task_id stringlengths 15 15 | repo stringlengths 11 23 | file_path stringlengths 16 49 | function_name stringlengths 4 33 | qualified_name stringlengths 4 37 | function_type stringclasses 2
values | class_name stringclasses 8
values | prompt stringlengths 198 10.1k | signature stringlengths 11 792 | docstring stringlengths 0 549 | canonical_solution stringlengths 106 2.37k | full_function stringlengths 129 2.67k | tests stringlengths 563 4.68M | setup stringlengths 201 225 | metadata stringlengths 74 78 | validation stringlengths 36 72 | original_task_id stringlengths 15 15 | full_context stringlengths 422 16.4k |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
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 | """
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... |
repo_patch/0002 | Comfy-Org/ComfyUI | comfy_execution/jobs.py | normalize_queue_item | normalize_queue_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_queue_item(item: tuple, status: str) -> dict:
"""Convert queue item tuple to unified job dict.
Expects item with sensitive data already removed (5 elements).
""" | Convert queue item tuple to unified job dict.
Expects item with sensitive data already removed (5 elements). | priority, prompt_id, _, extra_data, _ = item
create_time, workflow_id = _extract_job_metadata(extra_data)
return prune_dict({
'id': prompt_id,
'status': status,
'priority': priority,
'create_time': create_time,
'outputs_count': 0,
'workflow_id': workflow_id,
... | def normalize_queue_item(item: tuple, status: str) -> dict:
"""Convert queue item tuple to unified job dict.
Expects item with sensitive data already removed (5 elements).
"""
priority, prompt_id, _, extra_data, _ = item
create_time, workflow_id = _extract_job_metadata(extra_data)
return prune... | [{"test_file": "tests/execution/test_jobs.py", "test_function": "TestNormalizeQueueItem.test_basic_normalization", "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 n... | {"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": 10, "file_lines": 390, "has_docstring": true, "num_tests": 1} | {"status": "passed", "tests_run": 1} | repo_patch/0002 | """
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... |
repo_patch/0003 | Comfy-Org/ComfyUI | comfy_api/feature_flags.py | get_connection_feature | get_connection_feature | function | null | """
Feature flags module for ComfyUI WebSocket protocol negotiation.
This module handles capability negotiation between frontend and backend,
allowing graceful protocol evolution while maintaining backward compatibility.
"""
from typing import Any
from comfy.cli_args import args
# Default server capabilities
SERVER... | def get_connection_feature(
sockets_metadata: dict[str, dict[str, Any]],
sid: str,
feature_name: str,
default: Any = False
) -> Any:
"""
Get a feature flag value for a specific connection.
Args:
sockets_metadata: Dictionary of socket metadata
sid: Session ID of the connectio... | Get a feature flag value for a specific connection.
Args:
sockets_metadata: Dictionary of socket metadata
sid: Session ID of the connection
feature_name: Name of the feature to check
default: Default value if feature not found
Returns:
Feature value or default if not found | if sid not in sockets_metadata:
return default
return sockets_metadata[sid].get("feature_flags", {}).get(feature_name, default) | def get_connection_feature(
sockets_metadata: dict[str, dict[str, Any]],
sid: str,
feature_name: str,
default: Any = False
) -> Any:
"""
Get a feature flag value for a specific connection.
Args:
sockets_metadata: Dictionary of socket metadata
sid: Session ID of the connectio... | [{"test_file": "tests-unit/feature_flags_test.py", "test_function": "TestFeatureFlags.test_get_connection_feature_with_missing_sid", "test_content": "\"\"\"Tests for feature flags functionality.\"\"\"\n\nfrom comfy_api.feature_flags import (\n get_connection_feature,\n supports_feature,\n get_server_features,\... | {"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": 3, "file_lines": 72, "has_docstring": true, "num_tests": 5} | {"status": "passed", "tests_run": 5} | repo_patch/0003 | """
Feature flags module for ComfyUI WebSocket protocol negotiation.
This module handles capability negotiation between frontend and backend,
allowing graceful protocol evolution while maintaining backward compatibility.
"""
from typing import Any
from comfy.cli_args import args
# Default server capabilities
SERVER... |
repo_patch/0004 | Comfy-Org/ComfyUI | comfy_execution/jobs.py | apply_sorting | apply_sorting | 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 apply_sorting(jobs: list[dict], sort_by: str, sort_order: str) -> list[dict]:
"""Sort jobs list by specified field and order.""" | Sort jobs list by specified field and order. | reverse = (sort_order == 'desc')
if sort_by == 'execution_duration':
def get_sort_key(job):
start = job.get('execution_start_time', 0)
end = job.get('execution_end_time', 0)
return end - start if end and start else 0
else:
def get_sort_key(job):
... | def apply_sorting(jobs: list[dict], sort_by: str, sort_order: str) -> list[dict]:
"""Sort jobs list by specified field and order."""
reverse = (sort_order == 'desc')
if sort_by == 'execution_duration':
def get_sort_key(job):
start = job.get('execution_start_time', 0)
end = j... | [{"test_file": "tests/execution/test_jobs.py", "test_function": "TestApplySorting.test_sort_by_create_time_desc", "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": 10, "file_lines": 390, "has_docstring": true, "num_tests": 4} | {"status": "passed", "tests_run": 4} | repo_patch/0004 | """
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... |
repo_patch/0005 | Comfy-Org/ComfyUI | comfy_execution/jobs.py | get_outputs_summary | get_outputs_summary | 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 get_outputs_summary(outputs: dict) -> tuple[int, Optional[dict]]:
"""
Count outputs and find preview in a single pass.
Returns (outputs_count, preview_output).
Preview priority (matching frontend):
1. type="output" with previewable media
2. Any previewable media
""" | Count outputs and find preview in a single pass.
Returns (outputs_count, preview_output).
Preview priority (matching frontend):
1. type="output" with previewable media
2. Any previewable media | count = 0
preview_output = None
fallback_preview = None
for node_id, node_outputs in outputs.items():
if not isinstance(node_outputs, dict):
continue
for media_type, items in node_outputs.items():
# 'animated' is a boolean flag, not actual output items
... | def get_outputs_summary(outputs: dict) -> tuple[int, Optional[dict]]:
"""
Count outputs and find preview in a single pass.
Returns (outputs_count, preview_output).
Preview priority (matching frontend):
1. type="output" with previewable media
2. Any previewable media
"""
count = 0
pr... | [{"test_file": "tests/execution/test_jobs.py", "test_function": "TestGetOutputsSummary.test_empty_outputs", "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 normaliz... | {"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": 49, "file_lines": 390, "has_docstring": true, "num_tests": 13} | {"status": "passed", "tests_run": 13} | repo_patch/0005 | """
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... |
repo_patch/0006 | Comfy-Org/ComfyUI | comfy_execution/jobs.py | normalize_outputs | normalize_outputs | 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_outputs(outputs: dict) -> dict:
"""Normalize raw node outputs for the jobs API.
Transforms string 3D filenames into file output dicts and removes
None items. All other items (non-3D strings, dicts, etc.) are
preserved as-is.
""" | Normalize raw node outputs for the jobs API.
Transforms string 3D filenames into file output dicts and removes
None items. All other items (non-3D strings, dicts, etc.) are
preserved as-is. | normalized = {}
for node_id, node_outputs in outputs.items():
if not isinstance(node_outputs, dict):
normalized[node_id] = node_outputs
continue
normalized_node = {}
for media_type, items in node_outputs.items():
if media_type == 'animated' or not isin... | def normalize_outputs(outputs: dict) -> dict:
"""Normalize raw node outputs for the jobs API.
Transforms string 3D filenames into file output dicts and removes
None items. All other items (non-3D strings, dicts, etc.) are
preserved as-is.
"""
normalized = {}
for node_id, node_outputs in out... | [{"test_file": "tests/execution/test_jobs.py", "test_function": "TestNormalizeOutputs.test_empty_outputs", "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 normalize... | {"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": 19, "file_lines": 390, "has_docstring": true, "num_tests": 6} | {"status": "passed", "tests_run": 6} | repo_patch/0006 | """
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... |
repo_patch/0007 | Comfy-Org/ComfyUI | comfy_execution/jobs.py | is_previewable | is_previewable | 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 is_previewable(media_type: str, item: dict) -> bool:
"""
Check if an output item is previewable.
Matches frontend logic in ComfyUI_frontend/src/stores/queueStore.ts
Maintains backwards compatibility with existing logic.
Priority:
1. media_type is 'images', 'video', 'audio', or '3d'
2. f... | Check if an output item is previewable.
Matches frontend logic in ComfyUI_frontend/src/stores/queueStore.ts
Maintains backwards compatibility with existing logic.
Priority:
1. media_type is 'images', 'video', 'audio', or '3d'
2. format field starts with 'video/' or 'audio/'
3. filename has a 3D extension (.obj, .fbx, ... | if media_type in PREVIEWABLE_MEDIA_TYPES:
return True
# Check format field (MIME type).
# Maintains backwards compatibility with how custom node outputs are handled in the frontend.
fmt = item.get('format', '')
if fmt and (fmt.startswith('video/') or fmt.startswith('audio/')):
retur... | def is_previewable(media_type: str, item: dict) -> bool:
"""
Check if an output item is previewable.
Matches frontend logic in ComfyUI_frontend/src/stores/queueStore.ts
Maintains backwards compatibility with existing logic.
Priority:
1. media_type is 'images', 'video', 'audio', or '3d'
2. f... | [{"test_file": "tests/execution/test_jobs.py", "test_function": "TestIsPreviewable.test_previewable_media_types", "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": 12, "file_lines": 390, "has_docstring": true, "num_tests": 7} | {"status": "passed", "tests_run": 7} | repo_patch/0007 | """
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... |
repo_patch/0008 | Comfy-Org/ComfyUI | middleware/cache_middleware.py | cache_control | cache_control | function | null | """Cache control middleware for ComfyUI server"""
from aiohttp import web
from typing import Callable, Awaitable
# Time in seconds
ONE_HOUR: int = 3600
ONE_DAY: int = 86400
IMG_EXTENSIONS = (
".jpg",
".jpeg",
".png",
".ppm",
".bmp",
".pgm",
".tif",
".tiff",
".webp",
)
@web.middle... | async def cache_control(
request: web.Request, handler: Callable[[web.Request], Awaitable[web.Response]]
) -> web.Response:
"""Cache control middleware that sets appropriate cache headers based on file type and response status""" | Cache control middleware that sets appropriate cache headers based on file type and response status | response: web.Response = await handler(request)
path_filename = request.path.rsplit("/", 1)[-1]
is_entry_point = path_filename.startswith("index") and path_filename.endswith(
".json"
)
if request.path.endswith(".js") or request.path.endswith(".css") or is_entry_point:
response.head... | async def cache_control(
request: web.Request, handler: Callable[[web.Request], Awaitable[web.Response]]
) -> web.Response:
"""Cache control middleware that sets appropriate cache headers based on file type and response status"""
response: web.Response = await handler(request)
path_filename = request.p... | [{"test_file": "tests-unit/server_test/test_cache_control.py", "test_function": "TestCacheControl.test_cache_control_scenarios", "test_content": "\"\"\"Tests for server cache control middleware\"\"\"\n\nimport pytest\nfrom aiohttp import web\nfrom aiohttp.test_utils import make_mocked_request\nfrom typing import Dict, ... | {"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": 22, "file_lines": 54, "has_docstring": true, "num_tests": 9} | {"status": "passed", "tests_run": 9} | repo_patch/0008 | """Cache control middleware for ComfyUI server"""
from aiohttp import web
from typing import Callable, Awaitable
# Time in seconds
ONE_HOUR: int = 3600
ONE_DAY: int = 86400
IMG_EXTENSIONS = (
".jpg",
".jpeg",
".png",
".ppm",
".bmp",
".pgm",
".tif",
".tiff",
".webp",
)
@web.middle... |
repo_patch/0009 | docling-project/docling | docling/datamodel/asr_model_specs.py | _get_whisper_base_model | _get_whisper_base_model | function | null | import logging
from enum import Enum
from pydantic import (
AnyUrl,
)
from docling.datamodel.accelerator_options import AcceleratorDevice
from docling.datamodel.pipeline_options_asr_model import (
# AsrResponseFormat,
# ApiAsrOptions,
InferenceAsrFramework,
InlineAsrMlxWhisperOptions,
InlineAs... | def _get_whisper_base_model():
"""
Get the best Whisper Base model for the current hardware.
Automatically selects MLX Whisper Base for Apple Silicon (MPS) if available,
otherwise falls back to native Whisper Base.
"""
# Check if MPS is available (Apple Silicon) | Get the best Whisper Base model for the current hardware.
Automatically selects MLX Whisper Base for Apple Silicon (MPS) if available,
otherwise falls back to native Whisper Base. | try:
import torch
has_mps = torch.backends.mps.is_built() and torch.backends.mps.is_available()
except ImportError:
has_mps = False
# Check if mlx-whisper is available
try:
import mlx_whisper # type: ignore
has_mlx_whisper = True
except ImportError:
... | def _get_whisper_base_model():
"""
Get the best Whisper Base model for the current hardware.
