects:\",\"tool_calls\":[{\"id\":\"toolu_bdrk_01WSSDEGVk2FHv51RevTiES7\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\": \\\"\\\\nimport bpy\\\\n\\\\nsphere = bpy.data.objects[\\\\\\\"RedSphere\\\\\\\"]\\\\nplane = bpy.data.objects[\\\\\\\"GroundPlane\\\\\\\"]\\\\n\\\\n# Select sphere and apply smooth shading\\\\nsphere.select_set(True)\\\\nbpy.context.view_layer.objects.active = sphere\\\\nbpy.ops.object.shade_smooth()\\\\n\\\\n# Select plane and apply smooth shading\\\\nplane.select_set(True)\\\\nbpy.context.view_layer.objects.active = plane\\\\nbpy.ops.object.shade_smooth()\\\\n\\\\nprint(\\\\\\\"Smooth shading applied to both objects\\\\\\\")\\\\n\\\", \\\"user_prompt\\\": \\\"Apply smooth shading to sphere and plane\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"toolu_bdrk_01WSSDEGVk2FHv51RevTiES7\",\"content\":\"Code executed successfully: Smooth shading applied to both objects\\n\"},{\"role\":\"assistant\",\"content\":\"Now I need to set up the lighting and camera for a good presentation. I'll add a key light, fill light, and set up the camera:\",\"tool_calls\":[{\"id\":\"toolu_bdrk_01AMvEaBThonE94HvaJjwfZk\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\": \\\"\\\\nimport bpy\\\\nfrom mathutils import Vector\\\\n\\\\n# Add a key light (three-quarter lighting)\\\\nbpy.ops.object.light_add(\\\\n    type='SUN',\\\\n    location=(3, 4, 5)\\\\n)\\\\nkey_light = bpy.context.active_object\\\\nkey_light.name = \\\\\\\"KeyLight\\\\\\\"\\\\nkey_light.data.energy = 2.0\\\\nkey_light.data.angle = 0.5  # Slight softness\\\\nbpy.context.view_layer.objects.active = key_light\\\\nkey_light.select_set(True)\\\\n\\\\n# Add a fill light (softer light from the other side)\\\\nbpy.ops.object.light_add(\\\\n    type='SUN',\\\\n    location=(-2, -3, 3)\\\\n)\\\\nfill_light = bpy.context.active_object\\\\nfill_light.name = \\\\\\\"FillLight\\\\\\\"\\\\nfill_light.data.energy = 0.6  # Much softer\\\\nfill_light.data.angle = 2.0  # Very soft\\\\nbpy.context.view_layer.objects.active = fill_light\\\\nfill_light.select_set(True)\\\\n\\\\n# Add an area light positioned slightly to the side and above for catch lights\\\\nbpy.ops.object.light_add(\\\\n    type='AREA',\\\\n    location=(2, -3, 4)\\\\n)\\\\ncatch_light = bpy.context.active_object\\\\ncatch_light.name = \\\\\\\"CatchLight\\\\\\\"\\\\ncatch_light.data.energy = 0.8\\\\ncatch_light.data.size = 3\\\\nbpy.context.view_layer.objects.active = catch_light\\\\ncatch_light.select_set(True)\\\\n\\\\nprint(\\\\\\\"Lights created: Key, Fill, and Catch lights\\\\\\\")\\\\n\\\", \\\"user_prompt\\\": \\\"Add lighting setup for presentation\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"toolu_bdrk_01AMvEaBThonE94HvaJjwfZk\",\"content\":\"Code executed successfully: Lights created: Key, Fill, and Catch lights\\n\"},{\"role\":\"assistant\",\"content\":\"Now I'll set up the camera for a clean, well-composed view:\",\"tool_calls\":[{\"id\":\"toolu_bdrk_013cajKLXaCWr4wzNHHwZfby\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\": \\\"\\\\nimport bpy\\\\nfrom mathutils import Vector, Matrix\\\\n\\\\n# Add a camera\\\\nbpy.ops.object.camera_add(\\\\n    location=(3.5, -3, 2.5)\\\\n)\\\\ncamera = bpy.context.active_object\\\\ncamera.name = \\\\\\\"Camera\\\\\\\"\\\\n\\\\n# Point camera at the sphere (origin area)\\\\ndirection = Vector((0, 0, 0.3)) - Vector(camera.location)\\\\nrot_quat = direction.to_track_quat('-Z', 'Y')\\\\ncamera.rotation_euler = rot_quat.to_euler()\\\\n\\\\n# Set as active camera\\\\nbpy.context.scene.camera = camera\\\\n\\\\n# Adjust camera settings for a nice view\\\\ncamera.data.lens = 50  # Standard lens\\\\ncamera.data.sensor_width = 36\\\\n\\\\nprint(\\\\\\\"Camera set up at position:\\\\\\\", camera.location)\\\\nprint(\\\\\\\"Camera rotation euler:\\\\\\\", camera.rotation_euler)\\\\n\\\", \\\"user_prompt\\\": \\\"Set up camera for presentation\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"toolu_bdrk_013cajKLXaCWr4wzNHHwZfby\",\"content\":\"Code executed successfully: Camera set up at position: <Vector (3.5000, -3.0000, 2.5000)>\\nCamera rotation euler: <Euler (x=1.1255, y=0.0000, z=0.8622), order='XYZ'>\\n\"},{\"role\":\"assistant\",\"content\":\"Let me take a screenshot to see the current result:\",\"tool_calls\":[{\"id\":\"toolu_bdrk_017xgYeoUTciryqhVxri7bM3\",\"type\":\"function\",\"function\":{\"name\":\"get_viewport_screenshot\",\"arguments\":\"{\\\"user_prompt\\\": \\\"Take screenshot of the scene to verify the composition\\\", \\\"max_size\\\": 1000}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"toolu_bdrk_017xgYeoUTciryqhVxri7bM3\",\"content\":\"\"},{\"role\":\"user\",\"content\":[{\"type\":\"image_url\",\"image_url\":{\"url\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAA+gAAAJFCAIAAADBNEo3AAAAAXNSR0IDN8dNUwAAAARnQU1BAACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAAADZlWElmTU0AKgAAABgAAABIAAAAAQAAAEgAAAABAAIBGgAFAAAAAQAAAAgBGwAFAAAAAQAAABAAAAAAkHjvvAAAAAlvRkZzAAAAAAAAAAAA2iq2zgAAAAlwSFlzAAALEgAACxIB0t1+/AAAIABJREFUeAHsvXmQZUd1//nq1d57qzftau0rSAhJlkAYrSwCmR1jA/7xM8IGPLbDE7/wxESMw/MLh/8w44gZh+2YCTBe2IxsY2HMIjCLFpAQktCCFrR1S2qpu9Wbeql9nU/m997z8uW979atV6+qXlV3Sp118uTJs+V2br5893WccMIJxx9//IoVK6rVakdHR6VBUtX09HSD+jq0yGbVpK79vBVQqaQJ86ZCaxgXGCK3t0ZMwGU+/NY9XZnoqJQaUoEm7QbOxzhveSeW6b75MGSBOyv0WxmT51W9UJnygrKtZMiim1PeBFE2MmS2fBaRXiYol/9DeFaKLW73ZfsC5RdXpVl5T8S5VpQ3pFHzMpq00FcFarRQShmj5kJTYEX5HmlCgWK5EUP8CX2BV2fkVtA2krUwxampqcHBwd27d3e8/vWv7+zsNKlUkKwoIDKeEJ8kk6ianJxsN/PQCqMirdRJETKytOVFCZ0PtjMaMiNBy7VqgmHvdGW8oxIPuCYYLUiTRr0Z4lvr9tZyK+Ok+bOljPRW0ZgVAPJh1pNG00hoSEBzsSIfHx9Xk5CgERPhs9KL6W2NRQSJNTa7LEccwmW8q6tr5cqVEQFF+Bw6dCiLz2LKcEOlktwwBytMCrA5xACrbRoIRWSZSGiYiyZUJtvKMNIzFJFl1UJbTO78AaEtSIn8sOi2ROoV+KGYMjIkKhawXeCqYitMmbbV3zSUISw1wjRSGDIWGbO6e80pnauONyYAqhrb//TowL4QDwxPWyEpVju7ejdd0NHVH5FVpicHX7o/d+UM17eenp7+/rgtrQiRpTyasJzSJGvLWM/xHb3rYrmUJ8c7B55ByWwVnHPxrlGD9Vm+MmVGR0e7OGsfGRkRd+kaSlq9evUZZ5yxffv2w4cPh/hVq1Ylbh0bo/mWLVvWrVv34osvDg8Pgz/99NMhfv7553NdFvKZP5ieYPcSf1QiYbYsB0lx/kRnOZvcbFUTmMiWJji0T5PRjkqPP3dvLnZvoh/lPeVN+yHqUCuaPoZpWsRiNTTNsWVRzDGhZTwAMQpbThPTX82tNlwrszS5srJkCDpy5AjLGus4ac+ePbkNW4LUGosOY36NNZ69vb3nnnsuarDAhhqiG8s1GIBNmzaxG9GEZRAkSzT0VAHs2rXLWAFAdtVVVx04cOAXv/hFiA+5bdy4EaFg+vr6cCP66NGF/WPfvro9ldrcNZ+2CCKXCFMbjJDkhjSaLDLUsBEc8TEy4cPcBBnNjEDIPGLlLEkNhA+wERswI/+FJIi0CovelNoWGdpiGhp9aLXVZgExyWWVJTaMSTFMFiimidSLiuJWzCErcT4wZXRA+Vz9Q33K8AnpC2BkwW1GiREH6LVIgmcRO/vssw8ePPjCCy9kFVPEDL575aaVF/5mpdotVn09nZedt5mH/fuffGX3A/sGB1+IRKjICqxVvf/kN/Seeo2Qm9f3n7BhxYuvDLx6ZHR6cvTAcz+2c5aQCQ3RTdaxWhIrslSefPLJRvPKK68Q0OpgYs2aNcS3VJGvXbsW61599VWZc3D16zrXn8OxxMmbV25c2//KgaGd+wn3K9NjR/p37O7u7kbQhg0bYE4TGsIEfXgGAE/O0ooTWH55SKAquz6bPrCCCUJJaN4ly1VtD0kqsky/733vO/HEEwnEP/3pT4f2szrrkUXLNMH9008/PTExoYaoSG+xQxDHC4O1xx133Msvv0ynosHAwABy8QIaiyCb48SdO3fCn8s8e/fuNeZQwoS2SMm2Mgy2IEjEspa+4SmFJxD6G2fhxNAia5gL4DJ6l30az4oADjAcGhrKpc9FYjL9ZJs9JjCgoaQbYBsaaM1RWE3YLCHT49P69esL/GZtFwuY7TznxJ1+4vGc2J22dJbypvWHQ0Fb1RbTFDQvrhJb9CdBOU9SinVoVa0pL3PIDVNShJrIFfKGYWA1W4a50oUM86xu2VpTKUs8I4YViYn/8Y9//KSTTvrMZz6zf//+bBNmOrUsL3ymyaptNCyDLNDhTGdLYJHMXYiiNVZSWHZ+8zd/88EHHzzttNPOO++873znOyYdM0mYRkJD8KeeeioBN+skSw2r6DPPPGPEBmzevPlP/uRPHn/88T/8wz80JACsUABDSHAjsYCjKusqOpNr7UKWtcI6tgzW1fKrIiKsuYCQITqEtVExrGoHOFLPiq4/Ai9FqhpZhJ+nYoEmuRKhR0NTMmwewmprZBRDOKzN4nPlthBZLFFWZG2JFChmEhHPX7FYjdCWGS1CyWJussL4lCE2wzWvzznnnFtuueXnP/85ayDNP/vZz4ZLH8QKOKnq6F1z/Ma1xL7P7zqy79DIxOT0yNjkh99y9s69g7uNaQawFbJzxUZVXnnh8f/jNy5e1d99cGD0f/7DA08817C1rTxa32hOWMjCZQGeIlixJZLED5deein6s8SRzjrrrIceekjrdm935y03n3/xWRtZrpjoP3189z9+5yl93ADlDTfcwFp9//33swhj6WOPPSaeiHvd616H9B07duAo9gVCaFXl5iiMDs5X/pOK5CSfAizIrQ2H6CzEcLzmmmsI3JFqVQIgNnoicpb1L3zhC1/96lf/+Z//+Z3vfCfaWCfRbR/84AfZOdhyCMevvfZazEYc50Zof/7557O74BoApHzyk59kL0G/P/iDP1DVn/7pnxKq0pzHAyTCjR3rxhtvtLaRYlaECTAGA5AAcCLAddddx7aKT7ncz2MJ0pGrRx+ic2seAjfffDNx89VXX40CdAPG0hbn0BDnYAsR9plnnonHPvzhD0MZtjUYl6qWXRDit7zlLSizdevWN77xjVn30oqnBTzJNvme97wHnuoIFMAPuAsfQoBoFMA/H/jAB7DFZDUCaN500ghRXsDERDunl0tj09NdjEjfR7SwLivXOqYyBRYLkEJIl5cWXo2C3rEq680QALYEpWDpj1ECjIMwsfeDsvqRXIkagCgPyGcAW+jGSJJZClBGClZ885vf/K//+i8epFnEmNRM/LAhU5u1i+XiNa95zTve8Q5WmAsuuEALCBjWARzL6sFqc+WVVzJ5w7bFMCvP9773vUcfffSOO+5gzWERy9LLChYWVmASUlgoiLORmyXGhK997Ws//OEPs1Vg5CgA5PL4oaBczyHwNAKR8VDBgwrLcuTegmJWaAGxBh6uE6A8y6HdMAUWoWpoSwiHVoT4GWH5J/KStTK2BVqFVZqwhtFEbpSLTCJMYgiY9PYBzLQCAG1DK7K+bZU5oZQCOKuAiE0NbGnUR4aHxugbAaFPGtEU4IlJOGF885vfzGElyyCBSgHxocGxgwNjl52/+YPXnbmyv+ukTSv/9YfPTU7NoKQ0FNuuzurZJ6/p6XIXv9et6v3Q9WcViAurWBjf+973sjKzJr/2ta9961vfqqPxkIZ1nhUPP7OiynUXX3yxCN75xtMuOmMDTxqj45PkV1yw5dpLT1IVyztW01+cnrBoc2B9yimnqIrmLJVEs3xoyTMAER0haCgxCyNdXU8VgEtA5gKqCW0/9KEPEVxy5s0py7e+9S0W6ze96U1QXnbZZcgL6YH5TIEtBPuJuUmEoRy32zESm9YPfvCDe+65B+0JQxXyojc24C/c8Wu/9mvvete7UB0kB/k6p+f54/LLL0fcc889h3nE3Lj1kksuYTSwBSLO2qKAErXsoLDVEMEQuVi1PN7p3JoiHDiOgh6JbGy/+qu/Sm8B81CV8Ar+wIQnE7zxy1/+EhrM5NmDzRj+tELiFVdc8ba3vY0iPMmRErSugYyJH/3oRxdddBFDWf3EWMEbdBgiaBglnlWeeuopDsPwgMjY4/EJTx1EBvTF29/+dkSjAE+HPAzwvAgyYhIVTRt196xy5jz0yss0NFllgNFKhe+q5twIK9O4LWnkIlSjCzTFpGbUI1ERSktqmM1FEOFDPuaPgm6y3gwBYEvqa4oRExGARKKsM3FLFAgtlVfxsPyZaxEe0EdkLOKsJM8++yz0ISVLBKEwUSxkMH//+98PATmTlNnKWsf5BbP4Ix/5CLVhQ2J9KLXGSgHjjKCPfvSjrGzwgQO1BMrkPPaTs4LBB25K+hCWJmwVTz75JCsD11q4MwMNDLV50JxVhcD9i1/84t13300VrUJuphhNWDxJnB388R//MXzY2HhsMAIA1kOteyiAPqrSTkFumLDJbGGZFg5IOMAZ9UgAlmbLubX0pkYEREpSa3JFqaJgb1OShbXQJH3c4I/8E3opl9BEtxzIFSekDLE8ckjLNWkVw8ii0LcSYZ3FDDI4AqxbASLDzSHiFonLFkMFsrVgyhie2zBEwsQUKwByZREEcgHvb//2b1kAOXHnrPPP//zP9YkfKxhhEmsI581h2+HRiWdfOnT7T1/8/gMvvfvq03/wwEv3PfHKC7vd3RIlAicdmBJDp7ja39efu+l152y88PTjnnj+wEPPuPt7fT3JTWkRaWklp4+EsY5gJXziiSeIsh555BEiNIJeqmqsPcS6zak5N2e2bdvGHcWHH3447euOS8/ZND5R++RwbHzqivM2V7hH0N29devWkA/H2ToaJnzF1QR+3/jGN/SQwJDALUYs/7BfoLAhaULXq/e7BFkdANX33nsvjO677z6Ab3/727qtjlROwXlu4FQ/WrXD5lmY5y0OiV966SUUJfaFDz6i//AOuxe+YPNgqyNIpb/Z0mQJOw2hPwdXbDyIgwmtGAFsQsT0HLqrbfhsBJ69DfVuvfVWmGNIpMwDDzzA5R8eIdAEnuxbXMJhv8FBXGkisL7zzjujJhTpITSkFRstVtCF2qLoGGJrHmlggiyeXnhiQR+282zHIwLTaEjO6RfE5DzG4Fty0UcKI5SInM9WsHT79u08mXFRjFgfMvTHDxCwKeI3dk1ch2h6M6v/UsFw353vqo61x3tmsj2oERXh6YsQr2LkcHWrFmtgFUUTwrkYEWTzkDjLJFJgPoqhUPmEfEkPP/OSmYZF9FroaqMBYFngm/2sZixNRmbErLxsWuwxMGF6su4RjuMfonym6oUXXshuoVbynhqyvIRrLJqYMhymcDGGRYBzDcJxYnc2G2DWyT/6oz/iGD7UjSCbZRO5nHihJ0sWq6IORBDKKopQ1GNhYQGBA4shlLRiVYFbdABvGrKImdqhOGA4oBUAHOApGIasVIg2K6JWcyyKbcQcbaWwcqs1ICtUlFk8GKpoGLESMqzNZR4ho2IoLqwK4ZBG4pRntSpoFTFZxGKkZFjEIpLpBhzWhrDRzB0IJYbcIukzVqFegYahFJGRhyIK2oaiFxguoxVWkFCM3OiBWWpYRpj+t/tEgGRbAzEMcRTHtV/+8pezFm1a13/960/65r0vvOWKU/79zm0hAREzp8lI4WZHiBd8zinr/vn7z2xY0/e/vO+i514+zMn3bXfVNUeH3//93//Zz36GVmpiCqvIkkV4xmpJ0YwKF3biOqyguXFQw2DYCkFzB8BEZzq6KsIxCuswa7KqWCcJU8WKOB7OoT7EcmwTn/rUp/793/89YVr/p+6hRFUE/jwtIUMfhp5++ukEl9/97nfJP/e5z7EK13NwJXYCbCYSlcG4gGsbeEqH7kSWhMjvfve7eZqBhpibhyf2Bo6Nb7vtNs7jiUp51rnpppsEQMCuQyskwpkNBoCYGErUYLPk7J9zaPoADN1p+uCLf/iHf8B+5LLBoAwpdAfPUnoAoA/YAqEhoRJfzCIIxllswzQxhgI05tCWJxmGHYdSOAFWqIQVPELRiscPztHhA8xzIcwjJjiTpwIeOtEZiTDhUYFnOFqhDAM91FNtcSDcuBQEW5rgVR5S0eH6669nC+cxg20eAj6OQRyfthAlsJdHcpdWsXzsnu0mLBUST+bWCh/mck7W86HTrFaAFY0mwmcJoDSkKWYY47N0AfOArCMHI+Q8GWVuzPKX9DAPadQQ3UQQVkWwt6DuyZ8mJJEx+zhTYAlideLBm8WNpYDJqNqvf/3rLBfMa049+KiQqJ0PFX/605+yKzCFWYJY31gBOGhgQWCBZilQw4I1lsVd35TiSZ6onfstasJhBNyyyzIYEhJZTvnAkBgdMjVBIqsWyx0rGDsTawtyVcUyglY0VFE5uwt8oPy+TyDZHVhsjYYqTjG02oOHPxxwIM5hX7A92+jnDlhHRKzAa+AJEJlylLQqWglWbkyi4oxkuUyMW8sBUy+Si4FKRmCisxiraisg0jMsyjS0BWiks9FDY3Aj4iw+ahIVQ/qCqpAshHObGDK0LkQaDKsQDjkvOmyKGSCVWFvuuusulj7WQEKscAUA5iM+wiEWClYV6NWt61f3XnfpSWtX9Xz5e89w54Q4dmVf1+BI8rVJyKDnGjbrSRg3h0OC0XH7fS9u23mou6vz8ODYjj3uHMES8STLoA4UDInapjmcidxQjOMJrngQ6VFlmmMRlx2I06ABZs2nyqfpR57d/+ZLTuRevtj2dFUffGovb7mGIQsgIRmu4LyV/YKFkcAVMvhwugF/PEBAz7E1lylYeE0xAGLdn/zkJ5HCRtBB4GhLMCsvkqj7lV/5Fbjo259cxuC5Ifo2JOcrcjqnNehH7Ih+LNbyKaqwW6Ai+pkktJSX5SnuvdBQ3jF82A3WUIBaRZTGMyKmyHDRw01UpSaWUwtPKxYrYNKtlZgb3lhFQsOiyQqZlJfLIxBdzuOK+MAEwFgV8Al1mG+42A9Wa64wtYXpnp5mvjJcZFqxtmVoijksfG1o78JLXwCJGFjGRmjCMWBweQ0XuPdRmCd81HvDG95A7M4DM2s3RU7TeZbWhRMpL8UiA6kK8RSNQK3CPFpjWVF55idM51SFnI8fQ2LBLLwwJEIlTCcHSbhPQ7ZP6FmNWd6J0bnvx9dbWUa2bt3KKk0QT0MOTSKGfGggbnyuCB92LFZ4WOlAh92BxZ8FnFaqoigOaE4cr8cMYeDDgkweibBaAPW+AMMX929xrZgU5KE+wOI2R54F4ha+ygw0wKwzYIG1Mk2ycsOqptWDibU1ICtrCWFCt+Sq3W5mojCLJAEe3837i7/4i1ydhSSGZJFB/571Z773v/8xy+HI2MQdD+2k9orzN+85OMx3VQ/94isHdzyUy0RBNlVrzn/Ppb9yzeknrHnomb0XbD1u1/6hR551J6e8VWbXD/+nDhSyHAi+EY0OrKiEVbaiEs3y/SVasdzpBJaPDdGTI2OWUKJKVjYwLKHQHFx91cot53/6PRedd9p6HjY6qh0/f2rv3/3nExMjh/t33IoITmq4N05zFlvuWXBCTdyLUM4+iObRimWTAxTuVbIUsz4TbGdVBUMTVmltN6zGHVxbtMAdMwjwyXngIFIHoAHrO+G4AnpxZEuAhWDWcYzhSQLNOACGjG7jzB4a1nSJEeVC5ujA5qGtayHlLoAsTMOr5E3IKt9KlAyAbBONCvACyqhRntK4La33u5vaswLMt034Z1aCGhGbAo0IIrzo0Va9rzyiyRZNisxcLGOzijWBwRaWu+xOwK1xVr8WmsbaxcIrDVl+EQpzdhc+dWVl58w+qzwrO00gQ0kWcGvOSsiKoQW8YGOIGIobSLhxGqRHAnZK8DCRB9hy+JChjNUwIXCXiDL0kTILWUTVXHFtrnauzo2QjWwUvVlaTBYyh5JWykN8AWxSCmjmUpVVPqvefOswF/1Lti1jJqxaZWlWXFZPaFiviAxZhbg1kCUwDAEoywK6dfas3PT6337tRec++tz+sXF3lYU3w4DnxL1R4K4VUvr0bb6w/6ybt2xce8YJq1/cM/Dy3uQDw4LAnWN1FkZEwIHVm7MMM43VkhiaVZcYWAferKWcTSARiwiyOXG3IxsCd/c6yGrHGSeu4Z7Prv2D3MsncPavg7yV8wtacaDOygk3PtvENBZPVlHw6EAVTwJw03lQwfoMB/kKDi5wD0/cQWEJCaAgYaEZCVmZJgXc5l6FMpHOXsH89Xfu4uaVQ+jYXEGhsYKL81wmETLyXlS7WEXugvE7N+48c7kndXpxL4imvCeMZzg8yjQvVqMMh0Y0aEKiFhHzJ6WR9BbiS55HyN7mjFVb0zmXiZxpNOwEBlPFrmBFAWKimHtG/4sbfKDM5QZP/CBuponYZpnDzWgirZZE0ZQHCK0L4XYzxHQuUEw0WJFLbMaamQYU8FwSVbn2hpovS0tzrVbvZ+213g/dEsLZJmFtmUWybsnq6qt2133fXdwmR49MTSQf5YX8UY9kmM7etfwOkxU5cOA/dpqJoZzX9aJ5nej6n4ISE2h49jCGCrWtaMBkR29HZ3IkYUgAbtl0jB8JpVgtnomUD6u0ohrGALGSycT6ceBudEsFkCXFY6iFtoRjpZgtlGilHMpsQ9VSlat8LlJ8GlUV67MUa5fcuXu2l3PdbqPCasGQKNK5xf1bXGsM2xyQsVJyeVhU4HDft0nnFpBZlY0EAJwT+UpIIxbQJj6UqspNJQMinZd60fol6pG52Gs8GznHfBtRNhLaCN+IfyO8iTPAOBvQqO2Sw5uNuZovrr3Fuklh0aCnAZEhMiHLCkzWuiwm4raEilmTc5Vvc5M5cY9PZXLNWFwkzzq5CugRJHywU6+QK6kVcNRcvRLmEFCMKIWxPGKiopjkVoEUw4itERveAKtqBGAyEouFNmq7FPF8Zrbav2cm//2azZpk/WIAnMJeMA8LsBwa4DAvVsH4ZMlCcWEtXaxeDgd2SLDMYPmBOY6v9A2Z9jFQupEroRiAqWejQhgrhjRUgRfGAPEREpPV0NgaIIKIWy7SmrQVEKoqmIENgMnLb2zLNKwjAdMRANYd2Euyjg5HgpAQW621ymKsKmQuZBZjxPMESKLJtaLMj0yeJx0Wnm1or/oRjEymKKsLtILYEmTAIqYtCTjMVStMlmcjvFEacwOsqgCIiK0IgEQVyZXAaGDPqEyBxLaqwi7TB1h2JdamVWayzDf6lgOhMiFz8HWBO+VqVy+jKSRyMMf+08k7dOKqlpZxBEksAZgP3C6KtFdR0Xz4WYZTMxj6KkaYliq7CMy4MoXJ5qJF0GD+RUbdzbA7ZbpjT7UySYUfmREBGhkm8ozhoQmrolFBMcLMv5UzSOCeA4OfBSIa4TM0W8rV+iySLqMvZjXIw16WA8AoUQSwPNc9iBNNWKvxAMYGRgSExE3DdLG7reg1hL92fRPdNNt2boix3NRUoIOeGEsqeHqZJ1vCHhdMbgBCBYfS0VNFqoCVG4FqyZXAAxi9rrqKp/Dktv0bk2UDYKlMxiJzF7CsxnDBS8he9Z0UFkyuZEhGdTiwwUOg2shwipZEsERz7MVGszqyAhs1yMEDR7VLtCh71bPk3GzhQn9oi6pCTC5cksz8plkjViBrgTuMVm1905ozrhudmOZHXPmKQHdXlXfcdFY7hvY+te7wT2gDjb5jJC4suCitl7fQecxVaGBKYuvlor2q2JJ14TJbBR5WEMAKDjThzWgWrIAx2IxHBwTxvajQEqtd9gA+bAcbo2FnRQFRMVRYVY2soNaqDNg9Pb15avpVvn89nZxaWVXIeTnBGvbMC6Yb4zw7C5ausTY2ZEJYpFtJzHqFs8DYTm7GhrA1D5GClWt9CDHGp00ANJSS6IMfMJz1UA5RldW2icJzV4PuiAYzVjPOsVqGQyDbkaW+M6EiUNFg1yzzbCaMNczyMYwA5cgNi9Z8jgA82d1IIR80NKshoEhOkg4AIfGSg9E/azJWYKZ1t4yS4cBm+HzYjhTzoeBsbvoARDqYksJbDqBkzHMBmucm2krWPA28XGVai0RzGEZj20RgNd1NAjCkAanz3F+Qyq22TYBQc8HkAqQhsSixOx4wK/jqasttQaK7HhN8bcntEySUwL+bzrjyHdec9c17nn/d2RufeP5Vvif78r7By87d/G/feEpa8nZ2/USobtDzm1i8mExVZ511ln69DxvoTt6MZq8N5r1pvEkHMvDU8n5KvUQMDF/m5S08fGOX9yTw/iBemMBbxmxll2Lir1wbG+/TCZHH4AIPmA8NgNhgAVaM+AhfPArDWoM1n1U0ZMR8VsVdlcqJk5W9He7nmY6exGQhMTH1dXLmRUucOVsHZgeJDRgAg0O2IbKRziFesG0DLC9gYGLBjZaOsEkobhnAmKbuli1uu0sjWqown7REzbfBEAEqGhLDMRCrtcgDU0WS+crlHOAQwDNqa7lq2znHBNvmTE9sx17GPBgAcsiUlm7vm3Uyh0EeYgRjrBvuQYSnfqfWDAcASQKpXIDBYiUkTgNPbkgBwlgOoBSRtbZYLEJW59qearcQSrbWZPN2bnerlg5SCm03Ncx2MMUOtCbFALIgUC4ghIvbogAEloe6gWdkksRBO1cxtyZqkUjQq1M8Ne+SEhQAjgyN/8fd2weGx/UqzT2vuo8ABoZ4t4czmJCfiyu8Q4ci76YhtwN11hqePIQET9LBPEswSJ3Eg4QDlHpuUM5b8eHMO4N4JSVvgs+uZZ6ZyyCjCQ4qoDHipQhkh1GEkQcamWbE9GOWxpAGQAOsonJhsm3bAYNJu6qVE6Yq+zoqOd8wbwcV500HxjyTVr1Pzvi3ZaKRTA2GMBdlFgMeJANAVVmGNjaoyo6WEBNSZvk0gYGhTXYWd6a/VGUzmNEDTYhrqyYYaDZitZmPkrhlwTygURHmKBAV5Tchsz4MR4Vgw5iBET5ion2dXKKNmObGKmqyRItySBjr4FUlDX7sMvMBlpYHZIg6UR0EJixmYQj0GBPSA5vteEDuWrojQZ0uA6PcxrymALU4JBwA5oqo4ZIoYohsydU2HC3mB1Fq2ABHHAyfyzCit7YGNGrVNJ4f0OAHkVD+G9/4xv79yctteJ8vL3/kF0jvueee2XLGQLihMEDtqgxcVq/oPnvr+vufrHtJ8OXnb/7Ojsd5le/73vc+eTCSx9uFich37tzJKTscr776aiM4++yzed0kb6r/0pe+BPLaa6/lYF61/I4Ubz4yzvvIAAAgAElEQVTWDxCC4bcGWZvC3+wwJgDIpXaenmZCQSVhDZEwp6GKIdCIm1HSBxGNYQSEOU7AA0t9nYrsLV/kkTaJ3avLMHYPx1IWDr3EXKPIct9oxc8OoSxGDA0f8m9P2OzFOfbwD9Ii+/ZUuyVa0U3Mek18GIYeoIgTqMp2pY0iA9RWKgkpuCAX2ygPi7TNii5g2ESVxrmZDwdWQvS3WBaMhocomxDRtk3wrdwbmo/tSqEHREkeUrbWLgmFJ4ByAYbJFSf9VSXYcgDB1BqQyyRCShNyRoJF9uLgOC61R5rIOisWjOesB8BguNp6HyR+MG5tCKAzWpkt0lDIAm1lJuPcGoZNsk6QNwoYzlMVv+v0T//0Txx233TTTYxSfs4JPXkTPHEvx9/U8ntHXDXn5e78wh2n2z/60Y/4rVnuit9+++1SiR9pgoCoj/Cb2BhDNCQA6gL3I8PjT73oXhEfpgd+6eJ4ZogihrBKMLdcYIcfueUCWRi4oy4KoS6X1yEO1xR+6xsktSTx59w9dxsWQdPXY9Sp5AZIbRUFF+dQ2mgwSsMACM6dZqqyVnMB8C0ObCHDuSizKG0Vu584VdnbBrG7DScD8Ek4qEK42F3qU+tZAEtRQ/CaCOzZTC7GQ+6UiVotpyIeMJPxgD7ZE5J8eVjKyFHCnBDItY6RICeIWEuxuQJAsK34IRNVhZglAcsW201wEWozGHCF6R/5wfDLAPBd6oa6eQBYTmCPYLsULEvxA/SiFJ5cyVqFgFplcw0VywHUC0apKivOK4AsiQs9gETsIscJGgkqShM1gV4N51W9BWAuc3IFyWpy/MCkgAY4tBqYvou6L5dVeSQiLEmi2oIMRVMU3pAASpE+IMtLz1JKkFTCDyY3pDS5c5QV8jRYPMnZrbRPcXj90Y9+lKj90Ucfvf766zm5/ru/+7uPfexjBPR33nknB9wE6KRQGRb29773vUT23EyJDrVrKx0i+bWqs05e++BTdT929bpzNn3vRdMnAfhuKI8C+slVfjYV2fpRKKr5UABPKVIXNb+6x6k8CtGK4F6Rx5lnnsnPU9GKJwzIQF544YVE88DmZQzm0YTJRqcqfLfa0DxJUU7bqMqKAJYgBg4btj+cNa39dW65hsTuL1crJ09VXqlWZvtFB40r8ghASTCMB+FNZ2GyeBFo/FjOEDXYOMwfwHKgFYHraohmws+frLblzMpAQj06jq2aZYcuwC3RHrBg+ofjKoRNAZAaJIbJBWwgAcxqXMGfNdM2KprjnzISc9VYKkgZSL+HCuMEhoR6QXhcYc4MKZcWjEVhQnmKjUygSqEbEYBaQSkn4C7NneU0PGQLdsm00C2YHw4JKOU3ABwin4T0SxeWE2RXrhX4gcTAMA8YAD0N5T0jEBOjAZAIA0QAkmSeBM6VvjBISS/WAf1DP2QVky0wKeaTbQgG5o888sgtt9xC4HrbbbfdfPPNH/7wh8Hv2rWLL3zCme9zEscTRQODx+fEySxZJ598MuJoDpKz7G9961sE2Fwj10QGqVT7ASZs2HTVH65au/HVI3W3iLcct2LntkfP6nnqN37jN9JWFXS66667KBKLr169Gtm2bhJqK6CnChW5HmOt+Frq008/rVYc/ouMooJyBR9cADK87tM34TWTuJwAFt+mP3ZYKn7QeA21FYbcAGoZ1Mdz373qflpV+LBJBIfjR7DlBkRNllaRmasTpuh5fWlZ0SptFcHDjUWJ1TDs/VBEdti4EeaXyxCImjTiJrJwOAFbCpksJBxtS3gDn6DVQurQJrLoUwUi6mK0wg8aHgvgEBOKXMHkBhhyRl9JVcu15TehPwNDySTChKThAWD4ZQ/QC7jC90bSIzJZ3mjaw4vlNw0qkx7aZVUGqKNVNBhAMaIwYY43lpxDzBWzBeQ6jY2wLXjzCQAO4ZCalUQ0bMF2Zk0tUTH0bEkf+MAHvvKVr2jSQQwSmFq5WjzJwYCPYnRxBklkL/9z+cWdSUAqVXqnB6+99OIfPPjSJWdvfFJvldk7eN5p63ZucwE6h/wQi8uBAwfMGN4Js27dOjB8DxXB3OlRdA7B4cOHt23bpsd9igMDA9bqtNNO4/Sd0JyPAHhZDQ8ihOwylVwq4QVFJNbqqAXUtW1ovg0JdDNYQJirVsPMyBqZIzLl0FgRwGA+BDplvPLKUfaemVyPMZl5osOregBmDmp65xIvA6SNnwhQkZxBQs7SwccRfsi4LNcn4M0hoqQoSisawZIDMES2SHOWfvvcEusUxy85o5pTGHuZF2FbRggOIScJL3fl9rvRhIDgMA/5RzBshREgKZZHxPNdlKWRFPZcEubIImqlHsQCIvrlUcQ0i7pCi3AC3tAIAS+fQAxMvgA+kUTrCwDBYR4qLMWoVWcZAH622uoINWIOw6xDRBM5RF6Kmi+5otyI6xpprsnCCAGATP0CbPRgbL299dZbDbaY1jA0UfMQY3wEwDl0bO3EnequvnU9Wy6uVONfKp0e3t83mrku4/mxGpoekaQmqog/dNwuFYn4GzGPZC37okZJ7hIzW9s1RJTT1gCDs5hIBL0DTTiMIFAxRIaYEB9xa7rIFDlpqsJvM9V9QtQ0u2XRkH5hyjBaWHwLFp1FsVXjykZXcdE0FFk0fqyYHWMhRkzwhpZXqhStWnOTcrQB+EQJ9+INhgqeWcZuCQdbFqb3QZpDbDDIMzjHPBMCgsPcGi4PAJ/ILQLIZawcErpledhbxgo5BEpGCzlFtcIzlvBMyEreC4kNIyQNjY9xCwEIKFouQATtkKO8vKEclWROqDCwUjso3FodONS2wAzD5+laRBjTc+JeF7hjD85trVWz4qaOlw7stXZUPysmy4+Y0UA0poVSUwIbs4AZblWGwaVChv0LbEUDaCI4xBifdgNYO3Xf/VjsHnWNwnfGTHTQGJGVLzJ+NITCnOYenexeETeqolEUDi2rigArRtzmXkQfVhgWFlghBc/Mn6y5a7tgHOQWPEPCISSGjW1FC6bGjII00sihDHMrhhxEgC2GFOzMS5ECwtyIxRMmcguAa5YetUaRWdhq2cO4goRbBMheOcf8s1ydYCaHAMaqaDkYXGE0covNKYoisBxguSbziQFYKg8IALa0RJ1AzxK7Y4X0N6CMObilgCxkFVK6qzLcbwmrC7jMaxVq8UjBVmprYqjovIqeD+ZSvlEuiSUNVO/gHF2HoChMmIuhMPNhTtvy1HdVOXffPfvvqratUS1RTPE64buui1C00WgDz4BcidSGIyocb8A2T43GgFxu7YBEQ+JRhaREHjhHHgiXnXbQc4F1CN2CaHyiQxOpQS3+aUnnwllJUoAlwgAVI6SJFkBOWoDhFwmSzgwbnMPIMVUZTigDsWGWN5B1izxDJ4ZzSk6AWP5pN59oHCqX/tIwdyiaLQZgl0ag9bsBorHcBGnkGB5ATIxVWLVsYKwj5ZojV5PjGXnJyNTEN3V+bsTB6BcXQP9Gb1wso5iNkNAJwLS1KvEREm+w/ritS+UyMuaJhqWQxIf76LSIynhP1PwlxynPVSwXCT14OUoAuSXhw9oQUwCjm0KxApqjs4pvhbxUrZwyWdnVOev3zLS5xzQXWBfQMxycKpIzriIT1CTEA4Pk+ZwVkGe/7DoYEkfclnERP5AwEOew+GglJMg4NssYD6ETGH44R4OQKpyGlyhqQGq8yY3k4ViyqhAJrCTnA9sYC2FDthWAhthOMq2wEVfYp9gU5R9ZZ2TLHgj71IzFGySNHyEpQolzlIxyLoAXUpeV4SaFc9UGWYZDSRqJgDgcNhTRmBzn2OITysU/0IeYkuKWEJmsI8fYrNrmH63MEYFa0bBNXCRtTUmKtkKCjGpFFmoOrGSuoGjcsgCHcbWTg2z1wmBY9dCy/MWgyAsqkhuA2sDwFKaMFeYmAeQ2JqyqDJ95oilvyDwp0OZsmfc7OiunTlZ2LmrsHnUTRSW8B2C5nClMNLoipI3GuQ9IPsuDuSIMPSG3eZ8umHr41uJUNlHWRESDXE5e0rgyl1IME3iKVitAYy+Lx0XcYIQGAmILXGebTcThaCjKCWFMhscUimE+MAT4RwRy6dHgFtmIvaTs8LBoVWQaY5BpGlrEY2NPbjS/GR7mIL0QB8DBYCNuW0DKMzA0NiI9LZoP8bJO9Goe1i4zuNg/jAHGSTamVysbCXP3kg02gDCZtyWCKmFUjNQw4lYB8J/fwN3skcZhERjXE0kwEHE0QFhrFqIieOVCyikhLAxMQAKraByWAYAHZN0ysGWeTKDvX1Tszp2ZOAiZQaYNvBAQHObGBWTBGLMqAeRKNAcwJgsPIF23rTTX2vDbqwvvk0ii20XTF8PbiqQIfnH7LhyZ0hmMJcNE5lCM1LYiAMl2uGzDMhhFYDhKgnCdGJZpu1xp8Ko9B8pGizDoLzkKLyktMydoQGKULMXwvr5+cj6e4TMtglGg/hUrgDhn7urmN+QIvbp7+3o9QG0nr7Xm52F4+9zhQ4eGR4aHBocmJtzn8KRl5qtic/w6VPtgR8R4FQfiRvOz8PIPI0pAMeflUYuluS7COnnJjTmfoOSvWU1RjhKlaAQbjQA1JKdoefs4uS5wr64+rfOEKzkyiGwoKE6PH5naeWfnlPthVKXQ4BTn/sp4A5iywLzzErxVObpjKeMB5mq0GWRIjkaEJqRNS4CXOyunTFV2VivuQrefrspD74DJjjfDhIBgcgEhk6ULY4s+3SLeIjGuFKouXYvmQ3PzEszxkhtMHR3aKpobDDSHVW4upKwIYWEicSpavrgbCdJJpjnLlHyFeiTVAojgqM3lBzOfLlb4ZeeF5ihzphEvMGCdJQ0lHXXRuaebD1q6wVddtO2uTnGvl+6m013ZYdxHMd3dhOVdLi4nvvQNXRPaM/79DIBnLYzKmMdvwgjHoOFzCtamsdFRft5xcJBgfoCMsJ7QvlHQluG3rBD0Tu5a7b3rBpUAsxl6S4s+tEyr1gKhyYLJEaE8BExu9ORjLooAo29PoIu5R8JOurbzlGs7+jfOVtGp4X1ju+7NtoIhb4xnzjGfgW3MIQuJFjFQtWnTJj6h5k3w9u76LLdGGPjAgZ9zQlAjmqWOx2OMqqVuRag/FpGEERDm4K3WWoHJOkEYw3Pm8GK1cuLk9N7qNO+ZiWqN1TEAD3CQTM6Oy9RjBql4zDNZD8gzDD9WfHzFBom79CA946AVNxufNiABBMNKgPKs9KWCQX/5RAq7cM1/oKppi5lKS8Wc+dMTR0VxJ47S6GJbRC4EJLkLoDlNrCHMmeMwQYQX5IoccgOTOPDWqO7p7emq6gS8i/NwaDgRp8qnLvRz7RMOjpm0AlNavTxDDJfhA1+U4H9m3Oo1a5wUb5JcN+JP5onmjxw5wvujh4YGR0dGGH6k2ahUWvf2JsQxJAZMpKbrMN9lvqPrjmghbtQqYrLwRalNjmjBArKaYIKQsgVYTrBitkkjjAnFVybUmAOYu0xoI1YLg+9iW9LOhLzOqls4SNWOqRs2/3xl18iP9lzyq5sePTi+6qWhTa9f//TPDpx31qqX+zrHvvvK5RNTycF8R9pKbS3HifzK62c/+9mLL76Yx+WDBw+C2b17Nz+9xAR78cUXzzzzTJDE67/927999913Q/yZz3zmjDPOePnll4n1+V0nfpWJH4B9/vnngXkWpy1Xdbdu3QpAmA7Ajze97W1voy0wv/q0Z8+eDRs2MI/5RacXXnhBvWj6HANa4gEb1iFgnEEaLECYaLhbUfMByrCzVGs0EcMZiy/zjsgJ956ZY++InNFXfrNzv9/GBonDWQqadvuMshadQCM2O241RMllu4qhtuYT4gY8RhGPEdsAGAZ6IwvbHp0w09lmNP5kO8RjFphGkevR6SKzWo7CJ4bBXSQGmIYi44oEWYpx/qwSX1c7+/r7/F8XdLtIt7NzRf8KyNyJN388RAnv+x5wl1WQAlslwSZ3PoF4X3Cy8nANdUBjf9CATSxWa9etEyWe0XAimh8ZGXYn80eODA65M3o+WiThN9+0IePlWuFHTcOAHp9oEQvNtyY2ecPa5mCNNOVwsL4wIGSLAhSlBoDUMGRI2VrYJGbZhpoz9iK11VB5gdOmu1aMnXTN5NpzKlOj3fse7X7lp5Xp2nzPCp0RU/ccZtQ91YnXrtv22W3vHJrovWPvJZPTVcL0fWNrRyZ79o+t6a5OXrL2uQdePcfoZwRYsj/1qU/dc88927Zte9Ob3sTE+/rXv/6xj33sb/7mb2iLO9auXYvZYCC48cYbCcH5XdU3v/nNe/fu5SO5m2666fvf//5b3vIWkETt733vewnWN2/eTJzBj7Yy/s4991weD/71X//1rW99K5gnnnjisssuozijYkcPgQZcOAoNLnYCZJo5IhOsPJxXIZOQPsQvDMyE0HtmuDMz2/vuC6Nhu0nRzsemzgMzurHbLW4PNvKPxrCCDwtBIBa+USvhbdwCKIEHsFYhbMhGAB7DS9Rq89McWd6PPVlXRCtDlkAYHKsBpiKtcBpbgNUue79lhxYbooIAHmrwBoltjkNuRrU/6+a2SbW/r7+7R3dRugGqPlyHwAXs7qKzPxpk8FcJaHHqrKLgRn219PAYjidJLpqvrNviLXDhqv8IiFCegJ6DeeJ5DubJx8fG9MH+UesxRqMGJLMy7G85hJzRhT9VFCVkeFQJmCotv+QiC/mIwBo6YX6ZpTlVgkMgattuRa9+sk1EHkNVzFeS0yLl1Xaia/XA6/73yVUnqnbshDf0bL5sxS/+Zi6xe37gTpj+432vOTy+AknKAUan3Hn82FRXb+f4xetKBe5clcEqIumXXnqJUPv1r389J+XHH388B+2MjO3bt3PcDk/OyJ966qlvf/vbf/Znf+aekcfGcNDPf/5zovZzzjnn9NNPZ6Y9/PDDV199NSfrxBacuxO1g4GMiH/nzp1I+clPfsKzwVe+8pVPfOITsOXDNDgvg8TcoPuT0ZH+wS5AWQdgk8HsVW2EV5GcxCwiF70B1nypAyxIfFf1tMkKt95deHUslfAAQ8JePsP4IZzSUluiaVkSDUtyki36wCXba6BKK+a+jVsDSvJpIRlqkGCIFURjGIV6hmyhoDZk1ZzbaaXPeWQRHsNvNgaokj/b0N5IJQxBbTZsQkYABjSwzOnr7XOxDwFQdzdhdg8UvT0eQ1gOxoU7Pv524ZGbCJ41MMMoklJQ9P5nDXdN1BeOw7GER+gL/1Dtovm167Ycf4JchJdY1ggwCOL5uH54cJCYnlCB+B7kUes9DI+SBpEGFTC1DGdWNka4KKkigSExYVU8CoeeGZ67auErZvfIuf+NqH38yJ7xg9wGmO7bct7Ylst7Dr2l7+X/wnvGYVbeyw/cCQVXdibfN924pmftyu7ndtVukI9Odv/zi9fNKIY+/pd/+ZePfOQjzA3i8gsuuOCXv/wlh+4bN25kpeY+zOOPP64xAZIgHvovfOELl19++SOPPEIAocdiwvSHHnqI8P3Xf/3X77rrLk7ir7nmGkL2Bx988Oabb+YA/t57773kkkt+8YtfgDzzzDMRceutt0IPZkYN54OArgrZqqixLmMtN4CeC5tEML5irWHaqIPJNUSKW0VMjsIiT/cvdFa2TrrT92Pn7uUHAOOKPQx6ph7jVoFUOKrDwcyqJM4hQYEsDVpykq1ZwAVNlkoVVshvuILZSkJzpippeRg4Tx3BMJDfxB+/EWwJVkxAPk+iNZKTyNe9VNsde7ugGoggnLi72tm/ot9DSRzOXfC+3l5h3HOai9RduA0At9p0QOP6jWA+TPCTrrbdMMyikebq5l+N+TBtPniqu90zFPfmV6/esuV4pMhpiua5ZMN//ur84dGRUYL7JTd5/ZBwzpOxlgvjKtJnPMHK5QRyW5PD2mKYsI1kco0YVk1ws+bLA8Clk5We0eMuxJxqV29Hd9+Rp37Qd/z5FEc3Xdaz47usGLZohCbLdeQgYaIqAyh2vP3tb+fAWxW9F32ig4+bKhXO1N9z0o+/+uK1wCdt7P8f7z/7//3mtqdfGhBZT+fEB0++40sv3KDixO6fTbx8l+AopzvZ/ud4iIL2l156KZE64yPi39qiDb4IoBhhkCtM6MpQGcMLsNyAkLgYZpXhMaaY5lhtIw8QV7rY/ag/d9dwDYcxsIrKcaABoTMJpMAzbtnbhLcxDBCtLGHDYzAe8Jua29XwFcug3HXMMyU9wJaGA21jI4rCgbk+xMMpGZC7REII7uMJvmjp/iMS59WEXE9xAblPAITgXd09/gY4yOTdQfCBFwx91CtNa8FxSc3bhUxztZI8mxyL48v2C/GW+wL6ODsvN+YJkIjpBwcG/XdihxiBDMWyrOZGlwxDDUY3KF2CpQFin/RzKktFzRTg3CmT0s7XX+aRm0o+IYO/ppUpFqk9X6osKt/p3g2H3/h/TXf2EIAffOw/+0+4qHfjGWjUNfDyqnv/t0aqRd6DjNMNiwPdtbrclmOT3f+58yqrWtnX+fvvOvOv/+M5xe582WPH0CarbQSwbrJCIm+O3YMNnKbDrZEgxgRVyiMgixQT4bOKCWN4ATbOaGtV4rMA+cJLXACjFkwET6zP69x9icfuNsjDIW1I+dOqsu61UQQgWIBy6IXMNtRiwURmAjIRiD4bUWbbthaDdYslumlD2OC1x7OI4UDOaGEl5JKzpWknZBvmdqUcIkdBoMSyjtN6ezjkdm/+5hQDgNgcpCJyHXt3+7ejuRNyl9xNcdc8+ePD7iTzf7IKLVcMTnCmJVZPE8Gnp3c1ixOaGkJQ8adgWT8W08fc27zsb0Axknp7+9ascUeZJMann7kT3KzhtjzR/OHDh3k9JS+b534AVayNDXwpBkkOjRJlAOUCDCNSWyIABJOzCPtSu/sbPUkyxHIZns7LxHaZY3m2lTVfikDH2IGO0YPTKzaP7HlqdN+2rpUbu1Zv7uxdVT28vcAc77w673EDRVuJhkp+4N7bOfb+k+/6p+ffYqx7ujp++21b/79vbnt+99BUpbpjaLNV5QIctNMT9oiQSwNSHSnAcgNsNINREgbOKcL9pYidjAbhrdZGQIQP27Y/nHVC++vcVhoy/Lkzc6p/v/sifnKhfiRXwkUAlstjwuR6LxzDwGGCXsXchi1BsieRmGKa15y+I7ElnMszaaFE/DxHbjNyiAhsIQZPQIAbMRwka7EtU+Vd0T6UkZlSTL6liqia3CW/V2O4+0UewmvOwf3bBl1Q3tvnT8B5qulxTzddnUThUNICmNNvbRI6u4M/zBIp/AkGoeFVeyzPesC9Sj1xXq2SzopmsjyZIaw1yYVmS5/LpJ2RbhS7Lw538QWG9f6dNvgN57lofoJonuR+Oiq5ND885M7s/deEbGSap53PvdezQDt7YO66yd7sioeLlJj4rAYIohi6yNbPueuw0Bymp/u3fX3wok/0rD/1uMt+E+nVrr6OiZHeF77VhCY456STTuLLn/mB+8R05y8OnW58d+4fIWQfGJkcn3APAd3ViRu2/Pyz294hAlxsvgZDUddj8DXdwH7PMYkoOX2HkhVZRXL1jQB1aogRmWGsVQTwLPLud7/79ttvj/DLoIgD9Zi1DGxZRBPcd1WrLnZ/mfvuc9BDi4vlcAI2fiFsSAM0jC0HsPXLRrgB1qqtABTmURwzFXcSvpsJi6InmjTnsVm1ypUyI4dGBOD9Q9AEHmMlZD0Uf7f9N/78PVeHFvo8l39ogjvhdifZEBJQo7h75wl7LKAPwd0HMnyAKzKMIoHRoTjEgLQkoXMC5EaRszFJvKyFO04mKQiVJKs7BjT2QNZV6nccaUtblqYxv+VTE45/Rq2eEhO3pEWsZaATzff29a1bvy4dg9NTk1PjExN8UU+vsiGa5wWVwHbh4eh0aaPBgavl7WgNxEtKbi1Jl0oxURM2oMXdgxpZFOK7d929orNn5Mz3VVe78+7OgZ39v/zHzoEdIU0ZGA9cccUVzzzzDMRuhdW5iByXtHcfqSXT9vDQ+P9z27MHB5IbrhCMTXbd9vLVJkkeVC7n6reQwPA+9de85jW8YR15vO/lhhtu4PXq27ZtY+viDY9btmzh1TF8i5SVHW4Q0208T4A05gDwgZLvgJ966qlSOqwVvGHDBgC68F3vehc/xnTnnXciKEu25DCMV7tbvOSUbyuFFbufNsV9945JP94YdSwKKKl8Rm0Zh9BonAMw2AxWWxHMyGepE2CmPkkjfMeHjM9otZ2VgTi/ab+VbDgXEdgiKRokJSU28kCkCUOIjRxi8Mx0DjgALKwPmZjciENIIz5GaVWGEcBqz0tOsErxNHIp8ruYAG5v9AE3mtC5KvPeQuJuTr49gYvZ0YEkcS7PO8c16QsGJIfJya5VmZ7yIbwvStsF02RpC5rW+yVrUbu7X+P/q9mV9H8NEUPe9zGyDco1Q/z4ZRZIKQwCYoKABiB3Y8aPGxUdmca8WVFf1ECT12hZ5eesurr6iebT98273cLPd6Y833/lmg1hEgf0HNSznPKmGy/BszERRz2Az0i4IdpifM8kyyZ+VtG8xboKPblhFhhYP109c7qzu9JB1HG4Y+qpjomel75P+D669V1dh57rOvBIZcod3Mw28QZFFmFemcj67F7xw1Yh7yQH44Tz1cnXrX/moYNnwfrIUCyD2us2P3TrjmtMMG7Cd6z1AOH1mCuvvPLHP/4xJzG8FxJirqpfc801BO7nnXce+8fnP/95Xs3O2yF5cTuH/6+88gpMeCM7ewP5fffdB7c3vOENzz777Ac/+MEvfvGLnKzTnMie18ucf/75mEEV75wxNSDmtTO33Xbb9ddfj0Te7E7t1q1bV65cyWxZsWLF1772NVgxYb7zne/we0933303L7FB4a9+9avoY3zaB8Ah6pr2UWlRNIlmJsUwoRLFMortnJ4+aXJ6V7XCiGfkk5a0e7F67vo3zURBJxOHxBrCbC1QppGUgiZRh1/0cawAACAASURBVDbiEJFliy0RUZ5JVgHDGJPIFvAWr7M0K4JncIIkt+a0AlYOwEF2Xx+xvoul3bWSTvcrPH39/e6s2/9jSyMo929BcUE43UQNzV3IXuUX6R0g5m0SfJulcwf8280TNuZ2ysvP0rn7qo5Ddh2VywI8oVToUufVaPl1RRdvLUCq1yQ5F1c3ew1crO6K0pAXeNY+73d6m4ZAojGUACsaZXMAz7sVXlLU1c97itauW3viSSf5pyG2Ib73zxcPx9wbbfjpKGL5gSP+qN59C7beuuYkL8NWcgt5uDyanW5986cO4bCEmKRNCsCIWw6sr3ScNd35RDUJm9dNVy+e6n64Ot4xOdoxdrBjdH9zUTt67t+/n7cmvva1r+UljS5wz5rBCx+/sfMNv3bivc8PbmHQb125+849F1+98ReHxlfuGtlw1YYnVnQmt4X7uiu8a0ZuYiOPWBHK88ZG8PgLwRzOEaZDwwE5J+JsJITgv/Vbv8VbIz/0oQ/xO6niw3sef/aznxHTQ/CXf/mXmzZt4lSeX1p94xvfSNT+V3/1V7/zO78D58997nOf/vSneduMuZ5Q/rHHHqPbeFrAPMRx0g8TfomJiJ8i76akll9j5UMAHlyQCPKqq6760pe+BJnxOQa03AM2hZLF0a+YwEqIAygWakNLADlJoxeguG1Y+2Klctp4ZQexe4hdmnB5w3FvI2LwBbUzOoYZRGLmMiVhBZwrKBc5I/OQoDyHps0JRTTNJNS5AGYhQoQIgDndcOF0laN3F3SDWbFyJUhUouDeB+6/mulOxN0t8B5/Cg4D9zpwl6chOAyNbYH0VlfJkOw0nGFSt1qNfH6hQ1wXOzWlqmraQsl81dsSq8W7pho+TXqeP8lICH1eo5wBSpobO3h5xmmWzhexkRpOXogPYK+Ke+ywFFQabvEA/0TESzc6Ozma71+ztvYtWI6Mx8fGOakkiHc/AssPSA0OjvJTUsPDbHmkxVN6CUhu5CIWSZZWRmY4OBls0CvHNht7Tdt5xlTXk2nUDpODHVMbp6srKx2Dbiz7kdyYtRSzXKu6ioTQAITTJM6g8++483HZwbHV9+6/gJ9KRdiu4Q2DE333HTifu+9gbt99+dBEH9LP3lzhA8mnX3L7RnjQboox8ri4QrB++umn8x53nhg4EaeW8JofSzrhhBPAc2p+3XXX8YkSx0uE1McddxxH72i2fft2Ts3f9ra39ff30+S0007je9wEB+94xzuIuYnmQUYjmPieH1V97rnn4AYrjttpCw32E0+w9e3YsYOjeizngyocwTWeNWvW5GpuJhwD8ICGjgE2sIQJi1l3MRMgsPkgwHLNGVoZQZZDazE8Qb5Qde+I3LEg75kJbc8aUlybpW8aU+ze4toyQplNJCYap8X0aaPwHVZlTJ4jzdzNQU+YFKshKdoMvItYBmmht6C471myS7iLJ9VO7r+y+OhUgoNvAnFeB+6/5yaaLn0FE1a6h+KcJKcHMYdiI4eoIWuQyMvl2jysbVgENnw5ZnVUc2lbx2j+CnSQZ56o6jsRq53hDpU6fv4UWIacU5/a4MGr7iRZZVzqvezd61yceIAB7RtCZknur/FLqH2TtJ0RJ8Cy6zLMd0/v/e7Ts3Xr18tMXOheWzM5yWts0l+BPcLdAd5pw3G9lt/Qb7GXjpV9uBhFjHgFp5FYeXV0C2wOV3DiBrNPJV3IDZlwSNPqQMfUpkrncHWKJzUeHHS3xwQJQIL4d3ROrz5laMXGkcmxysEX+of2davKPpKFDDj/Pe5lVOztqpy8vnJgsLJ3W8P3uPNbS/wW0pNPPsl2zsPi2WefvdMnVCGkJmjet28fY46fQeXbG0T5ROpYBcyRPFG+qoizsQ086qIYQTmRPbdfIIOeaJ7I++Mf//iXv/xlHM0PKxD0c/EGicAE6DRkfFOkbyBGJdgS+sMBiTwAUORxArIyVi8kjZ4OZfXc5Wp8kBsATxXJNThUlStLBAW5qnLbtiGSd4tunXLn7nP5rmoL7bIuKOBZhqag+dyrNDzoaAPgmdXKFkHC9+xCGaqRbRvWloHLcCimsXGL2loEoPcruds+3UG4O/6mykXeXP1mJXHbKsfePhZ3V1Coct/SdFfE3QfiSXvHxPlHZqT7QRmjZqJJVHacW8l2JrHLvt6GQmopXZiCx/4mhyveJbWDGPwCJo06nJfkNPOlL1qpzo3H3FvnjlkWnE+5YudeaTM+POT+c9dsjvCGymFu24DU8nvMybP0a0KO31j2KZADk4A1jslJFtZTJUpP1XHpRCcXWTwX/2nPdOXEqY5XO6b2dUwPn3ht16FnO4+8IA7kibD0T7Vr+vQb96w5OfnFpKnJ6ss/27jvsdUSbaEgu1Jd4D615jz3ovhZpo7hPdWR3bmNZB4bnmoJkeWFXOKmkVzF4U48J/RNc2jPhkQGeCwMfTQylKMzgOUhkDXHhohGDATChLkhs81biEFnU6aFbJtgVTJ2L6Nwq2iasKKJJgXahlUhnCulmIBaBjDznf2DYZzLoSQSVlCWGTZGQ3ztI2i37PrD7yonBZxv+1NwdxcFiMumCr4JuLmKAoa/vsZRJSE4syy90yI1Suq8IGSJue2n2IJYP99CbDB5QcvTyamNmeCb6Vbzr01A7Te1ijlAqeQaC+1mtfIxqCkPEDC4mzbjLponEcoT0PNGG67ccJle9wuW52Buyl0FjfASmwe5EpQAygUwhkUAoITrT5uoTExO7XFfTHWJMOOCqe77qmPMp9GTrus6TODOdd38dPylB4+/dP/Lvxw9/syePc+PbziFLyl1Pvm1U0YP1l3k5uJJ3THzgW0/bmKL5YSbs+1cRTDPonYIQjiXvjkkZiyVqF0jwHLs1QhQHplPVMEzFgPC8OnwcH9BMkrIjcAAo29DoH2U1J2Z0ybdW94b3Xe33jG1wRhs7s1irMoAaHLbGoGAGWmkkiSGxBEMt0ZaNcJHTbIiIlUL+IgVT9Ro5aNiN5JJEQcrhkbBNrRFzcG4CJzkfvDS3TwhCNcpOLG2u4vCWka47ZI7Glfyh+XuwBxBrLhOCv/84muilzIQmKJVYvmY1gbdEngXbcLR3sL4teV2mp6obzBSskWJNiujsRMVxaGF2prcGk+nbm2za7nEmqBlDflV0q2NfA3WDCVUwLt853V0bJQInnf0EclzQE9kz/diCeiPKm8z9uQlWZ0zFP18x2NK8h7EFM2lESAm5M93dp4zXb2ws+dQx3T3VGX1VOWpvqmuaXcI69aNhgwcv/VnDTLvuno6nrl/eHJs+oSze5jEa7cO73m4LnCHsi5wd00ziZ3wlFNOqQwM7DlwYIgXR2QIjiqEdQ9AEhAAZRe5Bk5JR4Lzop6RNBQaDYhj9+8bOHJ2aHVQ6GQwFAkkidpP9u93r73uNOBtTURPTRYj8qyIgE0CWtuwyjgLgCbEQBm1Cotl4FBWc3AZ00znSAQacvZDBM8ywuU0kZG72yadVfdiwl732kHibHf4XXWvJuRNKe7E22H8VzD9y08g0GLrYyaEwMOBx1LiAfOGbS2GmbOP5OjWrvxZnmDmIiLLcM521zGIxls49aKqumZBoUkNA0kICkr5sXioTAijSFR0qrVukASGNgtm9AuNFdMMSbOyjrJ2ilX4vjv/r19/3Eknn4IDuO7BJ/r+GH7M/XAUZ/ODg/yxb8EuOScxPEgKygFy9degIha3ACw7zHIbFiPFhBzOj1cqvRPjG6arQx1Th1jVxp1WbGF8oNvd414yYKycGlME1VO9ayf6N4z2rHRPUFtO73npydFzr1ohCzoJ/zOpKHDn9ZiXbzn+7b/7Ozt27nz8jjtOm5zaetGFT46O8sqXlpiaUWYeEWEvCo7C7pCgQA8ZjruhIRcwT96YJ7YF1rV/Fd0UuiUqNtI/bCIaw/BV7ZeqlZMnp1/urBC7axhYrTGcEZMlMN2yPK0K/tYwBNTEaouLpmQxEKkRFiP+IR9pBYESYSGrCTA3T5hBLJJ2tr2ifwUXTkC6U29396Sbq3guAk+ug7u37brPHdNl1YIGcKHEoxmOYrtivxDm5hCkznRBsIL41L8Ki3OalPN40w0L2Ec8o2JBw0ZVc+fQiHOET53q0LWZ6yNhf7M1Iq8V0VD0SfcFC5pTPiw6Rh08x7sPjNIUygUXFVOqsn9nOyREX5b7nOmy1pmrxdsP9gXr8znb02YMGFl8K4e1mrN5e988Hiai5YY8F204kueajX+nzQCn9cMjI+47x3Y0sCDmaAwo9zsOoEu5wtGNRFTGaRFALk1rkd0rp1afPDI12THwUu/ESG2mjlamd6a3ZdyE91rxxYPpsbGpsdFq97SL1DeOrto80bMKfadHDnaOHVg5sHPNmlMPMuFXHdfZsyKxcWCnexNMlOruuOuroqLo7e6+9rgN2waOPDM4iAO4jsKxGVdiPvnJTz7x6KPf+u53jVHBVRmjmTugrrIOc12nWZsCiBCmkSzrSAHOl0Fq1Gqx8MRDDFPG32IpkJWLe3FYFt8Epgyr2dJE9BRRTApHVVmFuYh2ur8zU+zuGfmIc0QWFoGllZBWBBnCuXyEjCijVmEROOovigqbHStPSpFDb9Zu4mmF4Bx7c+jd4+6i6OaJOw7nIByMEGBpih204I/ugks3hy2ZGEjliUvynB8yTGLQyzADyotqokl55iUp3Q0Ep8cS8XhJqxaVzLpVQDjRwPgiv+bDfy5RTpRVLJ4Op0W1oH2Fm29RUY5rNPvM7TUyP8hTd7evjUtLM/zsrzqOu6vy3JjnYP7wYf+F2GHeaePOL2f/hko/M1zHCgiLoXPUxeSWmpAVMmwZ3FE54bKDvP5l9GAXE71v/cShF1fu5YukjQdf7+ZfOf2sR8bXvcAXp0YOdA7t7R3e3zt6qGs6PVLvWT15zq+91L2idqf04Pa1O+8+STrLA5jPoVjDwP0Tt9zy2IMP3pv+vBGvZbz99ttpzy7+zq1b79mxY6//rS8wJQN3P6GSTpIe2a4SjWqjHKWFkfbAwkTFqNXSLdI3PPi2yxhN/UgHWUekuBb/DUXYeJij0JBnI3Vd7D5VebHwPTPSB2UMELeoCLKMxLCVYBrKUisaRoKUi4aYm8koSmBk+ld9+7egcJTSzYtOXMAd3EXh5kmVV6BA5g7CSZy3uFPzCrdRCNxh5ddRCdFeGYqdD7i24YbcF0Z2KPHogXWN2AeQhUbTM3SDbULqkrC7RFDII6mMKCMmxqElvR7JMublAG0yqa3JjWumhVPZz7r00afmGk2aRuw1Vf1TkyNpSFxe7fKUjXQSviSfOZLRfGFT8ozqR676saHP51OxBbd7Po1pwJvghBCFs0WCeRfQ87L5gQH/JVjeaZP8chzOtxSx0dQgj1JE1rbFTRcd6V0zOryvdu9l9cnDB7evPrjdvb48N23s7Hp/R9dtU2OvNH7O6Vk9seWSQyu3DE1NdL767Kp9T66e9nfjYShPsmXHX041Ybxm8Ywzz/z83/+9YdjtBdNVd7/44mtXr7njwH5hYAcjciPOBege8JartywwVTG34dGJpHva6rhdvaDum7FHGAwlKbOswoYhnKUsjzE+NkqFCYt8V/X5aseJU9O7G8fuxgfRBotJliE0YVVWW5pYK3fmzQxyB9guliYm550nRN4U3Lk3F757etxPYPrXnnD524fd/BCPC8GhcrkPwZEpwEdmM0zJrEq5mNZwyWWdHGRbYJNP1C5Yc4S2ZRVtiw6LRhmqDmUuPqQxOCIOiyFs9GUA39ANDBIBDkXTKKuYMBE+LIZwsUohZU2kk97QIRHDqJi09AZ4NrWsXpbml6+lIqX3LxB3hfQwSBM1rU60Cp9tHFdbLBw7EJKkRlnYEZGSRkm91KEJZZfXHpSTVz+nfHylaMSnloukVgYKdairCAoZmhw+AbmBMZn4WLUAjSmndFSRRcQEpcp5nBs0tF7zqlgY75ZrU04rrWeQa04D1uXRDLDyxK2mNGeZbVLGiggEExZnq4LGsd9o3LsHuDa5cWOy4xPREc+PuV+PGuZ2De/j5qYNb5sfHBjUGypnK6tt6decOnjw2ZWhekde6l931kBB4L5vcuK26akPTPbd2jWyt+KO2bmqTl+En/OPHenacfeGSoX/44TbSfgXz7vzN8JuSEAZ4XGVyvc+//dQaGmh6v7776eWIvCB8fFf/W+/9eO//mu9JgIMgJorNz7HgKY9IM833XxZNtTwk2k2MimarzT8wqrID9H4DIu0InbfyW8z+XN3vk8StaVo9ADQqxjlLGRc8KY1s8ufiLsfxdT5twvBibT5pfp+9yJCXwDn3gLu/vOXo1yTNARHBCk0MKtSGUyOJWWaLSCNNHSelMFNiI6MtMUswhdzplWWXkhjCIeQJoSjqkhWRJkrS02yVWFbg6VSo2IkPdItid89URLEO6nxqE9EmIwG+kX1jkotRZ9TrYqGedTCippsrpn7NRMnw40ZJw3VvUgXq7lCcj/FQx5V6zbPzWX11poQr7yVZIgVnewghfgQDkgS0Ma2yNzq4ayR/tI4obS+KWZoIsqQlaExhgVAAZ+CqgKGZaqa5mxhvPOvJHmnuy+oJf3q0BokZTQpRxPr60akb+llJqWYqBzrlIphE6WUX/o3XaVSQsOrXVQE6UajDf10d5MHqRI9nnKwd6bDpF71oEO4EyX/XgFeRUDxRG55EGsSbU5O8aaNkdERvdOGH4E9fOQwkT0XsHVAaR1Eq/ZP1Z7K1FjWhZXOnmluqNvVl6whr3RM/Xvn6G9N9P+gOvauib5zp9y3TJ+rTnyxe/jpjtoNmWzDCON+1Sh7sssPbR945RU+Zb/iiivohscff3z37t389Cm/lMRPFz366KP9mzeDV+AOR0L8iO/RUNQgNkujouGbAFrICumt5VbGHC0BBZRSiVw0Ri+MigaLGEojy8JGo1YQCKNiyDBqS1EJGhLjeHtHZesEp+9T0118/5sPwuDRoaslvfziZY8/7eaI270Fpequg/MrmK7aPQP7I3J/Au6jcB+Ca32r5anAor85S0IR+ZKqk23aToABUgwfN2CJtiTh0s1n9gamPOOWJi6u8OVEan2dkGGVKS/CbDEkrmdWKxXQuCpVS90GSkccomJNUlY/4++JkkDRtWcKJHWamzPwDPlESjZs6UXWMi9RbfUwTJUUxgVBaO7mYTWR4Xgnr/gE63iFWSo5XV3SakdXMqUsHHkI5zYPDQ/hiDjm45emxPXO73ie/xMqt+ZE7Y8VW+KBxLGhf835JsC5v1n/u06M2lrRA1YycU0AsYjGLLw4N7os+YJDw0R4WetzReq+sqZo6I0E9vxqFMY8C7B78g6DaoUj4lWrVlc2uN+8d2PdfwuWF8vzjsqBgcO8zYZj+SOHj/DCSsJ6OzUWP4SaCSGclbYwmKnxSmdv4NJUKvjQ0ym67i9fWv1JdezPRtfYm2Eum+zhXe//R8/hJ6tlY/cu57+MqMHu7tdccskjd9xx9913S+Yll1zy8MMPP/HEExRZMnc+8IBF7XVKHU2FyG9RcS6eYGguuWehaDqFRWC8EfpHcIiRuwyjJiAFRBzCIjCtrGECeApVwYSz777+Pr3SxB0IuNC6yncziLYpuQsnnHv3dPf39bta4vWu7gvogi7/CkIXgsPJ/fP6uIx/5dPsqMvzXUTKJkyKQhrjkAG0JbEmWU1iaMRB2FykeSZmUdh1MSsag7IU8ApAV92wqOZWHRWNcyzYKlLWxiGoyQdzWUXNo2IdIw1yP1mdthrydRSukMuhhrTJKFJ3Bu7+c83clLSCK0mC3sbgOEQSDeXE+uYC2imvGe5NLKNappsSPySs3HKWnNrJAaGIMvyP0czGA/XOp6V3vzuSd6PR/xFJKaZ+BJeibAURqjo2NqBcMX0aTPEoX6d/WAi0nd0gmx11bCoqkvhQmtOulatWbdiY3AzBHH4JlkN4dyR/+BDfgq2902Z42BnkU8yuRBlxia9KEM9MMl0ZfrW3q39yYpgvxyWpd+344J4+1xczpZsn+ojad42PPD18ZENXD3HG+f1rfndixf/ac7jkEbgF/XWi9g4NXfmOd3zrpz8dGRlRBa9yJ3AXfFJf/89+8AMuu9e1WZBCi72/IDo3IYTosYWBeyvHq987cxlGyLAYwqE36E2KubWGhEZJDZnqnIITXBNIuyCbyLvq7qL4025/FcVdR+nu6+/lJYSu2n8TUxwI1v1agdDaghzqE8IQdfKG1U53yEea2zIVMl5suAlLIhdQ5P/Z8snSR2xxTMA22VAU8KltlkPcMaII+YpjwNc9EOQyyrUoSxmwSlboLI11sekjoBEl+JCtNW8CiCQ2wUFNmDHW1s1F/hnKq5tG4s6duqOS0Pu5BlxrrzM8K9uRnjUwSUcNYM7It9i7ukbjF0NGiEusYAKO5fPnAed/3wdOhO+HZD9KVw+HC2bIPGjiFoREaMKdGeceek0vr4IUcVMqSZFWhk/r2/wv1rktu7ubY7XjNvho3nuBWyGcFHPNhpfN81VYrtkMDw25a/Sjo9SXCZbqnVlzAxIbVdWI8qBd96/besPesYOdo4f4/ud0/4axjq7q7rvW5NHW4VZWOs72N2SO7+779qu7Hp2a+N0tZ0JxzmR3X6VjKNlW6ppkC/mBOxePvv+9791w8cXfuu8+LRh79uxR47Vr1974mtfeeu89WV4LgGnOxXNRrOl+LS9UU9FMo0h4mr2/VJ5hRNlyE7IMsxh0MKRMwygufWsloQoruXbS08Obv91XMAm3+evuogS/yAM9byEkEYtTy8E4lG7v0hm4u1kBH5lbtESpLtn6aONbFDXwBETtVZYE/7ubkrGgeaj0jLrOq2aR9Kg4O9FB4wR0W1TCIw/jcO4YSVSiUE+mrZLG9ifECw4w6XAx6lkCAStTewYWYZNGpGVoGrXN4htz81PR+6Buu6JBsv64pq6+NlncVE2mWUrjEOEsysjLILI6HsOU9oBf42ouTXrBN3dY352lmR0jbMYDyTZjrqYPtCgFj9wJTQH7ZB2rTT34ORz/jHPaHEQ9Q8252jAQYT1N2ng5/fUW+lChh2h+4yZ/08b7X9e8iebdV18HBonmOaenwO1vjpXTDprBFyXJslymxju2f3cLv3XKGyGpPbRj9aEXSh23c0Sv1ZPHMI4SXf977nRtehcwK60Og0uS10Eqctq5c6edo8Pl8rXr+ru6Hh0fe/XwYdqBOf+cc/77Jz7xj//4j9x6N04lXwdp9PMKYFGZnihJVqxqyARYxMXSwyYFzDlCts86RFayYS7PgrZRVVSMuJlpGi3UQk+iSPDtfpHHXSpxIThH3f5FhByN6xTcH34TffMiQv8KQgvBcRttYeK5SWDiybBgI1vIhcybid1lQaQ0xTrLmjUi4hNJaZZrrV0d/7lrLP1gb6zmoHHeDlfT/OiDbEo6/6YXVgM3JK52rvfVYTkgawr0sjVzm2p/rFELPBAOANgd644W+HT2LJJe8HF5AtMTflMTM2DrqRA/e1HHWjT0AB7mnI0Ill/qJnz3b5znos0RgOGhYaL5gtvd6hT1UdhZDYXNreL/Hlt73mTXcyMD20cH13X18KNWV6w67qXqxCd7D5W5x8JjjAvcOdwl8uJDh/AHmFCM5f6Udes+dMstK1at2n7Hnbsfe2xq06aH9+0lhWpb4G7GL4DloQIGNyfX1M7yaY6h8YmAktwg4zZIFLhHrLLFAua5VbaOMAjofXrbBd3c5+7wb0Hx59+KusG5t6C4F6N0dfGDvf79g5yB855CAnVCc9fcL1IAyEI35VklQ4zFcSGyvWCpOF3h3tKks9FrN4ews2YdTOCWZSW80UVFwzcNOIYtcbw0I7fUErbGrQDwQltmSIGgFlflOkgetLHgRGr71ySyWaqbKkFoTpO6eV1myqUGOeelsP5mMfX1DUvuxkxiQzKwIlZRsSGjpioSyU21jRqJVYiMXBRWzQib1QZYkyzGqsoDWW1d22C0JEtw4agwJrJURbPaihFZpKTVRvjlUTSPMrj9vHRm4SKzWnCSR0tr0hjaxKmFvbE8HNZ2VtAJiub5DiyJk3n301EurOeXYYcIfUm5/QLSer/YqvKUxufSqe7/c3RN8oZ1jyVe/4ueI3d3jhlNAeAC93e/+908lOhZJArcrSVxJEbwjQGeDAxpgAXuhikDyFklXVOSYcStkUMb4SMpJcloVZ4yFEErK4aaE/4SDTOerLYACBu6y938pJZPPp7upOP4ziUwiUpyMPS6Owb372zijNzVuNjbQcTdWvH9+z2kYE3JAjVKERW0n9cqKWcjNywC19bVIiV0372IQnURt6g4c/uSFKX1zuFXRifRlKHMEdAA1VpuSYDLcG0gLh/diBrlLMm5FI04Uj0qWsMIEFk4Q522rixpieoO4ST5eavoS5+KR+xmKiZMU7KomKKb+RuxiorGETy2ODMM1RQAnzlyaCS2tZyNmwGN5JbHw0rJPCCMFVVbRiJ9UdfW90vU0IoGpPLn9NfkGlDAzmgiHQxvJhuTyBuGB1CrEJMLp76JK+FsHHCYxnNM1HQ5I3XOc6VpVY7qhvRDGs3zhsrhgSP+YJ6fhB0YcC+5GRmhNuoaFa0DNTZm50ReitW3scINm9FXaXjFVPcnxleePOW+2/pKx+Q/dA/d1TlmY6+Yswvcb7rpJj5WEN3Bgwc5dy9uk63l1jtXO8BHtmUpm8C0nGfEMCrORUOxEgd1cBPMaajgmzib6Nl9m9KH3T3uRYTutJv/OPwGSefh9iQk5xd53IE50Tdf2XQ/X09CE7+FokXBQpdv8awb5LNZKKypGy32c5EfsdK5Oy+1aj6ZlrDIcHdsQ4KmxcC5JXyaVqCVDWe0xPnRBb5FQ7zlHiHyTmIi910xXttLKREihVV0MHrNpN6s/CUxLbdoVjqUIU4cVNgvZfgsP5qW9x0MlYonCz0iSpE1YL6pHAAAIABJREFU3TMhk1Ry839DbiUNKRbmmHhDAbAxNdqtiTVZbrXgxeT5DnMNG8gQhwaVzaC9ejW9YNF0vzQj/libwANugvh32hADD48MczDvAvkBzuiJ5gnn3Tttmk6Ta8+aXH16x8SAu8fevbJ77887RvYRsx837QK1/R1TZV8D6TVwgTtXZfjUQArxnMHTxqyUI0B05/F+3wznSQGTkmQFHKiy8Y2vrShYRYOL+YS14tmIIZSNqkJZppgAcsJrHWYTUhNn4zF/88SF4O602x+TQ+MPwTt516kj9kffVLsQHN9698quUOEysNaggsWoDJP5pXGraCrB1AXRaPlMaWf9N/JCVCzBjnN3vqua/gSf6SftrQij2bOeWfp88JxZ6hwpGjlljsZEHvfFhjtxsQkE2UwwTWHBTDdX5H+vvZvILhKXyslbHRrt+sWylmEtLlJn5NmmfvGeS5ewPLKGuHD0GJGJy60NyULdjFjNrSj6XKSxCgEoQ7ZhlcFiXpKnkYVAKMK4hUiT1RTA8JY0tYaxbVtN8Zv3RmjrrDetvSvMYYgXAQCGGNwStWbkNiOBqQElCd2VcouBkY6qzfslsWRZ/yEq5kIKv3bEAbd/Q+XQkSOHhklD7qaNm0o2LBv4YWrlSZNrTu888oLVT6w/v2fX3ZWJUg8DbkinIgTHgbvxbQIIuc+quYamNDMmAqxYzDBLZhgA2oZmR0Wryoqgrc6tqQKmwBtPuOmtU3Bd9e7t7cOJ7tjb3TzhBSnuMQbAXUzxV1Pcmbnn4zKUSfcvkFmJwvAxDsSNagvwDTkWtJmnKqmilQkRVpwnFaMl0IwCP4NEq450NRYCaoyS++5R/TIqmkcim7IOEqYRfdQ8W6z5NK0LMU0wd020wLmwuhaLa/oyrSTHV/qtPpXLZExC9hTT7n+zTg99h/ZyH0CWMrItcrQVI4ZRq/JF8UmefFy/zKxSeeYhZasUDnmG8ELwp7f0hFjcbahC8sRJHirq4VDbEM4QSqBGDLPH71ENpTuCLAcwuVhTM7cJSE8gqmTmipX3gQO1SyZSnYxcOeDbJJnFADOqWopGC1o6ob0LEsa5/I2nAbmeoZYEBwG5NMeQjTxA0EgimOfti5zH804bEu+pHBkeGRwa5D309ooXcRg/4U0+aq85e7qzb7prRdf+R3JFuP3I3zkkzyUg5sx/HWQudQEyV5KQBa1UFSpncFZvzWERGGxAJMX48LREFWScZ4uYnBCaONvdMyHQBvYRNr4g5vYReJd/RyFvR+H4u3ZBHEotau4CS+kXEUaKNVHMnZ9N8GlxkzJqRTRRsVghRmx5+hxKj3KZhn5Ikcs6JMhqVqvlW6qdfHPdfcBVQ2YbLCQGPWx+hzrl2hkqFrnGmIQ0BoechcxijHhG0VBmm4cYg5O57Gcv37EBnzBPziFcyRGT2TKnMFzoVE78MGwCvNJlw8n6VmZvHSA/zkgpMyx3JtSxmV0hahsVC3hFlFY0oKCtq4LODDYgaCM+QcCeLs767KJQjIvYCgkCOaW85xhaG0HqAEM2BmoNc2lCPo1gGoZV9XwS/mVGYqiKMTTAsw1JQrhepislAh2Rn0P0Txo4e5T2TUfpCQTMkKd97MjQyx+dwcfQIU+qXTHs6MgHYZXjmKakN828wAOzGjni5/QMU8AtGeFhbQKHRKBMFQ+QGVOrgSpBhqgc1h5ljjJi8yGRTYI0IsMEgOTVmvsqKxrQSIFivCwxJmZtcaulXou/SUSMpFWrVskc+oVok5CdezVE8kPDQ/Yt2P09K4kXfCiamN4xOTK18kQK8KGhcnOLutg62vAhUBe4c0+GjwOKG4SNgYl016xZQw4cNYyKEKCfmltVpLFqIxqK0FsTnOX5uBNsF0xXq+4HL/33K/0VcPcFTbv8zVvA0Y04HSS1EPtvYvq3ibsXERKAw6x+1QiVaAVsw7o0s9RNpRs0Tzh75XJkMV9DPlGRBllMDpd6VI1hDaqnKMk02zyLqWc8U2mS8cQE9WN+JtqZ65vQxiwHUBITilYFHmRYTGlrfyPRUbFGl4GK2UJewMomst+qkxABpKZhWuuKTEtX7XmJoXt0DpjHM5daNAtSnZ40rSsHdOXBMhyk64w8Raa57j7eJ0nFbEuRSrZRZsmawLSEmxmcAoRNJJmWUcr1mq92nZ4+bKXIeuoGHOqJZlPKYZjqPBs2ebQhn0Yw7cKqPDazw4XcQnh2XEJq3xG1wJk+Uoo6CGEaOq4DnWTrSbehJlVuCEir5G9aDAXOAY57M/BAXNVICpqmrdK/KWlYDuG03v/NqQhYJswTF1nDnEZWlwOYNyUxMc1uBrrJ5vpItS533VfrwpqFqvN0ae/VV5rqjlvKwWBJUPMkDywBdKcq85RMM+Ofo43VLQbA2PbHvu7Lh6tXr5YKdAvR/KN7V1TXDfA6ytHxsVH+jY8PT/CW6cGOzi6u4NAw7b06vYUP87C6LnDnwD865A9Jc2Houe1jjx25NIbU+bcV3ZUSrnGTiKDdFyv9W1As1NYv8vS5uyi6Dw49IZOPzjkrd17ykbfjBwfYOKDsjDUt5gq0drDShU2a0BI9NBuMlU0Ow+R6K6qNiq5XomZhOTslI+JGxZBJI5r5whO1u9+25b57KqFV2uT2QMTcigZIi6gIMotJ9Z3T34htdt1JrPB0vtZBmqBep6hom4TN30RCunnUaxs6HTih9TQhrBr/aJCsCvW19Ux9KeJWR+Eb1zgkJtaR5BciplFD1Xq+bJFFy5dk1zTIlzY77Fy4RXYFgousqJElK7YQ6RhyCpVrXmPUMqixRU2KaDnDUI+WMk/8n/JM4kDXF2mNG5xpL3k16D96yjrLgPoZGWrcfvAsx7/NUPknMLlmWpZlLlmtQQAZ/wBXt77V8CbGc9dcSmvpJiJG+pLkllDXS0Yvovqi71dRqpGrVvugg31VOkLEpi6v55lQOy6O89xStn2ASRTNSoh0bUiXbdkiDG4nUjhtfeW5kePX9fEymZUwppP3dZxw8ZbRydFNnJXrmg0vm+ftjmOjRPajHNzT0PeJeyTzTep0p7YucG+kLS985OUz//Zv/5ZLAGsY9fb14UqvKF+65Ecx9dpBd9ztf3fHPYj09PIVTBdwd3Mv3EXe7rycUNzH7clpWjAMg67JFTzPSMSbtxZTlbnIxgA1FyB7ChhGVVFxBofPjjpl1lyrtPWc/0q8dbT4ldGJqL3kffcZvR4aEYmOiiHl/MFOYa0Xfka7Quogrf8qOt2STcGdsYVbQ6g2a0K9qlGxvtKJ9pgCqrAqhBNOoFJ1wdimkbBVA6dvQh7+ycOF9QGc8nGciptFtY2LM/AJhNfABobUCJqASvKMDGlCUNAkGCV+tPkDxWYcEvCcNdicRQXuao5hSb3LMk9mL5PW+9Pv125