task_id stringlengths 15 15 | repo stringclasses 9
values | file_path stringlengths 17 49 | function_name stringlengths 4 33 | qualified_name stringlengths 4 35 | function_type stringclasses 2
values | class_name stringclasses 4
values | prompt stringlengths 422 16.4k | signature stringlengths 22 792 | docstring stringlengths 0 549 | canonical_solution stringlengths 106 1.36k | full_function stringlengths 129 1.75k | tests stringlengths 563 526k | setup stringclasses 9
values | metadata stringlengths 74 77 | validation stringlengths 36 72 | original_task_id stringlengths 15 15 | contamination_label stringclasses 2
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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
repo_patch/0001 | Comfy-Org/ComfyUI | comfy_execution/jobs.py | normalize_output_item | normalize_output_item | function | null | """
Job utilities for the /api/jobs endpoint.
Provides normalization and helper functions for job status tracking.
"""
from typing import Optional
from comfy_api.internal import prune_dict
class JobStatus:
"""Job status constants."""
PENDING = 'pending'
IN_PROGRESS = 'in_progress'
COMPLETED = 'compl... | def normalize_output_item(item):
"""Normalize a single output list item for the jobs API.
Returns the normalized item, or None to exclude it.
String items with 3D extensions become {filename, type, subfolder} dicts.
""" | Normalize a single output list item for the jobs API.
Returns the normalized item, or None to exclude it.
String items with 3D extensions become {filename, type, subfolder} dicts. | if item is None:
return None
if isinstance(item, str):
if has_3d_extension(item):
return {'filename': item, 'type': 'output', 'subfolder': '', 'mediaType': '3d'}
return None
if isinstance(item, dict):
return item
return None | def normalize_output_item(item):
"""Normalize a single output list item for the jobs API.
Returns the normalized item, or None to exclude it.
String items with 3D extensions become {filename, type, subfolder} dicts.
"""
if item is None:
return None
if isinstance(item, str):
if h... | [{"test_file": "tests/execution/test_jobs.py", "test_function": "TestNormalizeOutputItem.test_none_returns_none", "test_content": "\"\"\"Unit tests for comfy_execution/jobs.py\"\"\"\n\nfrom comfy_execution.jobs import (\n JobStatus,\n is_previewable,\n normalize_queue_item,\n normalize_history_item,\n no... | {"repo_url": "https://github.com/Comfy-Org/ComfyUI", "install_cmd": "pip install -e .", "commit_sha": "dff0a4a15887383c90a031e3fd48ebc41f6928e7", "frozen_requirements": "frozen_requirements/Comfy-Org_ComfyUI.txt"} | {"body_lines": 9, "file_lines": 390, "has_docstring": true, "num_tests": 6} | {"status": "passed", "tests_run": 6} | repo_patch/0001 | file_overlap |
repo_patch/0002 | Comfy-Org/ComfyUI | comfy_execution/jobs.py | normalize_queue_item | normalize_queue_item | function | null | "\"\"\"\nJob utilities for the /api/jobs endpoint.\nProvides normalization and helper functions for (...TRUNCATED) | "def normalize_queue_item(item: tuple, status: str) -> dict:\n \"\"\"Convert queue item tuple to (...TRUNCATED) | "Convert queue item tuple to unified job dict.\n\nExpects item with sensitive data already removed ((...TRUNCATED) | " priority, prompt_id, _, extra_data, _ = item\n create_time, workflow_id = _extract_job_metad(...TRUNCATED) | "def normalize_queue_item(item: tuple, status: str) -> dict:\n \"\"\"Convert queue item tuple to (...TRUNCATED) | "[{\"test_file\": \"tests/execution/test_jobs.py\", \"test_function\": \"TestNormalizeQueueItem.test(...TRUNCATED) | "{\"repo_url\": \"https://github.com/Comfy-Org/ComfyUI\", \"install_cmd\": \"pip install -e .\", \"c(...TRUNCATED) | {"body_lines": 10, "file_lines": 390, "has_docstring": true, "num_tests": 1} | {"status": "passed", "tests_run": 1} | repo_patch/0002 | file_overlap |