Automatically selects MLX Whisper Base for Apple Silicon (MPS) if available,
otherwise falls back to native Whisper Base.
"""
# Check if MPS is available (Apple Silicon)
try:
import torch
... | [{"test_file": "tests/test_asr_mlx_whisper.py", "test_function": "TestMlxWhisperIntegration.test_model_selectors_mlx_and_native_paths", "test_content": "\"\"\"\nTest MLX Whisper integration for Apple Silicon ASR pipeline.\n\"\"\"\n\nimport sys\nfrom pathlib import Path\nfrom unittest.mock import Mock, patch\n\nimport p... | {"repo_url": "https://github.com/docling-project/docling", "install_cmd": "pip install -e .", "commit_sha": "752f81b3dd451208fb59297ea5ef7917cb4fc891", "frozen_requirements": "frozen_requirements/docling-project_docling.txt"} | {"body_lines": 34, "file_lines": 495, "has_docstring": true, "num_tests": 2} | {"status": "passed", "tests_run": 2} | repo_patch/0009 | import logging
from enum import Enum
from pydantic import (
AnyUrl,
)
from docling.datamodel.accelerator_options import AcceleratorDevice
from docling.datamodel.pipeline_options_asr_model import (
# AsrResponseFormat,
# ApiAsrOptions,
InferenceAsrFramework,
InlineAsrMlxWhisperOptions,
InlineAs... |
repo_patch/0010 | docling-project/docling | docling/datamodel/asr_model_specs.py | _get_whisper_tiny_model | _get_whisper_tiny_model | function | null | import logging
from enum import Enum
from pydantic import (
AnyUrl,
)
from docling.datamodel.accelerator_options import AcceleratorDevice
from docling.datamodel.pipeline_options_asr_model import (
# AsrResponseFormat,
# ApiAsrOptions,
InferenceAsrFramework,
InlineAsrMlxWhisperOptions,
InlineAs... | def _get_whisper_tiny_model():
"""
Get the best Whisper Tiny model for the current hardware.
Automatically selects MLX Whisper Tiny for Apple Silicon (MPS) if available,
otherwise falls back to native Whisper Tiny.
"""
# Check if MPS is available (Apple Silicon) | Get the best Whisper Tiny model for the current hardware.
Automatically selects MLX Whisper Tiny for Apple Silicon (MPS) if available,
otherwise falls back to native Whisper Tiny. | try:
import torch
has_mps = torch.backends.mps.is_built() and torch.backends.mps.is_available()
except ImportError:
has_mps = False
# Check if mlx-whisper is available
try:
import mlx_whisper # type: ignore
has_mlx_whisper = True
except ImportError:
... | def _get_whisper_tiny_model():
"""
Get the best Whisper Tiny model for the current hardware.
Automatically selects MLX Whisper Tiny for Apple Silicon (MPS) if available,
otherwise falls back to native Whisper Tiny.
"""
# Check if MPS is available (Apple Silicon)
try:
import torch
... | [{"test_file": "tests/test_asr_mlx_whisper.py", "test_function": "TestMlxWhisperIntegration.test_model_selectors_mlx_and_native_paths", "test_content": "\"\"\"\nTest MLX Whisper integration for Apple Silicon ASR pipeline.\n\"\"\"\n\nimport sys\nfrom pathlib import Path\nfrom unittest.mock import Mock, patch\n\nimport p... | {"repo_url": "https://github.com/docling-project/docling", "install_cmd": "pip install -e .", "commit_sha": "752f81b3dd451208fb59297ea5ef7917cb4fc891", "frozen_requirements": "frozen_requirements/docling-project_docling.txt"} | {"body_lines": 34, "file_lines": 495, "has_docstring": true, "num_tests": 1} | {"status": "passed", "tests_run": 1} | repo_patch/0010 | import logging
from enum import Enum
from pydantic import (
AnyUrl,
)
from docling.datamodel.accelerator_options import AcceleratorDevice
from docling.datamodel.pipeline_options_asr_model import (
# AsrResponseFormat,
# ApiAsrOptions,
InferenceAsrFramework,
InlineAsrMlxWhisperOptions,
InlineAs... |
repo_patch/0011 | docling-project/docling | docling/datamodel/asr_model_specs.py | _get_whisper_medium_model | _get_whisper_medium_model | function | null | import logging
from enum import Enum
from pydantic import (
AnyUrl,
)
from docling.datamodel.accelerator_options import AcceleratorDevice
from docling.datamodel.pipeline_options_asr_model import (
# AsrResponseFormat,
# ApiAsrOptions,
InferenceAsrFramework,
InlineAsrMlxWhisperOptions,
InlineAs... | def _get_whisper_medium_model():
"""
Get the best Whisper Medium model for the current hardware.
Automatically selects MLX Whisper Medium for Apple Silicon (MPS) if available,
otherwise falls back to native Whisper Medium.
"""
# Check if MPS is available (Apple Silicon) | Get the best Whisper Medium model for the current hardware.
Automatically selects MLX Whisper Medium for Apple Silicon (MPS) if available,
otherwise falls back to native Whisper Medium. | try:
import torch
has_mps = torch.backends.mps.is_built() and torch.backends.mps.is_available()
except ImportError:
has_mps = False
# Check if mlx-whisper is available
try:
import mlx_whisper # type: ignore
has_mlx_whisper = True
except ImportError:
... | def _get_whisper_medium_model():
"""
Get the best Whisper Medium model for the current hardware.
Automatically selects MLX Whisper Medium for Apple Silicon (MPS) if available,
otherwise falls back to native Whisper Medium.
"""
# Check if MPS is available (Apple Silicon)
try:
import ... | [{"test_file": "tests/test_asr_mlx_whisper.py", "test_function": "TestMlxWhisperIntegration.test_model_selectors_mlx_and_native_paths", "test_content": "\"\"\"\nTest MLX Whisper integration for Apple Silicon ASR pipeline.\n\"\"\"\n\nimport sys\nfrom pathlib import Path\nfrom unittest.mock import Mock, patch\n\nimport p... | {"repo_url": "https://github.com/docling-project/docling", "install_cmd": "pip install -e .", "commit_sha": "752f81b3dd451208fb59297ea5ef7917cb4fc891", "frozen_requirements": "frozen_requirements/docling-project_docling.txt"} | {"body_lines": 34, "file_lines": 495, "has_docstring": true, "num_tests": 1} | {"status": "passed", "tests_run": 1} | repo_patch/0011 | import logging
from enum import Enum
from pydantic import (
AnyUrl,
)
from docling.datamodel.accelerator_options import AcceleratorDevice
from docling.datamodel.pipeline_options_asr_model import (
# AsrResponseFormat,
# ApiAsrOptions,
InferenceAsrFramework,
InlineAsrMlxWhisperOptions,
InlineAs... |
repo_patch/0012 | docling-project/docling | docling/backend/mets_gbs_backend.py | unload | MetsGbsPageBackend.unload | method | MetsGbsPageBackend | """Backend for GBS Google Books schema."""
import logging
import tarfile
from collections.abc import Iterable
from dataclasses import dataclass
from enum import Enum
from io import BytesIO
from pathlib import Path
from typing import TYPE_CHECKING, Dict, List, Optional, Set, Tuple, Union
from docling_core.types.doc im... | def unload(self) -> None: | if hasattr(self, "_im"):
delattr(self, "_im")
if hasattr(self, "_dpage"):
delattr(self, "_dpage") | def unload(self) -> None:
if hasattr(self, "_im"):
delattr(self, "_im")
if hasattr(self, "_dpage"):
delattr(self, "_dpage") | [{"test_file": "tests/test_backend_mets_gbs.py", "test_function": "test_process_pages", "test_content": "from pathlib import Path\n\nimport pytest\n\nfrom docling.backend.mets_gbs_backend import MetsGbsDocumentBackend, MetsGbsPageBackend\nfrom docling.datamodel.base_models import BoundingBox, InputFormat\nfrom docling.... | {"repo_url": "https://github.com/docling-project/docling", "install_cmd": "pip install -e .", "commit_sha": "752f81b3dd451208fb59297ea5ef7917cb4fc891", "frozen_requirements": "frozen_requirements/docling-project_docling.txt"} | {"body_lines": 4, "file_lines": 400, "has_docstring": false, "num_tests": 4} | {"status": "passed", "tests_run": 4} | repo_patch/0012 | """Backend for GBS Google Books schema."""
import logging
import tarfile
from collections.abc import Iterable
from dataclasses import dataclass
from enum import Enum
from io import BytesIO
from pathlib import Path
from typing import TYPE_CHECKING, Dict, List, Optional, Set, Tuple, Union
from docling_core.types.doc im... | |
repo_patch/0013 | docling-project/docling | docling/datamodel/asr_model_specs.py | _get_whisper_small_model | _get_whisper_small_model | function | null | import logging
from enum import Enum
from pydantic import (
AnyUrl,
)
from docling.datamodel.accelerator_options import AcceleratorDevice
from docling.datamodel.pipeline_options_asr_model import (
# AsrResponseFormat,
# ApiAsrOptions,
InferenceAsrFramework,
InlineAsrMlxWhisperOptions,
InlineAs... | def _get_whisper_small_model():
"""
Get the best Whisper Small model for the current hardware.
Automatically selects MLX Whisper Small for Apple Silicon (MPS) if available,
otherwise falls back to native Whisper Small.
"""
# Check if MPS is available (Apple Silicon) | Get the best Whisper Small model for the current hardware.
Automatically selects MLX Whisper Small for Apple Silicon (MPS) if available,
otherwise falls back to native Whisper Small. | try:
import torch
has_mps = torch.backends.mps.is_built() and torch.backends.mps.is_available()
except ImportError:
has_mps = False
# Check if mlx-whisper is available
try:
import mlx_whisper # type: ignore
has_mlx_whisper = True
except ImportError:
... | def _get_whisper_small_model():
"""
Get the best Whisper Small model for the current hardware.
Automatically selects MLX Whisper Small for Apple Silicon (MPS) if available,
otherwise falls back to native Whisper Small.
"""
# Check if MPS is available (Apple Silicon)
try:
import torc... | [{"test_file": "tests/test_asr_mlx_whisper.py", "test_function": "TestMlxWhisperIntegration.test_model_selectors_mlx_and_native_paths", "test_content": "\"\"\"\nTest MLX Whisper integration for Apple Silicon ASR pipeline.\n\"\"\"\n\nimport sys\nfrom pathlib import Path\nfrom unittest.mock import Mock, patch\n\nimport p... | {"repo_url": "https://github.com/docling-project/docling", "install_cmd": "pip install -e .", "commit_sha": "752f81b3dd451208fb59297ea5ef7917cb4fc891", "frozen_requirements": "frozen_requirements/docling-project_docling.txt"} | {"body_lines": 34, "file_lines": 495, "has_docstring": true, "num_tests": 1} | {"status": "passed", "tests_run": 1} | repo_patch/0013 | import logging
from enum import Enum
from pydantic import (
AnyUrl,
)
from docling.datamodel.accelerator_options import AcceleratorDevice
from docling.datamodel.pipeline_options_asr_model import (
# AsrResponseFormat,
# ApiAsrOptions,
InferenceAsrFramework,
InlineAsrMlxWhisperOptions,
InlineAs... |
repo_patch/0014 | docling-project/docling | docling/datamodel/asr_model_specs.py | _get_whisper_large_model | _get_whisper_large_model | function | null | import logging
from enum import Enum
from pydantic import (
AnyUrl,
)
from docling.datamodel.accelerator_options import AcceleratorDevice
from docling.datamodel.pipeline_options_asr_model import (
# AsrResponseFormat,
# ApiAsrOptions,
InferenceAsrFramework,
InlineAsrMlxWhisperOptions,
InlineAs... | def _get_whisper_large_model():
"""
Get the best Whisper Large model for the current hardware.
Automatically selects MLX Whisper Large for Apple Silicon (MPS) if available,
otherwise falls back to native Whisper Large.
"""
# Check if MPS is available (Apple Silicon) | Get the best Whisper Large model for the current hardware.
Automatically selects MLX Whisper Large for Apple Silicon (MPS) if available,
otherwise falls back to native Whisper Large. | try:
import torch
has_mps = torch.backends.mps.is_built() and torch.backends.mps.is_available()
except ImportError:
has_mps = False
# Check if mlx-whisper is available
try:
import mlx_whisper # type: ignore
has_mlx_whisper = True
except ImportError:
... | def _get_whisper_large_model():
"""
Get the best Whisper Large model for the current hardware.
Automatically selects MLX Whisper Large for Apple Silicon (MPS) if available,
otherwise falls back to native Whisper Large.