+O5cnM9mBfg67VyQ5Mk8pPYKe8U1MOxjxvZHkwdgpE1Ia1bICzC0GzNa8tBfy2zXNtp6d786Al3oq/doPtBA4Gkt1hRQb1Kco/gZYDXwb/gbUUQf0wueRBS3qFu86fMNCRkRKGVVExZSq0V83N2z3aEAEiUvJHxUy+XG9o6ObV7+wr786OcTsqvauvmjjxMqezkrf2jVr127essVx8ImvwI7wTpuhIXdn/sgRYnou0BPL650xipNd/Mz9EdrQa7TK7TvV/t7v/d6NN964ffv2Bx98MKNVZeXKlW+/6Z00d/dV+JMefmt4ZOnbAVOyC0uSNWNRyDocyiFchm8xvUkRYMUynN0ULeZeiktK1EJWKcvSf2e0pLxjQjO46V7md1XLMy9tUDlCme1p3faK6kos2dqwrVhX61ar5KqKNai3IVzzEx4J5Vz+pDKSv6Gnc9mmBMleKPPUOGUVtnM4nKDwJY8gJE7gVEROlVAzLewNG7aqoqQhsxI3HzxnoYDfiXxvpaO00d40C6bzSLqI7mLfxjD3j4Gob4C4ZZt/cp2H/WylPtFTzkxKKdK5R/UhxmEbJ8dI/zkaH3i4H3pJZJbn01jCEq5xjkgcXmdFLrKOYh4K6ikfkIm766wpdySPii6vGxOlFUgGTEqfZ25aF/wVWU12UCWwER83zNNRKspsbjQCsgSzwJTpqkTXRiqbtBNXjJxwamV4fGVndaq3U29urG9E4Mzl7d4qX7Xkpg3RPFPL9dL0FO+u4Yuw/GgUd1v4/aiDhw4SynddedUb3Gf+nZ28FPKb3/gPk2QAX+v84Q9/+MILLxw4cMCQIUCn969YEWIWF673x2LoEmmgMYoiBkgpK4reirNSOZJVtm3JZjOSoTQpJGtkRkhTVsuQjvYSFskLaQpgNS+pRCMbsqLduXvr7rvn6B9pHLgg2bOFEVlay5R0cbpeo5Jy0OKc9FW8KLkNOEz1pbAmgiFMpUYsIsJZF2dUISVIbEmLRYJSM9P4oojW1ZXhaTzww6zoreExIN8DyYil0veXj4WODg+zYycuSQdV3ojFP37sJz7RZ9ZJu3iC53u4hVivivRREG8PyYvy4UkLLWuK1YL7f1Zaun5K+ipZs9yI458fd6pJ62fFuDSxxuysZnNEnM6MOpFGY0Bd9dwK6ZxMuCAiwhSyh3xF97haeEe7eeG2abdtOHWNGZ4Xhr/VSpXL4dSuXlW7N8+Fmi5Ce/1IZyNLqf3/2Xu3WN2y6yzwnDqnLqdO3e/3MmAnjm1i1N2JYwPtdJwgaAckIgXxhHhEAqTkKRJv8MZDhISEQOqkowACgbGTdCOSmESRCB0TY+JESfmaOE6qXPf71VXnUv2N8c35zTFv6/LvvcsVU6vq/GvMMb7xjTHHnGuuuf+9/n9/7Wtf+5t/82/++3//7xcTOyljrM8syZOK3fAqPGvMZpQbfNOUO/XLzcZ3pdlwraD3mNG9BfLe1GsG0QBa5h34LOYxxDfKTZm5z3Yk4Hze3b9npgk4ajY9twvXJpBdqPY2mcn+at8jape3fYAvXcXh0jaV3xpoyoFC3tkrm3acA4t5rQ5Ug98YyXp65CEdxlpNGGGZ8/HmoDpsSGCY+LdMmRJWB2aJsF6wxvLNwDP9MMpKyTjXcYGky8W5eQXkq2JGyzSGVpjUo1m2q/oZsxxTCL+0beLhkrbA9oIepB55X3wpMD78y53L9GnGivVtLaTkU87+tC6771nnrr2tu/A/W3IclDg0nKc2UdOUtJJEwLe4RPnKeOvS6CM2mrw84eZr1XMry1jVDQ1rmzndv7EYoJVWirpDnR6ueMQce3lYjCK/Jjd8MctHP/rRX/mVX8H7iS+++OJP/uRP0nDXXXfdcccdv/u7v5vGNcEPOTHqFs/tyC1sLYbsqpqaC1GjKcottbdXAdkLJbUBLUeUi3YusQ/Rq9fMvceWyDZGQLsJVHsf4FITvOWtecaX8IkNXCdXnEEh6usCPj4EvCfjPs3vJ01vQqUfuNET2HENc/Q1B7wdo0Z5ufvbkcs8sB4jVYx1AC0qucVrC4aZEBn3SzHDqdwH4GWWHXr7pryz+8pZ6QKXO5BcBoFXyGhOfs1isau5Kc4IFKPEMg77Yve5bEAduBvU9dLTEwvayBz7nMl6V3NZsNLBmLUltWsfavuHLO1kl3W917HbuS3xyjn2qPTNfJ2oywG0yqqR4SKTE+x48WwrPKga5QJ5g6yIQmehR7msYl4HwlSKxuud5re2Amlc8obERs2Hj7McMxjN/wnHjnWwoeGkt+vESlOVAldyrpsEc5kdvLRmF1ivP31q/OFUPGTzD//hP7z99ttvvfVWfKb0Z3/2Zx9//PEbb7zx7/29v/eVr3zlb//tv/3TP/3TP/dzP1f6MEvI9X3cRfiJGZfzaKxNs0/KxqrXbteMnDEB7BvidSzH6K3RlyS9RuRLAtzAjoPCgSwhQpNr0wzAt1Dc0itPNM1zVsRLYvcdZuoFggU/cmHvfvlM+ctZ+ZbswHxPtt99lQMXtZJoShKbDCxk8T+a5CGsJ2RmFFAeS6CY/9HSLN77EgN6W+FsbBzpP06VcK0kNgowKyEJrc/OdsOsiJEmTyXLGf8j9Gr0hic2KbMv4okAhZaVGjWHYGCiXlEaXzTF08hwiSY6xtd8fXkgu3XCwTx6r0azyqwo9sh2JrRFAES+8fafnTxeRc2rOV1ObtFYibISYibegZQ8ZYWGIGsMOJOJj1ZFbSIC08cSuGGIvsJQ6JHQizxa04qX1z2rat5GABbWwyZCairbaGagGAXWITJ6vSPvqkAZmjB2HD7jycoC28X+dgLn+eg52VXPn8h9fvHFXsOlfax9xhuA+BNOWHnwZ4UWiMcbd3ya9VOf+tSP/uiPfvrTn0Y32BMof+qnfuqee+556KGHfvM3f5NKFby5bKR/i4QjhudVHkkWVil2KYJLJxttw9KEaZqFJUsNW1an87K1AbfNVWcBJEQKpc4eqhkxUW5ImmZEHknexuu3Y7/5MntchGk2p3fC05sKSAWXJ3R2xIsoyrqET795xamzly5fTHv3PpWmSGim8OT3V71E9ygLMBPI2b/2eKfNC651Y9MRc15w2MQWQeAdHsTIqmb0HTpCuQVDZkfmd0l1F6p5xSahti+1Yv5Rbnwa5qY5BIf8zY4mvWKUhic2oyx+KcUmEwXpFYWh8SpfCVFJ942vjKIQNlXVCBQWyKcxjfba/uoyoF3k5Q5kpjQRh31Nih9+hZtGvUiit8NNO3S4DkJy6dSUAJPIF2QyRKQ4VwXE0kGGyNNrBB4KxEdOwiJno4mm2HHxs6pixriI35Rujo6RUCQRIOUQCWsEKxa9GpfGKuZ3hFQuXhzlEjG1DSA3u7lM4RrKqm/1OcyynIptB8rzDiXnUR+zzzGf8SnTZ5555puvvWYVfPNN/Bb//HXX8a3zPtJ44w63//pf/+vnP/95cEHG17RjE/8zP/MzTzzxxHPPPfcP/sE/wKdcI1cz46PpcLkhjRfcMikvuMZ92aUHVxo2mgya5jBAxVIv1MA31iHDJiWI2OkZ6WqudFc/ZzzKRqlTUFOA4xH28GKixo9jomm77nw15huCvi7Fb77iT1dqbteLUb5Pw6oys0KxbHblY9euZ2a6CmTyZGiaHXyg4DDydWB2FWn71xme+mXO6JvSPiB7sLBeEOgem9IMmRvlMliBBFMHGh5lQoBb7cWmzYbLk1VjLxSrDzGKwoArr5F5AFW6tDnadI1+4Okqss+srk9sQjozLPlchKhcpMyTgBQoNNnEmJ2lkGCWqpGh+exPnhkj/rPr3V5sHO3izj+bs6lVIFOm37ngK9Ey2fwcq0FZWaXOmK901kM0czufTbnjiIEku4AXkYuQUTbFIpt7GlUQaGnIx5xW89Bl5RGEAXM9FwxQdkxGiAG0HPBqz/WOI5tTiBLTGMoRHOXAkcRojVQN0jLMORBGDWGRhJr+NTJEa+SJ+rez7Nda6bSNYL71Mu0wwifYjyZoiuTDgwQGOdgKUdI+wcwm1E8//fSzzzxjaWOe+/sOly5fxjPq+FrIu++++7r8F53kfRbJMt8+a/wVVeLwzZF45B29Be8bb7wh5+MR+sA9r1e8qJtmMbi0hbBxsRroMpF/E4Z6KQVruQ5t+5h1zhvD9DAlSsqm2cSJ7lFuYCfS7ON5rna92++qfQtFDDR+2FTEeHnTsH43tnO47fLi1NWYl/0+GDSZt0wDwWhSU0JKJCWUTmbFNzSdvYz33e1vCW8+rBMbwMRsQW4gyxDQYeaxtZ06ZhzLB56mKQ3IG/6mCSQ0kZlZ6TUyU+brkEdevVWmuVDefQdmxkB9tEa5kEubOzBL21wErsRCNpWCY4uJ8SQTr2brE9qRGfjYDKg08JGW1gYfm7o+I08je4Z84f0YTuniRy52V4KD/fNL3haBzOqR8koR934xhRSNKg9jGjQhx1fioCEyudXNxiQXgcnfwKJyJkcXyS6kVnRUuCgI4LWDFxTmm/wNKpGCmmZLaBMHByuemQEwOPwVNPs0zLzMSuAMwxlIruHMC0PvA52x2VY8nJr8VCZyb0R9camlYReppLsAsVuRmXLUIIK86milFckJjvwFN5EafvpOsG+1uh0ljiIGMk0O63qL2ZZjmg0N2JlBOOZsBqbx/dY1n3/++WeeeRrZ4bn073jPu/FG+aOPPfbII9/war352GOP3XfmTPPNjeN33GMXsGX/8R//cbxj//GPf/w//sf/GE1LsmoU5yCnWD/RBJ4xNoCmOfOq9PRpYkdET9prgB8qI89WuSHCt6pC0ygjVywk9E0zIilHqij3yOPUjCJh/pWFPd1H05Vll5/3JLc9F5BYNfC9prYnL+lVYrJIl7fmgLt/cpMsoCoHgUqZ6FsCZkDUrMh4cwjfMOPfEYkfIyLzguNGmBiUuTRJ6Imm0OAqrwiGzINWNoWUEoKUEZ+97dwAogkygy6EFj7yRFkkQq4L8G+6FHwyuZ25XVjpg3zJySZZoFGgyCuX7QJ4MsPAqbEqKKDRK8qNaUDqKjLzFe5hfzZljlFiJiFEKqxp0l2YTgnO65kgXx2wJOBvhJAgLgsOhD6GDGEM6ia/DRJm7fzPBliyA5Nzkr3bUR+4SZwUkadgWCz3z30xY5Qj+UwuRReCdfJInmOaZtXoCBzKk2uXbCltZhzwVOgVFuYsTfIILpkZlqxVklnRuoe2+C2W6+nkMqZAnBFIJn3brWGSKXAFd2pZohbhgXJqttHPEU10ORvNU7KExOcZ9tzURDAjCB5NlOPrjLDR00WcsEpDocFvb0bO7V5LSA4UK+tp+kXpceIVYcX3L2aZcMGKY2AcKge4t5EKX9uIt9sx4a85d80Pf/z/vOWWW/CU+3d/95/9whe++Buf+W+sDwD3P/BATDq8RRnVQcYfbcL3uGP7/mu/9mtBPRJRSRYzllRKePRWKUd8O3WMNHslGTOIxMc/OSO7ovZpNTDLzN5Inh5kkLlpSn9SQugAbg38H7FM8D8TYCdbTfG9KXZA0EEYRt+uNvtruThMxOFybPv7Ym7y2WJV8XkTwruq00fYUJabKgdhod7CS9gKtr07fkt1KT0vK/+NAvsMcOxzLdv6NPy/DwFYzBsy/ycyyoyIV+LFTySblPUK5ewgiawM1ISjlSSiYiC+yr0I0RBkiFVEmhZeY+zCPpB4L+CMX6qdCBW00Qy4s6pKfa4EM48hXlZimIZfRa5o0kpMdkps/rgIZP4f5xenmyZdujzteh5Ow7Q6pGXCVgJfDE7ZqpAbwPAwirwO2MY8rwgexAz2PwIxaMyqkdMIlf6kADoZQ1p3LLivXLaSpV4IVwtmBTinWxtDK/CUTGBnlRzovUguUd5CTh4bGq64kELx2YX8ynJ5v0KCM5FeYCM5YCl/H7/oxZyb1+XkCbZUReuMFkJCDiRmdhZN9rGQmCYdZZ7YHsbvOz67Eln2tU7B7qNAATKV0jMKMZQ9RlVh+QqcBO+F98+l1DQ4jpyFBPYpdtRMI6RckuD9IC1emU6K0hG4IhFHDPUbX+nIHsCl68o+TQzKnQI0JjizWdMoU217B1P64ZY87mlosu1P/hkPtuDTqOjHn/vgB7lr/8pXvopeftf7vutO/FFVX/lex59Tfe212Nf1d9yB/vVf//UvfvGLenIm+ieZk4+NKDdQzYVGv9JcYOw998bYSg5cTw1N9G+afXILGiw49jvet+CIQTxjv0bYOZsntqbyP1sl8z3L3zXHjS9eUYbWf8yc7X29UELD+skq0l4D01BJlyEtTQteCidhF/gUvmEGe3d8U+Q+N4+26pJGK3e7aSpjCpEtyrA2zR7fUI2bYOlq3DA3zR5vzE032CQ5rJFiJEfdOE9pma07YPLbednZZrUfdl3YFWLtZZeM5zmB6Wiq2rm0gMjWonRdfCmmwpjHQBpSZTZzp6k4J0opcLVXBxmKMl/7zpMbWCOyyL1iwjsZZFtDeBSpirKlkSIywNhBYZrajtDMJOWVs88Jjx1G2qjLnYSuZBIBc3kdn8kzMrfnnBML+jrlyIaS/5Yw8sLsslGCjw9Wn0BBhhJFZe8SNT5M6UKMidlPTFAnIg4m3jmCq6GgV4heiPwzmb2Rb4KpjzmVfKa9hc/IExo14w9+LkSwiLQEsV+OkbG4R19igsZGH024pWK6HABRZIfMhaHR4w2HocKe21qeML0ZXaWyX6j7IU1q1W1PtiLloG7LiBHevq+vvmpPpGNndfsdt7/y6qtf+tKXn3rqKVxL7/3O9958802PP/E45jH6i2fUz507p26ctdnNMtXFEgICCgeuqBnIZWQGxqQahxhr3aUd9ExNfW5V5wW2CrfQEEXs0zCkkGRrmgshTtrki6hfNIhU7qx8ZAU32vRmsE0JG3521OaHwfWfZ9lcXGUdXO8DmbdXZYYc1n4hfIOf0S4wFBP7gLZYWCw1C7SVTl8+c/osfu+1+Xn3NAw51kKgJnrTbBPZ2xZdU8gFHrkMMbEn6mRU0qshUVPCkLxXKgRMQ9+gzNO7ZwkgGfkdQ7i6PES6XMyqkLVX3RonI3ITGofKlhuIJRgFaig3puxkZ5miErLfGU2XmNlm77hWwDNc+BD9ZpJpgimrtp4ndWvdPfP5ULXw0tZusqhaKWSfOz4rVOv6dmqzp8hoKfnQ1125c5j8XZzpKPidxFiXEliKqs3rEDRLvY5GlLAYUB9Te+F8lqkKkiZ+1mW0tWv+jAjaxjcjpmeSW0XzZh2iqiph6j8yyCuR5/GKISxITluSksmWhl3AVAevJjEQYaUiCqUvXjwnLjS+xsRgTNUoZ4WkM30SWjsYT8VMIYLrpi8xSok9hZ+4gQXEr4yee/a5u++668998Luxd3/fd30XRuaFF17ARYXfOeI//iyqbMI77m/ao/GNWbgF4cxZJ4lj0aJli0VrQEMTHeUul14j07pA52G8xjmGiXIDO5amlr0ZGwbY55m94He+3nIZl1R6X7w0nQSoRJZndepEMRA3WZySc+bITc9ikws8dpWhWsE+AAAgAElEQVQNzBGvZlTmLJbOe/GJa+bW6JvmUiL2d1WvuMK+3/2MOsPywavnoWYBsBxrau0jTaG1AY7Ku7ZYi4k2SRPWRIzNKJOk0ZBh6+vIudItdKCLYbcxKkXRjIbr87WKaxBXnt+ohO84j1/Rx+o1OSqXCV85sAikJcQWkEpOfc7rRV4uTF3JS7MhRyznDZvmXO7idMxSHqpttOqsjayXSJptBCeKWp7J+3o6SNQm/2wiZf0UMODbrToB8jx8dubk1w4HKtPyyOfcbNqLXQlYmzbAerBpMRnUcYu8IyO3AAv8iVw5SRixMVV7ba2sVbkKHEkl60YT5hz2lHQuxWzJQnswhUtsSSSnW5KzYxvMlmcvin2lnDV4WGIGFWU22Hdd2EE+yPLJ148UcklCYxhQtx6b2njmC2ki3d/+nd958MEHzp8//x3f8R54XrjwBr7UEQIeMDx9+TSevY10YeN++hTejcf7g9G8Rb50kS7qCGsydBWmty6YenClOczzMK8q8NEaNg/SlMPkxH2Ce3HT2hxKe3Fv+rUCyX+1lBL3iVk6MZmmcNp+FLZFn42wyNFPiV4DfMPcNCNhKxNqJQ0sTbP1Ce1hNsHeimSmVqEhjHjwvjuedy/vu6/2ahWQsom4UeA26dhewKtv4p9twAQAc5TRXOCPadCREQtDkRrsQU2wtQGcZ5Qib3tVmKVkeHPgVTy9T1dsbIxCD2BS9XjT2KrhC4hl7WNEBzTz/d0s9n/+lsOUsOOirEhrgqrRp9S75lthb3nba1gcqyu3Ykh4MDeOpRtbKslAKv6xxO1ITqqDXaATVWDqTzqSxjRb7eLBRy48GeiaN7PGOc7JiS/XVI4y5lnQWgiYJ2MtWgkLVIsmWzrsHzo+eNfcU4AlnclUeudei/QTY9OtydwfBKIj8dGLCSIa90w5rI2tdQ7/p8M6Y91JGaST2kQZ3B4VHq5ccsmU6Uy8vCTUMKPGP5IwLcrXXXf9iy+8CK+XX37lUz/3C+9///uuPXfuiSefvPqqq37oh37wl3/50y+++BJ+Orr66qsiYdi4R/VuOXYqyruJlh2OTq3aMVDTXI6+akX1038GtWlibRutPBPCiEFl7+q4DfPMZxUjpF76VePerg4Awra8Hr1gwygsm4rHXgE5C9fre80wUKWEz3KkhrRpVlx1g8x6rY2DVs9MTa+ns31W9fJlfNvMgAsqurFvolB1xz61Fl6r+KZ2TTjxKQFqvGkvU345BIR/UML7RessmMdIBMFdySwJ2/HKMNINlRGwWfYL1S5heOSLdtF5IbTfS805CYSmZQSN0Gm0tCPXAsO4m7JYTHHNuNCFNde3kT1Uc5hVu+baoLS6oeMO5bdHJXd0+EhQH4Alhs1T38ZRY4mBBbP9swNqWapYm8krr6qxPuUq+KENdAVd4PaVywaYUqdyDe1j386vzko4NGzwY5QcKxiCuHfup3QDg4vWWd5EMyE60vQFGP2cBidaG0zmyCxpFc6oMHQsL/Ng6JSA/dhV3EOippRBApTnrz1/5VVXXnjjAubfq6+9+rnP/Q8oGQo/V/75j3zkl37503i6/eqrrwlsp7BxJwleI1vB4C+v3n///ddffz3++tLjjz+u3zQVxLqULodV4DiDNbdQT4M2zca7CdE0CbbbZErZ7X45QxWHxKLQOceD1UaY/+Wo0cV0dTy1lqd3JuMZTkyuo6txO1u5G8mNzUYpSiau9CUIsEUwcnpC4rFKtArITNU0IL98m2YMLYx4xgJx1gO3N4yjZvqOyLPVL7wq8iY2muKvcHWDmPha20sr8kc5IQaq4gsp2WPfKDN2QbiXJj6ta+TmswXj3OllL55uyjZSLcubXPLq7u/RNjd4LPOKkGXDFxnm0h2nEp95BpuI3hGOXoFS121ceVB84HJjm+s7qGOpQHNlHQsnSDiWGlGMrx+95cgB9065QUCkFrTWQuJUqgcGMFjanFdRT6iGIScTGeXosbwXYQVswjBU1b8BgiNcVcdW37ibpbHevqV5kQhDkJopBAyYoJ2K6Bze07vzjjsfffRR5INNZt5n2tj93kMPfeX3v4pnaW6/4w7dUMEFme+4j6Mhue/7vu9773vf+/u///vPPvvsBz/4wb/8l//yf/pP/wnb92kiY8NRR886MWY2bWNqmgDYNLfh9wO5uOx7c3Qx3T6TF085XlZyNCvi0ihSinDQKYdMzsuksDb4WcyNMLg3EdlslLMolV4+iI1DzQpUW+aYyqnpTNMENGrAqWbD3zRLjOhTtFkCHQ46U0aTMRrGSfPSWfuOSDz17kQbXpSPetI7MVZ8NUyTQe8mTeyJHFfj0X1LlC0YJfMWCAfkM3fh3VJZe5MrCn5Tq92A7Lba2L98RDnrjuVcQmQ6DXNvypBNZ/KQpOGMpi1cW/AxRJS38O/FNPxL6eWBS+Ofm1siglZDEENEOfJQD428olWyYNK8IxxeAR9QjSpGGVu8xOaXr0yHh1j0TPNKGOxY+IkLaJr1AxZ/33yY0vKcEf3bWgj9Pd48UbFYNNbcxrr+3WnEHD2BtPfMtwF2Dn8Y9a677sI74/aDhG9ILSgu6dOnLl+6fO9991111dXV9X/a3nGfHh/5yEduu+22f/kv/yUffH/ooYduvPHGG264YepQGzRpkIDnUJv3tEQVnVDf1ITZRbaty9lUfkzJFHb2apXxKFKmz+DUznGyee+5oRu4+wN2yzAkQQCFZbBCbIQJPxUOIFp1YU/UsWnsbGgImyZQlYZj37NDw4NoNivPDGjOERPlBjZs5jTsffeLvndfYchmO9uqPWSdKBEsx+OFYbgZA/XCk3IGjgEbl2g6LnlviL34pTy1hhjIGygKloqi93VDlbJWHKYzXnq74/p/S6GKDR3goVFBUyGysTpv7POMhO56HYaLISJPlKMj8LHpremLQjdsdIjKKE/pOkPkX06s4UeT+I6yKHTrCLMC1jQPGLqgXeqjENDo5RX1PaE0zDaCxSCMNAvCDCw9Bbz2xzB6Dxv69rC3jyZe2hxoPnRh/YUtXvWbky4TxlysZTyujYRJ9hBRX+IcFL24vyPlCuRSl+W6HWsu5UcrOEcyxyznG2688Zpz55577jn7WvdLFxEaj7qcv+66m2++ufpYarp43pxu3PEXlz7wgQ/81E/9lH+s7sy99977x3/8x/h6GhwPPPAA5BJz7day9XrOu21jDptva4X7ZXUf1Fz2GAqkTzrHJA+XxbtOQSgXt3U0EZwfYbM1dFQSEoawfcoFrn196MJqdUYIUvWEjL6QQ8VKXGRpZKIFg3V4NPH6ZqQdMlApWAzUsMldevwMje93x/Pu9YfEBRwJcO5jKHzvwWAEKHAPi5qNsCO6RPct8r6sZj+pNZFwuflv26C2VUbWapHhwqK1JizWBltb8chp2bujhTOf1B31queRqTAov4nQuERU5I8yMdTQPb5GBsoLIXowNMSDn8fMPeqjnP02nRmluBepik51MrY+00DCs1ZTnI1tCQywRfCHjBdcYCo+yzi3NjmwGRmiHPmob9wjoJct/3CAQeQU1AyorSJ9+xDUrDLLcRWJhATemtwaLg10GG9fTmwdoU6WeFuHbNlikgCYp4qQ2bWc17J4x/5WVCANUBgnjOPg6Zo8oEfMCV/qiL+4ZB+qtV/XnsITMiFy5k6TPj0qk7X5jM3+e97zHjwhc9111+EngL/xN/4GNvH/5t/8m9/7vd9717ve9Y//8T/+iZ/4ia9//esZPj1z7vI2iZtgvl8aHjnRarK17ACAd18CDJcPWLPoZ7RqRWVlwy6mgfpoqlVGRaWg5kJY+0qZBfMu02p+u9gacOxM371ojWlQjpqGtjQBiiyQedCZTQEiY5ThEpvCZ7KlsyISRN8hgyakxRoixnEwxfG0jH3PjH0PFJyjL2QcMXuSbNEQqdfeRaYFISazADuKKYXYnt+WnIDBemIrTFxlmCYiwewHlxFcbil6HkSGaBeZ5NSehpnHJBlNMO7/mZfWN5AK0AbI7ciZdeUcrZIlAFf4Y9RMkKyeVi5Dtg3PROZamnsJMHBYNA7wUcWfpxIDT+gYjo60KAiILDO5+HQIRREbwKqerJ1fVCT6cIMDxbjCMUqWS5A6z7oVKlEcYhatnNzrq8OUNS9bArcs29q5KxU3lKDlaxfWeOtEppEEE1UDZXTBJADWmBrHYZMuY5OtN27H8LqMBjd2CAqdf3bc4pfhH8+DIf07yrddBWxM6xHEPUdb+TTSNWB7H/BUzAsvPP/Syy9dvHARXwGJD6Rej2dobrihiUjCszbDOm5s9n/8x38cz8n8/b//92+66ab3v//9V1555Uc/+tEvfelLeOodW/YPf/jDf/RHf6SdN8KAA+ubz2J7gWzxPKbWPQkM2CfUACwzZzQ8s2RTMgFskjS+zvQRk+QIXQhJNAAR39DJREHNBhaay3wZGIm2eWTPtTM7TH7K8IjhRBCVlHuNwEkgQrzRDFPUrzLGYJFnWY5RyICgMyrT08ERhPF1KUpwGcOakKcvX3HF2cuXL9r77pE9ymOinVrWdzvtduQ0kTUKs8dhnxK5gUj7bi/eI6EEQfbHGpJWIS412JEPww+VIfCyHdEEiHIgKAAmGE2UubhZ4nwvVow05x6pBbv1Je+0+n5FAslJmGVJ9vjqaZXSesRkFynbeXUu6u1RyOB9KR2Ru4ScWMQgN2ta1BI5A8O5GEGHg23KGaZACZIxSZ97KO/sl84lRGMYNw3u/zBFrcLek+quFwmznM+ON4aimIcxMPtEjEWhonEPXcxs8usCdQpzSbTBGzAWkEyph6LNHs42pMxc4cypAgXJg0VipGJ8mqI+yiST+6pgHUgjZxsZLEHGZvW05KCBWN5xa+rsJYlPxsPNHOqj1KjWv9P6k1IBnwbVsKZLPQ9tDxh2De+PP/bYo5cuXvQZby8vv/zySy+9dPWzz+JpF7wZ33jhURmbm9DylebXXnvtn//zf/7ud78bu3No/u2//bfYteNh94sXL37iE5/A6yc/+cmIx192oqP3oHSjSDQvvArKDqspIQRINDQ1gHEIgHIhC0CeMOF/NSX0IWeaQnqY1IQ8jCR6qbdkZvdiJyM4Ro9yxFSyQAoTzQrZK6OGsqh60xbNrEuNb7NirgcFYiO1YMukrRW/ZEnfM7PjmZmmV1uabdzGJ5pngyl9BDc8qkKjnzfL+mG3QY+BNU7aJHtIuz9C8EUm5RBScds0TpUYvdSf2ikh+dtsmGKIgAzqoK3CBH0n+nM2/gaHrbo+L8lY85ZW3GltjsLky85NjhB41FdEacEnxS4pZJ/uTEgJEwHceQFBASbrSSaV1V2yNvonObosX5IlDQ5hmkrV9FAgCRamxBgksKDyCChdqFnFy+G1AKyvv7qTD30p+iSGACkQyXPpSn/dveonND7SYphEoGc2cnLUXTAmTonQT9HKRKfKtTQEt0BVX3Lk5uxdsw4BrBmrMjZgb5ZgslpuA3WyB2KKgFqo4GUJ2O/mEsmZLMQJA5dZDOjLt7V6v9O7DzbdjBnR6Dupu2Uaej8NZLgaSc07r9+SCviFWK5Gm8JpEpfJUsyeIv7Q0qPf+Abe/k6I06duuvHGF154EbA33nj9kUcefuD+B85eeWXsDjbusPocKvPSAJ/D90n+D/tGSRx4QgbvtWO/Dvkv/IW/wG+uccuGF3AvTMyegHO51x+oEZ2EnmjB1IMP1CzE8B/kN9HqMiZb04wUTTg2G2XEb5IVNc2vvPAMnY8aLJAynILCInkQZaByLvkE5kqMAMpRU0FDYxYuQCai7d3xwZPwt5kmwE3qhTzQjf4gnibKDYM63+h7Kmgym+8qMiIvWLbG2JjlYHbf0mHvWRVFXNGiLPhYyOGjlfkjMpfNErJIEV5yqhfCCjNtNFEmIczdTXqxfcKYdKROOtYxu6VxyhlYiITLZ2gSaBYscxXfohlI3DtapKq4qdCMrjGVEImGyggYy5hR1il1OaJKb1MvoiIC12TrnUWAvw0PuwMFjlTYxJAn6K5A7mQ8dnVkfms3gWIxcyCPm0u3EjXDmGsOxFb1WkzsrxfXlEB5fxWIgusSg0xsF6oqQtVgX3xpgGgDSnOkklx1XDRp+NUeCjZgyMfH0mQfUA/rI0sr+P1wez26W/qiCUFw72IJaGT59xNLRkgmJZYyipW1/MzV/NOBNpp8zbpyFjJzun+xt1TB8o54ghXA/MrsSbBxx8jbPxuUS5cv44vW8W1zgAH85z/8YXwL5A/94A/+yq/+6vve912f+W+/iSdnnnrqqbvvuSfz2Hn64VSnPfUjP/Ijn/3sZx955BHs2vH8DHbt+E53vAEfKUxWbq3B28vWoUulhL9mIw2z2Vu5bW6Q7RDOpmfbKYDEYR3z4VNzOecmXNNc9p1a97Is44clkJKCmoOcRuzURUuUByQIgEMgxlNz4FDj5bvqMqPaofe/q3oZn1jd4ZOXb/aT6Ua5oVroxnYTFwRVNu1ufK2hydLwRSqVO9wRPZ/Tpxa6SJ8m79iM3XM5ecQORDkMf1k5V6P4FVmF9VjWL95kLUQdJqNDlLTvy5alM71YQFbPxrbk6eHbiHUCapUM+ogC9aZG0ydfssnQsCmMQSWjVpKzz+gcepfKu5CnmRJrQvWJjYJEHaMUoiGD9S5FKL1YSCwGkNwwx2aSbbjFj8R4J5BGgigHQj9YEaSgYTj6oYmBJEswPvFE8pFML3WkjyWnPNfzWYYtgo+FR8ELGsiPB9hcnRYiV4b3zNMClYtW9TFT6Byt1hEY4hzIJFCrm71L0JSe5vklDXK2N+O9H+4BC2LFcJYW+mav5PSgXWcdZi92tP7UWr78YWlmzzg/ez8rjS6OrFX5syI5Vs13GqpAGsF0OvXaK6+8/vrrGHwb2DffvPlmvNt+E4z4Csfbbr2VkxsP0rzxxhvxgZnpxh1hMMD/+T//54997GM/8AM/ADc8LP+FL3wBu3Z+O6TyOG5hOJ+iEhMlNofxiRGyaTYuZBtzjrUNQWjGkFDP3KVHkfETUSA4JlEBjsKnzvQk0QSZxyxo0udLfgCLdJltcN4IawI0zQHvsat2hbTvmanfd2dFRdI0kS1MTS0EXu5L4wWwLc38z2jzwyqQ8l0Q9wj7fbGH9LuGvVg7T1u3lbCWSR+m2Gsp983vShbE/9WYRmmgMWrRDT4WzG95oMiBqTJG562Jkw7gLHUhOsUS0u+DqZNFtjUcafl92GQmYsRF6sLsUsTxsDCewpA8RmcIwSLJYvRUgdLByYQQs4oP2s1RquLIS8KIqh2akMBih/YYmYCYmyaYZAqsnhgMPgWS3mbCyuGDxSuHhatc5C4h1nmFOpizew5kpipQwLopOSxgao9xi7Vg0LwWCcni4BWxPI5i5mwFbYV+hreI3I5zKdPaxWMxyzAOuzlUZt7mbL3IeHSaBzEWVdaMMZPL8iLYlzazpebkVLwQKWCgh4IRpQ79lM6mGv4zk6PNEIkc2CeRXDKNrw5GQNcmkCs70uz77XR+5ZWX0Xc+bI7xePGll2666UZ08JZbboH8kQ9/3//3G5/BuHzzm9+cbNz7Sp86hafjf/7nf55fTIMPz9YDfVj1YhgMTGxuIYxjueBOWpE3zXEgoRszQ9Ia5QaGZmSIco8smq0499gFLjF2Sk1dmybJYiYzuarH9hyG8fqo2wlPEBm7viVMP3+oaZ53b2ib5vZa4IIt1yxYPNhlrLlY93OZTbZNuD2xgiMuoGGJ98k9zGPY7RyLjAmSI1YemTPdlViOCsFGxvWmdCuNBrLQJcqm8UBRmVTRv5NFNU9DNyBzzt1IceRVVbe6Pjwtw/mAlcp3qXQKxFj9hYASgHfkdt+OcaIAid9ty1CJdsijzg6tkyClKEOvqIyyMpFgPZ3FCHqQ4NiCpBODLng1VLEZEyZb+xrGBvMAX/lg7kHZ4q2dhmNHH0Ysw9xqZQ40ct+lK7S2PC2/a47OpxrESu4KtwGMJDQJSjdzxKIp1d5A2kFyFMTCYR0rURPYuhvCWXX8oLmxJh+dgqNv4n1ZkJK7dAua34aIkZyElRbfVLArOxeHbLnVuCB3aCxO+pHA7XSxtw88YOPjTeebkAY8KxkUEnvLjK1HiuStEPx9cKvTgw8++NJLL+P71t/1rgevvubqBx94AJ+R+MY3vmHb7ytO45kXfWERVoazVrpZj3La+r6brFg+r9EV7xkSpZyZ4CyThMLYS0OQxopWNmPUKEeGKPexDtHESPQ//hh5IxGZ+7jKXjAKaiZA25ZfFkQNAYfwTTPDK0zER8BbJCvXw+LNei62nj9pdj7vbot52kWhvuVtcgWCYMslduTD3+cg0fx+eXSZysxS3SMujmeUaa266o1KMwkFDJf1ib3bO1o/DWteODEGX0kRZWoybCWQwBRy51OgTFJQMVCW87mgVqV0N7UBNu/8b+7nMVgEA+U85w7BsiW/SKj7dVSCb5ln2RrSqUR5xVhSrgatuOaNSBgDzTyIj14zZK/PXpsmHuZBwttUSBMhM/TcR9UMmYfKfZFspcr5Wze8I+ndVu8gF6gYSR3fF2mM9jEN19IYdawhxyGgzVFKZ4s0doJHdjIAKumbMesUj9bNu2tKTp48hWx57JWmqg/hpbbSLWYJKzEc5uxoKwX+k68EAFyWCX6Ss/fi2QMhJ+9SQmphyp6DnDHrAON7D4BlRDpHtkzCMy0ZXoUNNMlnTlOTquWZ2wNdeIv9B3/wY1946At33XkXrOeuOYeO/vqv/7rV59TpM7hO8q0c6KVHZUS9QVCvIhZ9oF6dGcKiC+UF2IKp4gGOUZVENDcsTRPIXhPdl+VhRHMZkmISD/XLMWhlpGm8QIEQgMWjDdq2I7aTxSXe6C5ZAgmaZsd6rIpZsC3VahKJvYWJzSG/lBDk1bAtNP3vql66eIU/0oH5gt9dgiZnbE1ff23G8D3yvPnePYeU6DCb2EN1Ay7slZbgSBLlllMUraFrs7N+J4DNOHve2NdgVVYd6VRBF7vrKFDseeOXY6VA2zvV8MyaIMwhDJLvZSm9mVevjyS99QDNkHCo3Ei+UOQhw8Gx3rJAw7Tnyp1zNU8F27eRVJp5jNpis+jgMtZUdYsppXXJLtw0jVMwD1o2HNn3RFLJ5PXZQ51M3+tAb0UL466hR6nTO6o+uMk0q+zBFYCj34Rq4rgI21plnVdmqgTnQpoR0rZC79civI3+JmR2qFJaiwIOJp1ST52yO2zWt2FpcuISCk1jmh5D01Jy5669Fg+24FMOv/3bv4PH0f/sn/2AuJ96+qlnn30OaeAr3q+svxGy2rjjGZqdb65biDNn8V3Uw0N9kDCEHahcJV0FzAKzzD5CS0NU3OtIdaugesnnYq/2TZ9YlI008qCm1wtQBEzO4explLHTUS5ELsWQUW5gJ9VUyCb7LfHkC3DTwxlbdIFX05wFBYz86ezsfmMzBRYfWzrSQawtSJfOXnEWnzDP11RaZYQ7eP3NDOUcgzd9ij2MMpyt6T0pRKuS9S7vHEjH2A21s1OXugnY3liryXSEVUn7lGaE6shGlxw3F6LjnfAwvalXR1MUOWLRnKi0Pdykp8ef3VsW6PhTHzLmHYsZuZJYD6sJPPTbhulcFQJCGl39TI2geU4iKyW2JZcuzp8cRe7yCWecL6XFcLHsyAdDpM0bh0ODctRs7SZ00KVEJ7nmbg3ygQmHkBGRi7DSnaFv5Gnk3KnTvm/nVBckFdATavpemp5z3vcv9A2sVXLhvm8B8UdOn376Kf8qyFOf+cx/wyC+/33vw7fN4TvZP/vZ/84/kYSd+blz1zoPA9V/ORWfQD3gg6eXLtoX2RzLUfWvZlwuTI1db4mNgpr0ZBrjZMba9YiGiGEg+8WmdyOsrSNGWcpm6EClBzCR/mpCIxmmGElgc+tMVL7Vr01+TfgFKzsJgAT5QkPHxr1pCj8T7ArM7LrycWFnLaJkSpzR8ENsxWqqjMTf3jtz5uzFSxfz3l34FSFnkmCpk4W2dZ9bWmTbLvdstzAwRDKmwLUTV8IGWUMGrS0pxnAzOVJv4Yz4ZZmlAGaVNgMO29qYV5puLOVyWm7NETdAjwNy1HDRXxNlmJjXIlmi1xAclaJd8BImOlKeeS249CRrGpDN4lSumgZl5dk9teTqtyKnBy21hR8J4SnbHLwEKVK2HfN5aymOJ2zeIC6xnXiXGTwXe084jFcZMvQl/w1XCNmSaZd6eJK2GN8zTAOMbkZTk8KeIjSubIZZjjhdLA+t0hUGJrcwK+iYUk8dQCyjijM3yzI1GVx15VW33HLrM08/xdC/+Zuf/epXv3rD9Tfg7fZXXnkVShDib6HmDFMgvONOCa9JVVI/Dqkhzb3YRz30Gio38iorCrvnBmLjoDNlNd0yflFUgeU7diAuBoMmNhf8YzDFY5jGROVJvW4Phs7MwDLFzi9nLCoJwidNbwDCA2hHDgGXmTbixpBTSReSk0BOTY/RXJlyceP6C0LiL6qeuXj5Er4j0vjxz1NI8oTBMwn5TWBjde5Va2W9oU3syV5dMLWpQVaEDbKy5UbMZCZnrJ0j50yO+OOWq1KMyWNaEaHiQkkMNRO8TSuYFjGRvpVjNWmLVFGOnr1XtB4sK5wEUEmeVKBEi4DtGap6DES6SAVN0ywh59KyizqloEpj5DjSzUNbwnkGGr/vh6hiuLhrsa1DoKrWrGprHlHB4a0WjysNVGIDVS7jW93LE4jHKVEmhr0ZjyLYjPBeViN//PEXNrsKxjzUPFjwW7Xu0epy5BsqI2Asc8o0s6KfSrUmxYrTrbzN5dpoyoFvvfXWixcuPP/881Ag4DPPPIsnZHy8DH3b7bdfd/31GWtqYLBxryMX+w5plMzYfRVpkytcZ00zkq5SRXBF2hpym8HQInXTzCg7x9hRjphNcu8cY1NuMLEZ5U3xjhd09PCocSRpmqju2zMAACAASURBVMpWGAkyDYQZyC9y4Hkzs6ud97maIl97YHmTfxLY90s1aGvLU7GX2LO4C49yIMUzM2fwzAzed+fDbNtTsLTrogbaSgSMB1xmXpb68R05sby9GDHHiDN55DfV5aA1IFLXltRidVZhjW8fDBopG06RUw8qaRra2BTG9mHNfSXiPC45xY9mzEFUhNXeqUUMvCJ4iIzKEDG5Nu5qSoC75F3h6MWIkiFEjXJTCGl2xZLXUFBECsAgnCJKoH7IsFdpgTAPfCbgNbljo+ayTQ+fImFNW4mgLqzggjlHTarYzYAaiHJcdRFywLKuWqVfp3hbIzDWi+sARj9NAO8G5kl8qCaaVrrJQGvhlpNZCbHXbDdGG9/tN8i9ERIeU5DzSAINGyYXB6cMUcMA4tOn77r77mvPX/vM08+8/sbraPKKxuPv2NOfv/Z8ubQtqIXUO+7Monr9S3/pL/VPvZ8/f/63f/u38WZ+BV1r6MJT3+nR97rRoCnftSAjexMPENE1kWBiMOkljIgP0jEAI5GgG8PEe/yxvee7aXc7eP6scfTtNexoxEQ5lWHhFNC2kvD+5aOLp9Z8Oxycc5m5SKXHk+BVfgMcwEUMMYpyTUqxLCN7A8w48K/83G1yIY5yzXz6NHbtZ/EtUGnvXlvHLcbe+PxGScLJmuY4wEibizuy1bocoqxhtX1ni3QIzwNNyjmM1Kavldlnft6C70OTL6YReaIcI1O/O0n7mITNe6Ma3X4VToIBDb37UIbwhBw7CE2feYhiIvHbowb3gVMMJ1kuvWZAEVR0ZIYiCfZWFD9daKaj3CW0zrktkqyYnnHPtkC2khir3dFxwj8Pb8Nu30jhutFllb1tIZqvNSW6EfnB3sUm5UYPrDCQYcX/UUO24WsDmzkqogSxUYNmQ9UDpPk2FEbjvtBNzBmbNjzCJh6KytRT0Eu+PeDbQuOXmNUH9+9y0bBguWxH6mhHwov0hhtuvP76G/C8Ou750FxzzTX4vTtHqr9y+X7eOA3s2vEHmOyvOvnxnve853u+53uwZQfj0GHhKmpSbZpDNinXwUTEyxpydJvJiiEhIqVcFWJoAy+wFJPNjwOvgaZ7q/kRUELLYaCSbS7E7g5T6Wl7zZy+WDyQvXBm40pCxdKmnEkIi0rii1b4ZwxsADYEBGSYvTh7IeKT3AUqijqLg8b6kj0zc/GS7d1j7D4zanLsg2LNSNf0OegabsHeUKCrOKiUTIH6WIvoG2XnSC+imgEiWLIiSgMhMizLMcnIMJPBxoiiJUOjlHuC+VjjAjlsNZklOdQrMQlMpmkqQwkEDDmFGQrsO0wxxEwmQ7QOOYdKZSiSWbbilzAkXFZW4bi68QewHB5rh6ltCeFuKkfjcKMgWbESKOPQHcYBfnjfif2NcsO/HDZaQWKxGv9Rk0hYJERHMUgQR9QMc44AeVGIsRiOmujSYBqGb4dm/qkv1STs4zntTraP+cdR/Vx6suG2sevqqO/f25wPQikiNtsvv/yybdwvX8bDM9eeP49N+JDyLKYp/h9O+ugA6g9/+MMf+tCH8HY79hLRJDnOeCkprPI3+La5QC3oFozAM2E90VEYe+drpJ9FcT2W0fEnUxe93LgjlqBbUgQGB1wkoNk7ipNg89lzODkjGHvaiJMhBbOZzNsM57TtyHPU47icchczZ+4k28xNttC3oktvgAXbUcTUbaeIMn7kP22fVcUzM5OL7ihR575VDnOYWVSSedWmBO6S/HgioTgjf6MEPmqmMYKBeDmmwA6QMsB380dfyAg3pCUsmpSJekQrm1I2/LHJy8QvpbxTi+a5TPKYjGFxjTGnxjGmIkBUNvi6mWK5o8n4R5I5Q2VZA9fRckt5ZkV7DjGCeOjo10lqr+zLXJMKopkGYwcJO6gm5MJCpzx9b+8dkqrtXiqy2X1qICTmCR05begRCaIcrfDtTX3A6AI5dhvuPcmMk44zaxNXMHnFQFEJR4CFJw/BhIm5wUgfBTFHJeSGqrG+PZs2FfP6gSmiJ2qQbTQdW/I5FmfmsdG+PYk4wya5YbP+xOOPY9eOsqPUwL744osQbrzpJnwytd9yp3fchxP0i1/8Ijy55cfb7U899dRP/MRP3HPPPT3LJJmiHvKbeWpw38WuFvZe6ml1GfWm6K6ZFJXr8jLpuv9hiI1RYxWXyyBCCUgsyst5Onnah6c7gzvYNiDfYbglME7nHSwHOV2YlsN1VuHVS4ckQli5saFfBDe3SJm6CEXhbJtqw2TIGeXCVde4jY7rGN/vfujePXU+BluVyxitQgOgyZtdhZ2VIrDBeNNeDshStA1nyKiIse7CSwAuysXtyBJph70zU46azyVe0gw9C6qW/P1ZeKQrsLkZ9jGC9yDcIt5cBYjFJac0FCIYftFRMh2XXwkW+QwMwBAp/czxIL2tbH5IsPBJ6X31ZPxxlr63nWZnkqmYthO3JKzJdKJQB/FWWVyZtq2ANYydal4LRFEigusrQFpoGSxg8n0gjxFNZAswigo3jNbB0zLi8c0YBVFJaNyp5yvCzWCNF6PgtekB3LczMNzGoE2gPp/j0jS3ZswTfsTVuszpU67k44p5TDwbS3ks0WxC+1CvBp1PiAsXLjzy8MN8toWlPYtN9pkrLly4+Pxzz+Gt93vuvZd6pTx9VAZfBf9jP/ZjN998M6E/8zM/84u/+Ity2yog19X+zLhmjtRHa5RnbCc4ybaEn6U11s+HuCwQxCA2jgV8Y11GOln7wu7ZUsxgXJa7Tpel32PYLxMCJt4WVnJYMTM9gur7gy6hph6FMGahbhazVItC6FXs4dQn8kd56tAb0t592/PudQxrxYx78qEGbr0XNDxolZzV6UzHmEaUG3Ak6SM2YAGYiWilb/BqCikNhNZrBoo+jTx0aTDx8mxDdtBGQX5Nc1izxoBNdG/qBVcD/muW+4a+bTaErTmULHYkelEvjYSeChpZI9sQSWVDDiU0OJq1XbQSHFXCWXNzSC8j4dXW3DlzeXHOImMNXrdFbHJO+1xowyRITAEaFzb9rsTsATNIyVQ5b9uYOYQKjxhdlH5CZVvUKw0JGaVzNVZvnnr2uWdvufmW1TRTn3JSTAGcFVvorbotISUQqqiUeiF6KZZg0bqsVG0EGwokjLQMKo2aEoY8VG4MKvACVTTZtMhzhb8Nxz6eAKhliS7fMlmFewsy0BQ8NCiuuyefeIK79quvvho/Ht10083veteDeNgdFf69hx56+ZWXn3n6aXy3TOzNdOOOkbjjjjvwLj3Rs+fazYqMF5JeMMVEIHNWkpDycA6SMNJGueKcGipUagyDjYCtbleU4kw3OcfeA8SmrHJrNE1TsF6YIfN6bXcGXJrWFFQl4ex0vV+jQvRxgmYbKjvEj1R67y36MkV9f1gB5ziDs/opAaAoRx+lJCFaj19OYfC+Oz6rir+zsP+ZGTDMOhPTBQYHw1FGMzrGDkfZ/crLgqmARhIcFU5CBIpZAq3R0TSNOVIE2VDsJ/FDeYGK+EC4Li6wReeYCfSc5lDKXUL0qmW/GnBN+X/llluDmlYMgdp4GtUtWXElNAwzfQNrmvTKK5FFLBvWAO3Js6OB9l3+yRN+ab/qJ++xv+Rae/dtAGzdUwJVetKGVAdiiRgGMuK0qQQy52AhIeP/sNatBIzmzBPjjGXvncNLJayomS2fc2EyS6OvKpMxs/PpU3gMEIQe0UBGhn8pa1HT4CyuCwZXLgUll8OcPPlC3bKUSRf5GpRSzYxL59Z3ELNyZ1Kp99kSmw1hhlRnYaJjhQgNgaEjPhj7CmWjzwp+JNq8cOX6901CNEK7UiJx9nrnPKrAN7/5TWzNUbEHHrgf3yFz5Vl7t/3LX/kK9t7Yu995552PPf7ECy+8cPMtt8RHXaYb91GI/TqMajOCvUasERllAYqwYi7AHdJJcFbhGSBdHv4kUwwZZbg1zYpoQ4OXE4B+N3C+tBq5cx4Fu8AsEs8uHTWy8/MlR8kLcQ5tAdM/AtNrdbWHG1UFWm4oZA+LpigDqWJLiMqe6lg0/on1nUyXr7jizEF7d8RBn3GghxQouy69DDtPfDRFl2W5qfIymIOQAuVfP664BLMcy2AGq4mx26qFOtbkKn1DEpvENI4R0MsZzNt/Nd8jeBh9KdzQwRntMrKfxnHYZQ6dvcQ9Yghc0+T0ctIBuEVExO7+zbiTBAqa7x3U2SyEzIn6GA+8vO/2AqBXvupRCotTkhYiZVOJmDVbz9Z3rwyTjdnGFa/WbyXvcZnHS2D9YxUMiCLgyADK3ioqy5Qwv0O4w8pLqUzPb64vvfgiPnJnWxDL5BS2JjYjPSRivfTyy9dfd50nVbJYiQhz9jdkSYB+HY/HNRgFGw8GLMPfcpDJX6MJCedmFyW4ZNG+Dgix4lFC+nCQpeEqTffFS9FErk6OMIWFkgzUREyUSSZkb5LGBHQjFwItfBWp3oyniWDl0GX6P6WCxcUV8dKLvMpuv/12/LXUJ57A++9P3HXXnbfccsvXv/51vGOO0uLvp7726qvx29zDxl1DMSojBgafTL3yyivBPv4uyH4igKfn7DWjcLVuwSf33hw0MRbwNfHxtRByWIA+ApPD5D70k6kI5P9xDSBdUwZX+sUUryhC+4yOrBlEZwZgdluMHOVp5E2g4D3IIFgpRs4od8DjUGwIYL/+3QCrssGNAp9VXd67D2ljoChX7JPGXnyi4T5kmM0kUFEj5F5H4HHQkURN3rEZ5SGYyi2vzV5IzG3+bsjWdJvLW4YtcRKGDGPHsB1Pi0OK57dVk+0Owf10zmRD6BTSkatu6rZtU3DY5gan1GHtTSU4qH3ZFdGcEQWH01gHTfA+U5VWQv/u17DBcPixvajjkXGo9Mwsw4g8Xllxs5Bj5oHYFl5enDmeY9DNc8YQsNBpUJInTti14y1GbEdw+7t86TK+XfrFF1/AF+Fh+KDHc70hQ8Bz/vNQxaKLyjfi8MThcy+dXaGpWIR6SvAi2jQ2dSlqR6uAx82zMeXSEaPjDuwMKd1wcohwnPCtbyx4dC1upt1YVjrVruvT1t6J5zfVegJ8M57DAVNdtJDiSKTXyPInX5fLeuGNC+gMvn4Du3b7PplTb95x5+0Y3G88+uif+lPv+h+/9XmYUDSYYp/tF1XDocCPwv/iX/wLPHND9EMPPYS361n0V1+1P8TaHjmPVr/ShptGRxSNhk1ZI2NURjlijk1eDrBsXU3COulXHS9ItFDtSmahsCDgDoSvPsRD5G/BgbSqjlWNFD/qonyk9BQ4CmAcBpBSwpFiLzsfV4ztPCoBEsNzhfiZL39H5CDTZdpINXDerloOIx7ADgtJ/ugrGQKOJgE15agcdgkWRVzwZDCnSAnAyps0lIbM8LxZcWybHpXNa/JMvI1x1GQyvus10Zs5vCWjxIuyouECnm75E0zlUBpOrW2B+eZkCgZSyYAiI1aQrQ2j0gKYZCQQ0obIjBDF//SnoqcYbXtraHRt2VUACcJLg2BSbg28G1eNiLwVV4JMBwoaRxsRGwXwQEf+2GXn1xhJyGGxNceu/blnnz2Dv1GBD99dunTTTTdhU/LC88+fv+66c+fOZyDPyr/eFtNoaQggv5RUMlh9eozAvRAuot7YapQVolg9sj3Vp1ZmY33ONaRWhDVo1EqOGA/ro3XSulpyGPk4TlmilXxXvZwsOXo473CqrMXtlgNFwZWpVBAO31RDvE+flcAisVRzlKgkM5OhvGxVJm8rgXPui1/80m233/bgA/dffbV93/qTTz752f/+OZQrfcl1XarwjnvdFVxLv/Zrv1br2ha+cAafXo1VaxFtG1UlnOWFjP+bUveaBtCSHr19cgF4YTDDIuN3H/mDHWayIcGP3Z5FvSOPq0iS+/Ic0H+QWFx/LfKoDCOdux3LS+xMlJXcUDiW0IWkLUOxbJWa1Le65YsB+OUc4iCYbO+72/Puu78jknOII84sQadmI2/vxjKS2a/WSAAJ1tOwQpCHyjaibNlgW7r+UY1sHZ+9EP7TstGlf4G5iPG6FBfNMXuZlgVtP9lZ8aQkgrMyiAnsjlhu+TaVxKk4IpSQTQXcmTJk7Txw1MKYBWBwWGKWnu/PYLJ38sImwDG+dlI6zleE5hYqjkgMoKJJkFWaQU8FWhK8v0uAaCsjErUnKNuI5KB5vKTYEhcbuSvw5vr111+Pr7rDE73ffP3155577o3XX781f6BuxBImvOqT0xjhs67F7B8U36dmOpuRRcYUTa2ohL1pBo8VUYQzXLk0EiJcwDnq/t2/c2E0jaHqYElDVlYwhMU14j9BbVhvbeqELMHJ/oAhf6E8zElXYrtEQ2+GBv8vHI2V+fNVXuOQMp+kgDfHX3r5JVbinrvvvu78dV/6yleuvurq66+/7sYbb3zPn/kz//1zn8PPOvhFu5XLC4Yff6Yb99VU8Um5j33sY3/4h3/47HPPTcBNxYiKyiiLY6iU9XBhxnvYmLGCyqZpJr2H5LWICVvmLMxnmrfMZ9kpQhZWgewPYUWu3WIryjlIdwbRJlxwpAu9Zu6RM8qB5hCxCgdetHksxFgwZe/pecFXqUiILMxN7hIiZihjib105oqzFy9dPHtmZWRi5ykvRF3NYNiNYYqdkst7Gow+UNJ0ty7THxLVLze7LvN1NyxEzDJnkO4twkfMqkySWcLk7DC6m6UUEKVIayEdmYq7hk12T8/qgnu83QqMQjmU0As5LJjaHPLaaHFcZhH4iiQSlUlZrihcOTZVuONt5HmYsktF2hNjR4kq2mERKsTJNGwi+LE5AQ1KHmJdaysZ3nLrrXi7HW/8PfXkk9iv8z14/EofO5UVT5g3pzei2j8oRwoXU9gzhaaX85b889RFcI7pti40o4lIlnH27QX1jSYBOBnUFMyEkD6yLBj3wfPDp9709+PdgJeYALKhd+BI3HBL2TYxZE9CMYuEk15NAaW3HFwrAS1aBT6KgCfXn332GRLiaZlz11zz+OOP4/WWW2763/7X/wU/4sJ05ooz1157LaLwOQv8kHP4xh0s/+W//Bc8UYOPu+auMf/YwaP0aJ9vX/qN/o2j1qDUGS1nkc4ujeJnEzDPb0MVS/QpMkJU+GLZIFXVHUWKuihv4F6ExMCQcSyzR2uUF4PsNE5428fHJ7CdwdbgsUDCKrQEmShU+qrRAEPTy485dAkfQOdnVRcco0myhMC6SXTHdFuJHY5yIqpj6IJpB2dLWFL1MbwQaR5SBluMq+us8Y2YWQKzoEO8+Cm4bypTxMe4UY6Yg+S4/qwTKHRZyJZutyuEdE3v82n9RAwOSR4DnO1YIXubmo+ctk0G9O3IPHvrw/vXlrJvwUyil2HNw+9kVWdfffUV/Br/pZdewoN+11xz9Y033vTIIw8/9eRTzz777He+972vvvYq9myvvfbqDTfc8Oyzz913332TUH9y1VU1Vi63I4xFVaDI4yGRhK1Q0A9Wp+Sq0awyrniXGnJfAtGmi8J9oiOStX08svVX27DOs+G9JdrhZWzmC2O05FXJznzMIa1SytYXMkUzFpoiS5SDo3l5yPQqUyMYIzv25pv48fX8+evw55bgjC+Tuf/++//cB78byT/19NNf/vJX/vSf/lPA3nTzzfZ57nBUG3dQVI9wBNxQxHWIgmLzUFuHnaohB7VmvKzUKmUqlI0X10+Oa+un2UNBzQ7XKra3kQl/crJMcLBjUR5zOe6Qfc+YLmhVQiahnARpat80BTtO4SgxDvONnY/yxl5Ng04NgVgjAB1jw2uWRCbEwnMZ77vjmRm8737sR5OSBU2BbSXUmsu4OaUNWZizqDbgCWYt0u4we8WoUc72cl66URXUQMqrhNEzxDB5RZcA+AJ+EKlTDQMRtWDqaLYpuNBxkewXPdO7DSZiIqvj088A8ZHWUIsIP1bZ7+u51MfKvEB2QNBm97BAfrymtzhumDppLqE7nsO119rz6/gcKv48JN5ZxFt+99573yMPP/L+D3wAu46bb74Fv8PnZ+qIPEIZmlXiCEy7XG1J3DrlQ512xdgPVlY5ZEpxc6r7Q272mOeAZHO+tvDEfWk0zSIlX/DD2X9k5v3D+p6D5u1+HjIibJXPmsSOLRe3+LNoGednOuPVZyFv4lwv/KaZyb0XyMXgd91114WLF/DmOuRH/PBbvz0+9NBDX8Bb8viGmRQjn7RxN39cS3j/Lpu2nvH5kgTVFNnqOsWx81Nza7CxsX/94SNHNWvk9cr0eQh7v6TJwAFg+dZZpglHzgl6tqhJclQNwu5XKVHVpw8hjYT9cdY8SB0Ls+axb3+3yibALIfY+SjLcUlQ95Y94yBEGV5o0lcMEuaB7X13fBkyn5mZwwYWhWMaFquOF1tRJle6dsQyiDBXcRstO9lLHjK4QGvOIJ9rzIbWysWujkjInK3j3gz24nNcDsd4Ud3BuQVqpbc7nK2juG9d8k/g2DPleVba4qmlsy2IEjZhNooVaNKIvpIljJ0Wkxm71NoFfpoiPBUzBxVga5Ej11Hk8aw4CuM2X95kc/eXfXjDJcZmlh9QXnfddW+88Tr27phg9957L94ufOaZZ1595RX90Zhl2g3Wbuu1wecYINvKcgyBJhQoss3DJo2mOfE9XO1DO4h7OGPrqYWHBgTUPr4xYRFD960O6nWWyhWKmej7+WzJ4Qoia/JZ8ykxD68+34LaZp0zPbBZkqQyIRgy/9mzVz74wIP4Fkh8wxJ+z+BwyxJv7+Kj23iWjATZ00iwcUczazJRf8ZTaPyBoDcVTU6vaNYkBvZKDqC8i6BYOAbmoELH0MtxN1azGrsF9mVxmH3kLHKRttR8OWx9i1R9YggSSCNhjfggO9iZxHKYZetBkZNTHIflTDblIJBKizhSzhMtF7Uco9dMJnnswzxEY8FqPdi7JyrE86XE4sbYoVWrG/K15qpz7FKQB1flKtVaLlvtIY3iougSii1IQ99gXxDLzCig5WCG80IdIWq6Ku2WhqngKymDkpOTAxouofwCCL5y1eX2vWS8JqlHqzlbQj4NJSgx6t3ezFrqpq+imiImBnO0AfL/MkZ9yYr2LADcJbeg3B4CmHCGGMmmLgwuH3FsEpiw0m7SmFIcGpc7D5t+frz66mvYpuNjqY8+9o1z116LtwtvuPHGq/JX2E2jv2NYrACLvAg5AeNxRdVcXMvRVipguDCGTbzrTLuSkV+HK5hJDvRCpvYdl/wxKV2wZsG/9A0wE/eittU4vJFvn7o/c/fd99x22+34Y0wXLryBVeCaa3BlXItfQxUvl3zFfrPVNiA8cPbRj370scce++Ef/uFf+qVfwgX2+c9/Hk/INLBZ00vUGtFhqdJjTPWqh/5bcmEg04MlctsrDPMYkoSgQ3tRilNCsUVpaB4qoxdLRJjKxaZ8JcAxypHn+OWFSAumw/IQYV8OmcQcNVEWoAgr5gI0KYJX84g/UEfHmnLaGrtQW8dGy9T459dK2bvnK7pQxZymkX39KT4LuNrkWVkynklVLAIjZ5RFk3qi9skLTGNr3Dppa6Hiw5p6LSx9gChXvrzTcNx2dpI8s4wtVl5UbV9k5IZN0XM2dl/pvpVlIRG7F7EjoLQOJ74Fl2jKYVMmdorm0BzqvRetS00waJGKvjCLWdF7WmFYsB4wCNOpFLewdRgoRC6hx/eaEVOlA9suL0YnheRdDNF3uyNmFO74F/C5uJtvvubqq594/PE7br8TuxP8cIg/EXPhjddPXYmvmblSKVWdfKfx1lcg79W4FJxs/GYa2ZoTVM0UzyYkFnNDvnozHtk21vX8FTR3HC5BTASkbdZSwCyTJs9ZSM9fK6p8z5zFxz+uwd9OxQP+15y7Bs1yYftVoVtPvs2PAjzwwAMf+chHPv3pT/+1v/bX8JdX8Z1NyOxHf/RHP/WpT73++usDD94y3MDeDq5AHwtmvFDWHbeJMLiDlKRSKsBHWQAKG9kar1Ld1tC3MbPyrIORqfRRoybKPd9xahYiIdEF63ISy76xBFGOnDF0lCOmyOuIgq0kheePjT1P1ES5YtneGFH4Jqm+/lm/CNYlj1hJzu+7X7zY/Yy+LSE+vhKjwE8VodxYPXp6YZJo7D16zlWGGCvKreMi9SEfFsnl8Oe80+JbgsZwUS4Iv4iWMo7QIGs5hS8WEN7SSgiXOA+a20niyNADQpur93R2TYgTgmH9lS8pL29EfYBMxSm+MTBqmKkpDYfxjoT05SSh3AXqFATou1MDBy365owSQIRoS5bQsxwcl1Ry7wXFUnQJMAkv2KoQ3cnQaMggZgj4vnZsTa47b8+7X3XVla+88op9XUbaOVyFj65iTz8kicmQMGr+RMq21K/29VvaM9uKfouOJvK2POKWEmsmD3YgmpouAQYNX/NUTBB6bSlDwmzLs0kAvoj+PHbYzz37Bv4eE0h8buBKufXW2/BomZE7cz7ZozI82msBPn/xL/7FT3ziE3jw/Wf9AA7suNJ+4Ad+4Bd/8Rezo519M1qlPL5/RJ8D5BiB+VKj3COg4Y+mKDewQXOIRkjpISsbCQMiqoAIv30WyRR/qAFxErliFNV+UpHsdV0NGpmj3AZasrXY1OZeAw34Ig8Kfh6/KES404+R61pxEVqnslAULliH7CrxPjCfd1/du8fwQbbQoWmJx15Eue//srXHN5q0LdzGElEmN0k31LMmWeDLIzZjgGy3s/Rayg8ILZLom24e6IonZCYMh2sN7/FcOHWFfxlA9I0ZLsvD0MmFNo9OjUJQQAq4m1hGuDo4Sx0nTglLOYh0CVTbUkmgNOf2yshR87mMERxKmnC1tr/yJcpBLbHY09y0+ErfUxG2Eooj1fKpUNNGdPcdRehFdip65ZZjpS6zv64sRcju5RxhRbtDIgFyFpMEdSQK2JeLHY8H4POpZvUU8VQAvj2DF0KcYMJLEKE0EBSXyiEm4hu5cW+sJ9JcGpgTCfg2IrVlZO8Q7UsfUyjOD2gXqAAAIABJREFUIsyry5cuYZQheORiJczaJ5zSrAP4LQGeankJXyyDA1Wx2uBxxTdf/+Y3H/3GN/BXmbB9b3zPohs4rDdcDLL9Ax/4AJ6KAePf+lt/66/8lb9C9T/6R//oa1/7Gj76jZ+S499PPZFtes7Ezv0QR02Uo9dWecHfKpNnGC9tgqPLTF4IH10WYI0JCcgxykUbHIQMuiOIMZ4KQQGsC8EWTFU6C7gYO/ooDyijLEzcfy/wC78i9BTKjOHhDwzl5o5ap9IztaHXEa2Ht8veHds7W4aUYYRH8igD0zSj10Z5EJF5LLJbfVTEjZEEW02azIRFmSnFuKtUCkqBXdvi5SssZ4UNC4JifPisoO4ZviV2ssQ4JU4GY2ky2tSEE9d787ZGXucCm8QiQPJ7m/v6r4Y3RQsgcvVpcwxgzZUpPuXSMWcLrSW5gNYkxjX/tYopAQlMgE0wOJX41gLncGJbd8gIrwnqzZGyyDmqBCSTde6lRtCngZYp09t5qAyA5MuyAVwXIRKISYLRRDyHWOSnT+FxXlwU157HO+5Ja75pgllkNivCMIAJkAkbWFZvPUd3phM1Q5acdTIS3yiHju8oy1Q+mVrYbpb/Bf7mY5Bxx3viW/ZmsjKrrMSHs7Fr513gPC4HXpanT7326quXLl1++umnr7zyKjy1Hrry5lnMNnsD2B6rryYq3mj/O3/n7/yTf/JP8BfO7rjjDvrgQbQf+ZEfuf322yEElh1iTtVcklyF3UF1KNTjaUFKS1dcYCJxWP9WF7not1seFKbjSJXy00lVTXlA4MFIMZ5kCRm7eN6H9vkBPno12cTMenkxiw3GkCjmSboukAFrE1MRUgLos2xnumyIuQ8iWgnFP+3d7Tsi8fu0nEyx75OW/VkLYCiAGhcMrqV42TDeMs92zCz7kEBKJkacyTHuoJSzYFHPrvr6gR0Wz7SnucOlxWBYmccfXiqzLDJvkb1jS+45OopiHQyFoGy+3vNgWQ/svv68kI/4plnGBMTtTbRSSgrfTx25uFC6cNiANftgkOBQdJdTq8mkabrf5CXSZUh253bBNwoemwk4yjoUXbNLpUx8EZdDVOfCK3cba2DyvwBPYAEkyNecLL+qVIFhIgrPCHXW+K3+3ffcbZ613hWepQ+WzZTkbmUTVsIkdlY3Ey+rF85kDkXxZLrVfJhAVI46vRDWTCnoCuodc6pA2nzHPbjdsW2W+I42jkZbNL8GTQnvIz0Zn4nBI86sy+dhIq7EY+fPPfssp/UDD9yPDfrtt92G7/V69plnXn/jja989avoCnb22IcH8tNnQyPHsOXjNN5TP3fuXG/F98CfP3+e37daHFzCnFN6US7aAICHwA3PpmYVoPNIVkbghVtH04KUXMN60JF1iuXYHZwLXlJjTzM8TJ1N+dwB9wbuCOpUSsQCVGwJxbYqLfgg9eaghi5RFkxsEmQ6RABLn0QOD4uJxIRsbJ7QRiQx28PLa7sLkCmbnHBPIo2Eir+87z563n3sUxGUSrEWMja+akoAEteSOiDHXcLQXUqmFCN60BRBsDZi49Ca09VnW5YJEiaMiN0jPEba3Gi00G1bLps3QLowI0WKOE195COdZRXelEUj5q+FTYIcXSihG8caNmzx7mBlsX/e8mR4GbkqkHYJpDTN17yHIZaU9LBhmPiKWYLT1fC5+1Jst1loCJPolbvNGsZNHqbwkgXvIFbOg0bdowFglBUT8MhWc4RLOWSwKiOhYmZ+66Erp9TIffPoqRR4QzEtNL5BN4gC22zxJE3DqWOu9oc1rYX/UjajYLWO3vkaUYQaZLWwariWQkJmB0SnNZ+8fC1L1U74DFSzAtUNJQA1ZCtBPpbdIzJ7fFudMfw2A+rDllwMkP07/HCOQoAo2sdDi/fpN1IflgX+rAHDYQLec/fd+B0sv2fp+htuuPnsWWzcER2f58YTLth4KxM94y6NCUgdXx3zkz/5kw8//DD+pNOjjz5KM96G/w//4T/80A/9EIJVDt7r0vV8AVSYpsGLo1FubMo3CTGyU1SKfOEWcvkX1XFIM9qSjU28MMeK4ZjCg4acw1SacE1zdwoL/gjPAxjJWVdM0ixQCbMuzFi4JLMkyiaC4wyJeobsNeupLCKYCSFRlpMiQlDCso4F+SQzvsDhzJkrcRmP9u7EiFphREKNmuOQc+0B7jEZEGPM4qhAo2RMUKPLgRa7y84xnZMpdFfgk5CIzpQSDxq2wKc/Oj1kSEpDLtmntpR6557ycL8ok6gpEwFVjA3ZOAQV2HTv4RYmZ+Iu9u471anuTVZVPnWD9bWV0Spc2za2LHT2Dgxik9DzJRP8g2MPG2qyb47O7Sd4tAcUZ6grdaU+uY7DEDNl7hF6bny5WcFtPCwZPysT6IK8eXkpzMHdoodmwfQSk/FKucubFy5cPIc/yZTd8zl6ljxd636Osy7hn/0dQ2AaWGSoZIYw33CU/MuIRMYCVobWFTDsiJwJnawZbzTB1CjVJQlKuUFK3yNlkkBfNiM+6gX+Vgk+sIOMMFIn/ki29xmBcLD7low9zpjycUOYKMdRI/xBA9Dw9wP4M0y/8zu/+973fieUeELmwQceuArftoS/c3r6CvuOyFNh4z6okGfz0EMP4amYP/iDP/hX/+pf/et//a+RMQt6N34muHw5PuB+SPKIGidOT8G0DDPC6R29kbEn6zTb3YaJztzH+qjldiRqutzGijhMkhue2IzymHGLtmFRMZgBm8NsomOUtwRdwsy4lISEiIxXWtQvRTqCjXUhAeWogT7mEOUSM2g114t1o4Sb6kX8ggx/IA1fL5V9AvNKHhGZvXec6d70XE3mk5dHo43hXPZ33KJ2R3DbD9j9Nbhj+SJBEjyVcg92m6/Xp8+ccTfluieuYRl0rzuzY+S0M8jZ53MhX0jJOr55O1XzePetPCmglY970JgBOujNVpfaLK9T1ezLrThULZI5uNaq2gaGIej21p3ByDnqbJuM7YRTvCTkvkqP+tU59hxBI2iYAcG8LJqzjzhhORVvpaJUuo5N0TvLBkWiTldU7jTiohQ4wqgkueRy+pVXXsbXZRjM6mZok7cdpOGrBffKF+5E4jPZ9y1On/gbGEDR2sUfZBW7NehpR0GFBfJk0Aw5pN/AD8JMeAzp1T1guigKfOnOINJPYlbq6FgZdjVscGzoI5vK8tZs0LfkaynxP0cj38tvXmbtmK1y3sK2gAHzjTfegL8f/KHv/R586gM/1t53332ow6233vrkk09hW1+X6pQ94z48sOX/jd/4jb/+1//6L//yL+MrIL/3e7/3n/7Tf4q/wfShD33ok5/85NBlqzKOlfmMUhjpAv+KOSAXROQhnihHFwGi0uSpoQXWbaxRo78TpfCsTU8eNVGu2fe0FliYBMliQnKRAEyU98QfYYdc4bZdVpsGqaaEnl417k1bNNFdsoTIEHOgbK9RG9GrMhyHYeAIPQ9iILdR7H33K84uvu+eOY7nrFQp8CauW53Sk7AU1UF+U15CJZtNE/5nRYBER5y0tqY1GG0ltEBcpVg1FpyKKW2goZCvpec7GArRFFB51xczL7QbJPZu3d0K5DlIMPJcGyi9WdL39upLiQ7oljqLkQOWB9G9WbpcEVMJPRPWETNPY8+bYC+MU6WsgpM6JYHGphk8tomLmYfiVFX1oEeNvC2/glJNKOQpUwAbEsLWxL4IEkcB6wIpTKsSRwqT1S74nJhnlNLqs4ucxWr+mv0Rsijn5N07IU2XulLGlIFKuIzl2a5WSJktGWcnZy3U3vfO3TMgg/8kFbmjLyBWtlmsTt8j80rRQT0xWs1mBc6HyhHTysa37mxD7tEkLMZG1kwcKI44n3LxThiRrIs0lRHf2m6Op07dfdfdn/nMZ264/gb8ZPDaq6/dfvttDz/8yHd+x3sef+KJ05fwdlL1dMxZ82DqfA2cf/iHf/j8889///d//2/91m+9+93vxlPzOP7dv/t32AYElIvr3Q7sQWx5jtRWEhBwrIaJgCgPkpBZMQagoGIGTGIoB6yJ4pfQAPY1e5YtSShGdI/yxs6LZ6OQaGOk6Blu21G9W57xkyj2LcoKE92TvCuzIanYGwFgHAgThZgB8VETZVr5in2r/13V+TMzU08ngJU3IbR4A3CB5P2ryIrgDGWK9z6LGlsTc+2wVnK5ROUtKdfnmzVYbNkE3BdXxS/uIU6wBu1E5CBMjKZmJp5oStX4yyKeUtR8kbDA2bsvgSub34c9ei7S8O4opYTIUpS5+tG6JrPvebCsFtljzpYwVhyhs9fxnpWDhMJfRs102NEcJRfw41hjKDM45hNlFXBHaVB8C3xA/sjHLxrL3Qi6/KWRQOieVzy2e80tt9QeVe8s/8hfqtQ4eS+pczwccWRFl3ztXVpVsKJupZwGQkSPKJuLdSXl0DKM2q27d6BVjhwtDYvGtTAjOvdYyAwq58ZKzq0zJ2364ZSL7kKqe+6DnZswJX4lxapp8KKyQh+xoQASNhOyZ7l/1T4eHNDLtEyJT52+8PwLWKlvufVmDCS+LxXvj1+8cOGWW27BO+4333KzLUJXnMYnuSNPtYuPBsr4Rvhf+IVfgPzlL38Z3+DeA5Km6nbV6FwwBMuAzqNSaAR7EmkkVJ7LjY0+hDEJyLNsIttMXs5nh1V5MBJywv8xKrhkkrwjQIY2nFndnbW/g0WyMuhYOkVHeCwKFUVCpI1JJJknoXFpVTeW6L1TBicO8Ut2dTFFfcxPsCUhONje/fSZsxcvXbzSfrgfHNx5uiH1F+5i8G7bDQs6KQcsc9U2L678af33PNJdkun5SuhfcOvL4jxaa2H0fMdtrett+FtSRoP+Zyn4aV5ICMbita0Ktau1rOiahtms7kjIljBIlk+O2jEIvyKQYNVdAAk2TOBG7fiLXm/mdFaC1ua+j7V91FIaPnuAKHNXOUgYEZguAw5JILv7hDailIASswB2DBIzrdsOfwn7BkTE0RAqDQk5ludT0Oi7eRdFxh3pjN0JnuOrKTyTXOqQv6M2JyBHFN5rn4JIXwddag3ccxob2LxiuTtLYXqb2L34eYTsYkZKcXwErIZHWjLvzCF6swKNppTF0/OZBYjlFZEWPAFCdj6b+h5LE6BY9fyHz27uCiyBrPBdo5fHMQvsuLqPEoVPuNrXic3inTt3Lb7rBZfD5z73W7gi/viPH07l/drX4GJNPNt+/vxVV10VGeLGfUoNh4VdO8crki7KS1EWHWk8onu7evURNfCIpNlAGJoMryQk9Dy9xsZjl0NDAd+YXLRG2ihHzPHLnlAqSh01r24WM8rHn4MYNTisUZ2P55GhraltZxzPsmLVVIwaMmgpCQngkbs44RnlAdGqaps/Zt6lM1fY8+5n7Hl3c4qOZXtXqWNwG0Z1IBq2y7aDSv8NLgXLAf9hiVM2MUULgytIK+P2sAkJWovacmYeS8w77zBoK2QOu8SQmYZnsu1zR0I4PGHLLa8fpFJ6EoZxi/KI42fhU+0QcdARFVZCis3aWXgybE24pF6PRdAXsZ+aSkPTqaB3SwfkrBh57mSFEsuKkz2HymDUcKS+KA0JkzxK3wPVBLugtunL2eyEV119dQf2TEq4zr5TEQuP6J4AFpDuS0I4mQdTGqVaK85qSgcwpHyc2t2ZBPLHHeiQlJzE3EG5PZ/0sUzbtPC5brjbDMjHatcNEMJ5/fflXy5d3jdCSJigE0DjlAWf6gEf28RETQAej8gikQsd1yYemmhCE2+r33X3XQ//8cP46hcbIw2W49DHq6686o4772zSKht3PAbzwIMP/tHXv876NrhZE3/t7L3v/a6Z1QeaRg465FzYuc9xWfpIGCqOlrLpE4peUT6WrMZf49xTzwLP9D3DUTSqUSFRYNgkw6wLp0AXpei7CGyNijtIzrFiNkGNhkYsjX5vMyYRZfEoAQkwQd6RQPQU71yw1T7HSFEiAxZfPO9++oqz9v3uzRtdc9LGMtyw1RikYRs0dJRJ4BUHFqOcH5LCX0ysnba2sJRpadvqU+O4uNnaaOnVBVKr0tf+2bPWbm4N3FkmMEgQmxJKqS7kJZ9FgYR9oJlTheQdJUEPSkX3JJBUbLP4Q73NsGbsrD3EnojS55DFO6gIx5RSNTSLnKEyR004UC2GdKMyTEIccZQw/Z3IdZ7jQeTo/uMvRxAqFiS+Hk+0FRauQgClBGxtDLVN+bQkCUy1qtuipu3K3RdpaZCPUjJ/b8Bqx/DLEBHdYVXarlt4AVmx5hBFM5KAMh8s+3b7qtzX7qYRbNShbU2/P0HZqM0Ujwyz1fngw8oYkkentI+n6Zprzt3/wAP48kY8P8YoxAOJ72S/5957u99NnTr9f/30/33hwgWi8XmRJx5/DH+raZQiZ0qb/zXnzt1xx50hq5VCjJiPQceoSg7NKDMAO3AMwQ6lwGjZx6W/NRXypFkClkYlM2F7TidUxUFOG8u8PfWNhIQpIQnSH1vEXUR5J1x6scs9u+HGdebS5QP37pZD/g+ENhe0l7cGdFxx4jqVI4dzc6MKls1iCtfhw3DlHxU6jN2Zdt1yBgw7VCElL5m5HkMCswpsywwJ4AgL9za3iDpSAumWfNQcYj4HyEfqwgHxOpejJMAV4aBVoMvjIAXGMF3ze93x5iI+Pqc/7LjX/bjw1oF8rCxZGTY6bxlExxzDVd/F9z5sTN6x7HSePBpCUBxpNegS26xgQr6HtuGwSu3JBO688WwO2AJDApXJa5Q0C1v8CKv8tzTMGfG945fffOnlF195+RVsyKE8d82589ddhyfgexpcPnjHHb1OFz/+Hup99z/gOCPyA6YoZ/Xb45zyzsnEZpRp7zXZ72TOw3hD5bHHt7nvpBIYwpT1DNydz26HnIeHtkaTk9vzDPRGCVEkoo7jleHxioP8UVYEhZZAU9MUvgjriIKtJObhKibZVKUQJ3PlvbGBNy7wzIw9717edxdxWkBsLTE6X1RYJYMkGFbV9NPn/Lm9lWTKdn8FODbzZpNvOTVGfUG+Qa5BZjL3OaDBrzc1JF65XCv3i1GyfAyhM9U0N6U0QrwVCYziZh3vzTbh8m+Hs+WtPNs0sDJZLVfreRKJHSVovZKfRHYrnLv2VxXXa6+96l/BXimP1BivBiuU6gBmgU8Ew0u54lzMNoh093k0HFNXrlx0ixdsCVdLXbqpIzzZ7K5WQsCnlbcaDpOvI85biDmvQHEzGAN5j0MP7Hrcm0Fw5zBUihJ1LsHBq8ViJRyU4wusG6XTIWUsZ/Diax/QB6NWWwyLb9orTt144034n0PnlhocWuVRGShffOGFr33tD/Q2foBNRbyT/57v+I6zZ6+cInYa9g7bRvqu2hv9RrCDUzzYEVmoAxz8RDVhlFrCqB8H6ZRHzKmRe2LLg7kcdW0IlejDxNpEOSKZRixMlCOykjeBKo/SUCq9UEC5QK5ZiQYzqFZAIOoRtibg/8tYvt+4gO93TzzGZuD0z9YsKLqHQWO2kI+y0HPhbghLr1SpFmFt+lquhI0wq7qUwGodIyDKOUAqgmXjh4SsWD+PaNe9iIAvJ0Mft9cskB4lB58J4B6P6UJQeDBJm5Yu7sp5iXmrLWewFf/tjcMw8Ng/lLsKg9/q4zN5U5c8G6aA3nC0hMsswEzMRSjKPlyn2QXuvKnYPfmZqhLmQpC4PKHxgy6T8IPrd+dAxCKErHSZp8AF1ve4H8ddORTq3EnPg2qIJnCO16EJqHXGQJfkSMoeFDS227bbaFGZb97KFy2pbMDyFedeKX3HwWCfMsCfCjYMOKocy8b99ddf/3//n59/5plnMuemM8ieevLJ//37/48R2uOZAQKOknaR3LD6IqJV5AygiDuo5DMj3aPH+C3BlRZRfWjTuLY3LfEexRZTUX4x/EyugkZQ7kMF2NVgTnCJyYkhxoqyABTUmaJfQBdQkCKFcgLJclqKIiFQbhbd2WINfwiyiZYmGz5SZDBLynz8ny2M+Of/X3UZz8yUFWBzAhloXLEQWb9wtpxhtn8pM2uxaZKp7ZDA5uzVYeM6zFwavd5zZcS+OzGTKGeeknzW7D6PaLeSuO9RCFKgo1EctQh+T8I4YjodkepIk2Fr0ddw/Sxa83ib2I9Y/M29wAPuNz3A3+2PfN6qNAaxuTtyg6+iXD2hPdoFMoi0QxXWdPNC09OxlJCYf5p/JT13WcEMElKv7dqM29EBtrkVNBWzBFYZRqyHLQihvzkRW1pstd9FSOdE4TUIzMN0W2Vi4HcLBKP0QVeJeBfe3jvLg+Z3dVsh0QNs6Mtt+9VXX8FjZ5XrhgZoHn3s0SGQ47xhwIfelTInXymHDQ+XLFEWeEo1NbjrkEukqwJqffnNha8EysOzdm2sBtoHUK8g4GhKEJtRTjEGqmQ5/BTziLIYFVSCTFOhg3aKqWsyKBUu49DGFSDSRXmNtbZv9eSF64ngKvY8RITpY0stXuyQeijYMzOnTx/5bzPFQuQ4WhwlZEu9aGq2o74r2YpgIFhPdzHU4FQpKo+QxiCznSorF1zeFjmwpgdkUxd3TwXyjLUZblOjqgNomYyEMbWTrGDGnpV2mYHWyqFqVJlXjQo2bazxTx0bAyq5ug40Lm9NE0/x2se93t6Hly5NQ5uTfpx0PRFIsbSsWdAcOJ/31e4wrxIjXJtcGQaEixO9waOPjabE2ij1N5fgWMiVFYXckYDdLDpDYsZ0sFvftBdcyx2VVvSSUg7Ige71ya7Mve0zIKkuXbqibNwz2/iMb4/5+Mc//qu/+qsvv/xygwBZHaKyL5gq3DE1YrgoG33bPqaQoEnj48IwEEKXS2+IOL5kCpPSgirKQqgiEmSqhBVzhV1pIA8cIJQQHWKgKEfMWN6H7jiUDQXYI6HktIyb+6ErgLi6HJLCLmf+Y5mQEXz4ysC41O1dlpRiscwYZ3rbu+MPsh3wd1XLomlvC7Trl0ojYZZB0lf9W8GOzWSADQKO5ZoQ48DyMlQW8zFK00heruXUt6QBBhzTKG4VRoKr4WZ+rTLZNp0Y9zCG5OVfItHc1dQdCQvZHCUH0haGMtVLwC05CI1O7cLDscE3xWRT/BIaL+PJF2DDIJeZMAsxw+/VN19Kvdf9cDxHczSmy5ylkvD1Q5plx5kV09tmONlsmPieEFjxeMuGH2n2d2GWyS49e93PtF0kAB+xer7AHz2LkDVGdRefg60XNm7lclG/eOUtUwpseZBj2SHki417iRr0lYhd+1/9q3/17/7dv3vffff9s3/2zyrbYmNXNZS5BHDvYmhz2VyF1rFul2skZhYxCiQhWsuMGJsr7I5GrE2URRHDRVmAVtgCGkYSESuEJqiGSIWQIN+p0EOH1FN/NzAzpgUFBTH3wjIbrW0aYllwtuUaB1dtI+B71lAlb1zOfmxZvo1pS1DDDY/53l1983SbKFyWjNGiI90h+R7l8TIchU0dL+mv0rFGycHWC/+vEOyQVmOtcoHBdgTWDyPzpjlFZskSIi2Vg0JE0KK8zEBmFi0mIFn149Vy2AQT22Kmxah8JBjDkec202CXS7D9kpUiD+heb5ViYxrC94G8OHxstzKqm5V21EBH3riQvvZuZD9JHUfzCGOqqciZyVyl7FMnLINL+eFix9ZFviM+QhdaLiaVZ1dr3d4+OsP2WAsTdDtJREbCMkoRkWV2U53VUpXt432PrDMhJjDDBD027ise+OH4x37sx97//vd/8pOf/MQnPhF8V8Sm+7EZZbEoDwkwRVnIIqyYHTgMVigmG8wAKNfISrgVc6DcKCp1CdExhotyxAzkHdCBN1RpziInHpFwJmfshnOkmMGBaUoSs6Hc8KgpYUa+rs8U+Rw9qpXaG0BZsgZODrZio51W7aYnkexE5VSmyd5dfZOwmsyRO2Izi1G2B13NKlNm4FqW/IR/8mKN4Coh01S0Vbo+0mtRRDMVNjIwMSYgF91NonUaaWJoOCeoqZrJeAL2Yinlg1JpZ31/xpUCpfn7qrPFpSehhvmQjTzSECByCQ2VpaFruLGtNWec0U/5SKA1+XopoGmskeH/Z+/deu26ljMxcu9NcvNOUZREiiIpidLx5fjEaaPTOeg2kA7S5xgdPwTIS5BfkBcDDvxo/we/J3DQyUuQ+CFtBwkCAwFyOgGc2EbH6W7YPtZdlI50dOf9vsl8VTWqRo3rHHOuuanjtqeoNWuMUfXVVzUuc6y155prUhaoWYGYsgmWgzKosqakhPtkTp08BU0HWGpRjSjU26TW9k89pX1pk5GpLLDQZ2RRDMng/fnAh+j7QnMAVGjq6BowaKhsjmDAMXlW9QwFTojvUN/X4Z18J9jQ7WsSzof50wM78BI6ruYIu/bf/M3f/OEPf/juu+/+/u//fuurqzZmASWywRqqeJGil01hWlhgZsyAbswyTwtgdU5mSK2i7t+ydhAS3yZkCsbMhEyhV1xiw3iWJxNSN2HMLsYntAXGwiZlGIl5QC9HjULqqTXbuCvpA03A4X98TQTBQBsy50WTg7lOGCPrddNXwXhBBfFiMxMMJPpt7N1Nc1AAYOmlZVvTDMlrmeT1MYC0hf922cRqWQEDnHBwGHIdjiAdKzbKX+bq5/bWbXobpeXLBDHxjrxsrZl+6ahfI5gdEGsywVxDYHN6yVr7TrNWsudHptHsS66imWJSNI8ilIFITWLTLczVb4GBjx0e02QTTM0L1moB+tYROUMwHBGsKFCmbIKvhzKOrEkUWq94CrWsn2bVAhEFazUhIusErTRFpSBBB4CiWTSOVth+zgSx9KOSaE8+kmvU4SI92ejJ6yKAb9nIRobw+DYC8R0asiED6NtIDYY5xhsohVF38EDvHnfZtf/ar/3ae++999u//dutXTsC8Xn28rwYBy1t5kFf5I6hb/LyBLMZqimSETKB2rG9q/1apHkxIQUrSqN6haHlzFo6UNZkgllBqFRWqrxFTQYhsZJNluAmOSusnBfAhUtnAAAgAElEQVQnFmrNilEjnhtPsFbwwfxsoNls5gsG32iOrbkF0/S9Dw3mtBS8N4vaBN8a5VX37oBN3KFgLNVloqCV4VwoZ+0TRf/hrqkCE4cwEYErkhfjVEVIVIcLI7GYjgkGH0ecVhlJrZg4i36JPGHGzZkVijgyAlY0gbVyPWulq3CJEmx6J7bCpYuuYQKBmsjQHCiGVZjg3SqGai84V98g2oLmuZHjyCJK4TosKS0GnaTbk05JKk5Yo9LGSslyZYIgKE7oWStWIIoqryx8fU2hThV4ft2p06erTdWIDdCE0tY3tWiIjtcscawGIPh9SxSNkvQnmfOc7N/loh07/jbTPK8hyGBzQ24N0G8PIwsEXTLYizXK6Jow0+aCOH1aPVyx5meszmIxoWEnFwK7HNDG3Q3NaIQfY8Jn7di147P23/md3+ns2qPNoDQZcEnIm7TkQe9RzQPF2q7UZwZTwzShizfV6FFK32ZtTdAX2Rt6WmaygpD5aCHalcyRA8lg7a9Wg4AtR1Q/BIGpi4PU5VWJESuejnjFWWRSmziGnE5gVJotSSZ4JXNaCl5thrx4724M2FlaUv/1Wm3NzlCuxpyplUWzgkAH785kiBkBE0Rl8tUwJzWrChyL0AlLfQlolEyoQuWVc7RFt3K9CZlKsRVZz2FeNUFSa1cygFjHWfCLgjX5xDAt07LxwBNT2CAWMiAPFS8G2hK0M9hRKLR0a/W8dMAxn0khLBduQZum5ZcYAkot0lKNhNQFl2FN4zoxDYganeGZUGIKiOdVn4qctszcwUozKnI9lLGXOHv2rLc1QxN861zZg8CdL1ah6GKAgzWNLqwkCeG1atmu9Alk7KBq9eaojfF3LY0MTPZow06qrQu6WhONOqUm1Caa/ehEUL44YcmfuJd5QGw/+MEPzp07h8/asWv/8ssvgbNz5NCbv/rvnL18/tO//OD9P/sreT86gV9Cg5yMWSEK+1KnrJlwM6u5gy7MhJOyxIyOvdSx7XIw4EJrEJEB8BJuwBWKBuZBvGwKs4U+ChFxkBYdKkX2re5KZlYxow5mQPS4PXVeiXFhN2aOFwY3KPHBEFkwPdi12yxZLBARH6DJJoz43ygc7N2fbMXnzMCxURzx7XU24qHjqwpilSaYX8tUEJJ8mtY8QZJgyH1jo2QC9GUGoEaOClSlSrWzs8fNmvpF3QTbnAi5qbuu14qHYJgsjH3fZSunhHY0lBXnjJNEZa5zS4fTCa1Cg3dF9Efk0sdQjUOpgQhBokPhkmd1pIKWMVWEfKwYImBKyoQqFNwaawKFzpQCN+PPOSGQBVQqRlWUaqWxDGnjhFDycIQXnI4dO1b5uDroGkQUAOYGQqwfkYQmIyBLfKRmUEDI9C1RUU1bfUkITml5iyBbSo0AOawoUhW8oKn01U5PA+hvTzWlhjdpNB9aef2ZTEdGNit2KdfvwX311Vd/4zd+46233vqt3/ot2bWfeunsD/7L/+z6T7780//hf8f4+w/+i/9k+1DvNpvgtBxuICf/oGFCl+L8xtKHepI+JkRI8s/Dl4Y0rb3GQlkXHnNgggB6Nl42d0yCXjJDU5gnGEpLyOAsVSZ4BQNBpci+dblsuCYELFoB8QO/T57s7T3GC/3Db/HZvye40eUp7mPZ2t62f9t8UBFLNH01FJhymKAVK5+RMjlM0IqYLA4wUjKFBUIzHPZBgCZk6FKPxD3hZ0QKUEs5sy2LMCwDLtVaNWJLILIfcnpCDBUmuMa6uAkTuJGe0U1cjKuEJUr4X0xYkGKsUQWrJ2H8YGVOSYquCNTE/4JgbmizQLTsqIEYpsJ1zpyUsnfaFshfYMZWdJWVgzlyypjhnI4NGABkzLbzfgt48EhTHOtYZcV+8NKDCeQ3ZRK8cPewO84bp45ijPWUPFY2IbAL9RxMj/BIW+KxNGhwI0bxAEno2Th/8PBBCWTjtBQAxLY8eGyEtgSCRuR8ddBLAl0pcFHAwKId+la8KkDc2tbLQQsx1MtQAIYNDh6oE1ZOh7wTAR5FyCtdpXDwfLWEAA6HgFql1XhnvvVvtUxJ4bTJ689MLuJM3QdK9c03nuz8z/75f/+//I//073bd+H03Kvnf+U//Q//z//6D+98cwvF9//0LzE1Lv+975AgB+eNRJocoe6ZnOBM5pF4tTlFC0XBROa/8Npflh4dcxOOhZnxAwmv05LXzKH4qKSFUhiqoSOyJ+S5ZvUr8Ksg8moGaF7WePhHepQ2KtGB734e3A6TdgUmiyGEHV5xlOFYjQmLHVUMS1DPw2QI+Adl0zehBKXP3fE+5/HjvU1+V5V9idPShdRYqwmmafTQz2WrqQ0KAc1AJ81sRtAo5KTR4kE3WGPYGYwJKV6jWpTC14xQ6KqliGkJ6wnNjQhBSAHNLl6T6DSBLLbUwXiJZyGRYb/EC7aSJQIJPSdcNHF6dl7YkJQnWTsjJxoNqZOia3diCNlIqsfEhqJYehhQCH4GDvcpJUFIch8TXOQ4Aww4wS6ErChzMEKPyGizQQYEAsP/o9yEC2BwYGmZRSEoazQ02pw9lVwNa8EbOdxqf0NULEhp/kExBwaDXUyEYQWiki9JnsjMhCMiNjP4GAmJQBiN24cI5of/dxaDGVg0uAaxG19O/eijjx786Ef/3n/+T+5ev729s413qf/iv/rnD+7cN1TcKoN3jlaMQq0utm4kAdoGWyYbrrnnaTJnGhjEMoEJ0fQUZn4WYWaGd9nLoNXKYtOK4tzRoMWN0kEUPTnB8AuGlwsPoYJQWm2t+mgQllk9ETN/6HjHOjb0UxTedr9kS5kJ3pOEFgP0bfPlZnKrDtLckTdRExQzMWGQz1p7d7izy1UZmLEyoUqv3zo+FGWg6fhiZpIuOJA8mic3C6yOciuavmrcvcYWtgs0IRMgbZczE5KUBW6mTORCYQIkhSxLTIAWqB6T0oxryLUIcXcnOBHMKDcwXLWGtEFE4h3h4AAyFzH6pNvUlZLTs9YnZw1M05w0jhYYpB5O6NGwKJsX4ySCpkQ2pkv6SKga7CBz17Oha5f7TlwCBn8jxX4aHTQOKb3Jr/QSxpQGBRxcJxI3A4XonYdKdybW4JREaBOKvJjQcJPrrDK08XeQ3nHnBzOherpEa3jW87l2Uk7QrMAjHhysgrHZUIadYPjmBFXXQlPzVpnm364iJUKTETv328nBTujfohs/f/vjL9795Mjx3b3He4/uVf629fTJ06NHj+I+eHq60yYHUmHeg2zlDNfXezlTk+KkQtUqq/TkQlML16ZKqWBNGfrsYgEdZjrqad5zIgsd8qIMVvj0ktEc89JfWMZwEl4y2mMqZdWUFYusZy+7zvuGIkjh0I+44kDMYC1GEzKFTYuGy30YPphRcrZaVLyYYdbWqs/UOsXJvbt1qAklGtMIXOZRqmp3PJW+NX02L2ixlWlC4DQlopMolUBpjWgS6LhNimClMFEEKUPjotRlLWYOQThsRibZxFhQIWt8qbLK0jVxCLVVLW8xJAeUqZ5u07NwJLtWHPKeKGlgC9Kbm8huSvtrbso2iCEJiAu6LPPYoTRrmKZqNUWLqQQhDzNvL8t4iPvu7m4rIHDDASt5hcBciAdMsEMvAaUG+qbZ0qnXW7BoZq8tbsFcdcgjqsTc2VBl621JN59+PWIngrNlg4UIyJpGUusgDUBhwMGbqNs61/UfATM1xgmwUWlA0kE/oPo3QoXykuXmW+PNt8o0yOA+sfu36FaZ6nHkyBF8gRXfXv3iiy9IAT3bwIlNUcepOrGJUGWwWqXRIsHTaYc0wzdANzkCOdlbGlNFjHRtdmpT/ZyGV9ep1yaWtLLo6sLqYZJyBkkT/9NKS6yaq2130NRJzK+V9Av5In024GIe53tILZI0pU1lScih3liyeXh7ZVAmlAj7WuP37u3caRInWZYQc9nDhQfhQUdupZ4ER0JFXwdN0g2X+bnunb6ATm5ceBfSoEf86H/SUa7OwwxxkEwFEa5xUEo4sfSc4LAJsaxtRq3ic6iKKIXkERPjqcYD9CQQQQlBqfW8c/BV6yYbACYIdKQn6RtcnefxmtY2VibQFCFKQktP00h1DQuz3lypvXHjxtmzz6NTpF+0k0lTeIEeC52rRgVWbCoNs6oiCrGjkuSNXokggamOnmsOLC0cZE+zZh2dyHO7eewHkNBtwQzwqKdXtuHa4FsGHNXL1KG2YhKxwciLG77IBRKR8hiAMBMsNJBlu2CVHoBZRtK+aT9kyeF+IO8r5o7r13mO8Nb5j/7oj3A3PD50JxB0pWQ79qlKeh7obgGax2RK29MifKOjhlZhArXsBxX1mJzNURASFjFnJe8EZbBgznr6YIDxjANK9D8dfJYCFgviyK8issaClzTUBQBiIssrySCYYfqilxc7yx1kQCWDkDRNpSfhZY/Tqvc6I3JJpmplaiaYmuzddzDPy/vd12JpzkjwoJI5lziu0N6mZT9qRymBqxZ00JbhVtXblXJlFdJCIEAqG5u0WlHBEh2NqqIwWBV81YIicKDoRiQCGq2QytUyE1zUyETvpQR9HMLKuGz0xw1BkbVMEUu/UzXaOzlO7F/L5BTUqu3g0wvq26bH+3J8U5S6VboWG/cXX3hRaDO7fIOOJk32qpmaBGOvQgwj0DIX9u6T5lUFhWFYaMweJAoQrsdcpI2v7GKk1XSqFJzP4J3yS1TClK9bWa10mxXd9om34IxErQQKRNmXi3rVgVRaU7mDZ5bTicp52UZFfA+/TmRvGOcZK+qtMuwWv7iE71/PYrCzs0PfNZFhPp3tSexlEDxmCNt6M+DwyWN6eZLMCgo0Y5mWOA5EUxZWMmEFxy0I80EzBuxwkKpVQyaSfNC3GFp/neRlruVkH+pD5lwunZO4yvo4nMIccQEEpzD6MK5SZYAmRNV9luDRyJSC9boRM8HzwtZg7yA/I3LkQVLeMpNDNxkR3ywLOMYbDUjtT6WjZyFMJVLy5svkciPbwWGG4jREQAVd9zWoxawoZgXpsOg3ce6QQU6go6IJtcz2YRDWlMJg+1ycmr6Qd8Ni0LdXC93Eqx35sIR4JS9n7lRfu9urbiBnXuYjrcxnPgGzQGrlsBoIoIcDz26xyuPHjuPRXlYsBeiHyo2TU4JTjcKCbfSFevab1GglWW12RFh45bR08AIxUowDFQgKwkskt2pNB6zSFK1SFxVVIpBWMykshbbz5oSSTrYjT82aJYdDGxLDaRpog+clmeKWwM/R9opqzI6s8DdR4E/cESxHh7vV9/b25oaxwKThQnnkzdwZoSuqOsSeI6h2Ug43WB7HqvNje5qE7tmD45iDJDtqcM0TU145P3BP+dMTllTI1S8Zd3Claf1IrGd9Oo2H+TPBmpYLs7CMlgnmOMMRBWtdLAAnQ+5Dmb5nCASqdwvkLEzxSHv3nfh89waNSWBamZkbvZulg04iRn58AeXWzguUMLiHVDsonJcEh/kJo5DEwJR4OrEARRuluaifVSHmkziiEPglDjghcmHGtI9X6ETpWRYsFhOWet+8r+GZM0IL4lN8PoVCu9dWcTcZ65CXOalLBvOk+4ZCFwTZ4wTK8NNBT7mUq0nco1fR8bOp1fpK5VByKnbVKuIs/a+w3PtV3anKOR2SY5lXJkQTXvi4nAcV5ZkjhFFM1aEjWNOAS/1mjXfB+SlXVLiIixr0uSAnWxCjgkThYklchyGT1PmCAWJ+Bj++uSGHSwq3Gj/R5SbqrcgwgNDNqHMOUweSyXMAoEtsVluT+RP3Iix4wafvv/7rv45XPGHmj//4j2dynKsuubCkZIR8kWRfnutpUB+EvBdfJNm3pZppC3lbMqNKlp4BWnkQkBYLYdKJFRgwRZzhuvhCTwYkNs/mFa5xSIZKGpY5E5azWgbB/CKxDMSKJrT4TSq0DLN64EQ22mY1QXDOTDRBjKhoZoozdc4waMnZ2z6482jv8cTn7jQiYyfDTZwttK4G3NbiPkUrtkfYWDcsgaI/6ArI89T4aQhea0LOMzah3mym9ySUQsJjzDxX4mjCHYWTGzZdTjS0cGK9dXmGZCSrgWTK+1qMDENHY07IpsQ47qv/aXAehFDLB/YcfrnttNeKBoOADRHCP148iBf9Ly+4qkxs0CuwXIVvpraa9rWeO30lD9IhcUCNwZq+CEoISSZxUcd5Q+osPhR4jJVpEQHuca0RnHloEoW8MqHEXPKm+J2zW4WJEzTVNNyKg3FpOiaUgL6JL0vACUgKWBrFmpBQqijVyxozdHbe0Hyb4gZC/TnuAJTP0X/1V3/1Rz/60Qb4YiqRSKiQy5itxoTgMy9vTKUFYLRM8JqBBp/mUZLQPdaYbPOQhAxEJznNiu7TasdcLdNqpcHyZ4LhexMvm8JCAVilsxaW5FJMoON5sOwrWhj7Xt8Mx03+eUSh3QTN0tCMjvfuW/x89+0wJiOs0RmjKN+p0pHcdDnSgAlSx+GJI7xC7I6kIXMdA/QSZOr7JfgokssdxqjRnul8sWHmh/zTOkRnjxnlMYZRP3OwrIj+wpG5bnVipsaWzCfsDFbg1nLNNOmlr2C93lcztHUEvr6gb4medjFk6WreoLvazVwS/r179zYDIZZCbkOcTc11QBGf1uCh1KmenrMB27Sdyc9wwEcoWU0VSXSQzcgQkpEkm83GoYGJi8RRlVFRCRNab/TuSSmyVsDmUVuY1SvcZQmBMXLWE4VdkgxtlVWQS5l7UzdBbeK51ZRBRYOWhI07sAQuAcXG/dNPPz179izuYm8Zu3o4TsxdE0TfFGWT+sYp1LySIUtizGOJYkNWdDqapW23RjxXVHh+aSuf8RL80kkGJ17pb5CbziLzH31Y1aBgliZUDS1zJlTVRisHUSRBADVB5NKNAZpQ6qxS009T4qKgYhUkzADyqIahlagYXT11ywvXOAgpoD3d2npy8ODO4734XdXCD9tMv/D4XhgboYdYmCLmkk4Y59it1UMkh5Qcfk/Mr4uTmbcFqIe6T23WCyZkjigz+0Cw5S7zLsVSWfsrya1WVjFqlTwOaTiRg42CZNeMAz81qEFug2q1YJp1NKNBDTHSwWeOFxMb/1HYxd9mRXXFjj94797d584+p7gj57LTq4kdgdovndjRyThkdxsNp4WEwUcohd5WVlJpoKHYY5iMQ6DxUDGAYUFcqCPGSQYV1YRn47gVG/BqklJVVhrXMI+omLihuQBMfpVzVIwSZg+s5FVrlQmXawqqqGf4aBwJFJOBYlbpTQ9ObMpxq8yxY8e8RUPu+IgWLaVWfbRsSJIIMfeyqRuyCb1smBl3opWWCRgP9MPLgLLv+wpFgcMooIP+4Ejclt1ovoBZTERpbCk0weuYpQm+dYksQHA2jmjMTPCOBWcczduuLoNGDE04xfIcb+GWhzXCIgwhgdHJHBJi5kJXNqtwfOlz963J+92dQU/s387BFIkveAhdYCmnsKwHqlxLqj1vg21LLgm56/RiIyEMut83Nb6SM7GMrSXNhHU4TMGFO02hZr2r4zMQsCYdkTViPBIEYcpjzVzqwnCiHAEkdJ9TdwxdbSmWlqXOPtcgBgpDDpYkLXwTOvgtT5ISB/gSEDw5+pVXLioIzoEmU6piLvHi8J+t6BO7ZA1ZmW3oaJkdtBfBRy6Uz2UDwKzQZyYvYBxs6fpDQxRFqZHXGYBptmn0+JpxoGBFmYmzhsY3b9MVU348q7MKSZO8IrRgnk6TydFMGaHDK0qdryEN2bhLm1pRfThu3rz59a3r3/uP/+HTvb0P/+WPb315Q1umz7mrhgW8djQ9ay8LmDf0cu6q15brjpepm8N/FIQEIq8Ewreq4rc/t+KmfH94jDOWyRJYRKYKYPRM0JaVziO4oJUd3spkE0y5rLGmfRViHgsGeUVe7vBKVLF6LFouZW3UCcZE9YKSLkGJtw6r0PR0S54z8+jxzqFp7b5G+KwFJEGCCRMZIcSvIvYWCXMQVK28UJi+jSf2ubpYybXC1c6l05pWUReZ8ZUNKJLrQnF2xTJCTCDxJddLve5GvqKUlxPTWBC1ZZScI2IBkHAJ18u2U4geRyQJdjCEDqAlzUOB5BO6BlmjzBuEsN+/Oe1ZdFjnTQ/u39/e9p8Vap9Tyhdi5j7WKIfep3mylNViw0H+so7raj5pRGOCKdGYWWkHP+m016t0/UlyC2LCcBq21PCWc4cSb+EAkBPw0woeE7Ilg6RGQqNXAafGIfuakny64eY4u5JZJPoVK9wwc+X1V+/v7X78b9698vd/4fGDRz/+0f+brBSMUrHk+sEXS7UJZuiRvWwKQUBbaZwrDZfVk0xgeqVe5HdgvgPQM3QbS/Y1HTXGR+17e9i2D3vtKo5HFydzaeMnS+TZdTy7cQQXzHBAUwTvY8Tc63u5DNi3jsuGkws1crU658ogQt2EurNMRF6acizWQKUOfhZ5hIoX391h6aDVO46QxMWsAn3uvr1Tf757Cyj4pcUsbNOFKzjR/MKREm7h9OtXiI4uIbSOE6Nqf1Ur+7RC63JL/WgAQJIs1+vMdMg/Z5ws+Q/fYyYtLYmFOrOhoTxjMzQryrKlqDZF0yEJ4DqShvRFqcITAwBtPCx56s1A86oWLNBM9gojsjB5gifhpET56kOb9BGQUmeQUuqzhJlRgx9tbGgvzk0Db7PqTXq87VkSuVKksi7VV6c2BQxCPkgDCxyGN6+Wm8ZLQOFIoObEmhgq2pKzOgUpupxo0aCknhbAkMOglBOwsswTTpSBjAopgxalbF4DXNZneeUI8jD8298KGcy0Ow/uvfmPf/nn/vGvfPIX7//V//EvX/sHv/j+n/xFRbVdZQPWBNOVnFgxZ2cNIkw0Z9pTRWSFEyajTl55ddVLmiRs+tOLdWk1aCdOLG1lRmlqKoQJWrHG2UCNRBVVmPkmM0SlySL0oTxIXzbYvlqrNdCQyc4FAzShZevqU10qbRKf2MpQZSgAog5H6Gh2Jz7llRt7L2S4CaWAzXv32j0zYY0rXBjhwNPRjYO2R3yobQUogpB1u4hhiEKptAwHVu4gDKqRay01SAJdGp12X8QYF/Alxik0EGjxJKAIJlIspybVkihTkNXm4UrDgUUGJSF7JFHI1ESBKnkshUuDXci9/bAsLvrR2ZVIL0aBAGzhfHsrPv58lb4zShRoO5BOU5JPjAEC6k0/xNVBMwoGC2WTrfVvrDASOoJDxBL3oP7SfEhP+RHOSN0OdL6EplSM2jjzQdF7GTRRNSNls0kjpsRWdvRqmJ+lHzRR6VY81+2XjRKpMS2pkQ26vMpUFHoJSTcTwGhi43737t3r33yze+BVOHr5u6/dvXH7qw8+/fhfvf3o/kPyrYelV8AlUm2Mi0JWD4WyxqwSYVLPGDgz6zAR8IpEhOyobyRODmfXESd5dGxnNVkiTfCuTTZhFvhs5YabeLkWlsA1TRNGnM1SHgGc0Gn4S6qTQobXa8tUx4oyfG137pdu8aVTmK6OKo9BN7SKvww29NrVxET27vycGbeShGv36llqcylbkrEpTIjwXE7Ql76peXiKX4++f/8+/iyJP6ydPn261LGayMCqnAAX2SH6KVstySWEx0hmNavYp1SFavJciRKcLmBVUjWe4RKAJZ6VNIOlRa9GrAFFI2H2CEqQQYC4CS19JQ3DhTDwV1oLhCglHmYXxFwy1oESBUMXzURfM9OBot9qHDgMtgMlMBmrAeyfIRXpfwpWU8fkKqkdIM3XjgRnwEhVbPShAqxwSI2vV109Wydpxb6cF3jBmEitkig2ylOcphTsYqgwamNvEUN6uk/KO0uobzzIG3ep8fVmcvz48efOnrWmK7/ycx/9q7efv3Lhp3/9oelAMAUTfOu0vMyMEhf/gxfOJNEhPMakPQ7+42OKxjISTVQb/amGDSvpvZZTqzchhVmj1IEOQ0u9GGmtoLPfP3agvMkzkjdi44xp8+eKy9nzrSBAsjwGQcCt1mfUTSrvl2b4Oqx4mTDX3oeTe75CZrDG41Ez8+6ZcR4mxU4HgH2W06jMHYcFweU0Nk469Qq44Q2789t3buPXyh4+fITPMo4ePfr48WPcLHrixAmk8dChw48fP3r//fcvXbrUegYXUxW6IiYDawEzmAQgz3W+LK4NyngakufmZVMwIYOy+gWCQXU8Zpy9ZpQ1zVCOlQsIwVxGFO3egzwJg0sAdGgfxKegL0B4pdssNyQVg9owQOFR8jTYcaKmabYSOG6tlmdMhzwMnDwU1LOiAVi91XQEitFBeU1p8jX7KstAWMmFW+c2Q+SBSVcZZAO3w4exvvFAXeuyBV7o7tCJVVb90VDmCVh9k1Y+S6iWZlZfdecreVfbHx7hczLqpAydiz/96U+/+eKrh39yF1+C2d7eQmfe/fLm4UM7uIVmxk+geWTjN5IvDkDWPcKQeOxTc4Ki6MJ/1V70rnPZqPiGEVpev5QlkXqPbN5uTk3INVYvtzwFotr1Xs3Lq/NZAFjplQ7Firb57JiZjgo0onpYqufPNEbDQmACj1BSsiGasEgKHqsuAwTz0KDqSqO1tA52oDpNzsMTussWz4h8HJ8R6VozcSTajBUSqUfIqesYwzNBdakf3F/YYnVdwu9G37lz++6duzuHdrBTRzRHjuw+eHAfax0+vzhymP47e/hI61PDEydOXrv24fPnzp06FT96N04myOBwEdXJjNQKZhxlIzZOp+SwGMqhFmPft03KLk2YRyFA3vdilGb0qJU1vFHLA+nY1BwxaADJZLFLUpg7QOaDnegL2PH00We5FIjaAXLegBQhi/k4lDqPtgFEpzwU8G8TVmYrvm7fvv3888+Tv5mH0BA+YmrIM5FI3WyFlRV90wismEfN9dbkiPktSmHsUnpotPMchBwG/AJiOq5mm6aJDbxmo7QN/Ahoaw210AoTxoVsSoesSiUzBhjoyWuqtiO08Vryx5/sfv7nfx6/mIA3yj/+8Y/ffPPNu/funv/lfzhWojIAACAASURBVIjp98033/ze7/3e0BvoEtcxkBVQXqna4hY+CEAPZ1SItcAKpS6PQrtbwSxlIKajiq3UEbZEY38Z7PpaodF6H1hKjmBNNmEFZ0shQBKHMIn9qcz0vAB9A9O6N2ZqFE0I3GnOhcMEKqPAhtq40XneImgUTTDny9dhgxDhCR6ddHD70KPHj/h3VZPQM9VaUVOjDA8ewCcF9nmkQ4tilGqAVgdaCkp1gL1/797169cfPXqIT8rx8fmhQ4fwkcT9+w9OnjyBDfrZ55/nmu1qagClVJMeBRns71977fXPPvvsxvXrly9fMQJVAfqCMxhFFUQqBQFoLSgj7PUhk37NprKkieWc18Bq/A0msyTXPjOMIh2xAiudmptA8f6cXmRG0xkSkOkhBXStmpEk1ZWzQc1AKFQDpLtA+UsqUQ3/V3te4SQoV1KmWjXzLObffPP1+fMX+qYhraxkTkWwoiCIZh+t32qAHG43IQWQmFg1QXHXT0JlhoawvwKNh+4HNF33Nqpl8IuuVXZNFzciT9o/43Nqk4ntmQ6mSzg6pqCsa4zCSX9rKFo7fBZDfs2Cc3empmj4c/D3v//9P/uzP7t16xYe5f7GG2+gHZe0U6dO4TMnbNl58XI2DXKxsyUGXZdxljBptdvwb4YV15Uqx3VSVK40esrcu94ZH1WTPldSaEXeql/J7SgMspkw0YLfXmndKKbqqZ2etX7BmTtduIYrqoAAmtHNhQnTTtb8pLz9oXueYJfvGVyzaMSSk5J2n9PDlWFve+vww0ePDjefESkIzohEgCszPdMbdpp4Vs5MOkVZcLBA3blzB3uFrS18qW8Ln6Pjt9YBintazpx5Dh894J4WfDBBbtpHyTbTzosHD54/fx6fdLz99luXLl3u/7q72I7GmHkqOfPFBtVYm4S2RTZpmoFR5sc33JlxWiQOREx2NdomNVwK4UONWQpVeVXtcJZwViEWoYBddSbc6MF5ICjpNBoYMvld6HIp7I+lYN8+CatKutomSQvTlHyGV164iG6iN1BIDaREmYdp2jSAFVXwmzDYP6DsmIZWQxXBitG4kESHoTYhRbiT7sSLUOgzNKgWMVMoAooV3l2s3USSndYmCGzrh1LY/uHTyaUPNYp0GMuDD/RJtI6STaHpLo1GFWkwXf2hILhpfyPQNMyK81aVn8aYjHHjDkQPisvP7/7u77ZQfD16LjypSldkjDxzgyTI4U3WlL2zebg2uZQ39XfZG2nux10stat5MKpopIBxWhO+5nLNOsfViamHZkOqFkpT2kNXeM5qSKflFQ4AzvjByUq7GAKeol2NtVoJqHqMi1yMLSiT0LjZ/fHOAXxXle+ZSTIrtpMIPlYoY2bSKlocWAFxyyz84Lh7986NGzfxgTcqcbvL0aO7qMR+/bnnnrty5VX7pp+xKcBkJxGrzZ8JsU2lHM2pHj129I0337x27drukd2XXnqpRl9ReJzVuzGqpJKtVTZg2TWPLHqvQ8QcmdR4tCQI84iV2EwG1aATLs1ScD3aZVppZGIEFuItnQ7XEAKQkC8+CUOzRiM+TcKfYqxGhSorqsSQxCtf9Cs6aj51Dsmxbi6hiHHoaDrz+IKWy4jYmCXHuOmoYHj1a9BT0RBT5gsBk/T8hZchBKZa358gfQ9EJOSDFOfwqgMbKY+2ANZMBNCKda9pbUvZuEHB5NT02ZXCXpEfm8tesSUtJ8sYH7/vHLPoarXy1zWqNiLL64HBQwh0Y1jMoLhxx0fpV6++8e677+CKWI2iWon30L/0ve/N25ov513LYq2OqfLYNl9B8NomuytJNcglleZ4iTFPzLBUwj7dORrtZcjrWEl4eZArU9sAjkyNo4pWRzmABuObExOo1R3UD3mgrnmuOAClG5MJv3GADHFoxUfGxQ3EI4iIpHLgbTz+NIe9846tLT3PFQRXhR+YefDwAbbjWJTwCTp+Mh1/CUQRjHeP7u49fry7exT3lJ8798LI4k+ENbeek5edcycWGqhQJKemIshcuXIFN+S89957l69cbn1jVdSlGyOaDBDbudkgEA5urSpI0YimyoEhpkx75zDwoVLxlBo6tqEBJpEqkJgX3h9OY6XIlZJe/sbebQqA7dDRbX7cEi26evGv5M2cUBk12bgQPHsILDOl0SLnibHEAjxDrwpJzSPxbx6hMYTFgW/GKvYpwCR1iXNxwVRRLwqW1OMnjifelTob6W4+gesXtBsZJ4DJOFTkpr1OEJp0lljW9qbiwNc0AdsNG5obsEZLFXOJeVsDXEvQPqVApSshUME6fi1Pm+BQT8/vivkWQxw3hOXu3EGu6cBgOHjwP/rBD3753/174eNzLA20OvAJqg1nu0eO0GNnZiWlAVXEPKDHS2TgFkiaFQtpqXCRVbTjzBSnijxOzHdVm3y1NdotVbD9qbR0mGDDIPCLDZsw0GCXza7g2e0dElYMrh6M/yZ0N7MtPylP6BJ4HBfGu+dzSKkHwD7pelDfV4Gfu7oRW/HY8ovP3fcOjn5XVXjJcxWxI8fHlrdv38ErbkDHN0R3d48cPnwEu3bcfY5Pr/s7YIFitpUlIbClj4g0ADFY+kpZ8M4KnDNnzuCxMx9++OG5c+eaD4vUYUtgsq8VorTyhsMErZg6iwF1VKlZqSqVYg2r0/VBel3i5WaCt4Hq2EbbQhJ9CtMMC53xCgJhZhka6rg6biOAKTp47f1lf42RIY6MgxTHgtKhQCOBCauxhKOlMbBSK9rXR0Zp0anh3DNRVgrY6qI6yB4/3qsCqhGPf7KsWpemNTUbhzxieYiyIadWoP24lWFIf6iqHeaAwaYWjTpGDXegzoMJDSPjrUWt2uTVJhVE2Tv15uOy70oaxDrvxhH2S1MXHJlKP0PEFgTM3Rk+FUPG8ZfBw9uHX75If8xKDuR/sOcTs8FCCxpTDasXg8ThWShThVbqedBxTW0FCIHFH/Ll6RPt5M3wNUO1FlW1zoiZkKvZIhhTXKjMYDag2lSJlzRaY4MaTrFAtWYeBLSudWS+NoPVRURRjLdWuHOvjdU2D9Ih0KOQ3HYs8GAORsQEx7IQ6X73rfy3mQCOR1Hdvn2LvyFzAI9wwe4cCw+e3IJHsuAWWNyZsLt7/OzZ5+3+lgKYKoQweEAIbFzB8/OyQcmeg5qqzaY3IjAC56yujXcaV1+/imdz3bhx45VXXgm/sgwro54MW4HrExsmHbjN3SXLEKBwwgJM1waU5A0nC9S4PHkCBzciMNiSFyDggCX/iTgklJkGxv1RVHGpExOwG3KzGIWhRyNw6X/JauQRh4ISCW1mXqJF6xkSOR48Qio4z9zlZmukhpDw3RK8Be+rBkQJEgMsy0LfmFtDftz0MuKGZgE4vFqdb55oZ9VpHRqr8dD3ElKTWWfFaJVKXk3QfU2qO13ytlU0H4Ao+xrvwLKNSu3PJR3qMVeRPbFVAHMQZMTnMW+eLocZN6W4Q98LxRMh+Pj666/e+uu/nnWrzNGjx777S7+Ei+6UI2mHIxcZzSr5+Et2Zj7iuIp1E+FNxihspGXkTUjgSjZlTWKwzwVjaQIcimzEgmDljShtgCJLLI0HpUhMtGDAJoTWDt1EtaM31lRF07yC/PzLTBVxjEzUAggmj5stsclLIOqOQFsIJDT4nhmnuUSUL4bevHkTj6zCLTP3Hj7cPXr0yR49xfn06VOHjxzZ2d7BioFP0GkZTdw7d8WbNQnBWySmScHh1MT5fVVD0Tqg8cWJGCQsuAFV5y9cuHf37vvvvX/h5QvHjh0nu0RPgeRMTTqu0pYFJWS4cSWI+XWDF77jpc1lielyuYE2m5q6mY4UHnEgJ8STUyPOgCAgcYPuIplNyBkwLLtzKXDtM0RAEXu++5Rg8T9jckJn4Iiq2AKPzDfmFtxToDoeGZfwFTwIWpxk7G298q1bt0+cPOlrmrL55i5vRkrjl3hqNqCNUsirnEIheJIBn9Y1SUw1UM74UI9TBmHgihmRMK4cJnXBOFTijCF5akh1dJGozSuAIXB4aISRhpoyd2VN6UbiIlYBbnGkJfbPXs1IRrqsB4eB3Yd6AH+Y/p//4A9wX2YXNm+Em6+++uqf/OAHeYOV0WM0Inna4NWPzii3r9+GUxEIulK9j1Xkjl1O+B3M/iDTTpzWBEG5RVRLcELXbKLiXMnhzbtYyjDgJEYaklF+lYuIg9+si6OPuRE6fQWpRKpEOdFacKZtEaBrHZg8dA3Tx4lksMIq5ZaWUgO0acBpQ1HCx+fYnePAF0P39h4/evQYXwx98ACPWdw5ffrMuRfOYYO+s3Vw58nTx/yMyAKAPcn1lNuCX2VsLLWClCqdUMEdrVqC5ggHN5jtNAD4umRTTtq0ePTYsdevXv34449u0lf0Jh6KJ2OediRqPhpPTQ8YesW01Rds45I7y0mgtCRxNXKymioaeMrhVUGPPlsiljIQfGMqh0gGx29qm5cs0LFeQAgycVw2BYJeuRKhoScCbu5uRjkiaN5mGBNLokqH/BFF4IhcGBMRX9TmvAZbQXOpwF/VTrvfNxiEdNTIgvCEO6W0YDsxOsgAB1MD1IQ2qXYOM6fVoOxTcgJ/1BRoCnuhkONGMtI1Bp4rVssciIvGxBCpEYC19nwKRLXc++FVWgnHMQmJTy3HS4Im3AAFbr7G44gjX/Nvgyz5XS+SuHHHZ0J48qMhn3v95ecvvfTX/+LPraYqoAO++Pyz0BTISe+oeijxMK6PW9WcfU4dzTC3LJrQM17gBrj7cZR0jZubYqt7Nic15EojLWahq00I5bB6ElDFsFFZcztWV/UxZhq1FKSWYm2L2s9GKgaYf5zmphQQFOHTB4X0QfnDBw9wpweedI47zu8/eIAPOPGVdLzPP3nixOkztEGPF56a572DT/Gcmb3tHRkLwpzyhv85ffxClibUYEIdOoEuiiOqHRRtIhyilcExRzeKST2oxAuxYtg5bDNM1RpEQDOeEXnzxo1333nnyquvTt6vT3CzQuULdYhIXfI59I+0Z6wWFpsf5A/hgYkcTpu7gXfoBzZ9wFzrjwzO27AYchdWtDAwaMDw1iz2NQ+hbBhlTgQKgaNeYTOVmUV2OmHD7gJhduxHewRYh5ByKdCwkuBPbdo8dWbONCCYcATLcj4FU203NHSE4FfVRit5XyN9KiaMH5y4q90YnvbHKoPERwr3VJQByugoUgbII6eBCUp7zpVHvTbJeNLlMFcdKpPLNFKYWY06ilDsPxb/RkplVJuFETfuHufFq6+8/v1ffPGNV+58ffMnf/Eenu3gW2sy81qbXM2R1YV5beW2UGoaUROCdV5ug3ZaMB/wAVFHodpkLE0wNcMKgpVNY44wZT3VHnzpvgZ0cVVLmOkCgZZBsDn8N9O17JowjWdBzLCZQgVm+DymrUmp5RSKIDLXVfJqOqIw/oo7WPDd0Md7j/HdUGwrsYLik3I8vwXfCsUGHT+phC9WvnT+/ORGkzwyCbxwdIEkJsPTA7jffe/xoW2oWDLHGWaaNLfER9ZAxQXwmK98CRc0AmAQN4qlZex1Yrd96vRp3DPwwfvv4z3P9A9JyjJC+xUXV7h0apaJrxBWJafrOYdLY4bmNebIdqHtGCGx2ODK/6ZG7GCMPXplg97sVzMfEQK3DSM1cxIoDmEehpgme4SP1xFulBVOg29aLgucDgSeHToIjKcJI24s9hHlSZ2nT2/fuv3yyzT9k4NpB6KeHssagLNQHeoLlV3zPNEhzBl1lhkTHBAYrNKzBrkCmow0JQaGAJfc6nkqb0lPuLyH7kuap7DyduUggaI1XevAlv/lZu0yzau/BYdu3NNwX/rOpfM/d+XE86cv/fKbX1376bnX+BurSKGqYdH96P97a2BD71O4UQd7oELuT7yK36yqb1+4m6jAY3nwoy9VJXNkgqkZJROsKVwqYnkTCfuR0nkLkHfnaAQnGJFg7HRfMwONESZeDH9Cr9tsAQbSTtnwTVg1u87TpKgZ1NRajtXSKJqgLZUzdCzsshnrojy5hbfpD2/evIX7W6CGP2HjgS18J8LWyZMn8SAU207ZhalEgyfLbHAqFPnVicGUflf1wLznzFScxirKHCLSRT829CS5zAg5Ii0SCSx1ktdDrbQxXoceMozbZr788osPPngfv7FqCS+gOMfyQsGinZkac60oDLsVMO91bde21ohIKVgefhBMhfkSVzxT3yqnBA6wN5CnAHw7J4q4+Yx5hSDzaA46bhiYFQtWGgCs+MiqjJKkLo7GTK9TlE6UVwdHUJLFju1kU4iW+tOwJ42aCgcPHtmln1kQBUknyUuhg53E3vQ63kD5EnoZo9A7TDXA2TgwIfWjCG4gpQqzSorWXdw9Io1lTEZclXXYK4QfFNVgPcyQrMikPNgXWVZ4RPD007XtwBZHwFEQvrEOg2eSmBmYpllak9WYzjxhMNh5oPO0deNOMVlcB/7N//Z/X//0y4vffe3P/+D/ev373/3oz9/ae/LEmi9+741X//7P/+Rfv4M/o8/zNkMbuRWHJmTGRidh7pW8hq8v5XHN0tZqhCiNCfq/fpgjE6JepSo2jklzIXjKwEgIk3WG4CZO1hIJNRuiyrOWjJIJeWDLGAFOenmWeTkcjJagWdGEWfhBmVdr7MvvP7h/9+5dQOH55xCO7h7Fh934UzUeTYiVED9feObEcy+8+KJfdSvu6ENGuZjpBYrKOkB4XOCLp3h00gSOQj+hj1dn7d0nkgG/dFlVduwnXAKIZrB2pL1qBZuqCkBlP/9co5eg4NnzeKeE383Aj61CcNcCI8dCfFmPnmTHuUyYtQt8vY8vpkjBUs6rH6Kb1rggGbDRNm5Y0ZRuZ9I8KgKqJRkmbpUbWCcEcHSnUmGUVKX0PCtRY7rVnpJO1IEeQLW40kgOsRJ4lUPwWjl5ApDxkwsOa6WRrMGy+01HC9FDjDxQqBsY3HOuBNmrcl0JWAXsWfTbErzwPhn0fJ7ZSxjNfkyXwD4ujpgmb6k2o8aZE6VkTgUYrueLCCpEn31mVMWWXnkLL2ib0GMn3AUunpFocxszd8Fa3TMWsHFHCJUoPv7X73753iff+6fff+9P/hK/ILezHT41OXv5pYd37n7yl+/NIVqdVFZpgoc0Sib41p4826AHlrRJRwK/w5i8DzIYVEsotAodrHJ3biA6ZTrWpvvMhHCFqOZ4mkQZykKghqsS3xQ10VTh1bxsyiKgaR49wXp64OGjh/hBIuzLMTFxuzn26/gEHd8WxddBsQvEb4vjjtLn6AGLEx92hmuKp+E4YXyQggVgArPH11JPnTy10/riaRbqgYP8ufuBnUe4Z0Y/L8h1Bst0rQmZ4AB4WWeiXJvSTEtdD4RD0DNMOnjhYqOAdAUCcMznwSNHdt94481PPvnJrZu3Xr54sQMlTQBkkHXohTCVXuYdX3Cg1FKm4wECfOBXReu/krgmvTjsIoExSSmHVFO6QJttpWmNBK40SCQioofcabKVLREnhfm+AkKjc8XpvNcqB8GveXEhHMDf+vC8V+8u0lsUnYdSebhPia0YFSYcY6wNmrFCfc08W+oAiMOK4zCcYRkeSJ1kT2iJPI5UahqC4Zc6s2oE0AYzbAPhuYHzFr6FllPSSZ/Xh3LSiVLg6zXGgjURhIexhiqm16wq7Gtl8wqKL6advvD8V9c+29reOnx8FzEI0dPnz/7r//WPv/vDfzBASyw0SzFBYmppMWEAklVmG4wCkx5Ir8Z4H4l6aIxATnXC2xR0ZFpFMjjnpKapazlrajQa1JAXpqATpnek27DNx1NLbcV6zjLwlDZDG1FN9DyHME/gYG2INaTbt2+/9ddvXbz48rHjx88+d/bQ4UP5x945HDmwuiCoC1tLtSINia8y1jn4iB3LqL0ZwF3v9iRZKGLFaO3nLA587g4CO48eT+/dZUibpQg6NuSccO5nLcPpFDfdHMvlgDsxJp3eDMgVKKOJyosXX8FTAd55+218YxVfKuhQQ1MAsS7paw+0guPTvT28+gOJ5bun6CN0Xz8pB3pxuE1ajCnU4435TdOt40LPzseyOeoA1hGFufFnUMsdtjz5n5KWerXpXU/gIljekVFqAR7mYSXRHvrO7dtYpnxNkI1epW3TqiSHPvxxp6YJcxxWXExtDCEwLzjrAFnsfsLQ8LmHdamZMNJmWvbo759EHmscDY04KpYAKrCcDQ1QgoZ6qySd6E0s2q/cmWxBNxSF9Zrnog7otq1rcQ4VUVvzstaveG5u3MXH1x99fuPTL72/c6+e90Unc+iurLks6xOlyYJLEOmukhTjZILRMHcmWNO0cJB+DYRuWl1iPA0fL4g0q6EvbmiexMPLsdZJZcyucR9E82eCOVGubpJbmwmqZBXzhGXm4IoDtiZIUav5vPgl41Qmpo2MO17OnDn9/Llz+bdFmagg8dLGORVHboBkntt+YgsBES6OLfx80qlTp3nFfIqP9cURVPGQ9lu3bzd/HzSCHaDfVcXeHW8CdnjxkYuiKoRMwGV3SKi6PwdTX7VMluh0crUw4E7Soq/lZNTkk2J3K4Y/kuCbBu+//95z+Bnqs2dbLmM9kuOv67GhJ2Fpsmue6XGv8lcQiO2C0WFIXmCc+Qw9RCLzhgA13DXWYmxFsKIpdIUV6YmfCiDvCnT+ODY9wiFGm1qzJ4LzY+IykEpEeR+Yh46AgYfHoXYU9qMJaYyTbln4RmtDc8PJhTAdqcddqsMAWOQ0hpz7mlEOBHjVgpkVPYQ4iu7oEkOjuqpslRKwFT3goOxtBQ2v7a8Jpag0HXndVp7xwsgzksKRrwfMWwnzlcfK4s6KxiaQsPJ8Yd4HKl38kp6ot+p7YLCxf5neOJxlxwSDMhATrGlIMHKZAGP+cG0IpKVkdEWgV3VDk5n/BaEF8WzrA2HjnXkHYTlM0IoYmEVIAg4OMrxG7aWSTdcMAIyzf6YgBFA0wZogtEL1OkkMPh7I6SHrdlrXLj09cPny5Q8++ICWLWEvuuxCxkVY3QpHbVDfknENuHCHz9rxgPaffvoJbtH5/PPPcTfFR9c+evTwIe7V+fjjj6Z37USY/uGTmT08u+bRI/IKxu4fMacYFhx0nV5g1jABB2bLKWZkzjW5EC8hLTRAiPAEZ+4Rs634xLXn6tU3sMX58IMPcONTRSOramQJVHEAByD+QAUs0IPZAb/gxvQmQsj8TxeJoeRqWtdpSNpRkdgqSae4odhI4HJU3ilK/hXEBvP83IZOoelCs2bfj5qLlVKEW/p2d3fXjoAJdzPDo3pttxviMWHqUzpkUPM0WSnVEnI6CJczFn6ClmGKI3kddyCA0M/QxhG8pqBhBasyNC9otTVbTFq0qZ6mLL3yrOOPR9E/I//gr5jlPP+DMbqce52KUl97zTGISvwXo7dP3BU2Nj0LqYh0wilYehNOBNWY4O1N0wTfKnIGWCpUajpwxES4lHZGk5vEceTtQE0MQguwdPEMaoQ3HJnA45A8G+86jYnmutFqtZZET8PLszwlhklhFkxUTtZtSS0zNjE4wenggZ1Dh86dO4etM358NEKkkgKuQY6RCRAr4MGDx0+cxE2rzz9/7osvPr90+dKnn3yK2youvnIp+g+XJaoQ/rRS4VBOVL+9/QR7d/vcnZo3PVrL8Siu0JaEcfLDygJcy6IJo6Bej405h77Wy+jWU/ywSHx7+NSpU74pk+lqRB1Ch29iqsR49FMobyxyl2Gp3q2Zyhf5EgDT5EsV1VlN18O30yjjWl4jAzdQYuVGkiJKL2tpI0g25stUHNao2zjb7WGDN5GLRmOR5MQFE05CmEgLckg2c0ymEQmvmTryiFZPm5ULk4CwVi9HfLheELIQVtoRDVBMMamZyFG92RBWCTmgMRa9VJnXiTRrZzOUPvRjgXNvDmxdky0iihDkNdPxGGhyxSDCyjbuqHIKhlQTtra3kZ2Zz4JMgEY9sZGED5M0D9RmOCYkbgYKdcN67QCcV8l483MXYru4GHE0ohNxB6RJQB33nPJM24omND1OazRMFxsi5bC1xBs8T484Wqx+hlBwikmagZKqerpEm8rkR3zxqxMTWzy38dqHH967d+/o0aOuQdS1giiKD63Z6AxwSi127Zj8+OD27Nnn8aS/Vy5dwo+m4sNd1AR4dyVLCSXu+Tkz7p6ZpHGDwnTHUBTh0JRT0dGOY4Uuu3z1VYtNz95LDQvfKsbDIj/76U9v3ryB29+hjoDoaiQvaiLXFbzKB052mdH2Dc7EkPKyAURhygEwyRR5KhsFkKuY7minvLIoyVk1RV2G0r+cxd5OsYvhG7Ei+uIacrsr8TW5RQ40ydbRbRcj+GvOEfGniNovqAXnOMJD+zBtxRuJZkzHuQbJJr5lWOOis7ONzhRCQ950GCnekmVWIopxGRYzRz0ODWg5T0MFFABp5gyCtdSYlN+1DyPGfrCNe6zqS9i1v/7v/+J7/89f4q+xfU20tpiLYUiqorSUfb2X1W75mWbYMkTbZIkACoRD11juAwY1ZBOWM13VksJmwGy6mpOYlAnq1myQhrGhUAKiJjvMu9VLTVlvCnVhtgHBUJJKllUHSj2qe1v1TiMnangg1fB1Bw5cunzlnXfefvPN76TVrhTu8nQ1c0UaIRQmfpqAHjF5cAuzHjfZ4/Z67NR//OO/OnH8BO0yP/sMarj3/fDhw3fv3Dn7/PODfvZl7y5Rg4GkLaTU59DJTmxx9gt3S2dJfTH7+EIDunS5wSfud+/eeeftty6+8goe6AkOSH7LS2BYALb0B+oH8lJBqQ9fUgwUSarYLauKy9Qye28VmdO1eU1k9rJS1wgxGh+SR1uDVsL3GVlLBlusFRuhrd4dkU3s91g3KLmcpwNmvRE+yGRMzQ8eWCSc52c4MeclK60Z4+S0EnOX26DCYz7brolJYlgDxAjEIS0tZWfXE725YPqa0rLq15v4LfsEYIggOLGNe1rNrQ/u3j//nUvPzFZaqQAAIABJREFUvfICSuhZSeax507+xZ/+5f1bd1GJL1S98MIL+NgvgPFJhq1NiAzXD2ove4SWPFc/wakZ08eRncM25bZDM5CQbzpZHZDw5a+5z2Ho+A9NlsppVdOwNxaoKextlppgdjXBB1hrl7rCS1u108IdIlD53B2j0cSGOeFuiNKEZ3RtjbkIXkND4jwpqGWorLapjp4x/1955RI+d7985YrW5WfoYDnwK0WuQWUd6OCNw3OQfNEg39rdpY/2cW/L119/hR9VxXcoH9x/cObMc6jEh+54xSfx+PT3+IkTkMePsHcfec5MBZQZS7rllXQQLq9WEslQLivQtaroo9Y6WocewYG0P8XTXHREEk0ijjOWECqdPHnq+PETH310DRv3F9v3REWvZAtYHGS+2pFPRAauVo76XSeNaYAbYsaMcRek2JuXQi9vSDLwEIY0hGhqc2U4bU6URqWNyTXgDmB7sHtk9RvcV6EGkNjvsxFXzPmIb/QL8Z1DuNaV+zK8aeUidhiTdJ5FkuwaB6+SAXADTB8yQzJP1G6ACcbRnKc1xa5XW84Dbw7nuIiAnEnvgRKU9rxs3OsLyjcffY5/WVLf/9O/khr8jf6HP/zh22+/ja+mSQ2QAZRhpe4ysH0ujvjWTQuoBOZmFTfl4+lH5zU/GFsYrfHJ7cFXD6IOPct9pJ53uFqU56afUrVSM8/aUQ9YYs+vTqw4Gq4qGE28TxsEzgaMS7kBJJ6TAlTyslmJwPM/q2sWMQfx+PYbN250vhjKy4Gw5Kxb6gMRnHS0tKkBRB5ig9fz5y/gkZT46P3nf+EX7t+/B/mbb77e3tqWJ5HHG2aarPOGsc/ds7wf2Nt7wo+ZO/z1V1/hY+k8r8l0yD0uLSNB1t9DGHRD4RPKuGUdMpIpxwHeoHeA8EboypVXv/nmm/fefefV115HsaPMTdKF80hOYMa/YLjxMb4iVtAdTqV1WdV+YC5j0rFikrXtVMem1YQhJE2yF7FiS3+0XmBXIgmneD+/YE0YZbuiHu+MaeZoYlfEXgHKsZI9YoZZqXQmmfI+FX3qKnzGvQpzDzdu29UUSHmViQP15X7oosT3qeunY3QppXe+my5HhkBptAu0C0027uTm1OnT+P3tDz/8wOJxahUR+/U//MM/xJz8R//oV42mCKteN4LrHqa5r9BUc/6bPwoBR/+AgBqzZsFKbax2C8/9dvOSFvAFaWEPbj4NjmoQXc2UrxmqU1Dc7okFA6ZuxqRgTk2w5uXCONa4JtjkG0UdKLrLhQrhpUGmUczyF0xpxs65ar50/vx7772L21fc1ZE5GbGAR/knH0tIJWHh3uuvv/oaX53EI94+/+zz7/zcz+HTd/yWUKI0syB79226U35Lx7jwNsZJ3rFG3bp1Ex/837p5E28Y+Ck3n7366mup241DTeG4VO8d8JEjs8ATW3BvYVY5t/jcc8/heZHvvPPOhQv4jdXeN1YVuU5SWyfPYahY+uQqIgPKKidRJhUYUO4tnNTtKQgx0UAvYAK5sdIzHGwL6RjUbqsFnnEVbKvOaQm9w9cekedYN3TXI/n48aOTydes10png/niag55xeG9mMikIfUyBjrrWY+bMGn+bBSED81H+FtvOK1I3jKGD6FEtpq+F15keKToUhMXHB1A0j+DgB13HoGTGVwd/G/+2X/76NFjscSb408/+Qk+ykJxMNX4zO+ll8579A6JNZs0QREzrItUEdaGUidqi1Jfw2sPyclX9IYsnFLYne8LMXGzcrTKnRKf7jVXcRQ6Ub1svuMkpAyUqOd1ixwh4gyakBceaTLbIIE9pu2H165dvXq1d+kZxWx7Cy1PP7p2Dc+Qwe+24qky9DH806evXLr82Wc/vXDh5UljpyDpsle0HNzC0yeePg3Pd3eqmYj169q1D+Ea392UpuvXr+P2Etxkn2muW5S1GPHiU3T/BohGO1ZAPtb1mKF9+umnWGEuXrwIV1lToxjHpBjwiKnrjiCK+Yhm3UetNlKstWZ1Jf8qmVmYmYtWcUPMkrmsmVX+LQ79erjgSzsNRmhWPPbt96cVHyu89trrwyN2f0j8Heq3mAEeiPw5WBiZa3Gh0T66Eg75DCs8L+fBAOQxmTa4eq5OEsTwE+nYuP93uPCjsNYKIpEO5WlYyWOaHIQlvMXGkIZ5TCniYcl4zkZbCx712IC9QjTPS1LSBIsNwh7gJsS2daXV+4cZJ7Q3ylFivB8zM10pUvYoFasV7jvHr+vgYYKdfticJ/7IdvPG9RdfCj/B9tWXX+IuHXw5VZziQSj4+L9GwBJvQk2L6/DtV/yb3LvjMxLcbY/vwr6Lz6FffvnYsWOf/OQn2E/jpv8GNGWt0VSplhUcryJAg4w57bhUrHu1cO6nSeLuoJ9+9tOLL1/cPXq0FY9lmQRccoy587SJaPibgGS2wPThiAuns+SD+QzToS0UxwPP+PvQMt/jmJlhpyijdnyYZmw7yAuaPvroo0v8HZgFtstMEP6+zdBljP6mWsnqt3Iy5faS4hK2aY4wiDvTbCa6LfuwWzf8VQbnwwcPdhDsevFSehaiway8OUHg0m/LGL4J5JWOokKqm69z9ZtA0qB/cOFlMAyjzIUrOnECt9u8Egz5cMs3sS+RrcaELrWica7ZLH03eIJjb86yryjIScWASmFJE3uDd+QBjxBYlJEDOWJyg1EzwTHBkxlxkxtuqMAt7646ETfnCfAXXoxPjj995syXX+FnlYXx03Mv4FvsQU4nozE2ISHmCyP3u9+8cQM/6YL7c3CHDNbBax9+8PO/8IvHTxz3OIUM18ItaaEFmhdpfglNslLjFUd5W7mPoYKYwM8tEHYfE/dEXT1+9eOPP8LP1p7nt0nQLw8hSa88jxFk/GNuqT2zBrBw2uc5Dmn8SdAxL/wdSFHh2loLv9gopDdYKBsJwmQMIy+IYSXwq2emUXiOmOmcKRRnVNDApVyCJlExFyVExq6jKbamP6lpvvCs2L6yYZrJhkI34g2x1zbnLiLQXi+t7XQYbySTGGUjatGn3l5CNej7/uCIZlOSx9kY1iKiSaR9ZJVTVHrtBgJYory03+2pMj1n+9tmGXfXFqsbcz1TnUAX963bIQaM6J17hYuxbiyCAa1VIEHZxU4BGKwJyyZTM6EOdyDKVKUE5QB8DDTzWW3UDymP6qZs1ijJNV+iIBbKxChpBT+CfYbHS5cuv//+e6+/jhtmDKIwb7cUqpUKQtZVDEnHt0LpQW+cfATibrLfyM3k3h0fNu/h7wtPnuCDdtxqjzcM+Jsh3jbjGSwV0loFiwN0i0vgq6wRE3+dXJ7kosqDZ8RJ628n4YNATo0w47BwDVxPZTxN6NLlGzeuv/veu5cvXT506FCiVCtgZV197w4/YRSP9Darir4RDJF6cxpgDOsrzWCRIJ1D025jzDj2JXZ0ffH14tzLsFNRlOXBVoVFEQcjGZZh30HhV76cP8xOMZVQ6Hotts6YlZPjMwSuECWlbNio4r8V53y4/KwHJbtY6iNlvtHq5zp7zYXUwVpCAz4GU63V1DLBR0dTSVd7X5+ZDBbnIphrwV9z4+5zYrIJs/KVBJ8nOi8nyusUwLo8xG/z7QUye+DA5GMfSthQU40qZq9pV2lg9mYaBGUPfVrBuVj1WQEcqSKstfA4AODRWT7cMmQV9MzUkgLXxJeyTdGjzsYSVrFka6Au5EwUlIasd1rqOs4wu7oYeVtb5186/8knP7E7v2sGcAxOI+5VLYkLpmZ7EDvmh48eupqaw0V1/b077pDBM+Zu3rx5/8ED3Pa9vUP/4c4ZeVNBi6v+xITGQLTlAJ25K2Y/AqBFL33VOa20fSXc2F+QLPVAOnP6DB6if+3aNfwUF/74kGB7PW2gZcsgtXLDs/jJBggwaSFMj9aorzDl9ZUwgVBpTnGHSyAgrOL4bds2+Wd8GIuoZvVt5MkWgdoQM+XP5PBUWBpPqw1+iVgckRzKeXyPHj+Sp1HlDUVZAFBNLAOu1a0wEGRIRkRHoBitru3vxDQD6y6eHnv/kGXLG/CrI8DzaMtAEBC6xOgck5q20bwWwNL6JyQxLh3bzNFGG3dDNsE8mUcT5kVgQHz5mmk7qJ5O2BCD8F3CetBmUG0kBg7Ack+CY29+TBiBHNYpUDGSi7opNNlJsJaFQSgKpOcWzlR71Q5JIq6LbHNA7gFUUiD8ly9SALKC6zm3GyzPJIlnqONrmng+I26oaHsoOIVsxB5gW1Vrc8BbhYcPH6qjzFyrl55l734I+3F8/7U4Thw/jt357pEjd+7cfvTw4Te3v8afGqCFH1fHAgdi2TLnAWhxbAflNQdlZErGgaZs0I7UdABFE1BjHDr1qe4c2nn96utffP7FBx+8f+XVV6eDYuQ+ZuQxKHEA8shLmVPEf0EiUneCsNo0ZXDGlJ81QNkoIh+Jb6RxFn9SZox5ZonPvKDhDw3UPADERsFZgAIuRfpiNbUlzxVN48+59MriiAjIgpr55J992NnZzrmkkFn+mR69z5BDUpFYaFNSOVUI8bNaRjMrVpHg0yNkOsjAir2fgT/TIvJeyfgzpbC6s9W7xgOi6633ff2yKAICj3DeUTTHZuWimLnMhqxHCjJfD8LUzYwXFr2ThRDOjJaVOO2ATcXCRVHhEMbEEnPMrqtFM8kphEikil+t1QSnvoo4BtzUAmVJOZPJAjArE1ahvDYIxyBhCDTRxf/KmqYA5BDcqt7nYeKJ5vi5zatvvFlbRBRKroo2XIOgscxhDyRdtjZ6O1T1/XRra+/p0208T257GzsOyq4eiA6PrseX61988cXnn39+1gNtYKuJULgF55SxlJpXPWbu6Ad/SHwKk/MgqlNX0hdefOHUg1NvvfXW5cuX8YyvHKIoCyaqrf8LlUYFr6O+jchLCIw1SdXbFnIlE4SK4UVUK60FwnQFgwESsE8kfiCvgM0YAr4GXAhEoHR+hUrxYqFCR9SspivgTS1lkkBkr0nBb5pbJUCohOUSevvW7efO4jfanIssgKDuFDgAxQjaWpQ2fl30Qm7cMM69tjFNE0I2kVEjGcjq22DffoukVTvO8UkS7ep/ZkVaHb5NcsihpBEplayCjdQsp8UR9S8OYeNu4ZtgXi0tJtQzFZvNdFxoGZd0Wpg8HQUmGknZZSBUQGPlA33mx3wrno5X5mTUSWAQjwrrBcAdn7WmTTxoJ0h+CQn/M6ChmlDzndWJruUka92gmCWVkMQLExf2Ak8U8L+y1rM0PpNXuJyVgadXXn0Nj2C7evWNwlDZU/g+yOVxnDx5AveX494VgqhklavH4MOyRyfHDT/Z+vTADm5hx53cxf3E2KeeO4cb3PF4meZXcqvOp3NazZDmr4qJSiQA5LHcuACCLpqmrOuosa/a9vjS8He+83Mff3QN31h96fxLk5cN6SjdZhV+mb0PIXieCiHCJlSTQuFsqoKDoawK+pR6aJfY3HJEJABhIMaVNDdjqJQEnAdAcy6o7sCZZ4H0gs0IjmAFtkSV3hMiQeQhpmWAV1slwMQMPH361ddf4Ud/I//ZziI1gl2lp9wwFmIyNhpx0RiyucCabiJbAxtbx0i1FavI0IFCClBV3JfKdTK5L9SaoKGzsjHQz3ITbP0GSyl4ClX4sMpV/AGWBi+HHDbuFr4J0VOlKjaKNGf1G4DL4a1cjnNDc9PR6sxu/wXktPfneSVgAYCjyEaWhbSkVvtyNlfj6GAMK+ZtIsEYlHaCVYxjs2bNTjzNBJpSp5kVmZMLcc2vNRZTgNIOy32h2/YOdzg844P4Nhh+sufLL7/AprZtuA5V7BRxt0zYuMvomLq+yqKGVxECQ4qA+gArXf4gF3xt5MmTnb296j0zR3aPPLh///jx/iNlKmng+HkdtEbi4I6s6FpElEWPdz6hTfjPWQwL0GqFMOmOLOTu0uXLuFHq/ffef+XSK9YjNbwQmOzcMGBRFuzggedCUKrZ9+tgjZ5d94oV0CTXTC/nwIPJAgnTeXI5Eq6rshWqSzJQCyHrhSWweaZi2ajSd0JQqCY2qk9LHAGp0arEnXXs6LHNYcWx4IiLdTGZLFEuD0qJjqKyVddd10sMI2XZ8LNsCtGLVFlDBh71soa/xUXr9HVnweoZ9TxBVdla9XKHBoHhUdwq0xpKXXe1Kb8AiIe62IEaCRlIVuxyajYG6Gb7VEOdBFZ/vnGwnHLQN5fe1stTPjdor7opWXoP2g+iRQD4P41BUBNsr+PR6nJiWleR2trY6ulrG08YKuhGynu0KcDavkXNl57XxGIO1Ak6+UtSdXd4OuS1Dz98cPJ+9wdNYWudWiJXakpnqLl//95Jd0s9MmugtGjhgBcKIhyUevxXbtBVoTx3vqt6+NDhb77++rnn9NuZJcUSTmvkiqzDQ2trZ96ExAYakvpBbTk8USP6ZVOEWCDxRpsxm0GeOfPciRMnP/roGu4jwhhQJ5z9OKntyxjUC9BBF4nQxFWgwXMGO2g1raYfEkeelmJxWVwwRjAZTQZoBJ427GoEOjT4Q5KjulTy3EOlEo9Sh4QlNjGM0EskwsQ/sOJRG/l0waAMnhQdU6ERD0vrDrV98OD+LFi1a57NBREo3DXNag1CTFok52EQbALrOk+WF+C7DLjmGiVbJ6f0oEhkN09ClcXPeOWG/f7MohOextbGm9UsZoLhsUMzb3KY9D2E6QulESC9gEBd573C21AXJJlH2vgtncuQlJaFbQJRLPVblSsHVHXc8cFhGPUgAIQlAUsgk0IDN+pEqaE6p5qvCg0DDoPbVCu4tunRXoct+gb2wupFsIF9aUvhtENoUsTN7vjOon86ZK1L4ia7CdRtwCe7T+7jRvRwiC53CV1WaUcgr8VdLjlqjZzXebJNz2uiZ8ccSj5rwANl7ty9U5913r4tgyXYE026EOZ6VK179KStS5jBbH3zdt5MnPkar1mRlWRvy4LvAr726mtffPHFB++/f/nKFfwZkIDwIsa1BYpakIKoUnG9oEpg2fuMGNVRfLNHxFDLJDWI2GWqv+GZGe5HEmhQ0e/shhTgpDGEmvnEFYDoqjwfJbNQIM5BCitVrE+cORRRjyFofB4VdngbKZoVWK86XyZYJabc2yiqKRpCu7SycNZla46mYY1BNZQsCsLFAh7WLLd5qhqjMmhmzfXaTGnVIuKwnKwGHJaJ1fBWB7KQfTda5Vx3yVVwprGMsupljQeDpFLGBemKvhtg/WG6ydW4F0nFa6VKh7m+zwjjApqqrGeZPFbquZ7d1kPttTlHOrOhHmKQENjcMEyw8BzEsEgOItKw2YQi08YLH9If5EQcRXf74Dn4XPWknRB26ood2MdwtGHhGU9Vf+nFl/BrphcuXFgIUZiBOv6q7tcd3NZy//4DWYizW1w0TkZZI6ywd3+U7N2x8O3sTD/IPA+FR5MwZFEeNtJYRafJNzTcW4KcAJUbVjVVXweWduWTvkiA6Gp+4IUXXzz98CF+Vvb8hQv4WS5vXpc5dIOt6yyo5VE9DcthIIowZiieeJmI0bkZ7pOwgFfdxJJA/qPbunJZ66JgAIQQI6EkkMl82NJRqCG67BN+VoMFEHohPFAVuEBeio4fecDjIIVsxMjWvWZ0Aw0GSu86ky4LaeFYuGVeggKwwKbI5GjpIbDjXSaa4o1sLV4jQLMkdj30SxXTbY29aA9VTqQzIVF3EBBFt6aU2XSLXZJdy05jEkZHb1ETjwQJe3PyHgFdJr3mK0coztq4l7mBUwwnjggv1C46/OrEESqpToBLK1coCSkBkp6ArLtzFjUM1RwIA0kIga/AsAmhhOoKZQzQg4ljFgEs8jrWZG1ESlU3xcWFKCRSkMiPrLnq0YWTul5QUswFpj0Tupwya8QAF/JqBup03qXEzKOgQLEmk7Bju0E/Mnrn+LHmLeAlPwHBxMZPHMk67gPA7txv0Hd3d3GPu6/JOITezGsXlqt79+793OzIXXnEsR9Smsbp57e0ScfBmulgOZZ1OatfUKSllg8Qpl6bui8ZaXnjzTc//fSTmzeuv3zxlZELQ2ALLxsPTh8gu5YrRdhpas4lHkzp4C+p9xA1ORiFuVbTWFQ3Bst9oS9EGxF0o5BGHgykvYhaxSiyFQ4VlW6VDiqEkkTBHQK2WAZwU1sXotmIH2Y/dfJU3hy6OjjMW5eWqSs0FuQkpGXDRAeqK787Mm6Cmxf5goFoKAYlMJgV0Uefifmg1aSaXHJ5EWXk2pfvHQjpuAPDKqtxjfstwvMqU407YhWkLGLuZAKWXoMw2OmdjbvLeIjf9oYWgvNidRm1hcWV4QILpMdNBufDRBNm8MYWp7d3mYHEqh0KPAVsNAZB9N2OtgMwh8t8GJfbCUd0uWUVjogkKtLFj2vT+UaVFnRoX+m0BizFYsGwEIKQUygsJuzt59K9ePHiO++8c/Xq1er4lMUCr3u4PKcrHCb2Fv/Xpw0V/DhiahosQNs6uQ8yq5X27gfxnJnkc/eAwGNJUsQi8wKPLDbS9kkN1uhDXh4qTUFj0QmZnA0rHcMsQyBAsanBNKiI/MaxVyEHnZdfvohH3b/z9tu4bQYPn6kopVXiZTbhFERLdL3w3cGwtd5QgyVnSkvs7SUIVZuQ7UBfRjIVaHTgfx4k8aUKUalcNb0On2nJqBEXrs1EyZIupRJMCLM2HyhYipRh+WvcqmyIfeGrr7668PLLDR3KHY0HnGbCkhl3gLySeRrzVB4ajNrVAV78zmXbhpX+ELachgDNw6ptNtViyViSh7ZvW0QDrA4ZnuKRk6nFKtaUYLnSiU5pX8R2OE13YVQ12/epIes1eLGaqke3cZd8IlQTtG/sHIUA9i1FWQ0lVhorEnzf9f+SFAHmSrQL9X7m2vM65I24C/iFcGMY7MQ8meBNh+TEMikMmc9QkiDYIMTD7qxbBp1TCmZ4HVYdBw3sCTnbLVkskeIo7KjecDyZIib/pUuX8PuaV65cwYKLIyjAMaeUL3oHd7Z2JhLcJornumROrYjEZKmypk2EJwe3nm4d2MbenX+bCU9zl1gk/cw0dsksR0gXUtRfMWcBijIBSuZzXqFDpDegHFw7Bu3Eh7WtRRj1yMz9+/fpN2UPHMDT/b/7S98bJA//LdgmAocigRBnipQWReEvr2I7G7np0hoYfrWhFt5vcBBApsCkQ3wU5nuBELp3NcKBQoQl6lwZFy2Ulb6ctTTJn2FJm3tYh+ikGT0L6kn/Z1OFMI0HoOVTQx2wVyjQAsV6xoBKtSPkARZqU9OaX2e4fcItYEkftwqS4qmBRNrKg2qNnw2fMrwerBDw+/U6uIw9pes6S+7Q0Ib6OQ5c154iuoaNRBokDsDLrvqZiT6ZMmR8jdHAxh1MmawxNsG06kIWcV1pSe3oihYJpJStZAKxiNpLOK1kI4xABQdkFoiYzoMwv1iNXpi0GLHNSi8Euy4qRUL9ZgR9JFIZ20xpWNjEdsIJp7iiQxGFqZEIy1O3eRBAaNFltnRxpQMFfsF5B49sPHzom2++wTMi8QF52Ep5KhKo2Pt6qZl6xY+VdlTgb3Q2V1FsXnArxc4RPMVPMh04cOjxHr6ruvd4b8XBDHzkr7pWVgmOVsrWXS750ok0QaKf+YkPnpnwk/v37t++cwdV+HxdQHEvzXH6auAB/IwufqMqehpjzPo0kmqGsiXiMIBGgcRQJgMB4P5kmD3PGG00msI0gSmPLc5NfL8hRcTHhClIrlnpRfK2Dpj0FGMRR4trTcIyEshT7O0e+8H4PKzAkaF1h46/+ZGEpY76jhn3uM5pC4w4Ecous6cJwsMrvNIcaahGS1UYz3C07UpGmLQGe8UAhQ0XBUdpmgbNDgKm/10vOTGqstRukX7HK6no2k+4k3as0HkhhnYki0SPjVl8O4KlWjL89MCTA3TbGm7weOw+cZ/Nbd8i9t1fsHJenVioPcsKy29lYXcTGJTozaawdq9OLFhvGmLLPhnHhdd6BUaP9IyOew9OMrdahHWQpbXe11KM3E5Xhjp2vTbH4HJVdf8SQZ2HzsDBi5t3hKHIB0YZ35nKzF6+ePHdd989dfpU/QMw1sELdWs1vG4lfeDdPTAqdMD09KCDg2iwFjGBZbj4cpVOHSk82d4+cGAP98zUgwoWS05IIJv5vM7AQbckaQxoAZVa1cEMUKeKhN+4cR0fZz56iN+eesSP0n9w7Njx3SNHjh47ij169bYoBzAuctdxx4TuCPsQ7ZUkznFYdCxZSipmmI2oSraLAZd0ivYI4QU5HVuFI+uxfeFM7uYMtjBVfPaNoFK3uLRirbP1HQALr4mTx9239Kwqc51Eg0KGpcwKK7x4ZJNXxA2EGZGnCaS4cPlOGnSqJC0TCzBqrtyQoGFMNHNkqpfl2lqVTQ0x1JmKMLZix6TZJIz4VVcZWihE32pa5nE8hcuI2qVXtjRuMcpToReilqtnUU/X94NP6FW+ZoLOAdmn2wf3tpdu3IuVcWkcICIpK4UAWWZ0ma+1cDLvNqooAL8VFH861hLvSSHDc0VLiasbFns+usAcA1trJwcom5DpuE8ZdaFT1bmlZdBiVbXVvplLxOn3suzUNhHxDQr85xEoGGzM8fk5zv5Ji202r7766vvvvYdvK3qcTAam9njW0iseO3YMj4/AQ2w6ShFZJ4gpE2U0hxeqbgdBrf7A3v3ggb3DbO7rV5EpFbRutumwBvmyGRMCaZtQmDM+b8YXf69f/+bevXs72zv37t/fpV+benDq1Cls0/EzWziyPTpfd3vem5nhVQyWYZ4QBuPYnEewdCwCZ8vyJWAvGHMllq9hQFmWOQ55WYe55cM7XEMWeiH9Bhg6NEtRZ0ya5T4LkgdJMlxV04Lx6VjkoXFTpVMMKsTuIDYUPWfzMh+TggZvnSlAClFoNipBzfcCCwPmt7hcsQinYiTQSphC0aWLyGtAFcN+VWSM/PAnFH39wdYMFla+RkA0FikhAtrijh3WX2KpDZOtAAAgAElEQVRhRRMymNDzae2wt9QsKz3FxQEfqxt32qnj54HoQhQPuuofXLpxn9G3HKcM9sq6b9kJwgqbqRhilKrJjs3DkiQQXA0Qu6rtrW1EJgFYPMOQTUVz0dBY7oos0SNyxO2aRqDAMzrZKKqtVTwjgQYYfFv/mFsNyirmCJtEI2zGEbD0YCwlUxRUeZZubec/yxvCHY4FG2v89vinn3zS/roYYSGFc5EPHzmCu6iLjTttTzN2jLxkTGU4voh3DAe2turfVfV682W+NiAE/JOkcGYMB3FYKDr3rbEvKDK0wuhArvDLsNev45P06/gK6f0HD/D66NHDo7tHz+DHpc4+j/SW16rSC3QARaDGrVSibnC1FB8PMq7zLU6JdeaOjMS+XehQbRuFlmyMCRS/SrpCNiZx5isQ8ibMS49ApAERQhLw4GItR2v3oCU5PKve8cRfhO7dvatzBsOqObLKTEiNppez0lKaX6+wsJyFDGUcFIhEWcbjkFl3vZeALCPEJXmmB7IHbQqb2ANVgqBBt/bQCGliypqymXSr6sJZYEVBQ5FYolFe1pYwwbTozmZhfe0ao2hqsYoHhi9msgBmlVLEcyH4XlNBxc944vtb6Z9sq2bLNu7U9RW40PnyAZQpsNAebaYHwE58FXf7VpXRCAwlDnXqaVeToYrLz7WcJW6HoN04jd1GMBGq5mgIu6IUfVQaN6sCYekZZp6tNLYGbebDrGN2rGq+UAWhRZKPDA9LUvIgl6qx2lC4M1ONT2pv3rx5586d48ebT4cE/FzkJ3tP8DHwkcNHpG+EIC4FelVQxnLOei1tXFbC8+rwUKf19u7xjwIaUYhl82mCbsdfJ/A+Bx+if/3VV/hIkt7z7OxwurbOvfDCuXP4udNux0/lKKTd8gyXjBgGC2LIwhj3JoaGPMVkbjuY1seMAVEodDRjkWb3mmfDNW0obopc6xfpCnmdzsaCALKuX4BQM0EqpGewWYeMA1p3bt8+f/5CPthq5lN1nI99GXWEXOSZOgYR8Kt0hRD08hRlbi+Qh6x6SpJZ4Qy9yd6kUGg1sfkiFpVI5iL3WCZtCkx5ZsoV54lBuyAIBEKhRxyqn1w6HKwzdbUsMkfxIE3EmR1KcfGrsQVROMEvstHf0CkK2qnzJlxcDXtYsHGXfbn3o7RCSnofQKnqMMF9VqT0sQsTUKKanwWixGmAB0YCjQDfI5o11DuEiEUmqrPu2bnbFFji8pt1QlTemzlSlMDR9/6mtNVeFpo9XMy0JpypT3CPQ/hRy6xxvLiAM54OiZvdX3/99ewWi8xpgYwIeHTpnlYi4ip8+XUHF2yElIeZgXKxQK4pzay7/+A+P9/96cy9e4woMicpLl9Wj64ciy9Shwk25Xisy+NHj7+5fn17ewt/T8GBj9KRLnyO/vrVq7xw8+SNdhtI6QoQOCtvi2UDB2w651afWb7AFJxhEihLODKsZCnQITY7Fhh6tFm0+spMKaS6oyne3RZKg7TlLDeWJEwj53bPpKzhMHueM5wH5gzK1Il4FiSeZ7Uam8E8z/fHwH5bGUaf1M/HixYBiLFVjq2bSAGNE50g65ImXTI5xkoOihwvs6XOspqE5wiEROemTAvB6mXgWXHESaZTdHpYaQKyuzTYDMgQiiLt1GmbLne/yPJ2YPtg3KxznzWXgQKPKsiktXEXOCiAOuQQAFk59lxsvnibppJryNy4lk1FCUbwEy/xGp1EuKm/efbtPNmtTgGQucuQ4W4JschwKwZdj0XbZ89qX9oQBI4aoRCRb/LyBJsZqhNIE80yq/EqArStXxABdmw0yvaBDcAxFuZ1+8GDFy++8pOPf3LpslxT67QYWfpFYieZFsQ0DjE+dvTYvfv3JnKkzQBBljZZWxUpng8fOvzkyR4Szd9VffQ4ubNW1DgW+cBBKog6008jiqCpNEn70aNHd+/c2Xuyd+fOXciHDu08fPAQj3NBsCdOnsSzOJvvlBb0IrhJx/tXCijpTSrMHR9p1K3Sut1HXsBTj5hqH46XVXPeOSDAUZKleSANbUlIHNgunNApyn+u7xy5QWB/q8tRpOHU/IbRgY8m8K+msLwuQEt6exxmuaA+Cciz7EaV9w2b12O76JAbHl5jS1qXvQ5TAd+vAKgfHVl2Bs9US7GEDlYuXcLcqBZUWJe5IRtsa/TxRQZXOP5GKRHBBQ+62Knjak3l4rD4rNlqCt1QAQW6xx0GlKtUyxe9nGqlpVG91GqVkgQNAmUkgk/cmN+6JNGR1qnDgUxSCB9sclB2x6pGZkPGjflh16Y4ycE0FwhKNZpyKFSE36x1FpPMNjowaRact5qERl9Tx+h/0RYjAAff5mKVXqALny+vJ0/BVtqPHj2KJ6/L0yGpL+i/eJAB/4+ABsc27nH/+uuvD5yNIH0JyOtuKI+fOI6nqxw5ss3PmTmw/fgxbnwPvRkSwKeN5gtdRyQh2Jffvn370cOHeBblvbv38Ak6HvOCPToeuwn59Okzxe3+/Xwg4UyPRkmg6wyy7lEF0fSvziaKPeSotVyqc+7iiQlecVi8Kkh4NEIwNJcsrV3X7JJeFtCuA7s1A2yFMzQ1nLrR/FpJyHq0Cwa0soG1jq6svVWfqaVF3BGGN6sM3EZOTUZLymdghJD/Zlyj/lbWY9rAbGS74U2s0nAIIYtuICcNB7VqD74uMg1meZYHrwQSiGRkXl5qtAWNWsgLHzpmQnHpKUsIYAAs23RZqekNB7aET/lnAjMvFhiTUmamlDZPDQ9s3GFgNoYyIcw2mMCj3cMkZhFqNDFbE4JDLtMn13nDFKGxdvzVe851OiUho0rqAkMphC0Gdgo0LnwkWviZPGv/UCwiV8lb5YIgyHYT+0GXWKToKHa1mLc4sEWfRYL6MPTvoP9htWQP3EsMojEWL7zw4jvvvH3y5KkdfEqNeBreuGWaN8b/3Xt3GxiNat0EN5pHquOOdnd3Fz8zdOTIYZjRbNzb2+Hnu4+g9HWw+bh18xa26UC9c/cO7uPHbTknjp84dvzYiZMnjhzZnTP3+66oVfqIJOoS6ZbN3m0QFpBG34OJ+ozXMHaqgyR2kHAIsGLSHHTROQ2/ZAGMTZtK4n0euCxo8Oyni5MZU8vVhGzKOlwL5tHuONWIiCwNEVJdDfwAPbCBgRk1+GovNuR87mFotFSrrxTEVNLq8dJ6CTGfkVMXnFYDN3PFanLeRHDzt5JgyEJsLmqwspDDfNEZw3DtfpzrTfU1X+QddVrU5mVnfJ0UN6kDkUcdMJ7ij+r469Lw35c46CRyJkJwCmkVLJS6zVtlWL94qQAUOqtUgD98URR8iN+e914bYynUMzmnbHg88pjhixk1soIfRqnFMyG5oRPuIpuHBKYxrPE+SbFykjYk8oZFZfSMHLCWIUcw9K6Ztuf0xMUWj5nuIvhMw5p6wgkMEYKtqqSPTinnDkcilnh97bXXr1378PXXr9bwXd0Y790ju85mSAThnHbTLryBAu1Ah8KIO9rDh49c/+ab06dPCwA+bt868GQHN5jz76o2UdMGvAO/devW119/hRtv7t+/h9+swpdu8TAfPNPlhRdf2Nk5ZHv0YdqpA1/iDqKriAhogixdmEwob7ORnAyPjZCqxtSVaKAhFnsodlDVZqiSB62BDpmMKwXwvgHFRYPNFre+emilAbrCOKn6Wi0nRJIOOQssXrkvNx8w9IAsPQxNx4m6VoUFZ0mvvAb8/Zg7kpaVcpKHGbrSDxUbb2E9sNTltt9eWShNdqV0jdEMgWjIVu+F/Qs2IscEe88TMj/4JTySGWOXb33BNj0ZxpIQAEVfE6h5M8ElkKFAlP2VgtXq97gn5oovIWtp03MfTQhUaSSOpzX2a9nXHIMB/nE0si0nSrZBh6yXZOE9Qtgi7I5y09pPwQcCP549y2sw9KD7FIsMffoOZXbw09CxP5Pq/Vj5BZnyBBKjoY7qyVVWxptMKFo1uFP6EPjRojNnnvvi889fePHFLCVJEVADScGbnMRqrEBUS3C5RgoCUClptAUUB1U3iAU/GvqSc0rfVd17Wt27Ywzgp15xUz6+PLe1tY2fF8UOAwj4Cike6nL5Mt2M3l9567Sd91y0NTcEFTqIStxTif6M9zOJ3WihTPioZaZno5l7yAKp9lBmOru4L6DGorsFWe66P4rM+1JBiFkvZDAUExNoKWT6rii8NxsneNd8aMc/xD3gKzZYCT3nd6YoUPJq6DMxhtXVDdKq4rBtV1EABXN18Oh5s96MOE7yeaDRxoev9LKz+7ZFm9D8cVCL5NODe0/o2S/h2MJvHh2ojGdtp7OHkoT4Gq85VybKtsDikkjZli+3KpIFpRXxPH8BSGy1gD9YBCfe1yi4t1HEyfMo+CSQKYQNOoApi7y5gBS3F1S/iCoZPrvDJ4ZjIdfZXsq6axatSvCVqlmQY8oyYfCKB3gkHmmzdHCLHoY9BrQPWj7dBbzRamnxMsNmUA1KCCasDeOfXkfPZ8+e/fDDD3CTCW41ibWlBC9T6z5ASrvpGu0tCj5EZRGptSVGK8ozclB2K+6Z2cMPi966ff/pkxs3buARjNs72/g9o93do48fPzpz5sylS5ftQ/QSc6KmlRO9gPl1NpEncKm5EsyA1ajK7DcGtjKIB+uPUhilsIFea3bMgtSICIyi0ISvAt5isk/g4C+zSKIwLxrT4utQmFEG2IqrXo/HzuK5SfU2TbjwNqK5sqw5UytPbqVlgDeRVWfBeSPMWiwe0MsLuPVMyvWxpz3YFrZB0BbmYSAOWs9SY+iV80NbG5r+hE03imHE0E5d1jV6qMTTiZ16JwKjKjmxYsdkvAloYI5HCC+e3IkvTHEkAYgiUG9quwktV06hYqNVC8+Rx1wAjoSMwC+sYMxUOpy7nL84X6E/19Wz0reQJKIQVdIvy/OVBLFPSZGuoK0k5z9fLKhraGDv8M+NJYQGCgi9troOWE6rgJQkv5sYDgtg1jEkUI9wYOqlhOBOMyPVmzrjA+Z333n76htvsou2NqH3wHGvPDqiB8L9RQ4CdT5lBmVUbUa+BTDYguP59PjFovv37t+5cxt3n+O9BB5X/2Tv8akjuxcuXMBn6t5kFTksyoLFHeDWvHU8WL5WgVOmsxI9S7lHswWEgTV8yPyEegusiiRD1waw2upZbfKy1q9yXhU8LFISEc0iHooW4CqEBWQh7bt37/Z/LALoMvvBHAdkKUbqm02oHC3irich3zjGMyQRjfmXnIzpzteS1XgOn+AjuTrqNkj9S86JOWoWgCtO5bx2d9J7Dn7wCwRmiovzFj2iMfydqkJhWZURR1qAYMVlaN5q6HpG4ekI5eFqa0Ss5wHMXUbw48O52BHMMPWBNGRj2minag4ptgcCFjG31Fjhz+zbaz/xKtLYVKLB6fpBAnBhrHEneslRHGQZLdUGa2S4+1cY8mTDdMMsoMehBygX2SD4vqm1qMjn4upWVk83+shM1zuDMEHNWucUvKWV1iOBL7988eOPP8LHz2lLUUrGUt6Kj7Hxu0Jhc0yLu+7zyYoj0LhySykn14O6itXC0b17d7FBx7MXb9+6jSfk4Oefbt2+dezY8UM7O8+fO3f+wgVThoDvqiI1j33VEllnk4sodM0c8jM9Wx5n2g2p06ziC0m3a4eghpTgpnqENFbbykoeSAJlhilyCMdadRF0FSWsq8nAXUtFTF1XFPKqBaOlNAmzKUbE/QhXIfbc6TMv426z/5+9N/u5JMnuw/Ju31ZVXb1WdfcsPaMZkh4S4mhMgitISpYJAoZp2vCLF0B6oJ/1vwiwHwzD8oOeTFmQKAI0adPgSGPQlDS0NDYpkuAs3c2Z7pnu6qWWb72bf+eciJORkZGREZmRt6oJZ311MzLibL9zTkTGjZtLz1dlCwqWY0MuYkNhKkutupLy1ViYTuMcHxdSNKEr4ILBfk5gnNbyofGzz2dEePh7Be5eq/DmI7p/urGZGFK3LgtEpXGyFBDuT9x5JNL+rwVNUpkK6qEWGvBTD8BNJ49U8jF01KGUvy5x1RgLxvCqPcMLHK6a3QXW/OJR02jJJdbKpMJwzm7xNHJjAwF/KCEynpIeF6PjHyumUE5hAavMF5zAwbiEDkVjvMWQwK/OiBXIS/muObt169HjR7iYRG/uDOtoCScgIMX/fYXnIWK125yqBZegSsRmXeGpxhdjLN09fvwIX9AuLs5x1Sxm7Vhcx7MX8QxKPNfy/r37kgp/+fbbr7zyiscuh8RAz5lJu1eVHKhi6hLh7EKUffGJyk8pQKsOwin0QRqRQH0LMGjHBQopC3dxcmOxj/GmR0yhXmUzx4HgFCPM3U3Czyf4flH9FJ4iNtiGoNEmlW6VEW4xuk0dZeIICu+gr6vb2uu2/BLuIUG3i/E1QdHYRQO/sUIOY+y5barOyZlcGTW9CFFRrrmqqKZ+5kvW7y6OgkbbM0V2XxljA95Oat58xFLoXjYMFu2ZekiH6wcNcohwSF1TOCW8W9MvkbsIXLpEwR2+2bvqYu5ONMqX2MpIiVoiSJTE0cgdyjlWmk9AwUMFixWIFgrDmEouJxtdgS5J51iN9KUN2PoW0acdG2Oz3x6vyAAoROhWVBBbGZcJG6DX4XPwFykOcs39+69++1vfun371mLhf40no8hgY7I7BFtM1ISXpwp2ohy0gf3y4vzBgw8w+79Z30AGHieHyfYLL77w3HN3j46O7t+/X2tsqYi/ASo0d2dQJAdRkjLHbZAD2bDpwioJZTA3Dpp+aA0T+nOoMBlWV4J8HaGwDgLe1B84gq6RftE4qXTItEjwJYTa7ZcqJSlQMJ5RXQVE+iLgdnIOe74G5VNlH4tzxG/GUQky0ilFmMqXgqdhWDopFzsmc0LjWdB1KHkOx49JeOEdI6HLvP568o06qp88haKwOF+liofpWvaJRh9jpo5nvyCNuU9hh0tfMFmPfntMUDppkNUbzkgQsIn8JmMe4HFHBaNc497ZbckLAVFpVQmcA+XLOQryZdhQRe0h3DQpRZrlT4fKOfPWfoHlbVRF7GvoKCLRE4KEw6KpV0lo6FmLuE27tQWqWjRcMXLgDQs1tTCi9kwHJUVKUg8Exmqgcnp5JxTQTGn9AOEw+7NvvPH2W2/hGZGElx1gXABrFVR3Gt46u7XFXfhpG7ICF6B/8OABHraOK53wPiM8Hx1r9i++9OL9V+/jwYu9z3Vp65GHRrfrtWYLiu2O3s20xFAOhG583LJy5BbYaw2xDQkjdUiyuUJMClJ0bKx87S55wxjvAAkAad3h9cjzDsU+8KRYo31KdcS5xGaDX3nKFbKM71LbCYrHi4mMF7+J6rgPu8yO14tn6BkAdnxwtfBUIyag7ROXWmRCCCpVvkswtmxtHisnwj/VID+JPzwcE3m+rOkYsvDsFxoGMQpQpuB1XzSbpZEmnl4e2mfgUD0jnicMzg+52upaGlpjc9rd3uhUt4qpdE3G3u+9FBaeOWhBBOhJpqG3cUCEU3UekV2PWWJU8ienFQHTcy5YcaCo+Miej5PFphKKm0R/Kk+UDtmGzX+QC0DwNejBtCO4z8QWsIMM1szhk4fGCiYTAwicWD0TONQI13itbBcIINdyMuLqcLzy8wfv/QCr71QbQaeecWSenp09evTwzp07Tl2FL22b7RbzclyIgytesGqOtxednpxc39w8d+c5XIkevAoWiRROGFd0q3zEb1/iahkphMIN7oye777dLddbPKmuJSC7whVtmelSXUp6e1xqzyGi0QzfjUQ8VDgpWEAhpImWArJasEWmdCK30eByqoZpB9fhjXesbhRzQR3GeJg4zLeKLYhLshFNrnA8u+nO7cZQoEK04NJrpVcwwqlTUdoLS9sMjyv3kMVP0GvLDwO5yIbTW2cPl5DKGTqbdPHi0pc9nvKLF5NijKcBSy5S/+RN0wMA+ayFevW8pGWA0laNO4el9D+rqb2nDimd3jnbSoUZCXQCoYW2FFMzzpROsZ0N8Kx6uZMIJxSDR+fosJNNFXsPYnWHko7qDjCSScBDBYDSjUIDT/CDXLTSK+Sp8pgDh4PkxZkMKIuML5CDZh5/hTPOH7CyqwoycwasLjGp9aTLTm2EBwboecWievGll958802sfB8fH8ckh4zHFPzDDz7Aa5iurq8xTZcbRvEJOZivQ7J3w2hEPvcp7TMRQjRJrBgbFYUrNlLgXnK4PvF6d+uYuA2NVhjvdPhGU+KBQPKIjSXGNRMujUOvONEzoNShjCE8/hGmAR7uskREifcKilV1Rn5ozCelTnceoP1gxtfzAgXWUWjnYQQXmlzP40VmuP+kQ3B2NbKe5Mu3VhqPI4b0CG+DAsMYgT36DtgsPWsiLBMKj0WT5hl0UykvKfAHrn5ZzDH+YQvG8oAOL6UKvqWotc5aGkp4AWj1UPXGJu5gqXtJXVLenILIck5rRriIdexO0JNAQmB71/Nz7G/RwuP1I03cPCJgamEesIaShvcbLYkHakQivZIBGvUZGMBZo/WSYXhtTY9kbp7Y/WpUb8EzltwKbKgVB9MnxctEylBzba/oZ47AxouDZ89Jko2eG0Kmv/EGPR3yiz/0w6FGU4drn26ur7GCjoe6PH70eHW0wpXouBHtEpeZX13hDteXX365PcpEBIaagrYiJLzVoVEyKehhSCQFmDY8333O96riIThhunG1SCMyMEGIkDEqUwRvLyPLn2ruDqthAPq+6QwJKLpIalx2skvQGB/L7+IbVc/G08g1OgPDZkCs9CwBIsFSXGGe5FqRxn5Lyp9kwYZQfCKp1uYVvVovxuhhb0HpIQcDwtnZWS9LFgGMhwoIb4xsfSKaoPqzuknfJ/1Zapfg2nNaYcus8MJiPXE8N5RpOp3GeDBEf7MzdY962OGUk5KszFTze33LQyZ1L09+59mLfKfdUfX0Ftj9DT45sJ91EwenW15N2E1z4BbbrwOWO9Y6xVH2pXl/hDbKBMkG+VRrKZnoNmy60kUrA4URugPSsqoaa9YddjCqxg9p5FIilt4ibGFmUE7ZyWEBrOvttFGXUE8zmaipYgsEika/vA32vPba69/77nc/9elPs4A9FuAfP3qEMmbq+AQB5ut37jwHPXi0y8svv6IQcA35vfhLWFNtYUxivIXDrDZQdk/2WJm4CEfLtq5zj7k7njPD17t3jn6dzAkNYokX33YwhIw/nWKSfKL35CfwpZLIrB3JlTJ993AJEmiqcTWCSDZY+XX4Ui1LoCO9rJHTKIGhm8SFVuOywicaH1TRRPGFfMLV7F+oVL3d/khqgRwMGrh0bbz/2/rISPpRC08MQXLScOTRmJDx0E0EjebmUaPJHCiFuKgtP8TE/gw2xCvZyDhJfquLQMv5Yvo4CiUnorijkxhCSh5HOGmmTuODBLI4ApswsJ/1lVTgZovIFy+69X1+jbW7cmC9PXW1ILQqmkLtpAEiDCV2fNDDaMVYH9pjs0/k9rhCh8MlUVBpa2DDsZVo90w03Uet3tMxQD8yiR7lAkg1ONx0jQcsUhycnw48VfZwgErLWnTv2iH+YVB8JkKbVhEsN9OLGlFEWEf+B2XXUaPBzaCk8c11h8+Zo0B5b92+jbcefuub38QFM7hWFSvouOIF7zOidfTot7j1Zq1CBhUkdGBlTMb4pLkjePA6BfwU0J/G1jK+3n3CuTsFjL761T/6xSJlrcraI7vVZVmMicRIrqB8VLrbYFxgDMp3hY8p98h37WZILi5ppE+XzLGGq61/Omgc8oSiq57JJb4ojhfvyoY0Esj9S+rHy/fg4aZzuVl8IvkEwU7fjX/sWGew2EPPsPRDcRHZn7C8kuhANwpkiR3iJCDptqVRJhqVJqxFNfjUam4n5eGRLgDB8iA9/qVlbauiZcKoisH2x7ViwBfJE8lX7XDPkvpxy031GcdO0JXHENtJQ4O1cVBzBErI4tG9KyC2riIF9VG4ZOdDQlibZBntvs2NCGFBul1ftGaIAhgmm2cJMolW0DFTt1u/g6bGZy3p2LfVI16wms73vGn4iFKcJTxtTsORtSOJ/U7KEtkk9hb1LRwlquNvO5tpSsZXd2MV2l947fXX+4kMRe2f9U3vxN3GjXhrbFZXABRNftM2vKYRj3hPn7hDqlwzM3Ld3UKq/SAWm4Dx3H26QRy6kDRecqQ5rINK/W2TEYOJxsrtYh38edWkjRW1syFPUAd1p3wotZETVhig0DuEtaoti+OhFk16RUi91OXJt4ETzSTB2tm2ReUTVciANktKDe50F2868hN+XWxGJKjIJYFw6VmUonbCFOQaXEn28zcE2g+WYhk7JNDXP0AQXZa2oc5FrQSflALN1GlZnTZAxfMZ53tMQuya+icFRtROCh/3n+mG+rZ+u+IuAyhPVRpE3d2+QZZ04KbuIbPRYoORUMtdhO1tYnOt64OjoeojTG1vK3eMDgiBAbJ5bUgdbFkzmMYg4YkbcdhGlCDMYQI8O1xqQU4vlsib+CaIzyWpFedy9tETOE5GLpictMD6mJPbC56NwzphsfHR6dkpIobcc4YQ02ph2X1YVKCW+ZMmpnh1K34fWK1WASndVblzd4pZc7OQ7L7ZiiM4xLql1VaiAkOYkd9pQpIak4/O0pfJHbKfJEyVSjwGQ8WE8mleJ6EzPoKucd5quJQsl8G6oFBHA+RHQmCQWfoB0Mh+IJBvyaMhwNU368Z3eJZPWcq9wRra3nuq2aUuVRc0HnMoxARBDly20WW1q5FD48SKKJUhi4Bd8tUApY8XPOFx4rKtHLTdfm4eDQxL8HbS+S5vWC5rUp40AtD3JROomiGZIOn6rbYTdzuWwSTuAP2cCRRNfMpQToGK5ELd18m39FUWG2yw2OSIa0d+YJDImxy39HW4xqUjEh6OZCdwagIYkG2Dq3WqKFgLXV22rrkXChM1hkcfVIv/Tm/okgTqrqamoqd6RCcUstNYK+dJOZvBrqmjYNVO4ymhD10AACAASURBVAKKF/BRrLDDxSr0ukQ3Jm55iAnoujSSNmQG5GDKfn19hXX3QFu0KjJ3p7x0IgcbhoGBc4yLopYMbiT5cDqFwm59hlJGNjfNx2Y1HQnsiSFwFiHKfZa3zWvXeOhgP/2TYJaQ39Yohk/nIhsCaJaMNCYQtEKIjJyG+ADQ3ipc4H73uefaZDKcS2i6bG4Hri2nqwbyIRYhICFc7qIkFw7axNPCnet1T2eQfYx8FxDkeOrc1tJljP64o5TW1MV+XPrySZqpe+6QsYJPamihlMIm2GqEHs/TObQTd9VebPFD4qhyCxY4LdWbEGxGAvK6bHZfUGlhUZoGIlcGHDvwmI5HKDh38DFqjh62fXInYQytx2gCzCMr9mwPTcio2URtgDUDWMKe6Kplgx0MXXS2Xs4acqTITe/X3LTEB9rDSV6uDVYMOWazPY0jwJco4lESjN5SlNtDF6VK1H/zxfzi4vLFF4dopfeqbrZzejeTGQwlr0ijLQ2R6/AgBST9nbqSRTGz0d2seJ4M1QlAlIAlDJYmZU8Q7C/CKfS5NNJLBqhggEab4Aqj42AGXZRrahe9hRB3b5brG12K5cvSNVRkyekyuVXPUikKaOlUUY8DLX66ix2PHWvXS42IdENm6rEblJaeInILnWTwFk06uYQRAFsMgSfSPxS/qwA59Ii0VepBEyTzuJRYCiIkndGVlsLlGemyR8r0/Zem6TsLCePCJ3mm3gGVUocdVI9IKT7tkDZFdWvinqFkKJRwfwoqlnUSauIxVzTyp1NsccLlQ21ryRpcEbRARmJ8SqEWzoMMPmSCng0gqKyWftiSWI9PGqF5ygL9EnRnLC1gckYilfaAnHyAwYSK0TjoUvXRAOF+v0nlK0LnhQBQsAkmC4zgWTK7b+rGqRK99OTkBFeZB9+p1CQfcsQqYl0at1kH3tHboYpgks+pWWYLuyW/m8mZu3ewDq8mr9J8yGgdLijKyaMKqeIwkjIOHiuNMqY0IrtN1peRF9ApKjThfAo+G2glWSHx06q+gkgOdbhikKCCAi3G9dnjtbsnBR5LAgOKVAlloNmTOOiQxUKDLKy0RcR8hRkz//DVonGxcVraXGpRthUm1cjoRaRwfo+XRusUAYBQa3WMpFZVEaRwiLuKRgU3q7Au4gH1KlMM1EMVZQynaTqAmpk6r9LgHaWrMHJl/itQYI9M1MUC7qH+YM4PgdZWVWPiDl4Z2lpk7Yp2oNs0oZq2DrLYJjoZD8mMQWqtnmTDQkqL1rVHNGtjQw2NfLxprYDjUQX5QJs2eYVwQ7jWY009LCPMGblqgYJLkjACMtXSKF07naLkAxoRQ4Jm4FmUGjtbMUCyYVFRw0XEOWGi6VceHUfPtLo0jEmAyafH5x8i1Ldv38FiW8/Lm3y+jGO20k58W3z4uhu8wN2mJ6FQJHygR0YW3s003+7pXtUS71VtGUgV8JI71AVpsipFmgZOcHGH8NFliY0QS6pSj5gsaUkyFNSnADKHobEH6zBGzOxpIttJhZne9VDnN1vnkA5bbkuxuaktIE32qlByIMA/RbhVQwSCml4X8EyqF/mXL3awqefw+djMMcVBA1zLySyxkBaPwYCZJ590W+3DK8Rqj99HKM0SnLCBnoDAocc9SaStWmsjujevqaOexiy8tA7vKA3M1C295f//92M84GaPeDYa7MbE3eVt2hCV0SQNH9EYRqcYmGRPX1amq9XU2aawrN7akew98nm4dFTs91uga3VljBq69UhMbCbfJZL2k2UJE3BQLhkF6cYQIOxX9Umg4PMMYanxcAkAKXXr2knAFFXRtpX6njnJmwDSgExYhdb9HIgP70r83ve+98ILLwzkT2Bjm8Nzd0zcLy7ObXIyMo6kBZggnZ8zg+e7L9ebjX0sRhJbDpHkUXa0A3M80mqzshFwVJre2qjOsbKPVvqEY0AfQ6wdVhI8GzuQUtHmZw0yJmNAGw9c1CdI2USeYj+xCgcd2crKs3IzjJDlSM+eDgICQgklusJ2CAW3bTcb+dE4Su+IMc6HjjHfBmPw8Tw16CMFrCLVMMdGMDpHVAwKEbJgEw+0vhBPZuRQ4ZmuHSHNb8KrSffVlrog1LCN9NqjasVei4lTq2JERmScZIJWYGr362DlUOWSTkO5o3wJnnUm7tQ7PXH+sdccOuTI2w/rOgxSImpM5wxpC9QFYASoBlXRw9B3e3rw3KJ+eyg6av+7ijLV1RgGRCBNV6dgPomilWNIshA6HYw6udKUlqcaYBCPr8SnIF2EbRNtErdbitUMUhGEjqhx4OoMEqC2A9ZMdakEDFwkg/vSGlpLiPVk0HyOBl8GSU4jrKjEXOHo6MgisnuP2T3sINku6d1MS1ywP9m6OxnMc+tIzAHR3UDZYa9LVZdFcv5JKkOJVTFkPJf5jTWXI6jji63FXqyZ8OxIgSAlxVU0AZqBNATRQTuiOBGK2iJreu0oTlBNUrRLPoBlObDjGAm1ilp9mRIbSaMHrqmTckxuM4Q1vBhPfZZkFCD1OpR3KLLUi1HRttH42Z6jbXXeHty7+dacKGAlOjHN1DEqNeTIUdDoBl3fQVtCU08f/7B227XBXYdP03SYzCZXLbZZf5gjTNzhWPatvRo5WXE7fVQUyWgHLFnyUyZEx5NN7FAgCBU/N6PC/WxTmSjKkNqqdRpN1HlsB4IqKVJi23wsoL+AiF7wUR3eDAjCgNACJNFR7lp3/iSo5k0sRVV0mWkDWOuwlHZftyDE4eVqh2RUEYvunEcB1YPlMkB8qExaR6ED80ICU486PA6yphqsr6ro3Uz4WG+3q8n6OKehCTgPoq69nKFuxcAy5LAKdd1AORE2uB3jpNufAsSgaNb20LeI+1U0WXKPLArwDfSVBzEIkFCQhoEqekGxUjIkqL2XPU6gAKXAIAJIMCceeWYU48lXAfFxG5NaIRYb5HvTdwUoUogoSV6YSLgJBdp7JA3SY77JG6ujxlLnwxhj33xEBtNKx35ZK/b6J5HQJgTSWBNL27hPT5rqdzWO02C4o54pouGgQtwstSvu5jTi2QGXwpnOBF0qTEwzF4IgpyeJPe3DDr2s6BQiXsCnFJQO/Ih38UV0ld8opBrbYMo7cGIojBxRDqHRTrtpDKlV5dmcRS2pK9MDVmiTlOboWZKeJjGb2mUuENmNwdnzcxe9JW7s4Y6p5+4jVfgwTVpalLSn/+iwdI+UrRaQ2615eHAD86ADmn3s8V5VrL2XnLtLhroWEZApzy3IKVHhKi1bhvmOCgqgB3M8QPIQdPCwXNZ4lSZBcIBoi19gQxqVBDBhnBGaFBUN6XkHpIRVgK3ZPfLk1FEUt7cAQklgbo0vz3jLcq6qNr1VR8rbreNrrHw8fIYGDRxqzXjhKkFkwlGsgj60qVDBWG1UWDUcPITHXqdOZ0H00iWtqQe3RjVZi5mabtIokZBPbSpVUP2qVms8FUrg1WccIhwJvTVD4AFJ3SyVibs4xC7+wG0mROJA5xzZ5dEU643MFNLCNDTMUP/hD5ZtcMAT9C6vjNMziRgW+DGuS/dHLIa1BXUpXfIwyvJBb36NVKs0KE62auPYggofK6jmb4dAgInDLEjjPoeYi8PtcSTVtpQpXV5eQhDcnxJz0LibAHKMc4ouHZfRadENvTjjsTYtwuEVdM3MZtzc3fZEMQJ4QkEDELc+hnoYGCglX4V0DxPoccmQyp8Udt48khKHDGBSILASSlgFFV2jBaPUsiFuY17Z4iBdeZzJ1CI5w1cKj1UYw6x5HVYSlfC5QDBxxz3eyZb2EpLyDCAxeTVIRgarSbh8og2bCyQmKbNNVQCK6MwU0E9OsZjhKwie6GPW+DE28tUvdlm2X4ZScMBbUZcKfMJRQtrhLm71BneV3VcQLeDGX8sE99sECXIJMhRyDPoM+QS0I7QUCxMGdYYWikFonKCKSW0Kov7HiSWf0khQAI/O83hq3FhgkCzvOWtqDh2NVRWSqXU2VWVP2SjJXk9n+jK0OWVQwc9SwZm+wiwDqEbY6LtT253vrpT4c/gkclSUEmE12FJkZCDnNPGnvBn8UVJcZS7tJhllx6Asn8HHndHWDdqjP3sBWRRdHYdRcr174ro7jzoGiYmZDWEcH1xhgRi+OP2AVhnbMXBJYYAEYWkMqnZ0cWTSrNdJ4cF6OhlLAelUwLMBDiUWYkUbBcXBGGFNbzLyxkckotLxFSFwKZtxHA6vdovN4Kury7t3n3d1jS/XQAhHA0hEuGLk+DFnN6uqEIE1roiCzCYrE3bROG8PM6XU5OjMtKBuFwfglznW1JcSaNSO7uy1qmZJfdUBxPMygc3dVISoIHaOu9a3BUaaXOIh1rj8z1KZvpONTqOnAAgXqUlc3WAACG0wx37vT4xoOgAolbvmDUtxBSzXlUqjIlcCKRXkwFLUe1syhvXucul7BY4isEOQCDEDtJ379JpqzxyjTIgzG5M6ifptJFaJoeQsSWQuYaVP+02lU8v4BvjUJNJ4WU0J2+0GFRYCOoqNouAj4rrUZB1yBOmsw8iUX72HCOrmoXczbQPXu/PJ17BBPfkzFVmAzgLptqNEC8ZF1129IomYiRgdoZSRNcJIBOSaaddocoFEDJZshcXYCCzsNyAJ79SDinVXRvawpRkf4ivci2hxEU4DMUNMH6mJe/X40aNXXrnXRz2kXWymkJC3JDEbckyT1A3CqCowtaBySEtD5ZADY3sESIfU/W6GL5MyUyfLZKbedfUL1IgKSDMqO+QOq65lspr6UMTV8alLDUUURul2jWq/1soNk/qs/cdBa0oJ71dflGLAjynD9Aed1i8KiYE7SWQ6oNSQhQdJIKw2stpSdGpQS+0oDcQUkmYnriKSjuyJUgeQlrZWRUjwU67zbHSmPOgw1GjgYaB8ypYmqLcmR0l5IJDRAJBqFsYnIDuhwguGIapibGOJKYkg9C2hMBqMNKRr7vp0ZY7R/VXJzfV1GaFNKRvcjb7fznGvKq/oEzYgbAbQOXKKTTnxIwZiO3ycdERrpxbulRpQxghag8VCsvu4AcyF0Ct7nHxYKwuX/EqzitUQg7OxkDpfnRYpGiV0YF3RohldUd5d5kQiUIGBYJTX0gLOKo6OjzPi0ZLRW0FY5BuVJTXqBKatHLM37kKu4NqTcmI9kwQIKiM9ZTfbcCxNJOfVAmvqcs70pAUPXRUg0MMg8fBKK1c6lz3qk0dhM6FzeyXYOcAeu1lBQK3xhdc+7tDYYYU0xghb+QzuM1JhhPXwhucfXxjiJ5vXgFDyCrfP7h/rFMnjL3Wo+lDQ8jDhnBp+Ctr5johs33tnVY3UbcXQvqAoV6yW+dsHqaETJilDr+SOKRQl1TtiVf2wQsSqYBLbYIJP2uVCFBWjhWHmTMKFPhXAErXUznkMH2iDLseyt/4eRVomnsDBOzADtuETqgepc2CDv+lviAWK3XKJC2aW/GLYZnvJI+suwlRSblOWaJG6Wg3Hsj5ssgw4slgKimxbASWxeQ8lg/ZJ9ikx5G7MMiivMjSJYcO0KEzRR6IYZRuq8Rd1mXZjhrURUjwQFtLFJOqTBTcIZWkQSnLN2EKlgkpqUfAR7IdSVjudx4xkqKEhudrNN3oamdMjGgvcMCAqSIN0h7JxsS5TIFQRVyE+9RjtIbO7ByjXIZaSCQq11E01jzeI1w1eKUynEjXfPDYchjnbdFPWHGzF3YAQp9OFLk1UCAFP0XsyVd3a5J7yyFXpGd2rlunxARnySRws0JUaFZNMGJXS0Vgb1UGQWK1zdEtPPYpOIWL9pBisygL7PjsxuGowoY7ImUX43M/hxoiU4fzDOB2tdvaquUGrp3aEdOhaip5/4XmcufmhkKaN4q9iWvSlKsi2fXV26xbm7hlPo+NYuoMwDUESz5Zl9l5VzN8nHDCtl8u6rD5Psp8wzE4ztWo67ZBaoFnUiQmUowgkp6zkq3w2Dcw4UuEiM4Mzh1S0OFaHmetZC9qbMMMMbi0DgIQpgKD3nZ2ekVGMYawWOwyR+dQzWaiDpYwWR2CgaJVOsfqO4cc++EWuhUWSzubbFenEAFB6EyjQYfLHd2chfVasp0UORQcsGd8fRZSnpRAGI0aNlGjoIZrdsiqdIGgqO1CY6jwkPsUnXehikXLE6EOehh4wJ1oV9JdwDD/XRYS2jMHrl3RBsdHIQRMbJH4kFf9ZuGiQzwaXf+CQOEWf6mkeM0Q7pNKB6YM04ek2WRwztd3ZWroN1pGzlgli/NnjCGsBmOgz1rHjpHWbSVgojpYCRd4QTxNSbaoLPcacnd168uSJO3EHK5RYHT3sY5ph/vHx8Xq9WSzaA5r5VunKJ/fGMtalNeXDzN2hjD02+Cojjqo1n7OokUoSC66imYMG23KU3IviYsncMK0BEy3TaKlVTonF12L7DMeKOqkZjsQG+ax58ksTYdls1jg/qjnZWhgpscsAZIchFRgsGG8IbxpLUE68Ej/6g0BzblgI6F5SeUQjAosBngbf+Xy/aqjmVX7UDFPRENVxoHGR9okUAR5iMtJjHQjq6jAWGeNqqlCJelU2nZwkPTapjIhTgpAdw+va57k8WRwb+tAgCb/4FJ9zetiiBzamIoM0JibaNlQHXps6x+NQaSZAG8SgIF1QDvWTmvu3oXb0S06jIOujlBxUCxN7prbjY5y1lturpSadqhQxVULpKKZrXSy93dfN7Zq6rVQpbzBNtIijYOMploKz0DcEloenyjx8+PCVV15x/SAjuDrUbRpUDqOFfKy146EW9FBIC1IjC3+G2TItMM+ZWW+2A18PmaTPGIshtdfq5pjLjOlQ6YsLBKQzJFnfIoL8ElrsmAv55JaA1SW0tKxvVpDWieeIJqSUu3Rtg+RAkextQjEeLOi0m5t140s765M4dWixMZU87812D4B7KLwTh0axSIwCKeiaRPHTB79QOOl2Unrz0ZGZPTSJ9UjFktOIbYrgu/2Ho1BCi7iFTYbNioOQHQaLiUuK0yJO1dMGGd6gwwGcRc/ctO4inEQW23oJXOaGcrehVc6YuGPtnLqGxodlAQNQ4H94KZpnti2ljYosYA3O3INMTXTqpFDZT2WnU57ZtGArIvtkWlKcTBxR2Nck0CSi0GcOSbPVTrGlzR7zwTP64dodNhEA7UZYwWA9YKtpH8PKLDSzdxmmKNNI186BZLVyCnMNE2EtmZhUFcs1jABHq+YCElsA1ZlaknFK7lqc5+fneBodMAp/hhQroXdPz5mhdzNNe80MmdGa77rjLkOkyI3ECAHhTOt1RA6BmJmuiCi1bxJC/O/vc7lachA4tOxyicVo95sJjR14GWozpOlOc0zMKBoImf0zqODjjz++f/9+sIm0iMsor0FC/1NiymTJH03XJbPlEWqatXPArqkjnvgDSDyiEWv1nL15SohaFWk5X0YKh+lZ+ZlmI8pKYK0YHFRpmwyLPQzSDq80YqGEz38DtUhuOlYYo7kGztJHihs1EikK+LCtwanKG7UhwYGJOxlEyPnDmdvAEbJJ5wtJa9epJdTUOGjTDq2BrWHJ4dqmGvaQuqnmoBARpdSY+rq5KSR8lEcdllGylr5bt3xFFS7SkgqnkdXlVg0i1DJQVz0xOYym6NS4xE+/jJA0ItVtKI9RAr0mOsgpLOClRccl4LBn/NxAxiPVapLWQl0ulxhTtXW6wiGumZETD9LYPNqC0JheWgOrw13X5ZeM2PHh6VNNKR34OkpRrZFQCYS86xMYbBctIiVIUKRSLAzBiYrnsFq0BiqZ2s1EiqYPjTnVdZuR0rJer1f2ezt5hnn47IISnU0NTHJCudWCiGVT+s0kQLXbVmuca+TQztRLjkKO20JLORH4mU0GEaeoKnVlUEe1GxMEqSxFeF+zZPedsMBQba3ETGGdihB9q85D2sVu6hF+cVqsH7d01BXa+2v3olFra0LqNvVGD0vAVlfIwAmjip4Fg3a4SseWExRo4oFWXEBMzJjAnWJgITEpqnppFK1gBGCMnPSv3mi5tVFRN5Us2RF7sEzX5rYQ+2WEuw+Rkr4m0DbT8BpIPojX2MImchtS1U9DOXu3SZcDDuwQOzpEpBKmdDyTkaPi9LkUA3X0BLNJ3qiVy+VqRXP34OwwRWEGDc/dN8XW3W1cawssUoFzmE4Kh0sy1WaULlk4POjYuFqsxZQJisPAkdzsMN2sHpjuCpzDoApXqV7aYautpm43JA32+5sbPI/VsKuEwMDEcA4QHePton7DmvpuhskSQkqbXKROiczP2ig7ZRIV+mlcKmPFsERSWd0F1WIQWkpg1JjaulF7kUbpUlqya1a6FrFEePOQ0lTDpPlAOJpNrumhsqiRYQWv9l7gfvAQ2fA6q8ACGi4pgRPKvCyj8cNnRKq72W487VPFjvH1pnU50AAxMRUD2zhfyBQxB9g9tNo0UMFTYAt6lqPqhZagsn3YH+IiFtZFLi7jlIgYhJXu664pbFFrtDDKljJS2IRNcwmgYZaZUjfq9MAdN1GJodMdPVMsxEAm0sAIaS67ailYwONl8IzIQXN3wgpEdQ7ZuAbNOwwcqCaPkWUpzg5aGqrkuNZgoUPk0wBtO26Ib3wdFB0gDQgFbQyHzzomrFRtAJZxqPqtbIDYeueD4sNB43xwGvyiBYt64pnNlnRfuAmvT9w6NnTF862lCBZxXd3b2iSRGr5OfSvPagQZLn1ZhB6mLnCS/BZRltLEcCixyZoymeWqNRCcqtSIOizpxcP4TSF4AZJDWAs/Kk268W1KFw6JTQkQEjNz42FlFrhUJlMOkZfPoF4jrEr2Pq8dWxa4K8Vjljxjz5GwijP4ypEKWsiDFRJyRtuwqXFQTvXEktpWmwUro5famaZNqJZFmpSmSGGcIo+bzyk2sCaoBBYzD/osYnBUSLkJ2+nJKWsKG02IaErtf69GPbaoif2NeFDVekOvbsUGaaxorEyR1vUp18wsN5tNxwVCzEhZzBE2n2Qc25VuHDEcYIrDjhvnN9NlBa97PvTBHiRA4uhxiLqCL/XcW80H7Wxs41wjWrXzjO4vESNMgnopZ2fqFEpoZwM0rHDyMe4Lz92mRNG0RSw1idlsco8AEiPUlvIYHPSB69SXc3Phj0sZKGu+SZtxY4BwbJWraKwWRM6aA1EqTSOLRupB2E0WLFU6ZVclGAJUEate64AyexUrivQwLN11tLEvTOjVjpq4t5Wq9N4uopTpBc0wYqnXhno8ky6/g1JRoqDlDtoS1ew6cwqs5Um3cbEewpZa/7iSnw6u7RJWBoxqopRW/maZoZaZD7HobqyMmyYYWjQYPeRp8NzCYJnSBrbBBkcQfaOuJbFEBanwQ6RyM9S7z361AHXA5Ikcj58WrKooUMCvYddXVyqIjJ7ed/KcGXfurmcF6GeYlMXiwQw/KgwtHGSmKzZnnD71XOiAJSFqdncBzslQ1C2nt6WYIh8sNDPQ+uMQKSfDwSFcR4MPjc2EDwMED0NdkcWP9uubm95YRAgOgYihNBVhTorLXPDJH0SAvMST4wA+YmysyQxuUCPem2z4Nooo6TJ/YBTLCCqFtSumClJJshWpiLSCKCroNwuU1EO4h7SgoiA+VWgUIak8C8JswVqnkjMzdeKeotGRPapouwz21tesPmhDsHKU+iTvjtPgcqN7AwNhNZ+MKC3GrpxnsizRUXBsI4WX/hNi93pxoZVPbh7yMZK9T6WKF0h95NxjlYeoJaFtld13y7EdoJtiVItjgBuKYTL3+yfn543re9h6D0Lj17FhikJcGCVv3KmD0ZoWp5DA3joEF+7b4Hm3uIhus8ELVsGig3Uvey6BkUzDhRO1XCkJ9KKI0DUUud+sIYVtsATDDLLyJ4yRwO1AFPOFBNdQCMwUsJYmJrpE2wBECWolxBpMGx9w9mXdxcXFiy++mKCik8Qo48nulBm+r+a4Mpge1MiDMcBiTX2RuKbeaX27QSPEiBxXtknH1qjrqCMFMpDCavqYzWSNcZbuPkVZwjqJawhy+gwg6uRFAzMRAeTUokIcbmtruAsxDK1TRWKbHg6Uh+DNqs6J+7DQiinpIzEodUOAZHywqu1eKdqFdE1tXlOToMXJhk4xKQ0Ez9KR5XxgPrnetLpesfSfhL2YL9ajjOFCvpTQ2CVYCYUBKYAaB88+RgXC8BpQeHBkBERkY1sAX7kxJW5M5gqoHSBrobPZ3bt3t1vMYDtHFfLPZEut9BB3b7Px8qqHHFIS06bRlRgDPgBj7j7oencRmfM5mffaRpjTDBoom+1SQh3vNsewGjvuD+NO5kK8ol2pnt1A5NiTK58yxguJg7PyB2e5QNaI2hC3tcpwRsO5EjeIHrz//huf+1yjathBn6IBUvl2UlpWpxSmK/X46hceuykfILED1ABdPotGiEcPe+RTlTm20utuC7kEjcIaDtswxVbRMO4MLquoP0yMWUJpmTL0gFS4jOsmywdrm+gxSvMMlbnonu60MFvJ0FqZsjdnPKdSdXku8g4djrJF1Z8qFs9la92Z2s1LAxy3KnIB5sLLNqFb3eCWfBuCHHrGswVzvYodLkoPHN14YR5ssHq76YItQWwhSjNKmJkcsVFga63JgkLCp6yTdOw0D8MKoNnBpTaEKu2RmSMAcJuuqm7dun15eXnnzh1LHt53KQpTJ9cu2+9Fgp3ds41OwSaytp3AE145VlfYZtrT893xbHe8u9U+Ec9tLVsmxw8AFTNCxynzvQS0El5KdQs8JmBkm6iYXpeCYoWIpIxYEtK6A49EI+wTJXnINuq2qBd0IQKtc/EShzlPaXu8wF4LDhH4rp5xfoxrIbskImaw6SX3CHazDeGknxARYTwdj2bqBNZJc2ExHmPvWaWesDKHmngQlxCmfKUMgfzFWz7/cA5zdjAhGy4nwtn2niQ8WIwzzS4iI6nJFRPM8yQp/US1HgFSH3fwuniJGPeCo4tIL+lgyauWvgKR2kca8gtqYrso7w9DZQAAIABJREFUU5MMTKPqFgVF82Bq0smGz3bKy241R2PVqtCJCjH72m1wAoeVP6xFPKNRWino4TPpBt86iyS45zOdbQFwYrbdLPO0Z6Xk7qGOsizLbKNjCI8ZSmxSQ4TFS0LjEo+OVngPS+/EneR0fEkwhg/a6ejW4O5XJLMZC5iNa0hIO8D17sv1/pOx7s6ecjLKiapTZE+Ev8uluSSTqj9SmQKVXGZmOAQ66koCio+beJWjSMF2HDNuFJEZFGIVNRtr1AqywJBldDWHJL0vvGnByCM2u6koJHG/w3Xq9JRG2fB2Ulz6Qi84T924J6BPhN2YKqWfTuSbAdbpfv2cAQpOKgkxRLE0DbOSHwwUlhLIoLGg1PDOgvEe2ifWBSyFItWJhUFQ0IKKpFJo2o6tV9xj4kNt7X4BE/DHCcUM7TwKyRlT1xe7chYgLaWTsLkGIwFmFeX0jPFGPm8dqzYvx5c/6rbhaOG+AieN2pLuUhtTenTMNF1QExsAO9xOkdpKr3t2Y3JamjY4DcOKGB0gUcMMvEhzGSYGaML13njISqIlA+THJTeucXdIZQiuxz7pyApbYzzaoM3qcNfM+KAcvK0inQIELkEkvPifuuXQpsrsoiNdnH9dBKn1ApioGagL11TwroiufpsKgYooAhDezMgleF3UEd5hTc1IlVxu9+xhFDRMWTh0JqaZOi5Sl7GLZ+q7I48v91Dku4pyJSTSqyKhV1wJ7Hx+oszFf5PHcS7VlaMlLrKj1Wqi4aXQRsOW3SBeNNgKJAG1epXaWqTQED7lWCGK4ng9RD0T99pzrQl5V3xoqtPV5ikfeQjjGooaByNkM2jbR0gJchGynYwspSlspKMoTFCqtuFAOWDs5FeBWBBoQVEx/I3OJoQGUzNbzBzdiCLj8N/APpCpMRiNNsJU4ozijgxmyBN3uYCx0NBQnnOAh6mvlqtUjtIrrJt16DuDHf1x16x5Q4r2Lxd2qtE9dHzNTLl3M0W1SVq445Iht5Btk+kTY+FOeeqqgVJ0ZCyq63pKZJj99inB1RDHOUF2OFBFurDFoyFGBVBYvCbEBwQlDhybWhZWcI8bSfdzvNNC3nyEZMbAMZ/vkseZoNCOStNVpnegUYS0lbd12Ai27JKJiDjYnpZbRPEKR1f9/SfOMrDVohh+tuLEpv7PW8SMw4ESIwCNz5+qN2JbVpOclRFgV3KvAxsT9/YZ2+mQTtG1q1k9fcLXuu3o3rSgbk8pMWI7RycGEsYCbR/BgcQrRdwnh4aAswN1IVzcKJ+fHBxiadNqPwk1yFSgeFIPsfEdA9UOU2NkuLxNGG7LoLKMCMLKg4I7MkQkDr86Ag9TT19xhwVkkB+tiGE9TbhQB29JW8zpleOU2xIe/mTPltQVMYXfzYS5+xYXz0TIyjTp77ktyGXku1KKBssV3CpLR+DxqdVGFXx2Ny2wygQ6SNpXeThQI7PdWWlQyF3gxCHlelaXHqqfzdDlefYZo8pq40c0Yk0dCcBD9n6OGM/3vKYuJ2MBmCU0i/iADpTVBNxEJ2lskem5oMSJymKXIbF3UmjJh+9VEUSYcbhbGNnDdETC+BV8N1PdkqWrZhtQskgaBg+QQwMYJ7aAtSF3JRlVQhfy4ZIEyKCPZpc1Xu4g7aiOyxrYat2Yxm7HeuAlI42hvOvrGhiVJvwp0DO/7GhL0lSBg1tq+4Ar5zNRqIF0myNnN5PuFi8zOlhTBHWraLYMCtcQA5DtNF6EOrkdByiolN4knv+HiZsAQkdRXSEGWwfGy8sLe5S2h5GDnEjesAOXYD49O6OJO79ClfC3t6G62pLiNd7c/bi6uq5aT7yJiwi32jUGM4qBCC5nrJ25ERY0sNZxYNjDA+USm5yJHAGc8M4xFQWsfHpNgw+tA43wwXI6GBuOQi8GGSd8o97y1k4AjWxsHg1fQQbLGNiHdFmhAfLBVY8ePXrl3j1iH9SRiY8epi5Xv5AYLKjj2S/zHX0D9zeT7sN1+QIjx2XTLKRIh/SZLDfsd9yPcyMdEt1d57iw64TSzZzTooqYCanHuJqJjaYmWY4Ch1aFyMlfDx2SYkUVnqFLSC1YldBjk0Pn6aKbU7GFMyVc26lraLftFMgNHUaQsiBjvTJhM4Xh2U4YZgpKspVQNcdI8kxvMiDDSB2ZyWAn7k/d+joaGaZEvG7iXIsVWsQZmWGjnaFqGGmyogiSVM0QYbs/5b6GnOBa8XafKrOLjgXWvu0ia9cfH+fPUPuNbgab6A1mlxVX6WDx7+goerUrMQzB1UYar5G5+2q7/vLyj36o+re/uf+7F5W8VjbO57RysGuAFBI7e6trLX3se52l6du3pQY4ZDqY70NJV1dghzom5A9Kaz7N1/nt8pct22nu1LoItfQu+13LegZ74xKlGQlR5EAupRIDMwrs6DFSvrA/fvTouU9/msqMK2X6zhep8wtKWQTN1HH1i/OMO5Ec+xRdMYrCbfBhsdzgcMA+T6BZfZec5+bCGJriPO3NxpJH4joez3zIJdWwLANKPGz9XFyLCFQHBnJD0GqIlXSoKZ6AxqUyQ2WW5dOxpVcsnc5ltDNjHg9UdvTTfa+cXgKIP9SWhN7aU8NWtozxTHkOgm2QNgeoPdcYOcDJpYBYNE3cYRsO69YVsK3B2XvAnV+liDO0A2t9r5hBBOLEbNb5IrRO1iOmGTByqBNtSvLuYLuSZ9V6jdc3nrl1rbKKbrWUrjhZ3vz0/p9/vvoTCP7l2T/5rf1/iZ/Du5Tw0G9Rg4gg43/GBvLA+SNDAI2lXZtrCZezdbkSVIsZv23EGbGItyTsA5DZryy2foI9Kebpe9DUggrJzwyZ1lf11N6dGyNVW1yNgSodYyQrxLDlaiU/cxk7Q2mLmTpdpG7vn+GL1DH3SLci6oMGsijliEbTHXN1uWO4ekYLIXtEEfhw/QzKchgiLFk3cugImULIxXj3kyhzfRiS3lMnHsYnWZE7lPbIbjcTQFYkTdLjCiV3WxvVLKnvFNFAQQrrSKjt5azn6CKNgmH+UUUvf4INzyaJ/WrSgGjgjnA4gWUfHgg1LPbPvt0xo65mNimZjm8rn8F9N5h0Yxm2ASxcgttKKKHDykrZ82CU26uvr69TZNc0NIjTkZylqCRprWi1QG2xDZLW63WMwrQ1vyckMAwguV09+ZXZP35+9r7wvly987Ozr/7B/m8atKh1spxOLB5qR2WyA0jMBCdgxxSnOEyXnMx57CFZ9gsZQ+zGCbLD4bLGdZvjeCGt6Jy9SCr/1w8645vop0kbRiWuHqCr1w9XlxftZyVjpr6t+GZxo3i+2C/x/qNhxgtXfVrwpMhIhUrpRF5r2cNEFTSsMdihoQUfNsQLGyMb5bpeH0AXaKBLCr30XQSOhG5J1ocOcZe80fXWigl1QTSlnkUlbhxteFzAMrbMEmdttpLpzZqEoxaHpLs4ggdQQwGfSP8XoaPTK8E2QzJhvH0jyIXsAHey7sImhpbLfCmfmGMajmhjtNgDGec/SjXIusS0z+IHW5uZ/jwWiwMMbHIAge0FfMCE5Hhkuhx3g+xibyuzuW339vRGAx/NZZ3YZ2quViu6VCaJa7SuuJY3qu/83Oz3zqqHLtkPVd94p/rMd/ZfoEoYkIzUdhRXWGeewI0HyxDRxZFr2KYHFGTpGSbXwSEJ3pvmKsMUSBeKBxn5xTg6C1lzfWuix2InkZAg+e8P464AhkZuynaKKyWtPIUb+eo4BIYf/EJnMJzGKL8X+xU+yYtptnVRkT97N3HeuAGkV4lPQCnCOaJ6tVAomJqArGpYPvpWR46tutTcB51lMVK9w4guNGURx0X1toouMpgV99LHCCQYan8Lhqng4a/VGBOc3sYr7unk3ZRpidrqwuyCulakWKx1fbfeqVvaqVlIo3NKM77TjKpH+WfBA4Xw8gy9icdEux7Ym80jFKdl42AFvp2cJN3SbD8XCrKN7VMpWugWUbdMDK1WhBI6Ym7+37l9Z31zc3wiV7o3kHO3trlt9019jaPcA0zc01bcWXA+tBR78N6XL1R/8XOz31lWuGinsS2qDWbzH1b3HlbPaUNW6JUrXhgQtbjASKvokrM3TbBoJmM2NFGQBWEJnCRjmqhZkxt7F1qjoXVQA9d+ndlLxU25fa1lSFJFETfi7IU1dY737vzq8Xa+wWQdF6m319Ql8jQQUD4M2RK5KAqiQWfPQ7Ql83Cqw7ZG1AZC7FcqzpPxdKgj+7VYCquhy5O23tJZvjF7K3OMjF5eNbgRtV42EHC4iRAhVilxRjv8ZeuKi+XWARekBqQqqEAbVxEB/VF/l5KhhAvYEeKOLvanWM9OH+ElxUsFd6PwIwv4062nM53+NRpKH0DLdBv8ZoNtgTPWg0Xb93cSVHV8vODLcj1JwO3GdHzG4jyXDw6vLyHnmFemcxjG0FL3pK7bvwloXOP+8BFWmoXFgU1yercUmk4hvOKecqmMkUDQBmWJb4Fk+h4Pm97/dPUvfnH2z9qzdmE5qZ780uy3V9UWOOXPF1XoGNAQjkLCOsVIxKmZS4TI2TrZxjUQNEqvydHBTELD4DyTBbgd3ziUAnxc3zbQpg+cQMMnAUnzJMh2s+12vt7Ob/C3m9NPW7P9YntdPX/nxcVuRbN28kR4Y0emaQoL6K+VDmWS0JLXlRyarkNLHtlz0rkIkBr8J9DKjCQR/dwkWSbp10dbop1HyIA66halNyuTUnL6zUStW5HpGWoMx7o9W+sWULeoroAna6q8UpmbU63PSTeQNqLKbS5BnoFtalLQ0NAmeUo1knCwTQs09htj7N61rV13iJx1LRhTVqAWMO8l3sCt4Gxh3AWO+ZbCl1Z1KnM2i44yuESbnxnq/3SYb0OCrfDtAXtBWBUjlxSQWMsnZs/X11hszvV8jRqCneSp63tL8P/V1VUvWYNgwAou3KGbJDt/nlWXPzf7Pz5b/Zk2BguvVN/9qdnX/i+62H3aDT4c7MmgZWywWk2YvTBhbheZugVlDqvkrCuMLmbJDE8rJOASbS4MT++YIuk28qXLc26cbWgrJYmM2iEJu9lGH6ZO6Om1R/7s/OHDh7dv3w5x+3XiMkmgqdzn6MxS17bHJrqxtz+x2ZP1GZAtsUIcs0oUJTXk1KM5WUJwSMZB8tBVrArLDl+uinZZnIl6kwkwgj2rFW2WATVW5gBWn2XExJ2/qgMnslPRQrzB6yv6K3Zsu6QBLw4wblB/dGFWuhBBtDHEMKaujl2vFNjlLc7QycykojXa7kPSYm0h+tF1nkLvMFc8dW2IsIE3yLUrWul2nyv+2abXO0c55LCVVpsEqgd4tTpaLDJnz03sJJh87QluEoWOMHG/zp24SwY31aliijUleb2RbSHDblcXf3v2T16q3q1Ju0s/XP2b96rXvln9SDdJsZatfTCFgkoUXfd1dgCAs4SYGLjmYOdasoMnuMauRFQJZCbogGuzArpEC5pi+BOEJ5IcUp0g4iDT1S/0R1/AkPlwwWKO55pEQW8269WqvvSrF6CjLiq3V1AagVVHoZNyFx97wDSC0hLbfRebU8+k+nNQBqMjI6NoMx99jmy3hxkSnnHSwyCSIcvooi5PvjycZ+wgk6sxaeJu4LBs6gEEjbqz4MNo3dcpcq2K0h/Uq9ydAVA2Rs5Fa4TdRy2mxmRC4+BegUUIIlZZzLqngY/oa566lGRMfSpMIh9JROY2fClA0my2EbfgaViUgb+TH82H7AgteCPdVbMLdkaDSuAl5ALeDX7N0CitlsvdFi8nH7d1erlH7NHxcZwiKFgzQ06BGvQ66lGhn6q++7dmv3lUXUap6sZ5tf3J2Vd/sH/9cXWnri1Rgv14BdXFgwfX73xv+/3v7548wXN28EvQ/uhof/v20ac+ffvTnz69c6fx8D6rl7DDOwDPqWVH94SQWwnYI1VkPSfoZ4ewTJHUcacbo47CLdjt0Ga7ui+V3WMcVAZAVMrU6hApnqnXvRWXvrTX1KM2VnhBMH5ki9O0W8Wz42PXlhyskWSWfq1BlUOiH51CrlKWTx+eOpdGy7UNWpVdMNlKzuQOmCqAUacSP206oDM4S1hipMFhdGbTjCD3GfEHc45qzMTVmrg7qcS4BBukGkgWmdVzMISicGp1Ih+fvJkA5zvX95L1VsJ+BGuC9C4SJ+yGxIJ+OvZ02dldH7CTl44C9b4QDTdnOaX9IPCmv/jSpzou1BVowG/4SLE3as3prRfMfLE4v8h8eWpA6MBvQXgHkwjzbA9ooDOrTDK5kdxQxz1I366ElV+p/vWPzf7lqsp7COZZ9finZ1/96v4/2lSLttgBNZv1+uHbb23++E+W3/7WyYP3n79Zy305WDDF33aGmwdnN/P5B0dHN6+9uvjiF+9+6UvP37vPD903rjY5bxyX4r+wmWaSNPJEm6yfzOZAap8Nm+XUcnfXzsphT36/Xhl0jjHxYll18BMvqOtMfTbHmvq++cKyzJ53c32d9OoGuLm1Scg0Eq32whXIKeiyVz3ZYQ5KnGJBlZLCPLx2KuhN85DbwjYaZ3JyJ/WFTqPC8jtr6fzRi6OTO7FB0ZGmoerMdE7H+YjVquIg6BKd4JLR4yA51tYbDhgZNVzqv1JlgS2QNE5SsIe4QiC4OhX0g+O5YHtSZREhvZp4wsJU1OlIp/3fy/rsEAxyFc/PDF4exwm+DbdIHCSX3DKYcZhPk0ZnX7ScRwS5mJwsBl5KGMUgzrrT19173HDg0AshGkIclXQKdLo8m2lHOOaxqeHwRIvH1c2PV1//67M/iFJ1Nn62+vMfm732jf1PdlKkNeD3jYdvvbn+/d+/+82/OF2vF7jYAdMzeiIfMnKOz92s2szxqL7qZLc/vb65fusvr97+7nv/5x+88yM//Om/9TdfuHdf8z9NYRIV3ItBxvp34r7BaDl8odwzg51MgSjSnJ+aJlpIwiVEhG7kN5MMbTREN87Rybx7TFOJFW8+Ag99w5ljTb1qztRb0qSjE0OCb3peVKzCIapjEipK6vC4LFLuYFTCSIFY7agFRfTHqFCPP1EdYR/fZNSxDQPUBVki/qjVselyOB5FTILJkkO4cyScIewpfSDmnSFtciaKW0uPg2zaFkyVbvVN5m66p9oiwx5McK11yyOsy/RXTFPx3Lc9nM9dbKicSvWEymf3mEnPcJsFFx1+6fxqTxnkgGZvKBg7chQyyp4kDuO33tmDwhfsNf5hyNO4jo96rlfpco4XUcSqF6Anar0xT5WxouxXVIl7tMtnqTuprn959k9fqf7SMyDr8CvV1x5Ur36v4tfFZ3Fa4svHjx9/7V8894d/eO/JkyVuj8ZcmX4/oX/YAzx+YMbrWhfVDHM3LLRiGR5/WORf3dyc//GfvPntb7//i7/4+Z/6qdSrHdISQKzDVwcYIMZYeyfbsxpkC/U/Ai4b9jbxuVBQPWcLyYvmVDGFooXgeUNYUwMvqNNF6na6ijV1fHs76po0N7nro1R1+/31jf/Y01qKV4omDxoBjuF5bBLDVmV3BTmJN6DQ8FOFrUdRbJGKqF0saPSHDD+l1EUMJhX6lZL7g6gejaBXQMSoXt6BBMAXRkejwCT2dGociCDMVoPqBoJLZYBwKMhuuWGLxtbmxMO1rVwUh3oqETn63XANPC40JBhZZgAL2QAvjdAYkli2LsMbfFYz2okN6Q/gnAYZUsqaP7E0ANShpAHfJjx5oGSAk76ZpL4FiZ0TD40LMO5LgX9+ft48+8upO85atyaqe7V696dnv/9S9U7NOaiEi91/Yfbbv739Lx7NM27vU1Ufv/XW7p/95mtvv3W83ix01m47O0/caRqPtXb8ARo2Z0diZheXj373f/v6X/zFj/7af3L3hRdVcmeBh5i6tSd4GFl4qThn2K6FJ5doDHM2+rZg8j9un8MzqChKRLtROEhOOhNwQR0PaYYJM/UdhRffzmhiOqtw0Q/uaRjx/GLHGnFjBCBek/z83bsOx6ii+tAF2CtRzFMyCFE5WmkKrXQgn/F4AYJOLk9KM9m8xvih6BcBLVvirJ2tnjkkVpFw/DAwggb1tlN0igo2ePKDNE+rkruDMdADPpFJtQ+zcnSwNRpK/krrikEPB/Kh0anlujInK9dm8uhFfc7+tXWOt40Xaynjnb+2nqI1lH69GweM+qMFb/zCdhJs/TOiar+1ZSdpbLMNq8mPCWP1lTF254PSgAcm9CvZdPCKQffFljgmhNPr9LG34JeAEpYBgOztcCvX3nQswmlauoWIHNPUShpCDyN0YzqJ++1bt+H+MTkNOSS+YwP416p3+QEyY2ftouGsevTz+9892WY8hwfGwcKPvvPt5W/8z6+9+Z2zm5sj3CC42y13eyzDYDUd6+u0zjrDHy+xUwGL7lSPH1jpb1bhVgBcMHGyr+7sdmff+tY3/uE//OiDB/XA4UYoUu7wklst0Yu41CVOKdvYUxpyClBft3+m+0t9irTxNKSaIjJeUpoEzNTn6011s5ndbGc3uBgG0/TF/mixO8InvwKpzKxdrYlE8Mnjx8v8O1NVclcBGiW4QQJuEgL6tKE3hSBLpFLYQSBiI5SmKdId0JSwiQDpxQnkPgkxOn+eOT41nxbR/VGPi/uxtQnaNa78duszVWM6vEQxGWABCOzSAnJSRZjcYogUn8V/+p/953hFeSq7Syedxq2ZqGyzDfaS+fJfQqWfRVVDA/7gI0mLorLjwpyrQi2h09XEAURDf9jsqGVpnb1g0E+nxStCyiG3tKxRu8k4G39jpgTF/SQ3dG/kpwNuDLCkRspDHqkFBKFxweOY8qKkxn5vdagj06oK63Bnp6eLOd3x5/71i+2gwK0m1AJZvBF61wWmmnaXl5dnZ2f8QH2nNrMI8dL323xH1fpTs++9t/80JsYfVS/tq+VphTtxrWVthoSaO/OHm+3J+7v7eEx2kBwTVLce2D/+y7eP/tFvvPzu9482G1whgxuVeKYOp8xncPsCn3A/lWkFlr0lDsMnBjW2lxbj8Y9yB2jPL9789rde/tKX7CtvXYVjyxKtLpf2SufuL0MfaM2Y7GJqS6AE4c3u2yRFa9jHgwHGTcHUfDdb7+dbXKqOTxBjdr6olrivlB4CwwGOSxjfaiNIklyX4geuW7duLZetR1yMVulpdE8B3KRJPVoTC1CZosjFmKfAHe+iZWpEZxTtfTrc/u9J7WM17dAlG03fWXWE0VPhHUYYn3ITI3wKNsCjwzMm2V46DZjRD48OK9/lkg0JEZILeJIinyCRLsW0qJti6xK72++WiwP7R6/ONEBhm2OeFPnTqZ3CJ5PJhN16Dg4p4WHFmabS6DamU4xiDhnYWzc2MtoFrCaBMFaslda771fUPa8V4cfHx+v1zeDTuUkBayjFHz/+07eXftOWywUGtfQbyq0Sfw+dq+rm9dk718d3Hm9uXW9W9HiW/fymOvp3+x8D9R/vv6w8mP2+Xr29rNZnsyev8KPcX62+e1xdoh6VShYpfGX1tffXr727fX2LKRmyn8+tSi8JoIdPPvxw8Y//l5fsrB2u4ck5FtUxl1vQH1Zg4TESRCvuvJHraNyXy9zxdnpM+qpquTdL76f7/foH7/3L3/hHP/9f/1enZ2eGqegOKCiICZ2Z0NMogY3MZg4NvRa4vftD9CRq7BaT0SLQqPum2hgWzjN13JLA39eQ9LSgvuLQWa9YPihiX43VaOX17NWlFBPG+OTJk5dfeaWHbVCz4BLWHe6wpW2QoEwmxTi5PoYjmEwQYarF6MCn06WtzgQTIpfVd/QLyJVyiCpW12uMdN2YiL9ibSXj47umHsHo/Ff7fuDEtBbnK0o8pnRsL6WaFBXzaiNZJjKtqIM88Yl2lyJj/HJ2kqIITpqdEKnLVMqmSeR0u5kHJ+BwtynG59G56hqYUEaWpuYqo+dQ1hyS5N1uC1iQoTHA7VV5EZFWzxyEKe5VvMH09LR//mfxsxL2A88JPG3UShpp8hJoEgvlE1bhCvvUJ124nK3y/eWHT/bP7Tar57YfnR1vMBPeVrvj+Wxd7a4vj29OTh5fn252y02FS1Rm36veIAH76k+rv0EFNvOoun65+j4q783ePa3O8U3gU9VbaMSzI1sT+v0vrX7rn67/zuX6ZLdcRiYN6/X66n/97dfe/f5qw9e186ydFtex6rlazXDdAgqYu1OA6Mfx3XaHayvoj5dE5BOzdjxuBnN6/C3wq4FcNrPbPfn2t//v//33fvZXf5V/LCEcZbdw2vAElRWZ1GP4PYFONMxoRDzKyOtXa/oiCFM14tsMnvljZ+p0ksPtpCt+KpA/U2+rl9ECaU8K5aBNVLSGtYhCXE2/wLfq8eKNOEeQi8XV6NZbcjNw2EPdm3TS4/SCaGGrQgrTBaVR1kHkMILJiWRqGjVU9UEHKsgFQPFdWZBBi/ssaphf8kAUB20qqcaRxZ3RDaHT1l9ETNxwuGWXeeDEPdsPBozMOMHNApz0dG2arpxtdjlTvMQVS8QLqoTPrXqUPvQ7LM9KseVpSQAyj0YLaubot+ieFQBj7JDRMCTBYDeDieS/JMFTdQSUd9scwhGtw1p71/2pBLzOBJlqWOR23yk7wUS8DqZLdafYjgYsmb83f+lmjavB7zSe0j6rTpc3J+vL51aPbx3d4IsCrlRZnFRXH1f7Oyfn1yeX10eb5ep6c3RTHb+DCf0MN7G+oUqA/rnq45PqYlFtPzP7DupfqB48V30E9L+0+p3fW/8aEGy7rz149Cd/8tKf/tnR9fVij4ubaa2dZu2Ysh8dz06OZ8fHNHdfLDAtp+uL8CYsXA692cDI2XZLr8XElx+ZwfPoAqV4EM1iT3+rfXVrt3vvj77+zpd//NOf+5waXLaA8xDFn4YAiTcvphodUlNWIeUYaUtInpZizx6IaG8eDREAGfVzghhoRYM8+4VuGyaRIEK0lvgy1ZaeWEOKoLJ5yk/kHURmFN6skx8p46sh/6hDrZ88HkAXAAAgAElEQVQC7rJ84ssgxjaXCrbc/fu2EMkcaKQIcXfpl2IpkgyAZN0oCViNVFql2t5XcETVpG1MTTJoBAkQ4hs+CmJAzV605NnSNKSoJk+Yp9hrneKQ45gl2O25iVEYNHGPpBU18SxADFcMdSELkUOsEpy6eHFkyAav0vCIJF6oje43hjjMIGFwIbv72eI+mLq1bR91SUpE1lwjUE90nM+mM1Q1T6fCk0waacCvN7YBtaZXtr1Wkw4rQQEnz2DJuYxA4g43rtVyqQxqnFMzjsgHpIV94dInliMaVcLRqucZ1UrZWbCOeGH74NvV5yWInsmXm6NL3Nu5weX8VswlfnfGU9KvVpubu8cXx8fXuHvw5PZuc7nHqvzuZPXo4ni9P7ranK53y8fV8/gD5/erz1h+eGZ/t/pwvVrO19sFvn/wNTPaKoWLh4+WX/va6Tme/Lid48IdXNqOJc/Van5yMjs5nZ+dzlDAJH6JS2CwiktfYircKHx9s2M7Z3vM3fFUdwkLpSJP4rH6jvtW97hWHhdknF1e/Zvf+Z37v/7rqQ+I9EwMHjp5QDnASYA67gzW3UHG7MqwNAlfV7omKwkLD7IzNDsM0qP0cYU6YsK3agA3PfgFM/UMgUEtjUpVSR4uKrmhpj6APzlJaKRL0Uh0zgaWfCsNT18o8wU7hrlF0ceWN9ZBXZpQudMAdQLABx0gTgGZeKtJ03BgQ2+nwgZVcDoBFeCGOpq+B63qVtuUnnTUEJaYOn2CGzL7iJ+FdkTXjaxbTjRvyMQdbjJ54nZFyTjzmag9nazW6fIkpqvLkljGDbsp3vQyRuyxM/4+6wLtzarmUaLl05O1zDKDjM0Kp/O3SC3N9FY6GjqnmA7N2KL1AclBJ6B8lb4wVnAevyZk2/N5ghKoAdDtlqSaBwSczj/88MOXXn7FdoQEWWkkpLE55Hl8+M6Aa9y9ys7Dbh/RnPhkvr/iUBKoblIrHcvZ59en1f70I0zo+SExs0viW823mM2vZuu7R5cvnj08xsU3t7cXF8vlbnZ9vLo8P7renT65PgX7x9VLELZdLRd4vGNr7g7g59/4xivvvoMmWmuXiS9P3DFfn9+6Nb91Nj89neMh+ktc/1Lt4Yebm/2CJvG01g4U8uRAFKjKzKYJGFwKgfv9ck9P/P7gO2++8+Zbb/zQFy2y/D3Jrp2GfuC7j2rIomgX8Zny7WhwmORBXVRrg2fgwZ5n6jvcRkq5Qz9rLPHmo8Iz9aBtAjLaR4J8Ayqvr6/oIU7wJi0a8LeQlmMNfpZOdIU2o1J0F5IZEaOWE8x8FAOcYLWAlRLIHkYcyHnWwKDE7aYGnRwAFmlBv2WddPm7CtBCgA+jR7C2UakkkNQQxs6U1kZ9gztwoAIDbYlVfaNPoph0MhNKMMDRDDydt02ZMnF3hmF4F6OtihmtXiUNKwwIeaKi9p2prAsfBr3salckynXJamFaG6jStmkKKRoVpeQeWMgLxEkt0uOTrXsaOaMAkq2MEYoXGLk4gb3QxMVT95iQ8m0wYNB5ZZgl5AQ+YUuPMEMROwH3hhYYmTrMgmSo7hr4cHX7o0ePatahgcf16IuF+QIQ0SjTMj13kVXO+U7MWO8W691pVZ0+vLEPa39E3Wcx3y4ut7cW18/ffvji6YNby82ansi9WB3vnlwcbdfLh5d3z3cnimW73qz+4s9XV1f0FlQ5ndOl7Qu6POb4GFP2xa1bi7MzOqTJ+n6H5fYFvh1g0r7HdTLVdlPN8JUCrHSFhrWcxMOhsAefuPwG54PjzeabX//X6RN3K4qGBYihFAi4gRuZoC5x/nDKDI2TykorsHFIn878SRPTprIzdTk70v0DC1ynTjdl8DaBxrYNtsaCxDEXbX3pPe5joccW8SaKvNUuVEp9ac21SjgWBxNqaZouivqiKUYZTnIBp3hTUuKRYU2AGelBwSbprS0zuOfCXvgVF9A0bO/gaIkwFS550AJl1FZh0UMlaBeCNK7GNotfMzwovqTE4xFpENDQmrgjYuIVmq5zEQgbZ6OedZKAkgJVjUg1DgoI90VQBhB8cQB9QiMrHaQ5gymD1Dd64HGXRqq3IxD5AxslgpPuXZxCHPukHBvOHZM8RRufG0SwpEKSEwBwBMxh3jFxmsAJJFlHBpJPQ7rsgqexk5N6xknkZbeu5JlVy9USC4FmBBuh9Pbs6mJ/SwUArMDkHgHglAhotYZkhwv8m90Cf9frow+v7tSK6OoXzPg2nzl774u3/tXieHG5PPnD7/8ECC4/eHD63e8ucLU65p3UgeiCC7oVFdfGYMX9FH+n9IerZXCNO2hwaTvuHMSUEpe5r5b4JkJX89CoRj+Kq0YusECavtO6+2o2+8Gf/zme6YmfL5pk5og7hEjocoLrEF+XKxMgRNpgT7rS0srkuWDSprELFc/U6V20QAqXws94pg9fpuXBZXKjkrtNjpbhtKSRB3ApDBfUzYn7pO/efZ57hCGix7BKD7Hh7OYu03IAmG1D29F0nQC/F/e5woQxhYS7PbQNkUY2KAIu+0ifxhTQYajTnTty3dKTAjWfw8JFaemxr2aqSxGWkLaacYoSXBePVC9B3Cr8OkpDOYARbA6XYeiKVFzeJK3WukmE00nY2UiXuMI6IJIPDl+kqAr6JPXkekRFiaZmx0PaUdctIdiXYcLpV095DCCAlwSH5zaSBGKowzWJO0K4NWWkMVGvDLVFosZDQm0ZyXT8UDegRFddtKfKiSY3JCUeQDTNlUL2YOpwfoEHq4/dXpi9d764Q2ocSc5QWxIdJjt4yMydCvP3j/A0R5wqcY3Fh09uf6v6hfmH2y985m0x4eqb37x9fo6L1CmTyS6OCH7cwFNocEPq0dEcf1h6Xx1hGZ5uN0NgllusvssDIilMFCm+Ew1nYxYhn+xOKKFGAMP17rvHj7//9l/qojuPDKyRTWHHxz0AYneLEUsYHd+6jFOVc3sKuwbXqeMidYHGa+q78BebsNGcr4eHiZhS5EKdJWxnXy3LI6IH7z945ZV7vuQaJkhice/Tk9EuNhzUt/AnTWpxxwg7t5x7I7AVpuiMUJZqgkZscKz8loJyWzIPDlTNjW0CbyiwAtqEzRYR201lqdP2KXLUUBBrOU28oXIzMOgrV1ovgUvcLvOjx9JlwJ2h4LXlDqsJ+pfGnWHimlwih0NiijI1bwpvHjUlxI6G8qnMiV2rekzB9DcahhF+sn7KwPran5ljxq/WkAsoKUwwR8aUpA1J3pFqFU1iwfYINZa9kJwNBLKVu5dXuL573Bb1AkLkjpKqCZm86n4ki5IFC9wjzMhwtt88uDitM4EYoBFIC3STBR4rWV3frq7Oqsd44vq22m9myyfVrberz/Kb62vrdrhXFVewbLY38MZbb855uZ0m2PA5Zg6IEdbn+Q+r7DxBp7cv0Uo8ppj0xBk8qoSzmYj5bEQQcBkg5vUEVyoJFyHjTZ5UM5u9/cf/72e/+AUaFWgj+LVZE5SgIxjQCVQZkYyKvCDwPEX0qwTdGSCXS5Hv6jV1jzTn0MAES3L/yhEforU4gzBDDK06ThWphRCVc+e557Ts8Ug9BRQNh0JKStnULqs8I7MPW35QmIT0kDBlyOXOmY0inwHQsAGjdykUJHFLXGLvuGEGHk+KuNOOSpxEYUKPb/ihGEqjopXhma71tp3C4KY3vFE35ZYgPYe7dalMrr5nld56Wf2BQYQcw84RD3X6CQma/W4C0dcpMsVNamoKcQ4Nn+mVQbLLdAzuetp0mMIoJw010TiXehp3TTIC/0f1tX5bWOsh8dIIS/OxmE5KVccPZmYn/uiHFKBoK8XTXTJOZjCWbGpuWtMBhZQ2hk3DvjpKfrAMT1itVskEUTY7O7252t+umt8+0GWgccCGmfqt6vysujmpznHBCmbqV9XJeXX2oPoMrkGPC8SKOeG8vFq89z7uLmUDOIDsMzWHTqpY/8MfHvuIaTsm59ut+aOJOv7ziZcK1AY5snpMc3f2A0SJNJa+P3//fdRP9ED3IGQOKH0ZyTuDBWWlVlLQOVGBHZMv/BCB56nDEfiPNfX5PGtNPU2p5FlG70gTG6cyOFPHO1hn5HE0wjlK16RFN6P0gJNaGc7FevFz1MC+xtoNPJ52SJRqCijkhV3Vpyi/XZWyTtI86ea6VMp0WdSQTRLLNdgt+xKNO6GSz9udpzWbrj5//rFrTb/UguF2FSeYnTNxJ+dlik+wIC6xdxYiGujUwyMuf1KdFWv3WpFgEtIk4+WLqiFBcpSkhCB2hAOfHRGYx6mu/uSM2jyocYIsCtihJx9yh1Upe0UfYCtaxerUxQdT62KgjKg7B5nA/9mWyQw6PT3F89TDjxQMKg1WujBCZRvURpuGulGLyZgJg/hCnIDPgOJ5tVtt1ld73FHqb6RR3Bngq4mX1fZO9fhWdbOsLvF2+k21u6huP6rOflC9KCegmjRawoNIHu+ON6vl9vF6jkuAMOfGdSx4SgmdyWjTyTo9+RF/6zUt/fHsfLNe4w/1O9z1is8t/rY7vJCJ/kBBcmTurgW4Br/906r/bndzcQF6CMMWtbFkI+siXAdQim8u+JZD4eRnv+C3Cb6jFA/lL4moS5ZFWo9MXZTF6q1Pg+5FpSjiaFvSbt14dhP6eHe7bTGSSHy/UMs0cu/ozFeqjuCTRnrqj1I6FLAoFZOLu9fxRMONoohGHrgoe/qe7lHHKRZbJ1JfaqsPtyoc6Z1FkVr7wZrhMQyS7cnIPsyZuGcL72Tw/dxJ6Dc4PpKiOavYE/BgwQ1FjhauLyO1oaLAAVvpmkpmkiMyzM0gLWCxKwKGl1POfQvijFBJCm/CgUM+e7hGTF0mkw4yCxEglAyEET/z88NMyR2AbTOinL/bfoMOzCmNJiia4c0/69UR3l407dZ2r7yAyc5DRDsZZtOh3wu3Krw9aX7xpOMKZgqqD2pVbZ+vPsaa+hx/++XNbP+kuvt+9fxN9UrWTN2T+9zq4uYKZsxuttv9+gZgaarNn1iGp+V3XmLHfJ2e135FT6/EbJtewITp+Wazvbqiv5ub3Xq9W2MGv8EzInnevqVvADxxJ4HoGVzGNBazdlFxc36OydlytWp72DOy9CEiBZ3l5+5YUN/Rs/fhTkpVvsxoQRnLS6XkVuovpdHE5E2FNKYTENm/Lg2NEbZ7uPWR8s3NTc6aK4ln/2YritjQ08SQepUKgYii4Oc6wjMiTanHNPJQTO5F2qvFdQWI456Q6IMFGyjjxL2qEwmsFugkDnvY5m5141aF5WFBOLB7qdcj8KkWVOKvLaldY4V37lV+J0VfQ87EPbNvq+oBwISX4sMnFetX14LBUtWuzsKEojt19jRIpioRWchWhkyVrAi1KL8W0qiU/JkotH1h+9bTQvO09EoH0UGH3CBdhD/d3jJt4Bg/bn7kCREdwLANHmYy/QbI0CWf3CX2F+cXUuMoz4vP89UHlyd3qieOgGYRjl3tN6/M3j/BlRUVLk1Z4AWSD6sXP6rubqulzAObHOlHDVPP7jx++PgFMK+vLnmeTqHGH6bdsGGO6Tdm5/yKpWp5hSVjTOVxrypd+L7f08T9+npzebm9ut5e32wxd5eld7PizsvttL5O0mi+LmWewUMjLqcRo9XD6RhGU1Iit4KYLZW/1uAhmPwdhZKDHtHIM/WQKNJZQGlIdKTOIAUF649Qjm2CP1WExTpc6WazPsYbA3I2ATg+rDk6DUDBrh5uuSJLZD9xjRS0lFaH2Aw6xqZIexWPdAUUYYMQWoDnrVdjCQJoIjFivDkYKNdGx36xhBjCodLcXsOVObMrleIXavmOTJ8oepw8cQcAB05UZt3o2Vc3hEpmfDVfaeAfdx0gS1JIekqdVUKxseUUvuI0TraQbNjS4fvxVo6XMAg9hzeJkxKPZyv62eGLJGlFiVJ8h0EgHWvcOuogKosKdoRp2UGuGtRh4wb0dAoJEIu4dXZGl3VMufGobZBjUjvDXZi04RWiY59EeVzNHn/UGBhx8cxxtXmh+ui0usF0EI+dXM+2D/b3LmbDdBmze91zcnTzPVwYX1XL45MrvFKJZ9WYauMsQ+7FlR64XRUT98UlbsrBbB2PgKTXpvLEnS6PubnB3J0W3fGJ8hpvdeJLZWi1nqbs9JyUGV0vssWLVnEn5oy+iGCqi38nt+/o5YKUw/ztqNfgggT5SpHx+KWJZ+psxwwXPDoPU0+xTZSCEoUU+iI0oqu4h7mD1Aa2EFHFYKWPHz1+5d69WnpyCVrFsJY9ySLyCRkpfp3iyeWhgisAycMw+GDpxFrjHpZW8WKRKEAINog97PTddNI42Hiy9LuiFTgZF4zSuPQpWxvnp5iiFoAgceJox6d0Oa+Dg+cgdDZyuanszFOC2iapRPId8pYsgkmeMLMyGVW6ne26qBR88n8pWaPlcMwhBRaJXeILMXCkmd1uTTR7oP6hc+h6WGFvYHCsA9Vrymiwta5E73DuwmbYeXxy8tGHH96t7iaz9hByHwGN5ATOhXSqsDz01Ui34+Pkm1OVxylcPHn43cdvfe7L9++8d/5c9fi4WtMUd390Nas+ql56R57VLfQ070k5I6udjpq04gm/Mgm0y9NTXHiEGycxsdabwmjyvtnM8XpU+AJnTX5VqjxbBlGgK9oxd8da+7VcLXNDK+603L7Hram00I4/uqqKZ/CQPEP9bI1rnCjLZkd4JDyeVGM31kB6bMUh9n1KkWq4+kWu7kFqUPegNfWRP3kwRknjQ4C0OvrAWrrovh4x5EQSJUajBHQA2Kury/AdLH0axyhNkF2TeK6g5MB/rj1kEjseJui1fVOWVKkowaHnjSmUQ4soesan78NdQacZc6rRQMqxHg5zrLK7J7K4qMSJOwSq8IDAWBthVXuIEP+ZXpm00JYcaWoTp9X0iURcc67eS1PqUNkpCKrMiZDPFLwuG7AtUOUIK1HsiW0JFS0ZrJOzQrQbGwCWZg1m04KtKLE3mhJFlTGBAxzXyL4AiX5XJR6MgzWXU6wru0sghyzHm92kaMkUHpeFDMdJ/fHjx/dffTVK2d1IbrAuYT9YT4ihvrloRbcVdy3wutDdTleLu3WEW1YPvvGzL3/3zTf3t87ufFC9dFXxZfq+QsMrJ6pGmMJSB9Zebnl8xr3yq9X6uTubB+/jGBN3MgfDFD5x7fsNPSuSZrCYuOM6Gawy84o7Xc5Od6byuvsN7lJdY7md1ttxFQ34+I+fd7jHLaj0SMqKZu03s9kGeOazO/fve7imBhv0kShFExWAmR78wjN1yg6EHVCXI2fqEb2cdR2xD7KNqxSHayanCNNZiJgqElIYXRpxchbvajXq6zG0DwDr2twuqytEeBgO17KHiaotZKIaB+yh9LrzDDs2ToTOFQukCpagcl66BBOV40q93BhoA+WLnzNyrPLDeOtTmWoOVAWkK3mzAKWJE/ewuU1pcqatDdJJgxY8+vghBPla4wyJrX1yeRWthGYz+TCiNIG1kIavz9xE1BEyx6AIVYEmZyhhaewZcU8JfydZ2HLnATRDBZ8qGqpMVzd1GoNhfSUMHbJ8uA0Twlwja4FEetAi53nqduAjV5EL5F+OKRgxWS/9ID5m4r67/5P/z9XZy597fvbw5rh670H14nmFp2d0Oo7xll2Kts6oqpPlWry5PDraPP/CGq9UsqZgMRxXtM/x4BeclfGHWTueKkOvW5rDGrgCS+606M7Td7renWftmPZu+JIbuSQGb2HFfB01a5q1z9bz2Q0e/b7AUvvi9O7d9kmInVwWbCzGPFPHzwBAQ6+dou8Tk83U23YwfMTicHjFBqs3POnR5JDoyGfb+NwakZMOFi/WzVURpM/V6wlxvZHuCktJ3LbsCZ7k0Ohio6fQW3sD5nsKRCnXT4KtJVT0i0k0euoprkVZsEJB49dIV6PWF9TlilKwBq8HNnD2cKpwkg5vnQ0gT5m412d/R5ssiaFCpFMLt7okYXMmrx1nwkBumqbXGwUub/IBhtrPtaBPbAkZXEMin8IdtJOtLtmaA+0dxU5xcuWUGzhLyMa9fOqhxEJKupjDEpfZI86IfuSXdPVEPU2vc2N4WFgvnrBCS8sR7d0gSfXx6a0fujf/1ual96sXV9XmleqDV6v3n1Rn71Uvw5UhXookZXsNIUQVq5PR3lBAjIrabefSa/Ab4OqNN66/8W+XeAAbLz0jmbDiTnpx5S7NbHF193a/wAyc35BKc17+BkPPfuT/WGh31tpx47CssqOAJXb8YeJ+M5vfLGjajtn/3fv3gybDNgaLxqA3gkzplbhI3b6gFOJx50KFR2Lys19whB8Ihjs53QaXkhSOC64rLb0sbia8bnK4uZEuK51S3NuLFyl1dussXWwvpcIUA+L06hAkSAp9tzTiFmnj5HRrCLawslJ6U70hCEFNIxXMmqLzBtDWag9y96p6gzLD9h2xIWBc6SpVJGboYY+eYChoshBsMMJSJu4gNXMO2dGMlKSK3Jh0o+QAu4JW9IpqztHJFWCx7kjDGtARqEqTNZoKUR2n3JmIkTEyMBxyeGAXpGFoLUKPdp8ngPuI6S/SxDMxsi7NQk/eqEOoRnB8vf7xKBVBZoxZuiZns8P4BE2cGsJX2BRIXi3pCYZBq5qVYdU4la/Wjx9v7oB4XS3fqe6D7nZ18deqt7GG8/3q/mUVeJgG9PIZMSyzqde3rumQmvZosTnf1rru/Mi/99HJ765wUTvdnUqKIAg4EWCKMa0wYdGdcg2V+MN6PDZ84k8uaseMWC+SsfehEgf+sNB+s5hfzedbLLYvFif377/+Bbw2NbzBYNZcZBptZ+pkNeymmTquU6e+whWuBVYvIKb42WUdVT6wXvatMVjKzxpe/ICDr8ejfNpiFoxBvK5DQCaULQEDK0RaUO9AiWlsqpdzOSOfR3nD+u7AeKEWG5TiK5+U05yURKUOgRMh3OXRw8PjFTOG623gsJjqIZF+em1vdTu3NZ3RJp+mJmh5pyoxOY8nIEwSy21ozwHgjhF6ulm7W1x7ypfhuizV8Ajo+dsL8ZE7svgNAuS09qsioPys7RJK9g8xuFteQzOD8uXjWB3WJSe73lfSIaA03g41dTWPVrMXnn8e90HCG9bZiebWcoaV8PB4eZS7Zc/Tu8CNqHfnVz+on0CP6D6uzh5Xb+AFqK9W751W1zhsLcBzhK3Kxl4Gb1vF3SXJpDunT7br+kri5+7fe+cznz37sz9dQAROgVWFq2XkeneauKMjGkW8IG/n7nITqpm48w2p5iIZfC3RhfY5LbRfLRbXi/l8iRX35atf+tGTk9jrdRBU0jYktXimTjfZ8kYz9SWuU29P0w1Bcyd6oVqzqtk+1ZGom0yviZxY34Y2md5Od8XxXl1f3737fCfziAarl76bqjfaDhmhIcxqVaAfJfXNsJT82kS9bn5YlnxlDocIEbFFBDqyO4tQRLqglcYrdGG6SWfYJpaDF9EimX2b0AhXCn2fvNT2wnoVKJ5rRoMvjZqoo09u0naabdQHqdYWoBODAoImtgbLU/hVmlxit4SssKThfbLFnZjDcg9US+dmSg1jHco2R5KBhS0dyR4UOoVMTxF7wq1D30xTOzK+aUpcy2yZFy+Hs1sxHXu3s9BwTAMpKXvxpZcodybT22HO7Nat2+dPnty581wHQU81XnpKT2UPbdtq/r2Kbre9U53/teqtfbV4t7qnC/BAjRODwHXGD/LAsG/6R6urj5/QQ9xlw023Zz/5k4+/9U1c146THlZcMLuhiTvNYvFp6TCE8VCOT5q18xQfBcyU5ZMuascqO/2eQHN3vEcH8/XrJU3c90soWS5Ozt748S9bcZ17jjIQ98yhaXaPq19kpg6bcLreL+e74IJRpy63QbKrV6/LUqpsQlwipyVVJGgQGxdZUG+WK6xeMDW68YMHD9747GezRPUSwyFKQznChz1+UYZiBcAkzU9DLzBQ91AoTYfU9UowviAwoeigeI0yWWeA6tTp+3iHKF647pCQp9C7hFB7Xgkkh20anySjJQSsGyvTGStIFDRQ1zEPhB4jfJCt8cF7jDlR3sZoITN0oTfuYCzPUB4YMINcHHVEqJFOJpol4zyBfjZqiCRTWluCF5BWpNc5JbSkZFSwMyRljEEyKlkRnkFCaRsn2Xsaq6PV6uP1erCr8Q7U8743yzyubuFPFuBPqiuUH+xfwAtLgY/0znVEHQX49ur6/R3J1O31H//yv/uDP1h9+1uYDePpMGizi+5wQv2dQQKDaTqMefLGz8wff3/x4Zsya6cHSsqsnafsZta+WFwuFzeL5Ryz9uXyta/8+/de/5QqjRYo+J6rcdWOffYLR5++VswXeA6+bCZrolITGsf2pgQVQZLBeuElESj5Kr3Gz92gSq4Ues/V3eTFWoJ68cOM+6jQwcrUJ5AgilRUUK+2TlkgzWKYZ9KUSkW2zGWNnoNpf1quZr0EGRvKfNjyMdpsXZjAtqbvRY4ILiUzRbvqGqPa+GOP3yi7N5weSp3vu5WEWwIjGp8FwtRptc6cA8ME8cinyx3Q6zb3lMdwj4bXY1u7WTTiE3/8Qe5oBnwMoLbCcTWlbEH/QfZrL3KNso4wLgENKW1nicuTVU7BkEKTpZRPikj+gTjoJwZ2DCk1HmH9/YaCusvVmQhc8h69mHqen58zA8zuIXblSnlV3VxvYleJmLM6v6jou7gCflY9V52/MXsXDsb9rOezM4bcFpxdg9kuXinhsuHp+K/+x7/6g//hv68uznH1y2qHiTseCknrkxQZO8CBBchp4j6bXV5+vFxf4oIbmbLzdTJ0Nyqvtc9krf1iubxamln70Ysv/ejP/0Le83DxAHis4NOl96ySEg2/Wh7VWeNiKFeeJrv67RO9oEMhSm1OskLZRxyVZBufLmRCy5Bxs7O1KG+vHhFRvT4RAtuh4t7OsyROXesFXU+U45J6WtUhrAdqDUa3vkdEoYDwPaUAACAASURBVOYaMsemkNR+MYSZTxPu5e8Cn+YiaOuXMYRCBVN2QcBUegK2iWqLMUm15oMxm39xDYhGFZ+rJ3JaWKPW1ufbYfrriQZJMjIoB6AhKrFWrLbEC1FpcdZm6/B5VVNO9Ij9ApONV8R48U9zvh6VcsjGYv71jGa5cAMmYdRifcLV9YfHNP4QXkYMal+zqvFi0ySkJXfdd0gqJQeZO9xQDDSDcnuURjx4hfXSYm/udlJdXHrvQ9VRU4YPM3bWgh9Vt/GHa2zwCJrXqveezG6/t39hN2ssltfUyaX1bo4L9zzyV7/whe//7M89/upXz3Cn6R6PeZ9j7k5Rku+i7GtEGibTJy6Gef/P8YlA2hV3c2k7r7UvrpZYa19erJZ48P4Sc/fV6od/6T+4+0J9fY6nXQ7p0hcsq0M8XaCDAGOavqi29ONtkH66StEIrAdW3aG3ThT2xCRGdaiezsdGclMv5VTiJk6xDhmSIawa6TZ47SHRUp/Mxo8Q2LJPM+BYswQyu8RyfWG9KaaKPTZkQ4KVoiVII6rd6XuQrHjl04IsenmklhHa97bmCSAbYgd8bMXdl+SwFSsGddDpIE2DmWfUDDRA1BONRClG1w73TfWce/IEpmEgqsJy6axNm/GLAWX9UliZqMr/JKvqwCn/RNbxKMgK8QH/0PUM1CGMXrtXMw5RMFEqHv6A7Ujs9onPpgnoyRSZoj8VT7QsVtewXa3meMXRMV2YwZCzp3R4wsrj+YkOmpQgfYOCGIOHqOOSd5DfxhXws7fxMqT3qpfOq+FPzTub40J0f4MtX/qVX/lXb7+1+fa3sba/2uFqcVp0JzPN/JVtR7jlAvdbt3bn57hoH2vteNESXdpOz2vH3ajzq8X8ki+SwWubcI0MZu33v/yVz//oj/kqkRz8eieepkMqzdTpYe/IF16rMvSta2baciaqGRboIsbwHQSQBBOSE6WE4qcFmWG6fbMFRnuO7TfM0iLLruCTl0ltuPqQGyGAZtJKUR6yqVcgINkhhpBUD9U7xFYbOLF5OtWOT6DEOBYF1EuTVg5DkcWlug6t2ioWva7NtsWtq8uRiTuN0TVhwVJcavf3ap5nNKyibkCGxSWmmg7fhX4jLiM8bkQDVZy03coxh5VGCI8NQnUI09v2pNd0xzpdRpASrpDJqLaabsAeQZkJtLF0IeJ3Z8G9tNZ+ee1MsTwRiy3J0D3yEXrZ/xNqca3TXkyB7jvtsU9qbqxNX1w+b7Klrk4tIa/4CvjPYwn63v7B67P3nlSneIjkgHy77HjJxsnp2Vf+zt/9o3/wP67feut0s+ELZnBJjdzkhZUMGhNPV6ef+tT9W7/2a+++887yt37rhYuLq5/5mT/6+tfXux09+ZEf/njBT5KZ0f2ouLR9de+v/42f+A9/RZ5/zw+HxyTfWE3T9IQXlKZ4O9WPmXSHUU25pE4x8xtKaS+FMm0fSC4p2pveA6V3s+GNXsgLcYWZyNoFAKQLrJqwk7Pww0OGMwZ4W7OCfDLCK8J7eNTWZsJhy91pkdZS+8S6tM2nurD6jlYdzNuUU9SotyFcLZlCkStT3aKVvarDE3fqgYVmw2pKXdCTWKiLs2qitaMBFckc8iN94P9EW7NvTKiobX+GMg5yTY+SDXJd2VYQqRnIFpEYa3K1aSLEGNLa6tSHAjPQuKraUjTR2k1Da+IKWaozix2qJZ3P/B7lMkiuJBjqMo0uQyuP/u2AT2IJXsCkJrcdXqcKE9neYzhgEN5gtFzgOt5Z+0oVFdtbwL2q787uAfXt2dUXqu/sqjmm7xfVSS+jEJyubjbr8OAMgjvPv/ATv/7f/Mv/6R9cvfnmyWZ9RDfG0lU1c/kmvN9/ZnX81ptvPvz7f//hnu5hXc5n99brr/y9v/fP/7v/9mq3v57PL+aLNd61RDej0tXt97/yla/88t9eHGNhHm/ERLRmix0/TD3RXEvGgT70AqEon0i1my2SKu2UnUi1dWpsf3jVDz/++N69e7CJOjO/n1LcgqO2Z2KmD20TdYiL6h0qKZsvrtpLlbLmQZrILys2wQVG4WCHe25J0GhIZMrOF89gSPIvGkyXM4BSME/qcHULdIk6185e1V3nhgmmNbCru2ezodQsBZSc76jdbC7WMuVD6qot7nI3vNEwCE5pB7kWM6jUpXuQsC6mBoououR6ShJIFMvps5n6ZZXFrTqkrrgltu/IKRW07VzRXtYnaXB7xB0HyTM2/OrqqgGAYWtNvAO9s/jsy0cffOb2uyeLm+v98mx+c7E72q4X55e3n1yd4FEum+0SSNI2ytHz6vSb1efn1R7PgH+9+j49gqZ6UR5BExFyenxxfROb5d/5/9h7t15bkuQ8bK+1b+d0T0/PcDi8iBItWaJIypT0ZMsQIEN6MGDAr3q0Hwz/PhvQH9CbJT3YBmXKBiR5SFoSeAEpTlOc7nP23V9EZGZlZeX9UpXrdFef3qsqKzPiiy8iI2PVqlXryx/8vf/pf/4X/+v/8vW/+p3PHh/vXl6u8YEh0sXb2y/++q8/X1//X//3V7hhCDee0y/lnK6++u3ffjid/vLn3/vfv/n64+35818/vf3x+fR8fb6//9W/9/f/7j/6B3fX765eI+6LgF2dAr2Utaz0vTo98qCLahMs5DxOLTmQu6jOUbTtI6rRPphzRcwf//Ef/8Zv/qbSxW+LhactsKEtinA2e6iirXC2ndiQGMeObIP5N/4V1R2m6ta0SIsQjg55ZppplNk/qFnKd4jDBtV52oPSik6ILujNtjolXmRxr7ghSdX+wr3/5XY7zNYXAunMqsSIW5Sipua8gDtmsbHwLrzQgkG00GZ1mH13BFaVFa1KnYOFVYm+EVpDTPfQ1c2nFC960VDBkmAEqiX9hewrbIe6BQCPdQ7RpijrrTqIFAbiZx1NhsQEOtEvM+TO7peX8x//2Y9t6TDgs7uP79998+PP/+z9u8fn69P756cP17dvL+dvPn729dP90xMuj99tLSfjNeH4XVP8CCtavrj62a9e/QF4+5Orn/9Z+A74u7un//TN92wY2/0vvvzyH/4P/+O//j//j//3n/7T8x/94f3Ly83rK8r33/z7f/9f/pN/8v58/o9vb9+nr6W+PV2d8Jyd3//t3/6vrq7+CA9qvz798N318+nu5/7Gr/3mf/MPf+Wv/rW+n0dThJu6Zot7ZIuohi9oJ3uzogXjloHLXoYoGZgfaRkic7uMUW1lF+JEEfPFF1/YFKGZUB7h7jFWpzk30cIVneYlPa5XD7JbMKwc0Ut8WI4hHF28qi1U3vNh0akzEIcN8o/69ioB1P5OgV2dN9GCVrJhdTJxYLpDCA20RnsKd8p6CYGb0+EBYiyv73z3JakPyid8FriNmi4Nfqz8K497fhyj0qLF9sKLH2IX6wcIqUfrG6nKUZyyl1+JCl//OoPAdTrY+qlzQKZVOwNwuI4XgkbxUgyxYghjCSkKtW8NoLRVY7hH0qoJMu1jaPn+91GvroC1qIb0rx/f4R9p+XOlCtLf3T7c3338/rtv3n/xARe6P799+vrpDhnkw8M7VPOPT+8+PKKah+aV1X9x9T38wzPg8QgaXINH7f6nVz/CF1ttE7D/+dXDT5+/dBq3h7gl/bf+3n/9V//Wf/Gv/sU//8k/+9/evvoKPxv7zX/4D//uZz97QTF+OqF2ly+k4jOI6/Pph3/z17/8vd8739y+/Plf+jv//T/4G3/778gXebeSW1uY/xHuTgNj38dVm5hhmOSjtNi8Ho678wb16SVWxA2PaxJaiAsixJ8qPnz84BFynLvbrfaYs2lSzOhwcc63cO6Iyjw0EXusaoNWE9NtHhnJ9g6sJsOPKN8Jhg5y3k1YagKGADdvRgi5m6V5CvcaPbJ08nUWFrvUXBbsHME5fRpp0FjXYsBI38tOG/FUZCndZKVKi2Iw1vZ1/8s4avYWPR6RTMUfJQvxoheMZullJO6oTlsYBaioIXaEEwbYjLLgYmizrqh91SclJ5rhJqmZluszrju7lwDQbdtohpTuIGA/PN3j31fyyHgeD77ubp5ubx4/u3344Zd/cjq9fHb79M3z7c3r24fXuw8f3318+gxvADAWP8L6R1e4rv9jPILmr1z9IW4WRvmOu2gMjLf71+cX3GWetX3viy/+y//2H/3y3/rP/s2//H/+8N/+/h/8zu/86d3dKy7A06yiX1LFQ9/f3dzcfvbZD//xP/5r/+yf/+pv/dYv/ZVfff/ZZ7IUZemo6tSX8yIIjmpyvaQZCmqcHBjbIrxjsLUYnh4LoNyJSMmg5d198A4uDBdhOXLSwEp6iMa+nCtbAAPSo8zgLKumriWoO/QVYH0Nj8MSWshU5mRP1QoYbnjXqg2MOOaOZ00kbA23mTHdOqoWY6EFm1u4LxVUSiHP9qW7BLYO773DNwVWzkdRbVb6PJmeXqAVrVCm2CFSlB+DCDRxHnG7NwVBNiMRZkgMswNSQBVRM05lBDOUHqKXzQcV9txmZpgUpkY40ej0a8SWslPLtI2OozDmrRsA9vjKcK0i+LrEjECBiNRkkccRBCUOOwG+Hp5v8e9nHz+/+oufM3pur3HP+cvn1x9+7ss/+4Wb5/evzx/ON+9Ozx9ebz883P/pNz/68HD381f/8Rev/vTD1Ts8UxL31dyeX17UtRUjxtnBw2vpeer8lMarx6u/+IVf+qW/9Iv/+fV/d/2jP/rDv3119f99/fXPvvrzr//8qxvcdvPy+sX3v//Xf+3X/vpv/MZnv/wrSQIdTS2H0OWEeou07LEqauSFIoZjplscZ+CAxiMMJ2SiWnY2SNUnmnyW/uiladMx0PD4hIeLBjfh+ZA7Z4BJtFfTroJGy1G2BG1dnWhUvZJVfgDtJtTLR6dH2Mw4tBxouFGNeMP9kGkzuvZQ2vn72RCMQ2npqsQjTBS5hbunIzeZNVxdtlC1Vs9MyDVcSH9je0+cLhQOaihQ1ZDlvxKtarQr/Ihjy9ekvsSKFdzlaro0W8w0yV0pKTywjDno7YIGrBZQBUjPeTm0UOruHV+hS71f6ii0nyizSIhIzUyBgoeHBwjZDmTDPe0Foqu6Pr3c4N/HK9ToXxpU59MrHof+vfsPX37x1S/86PH9+Qnfgv2V85/85uu/+eP/9KNrz5sTrtRPeGAOwgPRg+cs3OApjZR3rq7eX92b223+/Ff+8m/8jb/+/t//+48fH376Z3/27v279+8/+6Of/OTv/t6/+4lzg3KVOaWD9qGdPM5JGPCg0ebPGwylVlT038dwLzCxXwzHX+lDaQW8NCQ+fHvks/cZP03A7F8G7cyOMLMKGi+tqUab9lTfzueNashtNwRClrCBOHs6+YBLBxmS7OwTUN9G6vh9i8TbPtoXcvgNAw6x7aNamFoV7rQacJ3mlm6rqU5BPtnWA1KeDHIPGw+KaIeDRlryBISYaxsdkhptF5XKkGjPzJMgx+6p4vgAyywUUe27TTZNjJBNswyf9um3ehbaUbtbFlaeGqXWJxdR4dDuDxvf2My28/UZF929d75ttWfK7NLN1o5nTeImlq8+fA//jPDf5azy2f3D6x8gZJBrcE39hRMNFlKu1PGDS6nt9e3t3/7kd3/t53749U9+8vF8vv/BD/7SL//yb379sz/5+Izvdl1du/fTp+R1OG8b3kGciOBJZdIXVKB5G+jUuAm5bhhSggTV/legaYoxNrWTKrxSdqjzX/30p1/+4AeZnQ+n3ckzC2wTOdbyvZxt3jvK6QAuqtlwOio2BSP1GGWFPsx5tbSXq85REO4D1dgAn1Ic74f7Vp4x3LD4lYFogVDpIPuVOrKH0aPNjKv4Og55ew0qW9hhHRcLHOyZiJjxldE8kC6Kmg279jTf9jY9p93piJmIWeY4W1w3WzpiMryPkGmE5+xQ4FihQ5EjmOy/ZrblSCztk6AAeBDzGlWp8Kb+kt3k4QAiqDuMzz//3vPzM55Q7gVKrkDoKo94uwxsZMdQLtnqwH0v+OUjVOr/iZ7giJ9UejrjC6Vv9+tQ2o7ztIDef/2n//H+F37x89//3c9/7W/+9Kuv/uDz7+EW+tsnEv1yqbU7xQ55jy2WsPHw6OGDJuBRAU9wdtEuM4u06YRjyOll+4ePH3/wwx/6CPa3sY/Ya0dMN9LOpBgeCCUaGcyq0Q+/rXUXp3shytTIdjpzxIIwsJ2WQu1eCyobSTXTLutLD2tUOQ5ASWnG8JzOlRbqYTfwk/IV0iGuAh60Za+kQxDq63NWuUV6Zv+gId9XjazxSsnkQBBXHURNhzmupOUb4u9Zb179yBUSVakTO8zMOnRWXe2DTtqVyL7SbJxN+8uyoBZNhTOZB6u1vn///vHx4d270Lfo6CvRh5GllnMyjst0qtSp1AI5b8i/eHRlTaVO4zfbw+3tn/3oxz/7kz8BIXLyCY+huaja3QQPMYSIEaI2luY0YHB2KZMjr6zPEO2aHeFFsePD1Us7PsW6Ln7Xx7iyF3gf/No25qeX7RUgxCNHRV1KO7HDfaRjhX2xIUOExhQu56BatMv8kP3ldHpPxgk56m96kO5h1A31O1+UUnWZLB9a/6SvjLULUvYOFgJIXCjmhWF307kWHqm1VIGKXIGkIlG/jelKEWRzFVUitKRvnFTUATULCr+FUdzgBXikWqemAnCV2gu1UHffRsyPuehuBw9sNInMRjFOO7Tc39399Kuffv/7wccpDtVum+nsywV1+pEkvmqMC+pcqav71J3OXQ5Pt1+c1pXW4bU72RWedSZ4JGy8wVPNjEgbFPZJVD20K3r4DUxgagVw9NB+9fHDB+8daAGdVjMjHs484ooXdBU26kWloOHaLXPtXcM8GjUi+/zYfVs7AWAQrHJXLIeQb9uetFamFndL9s1ymSWltP5Ky7+RsgPrCM+s9IBBPdb1zvqoWaW4RMQsgat5RUtnfYWAw6tYgaAWE4gfGa8CDNS0yCuAXdB1FKLEpOK1ADBpScAmJXo/LAntolRptg667JIVzcFnTy7IQ+BkBg91a9bu5eHm9vbrn33Nv3fkPU+N0A7kmVCDUlIncJP6Kz345VU6nt6u8e+M22CI+OHaoeXm7rPzNbTjK63LJrU7Iu91XdMvPUbvWeSb+KFohD/GZx6o2Id8L4tF2g05wg0T5JWa21ikfSs0/CnWtq+nRZzbkXwSpVMzCefgCSXn7to9FoabRPugjBdUq1evRXuw69gTBICjWSEZq20lXTRCOTbsy6H0QIvsOO2r8R0OZKWFdsgKhWeZGnUbaPFVzzItGb37mSTxYVQOdonRU7/TxZMSgDmiTLAaxEso54w3w3rtRGrXI/AwP1CsYPFkg6lDoID5cO06ROPKaVLBrJqSB274LMGTHLrpEPH8pm9BAy4Kvrzgd4cSG5PfuXrGNfXXE1TLdMTdL7hJ/ebkuzFnhPatwV/c003t2w21+80jndq/dkcASWTje2T8RObxcb6xfx/yN2pVA08Zmke84/YSfiSAvB3cAYXHIpO06NSWKQBDPnp/fSlzvO4GvRXa9Wh+x6uzs5hQFEDK6nDaNYqG7CjOB2U+hszFKCmALsfFfNhCfhMninr2oAOsSW7WYCjHBtvp9neMkOMdYUAh1AKAKM8CHe5kvr9VFPxheTVnmi68CRGiVvzhT4cRYEfN4Qik8lMh/9n8kFSOV1u8rBB2y977BrrZ2RUBTWZdl6OkIIJY//5o9teYQ/QqgsCN5idnbKIPREF6R4FG333wBnfTpcsOQgfl57M8TB0ScZ/69St+OCnLlePMz7Ht+e729vEJQId/V5XXKskzUCfuZoJGeT/H/GPJFxpM8Js5tuYnx47KPhXm4ymrX/6g4JupEWQSA8b8SE85RT2lWMdIzs8cP8lx4Q7DMk9YpX2GrOG6o9UOJZQDiGTh/xQ/peTbuLvsHwCA+RHwuKyDI2kQJF2MyhRibEf/Fu1UuHMAZeod0Y3yOop3DrqEfNBNIc9d6W+PMuLl9fV696f3r+yMTt7oyZUYHIATcic2oYkoSgiQjolOJHHMtrt6nrGilSwCP1b870/D7vav3cjmO6Wz5DTVr8sUW+tcHSXjc9U7+yDzK3Q+8+M6kIDeXq90pQ7mrvAzrbfVX3Yl8634i+vufvaJa3fki+7X3U2iBmZxsXdqlfPfkwMGRtEuCHuKTsrSkwxXAJkhLz1JKU0dxGryVGqNEDV//tVXn3++/Lhvk24eDL0x7byQ8SqPjsRPX46U1YfNPtIvUaCQFBNKoxUn5SLKRxTjiw8AgDbz4+LprMjHDnTZwUOHrL3jw2fSaKwe0C5HQGj2rfPpXbniTrMj3bdPD48icOgBwPN2pZPYpuEiwiNo1Tv74LCpqxH6rNfn9Ifu+ti2GuNUmc5nNTu6i37VQ2d63Q0bfQ2QeOKNwkdCSAeRalfn93hxLQee6tnbC6+V4CBSM9RLekrOCPNfcW953hbnH8FD3yilnz2SKIK3Ti2VugfUoQEgtTuCsvW6u44hiR7112Ot2xTn3+3d/5iQjojALVJhiOa/lYZwoJnbjtijpYj/27u7vpgkTgz/DkVEVV99W2k8+0gRlxbb80NblNKiCkTqIkLbDbLhf6ixW+HG+zjVzRhdr0NgXKbpoKLuuACo4P+mYszWASUtav0zQxQADkecQzzKX8pu374tYjMRJyEmtEhU9mFJUT6Q77Bh8dlVBok4kkqd9Mn/qNXLhPTvfTgA2yQ7hmiSgf/dk4CNZwiAh8eHlY7oAc8hNQW4UsfPHuEbpRJMOHk+v95OEEVRGxpOUu1e9YxIE0k0hRum8ScZgeIQm6IQQ9J+4BzM5P/Dxw8/vv2FhkDzDxWK5G+IIv/ITq2H809ZmLbwKrwU61ThdrJ7EXMsA8rpHAHVAaDiBzZBRCFF0n+GCBT4i2PCe+Ye93CXDme2oSYsiWi+JMpcS79t7w4QLkQEFwsaq0xXOZLgKoxILSj1Gs4YqZHR89mOhKGVli2X0+n2dCnRS2ssqK4HECVAn1x5Vd5Q6FP0Ct9iPpSmG1tCdN+ea8TzVtFgAFF0fJIgdfUBPgNNa1U9cJO6evYLRw6cgW+U3nq/UZots6xjd/PL1F9d5T1nZgkkCSH5W6rL2x+iRk4Br85VYzcXaJJKKTqWAUGbcMEb3sLSA5EaN82QSkSiWmQmADQqjg7PYiAqofkkrZcLA7L6y7oof5sVxAUczICOg4WBOFx9GVPG6dGpMeHzxnwB0C4wrMp/xmjMYeDG9PYLa21FLALGqnBhjUYtTnXIBdUwcziqFh4a6BS0xA5VLfjLRR3/pRpil+kaAlnZ7tiWK0UsT/TmQFpoohjiUouHVSpOqCw7PQOG1Wxbz7UyY3bunRUB2Zje3Yd+fQkiqFLHDTA833B4Or9d37zd4mZjk5Wy9fTreHTlSrX7+jkzEklqvlEuGksPSZc0KO+/+1GbK4nzbemKYOYbTX7KSCwlV+WqnwwtBbAS0XYQB9BStbNR7F7oCDMUB9BmXNboIwHoMkkiKsJSliW1nY5kgDHHAShy0BP9woFUa71IpYsIR93+LpbhbzwM+l9x1+En1NF1xBi9fZfrcnfBPdd4sPK+G4ymHKY37J2FIyn85K8+O/vrSLQcu4sCHUkjaOoSiManC+bx7pMJrvQkplsYDXPbhYSwjugZAIAl2sXRrhknr2+urV704Beu1KXtdH69QbGO3G/1oaKrIwBbcuZ+XwYyldrdcM8MnhGJIHjjC6vGFyua7AHd9zkNTu8FmnGKE3AUW94SBG2JpRnYdSIkEPhOs0VuKsBC+fKy+jUA39CljTjSG3HELGVShc4zxADgC2xtx4BXZklxLRRZSg4kQQw/HIBdJvGUA67tpLEo67RLaliRhLHsd5KdKyYOoLVwlxRm5iiMPcTIXDK2/WjmjA+FNU1EkY4/qN/7fcOWhAoCKoZs9a5bOImhSUUTh9IANWul+ihTUWY3LTX7FSGRnSUlmSjRGKhDKVtZoCMF6Sj7AirXzb2U4/6pr7/52cvZ3OaOa+o3uE99rc1zVOIFz/D2pv0BUNQxbsqDp5M8IxIFWvfnzOSTU0FCr8gRkA4AykeSwGmHWBo3TcQQAXDobORPP2CvthW/mfrll1+aUBGi3L9WZtLj3C6ZxzI8OyVmSi3opvCPyInMkopYjqRQ9AKDmp7jAi5KybFeENulGlDuiKIdcdIwAOGHYDBKnblQUrirgkoXVlzwitxQ5KWprB+Zln1kD12EEgbY2ErTkaYo3d09RbenLyGFfR2j3TWl2UNiDKToA8A4cDnPL21gaVAaJ7EBFhb1Q/cK3r2scNCDX67k2S/cjlA6n65f71edLuQA3nVydF/gdjhxLKkIN4HOz5l5RM7CU3L7qo5LMwCoG0/IUYEexyFnTwgp9ZYGCXxBskKZI6iyD4UBJ8hFdaWk4mEqKUuW4ViEiG8+fLi5vsYpEGA4oJ6y0qGJzp28EaMEFgNh2o/OSGyiWZvKbdAjFJHMkiFQnwy+iveHJoSgbn1iVwyWu0XvwpV1SkPb6dUwgGg/9fiaRwXuBQMPDhfuerbJK9GnKUQeq1DsGXKcJzxgWppgiNk43wnLpu1Sdzr5WZmvKnUIVV8kXQVSX101jFNO5XHHQOFaBSuEWgrFAgTSjrFEtcIxxmt/kbEZaWFdqWMIKgY8+8Wq1F/pVzYqqMOQuoHagg6vDNtEYweBsEikQDILT8h8urvj+93f9rzubuVQhielM6dTHO8QlgRAiGKO8Id4y+ErQWflaUqPNBtqwrhSJQ9bUa1JwD2/n33/C5xXLKGTcKMdRFA3Wrctmy6pBpFf4ogOStegOAQgdUXMukvoiNY8hBBOqziqkGHGMgkhTaPb1XQwYLrqoyBnjoQrv2wTaf7Tw1vJgzIdWJVy6HC1iwKTxrFjFe5OvDORGFQVaYuymfdqcyIzpSayxFsDSYfORu0djV+/6vb6QruHaAAAIABJREFUV5ilN1WmI5LoP9o2arKqNS2v86sNxt7vrCYszsxH6bJ/QligSXY+EgGw+JyAJ6nzNXX1rBg8+IWf0milr8UI2vve9z5/fn7G9y3XzVlHU5DAj/eudcQSUSKhQo483x18mdrd55YsPqs7ATZd9q5An6eSUpTOU0rLujhAI/rULhN5IFK9CAOtEfxfqvOI80wCvr59fXtDs4nCQFjaBMSmwTuVgxiXFWPbhTUyD1sl295uS2RMTOlKjJGBEWZ/1UMfLMU69Yz31WOyXjUJ8EhW/96dFr1dihbOUiJzkZyPuQuGfHW6p0AFdoav34/ps91fRQvEQq+oln08x53fRCMSjgmG7pYWCMQXbhLflCd2rE1NGGbKois5lS0R8+3CkOY5oHlSdCFdWbnFYipm/Z4seiABMSaJjTsGtvacmYdGADQarrZnTbdv0w5XKudXPPuFcxOZTpU6P6Uxk4f3794/Pj7WFe6kbpdgiNuSj8EOGw6mJaLiKsjScA/c747vqqKDqd3DfUedwbf2yTozQ9r0qM/8RIiQlJJM/BwdDBQJ+75/4DzOBSibj3z+o5/7ETX284U4wf4bCUXVTQdDuqctN7pfKoreQrkkOCtX12J9A17NbRfDpt/QBpk1pRi4P6azMkG91AJNzdxauVnjBDtswSdRtFr0BkNJj5ehkGQ8DtJJ3qWRnGXnttPiv+253VpMDC0adbZCC6iJ+mMnphZsXffa0HNgQYTkLD0VY2VAV/D1wtrMLtcrM9CMC81D6YCzh8+LozDg7hc8+0UqdYoqPNmk4WeP7u/vv/nmm5ZfaI97yjj0mB1O65TaOXm3QBUhISvUdffT6WV9Z+ees6gxIGkCClyq/yuLqkYMIXrz28mCwcmBI0GuFoMwKHS5UhjkRsd86F17SqjvnyRX00TeP7BdjEfCq6udSWFcmezPwwpXJgaagTwFub94cCWn/YDm+AFeIFvwDwZyQm42TcRQmkqKMp8172323vo8wQGyEVEqVel70dxsZcaNAjxKrgFu7dTqUgHFs0+KdR1YIrFWrgVNpvaqoc9BF2wFUDRXaogmqkDCt6Qrbn15PatKHSajUr+mB78of4HGFupubm5QuP+ojcpGDG3KaTQYMBhoR97WcDudbVcQkGBLluvuV9e47u795mFARNdmm4ekYLsAReclimyrklI2HYowbEb3aRAMlkmtYokrrjnwykQFlz+jCT0Qigi+dFczpvcO8cAy21xaAAuKRCNfbieuqEXKtQIxWkjJkEhfQcEddmPChUO+EB6s0lmSFXXl0nY4OFZNSi0MLtBxx1rrloc8nWoWQkw+fBTuYHU4sVsDAHZvrTpDKTBEEv8THFE00ZNb40pa9iGi0AAJQWPGOqAKZRkp++3sijDKVbHNa6qLh3cZAAwDkiDSqn33C3TgwS9Lpe4gb8SA7/4/PuHRKE1bI4Ym3bosgBB8HAsk2LDfN7IzpckzIqH98NrdW7NyRbX6pHRQVQkXyBorvmj0b91wUd0yPcUE0U7S8osFjbgdg5ZU/yqh28JDjm7FlT31LLqEh6KvYZgZx0GbAyHZh0SO5iEOwsQDuhEaiiljaHxoz7NQCh5I/xHajV6FgY8j5rHLqJPirJAwc8U9omLAqT3eG+lL6Qq+m6HisV5I4wCKGkWWGMDRvmQSxP3+oV+CN0kNbKmRB6vjUQHFMi0NAqaqRpeRsN1JYtgOmbCFpp9bqa+uqScxt9AKv9zfWQ+ZSSoLdMgJicDQmmZyvYyjB1oaAiQsawT2GmN9V/XQ6+6YfzS3VQFN1hFJYIr+22ETbYfP0NKwpMnICZ7ylRDWTJZgOKxKYvyEATtdywnmiWQq05Zp6KdM5mlpSGyjVen1K0m0ilcFc6Jr/9MCHBDIJs5gW+P6a/VKPJQHhcjwgNvfiZR1/IAf9Nu2e82JNB5UuEcQVZ9iSjgrQQReE6kctFpL46J176DrqC9TlMrhCCAMoEe+ILSc8Fro2G2PsDCiAo2ZBhdI3HaVmWbajyfKQBm5U2Emwkqe/SJlFXzZeJ86CahdkoH/fG3/eOpIskpk2yFLKZzzOAlgxkO0o72aihJ0sb5Uuz89AfFR31VluihJINJACLYY3JHnoPpwdyQxqMTFNNFaOIAtwkAhfNAtCuxiMovdITOozu3LTGS6SEhhdIk7WjA0+odMYMz7uIO0KI0r4MLfPhjITd6t0HdeGY2N4IGoABG6UheB1Nhju9DCnaKUNrwaIgopAaGytPchUvDs/DcPOkeOZoxTC4+TwfxXX+bbGb6jjpzpNHkOM7p4RhU0yUwzAwrDyoyr34FGYNhfr4M4AwPu4XjBDTA8DzEauK/Pb7e4B8YRVX1YTQUG4plR1XrtgaUYQsbLDFyiixNXvpdLYdgm9Np/ur29odp9p99mIsbWb2w4e5E1RKPJ/L3MK5FD7mAc+R4sEZ/VV1Tb81RFV2FoZSkLdKJoJyrov2OpgDtsKgJ4l2YVXWIAW7Gcq9oz7mBhoTRQJTo8iKxYb8oLPEFCILaj1jKCR4bhuK+ts1AVQhHU0vnEIblClGoiZGLqow72HVG4l/KI/rIhACgM8KJDQb8WMWEGmZ2i4Z07VwR2BLdk0AUihYqk8aXtwvYi1nawRC11WhJxpWJMN31bXyl03KnqVupnVOrh+9S7MFfnfoB/fHzoAgBCIM2sWI5MiRe7UWcraVsmJBmiZyPt2mPy9iMw8gR06IVn49884ssDNyPudyfqlmwvecvP0wxUiENDgdGB6wwRkr7kR17BCbaMQf27yBX9iqWsIxTlDkgMX/VYsr0wNYAuEUzTXhbejhb6RAX9zW8s7eBc5yWfrG2bTEaLpfIA40UEki0hWz1jW1i1TcVYdbJoru1VUcF8lnPowXtE4e6BYTdxgJkog/02BcE4XSRIFyNgOVG1WNrDj9wPGb4UBkBHnarCAmyFFOxptA1jFJ4ld7NlW7rA6Cjd2VwC1X6JJowK/ng7PeMGGN3ldH69QbG+a7TUUvH0iGvD3baQR7Z5RgIMSYsTuJQ0BKNPUNWy0Y2Iq6vnuzs83x0Cu9Tu6wQGmnN5Cnmko6U5ovaHQQFGJPH/TJdQxokrl70c00r7QDdjy3ZhqYKM/kKKnTwVXbS20JYho0MXmvbMxm4at6AVFYxDLLeTVYgIu8+q9NoqyGxhzm2PZI7r222sI6RYF8ThGBMMFJB4b7l+wG6psTMU7qtQUQtcKKwy7POJc4cdHkYLoLCbqY/Dw2qVk/MyJRd5l7wHax2DO1hD88Taxk5gS9El7nJGwd0vplKnuyKu3zp8y7OFDXYZnFgWG+/ev2tRuh0bnKkcYQocftBV99OvW0n1LdCyiuZ6SU0j+Tkz9dfdiTDhS+6vKHPsghyBMUMm3wHGksSgLBBYKBYPZ0OwHQ4DFScwSMpQdNXG2BJt5XtQLbP1COUKLqlmNuh9S9gEYgvn+W+k21ZAZjqaJTDEAImMrTGlLUIaRsWodYUSFeIRPhN1izvWHO9cuPNkgpk0p3gjg4vixCDXAtyGrGN5hFNW18GdPMZbTcyT5oqQwMvW6cHYdhG/mIO0YWmsnFsqwixBpYwtgCwhn+ou7lDH89SXSh0XAq5w94tVqVNuOt56kyHzodxcZyW3EuOoGpAcLWEmY3eek1AH7aVRnc9bZs+nu7vb7HtmaGJzLiPhtGxp1vVrptJtt0nYGAFDkUYXbyE+iymBYfG7JWyPlkNg2DFGdDFldKXr0E3cdviEZTLABdhYIkkHGGGUDtbJIcQpLYd6hJQrr+idEjzkSpLAIhYuS0RwX+URiKv67dWsta0YlBpgzRkKGLTCULXy1cp0x1k66JR4xO20OQZZ1zdDbd+o9DVgIlFa1puyxaxwajYtHXTH7q87qHAw+zSiTRdG2Z6EWMS+Eg4BHVIDBWnnKHWMzzmEISPSPVfq8o1SRoEfKL3Cb5RalboDbgwMR0nysIKNyJrtC74YBIovHWTiFHNZPTZs2LkKNkZgUbX76ebV97HvQprMyYZFLg5e2Ogx8+N6EmfbYXAak1xGZFGUlpNGMCbIXwyDKkV7gUswWH46GWPQLn2Ap1x8txHCBpLIUDaScIUN+UaELi6CtARPJNVsOkhMb5qPbiiyEDZw/76BRNI4MqWIyRfes3ilJQ2xuWQcixhrt6+7hgnuC9MvjYoB9WEaosJ4bW+bxuvL1EBLTqqrRLjikzqDttQYP/vhVj1Jwz0u54w8ovGNwgz/EG7nE11Txy8f5W79+c3VvOoHGFI0r1rXB3YgPD0+2ofrjokjYkpX6s4KB5kUgt1jLoHIPZ3DhjtmwDHX7k/yu6r0TomI402CZi+WiA32i6jVIPZ+rYChSKNIFbKqY3YxVqpDzqUdpCm55ZJ4BH9IpabQgrB+T4WYxBkzlooxgsHhgXl7YHgoNgDmCBgqobH9EqWU31LU1bsJVuYN5qJRAjZvwJheUlXEwqM8/ouQiuo0DC20unDnCQRjVjOI54gW7X9tjpXBBPpRd2lVOWeRRc6KxcrSc/Ce+LE/s+0SJZIX8yFxCsYWRAP32FJMsDIaUam/0iMa1cMQ8YhGfkpj0+/mUHptnrkDmdKi7dXi6emJoseCHeGRB1oTFNZaA7V49YpTMxByOAyZnI+3NzdPz/RRKh6cH6HYIbH3ITQjRkf5Jdsu7qhhbEdRiC1ByulfVy1Lcx9qIJd1heO4SI8X3tbAjUyaR8BBF/W0pZs+yQaJNHlvw/ZkKF4LxQDkAhUe29Fe69YSuhyR5r3eRSibOAeKXmPCAqN0dTEiUjsOxyGCdZrFeWdESkHX8wJDh9lhNUY+jOzCHe63N8we4bmM7Xr3lOmxoW721ezdtHdvoDigNKOo22Yuh9TuAPIF1jvGr6PeXWbyQDBFGedpv5JhrayzNyVVaFUERcfi7pfX0/MSZvSIxuvTVcE19ah4OnmIF7aoACN/8n722ecvuCOOf4bJG448PTVtZKHOaVvFs7YUEdLFiPX0VLy+3N3ef/z4hGfn++6Z6aI3RwihyYkQbzTkKMjsw1mLAlXujNgU6/4SxUGlFo1Mlb5uEtBSNONjtgM3RrIQkoeEI03dtdgl/1BWx/9CCKd4AtLOc545di9yRk6g2mPy9m1rlMuNpRsJ0oGGjL8uo8CsMVhovefXvYcdUWTa7ji6LJNoByr8FAn25dCxPlC4U71AMQ1miVH4Pux+R2L3w+4uFT91x6nYEupIOjPO9HXXNZ/Aai9xftb24OB86NqvgUiBqI+Oe/VOO3yd9PW8qtSvBz9MHQQMmjUhaoPxBEbylpn3798/Pz3dWL+fugo1rq5Ie1BTCBoPshN9rOPwc8gyQ10j67oxw7uK4OzDu3f0XdUb//3uZvjAHe3H0YRkmcAlCW4mxgrKi6cGlzWYO5WP8MvmOB8aIZspZNVjFib2S6JG5JLJFB1MgTcDWmKzdzWh5l0ERpJvNgL88DfdEg1pKTKVurhGKctLjA5wIgA5hFu3ZDid+x5u1REMqqAYVKGyNOMBgeII+UtddMh1cU1AZ7oZeLABAzYGtWJLF+58TglDB4Hui+q0wliPle5YR0CIn244i/cxNz2+mSqEGiBgmfa111W7iaZx9hgEB+xUWLWiDWwp3g4AfxEqMXVfr870y0f0Tpo33Ke+Q6XusFPhaUdC0aGZN9tRciqJ5/b29sPHD/f3y/du+0aaZNW+MrfGZrXQuitXd7O6xzsRvavlACYmySaR9v3ucRX1Z7OAYO0Y+2YmhN9ObcQa0FLpHonlkKTO7YACbAyos2QWFzdw8RnHkYvEIW3kyr/YTlRAMdIqHqPlbAte54Q+zDI3LGU9nFzDEbKpttb9tHLzak5Dk1KWN0+NBHtHJHRMI7bw/H2CQYSQc+xwNcbmi2rvSa7xFc3tkvMlKBIoVokDwwkKd+akweW5IKA4Q4sKwVyh+/UjmsgEJoz5MyTGQCTsSZyOSZ7inDOhPOZIwAlY9n8WbYcb57FkL0yUx+nuF12pU9Bd71+pu+aOXftdbaFj+AUBREHlSyZEnQ7J8/X5Z1/9xQ9/8MOQqA7t03ACPloqd5XchBHw6+M2h66nu1u67h54zkyOhEQf7dxENyplTm94zGnSkLZ5DtrVAsph6VFH5ZhbqibBj+gAbIzWO29GKDQyXZ/BJ/QIPGxEvsOZdG7zitEc3yHN7BpGEe+7OluBzuXAkQeJMoMJij5ndnQD9VEbzlFkVW5GjDteZO7oAxeAHJN1CFcc6PI9ZGrQko1gp/DdnPc3SHRirBOm/t7jWrV6U02hcA9xMg6FK/l4BC4is/pzYFCRwCuALANJuMkOizrIL+i9jNt7Lx+kCS2CSKlRSMsDjM7HTxKBWgI7z7hgLxRd8uwXrCPUCVmCHtF4a6XmKeKkhZL8ACICkrNCi2O+eNUTdtVEpYPr8zXucZfmQX+BQgEYpCBbrEwcmm/ZGyHXlQBN0pKxESXqGZEH3jMDcEyDSiY4KGAlYpk6xZwptxNvGdIVkk4MpyEGeoh+4B8AJE2xvTDIz0ZyqgfW9NiAQR2ayYPAMboyi5tI0cQ48EofAqx62xlmdWJY7oEWKB3OScp7ZOy6fN+OWBOynLdJk1aHVZKtu2876zPqdRk7ZvI46iKHggSu0bfKRPr2OmWFo6Gsl+x8ObDZm7fsa3XooBHyqz5QWpzDfN2X1zPHVGJUNuoN6nIGXQIVIzMX3fqCL5VyhmTW8I3St1v6NptvC8Ssr+vYtvq3VTpG/Pgcs51DZ4xM1VdZ4STcfDGHr6V2uSPO0e4cQv/IOHG0xQ6TSBYXcDwRyT7eYjpC59YOe7q/u314urq5er3e3IcQkjCmnTmhGkTCpFrJkuPU2rA2OEOuQtKP8hyd3Gdxu9Qq5HO2p5GTDABKEXpC51YdtRCSVu9kIYl00sgOm8gqmng2cj6xkWh0awNsr67P0FFxeLoiWAD+0Nqu990+ecdxoGkZpJ2J0UgSQyighMh1xy2Obct6hO+IJftO7NqGkBhVuLOzF2PsQFxaj9gDEvx4CnsXYSkxIS/hCzOOMUfA3lFn3FpizmwyqeYIZgNqzh2Umc/mYeoIuzM9+wUPfomzPZ0tvIrQfLGRrQ7sE532VcxBDWumiYr/pGIOq8C3nK93KRwVkgmmgYOEZqpMV/KQhc/aDfOnz1R5V90zc3UzQe0ODpDwbfu1aeHXVZqjWMthIdEHMiCWkSR6hnFVnPHp0lDy7CpTalOXJg49mJQRSMpw0wzhwoB3Ssd26W+oM2yYHVe+z6tun8ZjUkFxz6jWb66sGiClpA6oR4EIApgtJ1aiw0m/Rn9rCr1z3oblReL0H3c4qnC3LST0XWirpYHBKET49BzX4RSc9Ysrfg/Me+hw7Qoe+8CYXMKjMClCEyMo9WJP+OjIN+btBY9opGvqsqFSvzm/QmSNVHB+bJrQVtDrtgZyJ7vdW+8Xmb3++MsKOUeKc6h1ySuuuI++VcYonMhBpyt8FiErLhEXWMYM8mUnSubSLXPvdMXX3R+v5qjd8ZYvOYN0qiMiNG99SRGx9Jg3uVckk8sR3cRA/7czy/Vp6mikpi5bCg8QCcVjs5VkdtS0eKrDTAml3eLU4ayEY6nYjv0NJ5CpHOSdFp5lwNPUCAwAWCh9o8dI342ild35qbXRZt/wPoX7yh6fGrruk+zkG1jXRpMB6rRjOdqUeio940ITp+ODS84WXgQqEZ3Z1zZVk2UnEpqooMvulin5k+hWVi7L3S/4RqlsVKmfSn6gVA+c/ZVnU2w+14WLPWdJRYYUdKKFLZBA0f74gMJxvy1GykgU9pSF1dgitASB6AQQ7OA9EXWTqt3HfVfVC8nbyHlsS4tFHfG2yyqFB5nAQb7nmXiRj2wEFGGAbS/TZFGn67kyAaveAmDroFWnvQ4AppqWLIwWd3Hm6Sw6h7NclroenRgJfasY0UtfhM/a4t1yMs5Wgow64VmrNF09czZHbBb6mTv1KdxtqjxMjw+7ZSKQeniTUWyh4KSN1faMt7Pdofc+gOyuEzasdWri9Kvhrre12fLW+LKH7d4Rd6jjeer4UqnS/IbniuHul+Xhg30RcUgfFDJrS+Cg7UX3dZf0Ec1CejevN9RVkTmre21fMSq0quHUx48ft0MGtQgSmDFIvi12xR4Ub5QKGNXejsg4qlyUqt1vj3u+OxGncIOnV/WoGZhEjZ6nAdpED9tvqZj7gpIgyX0jYdYJoq48GlLQVdyi2wDhKeWr85qW4HWBVe+MA4s56l3AHneV4QWjMiAVd6EsfaYPr6Ryb/WRFT8mw7iYlhPMAL//5j7aQTiAj9xho48Flq12ATpaN8vvU7jbBoyGrQiSa+qkjLxWENAOVudwNPqVfJiyp3rWtc4f4M0gsHZXKPc+CJdieyNZ61tX6nTV4UzPfhlVqa+V01F7ubyV6bSYYHDanUOECuKoNGBo5ppiHYOX0HPEdzu0H+LeTWhYECyqoCUsb3WG8x6tmSrZedlb+Y8+Svb2WsnNOViJzRmw6qNqdzxnZpevHLBuGzF8wuTJCWIEoXvwt2aBRaJlXMCsfJA6kDcSxNomYhz2bGZTUmvOS1qYhpbWS++GvcZ0Z2hhF412QtRx+sMrJPNGoxY1XoM420kfvrDj6STRKh0JV89tSRpbqY4mb1enjy3E29/ukNzPLdwjIJI6GjuwkfqPiZRNfsnR8vL6gofEUc8D7VmA7gJiiX7iELT1ju/Fnk9sjx7ReHp9ucKH2mQZfvYIz1M/v+ZOme5s7BAuoYRSp5pCz0iUwKuatnEmkRJCyzx+gyk+tvvZCJhSXcScujxB9EsMXmUXnMQ06O+4uJYaYPVH7X6D58xcdXnOTE4wmnqJQDB99ig84v34u8wJGDtpEh8RGEDhgMFfIo43BqkP9nqVeQRth2i3rRQARAujsU+F9sexJ7SIj0La92k3tEBdXx+VscdxKiWNLDb9yhs7YzikLrNDTmy7wgp0CjHj9HfFOdp8h7lViIh29PkEum0VQabWeygj06XQZM0V6tdw6Cc55IuphqpmmWsNRUdsXtGInM6qWFLCETpHmpgDeJo+qNRfr17NN0pPb9f4d41b1SehMFyh9qUwJ2DAyXZq06wySz6x1i+LRi30guERZp5Hx/c9CWYob9Vsau7SUI45vFVs2TAa9UY1mhbVm7HP97c3+MpBh/vdgz7l0FPW6inrJxDfDc29OWRjSO8GmidTgOFSA9bhl8XxRKbeZhbLk0m9TTLFgnoMICdRbotdY9b8Bcu1HkC0cFYmqLqIrRNiaMHwRjAJ9oKTfgEui01D+Z6hY9GW2NPMoJsbM0aNyU1mxwg1fUyLs5NbuGPYVrojq+6QHLaIpvmxrPdLe51sPcrIYem69fBXA6seib1WQYqmj3a1UMSA2ddt370yA1Spmx8oBU30MHUU66vLtFYt9cmyVh0fRI7kGIigQKNtHpqenp73B0NMUE7L4mHJfjQCY9ajkvkb5q1HOPZCIlS4Yp1Oex0+4/nuj/x89151oY4+ogFGEhW5Fae6OYTH7EVAUE/kTpXgmB4nKPz0ZqYudqSd4vHQTQBMAgZMbJhZ+MMpbPuxxcomYUYMrwBj6ANxCfYcapewdSmXXCeJoTDvOTpcyRXHHBG4jkNwjXSzIwLDpsQUJgp3R0dMUt45lWkBFqLpL215Qwt7jZFaCCLeXViI93HPWpW60IcOxlSz44767lgYwK0veJ46Rx410AX11MPUB4VnnUf6TpaKcEHMoRJk7tgCLjeVLRXi1MgOL2AGy4DDz8PDQwfR5SK8YIwYtVwRXeioWdOvpltsp6RzHExMy4Bz9Hx3XHdv+K6qlQAp8S0ElqMVZjCuRUi5Wv8IBWYTw/7eDa2mWoKMkOECZjuhGtTWDxWQ84ABEnwoIbEXIrDe2pKRszED7DluQh+xsp49bwJUUkl2unwXCdYQgdT21y9uUeW7nuOY4hexgeUv3B1Zm1G5DeIh4ycMU64SBb3U2HBGyLTl99zPwqoXKlEs/MlVpazhPfHuJcsUM80KsQwixeLBL2o64D71ZKW+UeqpCDd99mvIyYxeNHXhQsTpPEtiEYDGPXw116vrmMaNhe/fvz/qc3+whIsUy/ph8rFNYJymjTnx7pGzBGZ8RbgB4Dfg6f7+Fm+oEt9VtcfaCwgCEHpyL6tvILkNwgxFuIlqt8t+xwADZSM8ZTMoWpJWjQOTVO3tIJ7KBO+V0NLoEChgMKftMG2R3zKWOGF8R5FjgxcM3hg2HI7CuXGGSr8oAUCRd4JvhpAtJlfbhmXte8WpkXROr5gRBnwi7AVYSVsV7r4xWXhNJ7XMQxAFNUP18mUGdNnJw/3y8jLDrXuWxZ6Jr11ERFLYqXgTC/PstBRc7G5lnUGf56wqdRQsFZX6xdK2AV4XMWoWizTkmPAUxilTnm6U792AueIsGO/evXt5eT6f73aGIgTyHKbanadxHoQ6h2XIJk/tXbsTAd7N93x313KzzENCJAK98osahRmUPkO15EPq5akuBGow5IR8Ewb1VJ4aHA8GfJxAiRbpI/tm4AE7jGAWMOwgmz0QAoCM8Qhu+MY84KEIRjIOpqU9sAkJRA6tCunNgDWdpYUKd9OUFrPuoYWq+1+UZ2SZZ6G6w3pYr6NC3CALP6bYS3kPOWSArtRJHo51jWTbZu/3UPsJyUDk0bNf6BulEmv4aZPT+fU2b1JcJBGYZZSDdKB4bSiNGMojZhPRUfmmL+1gaKm+1fieB0SOvs4NuXiqzOPj0+3tHoU7M6j+EIVMoyRoj4VHMAZMycjxQO3fRMar6+63t/gipNKwikLhr79ur0QJ+TnIIYAUxjTHadcLONRoUyhGhXrmtwsYzPNeAvNVb3sKhjpyttICLYrFHHuFHMZT5qmA6qZmQw6k5IBvUhYZbEchdzsSjMaZIMdaAPWIga8KDL+H2NTJGWt8AAAgAElEQVQqBEX9vzmHU4gz/JNnrORCpArdWLh4wydeFvT85T8XgtXPIBFrrDMz7y41Eu0tJIYx23ZelKlhk6rPqEr9hB91F1rw03/0dVLU69UykwNznJQUMq5DheXEnQlEmNcwTzF2knVLMWxV7nij/vj4+Pnnnw8in0OQU6JikF1h+YPImaPiIQYoT+/mLIuFAPuo3W8eHt5wzwzX7uAqPSYgqkszOSv1lriLohwhRAYVQOly2S6TBlEoYucih9hJk5NDNfo4HBYFoiEHcgbxn2mFdAMGoqYfOZnaoVEB2PAgpyYhByA9eCKpR5mVSUNBN1M3myWL1xKSwKnaK0qhWd0q43Q0nqAKH/2JeKPL6es9pDHeE90aB4tvx6mDWUkCXpMUcj8sWYa2w7kYCcaxCOW30zMFtFxTVz9Qige/mC57GIW5MEPeEVMJSSEeItAkIGIOMvoR2E9Suy9hFyUutg47Hb+fSvyZ+cz0sd2blLhmA6enCh64viuetbUZ/iPv6O2Z7nd/fLk57fjbTFq371WCp+fU8GnJbWMcXmfZHO6GVsgB+N00RokiFEQOA4r29J/sy6GQ43WWX/3IVnGQGDjUWYZD+CKiaDc8maQCD5AL+AhsJc2b4ZYclqkz2o2XZ/wBmM1y4gwUNKelcF+WJZzCAcmwIFu7jqTwYc2YRVrb6EUO71mLhXOm8yErIvpErk2ho0nbp1+d09/ew7cXVOqnFx1+uKZ+c0Z+HnlN/ViyKyIgJ29QYoJo7kpkVqjJ42W22hTGwnb8vbm5Kbg7zsePJPeFw6oHqxs8eXSO7YVAkGqnRI2PmpLxikYeQqFobXy/+8PVybpnxjq7/+5UzoL5Bk+Ew91YEt9N8dR5tpnxgJj8qyoLi2JLR+o0OZRwuwuvwDkOjyGxyMxxeKrJwUCxpcgQUrfKYRX6aYhDIy2jaOPFJrla32CwjCfoJqX2gFVpzRjVeIDTWX4ztRJWcJhdqQM7U5i2gWoqw3ZQ9n4n0oiHYaFgpUodd7/IRpX6CT9QOhU/XQnqIMwnQiYykchnVYD5eiqmP/UXFO7BDBumZaFRJil6hjtfIoWYWLAxI//Um71wyATFdXnudz+UVqCdpDY1NGL9ogVjjpQ41SPwaclNxbOhkabyYA5Fft782iPKO+JZaGzgsCOeLvQteCCuwa58MBEaqV7nC2109T266tCvQgr0fMU5PZOKg0Kca4n1a8dKA8g6V10tW0nBAb0hAkQFixMBWutQ2qbWSXDR1R+T/sWuejkZI/FdUpTpuKau+tLdLzf4Rulq6NF8rMDQAQB148d2vKMo027Mapr/vGQZCSMmshEe34Fq4DkQgANP8KBwf3nWYeb0sHxAu/z+m7pQvtROwKvedUaXHs7Jz9pfraaaBUnIWQtPEDZf7Y6fVq2/DSNhbfy0xePCIc+vjikoDiF5VsUz+u1S62TiYTg6jL00JgV16iD8rPB0klwnphqPxWLP9zwLHuaozqiOo4CHLmaMe3ts8Ui6IhufpDVIlZrWemSNWm6VsRp77OYXOVErekAhGeAtwVdIk1WpE1J7XQ8N8bRvjcwnyCNuQBMFbq8yxYHHD36BA6SEwhKEyL0+45p6fJPCNN5nx7Pj4GyDI2SWXWJin57HEc8CIUHfgnbMdzz+FV9O9dpKTNI7MWzMoKEx3xk8OP8P8IwLoXwYpqfG02Zw/oJkFAd2qHb/+HB1N9k9M/3ergfspmaLRSqPvC5hfyFiaxeyiPqqUwKzfmGtUhoZJPzQTyOpCe2nMSKh7ynDD8TKfl/5pdKK8JiAHId8wVNdm5VSEO8PQAgZWN4Pj02jd1IrRLIQbeDJJSS+UuyW76niaSMrpwEwxCvBzjEjgoOaTmRqFJJ0sqZBlZW6gM3U2mRZr8HktU6yuFKny+qKBTz45eqcrtTX2jviWQs++KiIZJrRKPdk0wuSOuL1PjHR1Mg9XoCkY8rrghiQvvnmaxHFJAqTPK35Txct+UJ4XZA3CvmDnJ5F4eOMdQ8ZDwod/UBG97zv2KxFki66xt/TO/5tJvsZkT4Iu7WxcTB4TK1sM8lTO2mXkD0KT1K9rwMgHY7HEAkWOZhNgw/xvm3Cz+EUGaNNCKFF9s0pmzXnlOnTfccomoUiBiRUGGylVqvhG4aDcqJJXZXvXAYoSKerIYU7KopEEahLEWVJFHfQ2uwTFBOholRV6hqI6tcIqHF4tmHTdESl/koPU6dKHb7lRzRen9/Wd79Mg7YOSPU0FnX5MaE+41HDoDZ/aJ1l/UZhFU/M/H66MiS9vr396Ec/T2lUSJyEySaOnNQZYiE3ZkAMpccEM7ISKV2pziFIWe18z8zj1a16RmTWmLGdyNwMijJBLExCbq6T1rIxrh+eteiqI0VQfplSpcUZtPDo0asQ1RLsqGo9VDAY8TyQAAcb8OCvWHgsNoPkWBgOFUJRZgQMZZJogQLtrEGFu2VpXXKyBLTvvuHir9zgrmoihYleghV9qdoJ7CyF3NCfK/UX+ho0bZj9Z/pG6af74BehCtMmP63kB4SajRjAFSaFZP5gQTbHX7KAq+Sj4LB2pRwkXl+ff/7HP8ZxZYk0wAxVBfZ0cGOsqBXBCWwVk8wAMDtnBxCziFTPmZnmujuQwXwQUkfCCCYVHka2EHfgHlNTTVEmcMMkIj7pC6GIGWqcIJnoUt12oSgFQp03TMpOksxMsY3dBMackEIUGSbRIdSnkZZluFYApYML98OnDFfquO/t+uYa1vIC1B1Td4GLp+bZo0qdblLX787xK7S4oD6yUp+n2IIXjI/NTotrlmvqEKcvB5PAQumYyBTVUzHVwos9No8KvgCxXIRW788tOVIoFzNrSei+u/Zanp3dQawFEgizBPGpY4MK193vHh6eL7Z2t7kcxCSLhZ6Jpv86sNcR1nBkyCxlUvrPRpFMtFJbGvhbhoaYlPZDIC3g9N6cXnMp0lQeQhqU9i/cUZQsFwyXJXWvpZNqImz4g8WIkhqB4YJdB0b3V9tICJ9iJW42EgsCvuazrtTpwS97Wkce3FdjkDbxseNp0ztJCg3UH3JhV6JSDU8ONmouYQemIaeRgY1biGuOCfqDDnpux1V1gxRXU3IWwDVLYTtdgc2UugLpWC9A6lwHx/m0VLQ9cu3+dHuLD/Mqho8Ykggki8q9aCQ9OpBGWFwsUwGSFFc8ehlgcZm+uL4M8+0pr+FUe1LyyS9u4+DY02uGTNbswSvte0LygFg3idfQFsK87j78SGDgErBAwuGQdJxtR//CfaV6tHGqTIdOWsZJG63l/LL8pb1+22iT+iEtkQQi+Zo67lOXSoLufrneu1IvQXxEX56tOtKiAIhEt1IfEjmANFXCJVZ65zSOSKnUSbQ9z6NOmPgkzIh9WjUkVIQOs4qzo1YLkH1qBu6odv/48HQ3b+1uM7aickf6pssAAMRvCPm1hAjNJjHZNYeINIjvK7bENrfvcEjlZAKSDJqEJUMRuDsWkuYST9igiwg4xHYspLbC3bu+UGHjhmm3Y6dSV2U6eTakAhSHTmW3B4VnS5iuo36YOt5BSl10RsWOr5NOdZ86XNc1gce8kOnjECQVZCbYMK13gx4z64BzYDKW1/KIZiIVqXxhfZnqFSbBFzFIFRKbhyA6LEh5pNQqtXNgJCwnZOmRnzPzMtU9Mwhvft4zvBEhs9ZXNeMmdJyCxBzFTLJCE0OGToN5Wepnt6GzjkzxgJWXYq7b55yBxKE0NEBcg2wy7XMHQjIwqgr3MHv9XW5V6rx+mwo9DMIYxzuAJO+T1s05R7kqcmQd3keuqfMjGqUeQi2Dh6nfmoKov+8Ot7kEgCoSN0MiQUCxaYbJbO6XgjdAwg2HKA3DwRmQAWL81BnGNhIsNqUk8gvYjMtqIEhHXybRQJVdQyHBWK2uoL4cCsngKdqR32Z6O/aeGYtNUCQsFVkxujMgQcU0EU7mEiQA8k06NAohRKXs7fJ3UpYaHafZBJNd6DQsKSfu4pq4kj0haToTZO4JaUtOduG+z/RSpRCU0cSuq9QdI/FImfN1/l2S+9iJmHBg9j8El2+nZ2JU7n6hHyilSj1UUPVHcJkSI57BlFYRCtNo4pp3PUeaSrPFtzoeiQm6QVU0ynnpJjqlwAeVkgoPhp2lPhIjWeNNJ5jcy3cUm9ZWTWZHSBacpl2p3Xe+7m7zCU4cl3MDuhz8iblD63SpQNOEV4dPB/mehwKGHOp6dU8UK13MU1kaN3xirBOcK9G1BySVAnyiCDcswaa+RhsySyWPgxT3W0bhPiIoDCheuKVMF8p0KdRNKzTk+bibRmNcdGeEureX07M8TJ1Vn/CIxjNupY3dTRvF+AmdrKPbns9UpVMkdau0PiF2VfG9togvuq9559rS1JdC53rQyCPoa1iH1pb0xZl6kxPSZscnxWanrY2oTiDWYqh2H/+7qoV8EuUNEbW2sMsRB8E8kAyfstMxRBvZEiTzECXmAFUckuETPbE1kpAezjo+Yd/ZfKbZCPcQX+xJVLhwL48KXn0Sw9Yrt7nIlhgVZqzxzFF6pXZp1Y73JHRNXf9AKaon/Drp6RP62aOOuclUi3bEbB1A3ThGpVtHALbe7vvACeRbc7orCgrc8Asw9K78DcDkHDUwn0fC5PddmRfadsJJvnt71RcsggSbE2axQcu4+GRUmZc8DLSxO/hdVXxX9fnu9rXvc2YsQiv4nJCowyEZRh0+0e60jI2YlPTDidoCFH4cokJ8boePaFkgQfoO7xbybFhQlUMaxKeBxDyNXT58hXu9xk3xoBbtZVVn2+oV5Pl06WU8tDSt9hZgVvM+8Gq06G+U4imN8ikF7n654btfLPif2u4mqHoYaNinCLAqdX4raU720LSbDH7fvJu2iCKeVDzzT1f47dKz/PYZDZiAWEAgd+cgYTsidi6ncqQtvbd7+J12qt0xm73bOovJ8uDt2L9xmqAS0/Bd1T61u0Up+Gz0HwRAXrOYnt4TSJC4GyrDKMgMKRVUTHcj5d24EqjTuk/sDPHZjYUMQZMTlUPREqKtMz7G10IUeg1TpAv3XvNIX17jupJMYMG9pMf48p7DunNelsPDYDjYsldDrAboi18+wgU5MEp0nvBIotd7R2CXw1nYWRuTw1UpclRkZg7TIlM4u9B/tkS/5qz3UZJfmvWmzBU6zRVk094bVa28jfuS5tVqKhkHvuygWuKT32SUhmiJ5mBfRVTw/DEn6mt3i1OY1tfriqvOUpsYFkh2UDWJ8w9WnBKfebajG0PyizuqVaPqGxRV1ugoFT4Hu68YoRCFYQKvePyAAQtRDGurQTO6K+YFVd682MKOt9xQZd0Srqs1m0RpYfo1rn/wWTww/+b6us3CwRDX4vEwdbmsTs34dh/ed1xd39A3SodvqLCm8Fm5oZHaEBbx2W1heaG2BtmRlDokn/r4XVjlxBTSOxBVkInkCQLLbwhnigFgen01TBOyMRk/yY7dQbuP0Njtx+5L7Z71fHe9aAufQ23QXA1VUkY8BTpfpJCdssHh3kKqorTc3EGownizzhyLSscpX0ey8B6LygKy7KpYYsRqfzl52J4hCghW+/rwEGQrJF3zub7inm0WFjxVC9EQAia1OvwoKLMlje6oM0pXvrqDpp89unrhZZGoPb9d01Maj7hPXfPV3cSDBJpIlRD91Mw7gFWzuqjUyJP/ABytKk15bHYcifvFClGKf5yjTmfcMDNbFiVXMyqHooMP6fnuHx9evPe76zAlL+67Jmmu9oufHDd0QaVJJUax5eiN94GQiaO9g4Fx83E2n9IZueIgIA/Ckh7xkKQrp4MQhZ9ZwA62nCE79BEk4u5eqBKFu4ktMY/IYEf5rJ2FJsSRBS+0Nltd9t1FpY5nv5igkjJ9qp892pePhDbbl9Gu7Gj8UQNUpEaHNJw0/muQ0X0obF4IyJQe5Zenv/SgghJbplS7G0ZRfq8aa8vpuw84G1Q11hWjslIq9BEtFjOzcgUPhm/BL6agzwD6rurDwzP/NpNFKleWfTTUSJnYg8U1lmEVRmGroSM6hmWSkhHCo5pjJwUMYI1AtVAqcz8GZHVuKKqVppKDSVAtrAKQzqiDPFhCz6qv4Qqtsr86XXjgFu6GApGTq4Bu6jh880LwNu4Kla6pn/B1UvUWApX6NX72aLnzflcwEWXFNV9EVsdTkQWDr6mLKhWoe3kbenClVD5r6mhrB1H68m2uKBWVS3d1UxFFA6VAPbH3YnYBUrdXirO0fw0qnVSZ01TWBuuzLTmwOfH12RpWWseApce7u5uHx7fbmyu6H3KWTXsQePaIrkyzJUMmQ0vHKkmVIZnya7uRkiSqWuH147QT+3jQsNpIqaCCVY1y6nnxjRQw+zsxzupRqHwMLW29UN3AeGM/x0NNpFIREymwFthD99wa5KC38nirjo9q3ErdTuKqNhpKxiclnCoeMogjVQUoAu7YkNOgLoPp0LRe6nSxQ7Ma6l9lLYTCdcP8VYmVUY26kGwlVayyYA3/V+Ksorz/oClqd4tWCadnfs7MEwKs7zMi2/gjbAR1XMxX4uOY96AyvAqrldJrh4VQ1crrM05QQVY1JyNYFTCQXI2qDzsbKUKXda1n06NTQxGrggqap6JLwLQ48QYiREonVmcQoxbIl5fnm5vhV2KQnVGp49kv6gdKESH4qdbvfqC0VyDwNMUySDPvk4vUXiQZOWBoSQeBQtEkPoxCntVlpZFxKTsB80rgczHabQlcEdvwrnLlxBJzRvdFtCzRNVqZlu+wuvV6wXdVtcw9XqV2n7XAshlAvGGzW/bfBwBx9OFIbNsFDMc8mnMpMoaMs2V6unK5stmO7JskAMOxRXpuT0l/ciItd9vzh7UQMLpwVrMAubfK1BkxBx9bnwy5KKoepn71alfq+Drpd/ep1wXPepSZobqi5PWvdK6uZQ45AqQh4dUO1swD4NMb7eo7i1Zkms6656BXokt9CbOXBsu8oMjB5i3RSivCitggpKwTTNeoTwOyEHg78WTku/7GEmvxmsUq1+4fn+7uprvuzh8TdgwMr1uKGg235MLCGqhIUWlnYQnwpqKLSZL3q7F8v7AK9LuwKlqIrvn8KGx04IHrWnFBaTg5/Re6dvGOoz14yG8mKujqULj3Xo+9NtatE3WjXAB8Tf2Ff6CUa4U3PKIRD4Cor9Snihxj7WGodEHJqwjNrz5uM4aN29kl9Evhgz9KBOROU9qyb8XBR5IbW/nCZo5FjICjxU/ICYMwZyTJyiEGjYvWvp8GGPyNOwPXP80s/J3vDmPO47t3dx8/Pt/ddf5dVaOgdqc0wGr1xMYJtSoLLKGO5oLIjynod07ogryKGOiHwpUkYFy6hFbuexRa0ktunMuPho06YBglDoCcvglWgIl8A9J19u7HFag6FO7DzGxfsE3hUoaRK/Vn+rF2KX3e8E3S6xO+UXo5JWWZwap3O+E5atWbaKWMJibt7qM7B9+F9uFYJ25BJf7wj3AyqdMwi/RUlcfZsphXWi1MAjMLCQUqh2sMTr9zMKyKsX4IYpI41GId0uccYhsdybX7w7P3GZFpLAN7JAOsv26LWWjH5lNBzRMGGKMFrrmKURAIYBavdOil1Uf1yDbNlyAcqalYtmEshyrDbU7nYijWAJJPRd5cASZWCwlJBjoU7gNit4NI/PQSLllZzortwomvp+er06vqRJX6zZl+rrQDEp/iQWJ9qiZooxnCMGipx5xZvwG6YC6Ozttcp/O7SyLRYpbecw4LXnbl0X+GR41ZRYjIoxwNvfri09GEr/SDEF72Vo2ZB0OJfby/o2dEfluvuxtu4aDMGaJdmdk908nt3cgAjrEpgNnEwrZ5gBmixeGXCMzh1lg0dkeXIbMxZiZuHFhr4d71ZoGeU/T19eXmJmgdvd064Zq6rtSvTufXm9MRP3s0NjqPks5zUbkTkagXEeNgs5MDEMPhr3W1nzNueB9Y0eGqYyFMleZYtyLWZpP3wRVN+1FvOwsRW93JlfXXOWw7LaE9dhkYssGiQnHbQ3iLjDbGWjQnxpYBU1FLMscSezp92667W9RWclvmykRc9Dx9ODDDrRO0Amx4MJdzOTMwertjTf8Qt+VGN42YmrHwW8RgadtERtngZbEsG5fubSQjYlAvyH3qXG2hUqdr6rdpGd17dH2v0x1dtUDMQ0M3JoPZrxa4Grh/gbxSHz4Y5E2LPsp29AZBbSd8w0I2/arPXMprEe6izsUMmMWDR5Ku7pFbjGkzQJaWywNmkQvwYx25Jk0/ZwbfVd1T7RqE76ibK9fc+lSVtQkwjNnXUWmQgocWl90iKI/bA4Cl2aIeCzA+yBu0Sy9GxrdCzJVpDWOGvV3oyFJisMmOGdNeuLeUVOOyKpXpuNVFXVOn34dCCr8+plI3ZH9aO5LfZArCMjuwljKTTR7n5k+L0cUaLtZxSJOL2bMoNORabcvIZQ/XttXgpe3IPQUX9uy6DG9NDi/M1pntsMNbWjLtKPCY9bY3bQJxKhGho0CRXF273071nBkAcxgr4sDQO4Jbdhc07FgiZxvfQlquEk1uEbd7AMs1YNVPgNHlyiNnoYZkcXvm31vghrkSmhA1c/yDTePNpsKdK4OW5GxqEHFwi6gr/s2jV1Op880VM1bqc0WrnllZr9b0s2MoNLbJnSGhk7VTfuxxGw/NBJpOeKFEq6nTr2L1+ijOhNTHi6R47z3OriY750dba4lt9risfR243BkuCykjb06y1K3t0sDWrdMcyVU0sBqm9gCsM9fuoCMz0kzsgtvx9JKGTGA7e1Rs745N6KWEUEvuIGDt9CqD2MJa45pQCLdCrZNyGQ/Oz/G+wrLSeFNgW2cO3jUeFFabCvcSUxzHlQwN9KVr6svPHuGu3mtzTR0LCe5Yxw8hBYZ+1xxiwHlboaYeVzq4ZTr7274h8Z9ku8NZto2o+NUtMJiUXPVkD83s2H/SpRRnaZTiYOybChW5Cq/Jein8dH7C5QSoYMJMwBZ+JXjpbRmnCWJwmk1q9wmfMwOGZBaEIlP45ayQNac6Uj5ZpK0s64LNxC6FbqegJTkst5fAldktBwwI0HYDpkIX+hLcqvN7YsskUpBNCEzwv7297lC4d8s7fPfLC98/QPhRqYcepv72+nq+Hv6bqZlB4HTrRocjt8ehTDmRxLHrTr3VVVNL48xGWTAP3iV6KZdxkUOV2Nivj8pS4rpwCAdT+H8VvVzpVtgKumw5FRLGDWFsBQ/L6ovEpgVIVosyI0OHdWtf/ZXSqHZ/eHi6ne6eGYnQhTSLX2J3xW+l7dXDoF3gHAvDi1+wVQBTFtVmBi+YVSNjWhy6OnfwAUFj+yt4y4ReTS8gEWn4mHmKZWQxV7iaxKGKXmCi7VxfuKfuoO3gBK7Unw2RVKnnPUyd7l44NPEZzJPvSDQApPYWBYVg1i1+C6SCj/fxj6xqBapJ5k8pfMUwMWW41bRt3wbpM6VajutfibiXQ00AC79deOiFrQsYRwgWtz0nQgG9XBk4aCc5fLzn2h2/q6qT2yTABIa60QhhNxMsgbNnsOVbz9j448oUZyaAMYRH5Sup7Aktc5JG68+AjxOFYSI35YsIoTJ2Tt6OxWYHsE1gfeFuS7H2W5IP3na9One/XL/eLVWlpeZyd1GttXDUbrgJBYhyJhtSDjVm6MjpkyHmU+gCDumNoqaNq3FTkuO98ZFUEbb+X1HtZ1EFODt8NwH8KcRT3AasjjxJ473azi4UO/khLpaDbdIrJlS7f3x4up+idl/45QAW3uLcHnV2Yp9SxgWT3hA1DHvPjiZTlIawjdYel98LmzAMH3RkGKKU2Ib3AHHzq8/ujC0ZwL0K95q1HOUOKvU3c586XQ85X+Nh6stzBat5/m6gYcDEgCrTvTNN/Hf4mwoD+oJ2QBpRrGaApBxrOpga3jHJ6uKc6X3Yy6udEUsqzBG6RDC/q/QGcF/SFLb51g+YOQSbRTHkV9s9BFsn1+rvqh7zjEhDMLjd8jszbxeAjWcF/gjJAIytU9TUiwEG4JkBydaGamyjGRa65uRtD2z0MRJVDMmwqSzc11fKcicJV+ryMHUuZ8ZU6vjk8TzxtyhzydrOtqIWmWHqik56pCkvzY6M2QltGuBcPYglKdbperZcd/hUqTrGLh2/5PdkFhsRHPDqnOuHENIBm0UxGO7lZkjqgG2ER80zIne87m44ThI8M28zY0OkSLUDkNjGBE6lVMEz53QowGZVk/swXICt0jP1wwZhk0QB4fRBUsZWV7hDNEqXtAJVqV+94s5MAkO/h3HOvE89A7y/C75yez5P+s1UP+L2VrM+UJUDcb6rOlpLxG1ZTtVyvlWvdqWuCkm5B0bYxBOM+t+R0odgpILylSMSI31QiRQHmxXFxxTra9tAAmetdeskRw51+agMyZAwyM3V2PKtqO6pr7sP/K7qiuESjskhPHiYZ6ppU/MR6CbBJiQjgBkPBfI82ByWgZCwobUkGBwhgw7Fm17qFMPALP8NQhAWS9gYhIAMdzzgTIS3IjQcGMxvYWyUFu6JVM+V+jO9Q9OV+pmK9Vs8m7HInpbOXELtp64YaqGHvPLZ33SGSm3YWhLaoWJkYsq8HAxvlMxFalTuYoYMTRaPaOOrEuYcDbmo7Ri4zDAeAaWqgZIo3oNd4KGJ1mPC7gE3oGMJY84T+5gzM3Wqdu963d2QjGBpYpgHTxt1x7rVIhlA3JSlsWEauKcCM2O/ZkF7EW4VkonfDcP78WU0MQgDyTRPsgN0FdjUENhAHFfGan7hbiuge1z09vZ6eqafPaISEvULblK5OaNM37FS10jMq1VSmbZL38HbESnT8WKtDYsfogZmdovKOPrk+CpKZpSys3RSSfxj8Hxcw5TwBwJ7w7VJ5rQFAPMWx4QwzN2xU4Ld6qduQ/IBSCPwDkCzVvl4f3f38Nj4XdVxJM9M3f7YhGfoxbZ2o3skHdA/2dMdue5wRRgAACAASURBVMsxUE2LDQQYnncho0DJzG41kZb2LF/bg9lmSAEFm67Jwt2ZKljE3l6vnq/OqNTpOiRV6m+o1G9J8iwFs4N5Y/SFNFAoaFLF2dWGeT1TLe0Q/gwVfbVLthKZ7owqJai0f19LEtLMGwtla6J719MxkimRzfsxN2hwiOtKTKswRKy1YCw0u5Hcqqdh/Jxve04n9Xz38mdEGpaHkrz2bAP/A4bugM0muZTnHeBVkyq2WHO2WlKHgV6SJ8G2NW8q6rzwhM9tuJp2rpi3Q2taIoW7lCFUqdOzX0745SPe8G6WnqeOpzTqolLa5/hLP72Uel9+KNJYGbBMJEDUl9VLq8HS/oeycYxyw7Mh2cVRTuLMqwVbJyaVG+ZSk3e8UEz9t7lsLQWovG8t170OOpres4prkJjieW8GFXV7q83V93h3f//48JhRu9vhvBvJMweeYAPRfdkwPDeKnZk6IQ2WNtqYG+W+fsKzF4A0HgvPB1m1zQzPYANWlG90hZ0niJfniI05pzyFOz+iEZX6K42nx9xxpf52r8XNu8QS3kPng6Yo+Lq9/CRTCAPg3S4ODrlnr5ItaPuBJ2QK8bVTelfn5/lTJogs5qnhdYIJmUYKTCyTFj/JXv3cefKZGwB+RLNFM0jGLwXMTB3wzQvvdPUQ/m0mQ3MwY4x3/szsARsI6OJcoRoCRWYXXkVUF3hd8DhCAM9Y7ZwadCjqIJxYTl3cRIdpqRMTZoYHhARv5E+4UOH+SlfTUanz+k2Vujz7RWp6dy3n0tNthJBJtte3t5vrmR8pY6aPqmySU6iR2Hld1WhYdDiFMiIVG306hSzFNMS5MOWrV3J8rHfIRI1im9dCGFZv2xLNbOzoYD6K0QmWMcU0uYpD2qZiAng2HHd/cni47n73YH6bSfNMWaN+XrgUNBxPzl49PGaa4xkyRlFdD6/BZZlDxWrQMM58IJGAhopSLdJ/NLxMrrbdFnicELcddm5xeeaINo3dwdy8nB4h9IRvlL6eu0v/TiAYoNDHX5AsK26PJDUqz03usKjZMknIApnTPXgurGmj+PbgNgYAfHR5173wrJnuYhmcRjOli9e6AFoLOQSeoRra48QcAm/NUOxoaninK3xX9fbh4Rn3zJxnvNFyavY4CdDMhf/jMcoBIiFNHRMRHQunonOiZ9rcMgie5hniY4tCkslB8JJ6MzscDi/OM8FDjwHr2s312/3r21MmTejWFgb5eip7NgVppc71ML7QK9c2JZdJbAkw3LPRBaH32ilwdBG+tmeqo5V9MmcEH0gWnvvAXenJFSmTtCeMXM2mXyguTIeqHZvo/RbcKqgXPshmmiO6IBDRf8QK0YvR2eAZqmnCnk5P797dfTTX3XsZ3U3ObOw5hhGHconKVx/YVEtPZ/gOh/MT2D55x/E8P3vyycJu0SVUI+jTGnWPdv/a08Rzj7t92tnvq9sR3n5IbPoSR7vkuASiha+po5u4qWC9jYvOO7uzujxQQ3rhVihjrJ4RKUVmQKrjpZ2vMCy7sqe5hA1/SEsu1W0MQsvMGWYUPEU1cbcPz21eqh89isB8RFGqq58zk6+/padmDzIqJn6L5tyxGiHBo4ks6QOtRyzKW9A2vO3Zw1sEHmCU0mWCunRgkckinNw6hzdd8AxrNLwWqgFQhnchsKxwd8ma7PgVj5QZ+YUAY67xH1oQxuKJnbPpEHVDhBraanas75UK2SV5bT5zaijwjKk0THJHKHGsolqtH5WKPJA/iaY4gUUmGrYhsxfLHeEV2ZLf+RCE+VQ/4ruqc19355J41uKJ4wAPdqMczUHdK7DzAyzeU8IPfQRevPP+ZwUVwjUHHnXTb412g/opEVhEmuSQHL9ExBr/ok+jqLLCvdsKEzGu4RRH/KhvpornBF016b0SWS85G7KHCd5oCjXQhV652stpSVGtcWVdMdadQyrGtQPtsE99hljVJao78skEZq1bHZXuI2pFdXPiDmH+hAkMmextX7Fdsm7hV5luHx+fMp4R6dU7vpEyIqyjl2k2wzah0lcWp0G3AiK8zUagDREII/BWVCON2CN32b8UAkGGQG1hRdiGnHZRBkYXUQWFeySYDKZPaMdMEOX+LnSrOw9qaRo/SxsBVhq2FOsy2cyaVGdwvLqvk1lg2SAO41YJvizb5JKYGmCoLjDwW90VeaAkE7ppZAfuChHugGilYhw8wzWmQXW6RkW8fs7MCvwkB+M4LDJQCAcYbPZAHJbMEXvoTvuTIxQ+bQ5DVO/E10bNxRG4sSDcQJ/y02oLG7GF+3U4Y7u4SFxB4V4k9xI7E4nqsyfAH+KyrdCxcVHshv3gSCYigEUP23Em0n54i6lsGNDbqoVrukJDF+scGhuwdh8KbNXprDsYr8AUwoVuIrq3M72QPI3DPvrx6CpsShFYJk7oBs/duMZzZt5Nfc8MCOrLYT7jim1BEA5u8cXME/kiEOrYBtij8kgwNC6CQKDPD8KF7b0+yajmsKBwny5w1hHF7lk3xY/ES1Q38kZTg3Z3M3M3RXEanLMDLxfLDTAL2TUEqDE1Qx1Dxx22gGsZu7JIRzc1qsg25y1HmLbvdloYcNnu5sZKUPA45cPK0XsMUwjryhGLbortOiEpK7+r3Q1Dhu9SttGf4nCMgwy8lp0JEW7ZnplDce7lItyy3RJOdWMrOMwt3Gd2jJDF30xN3OBOVvA1dQwBWbsvbKuqOHTrw+6olmAz5CxNDXv6EycSQWR34ptIDHFn0B5HIqwsv9bZB67JQYpww8Z6B76YfDpfBkL+Ep5Qe0AuWft0e3QZHJZUdSa8YVqfCbNlbd1C97s/POIvas/1mVmOhnrZJrza4KEIq1HZAydBGGF7EoQ2ac6+RojmqWcK4dNzWQg3h45F+x8KEqDKgZRbuO9vRqlGr8FmMkAa/CWMHBdZOmRKbbuc/opwUExP5qSbYPpstpzyurgPhgIpq3doGePib0Rs4x1hdoAvKcnp9N1hPwYswnk25yTZftrLJAEb4F40QovuA8Ib3E3+jEgERF8vG8IhFltZwAV6kxz5lK+TwICe+max9JDJYhMeMeBAhBFU9ikVLROvzoIQV3ixI5uNf5J9AMuJw0+ncDe8m5mAFrBg2o/aMQgI2AR4uvPgJ9yY3aKvi5AWAAeP9dpv890S4aXvLnblIjN/7YPJMA5/2CnFtO8D45PU4nW0IdZm+yjz8YSZuZ8zQzEJxlq4Gk44L3yNIEcHQDuN+QjrCN8TYb4tq54SimiarNQxhJ/PZ6CbORRlIscRZhXucRErtx1woD1C7+mpFhGzDwASUGmuppod6egtygIydmzOeHdBhhDbIJv+G0i4Q5nQYBonZZAYEaQNf1cSdIiTPKJ7dbJGCUTwpK4Z+60YYzHOhPsZ1zT6z85A1PwIwZICyelbDtEyA3uCga674/nuDw8TPyNSc1jGm4pysL0P4dDCaWci5zphNhqhME50l3lqgUkDWUq1hEXWmD0BNomjQ4QDpDk1hoZWqXEaswr3Vgi9x1NMSJEOybBPnmtLP700b0YgpMzD1BADnrLvVifCmfZA34bmfGryezbAaRlalbYsqySpaARCuT76trzC6ioaa/mxOIcnMjlXyYfnRK3iseP2prHIGotzvB/P5LxIQ6/OdN19+vvdYWxyygjlTPUBfE8djRwrQkqSxvy4IlHSG6K7JAoDceIpoyzNuA6Yz2R+Twly9I8Qbk519HU+wsyextUGrQy8kMKd/eBEv1XmXOV8MzWTqbpuNpiQBFqZQufmadcQTehz7Pe7W92xVKtzmqOHNWOiAo89acxZKAcgZ6J2hwj5MycssbfLMhejzqIchBhPxIa45zDIfAbknpvkeDZfC+tEt8W55YpJaFvB4Ovud3dz1+5AHPK14nxF+crA3Q7I54zGcv5uynMVhWjMHc/9dJxX5ZWkJqYPKmamEeEIO3YDaQgv4qSLr5Puaukg5tg0pgt3u3eL7qKxygFyWR2+T/nhEJC2RaGle8yUtTV32xfOd/vpgeo3MZz2JSF0s32AoKTnVUYzZV9GmA+AObvI/u8uJM7FbvgglVrSBM2f92FDu5lpIiI9LNKBxAsGjYen8YgFdEq+q/rxYebnzDBMYRIFkzKIKU9mJNV5jxeOgMndXReQJtL34bwO5B4utnSMBimctxCOscA7f0AKSPxNF+4W/wN3TbhDh13BzJRsCsy/FNhr2hm1PA2mwNbarnpRCY6PkYjBsdNBmWNPLJDkDjpJB2udhnKy39dh3X3kEbRPnqqU9e2ftxrWj+Z8pD8Tsvf3tWEdkbbMjTDMiwjIyZ8zYzjnSZPDetgfg8/M724J26yJo3nPi/SezBaA7Km2TNYQkPzGlEqBrOySBgw5Wb5OSxrYQ0AeWbgTR/p6Yy/qBxKWJ3rmTEmlorkIg32Q3ini09zMzEsafWYPIni9cYt8iqFP7Me51njRr6BLEkWFFXoxpTd5Q2lXIHebTRVcMAM7LEuGc2Cs4PwimHy8m+6eGUO7zfkO7q6KxGXQRbg7ApIYJmvSNwUsNo/Zi4Aco7BGai+QinbOt30rCyCEYZNPHIBMFu64g6rGQ74xYGNphljhqF38DCy3W7FQ03XPKdaF864aUsKmpSYFvOU8Qv3t7fXqip48he0A2kXxJ/E3P4JWKWZf2gFyhkQUdzjicAhIi/f2UB8FMk5N0dnTaZLfZjLEe2m/ACZ5kg6JySKHpjqvmNTPakAjttTQ/c4TGA6IqVA59gu2Oo9LtEOCCHEkdzwU+XUgO8KIiEoU7qg/2jgSqgkAkT0myPmbqapCiphad2oM5DosuaPsYp1IH8R7HM4lEhe3SJ0NGrbEOkU7ep/5b7B/lrbBnRAeM6cnTXkCpGF+VIrJ8QKcbV+ZyBlyRB8OR2SIDmFp095BnMUG+ZHXHqttrl31XdXHx0OeESnMgyUiKrqhw/wTnKyY293gWJiUHaypUdaPO8kBcREezweZH+19ebc93ldyu7RE4V6sQDjmYRxC/KdYStmA/AjIlDvrpAzC508y9B9KKt8V60Guak94g0LCnZgnxteLKC9GHT+wqgX+SYwjbldL+5JoiPc180dZDBjdc1F/WxSTlYIN75gPY2mfn0x8V/Xu/u5xp+e7U2jBaRzqRcxTZ3lLOcc08UeeYFvNcX/HnVt5RhPv9D+DnH+OXwTONEgdtNJzZ7+LOqWas96BMLa2Jwr3JFa2SIldQnur50JayIRxW1fpC/NSqXNmkT/jLEhI5vcOwT49zcdSJLk0qK33CWWbJBP5agYX69ADw3ra1ht5Qh7N8fnWyy3oV3U7KZ0B5GRq2krYoQWo5l/XwV0RSJNqdqb9Asg8XdFvM338SNfd+RcZ+8aYxTzIKEgybld4HMgwg0qEeG2J53jvkHjjCurphGkun1TGR40+C+ZphTmdPGCYQ5xvJ3OoFYB3ESBBwgqnZp6y/CQbe3oF8mhgscLdD5RpBWwhdfLYzaR3hwDhfNekRzKvsgi8zxDWTQZlOsfTDaZTcO5VLq+KdUW7Cn8PuAtsoiVqStg62RC48xnr0KT1+pTkxUAhe/jT+zJo4X6KVLMAm27viWv3x3fven2+JtRjSm6Zr56nGAh47WVxNYBMt6FQJvO3lmeOb+i2rLDQHgagGOBFKBMnr/4NyGqHihGpmV4rvd844FQxD1bDzPdTWCNpKjJjhbsxzuQRWuAno7VLUIbmVdckVVNoCvPiCCJeVY3GM4fudGXnUEs8yrlYV3GhQ9422N73DJcmDOwSn0EFn+aJJeo188bO0Ew1HY7fuRSne3Ea6nF2hmTjBXm8j9cIUBA/vH9/1/h8d315gJfYYHppnADgkz65GunaIPQ1aZEjcrpzFTbSu/kUUSrkQ3E2M2Qmk0n+SmFIdrAFNLrYFiX7ADjvMsQZh3hnFufFqWkFTkmSAlg37/3qL9wFGV3VpOBcIrMo/vYxZdw3UzsbS1RmUaLI577HBocHbp4JnoGX0GQxL8R7rfU2XoJ5YYySjI4NNkM++A0hmQFnmMVLPWOYhwEh5g+07VKcTs+ZCX9X1Zs1pGRUK8Nu1GNFr63dCXBqy+mTksHnpUJKV8VZwvydOPSJeK2lFLw4jcRoCX5FR7fON4n0M5LXYT8fTo/njNPX2D09xzWZwl0/4ohzt6JvwE17/S2hWyZyt/yeuRJ79KNkYd7xT7l2LlYmE9ucFC8GrPd09kArZ2+gvywD1uZc1pFVMCLhYLss+BG0sAXGTW2RJn/chY8IP6Wn5uTTjVc8IxLPdw/X7sbqVcLfPUoIdm18uiYbkwbt1OKMwDHkE/GdyMdUl/m+Nz8+O0NLtNh6eF7SAEC+D72uf3Q3f58ZWo/l8wYEoWykeiXI5AwsBTFQmGYv+aGYFumBQAqqrj6xvEniMCW92SZUKx07cDfu2s1Y2OfUTQIHoh8oup0KSwLh3OO6Eecb1gv2K8jBoPlzOuybE6dh35A/J04rMNXusThzA5WeM+P/bSZaeuTqjARHTexvWalvOZbPfNyCs73CNvxDYPfVFuEhOGd4sx6PVZvPeDmU76OcnpJ5oB1bTn90+y7PR4i6YR49VHqaImIu9tRuZqolk/RR5hhZK7Y5I29ekY7duPMbVPJgGbdYd6BvM5jTwY8gq/VCEhCikhJllkmlnVTsYxil7TE6SjF9e/ob9pl8D/toJ9dP75jROD3UVEQJavd7qt3xl+YUles8rZj9Cnnjhozmsxdywik0loeoCn6IAJry4UUmtOAsUrTtvF3Atn2WFuaBmBlMCF0Qxv+K++LpJU6bPzUJTprnvMAtPI/cM7fKrJXs4NS1wrqjaqcWB1E5PvajGgbXylYuZt8R4IWib1+lVdqQDcj7YawW/+Ae/Ub9RFcV/E98kEU+5W1yQL8N0sj1XWX2Q7dIOhCn4R8YsC2YLnwPtjS6fgcuEJoPuN/94fH57hbPQvI8T3AaLwifnefnAOvIa9mu53KR/pBd+wZ/Ec6OPNVENfNZ7Xrm128BzVCpI4j8Gmi2XPFg46y3BY7aZ6C74fQX7hdSvKF0ey2ama1BFPX5ki+4WxGwqOBdTg6lZpQFa9D2ZXVO2mu1kVSDjmtR65HfkiNEbFPewWDN1IUFv4bd/bWV0jJAiwNK+d8XZ5lVTu9MqPvP5yX/8zvVZ77ujm+soo53TJjqkPi8kMs1cdcr/ukaDTngQJIFJwBg50AYSdXK9VUXQbaGURRxyS5Wbzsk8UQ6CKWT8wn8grBpGY2wYJ3yF+5Wh6l3X1/frq+zrqT2DSNDCi2VEE05g50290Q1sJedQbwsCobuoVSnVcd2gNY3kWE0mS/kIyzNXv6rFItwAllJ/+2yQdkOyXEXU1qViAMgBZy0pJ9Pg9Kd4s9ymuHfdYDcM5PxXVVL2DG7IA2ZlFPU/vyVmbyNUsr/fMWAor9lApQBSfSWqTh/jiJ/t196Z/4hZ/vhklRGCbLyTl8Kpczo8OXJU7jPH222o7exYp/tvm9d1aVsJ8G0V7nS1ZrZU3TYWO0DShcqVzvGOIdhUXudYaR7KWvQA+LS05+W+GXjKTAd4Qu+o/fAT5rSUpCWC1QKKpXg60+i4Ntpqh8fRmoTnB0NDykKtUsGkkkdg8HPmcE9Mxdw3f3qhPsb+gdqiMGGdhAOnCajEv+zRqxA1VVCg82jhzKBkQf8b/M7LQE6V0SmwHagbYq9jtjtkX2hNKIxMnbPU4Jw3ITyFO57mteii2OiwvXFOlWM8jjyhwlGs1Ms8rgBl4hZXVIBaXCFeEDMsD+FvkzDjgsEv2aE96osN714qeQjngHHs404GJcWjdlddvpA1R7gCcArbRdwayE0wdYtxx4FwRzhfbMQ2Dkozg8yFKr2m0fc7z79PTNIr0ewGifQObtywWSx6kCVQ5n7IDYYyd5h+zYKNoEaf5sh/FOW4K0RppcTUhHdhMuLSP6DoMJ2T+HuZTPK5DEn+Q3ieevmLvjpmgo2ilD5r4vUY4haad3yZZ8WK9lqu/mAfX1ZHarpioqif+UF8swCNcewA+y4ZJW6VFRp+pJNuVjsxge0UI63AkoOXBGLDARULAGjP3GltCJvZYX/cidQ7R54RuR4f5ZpIP4PDYAQXDULNi44MFZDULftNKcoiCav3gl4BOriAvHC1s5OLaEk4CzwguJSWAU3HaCyD2A4Nrdw7yC9k/+SYpxvpob8nZQjHZgT1ZeIQVGIrVGokscv5RnfHl2/X2gCYNInYPX66kdKWPK7JQBgJ8SFyWe7CbCHmOLihjEXMbNkFry9vgr9NA8m3i6FVVBYBNXkIozCtrMHoPGoWHXW5qTh46AaF/BEaHaBfkbk/PfMlMZq0kfVHSwXkBO8ctC+w5s3r+qiRkLP0ypoSZG4kZ1tqKTHvGsNuGAklpVsJwIkV1BgUL3inFwNnORAkhXAhII5iJNnAo2yRrqFe3DwfCfwzdSbm+tqj1mXdJnLkb4/aiEkp8UXw2r6OsXDkp2XgE5icjo4h52QfXvEWD7gSUDXh6wU8e0hYqylRGl4jTFOWOfnsZBC0gF1RMraTtR4cgrBW7Vz6WbCdati1Tl6QGCkTBkR/1S73989PHxXu0edwOuVKRbjXfksPnLhj8dHrt8ZMHK60LSiELuAS+9kDqek0Z9o5fDm7bPMdPa8ZBI0dkgpXn09GiVN5QcAe4DW4sVYDWPqwn0LV8NWr8kOTn8hgi7RyvdKS8c74i768DjblReYPV9MJmm156a9bwYeZ5uBMPvO4gSqFNefWpBTwiXmVJYBKiypiqID7ECwrkPT9sL6zAHohqv0ztUWrUJZYwwQKlMpepbIFoDrsaerx3f3dx+/q93XtPCRmQk0lwu9gAFw4kXkAYrYiVPWygucrHZgtVtaYKye2JqsSVarCLHiBXSTnl74buEe6eodP7Qx5NGiJY5YwADIooSgh+rXofhnFH6Q4RKLTEgsHMsZs+2x98sl7TGCo3APRT4dtg9o+YjRdShQH/hPog2cW04gF0SdcKTNArUUXyykyq3Z5v+gfLzTxFcrnbdFUY3GEWTjjm7AFfeLeM4MyJMYSGSKKMnxk+RfecuUSkdxOThLgTFxQezgr5tcjpCOh5gLkvC3852gQtPIiyPBSV1qoYYaithtPinV0LH/NgbgBMlgWy9s9a4Kdxq5Ywrbokm2GB+zkf7uyz0wRAMM4kFmpH/QJbf2N01mcRMnnJDVTCEfDIyr/vY3WR4YDAZ4fgVOD2qGSi0ZNI30glaz+6smdu4wsB0xcC70ZD9O7A5056tAngfBycrdWhpgXL74nqxiiX3EbzNdwvPdYbbEQN+ygDISTwfyQVcvQBpFQVeZPX1vyRKox2ZkcQSXSDHKaJ4wsceitcgL7iqo6jqt2y004c0S6Q4YfCyBylOBNBXF7apwH4yzXvyWcViLH5MWiSr+5ICsp/++RVs87nakwirWJQhbdGeNpQtt320OAyYTUC6onAzkP7gzls8drd8dugwYPxCLVlZGu3XkjprqGDhttFlz8iADBKo3Ys0CQbTPYMPlPN8dzhRi20PWeIEkYhsTJxBM826Y/I6oBaM9vzoKj4gyjiAA2X4QYtvDIAKs1ymEFmwkMxG9GULtPjIqY1CHLhSoemWQ/XyhduE+o1NsTm2rXl5fbq5vlimaHX+2kEvdD5FyhD1WwEki6gUuMoMsFfD7d/Ul+X3xA2XjrHyVCBc4wCI60fnY07KoSPwdi8Q4AtSF8MyENsEWBQBPsZAtifH7nraJpfTB6yJPhoICZR/IWGsv654ZprOmQlg5YpdlmsKA6rbkBzD7uDqhRYI2mCwSowtOK0fITK5yhAxdiq4C5Xt3lZWLwqDwKkloySP2em38wR/IFD5Fquzno7UKd0ALoe6FmOXUKeFw4fH8dhpCbLO7ApxVWB1xva0hR9AGNJTHO3mh3ra9wrYDj+Aqf2am9WlPkDOoMqnn0KurM1qvjn6NnY0vBGZcAdJ6+6EQSkN3L4do7LloNcDLHPqqHmY6uyeQPememUv4XVVhHoTmpy8zIygx7T4lOJIL0GaG1qBuws+g51ry5KU/fD3HO8XLzAJaiMuPhDLpXXuTtYwWKawxBrfE1WRFGiMfB23lkeUSCTncLoU7ZPqFkcBuW74KtnEhZ5n8LGI50Q3arILyKRtmgc7CgGJfdOmILOnPjrqG0bSLYO0LykT033ebMFBSVXThbOWIwmWBFr/CS0FdMMeFBCfh+pGLcSHm7G6hSbDV1QRa+RS3BsfMOxd13R1EKm7D0Y6ohjvgevQ8nHhCu0tV08VSeq5lv5zAc5ku7bIjOvuCxHVF24XAkBBG63+nwSyFxvVsV54VKCnByVkGAUvhvs9E8zJlwmr58hCgAX4AE7FwWSWLsTDlM895L2V2vxbhtpz1Pmdg1WQ5YoyytWrraGd1luaZdo0vQIfli5kgToBln1hZfBHITplMwI+Ux9qEZOpyutUQBZwHoXXAm0PjiO1KcSC3Bl7mDiLggu6ZgVHgdntt2PgCZ2uiK5Os8m4Ac9QsKwdL3FIJhNeKwTyEigV1hxiEjK2SGC1prUbLkHf6o9Cuk9gg5EvJ5rsrJsdgmUaR0FWFe6RHjppInwg1yjwz6aUoiQzQagjtOaOf7n/8K03GQhSl/QvFb7vzfDcTf1xJsbthW1OPayHjQXQyM9uTQubFEZgp2a0z3REosnUOQmv7AowkfZeNd8+OjbMOw5EaKtJYXxvtKztxR+DspYSuqt0v5DkzcCiuDaN2l4iSkIj7om8MlEqTSKCiuHTkEf1Ra9NEK8y6GLLU6zsaqmKAV7T56SWEnBaGBsPaF8ubJ2ov3CJJbLniXigz0T3ixcUwValH+vq14C7Gm5tRyP0q92ktZqIVlinWWRDiRCHQr63yrfG722bpnmhX6gk/oKVCBP9jr5b4AXzXajOg3LHMCvtkj/1IXu4hvvhaQVypXlFQSwAAIABJREFUoE2/54xLqTq71OuFzsBacxF5h2r3u8t4vrss30hQUq2pJ7tVuXW3QRS65dXwbvAcRRSx2W87yR0S5APWbJad/iOZYdylvjSCkh6DgkEcIUCgYovINNk9t92cFq8o9FHlb+i0IyX/0AXHFSINl4rEZ1i+cL70U9L98L6Z9rqsrXEbz6+bS4+sYl3cvpULHNvGUj1O/1G2kQ28hjj6LuTQLta7z8IOHABT6eWfDlprRbSi1d6g6Ies7pNgY1cr4I1AuyE+5eyekX2bA6Dd3iYRGdt4annnxMtGqTTi9nLKNXXdfdbvqpr3TjQreGJQ3qWV+DJuW5UZfWGpDBEfuPRuTY3SaTGkP0XF5cw1FQzERIfoLfWFnVHFGZmJ2o5eLtx71D02GpUuBRT+8lS3O5gzdTu41aBu4CGjYqltFzusSh0EyBTbRXEu3a1gEG+tInKhdumnZrrIEn90kfudEDBAfJYkNOMMCqFLc8b+Yd/3K3TbiIU71HSm+rDVPozHygyR7aK2ULu3TFi7q9lB9NF/zkb08k9fXQS9AA+cUiRtTHEsm+KQo3e5bkLIeS0nM5qnxggLFb0XMt1M9NYlGuOOdl94o1EC1XaTKJIpSYU7enhH2mOS+yJOlEGB6MCodslb1SNkbrV0a/HyO9gGu1hnXwzW55K1szpX/XTHagEkXOQOConvKBrlJe+EWytb/GEy1brDfkcAADQVMA4LIKp+Olypsik2/gAPfe1CvUlXiy9k07X7w9P9PfaPQk188RKS9AZBrA3gQ6wDYFiXkSIOQecqFXov5QmnFAv0P707op3pN4O2KAOr2UGpaqCVRjSpszZS+vbWdqe4vP9joWKDUWYp6ryLIDa/mdpZ9D7ihnG0KtYxcUZGVYoqJ9ic7sMocPQcfGiqEbhi5Qyuew4Gl68e2IvyWr7kQT1DgI0/yB0XGIOTQAYMSjXNaKieljwx2B8Qf0EBzLX7/e3DI57yvnMOVxNEJkfJDCGGe7+XG5QcIFYid/6QMNMDBU98QR3HVZ1kThHqUZR1EvYcRfGQkSKUC+QiS8nsaLTFk2hPJ/rxUZmnmdLV3JboLxqZqSDV7VK/meqhP2VqxvlVsU7B1EVNFyFb9IPEbhUd2WImCEAcMT+OtH1O3cYj07oDwCJ5eMJpEwccCQNa/PTlHnLHXrZVA47YMu6UXHff57eZXI/UWkWfbBTWErWq+oxDSNAXNg5/PN2mJOcG+mNPD5koF8Qwzbirqz2/EtMYFgIY2Qk7jihyBrKWnNicdTrvcAh8OVfc1ZURBozHMLpW7QD0slWAsI6cKW8IJSqUevODQM1EnNmtN8C1vINBmMKQK/XttF+DvcijSzTq7e2VVj5Av4SUBZD2qnxwSGcEqQM4PkJWPkorvMU7DzpbBHgQhnyxUrvjujue8o79/IGZPc0yAlrglcxR8W4QNapQywRIcZaxaWlnfrPBUZkxalAXDcZcgyOPBNZfieEmwJkU9TCWzKC0RtZoK3vIHSaDQHIeJtx8P5WokvZhamsE+wt3KUU0XDKC9ufY9OdHc6CJoxDW2qeKybKkDu6YwRszYLDZ5/RgNwzdt4t1dkmRNnjQrtKKxh7V+RIAK6/QDFELxmxRGvSeAL0YuNqO+KyjzCfX18UletRhrxc170bU7sojtKAPWdQLvrs8ItZLZTILnDVKR3YL4Zx63VbGma0hGecYSlHSbUO1AnkNiLshyRVEiN9ep6m0vLBvEAc4oVY8XYJIoxkQ8mNODBghvXaG5Jte4Bw5QpyXviR3S7FO3tjX6ji4+FmHgl0O1Xo8XJc1TXb2yHDTkgrm8zpD1i4heJgoFkoczLxaWEgV95MD3kaIAHY+WqZsZ4p1dsp24FEtEiQzR4XDjKndH+/vkXGqN5olPDeEgWo57kAfJIkKXUq4I2Y6VugBWCog3tkJIOctzBXxStnT8NW8s1zaGbTPrVEViaupJI+zMaXoqKBjT8o0EbT4C/fAsH1Lr1wC6B535FmHz2nJfXl9mfqbqSHiQu1rN/FkliZyyFEREwAbaF6b8IkdWR4hy5xp8okZmzBnmjrYOIUWgUtzSXwWwRpYd0E2CWAglms/ZB2apl6dVQ0xO0g9G6V2v3t4KP2u6uodVItH4iGrcdqv4BZhPH0gE0P2RjfXtRBlywrsgxR9BqoMs6ZRn0y9ylCRtohJjRp43pgS0sG3iMPOCUthYh//o95aU8k2USS7J0I27tjOV9w9cHeEUKLq7fXtfF329rREfEPfZOAGZAdmcqD3Ls0cw1tNFN7RrZaCqNAjTi4+cWZyRzA05/RlsI5iP2FRxiv5TkFPjMrvP5q9zBkSmICj0RXLp4zAhQgqnjO+/VQs4JgBNPOO0VypVdfuj4/v7pMijFPK3kH1dh5PPXo3N8/si1PHRdDpFQXGgEDW5bqXjXrqJb9NWVhuyGYr5Y9k8nXVecCcNDOFQjTsBCJ5vm/ZLve4h5FvfHBcAxicNBFQFAQ2Ar2ckpVOH8Ma65xuPfaVACnM02Ebx8xSF+4UY/IAtXEG9ZfMobqO5v5KVhIXp9BEubBorIMLM8nqiY01TrFKw0t6GiAiTEi+oIhStftHPN/d/11VWn+kPIRVkeCpC8rVpMw9AArU7YiWGJ5cYTv1Q9XeF7D4BOiZhP7sC7d9MY/mGpgBmJOcRYi1awGguO7+PltNFuCIzBQLBKAJZvGideaw3aVwZ4Y8OPx8ejru0TQVmCyDGTHPXr4PjAMlM1yy5I/oRAlX3mt4g+LynBAgiXKH2WZ3igF65I6saqMRLH5pd4ok3HY5RTa3zxAEZruQIszxzjRRzGzZsAmodHIqxHF79Eq8MSU17LjzUrs7z5lZ/AJLvFXIoU65xNodNEqkW3SWkSj5C3/ZJ2Vj6+KLMVMs+GOgTujIUYJTiIpiZvYiFJLRmRt7lScMaTwV37jBOCEEImh8ptZ1twK464Hu0VK4u2f4uA6dV9S3rVG5CLH5dnWmIKn19XTEiWVzhQajoRmVYktyBfVC12jKSElqPp/E2qzhkgQYx8Ap1pJ5SSYAaxefwn5iYwIWaJVS0z3hFcF8WZ4D2lGPLxwTtlK736hnRJIOWlTsOOkSf43g1cqnpBAixPPr2yXcS6XoA6OYgDwFywj9/9v71jVZdV27vlR3r73f5uTRk/xJ8gQnP3KeZu81Z8++ZMgCY8A2vshGpou1vtkU2LKGhiQLF1DUx2wULS4vkzEa/sFwRueRvt8Q0NnLz9oDpxTGd6obdzRr+OtGycwg3iAQnb0jJ4upbegv3M/VKYRJ/2+mmlhn9W3Y6rRlyMbT8YDSgcMHwrqcNmnLnyptNqUZBP+rQTHprEafBKI41UrZ0DIDgVIy9yAgGQO1k++OWDgb7KaBnjq7+vP+xIupCtMnqHN13qNIOZLx+sIUcY3bgBe85AJ3y9C6+1+vO6+Rv6kgFZCbw9z9uT+q9m4xOI9p//oUsicDO5wuEnVGMxbTJ8kEVJ6mtkSdQ0IaHvclR8oxcFtc1zWYl+dVB7q6XeexLJfY6j3p3GtO8TKyKtyz0HjFNT349f31/PTcdAi/8JBdDKGuc3gWFaZ7TvyC1R4Fsh3o3QG12hvFlmRK9fpgyus2bZ12lphheenmTL4wrDN+uDclM64/MC+Vxgs66q0bAthV6wxXownGTDJQlJ+bfHz4eHt9/fX7/a+/9mk6gPL8w2TnVuvurSKSfQO2w87egjaTec/u2/c5Ete5jw7+UTwmnBpOOqNYkngyeOFlW6/79ZqOmnLO18KvN+lsvo70n/YJEjy2FO6nDJ+HhL/syOsj0XrNKLnFbCya46LOQVPv3FhClW4ySPF5MLszH9D6d4lYRNWYdtdq2kUvMiyFQIZXOLwE5sBFfJM96AwdKl0iA7AECGiLGzkqdY4rQoltXiyk2iSH07jksc4CeL17yEK21KymGDpKG9ziz9vb69+/3v/xFx8R+7ellwutu7dUcWdH9g3XPZxs1lWTnWrBAxzIrs7BpmpOkM7mDqWloJq9PVlHw8zS3/SzQg652jSwHcPD01B8ZU3V0qY/d0uQEpYfOXPDaN4BI33OOtXKBl48rlGmFQ9uBwcLcOSVo/Ggiy2iX2KziIQep2waRQnyaB4m6DGq0BhjmHgHFpGYoPnCDE8kOzFjHEhA2gpIoxs5KJHO9bo4NRA4VsUwkddZ7YBXLTGDC6no6iPV7v94e/31a7x194zL0YCZWgWcXy7FCGqAry9erRAPGf+o1UdHjETWGdYmIyeTb6MmHjJ5Fg2NvitDp/Ld79UhKXm67Fo/Livuu3PqDjSywQqnM4b1BjSoiNW0ImelRNMPDsKm47QU7qVmF1AtNZCS3blckFI7LMdSg5BB7tWzQRvolqiTJsVlTOgW65TOWnID4emmloFXLQWM4/bxVtuRZHMZRRRl8UK1+1+o3X/jfnfsN1C+hUzCyFkiC2wDdEkibUKD3rkEJQ3QstFYpmZLsFckJpATHGkfE6byIFNPd87sW6RznFjFfA9TuHd4MtWd23AVxQ6UbnLdLWucSQMyN39ejBoN5s3TgZLUqgie2MHUNnTUKAwS2LPmzSdMDDJbb2q2HpLnYKe0hvOSuVp4cGA65pkeYM2infG+QMuQQaAuPav6C7/N9EocC2+Z2qSODhOTqbG1MHaqFtF20I3PbzTUrLMXEOs/otpMwcb+jNGe8p712qHhwTnsTBTju0zyHN7PH3SW5em5CsZhCneY46nBjRBkDErWdKeScQLHcK6hnMMekw5wyAUTUlcbSA4B0jal7AB96KAikkMGvtLxhRwKHPmaoYGtoKXXQ7T5/QZ6SO1NM/fjlNYocAhcf4AYMSXjuDpr2GdTU7rJ3XJ6mJmdzMPs5A61aW/W3fGsaqPafTNawUe/adjU3ngsGEOqi81qIWomDxHiTkrtQzkjqs0UuB5yyM6hHTo0YFPjmpz1pxFlUqEbR+PcKoNlp+eCfOojiuifjGAkzo/q+9r+hGOuQ5yPdwnO0ZJjme04zpcgL5PSq9fCDorgqxBUk1c7BA+GSNGQ2piS0Kz2YMWng2oxtxvLsS0SqJ36FUeOgW2xPgdNTmerXGBnqd3/8RZo0uFwNiJOekgpp2c/N60dWoodW4Pah6q6DcZVG3dbAAj0x+YiUrvPerJT0b5X65ScHkY4zIp72uTlB0rfSMJMMJ/5d0u/14JeW/vFj3tUB0ibNY0ht+yMa92raG75gbus2OEycSiYpL+CQiHXZlA7VCjgOOc2qteVTWwRtXMt0LN96jPB3oljregy9dCF1JxvIx3nJmsxx5+m2h3vmen3jshSXddoTnQSm9mgUVbssM6hkFzjU/RpMLWn4MmjRo+5D6y9iZ5ITvBBGqNwpwixWc8HY39sG5Nspo2x9t34SGKzUHftxzXAW/gxE5oGlbTT1lM/Sw+IyZrSeiqZNZb1MKSSzCQ5jWMlZI0r1nhduy8E6aaHZy/dOnooWtQuYn1hB9NW9m+re/RJOWRq97e3X79+i78jkoYvMkSK3ibDwGLdnMSyQ6hKr3W5Y0+102x50MqqXYP9YIzq05agDTt8fHOwerSGAjKsHQqvwFw1TuEefTcW237iGyaYblh3KAnZxWky+O4ACJeArMiYg9PkUR/hDctoyEcLQQnzmR61PTadD+2jAksASqw965j0F0DoC+P5Ww4N3pKk91iNHHeZ3DunYM0KnxaGQRJ5x3tm/sZ7Zt6qX5Lj2KKFrmuZZO2vr9bv8xUniJ0EUMaKR1ZbYRpkgkLG5OMd3lOy9s3aT6x2obU3UTjX8WMU7nSDu+/JVEKBmYyxucW6i9bdr6VAZ3/FCG2mNJYLBaROs/4IrRyC6KuPhHp9FLPEosIsuscaaAJpKSJ2dFzgZZkHahdOWlnDFDeO+QE0R1AciAY6bqYhfGjdnd4zU3DPzBHOAzPUnkbVTn4CMYcWzxyK6DFbC4LYvUlzabUzUeY1Z22VqJ1FEF5Swu2HM7iA2hyjjw9jFO6ue03Fuo1DGy37tLM/kufbIq2bKtFUeDl8Jw5xVdVPSc6hY8VzuZWrek4UwVY19JjeiMgaGVUw9p1TVOFFd/H6YK9M8REbQXQt5VAEW+sydxpCQHBzeFqnlq1SvMQUkHPJt1XGHKe5yCy2uhRtW/b//P1kflf19+/3t7dwaKaZoK/27CdSV6c2iFrTw/J1eXgacWxwmqPPcIdigljt4Qxu/QTk8H4aS55WYxTuiGTi2PgXOZgzk3kwneGCHjWcLwO8ZysOqkG4YCB+eINylU65SL3vSVpg4ogjKB5D6cNSXCrwx2wVUErST+5k90u3TEFLy1AognAcXz+GzhaM2K0LdAa6czSvIBm+bUhZFJ84oiRXIbex3al2f8U7In/jzhkV8ZmMF1aFhbEVmxd9ebRiCcnKrhqy5p0HXWlQ9IEURiqsMHj2sBJPnbKdmeuxbA5toTa2GrX1Fu4zueQVQJgEUmEibaLSXCP7I6bJkN6h4Hz2eBJBtvV9Z20BWK8yktfylk+WIwqhfq6xKNBnb3HE5PGUXHKQ5nMcpTCU+s6TZDv0awgn73axJOfqIOULV0vTBeowUYTfPcE9M1S7N3lWtaHXwMQwN7aUcLB6oD3vZ/Wy3UV2WHOIOlGHEiBmSXTcZwxyXaXERKJ92D3YY8tcRVHhvkxg08r6tByWxIpcmhYlqL+wxoaw2TH9aqq/DX78iJYlJIWyvJBuQshPitB0iQktRbz8FM0tOBgNGY+AEEWZgMwambavCyy00A5AzphDTeqOZ1rxcDDiyNTrhqC8OvJQeLMGixWodqdnVX+NWLvDPrD+YWgQQ4akw5bNDL4SDDXIaxI0X3VT8IGfMYAi4paENRifuGSrLRncfOCBhviXrVH2rO3JhftSrLO7+CawA2BLmlJHloRqEjKqDbMEXoOortbuLoAs8BM4UhEMFe62SXfFcFCyE93F/SsgVHblskZ4/pa2wxJKUHQRLnWBukisNGZKd1S+g9buQBfxFpcjtEsxRbc27DTQ0PWebqPXDCSuOdPUwQ40BF3BjWfzsmdtTyjcN7PXYdTRK2W8kakrWj3xUv298JkIbWYEsA6B5zHfDztURral6USOOgxdZpxDD4LmHXK95YgCyZvKDhXdNeij+W5YgQNimov6hFkTPFgXhOa4Z+Yp4cXEUTNNo0TbxE/mIafa/c2su492vzussPWWOZZwPG6j089uNT9doWQF6jWfWepbOZg7NTrk82RDpjZkZ2ajJTp2p8IdJSy9DcHEIQVcTtRtA3T7OdU6A7Y7TPHCtrDxxkwNaLFdoh8IA4LCfOd7oLJDEoI8Mc4PZFafbpcuhV28GmmiAApd/oJYDUeJmvdpVr6okeIQh4lzBmkacuukUELV3s7VZ6VCf1OQ+/ua98y8vuI9M/hd1dE2EINv3TnRJZGkBiC05WzNyqvR61iRMs3t1HQi3jLNjy3SvgUbLTG9NCzcJxbNZZD5pxp6eeKqHrqfgM4g7SUVXUzZeLMR2A/3fSRUerikDdlhpgQcBduE+o52vCtA0UX3mSVeomiLA46A4UZ0B9Y87Otrfy2w4lEXZ30duhy1XqsDalOur7edCj/n6hYchu53p/fM/DLviBQTGxxP4oSNJnogO5IbJcZqJINdfcQ4pbiA3mkZBs1gwPxQamJ1VkOVSuk42WEOjSlcuLOxqAYUYnHlN7wyErLBGLkopL093gPkRBOnQiXRZg1w37EWmHkSCycrWXqH0029K/UPYowYD7kUU9m8V2+BlOFsGymzW4HddqA57oFEQRYcMXwm2OXohK3XDU2FA5h6Jum7sSN1QucLFQuJs8en2l3/uvuc96C5DSgYhQ6n1ZEWsoYdDlIob7Fo0CpJB6NxRHNLlEJo45rdau64/5au2sLdMscRJs7f59eX9zdTtzha5brtOA0+d1F94YkQiNPUwCw/USRHrEWOj12cww542s65MNnsucZeQsr5quo0Cw44sOe9li38YPlOEUv8RFW9qUztDv5zXSYysoBWEen2lKnd32jdXeU9MzamvJblOJW1u7VM0x2Gc/CajaYaVAgn5Q0xlpQ4TRVDCXdlhb+/vh9rn0sRVuxQ3GRqulD1Z5mSwn2ijVIN5LfJOFYq9B7N6IesrBscrvdZW6z7HX+y8UVfSmcxVTzksVL3Fj4LLFRlMuUTds4xOFgoy4QUGtHNLFNZARWyQP3xArPXDyomgW+BaOMHTZkyZq8xQxvMCRpNv6uq6R2R6Uyxt+fmmQSr9GjC7w9RkjfyABul8aJ3vu4dCwIKSHawsdQmgsxKg1f51MLdhpap1RsnHUf8YVWb539aWjsIpVWyTGGMck9F5443ckrbYAx5limoa5lyD44Bo0LL4ugWjx/YH5a3LGwwLYu2aNFoqWIzZNZHehMaXZwPt1Gaof+qdXcEzBHUnCleiMz0B0fR89iCo9M7Is9ed5+ZWrJfiknY4JFoTRFyVhsoz6gz3eYsfadxJ6bI4x9GXEgd3WdAw8bhY4X7NF3x94s9p6vVfE5abDcV2W+rVPJnae2nqJrGF8oI/BuHyZg0NTT21VrJOGR5meoZZ6eQJu39rUBQ1jH3RNMAoEqx3sjQuF9ctYqWpZ0ZofxmTrJt03eIKFpo6E0VbJ6g/A5zOrBmLal252dVu98zQymQyKKtGF+a5YvFN+zImie4TUMdEkUTU2ZbmJrumsm71kocrnUztjxGWeC0HlJO/kb5VeFuWDKFskFmKna5kfMlwW+eHp9W/cojfSWm1wevulMwBHTwdvG0dYLKfKPiafJTD2E+mGZxJRawXKVUgCmlgBJcWzU4uXjTYqpbb0X2+zxlRnYeDIvPFYVFP73NSJ77xTtrEB8uSj9bvsDapq6gdEp9TyKLlA/eRBuFHbdY+7PLe2a61O42CUqRZSz/hd/7bG8q4RHIArs1VOEx6sQxWaznRpJ+5TcKux+voTzKmxsYYpJM8gNGKte7buHhPj8/n5+fe+sjB35GNv+tljwxRRPVTFe1zEsKwGQuZvRSA93JOp2CdOqYLHYb73SVLuq0lqggFbj9BD+Te9jcLMImGc+JLAyTOVLSCNwoVTIWl6DS42aNKWOg05p2qN3XZAkjRdWOG69HrN1hCOPz5i6xk64592Skk8XKM4q9HOVHhlf+4eEGDNjONDStmPg3kw1P1c2vV9JRZx4KI5wkxTDaWELbk5lKwv3DGy10/UyyDGoqIGM+rcNHplsBOa6MvtCZ+BtAd48FtShfTjwIiS0bzevrcLHyMTyGCx46zNtLT+g96GzVqnafE2Frsqh2H+U+scVfpj0Yh+ykQH+mi8IvObZY+UGNT24J1RVYfucUBweg8+fnx+pWmYMeUqeTPaPlaooUmJAc8oti/cENSggu1FsnvhCA+/FUCxi20Jj8GmxluLd/BCOi3Hn8QnsdBXrlpS/omowrQVYvuyaNA0AAJ+CCSaMFGsG4RRtKBnqz+7p0IKqmh5wYVnV0FekW7jTrY/z+fOOHFY2cEazdKbZMdPWMLRVuH7Fv9BS5Nax2UtgSXzRzFdJFypvFDt6JAtV30ig9WaB+2m6P7+sTb0fH90uPz8+3Mwr3w8w+J8P2pmg3AhcHIagehOxAUAjnjEd52rRT9y451wILXw2SFrxnhEyy2GzlrPpUpzg0U9ThHAUywWwDShdbtd1bMdF2KJIuOhzdqY/7xYkCiObkiU+iY8iYxKOSUTuU8GVGbSfF1O5vr6XviHSToaxLJEJm4xMrGr3lCIRx8a5pZyZMJLis54tIOzKW8HnWuavx8xF8fHygE1S9vbxg5+Pj84zCPaK3kw+RwZ/GfoM7kriDZ0E9H5yDhygxrCxN7nt1FjDZO2T/ctEOY3xxVS7qMj1nb14A4YiSPOjcDPNT+MLiWQ/j71lf+C/fQ72OoOXaXV8RcIwZOvcwfrmBYz3N+91f894ROSdEMs3ZFTMbH6lHn+fEzG7P9XEeZgxjHXuz1Sxhh+RB9ODGB1BpwyTYLtwE9kTJDpXwqOdGMTWF+86PPr/Mk6lhVIOeARmsORBvyBgUkUq1KY/4r5ty1Z0JQ2q68+Uabxey7snT9im6bIgRZ9magOVxZyBC20H/KYdl23bqsCbF0EUSYfnZ+KWS5futdT2SP+uf1+tIaqfz8z0zv9//eosNuaRE4fovNmjCOTb+uME7OQ+QStuVGYN8bAmGLGpiRJPxG+hfpFBeJ7aMEufBLTF4NQt+sevFLLHvkSgo3MOO1NDJ9pYoPBLWngTyWY6a6fMIoAptoaobCoE4N1FtHcrOIAxjKskgeyslWbXmCY/9kAlHnMV1GC9JxwSpQzYB+Mq6egu7jTndet0NVh3OX45Zh/5bDhM/m9r95fWXv3bntAjTqA0xUgy5YPSlXyH97TTWja/J+WnVrDyCEn21RTPWH5K7WcxFAb6+cB/79xeW2EMlO7c/r3A/onVeNXNxKdwPTpEmZqbq8RQnUGgs5Sq5aW7MtNPWwEchu4zON2x0yN0uZTUXaovqZm+afoZ1Ahn90/nemC/6cUVZgDMZ/aNq+E6KAT5Jfx+m/GOo3T9eX15+/cavq3LvNWX5Ejv3oK/Z9C58HBoDzlN/7cGUkajum9Ef4499z1Jn/bHEjg1coWR/fnw+JO2Mwv0EXzq0Q1aDAACOlVkS3Jc8+L7ptoBL2p0vL1fFTlz3pYdXFzrYiTJ86eGuAwfVUXoCzlwy9xSTHTWDQ1lqVizUP6pG+KQ87L76h5EVnfnCOxZRu//99/vbG609DjiRDW1/uvLAtUf+K+ptoJ1MmRm+JP8Uuat4J7Ye6Q/RjZ0ft8SwoW45r4qRK9xFUx8uPlQ+mboHyZFCUzz4pf/mjU/Mn+5/FVnApebkBBe2ChTjeA436XQGzl2yIR5EvzC1rPWhDJFMIzbO2iWGTe5TcuFRSLaj05IDWBJzAAAgAElEQVQCiX+6bYes6KZGp/HBLkKgKQOOrgealJzWE8JZ2nOgYb304+3t7f39vcvvqmZpmNh4UPtbdBmvqJ+TY5/caDWM77D90UaVVnGd3bOsdvh3kd22Jft//vzBe7TAMhbZc/tLFO4Nsp95pYzCnzKepjUzIcHUNDOamb2BCXKZvLdfW2BPyZzcRs0ja3xtP+2tVzBefRFoCr9JzAnZf+SqHXzR2xUPv7AWYXrtHOtAKyvZSSLMj4mA/ghv4gKj+jW++IiOnXFyzRp1RO3+5/Ut7z0zGQP2aIqk0a7w6gCA9DfVRij7TWe1zmmsNpQM6d/BhpVD4L3psvpjVRqr7DBI/C72uNoVhXvL7EeWUvQuSFo7nKsQ/GUnBP6WJojzdj97aIE55XFDYku+AjhUYrwGsj5N0ZJvAyVPmkJz2ZSdb4nqHhGfLyAmqo5JkZQm1189RvscnRSdNaUBHylPpuBrJ8W1u53ZvOmR3hH5+ure754AWlcTdiHmQpdmadoQL/vHbWfavKylCe7XaooCkxj6jSo3EusPeZXWRr2Oqv3wwdMUxUsL98Y5cK6SUyA0bONMReCMB7LIXR3twUUZz6Hl5H2vpQXmpIayC99DtRzpOrJbuOuU7+bIiRuLwomvjrnua6FQXAPvWZo0S649vML6H4QVt9ce0oa10WZmNWnp5jLA1L3ykvvQwVGgTfs1azFL7J9VjbVWeW5KRIovnw7MZlIiBTLC2TQFIm0eFYcwU1Bb+8ZHaXeW7c1Rk2t7FOvY0Bd3sRfcFeMFlV+4d/GXLoN4DULzHM/U0MEER2nlN1hk+a0xzFEOKaZsDizOccNA2CnKyW5GszstceDEQGP1Z96IuaZIy6yFGZM0HDmWYVVAEP4Cc/2dSJltU3vBR6cVx9Qept3prr1oSxTkP2i49Jfbs+GWFWuo3T/X75mR06iTJMIL8AiEMWOZiaOwM4myk9VEh2HLj0sBjAEI5EPJXsQPnqJj1oOnKVbPL9zj1ZBEtsTFifA0c2QJqtWDl7JewIc4KcCOhr2fr7UAQohFIKLu5s61ZgeLcabbkGOizRCHulg9b14IuaY+sz3RjPlG4OoDQsykRWh6EsdFV3JG7eDX2XxmPGiYLTuhAxUbZL9i1qb3zDjviEwYVVkTAk+GKDZCfzyGNhp21jmvcOyv8OGIAMKgZkSHPXQ1YLXJiyJ3/nw/fHx+QG80Fi/Z2RxphTsy4VQghY0oly3xEvoOpJqSj1DN2SwdwHFLkWkybOsffcbNZR385Hq2PnbfNpgtcZTP1NfrbWzQS+qa48zCd6vkQtwD6s+16G3bVp/hMDRZxtzmHMXSASdASBeW1NISB9PUr4XxPTNDP6vKBfCRIyXZtmmjhbidw3MI6IcQsc9FIEyrGKu0A14+Pj4AkN7GvuMuYpPcU2mFe7xqX2meq4CnPR4Df37Jfj+OR9Du0BIP0xUsqy4NYDfu/UClBSxxkJMeDxfgNR1sooX72wTJgy7FTbWeqKS2ZmBhpJnSx3EBBAo6kDcvbLQkxafxbrwCCDsZJx/oBGFOlxhOFjBqdzyrGvpdVdmx2knrxEIRAKaOaItypxlCIu7hIZgldzsv8IOnT09PNe+KSTQdmgUKd+F4T9eHWsYvE/JkUdFg5cFV9sD2R3JHuLdvZQHLnY+4hEE9dCf0UtbEpoYavTp7uSUOOpuXEmZcbtXA/NF9JTi230PCkhR0EjITSLEp+ritCQfS7ripyhbQWySiPeCcqCtMmB6h20Omdn+51+5bu9R9ttSlE4eWrRypDkt67wkCOow8U3/8+YMnT1/w4OnLSzr2ypa7wn3UfOjYwawTcf0Pz8DmnHN3Q8fdNvf93hZwU1iYu1StRk9tqTjD7Xp6uctdWKMhz8AVkVe6VbIZNkommCCE7zbBKXODH1olS8zQctO0cAioRloOW7WTFRhDmIiNpQ4/GoPQ1QAx14O7B75nZuh3RBoeYuFwaHaZBhN5hesaoJsF9OFdBvJaCmsOFMNBwIti+HXsN1Ov0y9OmAS6xtfq07pwL0ynksqBQizRlUikep1WbuavdiNCIqdKRr73qbWAzV80r93ZqTUn9+9jx5k6w1yAO3A6Ymre0qCtYiwieDW5zDlzIq9I4NZKSZ8zltiNvEWzC/gSQ6if5jn0yDSIu0DoJbGR3wjPqj683tfd8w1ne8x5s3K+Y18aPbuOFdT8rhjo7N4Vwz8XgNWdPqsK68I9nk6X5Gm9T34H1zHpj9HMzk/F+vRfkkZxnBDRBWqSqqc1ooSymuTFNZnY44nnbvGQfYkIvtc41CJw/NDLvf0SibDkGfW8kq52EEh1TZD5BCNLAgJeMGNCGwQBUyLhJ7K51VAdEfm2mSDkd0SPKfQMeUUCZDqhdv8Y/B2RMAQTwTsydolLmcnDuFu3jncMn2VJECwnMjxYszP6UcDCKNlhANzI7n0de08IN6pRpTxIglS8C/Il8mTqskQErWEocdXFBUoY5SQZKAxkzTFnLcJD5MlKP8lKHYZNvIBqbc4NfXnAUfVKu1OeAhdoXUOwzZzmbpOOL4epUTrIGYReoFLJerm7jT49eZPX3d/+/e/f//xnkCr1J8ieMG7jwpfpazfpQXJrCB2Y1BnX9PtJX1+An/KumMmdTIXTzmI3WsuLrNw0ybrZcNjp0a2oWNeBIRv0dTos9N1L9TasNnVxEfo4I+NyrY0BOkml2bH/ne41NoO6uOzDZlKnNVPipaBtX7ETmV28UtPQmmLL23+ggykvdxeJvnY2Qe3+/tdfoz+rSnkJhm5Qu/ekj2t3cE2146CbUb0FEWX2wLsdoQzq9bzXsbdHsb5VxgV3NvXW46FUnSMezhxnQ3XNfpV9OfqqLGKohQNcmeIW2ChmuOCrjb4q+jR27ulNFdTaANzfechVisqgyACMSQEY66aG8/0rhILoM1dX+gFe5j0zJufJeNQSfX1raHan0eNiCgrw0dd6Nh3AgCjZ8RH1enEAMgoDIiOtWR3iO77CXX6UuA7LWdwnY74CpbKh2F6zuPNgzBr8wL9nJaygqafZPXh+iBNTIltnsRb+bemDWaoDcGtaL4ptI/WfO6EoY5fvhzGXFnH6OqHIYLMEsD4UGYBtUxeFDUCibx3vtr3CnWvU7jCsy0WZnZlB8uaT6CPPucSNZKegkH0dezsuzr/H3azrTZU6fqgl5S6itIgimeGtZJ4IS/vRZ9zJhj31R5ujPXhZ3+Vij7QGeSdNNu1tNsgIRdQuKdRQONTXS0WAN2SalenNseE+4n73oQOQa/fR3xEJt0EOxIyWmwndSVCD75Wh0KC5qwOjYFLc4+L7oM8+eOq+K0ZkoBZczPe4S+TPdJDWy02YoN80/MGTqekDHLRsjRbve774ZhmEU2K7OFod8GStbBnkYq8TxFPuEZfGBoeH9STdvohaYtAsrvMlVy5KCRRFeucqGm0/oRizducYhBHZl+JLTVEzqDiJ2v0T75n5/fvP25sKhUqVyAiNOY0yg6UDNunHKCBaoW7pgBmFcL51hs968NTpl7fLFAii6LfiPns4AAOFP+PjCZE8e0yt/dKKRAl0MpOILpUEUJGIiUM70wiJvYs5sAAbXCTsLYWhGDxQpe40gCA65uv0OlnX6J2fJ8iApmAnBvO7u2aDhFKnqhvYVaJ636D4qrVFtRpZAjgMVzFoYPB97VmiVDXGs6p4K9xPqN2nRKq7LCYHg6KyqwzdHY7DRBzF58cHfvEU73bMe/C0Ar4JcUrfjKhC0oPvHvcaeeu+sDUfMK80OM719ILhvC23fZ70e2vLIEwBbwtccN3t1NAC9S6+kHinUIgoGLJ2IsnidbmfCSmSsqkQjmIxU2JP7t9W4cfHp1o6kpEUN7RhaKLQZxB2quIBdHREKfTxMvxvM8GW3hh3SdRh7yMtAGP8W94XOszeEebYeTCIkh3563Z7fn58ijVtcM6wQRdTvFM8gnzhbj2bK70szXzJLC4gZf7Ilxof8wecZRLxL65HR4fLeUtBrZNtyErHtZFYmSOy9Y52gDJQTJVKUX0bnEzndanXYbD0bhk6Ex1f3/s3z2SIOGjaRO39mBhGqV/NcUiGOCKR6Bg/OjBt/Bn/t5kMXYYOs8c0NgrDvT/LHrmGX7HxiwMEd8XgRvanx8fbC+reTnnJy+NEh/Erb4PDgxKF+zK70HDFnk02fU4vE8+0+6FZR2wwzy9VJKoEzu/eVqlaQKli515INF9yBcTfDwtYoGQuTObV8licTtMRTj/WfVRTpgt0WiYDdvoU7kL/+QveQgmy3SyFmXMi+VXbSylZnH5ptO7+eoV1d8BjJjtEot+UQkdL8pXQ0LJiCoDY17GLP3haDI1RwLfK/Kq8cHfyUnmx7sLG9VCCWTvOBK5yV913WCxzoKsa5hRccecOJSz61smWLEOwiNKk/88YncIoDxrndVZsHYtpfea+yv6eo3woQDobx/JYHIstL6X6GYNr90Hvd1+RaL4GwZFiQvsZPTqSkgCJ6ph0MhEI3tRE2/d3zevYkxQqasTuVOZXeYX7/FwU1MSg4tk5ReD93pgiH1l3crNSitHXve+fmlggl4jliy6KxdzeTSAkCoWulElGUtmPDGY/SLsJGG1SPZHEYyB+A+yPJgDed5I7IgckWyc3qWZ33nU4EchOl/IDeFb14fby+vv3+zjvmWEeN8HIHw+CvdxO/XpeCYiXKTYlbt/gsyjZn/tZt2QkjnT03LhcXNZx4c74jRSWfHJqjuLRrFtU8fYnLY+o8bJcpL1qP3qEXJd1eKTHFEfdxtV8Y3H69sDHwxHAmUctSRV64BKi6ALwCOrGYo0/GiD9FkdnHuWTamcgjWj5en76eBzgPTMpPF6DERB9DSBAASwgjnd4n0t2/BwQtkYuLS52D+RwCF/hzkt503QEmT3ycnjO6DH6oZkGbbAko8zruUHxjqV2umfbxXUKSirX07sqNQkguAlXqZYJaoEJijKbJKPM2MV1E47RpglDSzchKFBMWuxJ8lxSGqiwpFZLfYNRMPleIExo3R13wf76/ecvde93z+XxGozAVS8GBDyiZEfyer7d+pSs4uFOaht3TNF/KdwXDyZOe9cGeN53fYV0lflDnN5jgZZJIvK4+Q9oYeygqyhJIQZEstI2HtGLjqV0/gG0aoFooyzAi+563VoRC1fwrq+0pBGAaoWdugMIuL01DUieoja3thAeVKXxRUhwXLkTVLsre1aVqSzgkbzLWeWVM1JvSQwEoxYYobeu4fH4wVN+HfvQQAiiAZDiXTc8wG48GD3OzMX0HP2zqwAqlsPNbX/Y+OINDImEkYi8G2bHtpK575AZ8ntT6E1U7oBc5wDmv0s8ogpOCcgu6gap1zcOheIdSWRzcPPx4PSm9SkfH/FLQHLVlZtdO8OBXxGQzqM2GA61u3nPzK/3v/5qID5VpAiVEym7kE9VQk07ylpIXnKR0g0ZdEbJjuHM29intxGOCGRvMfYuHCd2AtsND7BHTgd6NT0c1LXpqCMKtzkIyisjcURzNtc55NlusY6a6YdQCWtQaRsySnM2hAZg/b9RXYE4kjlmvT5Zg+ZxKhNDrISOCxlTVAx0rZ3I5wx7bkhi9FogooYtFmZq99ez7plhMqWovAwpNOMMdeMivyoGTrh/VwyTglNSLBe7emVH1j8S9cutMpUjSXQfaVaQwFskw84l43tnEf4hO3k9263X0xMN5aZLLFQPSaRVes0o1bv46tKcNVSuT9teI+yQg3lW4AZEZK5CCkw+p1ia/pXADpBSAO7kLnzPzMvv9z9vr31UYTaJSmkyIZAiBTCkJfexjDvKhEUxEJgav59ESz34BaVbsHBllokXxVhcy0f2I6QE8UfEiZ+aF9544guJV5JCQ+pNx9t4DKSSfM4RYxjiwE4/6/Tas1F4m/sRkFnm5LJuMHMdMhJaX8IJIokpBF3L8Zkww6RRyjBCq1fzKY+qA7G2+NhASm9NnuVgU5I1lcFWkILPWVgU6BtUgWr32631+937sAlS6GudC5WJoI1AadpgXtwVA63wGGSibmh2GVJAxR7LuYX75B9fXx9PT3h3jy53Od11wRbrAC9UFkqn22YABVxvJiJnNikvUn133wa0ANPGV15U2homZy6x6P6Q/tPPitEDFk0VlwAzYYkmUDfTKqaF3G0/hWtWOKQb3hH50OwdkcwmhWafDQNhyKvU7qqw4EUxuDEGVO7vijnklhzAJOp+nnCoU2kDQNh49bmF+1SYQidchKO08eHqFX6+sfsfY3owLmBfwOH6GzA0Yr807miwsInhu2ngKKB/l1KS/kt2k4ToystcfUFnuvLaZaZr/OCl8RmCCO8loONvfixzcBLUcWBC1WvwQuvuLy+vv36/C70jkvk0ZPZ2Wvafa/DCZce5WDA6v46dfj6p5nXsJk2fi0UqfW587NzCfQZFz3V5g817cO51mb/wrBlLwA7z6fvfcgvAl5qXiOyvls9GbELsNZLRRCbc3x/+5WSL9aTVNPttCQx/+Yx0eYC0pMjuMSiblwl/elb15Vb5rKoeNi/DC6LjLCxYX0fJDgUKlthDOf8sLCF9ao4zFqQwHYW7B8qPmj98y3cem/Q+ZKqU5sVuN1St60M9U0g3k154oIVNis7UdISm6Ij0OpplPAqbSQLfU1/h7h/Ao+/cwQpQjcfOaN6UrO8X1lMfH19//35/y/5tJo5QVWxCmTHD30NYZyy8xI5B8VNdHm3qDjEWyMBOnaTzexME3DvUURG/ySj8yJr+sx3V6zIUJxsz1Ag+xGmoi2VGHWR+OhEFzvhJ4RQSOKsqCYcpQJEbn4oyEjpN67mn2LJg0DjM4QsRItRMMfiJFvq1kEsEqaqQKfA5twutu99e0p9VtVOoTiqvRE0HLGCTlthxszTuiam5K8Z1Kd8+ewuG0+k2PpVjx/oU7rG5AS/5QUqN6Tj4uSXRAMg1vGZwRurVp/g3UojPmHfXD+WXgHEvk4P8CPsenS+/Sut1R9txFt2PHZfdzAE3zO6SdSlCJ6T4e5mouVIGoGdVHw7eM7MiVLcbXokaYIGxW0SNuSmGHjxFvc6jdGB1okbpLQ4ZBmhauB9PDND06+v7meL2WtucZmCCbk55LQuqQ0PJi2+Tvy+uS5Mz5dO5wJIW75dHK+NYi8VGISp3AcZe4h9TydGkzMy6EjW4yWSQtZU57wayLvvZVVbdTomaRh5M98w8PHjX3ZlTitFxNqYG+o6ldsjAsp72+flhqr7nFnfFhCDY48QIfQk39tJ708I97TvjwS1oHYJ8wVyb4gicY6Q0s2C48l4BJUsdMKdg0HpndmgvIU5BIWKVotSQKc0oxGqdGEqgkpXM3SZqeV/i9DDIGYtaJJmKKfa0TCRYwlvX7hmcZg/VowPnFq15INsC9Z7GD55CjrkppiQRZSsd6mDWaYampmnhHjIbH5+YS6vu46LOPOvkl2nVjiP2TJ3uY/stkJosHE6XLsueX/j9qGoLbDkFnT+O0VLAfB2ij17ilC7A8tY160sQVZa4EhzU7uDz9vv3n9fXa0yjV2KHscD5c6nhB0/R8ZQl9lC0TtRMVVuoldLjZxXu0xSCzJvrBCoMaauAfCdWof9PVSLub5bVvU+WljwNDT3lncMlxoYqSIpuBAecmtJuPdm0p7MRnFKL1wJWBWcVp0XIVMEp5XTpdw04E63Pz18PD6/v73/y3zOzWETT3jXYYYsCC3bAFO/EzYxmWGXHZpbY6VYobVsWHFXKCxbuJRkU1A70Lo4ps5hivQStKubvyswWcGmdjy1/70Qvthhnz+V0xeDqwzh4yjUVBMxXQOWq1PdkWgmSyPWq7vt/ssw1bnXohipD5ntmyt4RmWW0bo3HZcdrokM4KNaxyo44fa78BSXv8NIHD+FIDyggT6RwL58b+GpMAEc7EXNeAbvYjsbBlehRk/t5BRaYWaWF2BCtdyZ7E4VbMlCSlY6KvvycSZDTYtFFKkENuFnIuzJFZqlOlqgwpF81PJItB8c/hPdoSqh6O8YPTuzEG93PNrOAS+t+ENTuHw+P3mdV942HOCKaDc5HHIKDeh1FHd4TqOqumEN7MRw0w85hYw0N6gv3OpxiE5ukMZecksnkhVZwJO2pRRbdNEHcIjhT4pPLn1zl6+IhczSqdPGum65jZqqY0RwwpnuWMzrRTc706CS2Q14HYDSEPFf1Vi6B+EmJnRCMjONzFiYkbWZTAOkHJwN5YdNR4DCxh15k3hGZ8X73Qqt17DYKQYkmceGA088PXGrh7VODlewWLDvkKAmhvnA/nFFaTSHW4jI7nE7MOtVhTomOOAjeKIYrnZznCXs1fUDQwWlNpoGqVLIOpLGc9SjvmHqdojVS2J1qHHduk4MekdQWLcOJDC9yas7EdCEmIjAipDtBEV0ETmmGU0AravfHo/e7C1itowjNBBWYAXDonhi8Lhbvirnd8G+BEFVdRiGovnCPmP2ARYrkc5m2uUSgXo/Y4X6qtwUssYhDbM7w2A+Wum47p8t9t58FUIODuzVlq9FN0mAON8yumk0ffhajPdDC6HGCfDQkHJsjFhgi7CcIKmnSBFGJIgJ9WhFUqtpMbCGtn+Y9Mxe8Zwb2XE1MpfY9r597V8zFIghGVY6ooHAXmx7OMo2TSsZ8FdB5sap8ZIfZoJd6rxaDrZUDvqR6PjLALF9vTVWdr81ijPjZpV2PvfaFVG+0sohSYrYpTwyn6RCdhcsSVKw8MwtliiVwR35W9Zq1e6VpzugOWlGy49+b8+CpEpcTtIdyRFmFe20Ebsza9clUOz+csaKzAX7/KGsBy23BJCHs07LAEqQpzy8JCPZNlkdUXWaTmEpqtB9x3COjAiZmzTV0QcyKs3W9IDoRkRuzUkxd8j0ztKg7VDWCu2JQs8G1ULLvmT3R5fbKiBzRjMhDgA9zm+kh+p24T43MYzaFmPBogyFTpXtzIQtYbkErAqxMamG3ssHuvZItgPf/glU0z2NWK51tJoAz0RYjWsIWMVsatsl+lNGwGFHGGH2bdkc0cYtxWwA175l5uNK6O2c3WK2RxaRYgIZYYoc0vLn79vISEUtA6PUC2hFFIGxOcRDhoDaOEgt3ehLsaMsO1xShR4P6zs+TA00NP/PBPZ9VLnFsprY6kLKd9RLm0wyCsgGtQHGKRJ7MoSinrWYjJOumA7BZMk/R2cZtHq0pou9t4hZI5iguJnKWyaWIbeyV17tnBlY9fKonYvnWp8As3hWDN8XgF5RS2TVFFzqmtm+NoVo+A9GGKFK4t41CY4hqo84CII13SenLuMyM7qf/ndkFsfVzAzykrWf3Z4teCjkqqJlbE7Uzu6DZ5IcEohKa9CdkP2IGon3n5YgitBOiRTfP3opcz3ldh4Q4UgQKiOh+jDZV1CnkonZHsrvWurvG2h23xGCVHe92jC+xh3z9kqHEDk8xpWCLFO5TKRxWsgqAySZPYeHHZ5bEca/Vj601XIuJXgoToVhhf4Vbj1vmelkELjKWjoTi1XB/cGY3zO1hdjlssB/15CO4GilmSSPa0PRM5BqoQoHbj7YQon4aiI8kXbtPkSuWlbMBf96e8dMVl6rdjTFh2NPjBTrwg6dYYq/8BSXCUpHvst2ifQdODhpoAtZI4R63RO1Egiu62y12v5R/eJs2yNfRpFYN/yj3oydZwNKLIBEsRF0v4fArLqBOMswVhqWVAErldOWUwG74awSXznEMwxUU7hQdR+U0TefpeQle9HsaFSbnhwT/TDOOglZTxqvWhPnVYJlr3jOT/jVjNZV7AbzEDnJvzzexqspM4XAbDT6zh1xwhIFoQOQW7n3nw8MFfceu7pRwGSdw8P303YVfwWrdGLWvT5/Ho7TdZJFM/OL2R9YzgRU0oV4bXAkdZTUXlWa034MSHaOzMBD6/YUniWm7TGYmUBeqNpiaYkRT8Crj9167m5gT+OcDv3hqyK1cYg+pctVoYqOFULc+zoW7yvnQJgxlKSNCiUo7RvQ999RCcKMp/+fQAaT0LL/YUomAY0zr60aSDL/D04mVp4fvL/o3YUtqlCCnYZMpgA0eGYobKvvTRRfUT8yvWmbvtXuNT4NclOwgN+PB09LxCnyvdKhO/TgoYMOzogOFu6YZYpoKyPqwiCbNOjnE5YexDBO/LQluKVslSzru3DdvAyP7ELt1BEPCkhkvQyeBqjTMme43xa+BYAPYBvWZmomOvfI9UcknCksEZdm0/J6oc3zoe+0et4/3rH3wtNESu3fQRN/z9lV78ERQ7q0y/eyD9zTjHUPzeHaup/lM1arhrGHi35En5ESIZc3mqcDUcm1LsLbSy+D/gF52fZ0me0EOIEpQmgIiYB4TDRFUkVPnAViSNF+SrTQ5cQJb6SH64ceBmimmEB5nu3DtDhIEuUDaQcmOrf7B0zLv+HEBVWamtF7nFO54cvn5+TaXc+SdI6WKkGV1LHmGtCs7XkNMZ36v4EJlJHEvLOV2r3Inijl+a3zFCxyMfo+9Pu2HFVx01+jCbhR74cze53w9Emk31KkfUmowxWYW1uiBhy6DUvRq74icS3ZQU18ccb2OBYPn2w1V+6E92zVgLCKg2imZK/kUUF0LdzsH0I2etyFzRITU/mVTRBmxU9moJpLBbn3GyUIBTVO3x8evOSFexgsBhEwvXj3vbEp2xkDYQHDT4YCGQaWvug9D5yau1Oltc3VbinfedT/QwQLg1OX3AhTjHZFIEhd7RySnWDBVTNDn58fX1ze9jv3WtdKL+zC7XzGouPCzznYGBToxhZhpeIVYbCKxCQLiLVV2ZzXm/YNCC6Q5gmUZzDat5bwWStNx27Ws11bKj/lMOaJPve4lxntwWOMjSJz5WBE2CmRz+VcQxgTqy70Hclh6HMXXTDknht21uRoIClhWixtf4qOQudfuIAgU84OnKNlfXuw9yYqou15YcTRxcODziKgAABLtSURBVHUIKxTumDbkZw6bHUwZJy9fkQ9eXBVwt1kdXAA7LJ9D8TmjLga4/t5846uZ5Ptck62XEXDLPHR4xCtzIlv0ZKTf2ae06E2BbOv1GpbR18g527CS41+myJiqCtjGUGyzt6SxTpV1yd9mMnS51/kxE+OuGP7F054PnsYUCp+bwgrFp5YsGNY1+QyX7Iis1rW7u+Jeaz83EUT0hm85T6Ymm+Te8CwLbOv2iWe4S4Tl1srWOmtr/c6QDzq2XJWqYYpnKtqJ4w62jg8hharUGkf94tr7e6NPh/zuH3s+ajM2cSxBM3kgJq1Z/mX+TrgkTHSCTWaaN5E8NqiAHednVd//vL0Gmgx5OE4WGOYb2c0Se/7vWp5kEnZIyhiDRlbAbnGyAp1SD1M0f0+/nFqeaeecQKMmWh8edu4TEqkWurdjCxjvYKJpFzSX+4uATU8dXED/tiLq1jupXoeEKZZ7rYUcM4p1XOjU1nIV0sliydsMA1FkTJ3cUayhTdotIhkyIb+FZDH8P0bQlLTDZFySLKrd6Z6ZH1G7g2IsseNf3MU+aFl1SSeUB2WCGWKxYSrkW2VyMxknBOpFUnK3e1rPtdhp7ZloWkIrIVpa7XxXC2sgKSs8yiBnbDybpNDLNMnjmBJX189LlRK7wgxrw/LdImvFcimAlH4TXwVTQ4r0k9p0JqsGZRbRA+FKt8nX7RmRdtXaHXYAa3xXDHZUPXiazpHb8pJOCFDAWJ/hOZwhzZ08ULgnbjYb0MrXWkiihHuzISzgEG2chZYFVz5zAgrXZUWGx0VrfUSJaHKWEPq+jVeKiWVxA0dhlYxW0ieqRM3JvTKHi+77LjUKZPSleMb/XVkG2EODZEBQ0hQm1Jw0bOLODWfluMrYn+93v9q6O6yBB0/BNep1/Teyp3PHTqs5vtKxuC2n4ELVnD8DcER7w/mgcLe5AKp4+7sq3vfHtYAlGiWcNqLzHX5cHmo1n9JEuBCnesrcoUEsn2LZokGhrJqc7gXgPXjM5iFfxyICLZyIppAOtGp1uB2uVhqnydWJi7mmiC7ddOIqRTP1u9g9M2CZ3xWDkp35qrSPwu6X9EMOzKz56zCifYU7dzKs8pCCBOP7HTw/IShQj6iKtHkaCEu1ONEikMonIpHhLyTEEt135XVnwSswmrWWfALgKenD9iOmpJ3L3A+ELGCDWiR7QwgEiogKKdz/+HzPzO8/b2/9R5cake9iRypxl9gvyRcs9sNxTdn7KHXbwt0mAcr27dbi8PDz06m/3SUVSx45mNBPmKY9ihwesmRfLE0fAv9pDez9MCqIro+OcULMeFoSYJmJyoa0mq9GZXDpi9jTcVmqmwT1YCF27B/020wP3yPe7w6iuWTHU6feB09Pd8Vj6xe1uDgu2MRXl3NcJwb1Da2xUbHuk1Vk9lgnM1bSfBaTovNcFwNWQIftqTeI7sN1saoX9Y9ie2R2nH4Xlm4zJqJPt6aQAsBC2eN8PCl0CGE+HGqKaTKKzqC+ZMKfXLF7wme22xE9VIgdxsbS4JN+N/RzoNodRNu7YhYYvr2zXNGni+QxxgWJ7bxdUt1kWQxnlRU5qjOR0s1SLCt56HtDnwXM41++E+cem51iqtfPVeZ49F71zrEmI7aY1tdNPFOBq8GaGnSo4rIHADC2SuUJCtvA1py9GVcCmvGaFFBWDnImuwPXXXGVWyS7p1l3f3h5f//zqvf97uAZv5/09f2d9Tr2q1LGuLKZHqHDBho+5mptb5XJ7VjUnpd8i7reO6VbYM7zvLie3u/kltnOW6kvqiX8JKeK8rYcCX2JMocVxb8xovlmpVymQM82XHK+K0hzRYjaYChSxXZyQ9se1LzTl7KulugAjenGQD19sQOurjzNg1Ht/qG0dgfRnx+fmI9wS8xL/kOAV6WMHR/G6RsBs8c0+2vTOEYog9a1cMel5FV/M/X0OwOtKyDFl7lCMy9NFVxQbtbMZ+hLFW+NiFRk8u0s3f6LM7omGRSZvK1KJWb5Y60bIWYj85Ole8TQjkMrZefK/ZhuuNRZdF+VMoW1O9/FDm9+xrvnKy7QJsogqEKIzqC6kjduMjk+YivgvWvhji+CrvpKmbM8Hqzz0AXcn6XzZtzakmcj7tIfKcpNRR6nGyalhqdk8MZ06kviDQFfILovHY5UWIOjeDCmW0AV3bLQ0o3QuqWe2h13sQPs0+PjM92CL7CRH8KH5BxSQCchERfwRo7uTa7gjwWUyXhMIjvfX9+Pz9d8F2SiBaSasRNA2sYPpOT3kdOw5OkDoNcoLt2qjaZauRZsSQK2k5NLdwul+8u00PoP3XpEEWjMuLZkLgKttf0L5HPtfnt//zjjfndwzSU7v469QP9Yl6vX7sCuLUxidJhzh9FdEGhdC/dDhPcGUQvYCX083/XiyroXwUqQrJWsUJU74DtlfV2R7gWMDkynsOpENwjnBbNTvi1R5EmDqQLiCuoJottsBX27GagMWjf1ygZC7f6M+91/d/1dVfyIDW6MyXrwtAAd+9IlWRsOGgd4SnTnQrsX7gWh0bWLm9yvMZsL1ztd2Wg+GM3kPJ0jlDmay8bsdpt7dzphFVjo1FiQxLwOcLr14lxsZe522MuwVlLdHko+vQFDS1fDZTy91yktGRoUxs4pCrQbFLU7rpI71O6wnn0du/sLSu2gQfLlw025Q3KM5yrJrDF9cffoV7jjcvOqT6bGTVxy1qb2Ab8YiuBVl/upulXxYhmogf9oQ+xKzJEwNTmRhKgIoXTqnFV2jHqWNwmNO8f4nm6c2R88YGGE0wD1w6Ex52ORy6yN4F/ZOn7dbo8Pn+3umeEldhgQ74rpTzoT13/cbBryO0zQMAUIJeN8FYI9KmOcoR3myX6FO1TBcxhBuPcTXGkZO4C861lKISKoRKvb52lGRS9pwPW64X6Uf84zGlurz1XJmg0ZzJzZGcVaPn3ixL0/fj+i3AJMnDdzxxlXjgvqRaDpVz6uobln5lv8/e6fnx8mQT12W2L3wrwwcRxohwWu1yyNDrIy3gyQNWIKtI6FO1bcX16yAPyExm5Or6dcp8Vk6h2d2Iq0AunT9QIob3eN1sjujcRmW7LnJVc15jnODeEJ0s64LslmIL8D4MMSDX0+XyXBHht0M+dU+AqOcoqoDbRTdGg0KP2u6senSO0OxvnBUyyxP+t4D8eFiYM/KEHHOU02zOPQOhbujcJuTLED5fSaOaem75jExrR2Se9gGXyPKF/bdtA7ZsL+52oBu6Qnaj+l7MTWozWLT0ijofHoW8C4R4q+Qxcmzrxnpmrdfbor5uHh3CV2r9dcmDjgPREdRzoUwOa1fOVBFotR9vL7Fe77sStRqeqeSNyQOb3UJ0v7qSJWQJkzSRdfuDX39WQbpYErIJ94M1q2bi06MOX8ZXliatirIc7dfoj7EVELzLTTT9aLCtYiTHXQ1RnJrLt/5K67g3GU7NiwwK6wZLcmuTBxwHgKum6zjxddp8KdM5p1ox+1Y7GDAGzjYS9ath0GZxtF7cOmo5K+cdM2VtoMouxjCWYOdvSkSK8IdnSniUGZRaTUmdBV2EdKExE5boaHwIuh25jowuj4npnEZ1WpWv/8xNyI17Gjat9YSeFHJo79U6F6lSr1c0sztWM4bJU6p3fHWDQdOCN2Ktzh5FddhDDW39e2NpmbKTydIoUtM/0zs/nZgPfUVWhk63ViXYMhRJTQAMRPCn4pEIWS/1zd0TyhNtp1sF4HvV/vVuR1Q8C8e0nfTLTdVOowEGLjqujmZ1X//HkNPo+HdzuiZn98fLqN9sweHJU89qJf5U3oeNGkTRiw25/yLpsNun6Fu+YvkipZtoFAUWE2WFmkZKpUTKg7JeqU+iiv2BFSrlIMx0MlW1O9TlJOCeqgDaqmWPV0wtyUSSUr9zzMbrwHOSg9IeKZpYM37zejaz5QiwEOeWd0V62QeC2KQu9Ck5z1E75nZr/uDrwo2dHM/ILSq20/1g5TdlXuGqHjeIdwln8W4zw6K9OpcM+bD88yTOG4sCTV68B4Lq+F6qd0O6rbL81v0EDMO1FvKvZguxFPXIHRVhhWvLckF36Fsa6aVYZDl8X7hK6le9xlN7IAavfnD9zvPq27m5tiPpFNnvHe90tcqwwXellEC6LjkFdFOinz/d2pcM+y+xCNnSRubou4RDwHLR8tgaIngyLHPTFRjwC6KunjMAoKQEeAiOnrrzRPO8ZMvJsv1wLDpY1zb+VYYBRLcsjnaov2Yed0rDDm7rXR8br7499/v99uZok9eOfMmOxR0XJ3zgh3ZSEfESh56vGxR+FOJrhKicN0goN1Es8qESQZPFfWcbFzrn6io0/U486MsZwZCTr912F/FKOLe8Rgr0K+O/XXnl/BAMyrNqBW1C/ekrF3bfquig6843Xsfx4eXl9e/vH19THCs6cZTjk3vSp9jI+zSkF6oS7bAm82mZq/PQp3PHw9xGPXYVJsAh+taAtDyjvju1UmVuzkSdfberp5HQoiDXQv2kTsAproTq4rs4WlI/BTbC1/TxvzdLpCerFaP6Qjwqpgcm1qnIX6O+9HhlZI35HKsfPTXTFY0TR3xSBxooS/vf/5CD+rGhOn/tzF6NvbOwPgPNmjy16OtiM9CndteTmRAzd9j0BlIqyiZmtPXn8qEqiq0w4PfYFC1a65TtudVaW7jDIjY0RsmlAtw7Dt5Ua9jG2rpUDFQVNoNfTeAph92Zk7o3ToDVdmvAsABO948BT/7u+K4fvd77W7jK+cIeXYP03Jbr5K304HZ+ibNGaPwj1JES2NOHVf+knTfFO7K5rDuHYyTEQs30xiuacXlVzpWi3+1sTrMXpMvQczs08ntFFvLk2OMQ3b4nhmbQxtCny+UG8w1ukAG2BaiRwXIH4/CS93BBjcFAAUK1Tzh7l2f/94HfVlMjMU/18GjigIWcDfbZyjk39C4TXFU84n2H7q1ULsUbjT0qXybc7cYHDNrHK9T1CvgE3lMQHyif+ny3JPVybutZf1GuXEWD2PdsAcACaH7go2WcZsyd2PtGlwPhNgAw0aizwLILPfgfqzADbmbRE/HEDzOnb6eRn8ftKmnltQzXtz7X7Ze2YAdDgGZ3KS/nKM22liiLQfAda8cLeWiihxzqmZOqrUmdVz9BhgVLLQAGpmqEjFGldstLZO4C4G8NgWPw4wm8TAtrF/D/1jR7lgC+YfrtA58eudDSVIhj31A4SGn58fyP1YYs/6bRlTu39e+J4ZuADCQT+DNa4KgHiMAf9iq5Fzet/mhTuua1U9mQq/ZKMPT11H3ylYZZ+M3FHJlKHm509Miho8dFPwxtqUkao13ZlvFTDxxBCDdTf84021nUW+uvyc2hzgTP8p0zYzqM2vJPVB+M3Tq6RYIVnz69gfi1/Hzr+req/dhQjpLYajH9+xzGmgtwKC4zUv3BHJp2TJjY0sVRqU2eim+aMthOyOZm1Duq3r9VCrKx/noqHK+ZV7ANSjosGv5VxOqMhFV/azOmwCXhpQgPM/5Pv9I9BL/HA7gOKqlgnUCXC+K+Yxa4nda4H7PTNesyg/aMOf9dTppVk2bF+4Z6kj3Hgq18ETNmHZP0McL8vmLs4qsbWt14l9JToN6jZjWs/U61TNXyP8gQIZ7RpY+sSBnbD1GO3yJOoBCPZRsuN6/inzrpi4c861+2WfVQV8PSTGuTg8azPApuUEEGXBmFPbFQt35oqn6w6sXG4q9drMe3ATDEo+0mWGKdkQnKlhiZIotFqrBFWlGszfQCzm4zW5mO6Wmet17D9dm9V8I2nvIVIxzDMA1R/aAV9RPxESawyDV8V8ftKtzJF3xdTIn2v3+7OqNVZs3NdkgUgGYC99oN8AGS9LPP3973+3sx9M18soGIo2mrQxpNna4XIl565Gu30V7o/nwrMR2QHgAYCQ6wCE2pR7s7AL/SVzMBzzVsgLIdtAMdH/ZRIAOwCq9qttcGzKcpfeajByEuDwx7867VQDUCeivVZnYaRfPP3zByGCu2L4R5T2uokcQe3+/fT48vtdRJpOIUzicAlnUhjaH2UA02S8jPrr77+f/uu//l87p8HjIPiWqp18TtOGJ6YAK6xKk3U7I8hKRlFQ8L+sDunSrANQvc7bXKWmC7lySycaEBl071B8s9zHm2k6a30AAJ9oiR0bYDvINWl716WFBRwfoCzQYghZmVDSTFuyUnVJ64kRr2NHvY6N3xXT530YVLvjN1bf/+iyu6g2UzQNsljAMZWbAXo6qgg5KNpv//N//HfI+o//+G//+Oc/RYS6QmAR8e+q3HyXy5Crm9Q+9NGghhQcr5w4xv7zJPThCpQKNKF5Oo7Ra5bhDqZi7M9ovimNB9CVCDmAo3AqxvwR9fS4Y2Qu5nJiyAzMygtlLz2+udKkA0b8fhJui8H64D9eX08w5svt+eMTNxzjhTMr5Nf60IHHGoPNeaBq2VY5RrbPv/71r//7n//5f/73//r/uBQPcqrkGrQAAAAASUVORK5CYII=\"},\"cache_control\":{\"type\":\"ephemeral\"}}]}],\"usage\":{\"include\":true},\"tools\":[{\"type\":\"function\",\"function\":{\"name\":\"get_scene_info\",\"description\":\"Get detailed information about the current Blender scene\\n\\n    Parameters:\\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"user_prompt\"],\"title\":\"get_scene_infoArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_object_info\",\"description\":\"\\n    Get detailed information about a specific object in the Blender scene.\\n\\n    Parameters:\\n    - object_name: The name of the object to get information about\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"object_name\":{\"title\":\"Object Name\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"object_name\"],\"title\":\"get_object_infoArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_viewport_screenshot\",\"description\":\"\\n    Capture a screenshot of the current Blender 3D viewport.\\n\\n    Parameters:\\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns the screenshot as an Image.\\n    \",\"parameters\":{\"properties\":{\"max_size\":{\"default\":1000,\"title\":\"Max Size\",\"type\":\"integer\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_viewport_screenshotArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"description\":\"\\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\\n\\n    Parameters:\\n    - code: The Python code to execute\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"code\":{\"title\":\"Code\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"code\"],\"title\":\"execute_blender_codeArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_categories\",\"description\":\"\\n    Get a list of categories for a specific asset type on Polyhaven.\\n\\n    Parameters:\\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"asset_type\":{\"default\":\"hdris\",\"title\":\"Asset Type\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_polyhaven_categoriesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_polyhaven_assets\",\"description\":\"\\n    Search for assets on Polyhaven with optional filtering.\\n\\n    Parameters:\\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\\n    - categories: Optional comma-separated list of categories to filter by\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns a list of matching assets with basic information.\\n    \",\"parameters\":{\"properties\":{\"asset_type\":{\"default\":\"all\",\"title\":\"Asset Type\",\"type\":\"string\"},\"categories\":{\"default\":null,\"title\":\"Categories\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"search_polyhaven_assetsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"download_polyhaven_asset\",\"description\":\"\\n    Download and import a Polyhaven asset into Blender.\\n\\n    Parameters:\\n    - asset_id: The ID of the asset to download\\n    - asset_type: The type of asset (hdris, textures, models)\\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"asset_id\":{\"title\":\"Asset Id\",\"type\":\"string\"},\"asset_type\":{\"title\":\"Asset Type\",\"type\":\"string\"},\"resolution\":{\"default\":\"1k\",\"title\":\"Resolution\",\"type\":\"string\"},\"file_format\":{\"default\":null,\"title\":\"File Format\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"asset_id\",\"asset_type\"],\"title\":\"download_polyhaven_assetArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"set_texture\",\"description\":\"\\n    Apply a previously downloaded Polyhaven texture to an object.\\n    \\n    Parameters:\\n    - object_name: Name of the object to apply the texture to\\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\\n    \\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"object_name\":{\"title\":\"Object Name\",\"type\":\"string\"},\"texture_id\":{\"title\":\"Texture Id\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"object_name\",\"texture_id\"],\"title\":\"set_textureArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_status\",\"description\":\"\\n    Check if PolyHaven integration is enabled in Blender.\\n    Returns a message indicating whether PolyHaven features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_polyhaven_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_hyper3d_status\",\"description\":\"\\n    Check if Hyper3D Rodin integration is enabled in Blender.\\n    Returns a message indicating whether Hyper3D Rodin features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_hyper3d_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_status\",\"description\":\"\\n    Check if Sketchfab integration is enabled in Blender.\\n    Returns a message indicating whether Sketchfab features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_sketchfab_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_sketchfab_models\",\"description\":\"\\n    Search for models on Sketchfab with optional filtering.\\n\\n    Parameters:\\n    - query: Text to search for\\n    - categories: Optional comma-separated list of categories\\n    - count: Maximum number of results to return (default 20)\\n    - downloadable: Whether to include only downloadable models (default True)\\n\\n    Returns a formatted list of matching models.\\n    \",\"parameters\":{\"properties\":{\"query\":{\"title\":\"Query\",\"type\":\"string\"},\"categories\":{\"default\":null,\"title\":\"Categories\",\"type\":\"string\"},\"count\":{\"default\":20,\"title\":\"Count\",\"type\":\"integer\"},\"downloadable\":{\"default\":true,\"title\":\"Downloadable\",\"type\":\"boolean\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"query\"],\"title\":\"search_sketchfab_modelsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_model_preview\",\"description\":\"\\n    Get a preview thumbnail of a Sketchfab model by its UID.\\n    Use this to visually confirm a model before downloading.\\n    \\n    Parameters:\\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\\n    \\n    Returns the model's thumbnail as an Image for visual confirmation.\\n    \",\"parameters\":{\"properties\":{\"uid\":{\"title\":\"Uid\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"uid\"],\"title\":\"get_sketchfab_model_previewArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"download_sketchfab_model\",\"description\":\"\\n    Download and import a Sketchfab model by its UID.\\n    The model will be scaled so its largest dimension equals target_size.\\n    \\n    Parameters:\\n    - uid: The unique identifier of the Sketchfab model\\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\\n                  You must specify the desired size for the model.\\n                  Examples:\\n                  - Chair: target_size=1.0 (1 meter tall)\\n                  - Table: target_size=0.75 (75cm tall)\\n                  - Car: target_size=4.5 (4.5 meters long)\\n                  - Person: target_size=1.7 (1.7 meters tall)\\n                  - Small object (cup, phone): target_size=0.1 to 0.3\\n    \\n    Returns a message with import details including object names, dimensions, and bounding box.\\n    The model must be downloadable and you must have proper access rights.\\n    \",\"parameters\":{\"properties\":{\"uid\":{\"title\":\"Uid\",\"type\":\"string\"},\"target_size\":{\"title\":\"Target Size\",\"type\":\"number\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"uid\",\"target_size\"],\"title\":\"download_sketchfab_modelArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hyper3d_model_via_text\",\"description\":\"\\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\\n    The 3D asset has built-in materials.\\n    The generated model has a normalized size, so re-scaling after generation can be useful.\\n\\n    Parameters:\\n    - text_prompt: A short description of the desired model in **English**.\\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\\n\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"text_prompt\":{\"title\":\"Text Prompt\",\"type\":\"string\"},\"bbox_condition\":{\"default\":null,\"items\":{\"type\":\"number\"},\"title\":\"Bbox Condition\",\"type\":\"array\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"text_prompt\"],\"title\":\"generate_hyper3d_model_via_textArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hyper3d_model_via_images\",\"description\":\"\\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\\n    The 3D asset has built-in materials.\\n    The generated model has a normalized size, so re-scaling after generation can be useful.\\n    \\n    Parameters:\\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model.\\n\\n    Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode.\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"input_image_paths\":{\"default\":null,\"items\":{\"type\":\"string\"},\"title\":\"Input Image Paths\",\"type\":\"array\"},\"input_image_urls\":{\"default\":null,\"items\":{\"type\":\"string\"},\"title\":\"Input Image Urls\",\"type\":\"array\"},\"bbox_condition\":{\"default\":null,\"items\":{\"type\":\"number\"},\"title\":\"Bbox Condition\",\"type\":\"array\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"generate_hyper3d_model_via_imagesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"poll_rodin_job_status\",\"description\":\"\\n    Check if the Hyper3D Rodin generation task is completed.\\n\\n    For Hyper3D Rodin mode MAIN_SITE:\\n        Parameters:\\n        - subscription_key: The subscription_key given in the generate model step.\\n\\n        Returns a list of status. The task is done if all status are \\\"Done\\\".\\n        If \\\"Failed\\\" showed up, the generating process failed.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"Done\\\" or \\\"Canceled\\\").\\n\\n    For Hyper3D Rodin mode FAL_AI:\\n        Parameters:\\n        - request_id: The request_id given in the generate model step.\\n\\n        Returns the generation task status. The task is done if status is \\\"COMPLETED\\\".\\n        The task is in progress if status is \\\"IN_PROGRESS\\\".\\n        If status other than \\\"COMPLETED\\\", \\\"IN_PROGRESS\\\", \\\"IN_QUEUE\\\" showed up, the generating process might be failed.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"COMPLETED\\\" or some failed state).\\n    \",\"parameters\":{\"properties\":{\"subscription_key\":{\"default\":null,\"title\":\"Subscription Key\",\"type\":\"string\"},\"request_id\":{\"default\":null,\"title\":\"Request Id\",\"type\":\"string\"}},\"title\":\"poll_rodin_job_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"import_generated_asset\",\"description\":\"\\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\\n\\n    Parameters:\\n    - name: The name of the object in scene\\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\\n\\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\\n    Return if the asset has been imported successfully.\\n    \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"task_uuid\":{\"default\":null,\"title\":\"Task Uuid\",\"type\":\"string\"},\"request_id\":{\"default\":null,\"title\":\"Request Id\",\"type\":\"string\"}},\"required\":[\"name\"],\"title\":\"import_generated_assetArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_hunyuan3d_status\",\"description\":\"\\n    Check if Hunyuan3D integration is enabled in Blender.\\n    Returns a message indicating whether Hunyuan3D features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_hunyuan3d_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hunyuan3d_model\",\"description\":\"\\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \\n    or both for the desired asset, and import the asset into Blender.\\n    The 3D asset has built-in materials.\\n    \\n    Parameters:\\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\\n    - input_image_url: (Optional) The local or remote url of the input image. Accepts None if only using text prompt.\\n\\n    Returns: \\n    - When successful, returns a JSON with job_id (format: \\\"job_xxx\\\") indicating the task is in progress\\n    - When the job completes, the status will change to \\\"DONE\\\" indicating the model has been imported\\n    - Returns error message if the operation fails\\n    \",\"parameters\":{\"properties\":{\"text_prompt\":{\"default\":null,\"title\":\"Text Prompt\",\"type\":\"string\"},\"input_image_url\":{\"default\":null,\"title\":\"Input Image Url\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"generate_hunyuan3d_modelArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"poll_hunyuan_job_status\",\"description\":\"\\n    Check if the Hunyuan3D generation task is completed.\\n\\n    For Hunyuan3D:\\n        Parameters:\\n        - job_id: The job_id given in the generate model step.\\n\\n        Returns the generation task status. The task is done if status is \\\"DONE\\\".\\n        The task is in progress if status is \\\"RUN\\\".\\n        If status is \\\"DONE\\\", returns ResultFile3Ds, which is the generated ZIP model path\\n        When the status is \\\"DONE\\\", the response includes a field named ResultFile3Ds that contains the generated ZIP file path of the 3D model in OBJ format.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"DONE\\\" or some failed state).\\n    \",\"parameters\":{\"properties\":{\"job_id\":{\"default\":null,\"title\":\"Job Id\",\"type\":\"string\"}},\"title\":\"poll_hunyuan_job_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"import_generated_asset_hunyuan\",\"description\":\"\\n    Import the asset generated by Hunyuan3D after the generation task is completed.\\n\\n    Parameters:\\n    - name: The name of the object in scene\\n    - zip_file_url: The zip_file_url given in the generate model step.\\n\\n    Return if the asset has been imported successfully.\\n    \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"zip_file_url\":{\"title\":\"Zip File Url\",\"type\":\"string\"}},\"required\":[\"name\",\"zip_file_url\"],\"title\":\"import_generated_asset_hunyuanArguments\",\"type\":\"object\"}}}],\"reasoning\":{\"effort\":\"xhigh\"}}", "body_base64": "{"model":"anthropic/claude-haiku-4.5","messages":[{"role":"system","content":[{"type":"text","text":"You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\n\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\n\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\n\n## Harness Instructions\n\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. When you are finished, reply with the single word DONE and no tool calls.","cache_control":{"type":"ephemeral"}}]},{"role":"user","content":"## Task\n\nComplete the following Blender task:\n\n<task>\n\nA red sphere of 1 meter diameter resting on a white ground plane — touching it\nexactly, neither floating nor sunk. Smooth shading, a plain red material on the\nball, plain white on the floor.\n\nThe floor is the sphere, the color, and the contact. The ceiling is presentation:\na soft believable shadow under the ball, a hint of glossiness that catches the\nlight, and clean framing.