repo_patch/0003 | Comfy-Org/ComfyUI | comfy_execution/jobs.py | is_previewable | is_previewable | function | null | "\"\"\"\nJob utilities for the /api/jobs endpoint.\nProvides normalization and helper functions for (...TRUNCATED) | "def is_previewable(media_type: str, item: dict) -> bool:\n \"\"\"\n Check if an output item i(...TRUNCATED) | "Check if an output item is previewable.\nMatches frontend logic in ComfyUI_frontend/src/stores/queu(...TRUNCATED) | " if media_type in PREVIEWABLE_MEDIA_TYPES:\n return True\n\n # Check format field (MIM(...TRUNCATED) | "def is_previewable(media_type: str, item: dict) -> bool:\n \"\"\"\n Check if an output item i(...TRUNCATED) | "[{\"test_file\": \"tests/execution/test_jobs.py\", \"test_function\": \"TestIsPreviewable.test_prev(...TRUNCATED) | "{\"repo_url\": \"https://github.com/Comfy-Org/ComfyUI\", \"install_cmd\": \"pip install -e .\", \"c(...TRUNCATED) | {"body_lines": 12, "file_lines": 390, "has_docstring": true, "num_tests": 7} | {"status": "passed", "tests_run": 7} | repo_patch/0007 | file_overlap |
repo_patch/0004 | Comfy-Org/ComfyUI | middleware/cache_middleware.py | cache_control | cache_control | function | null | "\"\"\"Cache control middleware for ComfyUI server\"\"\"\n\nfrom aiohttp import web\nfrom typing imp(...TRUNCATED) | "async def cache_control(\n request: web.Request, handler: Callable[[web.Request], Awaitable[web.(...TRUNCATED) | "Cache control middleware that sets appropriate cache headers based on file type and response status(...TRUNCATED) | " response: web.Response = await handler(request)\n\n path_filename = request.path.rsplit(\"/\(...TRUNCATED) | "async def cache_control(\n request: web.Request, handler: Callable[[web.Request], Awaitable[web.(...TRUNCATED) | "[{\"test_file\": \"tests-unit/server_test/test_cache_control.py\", \"test_function\": \"TestCacheCo(...TRUNCATED) | "{\"repo_url\": \"https://github.com/Comfy-Org/ComfyUI\", \"install_cmd\": \"pip install -e .\", \"c(...TRUNCATED) | {"body_lines": 22, "file_lines": 54, "has_docstring": true, "num_tests": 9} | {"status": "passed", "tests_run": 9} | repo_patch/0008 | clean |
repo_patch/0005 | docling-project/docling | docling/datamodel/asr_model_specs.py | _get_whisper_base_model | _get_whisper_base_model | function | null | "import logging\nfrom enum import Enum\n\nfrom pydantic import (\n AnyUrl,\n)\n\nfrom docling.dat(...TRUNCATED) | "def _get_whisper_base_model():\n \"\"\"\n Get the best Whisper Base model for the current har(...TRUNCATED) | "Get the best Whisper Base model for the current hardware.\n\nAutomatically selects MLX Whisper Base(...TRUNCATED) | " try:\n import torch\n\n has_mps = torch.backends.mps.is_built() and torch.backend(...TRUNCATED) | "def _get_whisper_base_model():\n \"\"\"\n Get the best Whisper Base model for the current har(...TRUNCATED) | "[{\"test_file\": \"tests/test_asr_mlx_whisper.py\", \"test_function\": \"TestMlxWhisperIntegration.(...TRUNCATED) | "{\"repo_url\": \"https://github.com/docling-project/docling\", \"install_cmd\": \"pip install -e .\(...TRUNCATED) | {"body_lines": 34, "file_lines": 495, "has_docstring": true, "num_tests": 2} | {"status": "passed", "tests_run": 2} | repo_patch/0009 | clean |
repo_patch/0006 | docling-project/docling | docling/datamodel/asr_model_specs.py | _get_whisper_tiny_model | _get_whisper_tiny_model | function | null | "import logging\nfrom enum import Enum\n\nfrom pydantic import (\n AnyUrl,\n)\n\nfrom docling.dat(...TRUNCATED) | "def _get_whisper_tiny_model():\n \"\"\"\n Get the best Whisper Tiny model for the current har(...TRUNCATED) | "Get the best Whisper Tiny model for the current hardware.\n\nAutomatically selects MLX Whisper Tiny(...TRUNCATED) | " try:\n import torch\n\n has_mps = torch.backends.mps.is_built() and torch.backend(...TRUNCATED) | "def _get_whisper_tiny_model():\n \"\"\"\n Get the best Whisper Tiny model for the current har(...TRUNCATED) | "[{\"test_file\": \"tests/test_asr_mlx_whisper.py\", \"test_function\": \"TestMlxWhisperIntegration.(...TRUNCATED) | "{\"repo_url\": \"https://github.com/docling-project/docling\", \"install_cmd\": \"pip install -e .\(...TRUNCATED) | {"body_lines": 34, "file_lines": 495, "has_docstring": true, "num_tests": 1} | {"status": "passed", "tests_run": 1} | repo_patch/0010 | clean |