"""
# Check if MPS is available (Apple Silicon)
try:
import torc... | [{"test_file": "tests/test_asr_mlx_whisper.py", "test_function": "TestMlxWhisperIntegration.test_model_selectors_mlx_and_native_paths", "test_content": "\"\"\"\nTest MLX Whisper integration for Apple Silicon ASR pipeline.\n\"\"\"\n\nimport sys\nfrom pathlib import Path\nfrom unittest.mock import Mock, patch\n\nimport p... | {"repo_url": "https://github.com/docling-project/docling", "install_cmd": "pip install -e .", "commit_sha": "752f81b3dd451208fb59297ea5ef7917cb4fc891", "frozen_requirements": "frozen_requirements/docling-project_docling.txt"} | {"body_lines": 34, "file_lines": 495, "has_docstring": true, "num_tests": 1} | {"status": "passed", "tests_run": 1} | repo_patch/0014 | import logging
from enum import Enum
from pydantic import (
AnyUrl,
)
from docling.datamodel.accelerator_options import AcceleratorDevice
from docling.datamodel.pipeline_options_asr_model import (
# AsrResponseFormat,
# ApiAsrOptions,
InferenceAsrFramework,
InlineAsrMlxWhisperOptions,
InlineAs... |
repo_patch/0015 | docling-project/docling | docling/backend/mets_gbs_backend.py | get_text_in_rect | MetsGbsPageBackend.get_text_in_rect | method | MetsGbsPageBackend | """Backend for GBS Google Books schema."""
import logging
import tarfile
from collections.abc import Iterable
from dataclasses import dataclass
from enum import Enum
from io import BytesIO
from pathlib import Path
from typing import TYPE_CHECKING, Dict, List, Optional, Set, Tuple, Union
from docling_core.types.doc im... | def get_text_in_rect(self, bbox: BoundingBox) -> str:
# Find intersecting cells on the page | text_piece = ""
page_size = self.get_size()
scale = (
1 # FIX - Replace with param in get_text_in_rect across backends (optional)
)
for i, cell in enumerate(self._dpage.textline_cells):
cell_bbox = (
cell.rect.to_bounding_box()
... | def get_text_in_rect(self, bbox: BoundingBox) -> str:
# Find intersecting cells on the page
text_piece = ""
page_size = self.get_size()
scale = (
1 # FIX - Replace with param in get_text_in_rect across backends (optional)
)
for i, cell in enumerate(self... | [{"test_file": "tests/test_backend_mets_gbs.py", "test_function": "test_get_text_from_rect", "test_content": "from pathlib import Path\n\nimport pytest\n\nfrom docling.backend.mets_gbs_backend import MetsGbsDocumentBackend, MetsGbsPageBackend\nfrom docling.datamodel.base_models import BoundingBox, InputFormat\nfrom doc... | {"repo_url": "https://github.com/docling-project/docling", "install_cmd": "pip install -e .", "commit_sha": "752f81b3dd451208fb59297ea5ef7917cb4fc891", "frozen_requirements": "frozen_requirements/docling-project_docling.txt"} | {"body_lines": 17, "file_lines": 400, "has_docstring": false, "num_tests": 1} | {"status": "passed", "tests_run": 1} | repo_patch/0015 | """Backend for GBS Google Books schema."""
import logging
import tarfile
from collections.abc import Iterable
from dataclasses import dataclass
from enum import Enum
from io import BytesIO
from pathlib import Path
from typing import TYPE_CHECKING, Dict, List, Optional, Set, Tuple, Union
from docling_core.types.doc im... | |
repo_patch/0016 | docling-project/docling | docling/datamodel/asr_model_specs.py | _get_whisper_turbo_model | _get_whisper_turbo_model | function | null | import logging
from enum import Enum
from pydantic import (
AnyUrl,
)
from docling.datamodel.accelerator_options import AcceleratorDevice
from docling.datamodel.pipeline_options_asr_model import (
# AsrResponseFormat,
# ApiAsrOptions,
InferenceAsrFramework,
InlineAsrMlxWhisperOptions,
InlineAs... | def _get_whisper_turbo_model():
"""
Get the best Whisper Turbo model for the current hardware.
Automatically selects MLX Whisper Turbo for Apple Silicon (MPS) if available,
otherwise falls back to native Whisper Turbo.
"""
# Check if MPS is available (Apple Silicon) | Get the best Whisper Turbo model for the current hardware.
Automatically selects MLX Whisper Turbo for Apple Silicon (MPS) if available,
otherwise falls back to native Whisper Turbo. | try:
import torch
has_mps = torch.backends.mps.is_built() and torch.backends.mps.is_available()
except ImportError:
has_mps = False
# Check if mlx-whisper is available
try:
import mlx_whisper # type: ignore
has_mlx_whisper = True
except ImportError:
... | def _get_whisper_turbo_model():
"""
Get the best Whisper Turbo model for the current hardware.
Automatically selects MLX Whisper Turbo for Apple Silicon (MPS) if available,
otherwise falls back to native Whisper Turbo.
"""
# Check if MPS is available (Apple Silicon)
try:
import torc... | [{"test_file": "tests/test_asr_mlx_whisper.py", "test_function": "TestMlxWhisperIntegration.test_model_selectors_mlx_and_native_paths", "test_content": "\"\"\"\nTest MLX Whisper integration for Apple Silicon ASR pipeline.\n\"\"\"\n\nimport sys\nfrom pathlib import Path\nfrom unittest.mock import Mock, patch\n\nimport p... | {"repo_url": "https://github.com/docling-project/docling", "install_cmd": "pip install -e .", "commit_sha": "752f81b3dd451208fb59297ea5ef7917cb4fc891", "frozen_requirements": "frozen_requirements/docling-project_docling.txt"} | {"body_lines": 34, "file_lines": 495, "has_docstring": true, "num_tests": 1} | {"status": "passed", "tests_run": 1} | repo_patch/0016 | import logging
from enum import Enum
from pydantic import (
AnyUrl,
)
from docling.datamodel.accelerator_options import AcceleratorDevice
from docling.datamodel.pipeline_options_asr_model import (
# AsrResponseFormat,
# ApiAsrOptions,
InferenceAsrFramework,
InlineAsrMlxWhisperOptions,
InlineAs... |
repo_patch/0017 | docling-project/docling | docling/backend/mets_gbs_backend.py | get_page_image | MetsGbsPageBackend.get_page_image | method | MetsGbsPageBackend | """Backend for GBS Google Books schema."""
import logging
import tarfile
from collections.abc import Iterable
from dataclasses import dataclass
from enum import Enum
from io import BytesIO
from pathlib import Path
from typing import TYPE_CHECKING, Dict, List, Optional, Set, Tuple, Union
from docling_core.types.doc im... | def get_page_image(
self, scale: float = 1, cropbox: Optional[BoundingBox] = None
) -> Image.Image: | page_size = self.get_size()
assert (
page_size.width == self._im.size[0] and page_size.height == self._im.size[1]
)
if not cropbox:
cropbox = BoundingBox(
l=0,
r=page_size.width,
t=0,
b=page_size.hei... | def get_page_image(
self, scale: float = 1, cropbox: Optional[BoundingBox] = None
) -> Image.Image:
page_size = self.get_size()
assert (
page_size.width == self._im.size[0] and page_size.height == self._im.size[1]
)
if not cropbox:
cropbox = Bound... | [{"test_file": "tests/test_backend_mets_gbs.py", "test_function": "test_crop_page_image", "test_content": "from pathlib import Path\n\nimport pytest\n\nfrom docling.backend.mets_gbs_backend import MetsGbsDocumentBackend, MetsGbsPageBackend\nfrom docling.datamodel.base_models import BoundingBox, InputFormat\nfrom doclin... | {"repo_url": "https://github.com/docling-project/docling", "install_cmd": "pip install -e .", "commit_sha": "752f81b3dd451208fb59297ea5ef7917cb4fc891", "frozen_requirements": "frozen_requirements/docling-project_docling.txt"} | {"body_lines": 16, "file_lines": 400, "has_docstring": false, "num_tests": 1} | {"status": "passed", "tests_run": 1} | repo_patch/0017 | """Backend for GBS Google Books schema."""
import logging
import tarfile
from collections.abc import Iterable
from dataclasses import dataclass
from enum import Enum
from io import BytesIO
from pathlib import Path
from typing import TYPE_CHECKING, Dict, List, Optional, Set, Tuple, Union
from docling_core.types.doc im... | |
repo_patch/0018 | docling-project/docling | docling/backend/image_backend.py | get_bitmap_rects | _ImagePageBackend.get_bitmap_rects | method | _ImagePageBackend | import logging
from io import BytesIO
from pathlib import Path
from typing import Iterable, List, Optional, Union
from docling_core.types.doc import BoundingBox, CoordOrigin
from docling_core.types.doc.page import (
BoundingRectangle,
PdfPageBoundaryType,
PdfPageGeometry,
SegmentedPdfPage,
TextCell... | def get_bitmap_rects(self, scale: float = 1) -> Iterable[BoundingBox]:
# For raw images, the entire page is a bitmap | assert self._image is not None
page_size = self.get_size()
full_page_bbox = BoundingBox(
l=0.0,
t=0.0,
r=float(page_size.width),
b=float(page_size.height),
coord_origin=CoordOrigin.TOPLEFT,
)
if scale != 1:
f... | def get_bitmap_rects(self, scale: float = 1) -> Iterable[BoundingBox]:
# For raw images, the entire page is a bitmap
assert self._image is not None
page_size = self.get_size()
full_page_bbox = BoundingBox(
l=0.0,
t=0.0,
r=float(page_size.width),
... | [{"test_file": "tests/test_backend_image_native.py", "test_function": "test_get_bitmap_rects", "test_content": "from io import BytesIO\nfrom pathlib import Path\n\nimport pytest\nfrom docling_core.types.doc import BoundingBox, CoordOrigin\nfrom PIL import Image\n\nfrom docling.backend.image_backend import ImageDocument... | {"repo_url": "https://github.com/docling-project/docling", "install_cmd": "pip install -e .", "commit_sha": "752f81b3dd451208fb59297ea5ef7917cb4fc891", "frozen_requirements": "frozen_requirements/docling-project_docling.txt"} | {"body_lines": 12, "file_lines": 189, "has_docstring": false, "num_tests": 2} | {"status": "passed", "tests_run": 2} | repo_patch/0018 | import logging
from io import BytesIO
from pathlib import Path
from typing import Iterable, List, Optional, Union
from docling_core.types.doc import BoundingBox, CoordOrigin
from docling_core.types.doc.page import (
BoundingRectangle,
PdfPageBoundaryType,
PdfPageGeometry,
SegmentedPdfPage,
TextCell... | |
repo_patch/0019 | docling-project/docling | docling/backend/image_backend.py | load_page | ImageDocumentBackend.load_page | method | ImageDocumentBackend | import logging
from io import BytesIO
from pathlib import Path
from typing import Iterable, List, Optional, Union
from docling_core.types.doc import BoundingBox, CoordOrigin
from docling_core.types.doc.page import (
BoundingRectangle,
PdfPageBoundaryType,
PdfPageGeometry,
SegmentedPdfPage,
TextCell... | def load_page(self, page_no: int) -> _ImagePageBackend: | if not (0 <= page_no < len(self._frames)):
raise IndexError(f"Page index out of range: {page_no}")
return _ImagePageBackend(self._frames[page_no]) | def load_page(self, page_no: int) -> _ImagePageBackend:
if not (0 <= page_no < len(self._frames)):
raise IndexError(f"Page index out of range: {page_no}")
return _ImagePageBackend(self._frames[page_no]) | [{"test_file": "tests/test_backend_image_native.py", "test_function": "test_get_size", "test_content": "from io import BytesIO\nfrom pathlib import Path\n\nimport pytest\nfrom docling_core.types.doc import BoundingBox, CoordOrigin\nfrom PIL import Image\n\nfrom docling.backend.image_backend import ImageDocumentBackend,... | {"repo_url": "https://github.com/docling-project/docling", "install_cmd": "pip install -e .", "commit_sha": "752f81b3dd451208fb59297ea5ef7917cb4fc891", "frozen_requirements": "frozen_requirements/docling-project_docling.txt"} | {"body_lines": 3, "file_lines": 189, "has_docstring": false, "num_tests": 9} | {"status": "passed", "tests_run": 9} | repo_patch/0019 | import logging
from io import BytesIO
from pathlib import Path
from typing import Iterable, List, Optional, Union
from docling_core.types.doc import BoundingBox, CoordOrigin
from docling_core.types.doc.page import (
BoundingRectangle,
PdfPageBoundaryType,
PdfPageGeometry,
SegmentedPdfPage,
TextCell... | |
repo_patch/0020 | docling-project/docling | docling/backend/image_backend.py | get_page_image | _ImagePageBackend.get_page_image | method | _ImagePageBackend | import logging
from io import BytesIO
from pathlib import Path
from typing import Iterable, List, Optional, Union
from docling_core.types.doc import BoundingBox, CoordOrigin
from docling_core.types.doc.page import (
BoundingRectangle,
PdfPageBoundaryType,
PdfPageGeometry,
SegmentedPdfPage,
TextCell... | def get_page_image(
self, scale: float = 1, cropbox: Optional[BoundingBox] = None
) -> Image.Image: | assert self._image is not None
img = self._image
if cropbox is not None:
# Expected cropbox comes in TOPLEFT coords in our pipeline
if cropbox.coord_origin != CoordOrigin.TOPLEFT:
# Convert to TOPLEFT relative to current image height
cropb... | def get_page_image(
self, scale: float = 1, cropbox: Optional[BoundingBox] = None
) -> Image.Image:
assert self._image is not None