\n\n</task>"},{"role":"assistant","content":"I'll create a red sphere resting on a white ground plane with proper materials, shading, lighting, and camera setup. Let me work through this step by step.","tool_calls":[{"id":"toolu_bdrk_01McQwtp5Bq6813NVu2gUpRG","type":"function","function":{"name":"get_scene_info","arguments":"{\"user_prompt\": \"Get current scene info to see what's there initially\"}"}}]},{"role":"tool","tool_call_id":"toolu_bdrk_01McQwtp5Bq6813NVu2gUpRG","content":"{\n  \"name\": \"Scene\",\n  \"object_count\": 0,\n  \"objects\": [],\n  \"materials_count\": 2\n}"},{"role":"assistant","content":"Good, the scene is empty. Now I'll create the red sphere and white ground plane using Blender code:","tool_calls":[{"id":"toolu_bdrk_01PuvjJZjfHJZgTRw6jGDsdH","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\": \"\\nimport bpy\\nimport bmesh\\nfrom mathutils import Vector\\n\\n# Clear any default objects\\nbpy.ops.object.select_all(action='SELECT')\\nbpy.ops.object.delete(use_global=False)\\n\\n# Add a sphere (1 meter diameter = 0.5 meter radius)\\nbpy.ops.mesh.primitive_uv_sphere_add(\\n    radius=0.5,\\n    location=(0, 0, 0.5)  # Position it so bottom touches z=0\\n)\\nsphere = bpy.context.active_object\\nsphere.name = \\\"RedSphere\\\"\\n\\n# Add a large ground plane\\nbpy.ops.mesh.primitive_plane_add(\\n    size=10,\\n    location=(0, 0, 0)\\n)\\nplane = bpy.context.active_object\\nplane.name = \\\"GroundPlane\\\"\\n\\n# Select and scale plane to be large but thin\\nplane.scale = (10, 10, 0.1)\\n\\nprint(\\\"Objects created: Sphere at z=\\\" + str(sphere.location.z) + \\\", Plane at z=\\\" + str(plane.location.z))\\n\", \"user_prompt\": \"Create red sphere and white ground plane\"}"}}]},{"role":"tool","tool_call_id":"toolu_bdrk_01PuvjJZjfHJZgTRw6jGDsdH","content":"Code executed successfully: Objects created: Sphere at z=0.5, Plane at z=0.0\n"},{"role":"assistant","content":"Now I'll create the materials - red for the sphere and white for the plane:","tool_calls":[{"id":"toolu_bdrk_018gHZ2vPGV9JR5pgaWmu5aZ","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\": \"\\nimport bpy\\n\\n# Get the sphere and plane objects\\nsphere = bpy.data.objects[\\\"RedSphere\\\"]\\nplane = bpy.data.objects[\\\"GroundPlane\\\"]\\n\\n# Create red material for sphere\\nred_material = bpy.data.materials.new(name=\\\"RedMaterial\\\")\\nred_material.use_nodes = True\\nred_material.diffuse_color = (1.0, 0.0, 0.0, 1.0)  # Red RGBA\\n\\n# Configure red material with some glossiness\\nbsdf = red_material.node_tree.nodes[\\\"Principled BSDF\\\"]\\nbsdf.inputs[\\\"Base Color\\\"].default_value = (0.8, 0.05, 0.05, 1.0)  # Pure red\\nbsdf.inputs[\\\"Roughness\\\"].default_value = 0.3  # Slight glossiness\\n\\n# Create white material for plane\\nwhite_material = bpy.data.materials.new(name=\\\"WhiteMaterial\\\")\\nwhite_material.use_nodes = True\\nwhite_material.diffuse_color = (1.0, 1.0, 1.0, 1.0)  # White RGBA\\n\\nbsdf_white = white_material.node_tree.nodes[\\\"Principled BSDF\\\"]\\nbsdf_white.inputs[\\\"Base Color\\\"].default_value = (1.0, 1.0, 1.0, 1.0)  # White\\nbsdf_white.inputs[\\\"Roughness\\\"].default_value = 0.8  # Matte white\\n\\n# Assign materials to objects\\nsphere.data.materials.append(red_material)\\nplane.data.materials.append(white_material)\\n\\nprint(\\\"Materials created and assigned\\\")\\n\", \"user_prompt\": \"Create and assign red and white materials\"}"}}]},{"role":"tool","tool_call_id":"toolu_bdrk_018gHZ2vPGV9JR5pgaWmu5aZ","content":"Code executed successfully: Materials created and assigned\n"},{"role":"assistant","content":"Now I'll apply smooth shading to both objects:","tool_calls":[{"id":"toolu_bdrk_01WSSDEGVk2FHv51RevTiES7","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\": \"\\nimport bpy\\n\\nsphere = bpy.data.objects[\\\"RedSphere\\\"]\\nplane = bpy.data.objects[\\\"GroundPlane\\\"]\\n\\n# Select sphere and apply smooth shading\\nsphere.select_set(True)\\nbpy.context.view_layer.objects.active = sphere\\nbpy.ops.object.shade_smooth()\\n\\n# Select plane and apply smooth shading\\nplane.select_set(True)\\nbpy.context.view_layer.objects.active = plane\\nbpy.ops.object.shade_smooth()\\n\\nprint(\\\"Smooth shading applied to both objects\\\")\\n\", \"user_prompt\": \"Apply smooth shading to sphere and plane\"}"}}]},{"role":"tool","tool_call_id":"toolu_bdrk_01WSSDEGVk2FHv51RevTiES7","content":"Code executed successfully: Smooth shading applied to both objects\n"},{"role":"assistant","content":"Now I need to set up the lighting and camera for a good presentation. I'll add a key light, fill light, and set up the camera:","tool_calls":[{"id":"toolu_bdrk_01AMvEaBThonE94HvaJjwfZk","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\": \"\\nimport bpy\\nfrom mathutils import Vector\\n\\n# Add a key light (three-quarter lighting)\\nbpy.ops.object.light_add(\\n    type='SUN',\\n    location=(3, 4, 5)\\n)\\nkey_light = bpy.context.active_object\\nkey_light.name = \\\"KeyLight\\\"\\nkey_light.data.energy = 2.0\\nkey_light.data.angle = 0.5  # Slight softness\\nbpy.context.view_layer.objects.active = key_light\\nkey_light.select_set(True)\\n\\n# Add a fill light (softer light from the other side)\\nbpy.ops.object.light_add(\\n    type='SUN',\\n    location=(-2, -3, 3)\\n)\\nfill_light = bpy.context.active_object\\nfill_light.name = \\\"FillLight\\\"\\nfill_light.data.energy = 0.6  # Much softer\\nfill_light.data.angle = 2.0  # Very soft\\nbpy.context.view_layer.objects.active = fill_light\\nfill_light.select_set(True)\\n\\n# Add an area light positioned slightly to the side and above for catch lights\\nbpy.ops.object.light_add(\\n    type='AREA',\\n    location=(2, -3, 4)\\n)\\ncatch_light = bpy.context.active_object\\ncatch_light.name = \\\"CatchLight\\\"\\ncatch_light.data.energy = 0.8\\ncatch_light.data.size = 3\\nbpy.context.view_layer.objects.active = catch_light\\ncatch_light.select_set(True)\\n\\nprint(\\\"Lights created: Key, Fill, and Catch lights\\\")\\n\", \"user_prompt\": \"Add lighting setup for presentation\"}"}}]},{"role":"tool","tool_call_id":"toolu_bdrk_01AMvEaBThonE94HvaJjwfZk","content":"Code executed successfully: Lights created: Key, Fill, and Catch lights\n"},{"role":"assistant","content":"Now I'll set up the camera for a clean, well-composed view:","tool_calls":[{"id":"toolu_bdrk_013cajKLXaCWr4wzNHHwZfby","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\": \"\\nimport bpy\\nfrom mathutils import Vector, Matrix\\n\\n# Add a camera\\nbpy.ops.object.camera_add(\\n    location=(3.5, -3, 2.5)\\n)\\ncamera = bpy.context.active_object\\ncamera.name = \\\"Camera\\\"\\n\\n# Point camera at the sphere (origin area)\\ndirection = Vector((0, 0, 0.3)) - Vector(camera.location)\\nrot_quat = direction.to_track_quat('-Z', 'Y')\\ncamera.rotation_euler = rot_quat.to_euler()\\n\\n# Set as active camera\\nbpy.context.scene.camera = camera\\n\\n# Adjust camera settings for a nice view\\ncamera.data.lens = 50  # Standard lens\\ncamera.data.sensor_width = 36\\n\\nprint(\\\"Camera set up at position:\\\", camera.location)\\nprint(\\\"Camera rotation euler:\\\", camera.rotation_euler)\\n\", \"user_prompt\": \"Set up camera for presentation\"}"}}]},{"role":"tool","tool_call_id":"toolu_bdrk_013cajKLXaCWr4wzNHHwZfby","content":"Code executed successfully: Camera set up at position: <Vector (3.5000, -3.0000, 2.5000)>\nCamera rotation euler: <Euler (x=1.1255, y=0.0000, z=0.8622), order='XYZ'>\n"},{"role":"assistant","content":"Let me take a screenshot to see the current result:","tool_calls":[{"id":"toolu_bdrk_017xgYeoUTciryqhVxri7bM3","type":"function","function":{"name":"get_viewport_screenshot","arguments":"{\"user_prompt\": \"Take screenshot of the scene to verify the composition\", \"max_size\": 1000}"}}]},{"role":"tool","tool_call_id":"toolu_bdrk_017xgYeoUTciryqhVxri7bM3","content":""},{"role":"user","content":[{"type":"image_url","image_url":{"url":"data:image/png;base64,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"},"cache_control":{"type":"ephemeral"}}]}],"usage":{"include":true},"tools":[{"type":"function","function":{"name":"get_scene_info","description":"Get detailed information about the current Blender scene\n\n    Parameters:\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\n    ","parameters":{"properties":{"user_prompt":{"title":"User Prompt","type":"string"}},"required":["user_prompt"],"title":"get_scene_infoArguments","type":"object"}}},{"type":"function","function":{"name":"get_object_info","description":"\n    Get detailed information about a specific object in the Blender scene.\n\n    Parameters:\n    - object_name: The name of the object to get information about\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"object_name":{"title":"Object Name","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["object_name"],"title":"get_object_infoArguments","type":"object"}}},{"type":"function","function":{"name":"get_viewport_screenshot","description":"\n    Capture a screenshot of the current Blender 3D viewport.\n\n    Parameters:\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns the screenshot as an Image.\n    ","parameters":{"properties":{"max_size":{"default":1000,"title":"Max Size","type":"integer"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_viewport_screenshotArguments","type":"object"}}},{"type":"function","function":{"name":"execute_blender_code","description":"\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\n\n    Parameters:\n    - code: The Python code to execute\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"code":{"title":"Code","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["code"],"title":"execute_blender_codeArguments","type":"object"}}},{"type":"function","function":{"name":"get_polyhaven_categories","description":"\n    Get a list of categories for a specific asset type on Polyhaven.\n\n    Parameters:\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"asset_type":{"default":"hdris","title":"Asset Type","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_polyhaven_categoriesArguments","type":"object"}}},{"type":"function","function":{"name":"search_polyhaven_assets","description":"\n    Search for assets on Polyhaven with optional filtering.\n\n    Parameters:\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\n    - categories: Optional comma-separated list of categories to filter by\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a list of matching assets with basic information.\n    ","parameters":{"properties":{"asset_type":{"default":"all","title":"Asset Type","type":"string"},"categories":{"default":null,"title":"Categories","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"search_polyhaven_assetsArguments","type":"object"}}},{"type":"function","function":{"name":"download_polyhaven_asset","description":"\n    Download and import a Polyhaven asset into Blender.\n\n    Parameters:\n    - asset_id: The ID of the asset to download\n    - asset_type: The type of asset (hdris, textures, models)\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a message indicating success or failure.\n    ","parameters":{"properties":{"asset_id":{"title":"Asset Id","type":"string"},"asset_type":{"title":"Asset Type","type":"string"},"resolution":{"default":"1k","title":"Resolution","type":"string"},"file_format":{"default":null,"title":"File Format","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["asset_id","asset_type"],"title":"download_polyhaven_assetArguments","type":"object"}}},{"type":"function","function":{"name":"set_texture","description":"\n    Apply a previously downloaded Polyhaven texture to an object.\n    \n    Parameters:\n    - object_name: Name of the object to apply the texture to\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\n    \n    Returns a message indicating success or failure.\n    ","parameters":{"properties":{"object_name":{"title":"Object Name","type":"string"},"texture_id":{"title":"Texture Id","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["object_name","texture_id"],"title":"set_textureArguments","type":"object"}}},{"type":"function","function":{"name":"get_polyhaven_status","description":"\n    Check if PolyHaven integration is enabled in Blender.\n    Returns a message indicating whether PolyHaven features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_polyhaven_statusArguments","type":"object"}}},{"type":"function","function":{"name":"get_hyper3d_status","description":"\n    Check if Hyper3D Rodin integration is enabled in Blender.\n    Returns a message indicating whether Hyper3D Rodin features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_hyper3d_statusArguments","type":"object"}}},{"type":"function","function":{"name":"get_sketchfab_status","description":"\n    Check if Sketchfab integration is enabled in Blender.\n    Returns a message indicating whether Sketchfab features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_sketchfab_statusArguments","type":"object"}}},{"type":"function","function":{"name":"search_sketchfab_models","description":"\n    Search for models on Sketchfab with optional filtering.\n\n    Parameters:\n    - query: Text to search for\n    - categories: Optional comma-separated list of categories\n    - count: Maximum number of results to return (default 20)\n    - downloadable: Whether to include only downloadable models (default True)\n\n    Returns a formatted list of matching models.\n    ","parameters":{"properties":{"query":{"title":"Query","type":"string"},"categories":{"default":null,"title":"Categories","type":"string"},"count":{"default":20,"title":"Count","type":"integer"},"downloadable":{"default":true,"title":"Downloadable","type":"boolean"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["query"],"title":"search_sketchfab_modelsArguments","type":"object"}}},{"type":"function","function":{"name":"get_sketchfab_model_preview","description":"\n    Get a preview thumbnail of a Sketchfab model by its UID.\n    Use this to visually confirm a model before downloading.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\n    \n    Returns the model's thumbnail as an Image for visual confirmation.\n    ","parameters":{"properties":{"uid":{"title":"Uid","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["uid"],"title":"get_sketchfab_model_previewArguments","type":"object"}}},{"type":"function","function":{"name":"download_sketchfab_model","description":"\n    Download and import a Sketchfab model by its UID.\n    The model will be scaled so its largest dimension equals target_size.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\n                  You must specify the desired size for the model.\n                  Examples:\n                  - Chair: target_size=1.0 (1 meter tall)\n                  - Table: target_size=0.75 (75cm tall)\n                  - Car: target_size=4.5 (4.5 meters long)\n                  - Person: target_size=1.7 (1.7 meters tall)\n                  - Small object (cup, phone): target_size=0.1 to 0.3\n    \n    Returns a message with import details including object names, dimensions, and bounding box.\n    The model must be downloadable and you must have proper access rights.\n    ","parameters":{"properties":{"uid":{"title":"Uid","type":"string"},"target_size":{"title":"Target Size","type":"number"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["uid","target_size"],"title":"download_sketchfab_modelArguments","type":"object"}}},{"type":"function","function":{"name":"generate_hyper3d_model_via_text","description":"\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n\n    Parameters:\n    - text_prompt: A short description of the desired model in **English**.\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Returns a message indicating success or failure.\n    ","parameters":{"properties":{"text_prompt":{"title":"Text Prompt","type":"string"},"bbox_condition":{"default":null,"items":{"type":"number"},"title":"Bbox Condition","type":"array"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["text_prompt"],"title":"generate_hyper3d_model_via_textArguments","type":"object"}}},{"type":"function","function":{"name":"generate_hyper3d_model_via_images","description":"\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n    \n    Parameters:\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode.\n    Returns a message indicating success or failure.\n    ","parameters":{"properties":{"input_image_paths":{"default":null,"items":{"type":"string"},"title":"Input Image Paths","type":"array"},"input_image_urls":{"default":null,"items":{"type":"string"},"title":"Input Image Urls","type":"array"},"bbox_condition":{"default":null,"items":{"type":"number"},"title":"Bbox Condition","type":"array"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"generate_hyper3d_model_via_imagesArguments","type":"object"}}},{"type":"function","function":{"name":"poll_rodin_job_status","description":"\n    Check if the Hyper3D Rodin generation task is completed.\n\n    For Hyper3D Rodin mode MAIN_SITE:\n        Parameters:\n        - subscription_key: The subscription_key given in the generate model step.\n\n        Returns a list of status. The task is done if all status are \"Done\".\n        If \"Failed\" showed up, the generating process failed.\n        This is a polling API, so only proceed if the status are finally determined (\"Done\" or \"Canceled\").\n\n    For Hyper3D Rodin mode FAL_AI:\n        Parameters:\n        - request_id: The request_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"COMPLETED\".\n        The task is in progress if status is \"IN_PROGRESS\".\n        If status other than \"COMPLETED\", \"IN_PROGRESS\", \"IN_QUEUE\" showed up, the generating process might be failed.\n        This is a polling API, so only proceed if the status are finally determined (\"COMPLETED\" or some failed state).\n    ","parameters":{"properties":{"subscription_key":{"default":null,"title":"Subscription Key","type":"string"},"request_id":{"default":null,"title":"Request Id","type":"string"}},"title":"poll_rodin_job_statusArguments","type":"object"}}},{"type":"function","function":{"name":"import_generated_asset","description":"\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\n\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\n    Return if the asset has been imported successfully.\n    ","parameters":{"properties":{"name":{"title":"Name","type":"string"},"task_uuid":{"default":null,"title":"Task Uuid","type":"string"},"request_id":{"default":null,"title":"Request Id","type":"string"}},"required":["name"],"title":"import_generated_assetArguments","type":"object"}}},{"type":"function","function":{"name":"get_hunyuan3d_status","description":"\n    Check if Hunyuan3D integration is enabled in Blender.\n    Returns a message indicating whether Hunyuan3D features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_hunyuan3d_statusArguments","type":"object"}}},{"type":"function","function":{"name":"generate_hunyuan3d_model","description":"\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \n    or both for the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    \n    Parameters:\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\n    - input_image_url: (Optional) The local or remote url of the input image. Accepts None if only using text prompt.\n\n    Returns: \n    - When successful, returns a JSON with job_id (format: \"job_xxx\") indicating the task is in progress\n    - When the job completes, the status will change to \"DONE\" indicating the model has been imported\n    - Returns error message if the operation fails\n    ","parameters":{"properties":{"text_prompt":{"default":null,"title":"Text Prompt","type":"string"},"input_image_url":{"default":null,"title":"Input Image Url","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"generate_hunyuan3d_modelArguments","type":"object"}}},{"type":"function","function":{"name":"poll_hunyuan_job_status","description":"\n    Check if the Hunyuan3D generation task is completed.\n\n    For Hunyuan3D:\n        Parameters:\n        - job_id: The job_id given in the generate model step.\n\n        Returns the generation task status. 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The lighting setup is visible with the lights shown\\n4. The view looks reasonable but I need to verify a few things:\\n   - Make sure the sphere is properly positioned touching the ground plane\\n   - The ground plane needs to be visible and white in the rendered view\\n   - I need to set up the viewport shading or render preview to see the materials properly\\n   - The camera composition looks good from the angle\\n\\nI should:\\n1. Enable viewport shading to see materials properly (switch to Material Preview or Rendered view)\\n2. Verify the contact between sphere and plane\\n3. Make sure the white ground plane is visible (it might be too thin or need better positioning)\\n4. 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"error": null}], "response": {"text": "Good! I can see the red sphere and the ground plane. Now let me switch to a rendered viewport to see the materials and lighting properly:", "tool_calls": [{"id": "toolu_bdrk_01Bj9kiMq6FEp9CxLDp2ENaX", "name": "execute_blender_code", "arguments": {"code": "\nimport bpy\n\n# Set the viewport shading to rendered for a preview\nfor area in bpy.context.screen.areas:\n    if area.type == 'VIEW_3D':\n        for space in area.spaces:\n            if space.type == 'VIEW_3D':\n                space.shading.type = 'MATERIAL'\n                \nprint(\"Viewport shading set to MATERIAL\")\n", "user_prompt": "Switch viewport to material preview shading"}, "raw_arguments": "{\"code\": \"\\nimport bpy\\n\\n# Set the viewport shading to rendered for a preview\\nfor area in bpy.context.screen.areas:\\n    if area.type == 'VIEW_3D':\\n        for space in area.spaces:\\n            if space.type == 'VIEW_3D':\\n                space.shading.type = 'MATERIAL'\\n                \\nprint(\\\"Viewport shading set to MATERIAL\\\")\\n\", \"user_prompt\": \"Switch viewport to material preview shading\"}", "parse_error": null}], "tokens_in": 9020, "tokens_out": 424, "cost_usd": 0.0072009, "cached_tokens": 5384}, "error": null}
{"ts": "2026-07-15T00:49:14.441450+00:00", "type": "assistant", "text": "Good! I can see the red sphere and the ground plane. Now let me switch to a rendered viewport to see the materials and lighting properly:", "tool_calls": [{"id": "toolu_bdrk_01Bj9kiMq6FEp9CxLDp2ENaX", "name": "execute_blender_code", "arguments": {"code": "\nimport bpy\n\n# Set the viewport shading to rendered for a preview\nfor area in bpy.context.screen.areas:\n    if area.type == 'VIEW_3D':\n        for space in area.spaces:\n            if space.type == 'VIEW_3D':\n                space.shading.type = 'MATERIAL'\n                \nprint(\"Viewport shading set to MATERIAL\")\n", "user_prompt": "Switch viewport to material preview shading"}, "raw_arguments": "{\"code\": \"\\nimport bpy\\n\\n# Set the viewport shading to rendered for a preview\\nfor area in bpy.context.screen.areas:\\n    if area.type == 'VIEW_3D':\\n        for space in area.spaces:\\n            if space.type == 'VIEW_3D':\\n                space.shading.type = 'MATERIAL'\\n                \\nprint(\\\"Viewport shading set to MATERIAL\\\")\\n\", \"user_prompt\": \"Switch viewport to material preview shading\"}", "parse_error": null}]}
{"ts": "2026-07-15T00:49:14.442456+00:00", "type": "tool_call", "id": "toolu_bdrk_01Bj9kiMq6FEp9CxLDp2ENaX", "name": "execute_blender_code", "arguments": {"code": "\nimport bpy\n\n# Set the viewport shading to rendered for a preview\nfor area in bpy.context.screen.areas:\n    if area.type == 'VIEW_3D':\n        for space in area.spaces:\n            if space.type == 'VIEW_3D':\n                space.shading.type = 'MATERIAL'\n                \nprint(\"Viewport shading set to MATERIAL\")\n", "user_prompt": "Switch viewport to material preview shading"}, "raw_arguments": "{\"code\": \"\\nimport bpy\\n\\n# Set the viewport shading to rendered for a preview\\nfor area in bpy.context.screen.areas:\\n    if area.type == 'VIEW_3D':\\n        for space in area.spaces:\\n            if space.type == 'VIEW_3D':\\n                space.shading.type = 'MATERIAL'\\n                \\nprint(\\\"Viewport shading set to MATERIAL\\\")\\n\", \"user_prompt\": \"Switch viewport to material preview shading\"}"}
{"ts": "2026-07-15T00:49:14.659820+00:00", "type": "tool_result", "tool_call_id": "toolu_bdrk_01Bj9kiMq6FEp9CxLDp2ENaX", "name": "execute_blender_code", "text": "Code executed successfully: Viewport shading set to MATERIAL\n", "is_error": false, "has_image": false, "image_b64": null}
{"ts": "2026-07-15T00:49:17.970366+00:00", "type": "llm_call", "call_index": 9, "provider_type": "blenderbench.providers.openrouter.OpenRouterProvider", "started_at": "2026-07-15T00:49:14.660826+00:00", "finished_at": "2026-07-15T00:49:17.962574+00:00", "duration_s": 3.2969999999913853, "request": {"messages": [{"role": "system", "content": "You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\n\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\n\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\n\n## Harness Instructions\n\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. When you are finished, reply with the single word DONE and no tool calls."}, {"role": "user", "content": "## Task\n\nComplete the following Blender task:\n\n<task>\n\nA red sphere of 1 meter diameter resting on a white ground plane — touching it\nexactly, neither floating nor sunk. Smooth shading, a plain red material on the\nball, plain white on the floor.\n\nThe floor is the sphere, the color, and the contact. The ceiling is presentation:\na soft believable shadow under the ball, a hint of glossiness that catches the\nlight, and clean framing.\n\n</task>"}, {"role": "assistant", "content": "I'll create a red sphere resting on a white ground plane with proper materials, shading, lighting, and camera setup. Let me work through this step by step.", "tool_calls": [{"id": "toolu_bdrk_01McQwtp5Bq6813NVu2gUpRG", "type": "function", "function": {"name": "get_scene_info", "arguments": "{\"user_prompt\": \"Get current scene info to see what's there initially\"}"}}]}, {"role": "tool", "tool_call_id": "toolu_bdrk_01McQwtp5Bq6813NVu2gUpRG", "content": "{\n  \"name\": \"Scene\",\n  \"object_count\": 0,\n  \"objects\": [],\n  \"materials_count\": 2\n}"}, {"role": "assistant", "content": "Good, the scene is empty. Now I'll create the red sphere and white ground plane using Blender code:", "tool_calls": [{"id": "toolu_bdrk_01PuvjJZjfHJZgTRw6jGDsdH", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\": \"\\nimport bpy\\nimport bmesh\\nfrom mathutils import Vector\\n\\n# Clear any default objects\\nbpy.ops.object.select_all(action='SELECT')\\nbpy.ops.object.delete(use_global=False)\\n\\n# Add a sphere (1 meter diameter = 0.5 meter radius)\\nbpy.ops.mesh.primitive_uv_sphere_add(\\n    radius=0.5,\\n    location=(0, 0, 0.5)  # Position it so bottom touches z=0\\n)\\nsphere = bpy.context.active_object\\nsphere.name = \\\"RedSphere\\\"\\n\\n# Add a large ground plane\\nbpy.ops.mesh.primitive_plane_add(\\n    size=10,\\n    location=(0, 0, 0)\\n)\\nplane = bpy.context.active_object\\nplane.name = \\\"GroundPlane\\\"\\n\\n# Select and scale plane to be large but thin\\nplane.scale = (10, 10, 0.1)\\n\\nprint(\\\"Objects created: Sphere at z=\\\" + str(sphere.location.z) + \\\", Plane at z=\\\" + str(plane.location.z))\\n\", \"user_prompt\": \"Create red sphere and white ground plane\"}"}}]}, {"role": "tool", "tool_call_id": "toolu_bdrk_01PuvjJZjfHJZgTRw6jGDsdH", "content": "Code executed successfully: Objects created: Sphere at z=0.5, Plane at z=0.0\n"}, {"role": "assistant", "content": "Now I'll create the materials - red for the sphere and white for the plane:", "tool_calls": [{"id": "toolu_bdrk_018gHZ2vPGV9JR5pgaWmu5aZ", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\": \"\\nimport bpy\\n\\n# Get the sphere and plane objects\\nsphere = bpy.data.objects[\\\"RedSphere\\\"]\\nplane = bpy.data.objects[\\\"GroundPlane\\\"]\\n\\n# Create red material for sphere\\nred_material = bpy.data.materials.new(name=\\\"RedMaterial\\\")\\nred_material.use_nodes = True\\nred_material.diffuse_color = (1.0, 0.0, 0.0, 1.0)  # Red RGBA\\n\\n# Configure red material with some glossiness\\nbsdf = red_material.node_tree.nodes[\\\"Principled BSDF\\\"]\\nbsdf.inputs[\\\"Base Color\\\"].default_value = (0.8, 0.05, 0.05, 1.0)  # Pure red\\nbsdf.inputs[\\\"Roughness\\\"].default_value = 0.3  # Slight glossiness\\n\\n# Create white material for plane\\nwhite_material = bpy.data.materials.new(name=\\\"WhiteMaterial\\\")\\nwhite_material.use_nodes = True\\nwhite_material.diffuse_color = (1.0, 1.0, 1.0, 1.0)  # White RGBA\\n\\nbsdf_white = white_material.node_tree.nodes[\\\"Principled BSDF\\\"]\\nbsdf_white.inputs[\\\"Base Color\\\"].default_value = (1.0, 1.0, 1.0, 1.0)  # White\\nbsdf_white.inputs[\\\"Roughness\\\"].default_value = 0.8  # Matte white\\n\\n# Assign materials to objects\\nsphere.data.materials.append(red_material)\\nplane.data.materials.append(white_material)\\n\\nprint(\\\"Materials created and assigned\\\")\\n\", \"user_prompt\": \"Create and assign red and white materials\"}"}}]}, {"role": "tool", "tool_call_id": "toolu_bdrk_018gHZ2vPGV9JR5pgaWmu5aZ", "content": "Code executed successfully: Materials created and assigned\n"}, {"role": "assistant", "content": "Now I'll apply smooth shading to both objects:", "tool_calls": [{"id": "toolu_bdrk_01WSSDEGVk2FHv51RevTiES7", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\": \"\\nimport bpy\\n\\nsphere = bpy.data.objects[\\\"RedSphere\\\"]\\nplane = bpy.data.objects[\\\"GroundPlane\\\"]\\n\\n# Select sphere and apply smooth shading\\nsphere.select_set(True)\\nbpy.context.view_layer.objects.active = sphere\\nbpy.ops.object.shade_smooth()\\n\\n# Select plane and apply smooth shading\\nplane.select_set(True)\\nbpy.context.view_layer.objects.active = plane\\nbpy.ops.object.shade_smooth()\\n\\nprint(\\\"Smooth shading applied to both objects\\\")\\n\", \"user_prompt\": \"Apply smooth shading to sphere and plane\"}"}}]}, {"role": "tool", "tool_call_id": "toolu_bdrk_01WSSDEGVk2FHv51RevTiES7", "content": "Code executed successfully: Smooth shading applied to both objects\n"}, {"role": "assistant", "content": "Now I need to set up the lighting and camera for a good presentation. I'll add a key light, fill light, and set up the camera:", "tool_calls": [{"id": "toolu_bdrk_01AMvEaBThonE94HvaJjwfZk", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\": \"\\nimport bpy\\nfrom mathutils import Vector\\n\\n# Add a key light (three-quarter lighting)\\nbpy.ops.object.light_add(\\n    type='SUN',\\n    location=(3, 4, 5)\\n)\\nkey_light = bpy.context.active_object\\nkey_light.name = \\\"KeyLight\\\"\\nkey_light.data.energy = 2.0\\nkey_light.data.angle = 0.5  # Slight softness\\nbpy.context.view_layer.objects.active = key_light\\nkey_light.select_set(True)\\n\\n# Add a fill light (softer light from the other side)\\nbpy.ops.object.light_add(\\n    type='SUN',\\n    location=(-2, -3, 3)\\n)\\nfill_light = bpy.context.active_object\\nfill_light.name = \\\"FillLight\\\"\\nfill_light.data.energy = 0.6  # Much softer\\nfill_light.data.angle = 2.0  # Very soft\\nbpy.context.view_layer.objects.active = fill_light\\nfill_light.select_set(True)\\n\\n# Add an area light positioned slightly to the side and above for catch lights\\nbpy.ops.object.light_add(\\n    type='AREA',\\n    location=(2, -3, 4)\\n)\\ncatch_light = bpy.context.active_object\\ncatch_light.name = \\\"CatchLight\\\"\\ncatch_light.data.energy = 0.8\\ncatch_light.data.size = 3\\nbpy.context.view_layer.objects.active = catch_light\\ncatch_light.select_set(True)\\n\\nprint(\\\"Lights created: Key, Fill, and Catch lights\\\")\\n\", \"user_prompt\": \"Add lighting setup for presentation\"}"}}]}, {"role": "tool", "tool_call_id": "toolu_bdrk_01AMvEaBThonE94HvaJjwfZk", "content": "Code executed successfully: Lights created: Key, Fill, and Catch lights\n"}, {"role": "assistant", "content": "Now I'll set up the camera for a clean, well-composed view:", "tool_calls": [{"id": "toolu_bdrk_013cajKLXaCWr4wzNHHwZfby", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\": \"\\nimport bpy\\nfrom mathutils import Vector, Matrix\\n\\n# Add a camera\\nbpy.ops.object.camera_add(\\n    location=(3.5, -3, 2.5)\\n)\\ncamera = bpy.context.active_object\\ncamera.name = \\\"Camera\\\"\\n\\n# Point camera at the sphere (origin area)\\ndirection = Vector((0, 0, 0.3)) - Vector(camera.location)\\nrot_quat = direction.to_track_quat('-Z', 'Y')\\ncamera.rotation_euler = rot_quat.to_euler()\\n\\n# Set as active camera\\nbpy.context.scene.camera = camera\\n\\n# Adjust camera settings for a nice view\\ncamera.data.lens = 50  # Standard lens\\ncamera.data.sensor_width = 36\\n\\nprint(\\\"Camera set up at position:\\\", camera.location)\\nprint(\\\"Camera rotation euler:\\\", camera.rotation_euler)\\n\", \"user_prompt\": \"Set up camera for presentation\"}"}}]}, {"role": "tool", "tool_call_id": "toolu_bdrk_013cajKLXaCWr4wzNHHwZfby", "content": "Code executed successfully: Camera set up at position: <Vector (3.5000, -3.0000, 2.5000)>\nCamera rotation euler: <Euler (x=1.1255, y=0.0000, z=0.8622), order='XYZ'>\n"}, {"role": "assistant", "content": "Let me take a screenshot to see the current result:", "tool_calls": [{"id": "toolu_bdrk_017xgYeoUTciryqhVxri7bM3", "type": "function", "function": {"name": "get_viewport_screenshot", "arguments": "{\"user_prompt\": \"Take screenshot of the scene to verify the composition\", \"max_size\": 1000}"}}]}, {"role": "tool", "tool_call_id": "toolu_bdrk_017xgYeoUTciryqhVxri7bM3", "content": ""}, {"role": "user", "content": [{"type": "image_url", "image_url": {"url": 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"}}]}, {"role": "assistant", "content": "Good! I can see the red sphere and the ground plane. Now let me switch to a rendered viewport to see the materials and lighting properly:", "tool_calls": [{"id": "toolu_bdrk_01Bj9kiMq6FEp9CxLDp2ENaX", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\": \"\\nimport bpy\\n\\n# Set the viewport shading to rendered for a preview\\nfor area in bpy.context.screen.areas:\\n    if area.type == 'VIEW_3D':\\n        for space in area.spaces:\\n            if space.type == 'VIEW_3D':\\n                space.shading.type = 'MATERIAL'\\n                \\nprint(\\\"Viewport shading set to MATERIAL\\\")\\n\", \"user_prompt\": \"Switch viewport to material preview shading\"}"}}]}, {"role": "tool", "tool_call_id": "toolu_bdrk_01Bj9kiMq6FEp9CxLDp2ENaX", "content": "Code executed successfully: Viewport shading set to MATERIAL\n"}], "tools": [{"type": "function", "function": {"name": "get_scene_info", "description": "Get detailed information about the current Blender scene\n\n    Parameters:\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\n    ", "parameters": {"properties": {"user_prompt": {"title": "User Prompt", "type": "string"}}, "required": ["user_prompt"], "title": "get_scene_infoArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_object_info", "description": "\n    Get detailed information about a specific object in the Blender scene.\n\n    Parameters:\n    - object_name: The name of the object to get information about\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"object_name": {"title": "Object Name", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["object_name"], "title": "get_object_infoArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_viewport_screenshot", "description": "\n    Capture a screenshot of the current Blender 3D viewport.\n\n    Parameters:\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns the screenshot as an Image.\n    ", "parameters": {"properties": {"max_size": {"default": 1000, "title": "Max Size", "type": "integer"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_viewport_screenshotArguments", "type": "object"}}}, {"type": "function", "function": {"name": "execute_blender_code", "description": "\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\n\n    Parameters:\n    - code: The Python code to execute\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"code": {"title": "Code", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["code"], "title": "execute_blender_codeArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_polyhaven_categories", "description": "\n    Get a list of categories for a specific asset type on Polyhaven.\n\n    Parameters:\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"asset_type": {"default": "hdris", "title": "Asset Type", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_polyhaven_categoriesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_polyhaven_assets", "description": "\n    Search for assets on Polyhaven with optional filtering.\n\n    Parameters:\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\n    - categories: Optional comma-separated list of categories to filter by\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a list of matching assets with basic information.\n    ", "parameters": {"properties": {"asset_type": {"default": "all", "title": "Asset Type", "type": "string"}, "categories": {"default": null, "title": "Categories", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "search_polyhaven_assetsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "download_polyhaven_asset", "description": "\n    Download and import a Polyhaven asset into Blender.\n\n    Parameters:\n    - asset_id: The ID of the asset to download\n    - asset_type: The type of asset (hdris, textures, models)\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"asset_id": {"title": "Asset Id", "type": "string"}, "asset_type": {"title": "Asset Type", "type": "string"}, "resolution": {"default": "1k", "title": "Resolution", "type": "string"}, "file_format": {"default": null, "title": "File Format", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["asset_id", "asset_type"], "title": "download_polyhaven_assetArguments", "type": "object"}}}, {"type": "function", "function": {"name": "set_texture", "description": "\n    Apply a previously downloaded Polyhaven texture to an object.\n    \n    Parameters:\n    - object_name: Name of the object to apply the texture to\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\n    \n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"object_name": {"title": "Object Name", "type": "string"}, "texture_id": {"title": "Texture Id", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["object_name", "texture_id"], "title": "set_textureArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_polyhaven_status", "description": "\n    Check if PolyHaven integration is enabled in Blender.\n    Returns a message indicating whether PolyHaven features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_polyhaven_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_hyper3d_status", "description": "\n    Check if Hyper3D Rodin integration is enabled in Blender.\n    Returns a message indicating whether Hyper3D Rodin features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_hyper3d_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_sketchfab_status", "description": "\n    Check if Sketchfab integration is enabled in Blender.\n    Returns a message indicating whether Sketchfab features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_sketchfab_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_sketchfab_models", "description": "\n    Search for models on Sketchfab with optional filtering.\n\n    Parameters:\n    - query: Text to search for\n    - categories: Optional comma-separated list of categories\n    - count: Maximum number of results to return (default 20)\n    - downloadable: Whether to include only downloadable models (default True)\n\n    Returns a formatted list of matching models.\n    ", "parameters": {"properties": {"query": {"title": "Query", "type": "string"}, "categories": {"default": null, "title": "Categories", "type": "string"}, "count": {"default": 20, "title": "Count", "type": "integer"}, "downloadable": {"default": true, "title": "Downloadable", "type": "boolean"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["query"], "title": "search_sketchfab_modelsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_sketchfab_model_preview", "description": "\n    Get a preview thumbnail of a Sketchfab model by its UID.\n    Use this to visually confirm a model before downloading.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\n    \n    Returns the model's thumbnail as an Image for visual confirmation.\n    ", "parameters": {"properties": {"uid": {"title": "Uid", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["uid"], "title": "get_sketchfab_model_previewArguments", "type": "object"}}}, {"type": "function", "function": {"name": "download_sketchfab_model", "description": "\n    Download and import a Sketchfab model by its UID.\n    The model will be scaled so its largest dimension equals target_size.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\n                  You must specify the desired size for the model.\n                  Examples:\n                  - Chair: target_size=1.0 (1 meter tall)\n                  - Table: target_size=0.75 (75cm tall)\n                  - Car: target_size=4.5 (4.5 meters long)\n                  - Person: target_size=1.7 (1.7 meters tall)\n                  - Small object (cup, phone): target_size=0.1 to 0.3\n    \n    Returns a message with import details including object names, dimensions, and bounding box.\n    The model must be downloadable and you must have proper access rights.\n    ", "parameters": {"properties": {"uid": {"title": "Uid", "type": "string"}, "target_size": {"title": "Target Size", "type": "number"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["uid", "target_size"], "title": "download_sketchfab_modelArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hyper3d_model_via_text", "description": "\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n\n    Parameters:\n    - text_prompt: A short description of the desired model in **English**.\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"text_prompt": {"title": "Text Prompt", "type": "string"}, "bbox_condition": {"default": null, "items": {"type": "number"}, "title": "Bbox Condition", "type": "array"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["text_prompt"], "title": "generate_hyper3d_model_via_textArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hyper3d_model_via_images", "description": "\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n    \n    Parameters:\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode.\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"input_image_paths": {"default": null, "items": {"type": "string"}, "title": "Input Image Paths", "type": "array"}, "input_image_urls": {"default": null, "items": {"type": "string"}, "title": "Input Image Urls", "type": "array"}, "bbox_condition": {"default": null, "items": {"type": "number"}, "title": "Bbox Condition", "type": "array"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "generate_hyper3d_model_via_imagesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "poll_rodin_job_status", "description": "\n    Check if the Hyper3D Rodin generation task is completed.