repo_patch/0007 | docling-project/docling | docling/datamodel/asr_model_specs.py | _get_whisper_medium_model | _get_whisper_medium_model | function | null | "import logging\nfrom enum import Enum\n\nfrom pydantic import (\n AnyUrl,\n)\n\nfrom docling.dat(...TRUNCATED) | "def _get_whisper_medium_model():\n \"\"\"\n Get the best Whisper Medium model for the current(...TRUNCATED) | "Get the best Whisper Medium model for the current hardware.\n\nAutomatically selects MLX Whisper Me(...TRUNCATED) | " try:\n import torch\n\n has_mps = torch.backends.mps.is_built() and torch.backend(...TRUNCATED) | "def _get_whisper_medium_model():\n \"\"\"\n Get the best Whisper Medium model for the current(...TRUNCATED) | "[{\"test_file\": \"tests/test_asr_mlx_whisper.py\", \"test_function\": \"TestMlxWhisperIntegration.(...TRUNCATED) | "{\"repo_url\": \"https://github.com/docling-project/docling\", \"install_cmd\": \"pip install -e .\(...TRUNCATED) | {"body_lines": 34, "file_lines": 495, "has_docstring": true, "num_tests": 1} | {"status": "passed", "tests_run": 1} | repo_patch/0011 | clean |
repo_patch/0008 | docling-project/docling | docling/backend/mets_gbs_backend.py | unload | MetsGbsPageBackend.unload | method | MetsGbsPageBackend | "\"\"\"Backend for GBS Google Books schema.\"\"\"\n\nimport logging\nimport tarfile\nfrom collection(...TRUNCATED) | def unload(self) -> None: | " if hasattr(self, \"_im\"):\n delattr(self, \"_im\")\n if hasattr(self, \"(...TRUNCATED) | " def unload(self) -> None:\n if hasattr(self, \"_im\"):\n delattr(self, \"_im\(...TRUNCATED) | "[{\"test_file\": \"tests/test_backend_mets_gbs.py\", \"test_function\": \"test_process_pages\", \"t(...TRUNCATED) | "{\"repo_url\": \"https://github.com/docling-project/docling\", \"install_cmd\": \"pip install -e .\(...TRUNCATED) | {"body_lines": 4, "file_lines": 400, "has_docstring": false, "num_tests": 4} | {"status": "passed", "tests_run": 4} | repo_patch/0012 | file_overlap | |
repo_patch/0009 | docling-project/docling | docling/datamodel/asr_model_specs.py | _get_whisper_small_model | _get_whisper_small_model | function | null | "import logging\nfrom enum import Enum\n\nfrom pydantic import (\n AnyUrl,\n)\n\nfrom docling.dat(...TRUNCATED) | "def _get_whisper_small_model():\n \"\"\"\n Get the best Whisper Small model for the current h(...TRUNCATED) | "Get the best Whisper Small model for the current hardware.\n\nAutomatically selects MLX Whisper Sma(...TRUNCATED) | " try:\n import torch\n\n has_mps = torch.backends.mps.is_built() and torch.backend(...TRUNCATED) | "def _get_whisper_small_model():\n \"\"\"\n Get the best Whisper Small model for the current h(...TRUNCATED) | "[{\"test_file\": \"tests/test_asr_mlx_whisper.py\", \"test_function\": \"TestMlxWhisperIntegration.(...TRUNCATED) | "{\"repo_url\": \"https://github.com/docling-project/docling\", \"install_cmd\": \"pip install -e .\(...TRUNCATED) | {"body_lines": 34, "file_lines": 495, "has_docstring": true, "num_tests": 1} | {"status": "passed", "tests_run": 1} | repo_patch/0013 | clean |
repo_patch/0010 | docling-project/docling | docling/datamodel/asr_model_specs.py | _get_whisper_large_model | _get_whisper_large_model | function | null | "import logging\nfrom enum import Enum\n\nfrom pydantic import (\n AnyUrl,\n)\n\nfrom docling.dat(...TRUNCATED) | "def _get_whisper_large_model():\n \"\"\"\n Get the best Whisper Large model for the current h(...TRUNCATED) | "Get the best Whisper Large model for the current hardware.\n\nAutomatically selects MLX Whisper Lar(...TRUNCATED) | " try:\n import torch\n\n has_mps = torch.backends.mps.is_built() and torch.backend(...TRUNCATED) | "def _get_whisper_large_model():\n \"\"\"\n Get the best Whisper Large model for the current h(...TRUNCATED) | "[{\"test_file\": \"tests/test_asr_mlx_whisper.py\", \"test_function\": \"TestMlxWhisperIntegration.(...TRUNCATED) | "{\"repo_url\": \"https://github.com/docling-project/docling\", \"install_cmd\": \"pip install -e .\(...TRUNCATED) | {"body_lines": 34, "file_lines": 495, "has_docstring": true, "num_tests": 1} | {"status": "passed", "tests_run": 1} | repo_patch/0014 | clean |
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