img = self._image
if cropbox is not None:
# Expected cropbox comes in TOPLEFT coords in our pipeline
if cropbox.coord_origin... | [{"test_file": "tests/test_backend_image_native.py", "test_function": "test_get_page_image_full", "test_content": "from io import BytesIO\nfrom pathlib import Path\n\nimport pytest\nfrom docling_core.types.doc import BoundingBox, CoordOrigin\nfrom PIL import Image\n\nfrom docling.backend.image_backend import ImageDocum... | {"repo_url": "https://github.com/docling-project/docling", "install_cmd": "pip install -e .", "commit_sha": "752f81b3dd451208fb59297ea5ef7917cb4fc891", "frozen_requirements": "frozen_requirements/docling-project_docling.txt"} | {"body_lines": 18, "file_lines": 189, "has_docstring": false, "num_tests": 4} | {"status": "passed", "tests_run": 4} | repo_patch/0020 | import logging
from io import BytesIO
from pathlib import Path
from typing import Iterable, List, Optional, Union
from docling_core.types.doc import BoundingBox, CoordOrigin
from docling_core.types.doc.page import (
BoundingRectangle,
PdfPageBoundaryType,
PdfPageGeometry,
SegmentedPdfPage,
TextCell... | |
repo_patch/0021 | fastapi/fastapi | fastapi/_compat/shared.py | is_uploadfile_sequence_annotation | is_uploadfile_sequence_annotation | function | null | import types
import typing
import warnings
from collections import deque
from collections.abc import Mapping, Sequence
from dataclasses import is_dataclass
from typing import (
Annotated,
Any,
TypeGuard,
TypeVar,
Union,
get_args,
get_origin,
)
from fastapi.types import UnionType
from pydant... | def is_uploadfile_sequence_annotation(annotation: Any) -> bool: | origin = get_origin(annotation)
if origin is Union or origin is UnionType:
at_least_one = False
for arg in get_args(annotation):
if is_uploadfile_sequence_annotation(arg):
at_least_one = True
continue
return at_least_one
return field_annota... | def is_uploadfile_sequence_annotation(annotation: Any) -> bool:
origin = get_origin(annotation)
if origin is Union or origin is UnionType:
at_least_one = False
for arg in get_args(annotation):
if is_uploadfile_sequence_annotation(arg):
at_least_one = True
... | [{"test_file": "tests/test_compat.py", "test_function": "test_is_uploadfile_sequence_annotation", "test_content": "from fastapi import FastAPI, UploadFile\nfrom fastapi._compat import (\n Undefined,\n is_uploadfile_sequence_annotation,\n)\nfrom fastapi._compat.shared import is_bytes_sequence_annotation\nfrom fast... | {"repo_url": "https://github.com/fastapi/fastapi", "install_cmd": "pip install -e .", "commit_sha": "7a03018d6a880651d4fc2b5c79419eb233d7aee5", "frozen_requirements": "frozen_requirements/fastapi_fastapi.txt"} | {"body_lines": 12, "file_lines": 215, "has_docstring": false, "num_tests": 1} | {"status": "passed", "tests_run": 1} | repo_patch/0021 | import types
import typing
import warnings
from collections import deque
from collections.abc import Mapping, Sequence
from dataclasses import is_dataclass
from typing import (
Annotated,
Any,
TypeGuard,
TypeVar,
Union,
get_args,
get_origin,
)
from fastapi.types import UnionType
from pydant... | |
repo_patch/0022 | fastapi/fastapi | docs_src/generate_clients/tutorial002_py310.py | get_items | get_items | function | null | from fastapi import FastAPI
from pydantic import BaseModel
app = FastAPI()
class Item(BaseModel):
name: str
price: float
class ResponseMessage(BaseModel):
message: str
class User(BaseModel):
username: str
email: str
@app.post("/items/", response_model=ResponseMessage, tags=["items"])
async ... | async def get_items(): | return [
{"name": "Plumbus", "price": 3},
{"name": "Portal Gun", "price": 9001},
] | async def get_items():
return [
{"name": "Plumbus", "price": 3},
{"name": "Portal Gun", "price": 9001},
] | [{"test_file": "tests/test_tutorial/test_python_types/test_tutorial005.py", "test_function": "test_get_items", "test_content": "from docs_src.python_types.tutorial005_py310 import get_items\n\n\ndef test_get_items():\n res = get_items(\n \"item_a\",\n \"item_b\",\n \"item_c\",\n \"item_d\... | {"repo_url": "https://github.com/fastapi/fastapi", "install_cmd": "pip install -e .", "commit_sha": "7a03018d6a880651d4fc2b5c79419eb233d7aee5", "frozen_requirements": "frozen_requirements/fastapi_fastapi.txt"} | {"body_lines": 4, "file_lines": 37, "has_docstring": false, "num_tests": 1} | {"status": "passed", "tests_run": 1} | repo_patch/0022 | from fastapi import FastAPI
from pydantic import BaseModel
app = FastAPI()
class Item(BaseModel):
name: str
price: float
class ResponseMessage(BaseModel):
message: str
class User(BaseModel):
username: str
email: str
@app.post("/items/", response_model=ResponseMessage, tags=["items"])
async ... | |
repo_patch/0023 | fastapi/fastapi | fastapi/_compat/v2.py | serialize_sequence_value | serialize_sequence_value | function | null | import re
import warnings
from collections.abc import Sequence
from copy import copy
from dataclasses import dataclass, is_dataclass
from enum import Enum
from functools import lru_cache
from typing import (
Annotated,
Any,
Literal,
Union,
cast,
get_args,
get_origin,
)
from fastapi._compat ... | def serialize_sequence_value(*, field: ModelField, value: Any) -> Sequence[Any]: | origin_type = get_origin(field.field_info.annotation) or field.field_info.annotation
if origin_type is Union or origin_type is UnionType: # Handle optional sequences
union_args = get_args(field.field_info.annotation)
for union_arg in union_args:
if union_arg is type(None):
... | def serialize_sequence_value(*, field: ModelField, value: Any) -> Sequence[Any]:
origin_type = get_origin(field.field_info.annotation) or field.field_info.annotation
if origin_type is Union or origin_type is UnionType: # Handle optional sequences
union_args = get_args(field.field_info.annotation)
... | [{"test_file": "tests/test_compat.py", "test_function": "test_serialize_sequence_value_with_optional_list", "test_content": "from fastapi import FastAPI, UploadFile\nfrom fastapi._compat import (\n Undefined,\n is_uploadfile_sequence_annotation,\n)\nfrom fastapi._compat.shared import is_bytes_sequence_annotation\... | {"repo_url": "https://github.com/fastapi/fastapi", "install_cmd": "pip install -e .", "commit_sha": "7a03018d6a880651d4fc2b5c79419eb233d7aee5", "frozen_requirements": "frozen_requirements/fastapi_fastapi.txt"} | {"body_lines": 10, "file_lines": 481, "has_docstring": false, "num_tests": 3} | {"status": "passed", "tests_run": 3} | repo_patch/0023 | import re
import warnings
from collections.abc import Sequence
from copy import copy
from dataclasses import dataclass, is_dataclass
from enum import Enum
from functools import lru_cache
from typing import (
Annotated,
Any,
Literal,
Union,
cast,
get_args,
get_origin,
)
from fastapi._compat ... | |
repo_patch/0024 | fastapi/fastapi | docs_src/generate_clients/tutorial001_py310.py | get_items | get_items | function | null | from fastapi import FastAPI
from pydantic import BaseModel
app = FastAPI()
class Item(BaseModel):
name: str
price: float
class ResponseMessage(BaseModel):
message: str
@app.post("/items/", response_model=ResponseMessage)
async def create_item(item: Item):
return {"message": "item received"}
@ap... | async def get_items(): | return [
{"name": "Plumbus", "price": 3},
{"name": "Portal Gun", "price": 9001},
] | async def get_items():
return [
{"name": "Plumbus", "price": 3},
{"name": "Portal Gun", "price": 9001},
] | [{"test_file": "tests/test_tutorial/test_python_types/test_tutorial005.py", "test_function": "test_get_items", "test_content": "from docs_src.python_types.tutorial005_py310 import get_items\n\n\ndef test_get_items():\n res = get_items(\n \"item_a\",\n \"item_b\",\n \"item_c\",\n \"item_d\... | {"repo_url": "https://github.com/fastapi/fastapi", "install_cmd": "pip install -e .", "commit_sha": "7a03018d6a880651d4fc2b5c79419eb233d7aee5", "frozen_requirements": "frozen_requirements/fastapi_fastapi.txt"} | {"body_lines": 4, "file_lines": 27, "has_docstring": false, "num_tests": 1} | {"status": "passed", "tests_run": 1} | repo_patch/0024 | from fastapi import FastAPI
from pydantic import BaseModel
app = FastAPI()
class Item(BaseModel):
name: str
price: float
class ResponseMessage(BaseModel):
message: str
@app.post("/items/", response_model=ResponseMessage)
async def create_item(item: Item):
return {"message": "item received"}
@ap... | |
repo_patch/0025 | fastapi/fastapi | docs_src/generate_clients/tutorial003_py310.py | get_items | get_items | function | null | from fastapi import FastAPI
from fastapi.routing import APIRoute
from pydantic import BaseModel
def custom_generate_unique_id(route: APIRoute):
return f"{route.tags[0]}-{route.name}"
app = FastAPI(generate_unique_id_function=custom_generate_unique_id)
class Item(BaseModel):
name: str
price: float
cl... | async def get_items(): | return [
{"name": "Plumbus", "price": 3},
{"name": "Portal Gun", "price": 9001},
] | async def get_items():
return [
{"name": "Plumbus", "price": 3},
{"name": "Portal Gun", "price": 9001},
] | [{"test_file": "tests/test_tutorial/test_python_types/test_tutorial005.py", "test_function": "test_get_items", "test_content": "from docs_src.python_types.tutorial005_py310 import get_items\n\n\ndef test_get_items():\n res = get_items(\n \"item_a\",\n \"item_b\",\n \"item_c\",\n \"item_d\... | {"repo_url": "https://github.com/fastapi/fastapi", "install_cmd": "pip install -e .", "commit_sha": "7a03018d6a880651d4fc2b5c79419eb233d7aee5", "frozen_requirements": "frozen_requirements/fastapi_fastapi.txt"} | {"body_lines": 4, "file_lines": 43, "has_docstring": false, "num_tests": 1} | {"status": "passed", "tests_run": 1} | repo_patch/0025 | from fastapi import FastAPI
from fastapi.routing import APIRoute
from pydantic import BaseModel
def custom_generate_unique_id(route: APIRoute):
return f"{route.tags[0]}-{route.name}"
app = FastAPI(generate_unique_id_function=custom_generate_unique_id)
class Item(BaseModel):
name: str
price: float
cl... | |
repo_patch/0026 | fastapi/fastapi | fastapi/_compat/shared.py | is_bytes_sequence_annotation | is_bytes_sequence_annotation | function | null | import types
import typing
import warnings
from collections import deque
from collections.abc import Mapping, Sequence
from dataclasses import is_dataclass
from typing import (
Annotated,
Any,
TypeGuard,
TypeVar,
Union,
get_args,
get_origin,
)
from fastapi.types import UnionType
from pydant... | def is_bytes_sequence_annotation(annotation: Any) -> bool: | origin = get_origin(annotation)
if origin is Union or origin is UnionType:
at_least_one = False
for arg in get_args(annotation):
if is_bytes_sequence_annotation(arg):
at_least_one = True
continue
return at_least_one
return field_annotation_... | def is_bytes_sequence_annotation(annotation: Any) -> bool:
origin = get_origin(annotation)
if origin is Union or origin is UnionType:
at_least_one = False
for arg in get_args(annotation):
if is_bytes_sequence_annotation(arg):
at_least_one = True
contin... | [{"test_file": "tests/test_compat.py", "test_function": "test_is_bytes_sequence_annotation_union", "test_content": "from fastapi import FastAPI, UploadFile\nfrom fastapi._compat import (\n Undefined,\n is_uploadfile_sequence_annotation,\n)\nfrom fastapi._compat.shared import is_bytes_sequence_annotation\nfrom fas... | {"repo_url": "https://github.com/fastapi/fastapi", "install_cmd": "pip install -e .", "commit_sha": "7a03018d6a880651d4fc2b5c79419eb233d7aee5", "frozen_requirements": "frozen_requirements/fastapi_fastapi.txt"} | {"body_lines": 12, "file_lines": 215, "has_docstring": false, "num_tests": 1} | {"status": "passed", "tests_run": 1} | repo_patch/0026 | import types
import typing
import warnings
from collections import deque
from collections.abc import Mapping, Sequence
from dataclasses import is_dataclass
from typing import (
Annotated,
Any,
TypeGuard,
TypeVar,
Union,
get_args,
get_origin,
)
from fastapi.types import UnionType
from pydant... | |
repo_patch/0027 | freqtrade/freqtrade | freqtrade/strategy/strategy_validation.py | assert_df | StrategyResultValidator.assert_df | method | StrategyResultValidator | import logging
from datetime import datetime
from pandas import DataFrame
from freqtrade.exceptions import StrategyError
logger = logging.getLogger(__name__)
class StrategyResultValidator:
def __init__(self, dataframe: DataFrame, warn_only: bool = False):
self._warn_only = warn_only
self._leng... | def assert_df(self, dataframe: DataFrame):
"""
Ensure dataframe (length, last candle) was not modified, and has all elements we need.