\n\n    For Hyper3D Rodin mode MAIN_SITE:\n        Parameters:\n        - subscription_key: The subscription_key given in the generate model step.\n\n        Returns a list of status. The task is done if all status are \"Done\".\n        If \"Failed\" showed up, the generating process failed.\n        This is a polling API, so only proceed if the status are finally determined (\"Done\" or \"Canceled\").\n\n    For Hyper3D Rodin mode FAL_AI:\n        Parameters:\n        - request_id: The request_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"COMPLETED\".\n        The task is in progress if status is \"IN_PROGRESS\".\n        If status other than \"COMPLETED\", \"IN_PROGRESS\", \"IN_QUEUE\" showed up, the generating process might be failed.\n        This is a polling API, so only proceed if the status are finally determined (\"COMPLETED\" or some failed state).\n    ", "parameters": {"properties": {"subscription_key": {"default": null, "title": "Subscription Key", "type": "string"}, "request_id": {"default": null, "title": "Request Id", "type": "string"}}, "title": "poll_rodin_job_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "import_generated_asset", "description": "\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\n\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\n    Return if the asset has been imported successfully.\n    ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "task_uuid": {"default": null, "title": "Task Uuid", "type": "string"}, "request_id": {"default": null, "title": "Request Id", "type": "string"}}, "required": ["name"], "title": "import_generated_assetArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_hunyuan3d_status", "description": "\n    Check if Hunyuan3D integration is enabled in Blender.\n    Returns a message indicating whether Hunyuan3D features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_hunyuan3d_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hunyuan3d_model", "description": "\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \n    or both for the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    \n    Parameters:\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\n    - input_image_url: (Optional) The local or remote url of the input image. Accepts None if only using text prompt.\n\n    Returns: \n    - When successful, returns a JSON with job_id (format: \"job_xxx\") indicating the task is in progress\n    - When the job completes, the status will change to \"DONE\" indicating the model has been imported\n    - Returns error message if the operation fails\n    ", "parameters": {"properties": {"text_prompt": {"default": null, "title": "Text Prompt", "type": "string"}, "input_image_url": {"default": null, "title": "Input Image Url", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "generate_hunyuan3d_modelArguments", "type": "object"}}}, {"type": "function", "function": {"name": "poll_hunyuan_job_status", "description": "\n    Check if the Hunyuan3D generation task is completed.\n\n    For Hunyuan3D:\n        Parameters:\n        - job_id: The job_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"DONE\".\n        The task is in progress if status is \"RUN\".\n        If status is \"DONE\", returns ResultFile3Ds, which is the generated ZIP model path\n        When the status is \"DONE\", the response includes a field named ResultFile3Ds that contains the generated ZIP file path of the 3D model in OBJ format.\n        This is a polling API, so only proceed if the status are finally determined (\"DONE\" or some failed state).\n    ", "parameters": {"properties": {"job_id": {"default": null, "title": "Job Id", "type": "string"}}, "title": "poll_hunyuan_job_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "import_generated_asset_hunyuan", "description": "\n    Import the asset generated by Hunyuan3D after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - zip_file_url: The zip_file_url given in the generate model step.\n\n    Return if the asset has been imported successfully.\n    ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "zip_file_url": {"title": "Zip File Url", "type": "string"}}, "required": ["name", "zip_file_url"], "title": "import_generated_asset_hunyuanArguments", "type": "object"}}}]}, "provider_attempts": [{"provider": "openrouter", "attempt": 1, "started_at": "2026-07-15T00:49:14.660826+00:00", "finished_at": "2026-07-15T00:49:17.960573+00:00", "duration_s": 3.2969999999913853, "request": {"method": "POST", "url": "https://openrouter.ai/api/v1/chat/completions", "headers": {"host": "openrouter.ai", "accept": "*/*", "accept-encoding": "gzip, deflate", "connection": "keep-alive", "user-agent": "python-httpx/0.28.1", "authorization": "[REDACTED]", "content-length": "253146", "content-type": "application/json"}, "body": "{\"model\":\"anthropic/claude-haiku-4.5\",\"messages\":[{\"role\":\"system\",\"content\":[{\"type\":\"text\",\"text\":\"You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\\n\\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\\n\\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\\n\\n## Harness Instructions\\n\\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. When you are finished, reply with the single word DONE and no tool calls.\",\"cache_control\":{\"type\":\"ephemeral\"}}]},{\"role\":\"user\",\"content\":\"## Task\\n\\nComplete the following Blender task:\\n\\n<task>\\n\\nA red sphere of 1 meter diameter resting on a white ground plane — touching it\\nexactly, neither floating nor sunk. Smooth shading, a plain red material on the\\nball, plain white on the floor.\\n\\nThe floor is the sphere, the color, and the contact. The ceiling is presentation:\\na soft believable shadow under the ball, a hint of glossiness that catches the\\nlight, and clean framing.\\n\\n</task>\"},{\"role\":\"assistant\",\"content\":\"I'll create a red sphere resting on a white ground plane with proper materials, shading, lighting, and camera setup. Let me work through this step by step.\",\"tool_calls\":[{\"id\":\"toolu_bdrk_01McQwtp5Bq6813NVu2gUpRG\",\"type\":\"function\",\"function\":{\"name\":\"get_scene_info\",\"arguments\":\"{\\\"user_prompt\\\": \\\"Get current scene info to see what's there initially\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"toolu_bdrk_01McQwtp5Bq6813NVu2gUpRG\",\"content\":\"{\\n  \\\"name\\\": \\\"Scene\\\",\\n  \\\"object_count\\\": 0,\\n  \\\"objects\\\": [],\\n  \\\"materials_count\\\": 2\\n}\"},{\"role\":\"assistant\",\"content\":\"Good, the scene is empty. Now I'll create the red sphere and white ground plane using Blender code:\",\"tool_calls\":[{\"id\":\"toolu_bdrk_01PuvjJZjfHJZgTRw6jGDsdH\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\": \\\"\\\\nimport bpy\\\\nimport bmesh\\\\nfrom mathutils import Vector\\\\n\\\\n# Clear any default objects\\\\nbpy.ops.object.select_all(action='SELECT')\\\\nbpy.ops.object.delete(use_global=False)\\\\n\\\\n# Add a sphere (1 meter diameter = 0.5 meter radius)\\\\nbpy.ops.mesh.primitive_uv_sphere_add(\\\\n    radius=0.5,\\\\n    location=(0, 0, 0.5)  # Position it so bottom touches z=0\\\\n)\\\\nsphere = bpy.context.active_object\\\\nsphere.name = \\\\\\\"RedSphere\\\\\\\"\\\\n\\\\n# Add a large ground plane\\\\nbpy.ops.mesh.primitive_plane_add(\\\\n    size=10,\\\\n    location=(0, 0, 0)\\\\n)\\\\nplane = bpy.context.active_object\\\\nplane.name = \\\\\\\"GroundPlane\\\\\\\"\\\\n\\\\n# Select and scale plane to be large but thin\\\\nplane.scale = (10, 10, 0.1)\\\\n\\\\nprint(\\\\\\\"Objects created: Sphere at z=\\\\\\\" + str(sphere.location.z) + \\\\\\\", Plane at z=\\\\\\\" + str(plane.location.z))\\\\n\\\", \\\"user_prompt\\\": \\\"Create red sphere and white ground plane\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"toolu_bdrk_01PuvjJZjfHJZgTRw6jGDsdH\",\"content\":\"Code executed successfully: Objects created: Sphere at z=0.5, Plane at z=0.0\\n\"},{\"role\":\"assistant\",\"content\":\"Now I'll create the materials - red for the sphere and white for the plane:\",\"tool_calls\":[{\"id\":\"toolu_bdrk_018gHZ2vPGV9JR5pgaWmu5aZ\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\": \\\"\\\\nimport bpy\\\\n\\\\n# Get the sphere and plane objects\\\\nsphere = bpy.data.objects[\\\\\\\"RedSphere\\\\\\\"]\\\\nplane = bpy.data.objects[\\\\\\\"GroundPlane\\\\\\\"]\\\\n\\\\n# Create red material for sphere\\\\nred_material = bpy.data.materials.new(name=\\\\\\\"RedMaterial\\\\\\\")\\\\nred_material.use_nodes = True\\\\nred_material.diffuse_color = (1.0, 0.0, 0.0, 1.0)  # Red RGBA\\\\n\\\\n# Configure red material with some glossiness\\\\nbsdf = red_material.node_tree.nodes[\\\\\\\"Principled BSDF\\\\\\\"]\\\\nbsdf.inputs[\\\\\\\"Base Color\\\\\\\"].default_value = (0.8, 0.05, 0.05, 1.0)  # Pure red\\\\nbsdf.inputs[\\\\\\\"Roughness\\\\\\\"].default_value = 0.3  # Slight glossiness\\\\n\\\\n# Create white material for plane\\\\nwhite_material = bpy.data.materials.new(name=\\\\\\\"WhiteMaterial\\\\\\\")\\\\nwhite_material.use_nodes = True\\\\nwhite_material.diffuse_color = (1.0, 1.0, 1.0, 1.0)  # White RGBA\\\\n\\\\nbsdf_white = white_material.node_tree.nodes[\\\\\\\"Principled BSDF\\\\\\\"]\\\\nbsdf_white.inputs[\\\\\\\"Base Color\\\\\\\"].default_value = (1.0, 1.0, 1.0, 1.0)  # White\\\\nbsdf_white.inputs[\\\\\\\"Roughness\\\\\\\"].default_value = 0.8  # Matte white\\\\n\\\\n# Assign materials to objects\\\\nsphere.data.materials.append(red_material)\\\\nplane.data.materials.append(white_material)\\\\n\\\\nprint(\\\\\\\"Materials created and assigned\\\\\\\")\\\\n\\\", \\\"user_prompt\\\": \\\"Create and assign red and white materials\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"toolu_bdrk_018gHZ2vPGV9JR5pgaWmu5aZ\",\"content\":\"Code executed successfully: Materials created and assigned\\n\"},{\"role\":\"assistant\",\"content\":\"Now I'll apply smooth shading to both objects:\",\"tool_calls\":[{\"id\":\"toolu_bdrk_01WSSDEGVk2FHv51RevTiES7\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\": \\\"\\\\nimport bpy\\\\n\\\\nsphere = bpy.data.objects[\\\\\\\"RedSphere\\\\\\\"]\\\\nplane = bpy.data.objects[\\\\\\\"GroundPlane\\\\\\\"]\\\\n\\\\n# Select sphere and apply smooth shading\\\\nsphere.select_set(True)\\\\nbpy.context.view_layer.objects.active = sphere\\\\nbpy.ops.object.shade_smooth()\\\\n\\\\n# Select plane and apply smooth shading\\\\nplane.select_set(True)\\\\nbpy.context.view_layer.objects.active = plane\\\\nbpy.ops.object.shade_smooth()\\\\n\\\\nprint(\\\\\\\"Smooth shading applied to both objects\\\\\\\")\\\\n\\\", \\\"user_prompt\\\": \\\"Apply smooth shading to sphere and plane\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"toolu_bdrk_01WSSDEGVk2FHv51RevTiES7\",\"content\":\"Code executed successfully: Smooth shading applied to both objects\\n\"},{\"role\":\"assistant\",\"content\":\"Now I need to set up the lighting and camera for a good presentation. I'll add a key light, fill light, and set up the camera:\",\"tool_calls\":[{\"id\":\"toolu_bdrk_01AMvEaBThonE94HvaJjwfZk\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\": \\\"\\\\nimport bpy\\\\nfrom mathutils import Vector\\\\n\\\\n# Add a key light (three-quarter lighting)\\\\nbpy.ops.object.light_add(\\\\n    type='SUN',\\\\n    location=(3, 4, 5)\\\\n)\\\\nkey_light = bpy.context.active_object\\\\nkey_light.name = \\\\\\\"KeyLight\\\\\\\"\\\\nkey_light.data.energy = 2.0\\\\nkey_light.data.angle = 0.5  # Slight softness\\\\nbpy.context.view_layer.objects.active = key_light\\\\nkey_light.select_set(True)\\\\n\\\\n# Add a fill light (softer light from the other side)\\\\nbpy.ops.object.light_add(\\\\n    type='SUN',\\\\n    location=(-2, -3, 3)\\\\n)\\\\nfill_light = bpy.context.active_object\\\\nfill_light.name = \\\\\\\"FillLight\\\\\\\"\\\\nfill_light.data.energy = 0.6  # Much softer\\\\nfill_light.data.angle = 2.0  # Very soft\\\\nbpy.context.view_layer.objects.active = fill_light\\\\nfill_light.select_set(True)\\\\n\\\\n# Add an area light positioned slightly to the side and above for catch lights\\\\nbpy.ops.object.light_add(\\\\n    type='AREA',\\\\n    location=(2, -3, 4)\\\\n)\\\\ncatch_light = bpy.context.active_object\\\\ncatch_light.name = \\\\\\\"CatchLight\\\\\\\"\\\\ncatch_light.data.energy = 0.8\\\\ncatch_light.data.size = 3\\\\nbpy.context.view_layer.objects.active = catch_light\\\\ncatch_light.select_set(True)\\\\n\\\\nprint(\\\\\\\"Lights created: Key, Fill, and Catch lights\\\\\\\")\\\\n\\\", \\\"user_prompt\\\": \\\"Add lighting setup for presentation\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"toolu_bdrk_01AMvEaBThonE94HvaJjwfZk\",\"content\":\"Code executed successfully: Lights created: Key, Fill, and Catch lights\\n\"},{\"role\":\"assistant\",\"content\":\"Now I'll set up the camera for a clean, well-composed view:\",\"tool_calls\":[{\"id\":\"toolu_bdrk_013cajKLXaCWr4wzNHHwZfby\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\": \\\"\\\\nimport bpy\\\\nfrom mathutils import Vector, Matrix\\\\n\\\\n# Add a camera\\\\nbpy.ops.object.camera_add(\\\\n    location=(3.5, -3, 2.5)\\\\n)\\\\ncamera = bpy.context.active_object\\\\ncamera.name = \\\\\\\"Camera\\\\\\\"\\\\n\\\\n# Point camera at the sphere (origin area)\\\\ndirection = Vector((0, 0, 0.3)) - Vector(camera.location)\\\\nrot_quat = direction.to_track_quat('-Z', 'Y')\\\\ncamera.rotation_euler = rot_quat.to_euler()\\\\n\\\\n# Set as active camera\\\\nbpy.context.scene.camera = camera\\\\n\\\\n# Adjust camera settings for a nice view\\\\ncamera.data.lens = 50  # Standard lens\\\\ncamera.data.sensor_width = 36\\\\n\\\\nprint(\\\\\\\"Camera set up at position:\\\\\\\", camera.location)\\\\nprint(\\\\\\\"Camera rotation euler:\\\\\\\", camera.rotation_euler)\\\\n\\\", \\\"user_prompt\\\": \\\"Set up camera for presentation\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"toolu_bdrk_013cajKLXaCWr4wzNHHwZfby\",\"content\":\"Code executed successfully: Camera set up at position: <Vector (3.5000, -3.0000, 2.5000)>\\nCamera rotation euler: <Euler (x=1.1255, y=0.0000, z=0.8622), order='XYZ'>\\n\"},{\"role\":\"assistant\",\"content\":\"Let me take a screenshot to see the current result:\",\"tool_calls\":[{\"id\":\"toolu_bdrk_017xgYeoUTciryqhVxri7bM3\",\"type\":\"function\",\"function\":{\"name\":\"get_viewport_screenshot\",\"arguments\":\"{\\\"user_prompt\\\": \\\"Take screenshot of the scene to verify the composition\\\", \\\"max_size\\\": 1000}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"toolu_bdrk_017xgYeoUTciryqhVxri7bM3\",\"content\":\"\"},{\"role\":\"user\",\"content\":[{\"type\":\"image_url\",\"image_url\":{\"url\":\"data:image/png;base64,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\"}}]},{\"role\":\"assistant\",\"content\":\"Good! I can see the red sphere and the ground plane. Now let me switch to a rendered viewport to see the materials and lighting properly:\",\"tool_calls\":[{\"id\":\"toolu_bdrk_01Bj9kiMq6FEp9CxLDp2ENaX\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\": \\\"\\\\nimport bpy\\\\n\\\\n# Set the viewport shading to rendered for a preview\\\\nfor area in bpy.context.screen.areas:\\\\n    if area.type == 'VIEW_3D':\\\\n        for space in area.spaces:\\\\n            if space.type == 'VIEW_3D':\\\\n                space.shading.type = 'MATERIAL'\\\\n                \\\\nprint(\\\\\\\"Viewport shading set to MATERIAL\\\\\\\")\\\\n\\\", \\\"user_prompt\\\": \\\"Switch viewport to material preview shading\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"toolu_bdrk_01Bj9kiMq6FEp9CxLDp2ENaX\",\"content\":[{\"type\":\"text\",\"text\":\"Code executed successfully: Viewport shading set to MATERIAL\\n\",\"cache_control\":{\"type\":\"ephemeral\"}}]}],\"usage\":{\"include\":true},\"tools\":[{\"type\":\"function\",\"function\":{\"name\":\"get_scene_info\",\"description\":\"Get detailed information about the current Blender scene\\n\\n    Parameters:\\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"user_prompt\"],\"title\":\"get_scene_infoArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_object_info\",\"description\":\"\\n    Get detailed information about a specific object in the Blender scene.\\n\\n    Parameters:\\n    - object_name: The name of the object to get information about\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"object_name\":{\"title\":\"Object Name\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"object_name\"],\"title\":\"get_object_infoArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_viewport_screenshot\",\"description\":\"\\n    Capture a screenshot of the current Blender 3D viewport.\\n\\n    Parameters:\\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns the screenshot as an Image.\\n    \",\"parameters\":{\"properties\":{\"max_size\":{\"default\":1000,\"title\":\"Max Size\",\"type\":\"integer\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_viewport_screenshotArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"description\":\"\\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\\n\\n    Parameters:\\n    - code: The Python code to execute\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"code\":{\"title\":\"Code\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"code\"],\"title\":\"execute_blender_codeArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_categories\",\"description\":\"\\n    Get a list of categories for a specific asset type on Polyhaven.\\n\\n    Parameters:\\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"asset_type\":{\"default\":\"hdris\",\"title\":\"Asset Type\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_polyhaven_categoriesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_polyhaven_assets\",\"description\":\"\\n    Search for assets on Polyhaven with optional filtering.\\n\\n    Parameters:\\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\\n    - categories: Optional comma-separated list of categories to filter by\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns a list of matching assets with basic information.\\n    \",\"parameters\":{\"properties\":{\"asset_type\":{\"default\":\"all\",\"title\":\"Asset Type\",\"type\":\"string\"},\"categories\":{\"default\":null,\"title\":\"Categories\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"search_polyhaven_assetsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"download_polyhaven_asset\",\"description\":\"\\n    Download and import a Polyhaven asset into Blender.\\n\\n    Parameters:\\n    - asset_id: The ID of the asset to download\\n    - asset_type: The type of asset (hdris, textures, models)\\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"asset_id\":{\"title\":\"Asset Id\",\"type\":\"string\"},\"asset_type\":{\"title\":\"Asset Type\",\"type\":\"string\"},\"resolution\":{\"default\":\"1k\",\"title\":\"Resolution\",\"type\":\"string\"},\"file_format\":{\"default\":null,\"title\":\"File Format\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"asset_id\",\"asset_type\"],\"title\":\"download_polyhaven_assetArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"set_texture\",\"description\":\"\\n    Apply a previously downloaded Polyhaven texture to an object.\\n    \\n    Parameters:\\n    - object_name: Name of the object to apply the texture to\\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\\n    \\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"object_name\":{\"title\":\"Object Name\",\"type\":\"string\"},\"texture_id\":{\"title\":\"Texture Id\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"object_name\",\"texture_id\"],\"title\":\"set_textureArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_status\",\"description\":\"\\n    Check if PolyHaven integration is enabled in Blender.\\n    Returns a message indicating whether PolyHaven features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_polyhaven_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_hyper3d_status\",\"description\":\"\\n    Check if Hyper3D Rodin integration is enabled in Blender.\\n    Returns a message indicating whether Hyper3D Rodin features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_hyper3d_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_status\",\"description\":\"\\n    Check if Sketchfab integration is enabled in Blender.\\n    Returns a message indicating whether Sketchfab features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_sketchfab_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_sketchfab_models\",\"description\":\"\\n    Search for models on Sketchfab with optional filtering.\\n\\n    Parameters:\\n    - query: Text to search for\\n    - categories: Optional comma-separated list of categories\\n    - count: Maximum number of results to return (default 20)\\n    - downloadable: Whether to include only downloadable models (default True)\\n\\n    Returns a formatted list of matching models.\\n    \",\"parameters\":{\"properties\":{\"query\":{\"title\":\"Query\",\"type\":\"string\"},\"categories\":{\"default\":null,\"title\":\"Categories\",\"type\":\"string\"},\"count\":{\"default\":20,\"title\":\"Count\",\"type\":\"integer\"},\"downloadable\":{\"default\":true,\"title\":\"Downloadable\",\"type\":\"boolean\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"query\"],\"title\":\"search_sketchfab_modelsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_model_preview\",\"description\":\"\\n    Get a preview thumbnail of a Sketchfab model by its UID.\\n    Use this to visually confirm a model before downloading.\\n    \\n    Parameters:\\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\\n    \\n    Returns the model's thumbnail as an Image for visual confirmation.\\n    \",\"parameters\":{\"properties\":{\"uid\":{\"title\":\"Uid\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"uid\"],\"title\":\"get_sketchfab_model_previewArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"download_sketchfab_model\",\"description\":\"\\n    Download and import a Sketchfab model by its UID.\\n    The model will be scaled so its largest dimension equals target_size.\\n    \\n    Parameters:\\n    - uid: The unique identifier of the Sketchfab model\\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\\n                  You must specify the desired size for the model.\\n                  Examples:\\n                  - Chair: target_size=1.0 (1 meter tall)\\n                  - Table: target_size=0.75 (75cm tall)\\n                  - Car: target_size=4.5 (4.5 meters long)\\n                  - Person: target_size=1.7 (1.7 meters tall)\\n                  - Small object (cup, phone): target_size=0.1 to 0.3\\n    \\n    Returns a message with import details including object names, dimensions, and bounding box.\\n    The model must be downloadable and you must have proper access rights.\\n    \",\"parameters\":{\"properties\":{\"uid\":{\"title\":\"Uid\",\"type\":\"string\"},\"target_size\":{\"title\":\"Target Size\",\"type\":\"number\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"uid\",\"target_size\"],\"title\":\"download_sketchfab_modelArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hyper3d_model_via_text\",\"description\":\"\\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\\n    The 3D asset has built-in materials.\\n    The generated model has a normalized size, so re-scaling after generation can be useful.\\n\\n    Parameters:\\n    - text_prompt: A short description of the desired model in **English**.\\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\\n\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"text_prompt\":{\"title\":\"Text Prompt\",\"type\":\"string\"},\"bbox_condition\":{\"default\":null,\"items\":{\"type\":\"number\"},\"title\":\"Bbox Condition\",\"type\":\"array\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"text_prompt\"],\"title\":\"generate_hyper3d_model_via_textArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hyper3d_model_via_images\",\"description\":\"\\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\\n    The 3D asset has built-in materials.\\n    The generated model has a normalized size, so re-scaling after generation can be useful.\\n    \\n    Parameters:\\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model.\\n\\n    Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode.\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"input_image_paths\":{\"default\":null,\"items\":{\"type\":\"string\"},\"title\":\"Input Image Paths\",\"type\":\"array\"},\"input_image_urls\":{\"default\":null,\"items\":{\"type\":\"string\"},\"title\":\"Input Image Urls\",\"type\":\"array\"},\"bbox_condition\":{\"default\":null,\"items\":{\"type\":\"number\"},\"title\":\"Bbox Condition\",\"type\":\"array\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"generate_hyper3d_model_via_imagesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"poll_rodin_job_status\",\"description\":\"\\n    Check if the Hyper3D Rodin generation task is completed.\\n\\n    For Hyper3D Rodin mode MAIN_SITE:\\n        Parameters:\\n        - subscription_key: The subscription_key given in the generate model step.\\n\\n        Returns a list of status. The task is done if all status are \\\"Done\\\".\\n        If \\\"Failed\\\" showed up, the generating process failed.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"Done\\\" or \\\"Canceled\\\").\\n\\n    For Hyper3D Rodin mode FAL_AI:\\n        Parameters:\\n        - request_id: The request_id given in the generate model step.\\n\\n        Returns the generation task status. The task is done if status is \\\"COMPLETED\\\".\\n        The task is in progress if status is \\\"IN_PROGRESS\\\".\\n        If status other than \\\"COMPLETED\\\", \\\"IN_PROGRESS\\\", \\\"IN_QUEUE\\\" showed up, the generating process might be failed.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"COMPLETED\\\" or some failed state).\\n    \",\"parameters\":{\"properties\":{\"subscription_key\":{\"default\":null,\"title\":\"Subscription Key\",\"type\":\"string\"},\"request_id\":{\"default\":null,\"title\":\"Request Id\",\"type\":\"string\"}},\"title\":\"poll_rodin_job_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"import_generated_asset\",\"description\":\"\\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\\n\\n    Parameters:\\n    - name: The name of the object in scene\\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\\n\\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\\n    Return if the asset has been imported successfully.\\n    \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"task_uuid\":{\"default\":null,\"title\":\"Task Uuid\",\"type\":\"string\"},\"request_id\":{\"default\":null,\"title\":\"Request Id\",\"type\":\"string\"}},\"required\":[\"name\"],\"title\":\"import_generated_assetArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_hunyuan3d_status\",\"description\":\"\\n    Check if Hunyuan3D integration is enabled in Blender.\\n    Returns a message indicating whether Hunyuan3D features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_hunyuan3d_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hunyuan3d_model\",\"description\":\"\\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \\n    or both for the desired asset, and import the asset into Blender.\\n    The 3D asset has built-in materials.\\n    \\n    Parameters:\\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\\n    - input_image_url: (Optional) The local or remote url of the input image. Accepts None if only using text prompt.\\n\\n    Returns: \\n    - When successful, returns a JSON with job_id (format: \\\"job_xxx\\\") indicating the task is in progress\\n    - When the job completes, the status will change to \\\"DONE\\\" indicating the model has been imported\\n    - Returns error message if the operation fails\\n    \",\"parameters\":{\"properties\":{\"text_prompt\":{\"default\":null,\"title\":\"Text Prompt\",\"type\":\"string\"},\"input_image_url\":{\"default\":null,\"title\":\"Input Image Url\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"generate_hunyuan3d_modelArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"poll_hunyuan_job_status\",\"description\":\"\\n    Check if the Hunyuan3D generation task is completed.\\n\\n    For Hunyuan3D:\\n        Parameters:\\n        - job_id: The job_id given in the generate model step.\\n\\n        Returns the generation task status. The task is done if status is \\\"DONE\\\".\\n        The task is in progress if status is \\\"RUN\\\".\\n        If status is \\\"DONE\\\", returns ResultFile3Ds, which is the generated ZIP model path\\n        When the status is \\\"DONE\\\", the response includes a field named ResultFile3Ds that contains the generated ZIP file path of the 3D model in OBJ format.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"DONE\\\" or some failed state).\\n    \",\"parameters\":{\"properties\":{\"job_id\":{\"default\":null,\"title\":\"Job Id\",\"type\":\"string\"}},\"title\":\"poll_hunyuan_job_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"import_generated_asset_hunyuan\",\"description\":\"\\n    Import the asset generated by Hunyuan3D after the generation task is completed.\\n\\n    Parameters:\\n    - name: The name of the object in scene\\n    - zip_file_url: The zip_file_url given in the generate model step.\\n\\n    Return if the asset has been imported successfully.\\n    \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"zip_file_url\":{\"title\":\"Zip File Url\",\"type\":\"string\"}},\"required\":[\"name\",\"zip_file_url\"],\"title\":\"import_generated_asset_hunyuanArguments\",\"type\":\"object\"}}}],\"reasoning\":{\"effort\":\"xhigh\"}}", "body_base64": "{"model":"anthropic/claude-haiku-4.5","messages":[{"role":"system","content":[{"type":"text","text":"You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\n\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\n\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\n\n## Harness Instructions\n\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. When you are finished, reply with the single word DONE and no tool calls.","cache_control":{"type":"ephemeral"}}]},{"role":"user","content":"## Task\n\nComplete the following Blender task:\n\n<task>\n\nA red sphere of 1 meter diameter resting on a white ground plane — touching it\nexactly, neither floating nor sunk. Smooth shading, a plain red material on the\nball, plain white on the floor.\n\nThe floor is the sphere, the color, and the contact. The ceiling is presentation:\na soft believable shadow under the ball, a hint of glossiness that catches the\nlight, and clean framing.\n\n</task>"},{"role":"assistant","content":"I'll create a red sphere resting on a white ground plane with proper materials, shading, lighting, and camera setup. Let me work through this step by step.","tool_calls":[{"id":"toolu_bdrk_01McQwtp5Bq6813NVu2gUpRG","type":"function","function":{"name":"get_scene_info","arguments":"{\"user_prompt\": \"Get current scene info to see what's there initially\"}"}}]},{"role":"tool","tool_call_id":"toolu_bdrk_01McQwtp5Bq6813NVu2gUpRG","content":"{\n  \"name\": \"Scene\",\n  \"object_count\": 0,\n  \"objects\": [],\n  \"materials_count\": 2\n}"},{"role":"assistant","content":"Good, the scene is empty. Now I'll create the red sphere and white ground plane using Blender code:","tool_calls":[{"id":"toolu_bdrk_01PuvjJZjfHJZgTRw6jGDsdH","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\": \"\\nimport bpy\\nimport bmesh\\nfrom mathutils import Vector\\n\\n# Clear any default objects\\nbpy.ops.object.select_all(action='SELECT')\\nbpy.ops.object.delete(use_global=False)\\n\\n# Add a sphere (1 meter diameter = 0.5 meter radius)\\nbpy.ops.mesh.primitive_uv_sphere_add(\\n    radius=0.5,\\n    location=(0, 0, 0.5)  # Position it so bottom touches z=0\\n)\\nsphere = bpy.context.active_object\\nsphere.name = \\\"RedSphere\\\"\\n\\n# Add a large ground plane\\nbpy.ops.mesh.primitive_plane_add(\\n    size=10,\\n    location=(0, 0, 0)\\n)\\nplane = bpy.context.active_object\\nplane.name = \\\"GroundPlane\\\"\\n\\n# Select and scale plane to be large but thin\\nplane.scale = (10, 10, 0.1)\\n\\nprint(\\\"Objects created: Sphere at z=\\\" + str(sphere.location.z) + \\\", Plane at z=\\\" + str(plane.location.z))\\n\", \"user_prompt\": \"Create red sphere and white ground plane\"}"}}]},{"role":"tool","tool_call_id":"toolu_bdrk_01PuvjJZjfHJZgTRw6jGDsdH","content":"Code executed successfully: Objects created: Sphere at z=0.5, Plane at z=0.0\n"},{"role":"assistant","content":"Now I'll create the materials - red for the sphere and white for the plane:","tool_calls":[{"id":"toolu_bdrk_018gHZ2vPGV9JR5pgaWmu5aZ","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\": \"\\nimport bpy\\n\\n# Get the sphere and plane objects\\nsphere = bpy.data.objects[\\\"RedSphere\\\"]\\nplane = bpy.data.objects[\\\"GroundPlane\\\"]\\n\\n# Create red material for sphere\\nred_material = bpy.data.materials.new(name=\\\"RedMaterial\\\")\\nred_material.use_nodes = True\\nred_material.diffuse_color = (1.0, 0.0, 0.0, 1.0)  # Red RGBA\\n\\n# Configure red material with some glossiness\\nbsdf = red_material.node_tree.nodes[\\\"Principled BSDF\\\"]\\nbsdf.inputs[\\\"Base Color\\\"].default_value = (0.8, 0.05, 0.05, 1.0)  # Pure red\\nbsdf.inputs[\\\"Roughness\\\"].default_value = 0.3  # Slight glossiness\\n\\n# Create white material for plane\\nwhite_material = bpy.data.materials.new(name=\\\"WhiteMaterial\\\")\\nwhite_material.use_nodes = True\\nwhite_material.diffuse_color = (1.0, 1.0, 1.0, 1.0)  # White RGBA\\n\\nbsdf_white = white_material.node_tree.nodes[\\\"Principled BSDF\\\"]\\nbsdf_white.inputs[\\\"Base Color\\\"].default_value = (1.0, 1.0, 1.0, 1.0)  # White\\nbsdf_white.inputs[\\\"Roughness\\\"].default_value = 0.8  # Matte white\\n\\n# Assign materials to objects\\nsphere.data.materials.append(red_material)\\nplane.data.materials.append(white_material)\\n\\nprint(\\\"Materials created and assigned\\\")\\n\", \"user_prompt\": \"Create and assign red and white materials\"}"}}]},{"role":"tool","tool_call_id":"toolu_bdrk_018gHZ2vPGV9JR5pgaWmu5aZ","content":"Code executed successfully: Materials created and assigned\n"},{"role":"assistant","content":"Now I'll apply smooth shading to both objects:","tool_calls":[{"id":"toolu_bdrk_01WSSDEGVk2FHv51RevTiES7","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\": \"\\nimport bpy\\n\\nsphere = bpy.data.objects[\\\"RedSphere\\\"]\\nplane = bpy.data.objects[\\\"GroundPlane\\\"]\\n\\n# Select sphere and apply smooth shading\\nsphere.select_set(True)\\nbpy.context.view_layer.objects.active = sphere\\nbpy.ops.object.shade_smooth()\\n\\n# Select plane and apply smooth shading\\nplane.select_set(True)\\nbpy.context.view_layer.objects.active = plane\\nbpy.ops.object.shade_smooth()\\n\\nprint(\\\"Smooth shading applied to both objects\\\")\\n\", \"user_prompt\": \"Apply smooth shading to sphere and plane\"}"}}]},{"role":"tool","tool_call_id":"toolu_bdrk_01WSSDEGVk2FHv51RevTiES7","content":"Code executed successfully: Smooth shading applied to both objects\n"},{"role":"assistant","content":"Now I need to set up the lighting and camera for a good presentation. I'll add a key light, fill light, and set up the camera:","tool_calls":[{"id":"toolu_bdrk_01AMvEaBThonE94HvaJjwfZk","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\": \"\\nimport bpy\\nfrom mathutils import Vector\\n\\n# Add a key light (three-quarter lighting)\\nbpy.ops.object.light_add(\\n    type='SUN',\\n    location=(3, 4, 5)\\n)\\nkey_light = bpy.context.active_object\\nkey_light.name = \\\"KeyLight\\\"\\nkey_light.data.energy = 2.0\\nkey_light.data.angle = 0.5  # Slight softness\\nbpy.context.view_layer.objects.active = key_light\\nkey_light.select_set(True)\\n\\n# Add a fill light (softer light from the other side)\\nbpy.ops.object.light_add(\\n    type='SUN',\\n    location=(-2, -3, 3)\\n)\\nfill_light = bpy.context.active_object\\nfill_light.name = \\\"FillLight\\\"\\nfill_light.data.energy = 0.6  # Much softer\\nfill_light.data.angle = 2.0  # Very soft\\nbpy.context.view_layer.objects.active = fill_light\\nfill_light.select_set(True)\\n\\n# Add an area light positioned slightly to the side and above for catch lights\\nbpy.ops.object.light_add(\\n    type='AREA',\\n    location=(2, -3, 4)\\n)\\ncatch_light = bpy.context.active_object\\ncatch_light.name = \\\"CatchLight\\\"\\ncatch_light.data.energy = 0.8\\ncatch_light.data.size = 3\\nbpy.context.view_layer.objects.active = catch_light\\ncatch_light.select_set(True)\\n\\nprint(\\\"Lights created: Key, Fill, and Catch lights\\\")\\n\", \"user_prompt\": \"Add lighting setup for presentation\"}"}}]},{"role":"tool","tool_call_id":"toolu_bdrk_01AMvEaBThonE94HvaJjwfZk","content":"Code executed successfully: Lights created: Key, Fill, and Catch lights\n"},{"role":"assistant","content":"Now I'll set up the camera for a clean, well-composed view:","tool_calls":[{"id":"toolu_bdrk_013cajKLXaCWr4wzNHHwZfby","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\": \"\\nimport bpy\\nfrom mathutils import Vector, Matrix\\n\\n# Add a camera\\nbpy.ops.object.camera_add(\\n    location=(3.5, -3, 2.5)\\n)\\ncamera = bpy.context.active_object\\ncamera.name = \\\"Camera\\\"\\n\\n# Point camera at the sphere (origin area)\\ndirection = Vector((0, 0, 0.3)) - Vector(camera.location)\\nrot_quat = direction.to_track_quat('-Z', 'Y')\\ncamera.rotation_euler = rot_quat.to_euler()\\n\\n# Set as active camera\\nbpy.context.scene.camera = camera\\n\\n# Adjust camera settings for a nice view\\ncamera.data.lens = 50  # Standard lens\\ncamera.data.sensor_width = 36\\n\\nprint(\\\"Camera set up at position:\\\", camera.location)\\nprint(\\\"Camera rotation euler:\\\", camera.rotation_euler)\\n\", \"user_prompt\": \"Set up camera for presentation\"}"}}]},{"role":"tool","tool_call_id":"toolu_bdrk_013cajKLXaCWr4wzNHHwZfby","content":"Code executed successfully: Camera set up at position: <Vector (3.5000, -3.0000, 2.5000)>\nCamera rotation euler: <Euler (x=1.1255, y=0.0000, z=0.8622), order='XYZ'>\n"},{"role":"assistant","content":"Let me take a screenshot to see the current result:","tool_calls":[{"id":"toolu_bdrk_017xgYeoUTciryqhVxri7bM3","type":"function","function":{"name":"get_viewport_screenshot","arguments":"{\"user_prompt\": \"Take screenshot of the scene to verify the composition\", \"max_size\": 1000}"}}]},{"role":"tool","tool_call_id":"toolu_bdrk_017xgYeoUTciryqhVxri7bM3","content":""},{"role":"user","content":[{"type":"image_url","image_url":{"url":"data:image/png;base64,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"}}]},{"role":"assistant","content":"Good! I can see the red sphere and the ground plane. Now let me switch to a rendered viewport to see the materials and lighting properly:","tool_calls":[{"id":"toolu_bdrk_01Bj9kiMq6FEp9CxLDp2ENaX","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\": \"\\nimport bpy\\n\\n# Set the viewport shading to rendered for a preview\\nfor area in bpy.context.screen.areas:\\n    if area.type == 'VIEW_3D':\\n        for space in area.spaces:\\n            if space.type == 'VIEW_3D':\\n                space.shading.type = 'MATERIAL'\\n                \\nprint(\\\"Viewport shading set to MATERIAL\\\")\\n\", \"user_prompt\": \"Switch viewport to material preview shading\"}"}}]},{"role":"tool","tool_call_id":"toolu_bdrk_01Bj9kiMq6FEp9CxLDp2ENaX","content":[{"type":"text","text":"Code executed successfully: Viewport shading set to MATERIAL\n","cache_control":{"type":"ephemeral"}}]}],"usage":{"include":true},"tools":[{"type":"function","function":{"name":"get_scene_info","description":"Get detailed information about the current Blender scene\n\n    Parameters:\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\n    ","parameters":{"properties":{"user_prompt":{"title":"User Prompt","type":"string"}},"required":["user_prompt"],"title":"get_scene_infoArguments","type":"object"}}},{"type":"function","function":{"name":"get_object_info","description":"\n    Get detailed information about a specific object in the Blender scene.\n\n    Parameters:\n    - object_name: The name of the object to get information about\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"object_name":{"title":"Object Name","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["object_name"],"title":"get_object_infoArguments","type":"object"}}},{"type":"function","function":{"name":"get_viewport_screenshot","description":"\n    Capture a screenshot of the current Blender 3D viewport.\n\n    Parameters:\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns the screenshot as an Image.\n    ","parameters":{"properties":{"max_size":{"default":1000,"title":"Max Size","type":"integer"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_viewport_screenshotArguments","type":"object"}}},{"type":"function","function":{"name":"execute_blender_code","description":"\n    Execute arbitrary Python code in Blender. 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