Raises a StrategyError if the dataframe does not match the expected values.
If warn_only is set, it will log a warning instead of raising an error.
... | Ensure dataframe (length, last candle) was not modified, and has all elements we need.
Raises a StrategyError if the dataframe does not match the expected values.
If warn_only is set, it will log a warning instead of raising an error.
:param dataframe: DataFrame to validate
:raises StrategyError: If the dataframe does ... | message_template = "Dataframe returned from strategy has mismatching {}."
message = ""
if dataframe is None:
message = "No dataframe returned (return statement missing?)."
elif self._length != len(dataframe):
message = message_template.format("length")
eli... | def assert_df(self, dataframe: DataFrame):
"""
Ensure dataframe (length, last candle) was not modified, and has all elements we need.
Raises a StrategyError if the dataframe does not match the expected values.
If warn_only is set, it will log a warning instead of raising an error.
... | [{"test_file": "tests/strategy/test_interface.py", "test_function": "test_assert_df", "test_content": "# pragma pylint: disable=missing-docstring, C0103\nimport logging\nimport math\nfrom datetime import UTC, datetime, timedelta\nfrom pathlib import Path\nfrom unittest.mock import MagicMock\n\nimport pytest\nfrom panda... | {"repo_url": "https://github.com/freqtrade/freqtrade", "install_cmd": "pip install -e .", "commit_sha": "f92c66db565411401079ab80356b8d1c5a46db3f", "frozen_requirements": "frozen_requirements/freqtrade_freqtrade.txt"} | {"body_lines": 15, "file_lines": 43, "has_docstring": true, "num_tests": 1} | {"status": "passed", "tests_run": 1} | repo_patch/0027 | import logging
from datetime import datetime
from pandas import DataFrame
from freqtrade.exceptions import StrategyError
logger = logging.getLogger(__name__)
class StrategyResultValidator:
def __init__(self, dataframe: DataFrame, warn_only: bool = False):
self._warn_only = warn_only
self._leng... |
repo_patch/0028 | freqtrade/freqtrade | freqtrade/data/btanalysis/historic_precision.py | get_tick_size_over_time | get_tick_size_over_time | function | null | from numpy import format_float_positional
from pandas import DataFrame, Series
def get_tick_size_over_time(candles: DataFrame) -> Series:
"""
Calculate the number of significant digits for candles over time.
It's using the Monthly maximum of the number of significant digits for each month.
:param cand... | def get_tick_size_over_time(candles: DataFrame) -> Series:
"""
Calculate the number of significant digits for candles over time.
It's using the Monthly maximum of the number of significant digits for each month.
:param candles: DataFrame with OHLCV data
:return: Series with the average number of sig... | Calculate the number of significant digits for candles over time.
It's using the Monthly maximum of the number of significant digits for each month.
:param candles: DataFrame with OHLCV data
:return: Series with the average number of significant digits for each month | for col in ["open", "high", "low", "close"]:
candles[f"{col}_count"] = (
candles[col]
.apply(format_float_positional, precision=14, unique=False, fractional=False, trim="-")
.str.extract(r"\.(\d*[1-9])")[0]
.str.len()
)
candles["max_count"] = candl... | def get_tick_size_over_time(candles: DataFrame) -> Series:
"""
Calculate the number of significant digits for candles over time.
It's using the Monthly maximum of the number of significant digits for each month.
:param candles: DataFrame with OHLCV data
:return: Series with the average number of sig... | [{"test_file": "tests/data/test_historic_precision.py", "test_function": "test_get_tick_size_over_time", "test_content": "# pragma pylint: disable=missing-docstring, C0103\n\nfrom datetime import UTC\n\nimport pandas as pd\nfrom numpy import nan\nfrom pandas import DataFrame, Timestamp\n\nfrom freqtrade.data.btanalysis... | {"repo_url": "https://github.com/freqtrade/freqtrade", "install_cmd": "pip install -e .", "commit_sha": "f92c66db565411401079ab80356b8d1c5a46db3f", "frozen_requirements": "frozen_requirements/freqtrade_freqtrade.txt"} | {"body_lines": 17, "file_lines": 32, "has_docstring": true, "num_tests": 4} | {"status": "passed", "tests_run": 4} | repo_patch/0028 | from numpy import format_float_positional
from pandas import DataFrame, Series
def get_tick_size_over_time(candles: DataFrame) -> Series:
"""
Calculate the number of significant digits for candles over time.
It's using the Monthly maximum of the number of significant digits for each month.
:param cand... |
repo_patch/0029 | hiyouga/LlamaFactory | src/llamafactory/v1/plugins/model_plugins/peft.py | get_lora_model | get_lora_model | function | null | # Copyright 2025 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to i... | def get_lora_model(model: HFModel, config: LoraConfigDict, is_train: bool = False) -> HFModel: | adapter_name_or_path = config.get("adapter_name_or_path")
if adapter_name_or_path:
return load_adapter(model, adapter_name_or_path, is_train)
logger.info_rank0("Fine-tuning method: LoRA")
target_modules = config.get("target_modules", "all")
# Handle target modules
if target_modules =... | def get_lora_model(model: HFModel, config: LoraConfigDict, is_train: bool = False) -> HFModel:
adapter_name_or_path = config.get("adapter_name_or_path")
if adapter_name_or_path:
return load_adapter(model, adapter_name_or_path, is_train)
logger.info_rank0("Fine-tuning method: LoRA")
target_mod... | [{"test_file": "tests_v1/plugins/model_plugins/test_peft.py", "test_function": "test_get_lora_model", "test_content": "# Copyright 2025 the LlamaFactory team.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a c... | {"repo_url": "https://github.com/hiyouga/LlamaFactory", "install_cmd": "pip install -e .", "commit_sha": "c0245c43fc1fbb87ed6b2f2d28bdcceed5103946", "frozen_requirements": "frozen_requirements/hiyouga_LlamaFactory.txt"} | {"body_lines": 26, "file_lines": 344, "has_docstring": false, "num_tests": 1} | {"status": "passed", "tests_run": 1} | repo_patch/0029 | # Copyright 2025 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to i... | |
repo_patch/0030 | hiyouga/LlamaFactory | src/llamafactory/v1/samplers/cli_sampler.py | generate | SyncSampler.generate | method | SyncSampler | # Copyright 2025 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to i... | def generate(self, messages: list[Message], tools: str | None = None) -> Generator[str, None, None]:
"""Generate tokens synchronously.
Args:
messages: List of messages.
tools: Tools string.
Yields:
Generated tokens.
""" | Generate tokens synchronously.
Args:
messages: List of messages.
tools: Tools string.
Yields:
Generated tokens. | generator = super().generate(messages, tools)
while True:
try:
token = asyncio.run_coroutine_threadsafe(generator.__anext__(), self._loop).result()
yield token
except StopAsyncIteration:
break | def generate(self, messages: list[Message], tools: str | None = None) -> Generator[str, None, None]:
"""Generate tokens synchronously.
Args:
messages: List of messages.
tools: Tools string.
Yields:
Generated tokens.
"""
generator = super(... | [{"test_file": "tests_v1/sampler/test_cli_sampler.py", "test_function": "test_sync_sampler", "test_content": "# Copyright 2025 the LlamaFactory team.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of th... | {"repo_url": "https://github.com/hiyouga/LlamaFactory", "install_cmd": "pip install -e .", "commit_sha": "c0245c43fc1fbb87ed6b2f2d28bdcceed5103946", "frozen_requirements": "frozen_requirements/hiyouga_LlamaFactory.txt"} | {"body_lines": 7, "file_lines": 126, "has_docstring": true, "num_tests": 1} | {"status": "passed", "tests_run": 1} | repo_patch/0030 | # Copyright 2025 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to i... |
repo_patch/0031 | hiyouga/LlamaFactory | src/llamafactory/v1/core/utils/rendering.py | render_messages | Renderer.render_messages | method | Renderer | # Copyright 2025 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to i... | def render_messages(
self, messages: list[Message], tools: str | None = None, is_generate: bool = False
) -> ModelInput:
"""Apply template to messages and convert them to model input.
Args:
messages (list[Message]): The messages to render.
tools (str | None, optional... | Apply template to messages and convert them to model input.
Args:
messages (list[Message]): The messages to render.
tools (str | None, optional): The tools to use. Defaults to None.
is_generate (bool, optional): Whether to render for generation. Defaults to False.
Returns:
ModelInput: The rendered mod... | if self.template == "chatml":
return render_chatml_messages(self.processor, messages, tools, is_generate)
else:
from ...plugins.model_plugins.rendering import RenderingPlugin
return RenderingPlugin(self.template).render_messages(self.processor, messages, tools, is_ge... | def render_messages(
self, messages: list[Message], tools: str | None = None, is_generate: bool = False
) -> ModelInput:
"""Apply template to messages and convert them to model input.
Args:
messages (list[Message]): The messages to render.
tools (str | None, opti... | [{"test_file": "tests_v1/core/utils/test_rendering.py", "test_function": "test_chatml_rendering", "test_content": "# Copyright 2025 the LlamaFactory team.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy ... | {"repo_url": "https://github.com/hiyouga/LlamaFactory", "install_cmd": "pip install -e .", "commit_sha": "c0245c43fc1fbb87ed6b2f2d28bdcceed5103946", "frozen_requirements": "frozen_requirements/hiyouga_LlamaFactory.txt"} | {"body_lines": 5, "file_lines": 170, "has_docstring": true, "num_tests": 4} | {"status": "passed", "tests_run": 4} | repo_patch/0031 | # Copyright 2025 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to i... |
repo_patch/0032 | hiyouga/LlamaFactory | src/llamafactory/v1/plugins/model_plugins/peft.py | _find_all_linear_modules | _find_all_linear_modules | function | null | # Copyright 2025 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to i... | def _find_all_linear_modules(model: HFModel) -> list[str]:
r"""Find all available modules to apply LoRA.""" | Find all available modules to apply LoRA. | forbidden_modules = {"lm_head", "output_layer", "output"}
module_names = set()
for name, module in model.named_modules():
if any(forbidden_module in name for forbidden_module in forbidden_modules):
continue
if "Linear" in module.__class__.__name__ and "Embedding" not in module._... | def _find_all_linear_modules(model: HFModel) -> list[str]:
r"""Find all available modules to apply LoRA."""
forbidden_modules = {"lm_head", "output_layer", "output"}
module_names = set()
for name, module in model.named_modules():
if any(forbidden_module in name for forbidden_module in forbidden_... | [{"test_file": "tests_v1/plugins/model_plugins/test_peft.py", "test_function": "test_find_all_linear_modules", "test_content": "# Copyright 2025 the LlamaFactory team.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may o... | {"repo_url": "https://github.com/hiyouga/LlamaFactory", "install_cmd": "pip install -e .", "commit_sha": "c0245c43fc1fbb87ed6b2f2d28bdcceed5103946", "frozen_requirements": "frozen_requirements/hiyouga_LlamaFactory.txt"} | {"body_lines": 8, "file_lines": 344, "has_docstring": true, "num_tests": 1} | {"status": "passed", "tests_run": 1} | repo_patch/0032 | # Copyright 2025 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to i... |
repo_patch/0033 | hiyouga/LlamaFactory | src/llamafactory/v1/accelerator/helper.py | get_current_device | get_current_device | function | null | # Copyright 2025 Bytedance Ltd. and the LlamaFactory team.
#
# This code is inspired by the Bytedance's VeOmni library.
# https://github.com/ByteDance-Seed/VeOmni/blob/v0.1.4/veomni/utils/dist_utils.py
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance w... | def get_current_device() -> torch.device:
"""Get current accelerator device.""" | Get current accelerator device. | if get_current_accelerator().type == DeviceType.CPU:
return torch.device(DeviceType.CPU.value)
else:
return torch.device(type=get_current_accelerator().type, index=torch.accelerator.current_device_index()) | def get_current_device() -> torch.device:
"""Get current accelerator device."""
if get_current_accelerator().type == DeviceType.CPU:
return torch.device(DeviceType.CPU.value)
else:
return torch.device(type=get_current_accelerator().type, index=torch.accelerator.current_device_index()) | [{"test_file": "tests/model/model_utils/test_checkpointing.py", "test_function": "test_upcast_lmhead_output", "test_content": "# Copyright 2025 the LlamaFactory team.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may ob... | {"repo_url": "https://github.com/hiyouga/LlamaFactory", "install_cmd": "pip install -e .", "commit_sha": "c0245c43fc1fbb87ed6b2f2d28bdcceed5103946", "frozen_requirements": "frozen_requirements/hiyouga_LlamaFactory.txt"} | {"body_lines": 4, "file_lines": 236, "has_docstring": true, "num_tests": 1} | {"status": "passed", "tests_run": 1} | repo_patch/0033 | # Copyright 2025 Bytedance Ltd. and the LlamaFactory team.
#
# This code is inspired by the Bytedance's VeOmni library.
# https://github.com/ByteDance-Seed/VeOmni/blob/v0.1.4/veomni/utils/dist_utils.py
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance w... |
repo_patch/0034 | hiyouga/LlamaFactory | src/llamafactory/v1/plugins/model_plugins/peft.py | merge_and_export_model | merge_and_export_model | function | null | # Copyright 2025 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to i... | def merge_and_export_model(args: InputArgument = None): | model_args, _, _, _ = get_args(args)
export_config = model_args.peft_config
if export_config is None:
raise ValueError("Please specify peft_config to merge and export model.")
export_dir = export_config.get("export_dir")
if export_dir is None:
raise ValueError("Please specify expor... | def merge_and_export_model(args: InputArgument = None):
model_args, _, _, _ = get_args(args)
export_config = model_args.peft_config
if export_config is None:
raise ValueError("Please specify peft_config to merge and export model.")
export_dir = export_config.get("export_dir")
if export_dir... | [{"test_file": "tests_v1/plugins/model_plugins/test_peft.py", "test_function": "test_merge_and_export_model", "test_content": "# Copyright 2025 the LlamaFactory team.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may ob... | {"repo_url": "https://github.com/hiyouga/LlamaFactory", "install_cmd": "pip install -e .", "commit_sha": "c0245c43fc1fbb87ed6b2f2d28bdcceed5103946", "frozen_requirements": "frozen_requirements/hiyouga_LlamaFactory.txt"} | {"body_lines": 49, "file_lines": 344, "has_docstring": false, "num_tests": 1} | {"status": "passed", "tests_run": 1} | repo_patch/0034 | # Copyright 2025 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to i... | |
repo_patch/0035 | hiyouga/LlamaFactory | src/llamafactory/v1/plugins/model_plugins/peft.py | load_adapter | load_adapter | function | null | # Copyright 2025 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to i... | def load_adapter(model: HFModel, adapter_name_or_path: Union[list[str], str], is_train: bool) -> HFModel:
r"""Loads adapter(s) into the model.
Determine adapter usage based on mode:
- Training: Load the single adapter for continued training.
- Inference: Merge all adapters to clean up the model.
- ... | Loads adapter(s) into the model.
Determine adapter usage based on mode:
- Training: Load the single adapter for continued training.
- Inference: Merge all adapters to clean up the model.
- Unmergeable: Keep the single adapter active without merging. | if not isinstance(adapter_name_or_path, list):
adapter_name_or_path = [adapter_name_or_path]
# TODO
# Adapters fix for deepspeed and quant
# Adapters fix for vision
if is_train and len(adapter_name_or_path) > 1:
raise ValueError(
"When `adapter_name_or_path` is provided... | def load_adapter(model: HFModel, adapter_name_or_path: Union[list[str], str], is_train: bool) -> HFModel:
r"""Loads adapter(s) into the model.
Determine adapter usage based on mode:
- Training: Load the single adapter for continued training.
- Inference: Merge all adapters to clean up the model.
- ... | [{"test_file": "tests_v1/plugins/model_plugins/test_peft.py", "test_function": "test_load_adapter_single_for_inference", "test_content": "# Copyright 2025 the LlamaFactory team.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n#... | {"repo_url": "https://github.com/hiyouga/LlamaFactory", "install_cmd": "pip install -e .", "commit_sha": "c0245c43fc1fbb87ed6b2f2d28bdcceed5103946", "frozen_requirements": "frozen_requirements/hiyouga_LlamaFactory.txt"} | {"body_lines": 29, "file_lines": 344, "has_docstring": true, "num_tests": 4} | {"status": "passed", "tests_run": 4} | repo_patch/0035 | # Copyright 2025 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to i... |
repo_patch/0036 | hiyouga/LlamaFactory | src/llamafactory/v1/core/utils/rendering.py | parse_message | Renderer.parse_message | method | Renderer | # Copyright 2025 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to i... | def parse_message(self, generated_text: str) -> Message:
"""Parse a message in the template format.
Args:
generated_text (str): The generated text in the template format.
Returns:
Message: The parsed message.
""" | Parse a message in the template format.
Args:
generated_text (str): The generated text in the template format.
Returns:
Message: The parsed message. | if self.template == "chatml":
return parse_chatml_message(generated_text)
else:
from ...plugins.model_plugins.rendering import RenderingPlugin
return RenderingPlugin(self.template).parse_message(generated_text) | def parse_message(self, generated_text: str) -> Message:
"""Parse a message in the template format.
Args:
generated_text (str): The generated text in the template format.
Returns:
Message: The parsed message.
"""
if self.template == "chatml":
... | [{"test_file": "tests_v1/core/utils/test_rendering.py", "test_function": "test_chatml_parse", "test_content": "# Copyright 2025 the LlamaFactory team.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of t... | {"repo_url": "https://github.com/hiyouga/LlamaFactory", "install_cmd": "pip install -e .", "commit_sha": "c0245c43fc1fbb87ed6b2f2d28bdcceed5103946", "frozen_requirements": "frozen_requirements/hiyouga_LlamaFactory.txt"} | {"body_lines": 5, "file_lines": 170, "has_docstring": true, "num_tests": 2} | {"status": "passed", "tests_run": 2} | repo_patch/0036 | # Copyright 2025 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to i... |
repo_patch/0037 | hiyouga/LlamaFactory | src/llamafactory/v1/core/utils/rendering.py | process_samples | Renderer.process_samples | method | Renderer | # Copyright 2025 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to i... | def process_samples(self, samples: list[Sample]) -> list[ModelInput]:
"""Process samples to model input.
Args:
samples (list[Sample]): The samples to process.
Returns:
list[ModelInput]: The processed model inputs.
""" | Process samples to model input.
Args:
samples (list[Sample]): The samples to process.
Returns:
list[ModelInput]: The processed model inputs. | model_inputs = []
for sample in samples:
if "messages" in sample:
model_input = self.render_messages(sample["messages"], sample.get("tools"))
elif "chosen_messages" in sample and "rejected_messages" in sample:
chosen_input = self.render_messages(sa... | def process_samples(self, samples: list[Sample]) -> list[ModelInput]:
"""Process samples to model input.
Args:
samples (list[Sample]): The samples to process.
Returns:
list[ModelInput]: The processed model inputs.
"""
model_inputs = []
for sa... | [{"test_file": "tests_v1/core/utils/test_rendering.py", "test_function": "test_process_sft_samples", "test_content": "# Copyright 2025 the LlamaFactory team.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a co... | {"repo_url": "https://github.com/hiyouga/LlamaFactory", "install_cmd": "pip install -e .", "commit_sha": "c0245c43fc1fbb87ed6b2f2d28bdcceed5103946", "frozen_requirements": "frozen_requirements/hiyouga_LlamaFactory.txt"} | {"body_lines": 28, "file_lines": 170, "has_docstring": true, "num_tests": 2} | {"status": "passed", "tests_run": 2} | repo_patch/0037 | # Copyright 2025 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to i... |
repo_patch/0038 | infiniflow/ragflow | common/string_utils.py | remove_redundant_spaces | remove_redundant_spaces | function | null | #
# Copyright 2025 The InfiniFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by... | def remove_redundant_spaces(txt: str):
"""
Remove redundant spaces around punctuation marks while preserving meaningful spaces.
This function performs two main operations:
1. Remove spaces after left-boundary characters (opening brackets, etc.)
2. Remove spaces before right-boundary characters (clo... | Remove redundant spaces around punctuation marks while preserving meaningful spaces.
This function performs two main operations:
1. Remove spaces after left-boundary characters (opening brackets, etc.)
2. Remove spaces before right-boundary characters (closing brackets, punctuation, etc.)
Args:
txt (str): Input t... | txt = re.sub(r"([^a-z0-9.,\)>]) +([^ ])", r"\1\2", txt, flags=re.IGNORECASE)
# Second pass: Remove spaces before right-boundary characters
# Matches: [non-space] + [non-alphanumeric-and-specific-left-punctuation]
# Removes spaces before characters like non-')', non-',', non-'.', and non-alphanumeric ch... | def remove_redundant_spaces(txt: str):
"""
Remove redundant spaces around punctuation marks while preserving meaningful spaces.
This function performs two main operations:
1. Remove spaces after left-boundary characters (opening brackets, etc.)
2. Remove spaces before right-boundary characters (clo... | [{"test_file": "test/unit_test/common/test_string_utils.py", "test_function": "TestRemoveRedundantSpaces.test_remove_spaces_before_commas", "test_content": "#\n# Copyright 2025 The InfiniFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this... | {"repo_url": "https://github.com/infiniflow/ragflow", "install_cmd": "pip install -e .", "commit_sha": "1c87f97dde78adc1d583b8bcc2f43502602db28e", "frozen_requirements": "frozen_requirements/infiniflow_ragflow.txt"} | {"body_lines": 7, "file_lines": 74, "has_docstring": true, "num_tests": 32} | {"status": "passed", "tests_run": 32} | repo_patch/0038 | #
# Copyright 2025 The InfiniFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by... |
repo_patch/0039 | infiniflow/ragflow | rag/utils/raptor_utils.py | get_skip_reason | get_skip_reason | function | null | #
# Copyright 2025 The InfiniFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by... | def get_skip_reason(
file_type: Optional[str] = None,
parser_id: str = "",
parser_config: Optional[dict] = None
) -> str:
"""
Get a human-readable reason why Raptor was skipped.
Args:
file_type: File extension
parser_id: Parser ID being used
parser_config... | Get a human-readable reason why Raptor was skipped.
Args:
file_type: File extension
parser_id: Parser ID being used
parser_config: Parser configuration dict
Returns:
Reason string, or empty string if Raptor should not be skipped | parser_config = parser_config or {}
if is_structured_file_type(file_type):
return f"Structured data file ({file_type}) - Raptor auto-disabled"
if file_type and file_type.lower() in [".pdf", "pdf"]:
if is_tabular_pdf(parser_id, parser_config):
return f"Tabular PDF (parser={parse... | def get_skip_reason(
file_type: Optional[str] = None,
parser_id: str = "",
parser_config: Optional[dict] = None
) -> str:
"""
Get a human-readable reason why Raptor was skipped.
Args:
file_type: File extension
parser_id: Parser ID being used
parser_config... | [{"test_file": "test/unit_test/utils/test_raptor_utils.py", "test_function": "TestGetSkipReason.test_excel_skip_reason", "test_content": "#\n# Copyright 2025 The InfiniFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in com... | {"repo_url": "https://github.com/infiniflow/ragflow", "install_cmd": "pip install -e .", "commit_sha": "1c87f97dde78adc1d583b8bcc2f43502602db28e", "frozen_requirements": "frozen_requirements/infiniflow_ragflow.txt"} | {"body_lines": 7, "file_lines": 145, "has_docstring": true, "num_tests": 11} | {"status": "passed", "tests_run": 11} | repo_patch/0039 | #
# Copyright 2025 The InfiniFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by... |
repo_patch/0040 | infiniflow/ragflow | common/file_utils.py | get_project_base_directory | get_project_base_directory | function | null | #
# Copyright 2025 The InfiniFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by... | def get_project_base_directory(*args): | global PROJECT_BASE
if PROJECT_BASE is None:
PROJECT_BASE = os.path.abspath(
os.path.join(
os.path.dirname(os.path.realpath(__file__)),
os.pardir,
)
)
if args:
return os.path.join(PROJECT_BASE, *args)
return PROJECT_BASE | def get_project_base_directory(*args):
global PROJECT_BASE
if PROJECT_BASE is None:
PROJECT_BASE = os.path.abspath(
os.path.join(
os.path.dirname(os.path.realpath(__file__)),
os.pardir,
)
)
if args:
return os.path.join(PROJECT_... | [{"test_file": "test/unit_test/common/test_file_utils.py", "test_function": "TestGetProjectBaseDirectory.test_returns_project_base_when_no_args", "test_content": "#\n# Copyright 2025 The InfiniFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not us... | {"repo_url": "https://github.com/infiniflow/ragflow", "install_cmd": "pip install -e .", "commit_sha": "1c87f97dde78adc1d583b8bcc2f43502602db28e", "frozen_requirements": "frozen_requirements/infiniflow_ragflow.txt"} | {"body_lines": 11, "file_lines": 40, "has_docstring": false, "num_tests": 9} | {"status": "passed", "tests_run": 9} | repo_patch/0040 | #
# Copyright 2025 The InfiniFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by... | |
repo_patch/0041 | infiniflow/ragflow | common/string_utils.py | clean_markdown_block | clean_markdown_block | function | null | #
# Copyright 2025 The InfiniFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by... | def clean_markdown_block(text):
"""
Remove Markdown code block syntax from the beginning and end of text.
This function cleans Markdown code blocks by removing:
- Opening ```Markdown tags (with optional whitespace and newlines)
- Closing ``` tags (with optional whitespace and newlines)
Args:
... | Remove Markdown code block syntax from the beginning and end of text.
This function cleans Markdown code blocks by removing:
- Opening ```Markdown tags (with optional whitespace and newlines)
- Closing ``` tags (with optional whitespace and newlines)
Args:
text (str): Input text that may be wrapped in Markdown co... | text = re.sub(r'^\s*```markdown\s*\n?', '', text)
# Remove closing ``` tag with optional whitespace and newlines
# Matches: optional newline + optional whitespace + ``` + optional whitespace at end
text = re.sub(r'\n?\s*```\s*$', '', text)
# Return text with surrounding whitespace removed
retu... | def clean_markdown_block(text):
"""
Remove Markdown code block syntax from the beginning and end of text.
This function cleans Markdown code blocks by removing:
- Opening ```Markdown tags (with optional whitespace and newlines)
- Closing ``` tags (with optional whitespace and newlines)
Args:
... | [{"test_file": "test/unit_test/common/test_string_utils.py", "test_function": "TestCleanMarkdownBlock.test_standard_markdown_block", "test_content": "#\n# Copyright 2025 The InfiniFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file e... | {"repo_url": "https://github.com/infiniflow/ragflow", "install_cmd": "pip install -e .", "commit_sha": "1c87f97dde78adc1d583b8bcc2f43502602db28e", "frozen_requirements": "frozen_requirements/infiniflow_ragflow.txt"} | {"body_lines": 6, "file_lines": 74, "has_docstring": true, "num_tests": 18} | {"status": "passed", "tests_run": 18} | repo_patch/0041 | #
# Copyright 2025 The InfiniFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by... |
repo_patch/0042 | infiniflow/ragflow | common/misc_utils.py | convert_bytes | convert_bytes | function | null | #
# Copyright 2025 The InfiniFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by... | def convert_bytes(size_in_bytes: int) -> str:
"""
Format size in bytes.
""" | Format size in bytes. | if size_in_bytes == 0:
return "0 B"
units = ['B', 'KB', 'MB', 'GB', 'TB', 'PB']
i = 0
size = float(size_in_bytes)
while size >= 1024 and i < len(units) - 1:
size /= 1024
i += 1
if i == 0 or size >= 100:
return f"{size:.0f} {units[i]}"
elif size >= 10:
... | def convert_bytes(size_in_bytes: int) -> str:
"""
Format size in bytes.
"""
if size_in_bytes == 0:
return "0 B"
units = ['B', 'KB', 'MB', 'GB', 'TB', 'PB']
i = 0
size = float(size_in_bytes)
while size >= 1024 and i < len(units) - 1:
size /= 1024
i += 1
if i... | [{"test_file": "test/unit_test/common/test_misc_utils.py", "test_function": "TestConvertBytes.test_zero_bytes", "test_content": "#\n# Copyright 2025 The InfiniFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance w... | {"repo_url": "https://github.com/infiniflow/ragflow", "install_cmd": "pip install -e .", "commit_sha": "1c87f97dde78adc1d583b8bcc2f43502602db28e", "frozen_requirements": "frozen_requirements/infiniflow_ragflow.txt"} | {"body_lines": 14, "file_lines": 134, "has_docstring": true, "num_tests": 10} | {"status": "passed", "tests_run": 10} | repo_patch/0042 | #
# Copyright 2025 The InfiniFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by... |
repo_patch/0043 | infiniflow/ragflow | common/misc_utils.py | download_img | download_img | function | null | #
# Copyright 2025 The InfiniFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by... | def download_img(url): | if not url:
return ""
response = requests.get(url)
return "data:" + \
response.headers.get('Content-Type', 'image/jpg') + ";" + \
"base64," + base64.b64encode(response.content).decode("utf-8") | def download_img(url):
if not url:
return ""
response = requests.get(url)
return "data:" + \
response.headers.get('Content-Type', 'image/jpg') + ";" + \
"base64," + base64.b64encode(response.content).decode("utf-8") | [{"test_file": "test/unit_test/common/test_misc_utils.py", "test_function": "TestDownloadImg.test_empty_url_returns_empty_string", "test_content": "#\n# Copyright 2025 The InfiniFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file exc... | {"repo_url": "https://github.com/infiniflow/ragflow", "install_cmd": "pip install -e .", "commit_sha": "1c87f97dde78adc1d583b8bcc2f43502602db28e", "frozen_requirements": "frozen_requirements/infiniflow_ragflow.txt"} | {"body_lines": 6, "file_lines": 134, "has_docstring": false, "num_tests": 2} | {"status": "passed", "tests_run": 2} | repo_patch/0043 | #
# Copyright 2025 The InfiniFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by... | |
repo_patch/0044 | infiniflow/ragflow | rag/utils/raptor_utils.py | is_structured_file_type | is_structured_file_type | function | null | #
# Copyright 2025 The InfiniFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by... | def is_structured_file_type(file_type: Optional[str]) -> bool:
"""
Check if a file type is structured data (Excel, CSV, etc.)
Args:
file_type: File extension (e.g., ".xlsx", ".csv")
Returns:
True if file is structured data type
""" | Check if a file type is structured data (Excel, CSV, etc.)
Args:
file_type: File extension (e.g., ".xlsx", ".csv")
Returns:
True if file is structured data type | if not file_type:
return False
# Normalize to lowercase and ensure leading dot
file_type = file_type.lower()
if not file_type.startswith("."):
file_type = f".{file_type}"
return file_type in STRUCTURED_EXTENSIONS | def is_structured_file_type(file_type: Optional[str]) -> bool:
"""
Check if a file type is structured data (Excel, CSV, etc.)
Args:
file_type: File extension (e.g., ".xlsx", ".csv")
Returns:
True if file is structured data type
"""
if not file_type:
return F... | [{"test_file": "test/unit_test/utils/test_raptor_utils.py", "test_function": "TestIsStructuredFileType.test_file_type_detection", "test_content": "#\n# Copyright 2025 The InfiniFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file exce... | {"repo_url": "https://github.com/infiniflow/ragflow", "install_cmd": "pip install -e .", "commit_sha": "1c87f97dde78adc1d583b8bcc2f43502602db28e", "frozen_requirements": "frozen_requirements/infiniflow_ragflow.txt"} | {"body_lines": 7, "file_lines": 145, "has_docstring": true, "num_tests": 2} | {"status": "passed", "tests_run": 2} | repo_patch/0044 | #
# Copyright 2025 The InfiniFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by... |
repo_patch/0045 | infiniflow/ragflow | common/float_utils.py | get_float | get_float | function | null | #
# Copyright 2025 The InfiniFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by... | def get_float(v):
"""
Convert a value to float, handling None and exceptions gracefully.
Attempts to convert the input value to a float. If the value is None or
cannot be converted to float, returns negative infinity as a default value.
Args:
v: The value to convert to float. Can be any ty... | Convert a value to float, handling None and exceptions gracefully.
Attempts to convert the input value to a float. If the value is None or
cannot be converted to float, returns negative infinity as a default value.
Args:
v: The value to convert to float. Can be any type that float() accepts,
or None.
Retu... | if v is None:
return float("-inf")
try:
return float(v)
except Exception:
return float("-inf") | def get_float(v):
"""
Convert a value to float, handling None and exceptions gracefully.
Attempts to convert the input value to a float. If the value is None or
cannot be converted to float, returns negative infinity as a default value.
Args:
v: The value to convert to float. Can be any ty... | [{"test_file": "test/unit_test/common/test_float_utils.py", "test_function": "TestGetFloat.test_valid_float_string", "test_content": "#\n# Copyright 2025 The InfiniFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in complia... | {"repo_url": "https://github.com/infiniflow/ragflow", "install_cmd": "pip install -e .", "commit_sha": "1c87f97dde78adc1d583b8bcc2f43502602db28e", "frozen_requirements": "frozen_requirements/infiniflow_ragflow.txt"} | {"body_lines": 6, "file_lines": 59, "has_docstring": true, "num_tests": 9} | {"status": "passed", "tests_run": 9} | repo_patch/0045 | #
# Copyright 2025 The InfiniFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by... |
repo_patch/0046 | infiniflow/ragflow | common/time_utils.py | date_string_to_timestamp | date_string_to_timestamp | function | null | #
# Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by... | def date_string_to_timestamp(time_str, format_string="%Y-%m-%d %H:%M:%S"):
"""
Convert a date string to timestamp in milliseconds.
Args:
time_str: Date string to convert
format_string: Format of the input date string (default: "%Y-%m-%d %H:%M:%S")
Returns:
int: Unix timestamp i... | Convert a date string to timestamp in milliseconds.
Args:
time_str: Date string to convert
format_string: Format of the input date string (default: "%Y-%m-%d %H:%M:%S")
Returns:
int: Unix timestamp in milliseconds
Example:
>>> date_string_to_timestamp("2024-01-01 00:00:00")
1704067200000 | time_array = time.strptime(time_str, format_string)
time_stamp = int(time.mktime(time_array) * 1000)
return time_stamp | def date_string_to_timestamp(time_str, format_string="%Y-%m-%d %H:%M:%S"):
"""
Convert a date string to timestamp in milliseconds.
Args:
time_str: Date string to convert
format_string: Format of the input date string (default: "%Y-%m-%d %H:%M:%S")
Returns:
int: Unix timestamp i... | [{"test_file": "test/unit_test/common/test_time_utils.py", "test_function": "TestDateStringToTimestamp.test_basic_date_string_conversion", "test_content": "#\n# Copyright 2025 The InfiniFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this ... | {"repo_url": "https://github.com/infiniflow/ragflow", "install_cmd": "pip install -e .", "commit_sha": "1c87f97dde78adc1d583b8bcc2f43502602db28e", "frozen_requirements": "frozen_requirements/infiniflow_ragflow.txt"} | {"body_lines": 3, "file_lines": 155, "has_docstring": true, "num_tests": 13} | {"status": "passed", "tests_run": 13} | repo_patch/0046 | #
# Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by... |
repo_patch/0047 | infiniflow/ragflow | common/decorator.py | singleton | singleton | function | null | #
# Copyright 2025 The InfiniFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by... | def singleton(cls, *args, **kw): | instances = {}
def _singleton():
key = str(cls) + str(os.getpid())
if key not in instances:
instances[key] = cls(*args, **kw)
return instances[key]
return _singleton | def singleton(cls, *args, **kw):
instances = {}
def _singleton():
key = str(cls) + str(os.getpid())
if key not in instances:
instances[key] = cls(*args, **kw)
return instances[key]
return _singleton | [{"test_file": "test/unit_test/common/test_decorator.py", "test_function": "test_singleton_decorator_returns_callable", "test_content": "#\n# Copyright 2025 The InfiniFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in comp... | {"repo_url": "https://github.com/infiniflow/ragflow", "install_cmd": "pip install -e .", "commit_sha": "1c87f97dde78adc1d583b8bcc2f43502602db28e", "frozen_requirements": "frozen_requirements/infiniflow_ragflow.txt"} | {"body_lines": 7, "file_lines": 27, "has_docstring": false, "num_tests": 1} | {"status": "passed", "tests_run": 1} | repo_patch/0047 | #
# Copyright 2025 The InfiniFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by... | |
repo_patch/0048 | mem0ai/mem0 | mem0/llms/vllm.py | generate_response | VllmLLM.generate_response | method | VllmLLM | import json
import os
from typing import Dict, List, Optional, Union
from openai import OpenAI
from mem0.configs.llms.base import BaseLlmConfig
from mem0.configs.llms.vllm import VllmConfig
from mem0.llms.base import LLMBase
from mem0.memory.utils import extract_json
class VllmLLM(LLMBase):
def __init__(self, c... | def generate_response(
self,
messages: List[Dict[str, str]],
response_format=None,
tools: Optional[List[Dict]] = None,
tool_choice: str = "auto",
**kwargs,
):
"""
Generate a response based on the given messages using vLLM.
Args:
me... | Generate a response based on the given messages using vLLM.
Args:
messages (list): List of message dicts containing 'role' and 'content'.
response_format (str or object, optional): Format of the response. Defaults to "text".
tools (list, optional): List of tools that the model can call. Defaults to None.
... | params = self._get_supported_params(messages=messages, **kwargs)
params.update(
{
"model": self.config.model,
"messages": messages,
}
)
if tools:
params["tools"] = tools
params["tool_choice"] = tool_choice
... | def generate_response(
self,
messages: List[Dict[str, str]],
response_format=None,
tools: Optional[List[Dict]] = None,
tool_choice: str = "auto",
**kwargs,
):
"""
Generate a response based on the given messages using vLLM.
Args:
... | [{"test_file": "tests/llms/test_vllm.py", "test_function": "test_generate_response_without_tools", "test_content": "from unittest.mock import MagicMock, Mock, patch\n\nimport pytest\n\nfrom mem0 import AsyncMemory, Memory\nfrom mem0.configs.llms.base import BaseLlmConfig\nfrom mem0.llms.vllm import VllmLLM\n\n\n@pytest... | {"repo_url": "https://github.com/mem0ai/mem0", "install_cmd": "pip install -e .", "commit_sha": "a0d8a02b948271a2b369f7d65f28805189a22970", "frozen_requirements": "frozen_requirements/mem0ai_mem0.txt"} | {"body_lines": 12, "file_lines": 108, "has_docstring": true, "num_tests": 2} | {"status": "passed", "tests_run": 2} | repo_patch/0048 | import json
import os
from typing import Dict, List, Optional, Union
from openai import OpenAI
from mem0.configs.llms.base import BaseLlmConfig
from mem0.configs.llms.vllm import VllmConfig
from mem0.llms.base import LLMBase
from mem0.memory.utils import extract_json
class VllmLLM(LLMBase):
def __init__(self, c... |
repo_patch/0049 | scrapy/scrapy | scrapy/core/downloader/handlers/_httpx.py | download_request | HttpxDownloadHandler.download_request | method | HttpxDownloadHandler | """``httpx``-based HTTP(S) download handler. Currently not recommended for production use."""
from __future__ import annotations
import ipaddress
import logging
import ssl
from http.cookiejar import Cookie, CookieJar
from io import BytesIO
from typing import TYPE_CHECKING, Any, NoReturn, TypedDict
import httpx
from... | async def download_request(self, request: Request) -> Response: | self._warn_unsupported_meta(request.meta)
timeout: float = request.meta.get(
"download_timeout", self._DEFAULT_CONNECT_TIMEOUT
)
try:
async with self._get_httpx_response(request, timeout) as httpx_response:
return await self._read_response(httpx_... | async def download_request(self, request: Request) -> Response:
self._warn_unsupported_meta(request.meta)
timeout: float = request.meta.get(
"download_timeout", self._DEFAULT_CONNECT_TIMEOUT
)
try:
async with self._get_httpx_response(request, timeout) as htt... | [{"test_file": "tests/test_downloader_handler_httpx.py", "test_function": "TestHttp11.test_unsupported_bindaddress", "test_content": "\"\"\"Tests for scrapy.core.downloader.handlers._httpx.HttpxDownloadHandler.\"\"\"\n\nfrom __future__ import annotations\n\nfrom typing import TYPE_CHECKING, Any\n\nimport pytest\n\nfrom... | {"repo_url": "https://github.com/scrapy/scrapy", "install_cmd": "pip install -e .", "commit_sha": "e02ad08672a5946f659acf4874c4a315e7886346", "frozen_requirements": "frozen_requirements/scrapy_scrapy.txt"} | {"body_lines": 21, "file_lines": 270, "has_docstring": false, "num_tests": 2} | {"status": "passed", "tests_run": 2} | repo_patch/0049 | """``httpx``-based HTTP(S) download handler. Currently not recommended for production use."""
from __future__ import annotations
import ipaddress
import logging
import ssl
from http.cookiejar import Cookie, CookieJar
from io import BytesIO
from typing import TYPE_CHECKING, Any, NoReturn, TypedDict
import httpx
from... | |
repo_patch/0050 | scrapy/scrapy | tests/utils/cmdline.py | call | call | function | null | from __future__ import annotations
import subprocess
import sys
from typing import Any
import pytest
from scrapy.utils.test import get_testenv
def call(*args: str, **popen_kwargs: Any) -> int:
| def call(*args: str, **popen_kwargs: Any) -> int: | args = (sys.executable, "-m", "scrapy.cmdline", *args)
return subprocess.call(
args,
stdout=subprocess.DEVNULL,
stderr=subprocess.DEVNULL,
env=get_testenv(),
**popen_kwargs,
) | def call(*args: str, **popen_kwargs: Any) -> int:
args = (sys.executable, "-m", "scrapy.cmdline", *args)
return subprocess.call(
args,
stdout=subprocess.DEVNULL,
stderr=subprocess.DEVNULL,
env=get_testenv(),
**popen_kwargs,
) | [{"test_file": "tests/test_command_parse.py", "test_function": "TestParseCommand.test_crawlspider_not_exists_with_not_matched_url", "test_content": "from __future__ import annotations\n\nimport argparse\nimport re\nfrom typing import TYPE_CHECKING\n\nimport pytest\n\nfrom scrapy.commands import parse\nfrom scrapy.setti... | {"repo_url": "https://github.com/scrapy/scrapy", "install_cmd": "pip install -e .", "commit_sha": "e02ad08672a5946f659acf4874c4a315e7886346", "frozen_requirements": "frozen_requirements/scrapy_scrapy.txt"} | {"body_lines": 8, "file_lines": 39, "has_docstring": false, "num_tests": 20} | {"status": "partial_pass", "note": "environment-specific test failures"} | repo_patch/0050 | from __future__ import annotations
import subprocess
import sys
from typing import Any
import pytest
from scrapy.utils.test import get_testenv
def call(*args: str, **popen_kwargs: Any) -> int:
# TODO: Implement this function
def proc(*args: str, **popen_kwargs: Any) -> tuple[int, str, str]:
args = (sys.e... | |
repo_patch/0051 | scrapy/scrapy | tests/utils/cmdline.py | proc | proc | function | null | from __future__ import annotations
import subprocess
import sys
from typing import Any
import pytest
from scrapy.utils.test import get_testenv
def call(*args: str, **popen_kwargs: Any) -> int:
args = (sys.executable, "-m", "scrapy.cmdline", *args)
return subprocess.call(
args,
stdout=subpro... | def proc(*args: str, **popen_kwargs: Any) -> tuple[int, str, str]: | args = (sys.executable, "-m", "scrapy.cmdline", *args)
try:
p = subprocess.run(
args,
check=False,
capture_output=True,
encoding="utf-8",
timeout=15,
env=get_testenv(),
**popen_kwargs,
)
except subprocess.Tim... | def proc(*args: str, **popen_kwargs: Any) -> tuple[int, str, str]:
args = (sys.executable, "-m", "scrapy.cmdline", *args)
try:
p = subprocess.run(
args,
check=False,
capture_output=True,
encoding="utf-8",
timeout=15,
env=get_testenv... | [{"test_file": "tests/test_command_parse.py", "test_function": "TestParseCommand.test_spider_arguments", "test_content": "from __future__ import annotations\n\nimport argparse\nimport re\nfrom typing import TYPE_CHECKING\n\nimport pytest\n\nfrom scrapy.commands import parse\nfrom scrapy.settings import Settings\nfrom t... | {"repo_url": "https://github.com/scrapy/scrapy", "install_cmd": "pip install -e .", "commit_sha": "e02ad08672a5946f659acf4874c4a315e7886346", "frozen_requirements": "frozen_requirements/scrapy_scrapy.txt"} | {"body_lines": 14, "file_lines": 39, "has_docstring": false, "num_tests": 50} | {"status": "partial_pass", "note": "environment-specific test failures"} | repo_patch/0051 | from __future__ import annotations
import subprocess
import sys
from typing import Any
import pytest
from scrapy.utils.test import get_testenv
def call(*args: str, **popen_kwargs: Any) -> int:
args = (sys.executable, "-m", "scrapy.cmdline", *args)
return subprocess.call(
args,
stdout=subpro... | |
repo_patch/0052 | Textualize/textual | src/textual/highlight.py | guess_language | guess_language | function | null | from __future__ import annotations
import os
from typing import Tuple
from pygments.lexer import Lexer
from pygments.lexers import get_lexer_by_name, guess_lexer_for_filename
from pygments.token import Token
from pygments.util import ClassNotFound
from textual.content import Content, Span
TokenType = Tuple[str, ...... | def guess_language(code: str, path: str | None) -> str:
"""Guess the language based on the code and path.
The result may be used in the [highlight][textual.highlight.highlight] function.
Args:
code: The code to guess from.
path: A path to the code.
Returns:
The language, suitab... | Guess the language based on the code and path.
The result may be used in the [highlight][textual.highlight.highlight] function.
Args:
code: The code to guess from.
path: A path to the code.
Returns:
The language, suitable for use with Pygments. | if path and os.path.splitext(path)[-1] == ".tcss":
# A special case for TCSS files which aren't known outside of Textual
return "scss"
lexer: Lexer | None = None
lexer_name = "default"
if code:
if path:
try:
lexer = guess_lexer_for_filename(path, code... | def guess_language(code: str, path: str | None) -> str:
"""Guess the language based on the code and path.
The result may be used in the [highlight][textual.highlight.highlight] function.
Args:
code: The code to guess from.
path: A path to the code.
Returns:
The language, suitab... | [{"test_file": "tests/test_highlight.py", "test_function": "test_guess_language", "test_content": "import pytest\n\nfrom textual.content import Span\nfrom textual.highlight import guess_language, highlight\n\n\ndef test_highlight() -> None:\n \"\"\"Test simple application of highlight.\"\"\"\n import_this = highl... | {"repo_url": "https://github.com/Textualize/textual", "install_cmd": "pip install -e .", "commit_sha": "431dc7d32c64948d0197851654729262f8ed35be", "frozen_requirements": "frozen_requirements/Textualize_textual.txt"} | {"body_lines": 31, "file_lines": 161, "has_docstring": true, "num_tests": 1} | {"status": "validated", "tests_run": "docker"} | repo_patch/0052 | from __future__ import annotations
import os
from typing import Tuple
from pygments.lexer import Lexer
from pygments.lexers import get_lexer_by_name, guess_lexer_for_filename
from pygments.token import Token
from pygments.util import ClassNotFound
from textual.content import Content, Span
TokenType = Tuple[str, ...... |
repo_patch/0053 | Textualize/rich | rich/_unicode_data/__init__.py | load | load | function | null | from __future__ import annotations
import bisect
import os
import sys
if sys.version_info[:2] >= (3, 9):
from functools import cache
else:
from functools import lru_cache as cache # pragma: no cover
from importlib import import_module
from typing import TYPE_CHECKING, cast
from rich._unicode_data._versions... | def load(unicode_version: str = "auto") -> CellTable:
"""Load a cell table for the given unicode version.
Args:
unicode_version: Unicode version, or `None` to auto-detect.
""" | Load a cell table for the given unicode version.
Args:
unicode_version: Unicode version, or `None` to auto-detect. | if unicode_version == "auto":
unicode_version = os.environ.get("UNICODE_VERSION", "latest")
try:
_parse_version(unicode_version)
except ValueError:
# The environment variable is invalid
# Fallback to using the latest version seems reasonable
un... | def load(unicode_version: str = "auto") -> CellTable:
"""Load a cell table for the given unicode version.
Args:
unicode_version: Unicode version, or `None` to auto-detect.
"""
if unicode_version == "auto":
unicode_version = os.environ.get("UNICODE_VERSION", "latest")
try:
... | [{"test_file": "tests/test_unicode_data.py", "test_function": "test_load", "test_content": "from __future__ import annotations\n\nimport pytest\n\nfrom rich._unicode_data import VERSIONS, _parse_version, load\n\n\ndef test_load():\n \"\"\"Test all versions may be loaded.\"\"\"\n for version in VERSIONS:\n ... | {"repo_url": "https://github.com/Textualize/rich", "install_cmd": "pip install -e .", "commit_sha": "fc41075a3206d2a5fd846c6f41c4d2becab814fa", "frozen_requirements": "frozen_requirements/Textualize_rich.txt"} | {"body_lines": 26, "file_lines": 94, "has_docstring": true, "num_tests": 3} | {"status": "validated", "tests_run": "docker"} | repo_patch/0053 | from __future__ import annotations
import bisect
import os
import sys
if sys.version_info[:2] >= (3, 9):
from functools import cache
else:
from functools import lru_cache as cache # pragma: no cover
from importlib import import_module
from typing import TYPE_CHECKING, cast
from rich._unicode_data._versions... |
repo_patch/0054 | deepspeedai/DeepSpeed | deepspeed/runtime/precision_config.py | get_bfloat16_config | get_bfloat16_config | function | null | # Copyright (c) Microsoft Corporation.
# SPDX-License-Identifier: Apache-2.0
# DeepSpeed Team
from deepspeed.runtime.config_utils import DeepSpeedConfigModel
from .fp16.loss_scaler import (
INITIAL_LOSS_SCALE,
SCALE_WINDOW,
DELAYED_SHIFT,
CONSECUTIVE_HYSTERESIS,
MIN_LOSS_SCALE,
)
################... | def get_bfloat16_config(param_dict): | bf16_config_dict = param_dict.get(BFLOAT16, None)
if bf16_config_dict is None:
bf16_config_dict = param_dict.get(BFLOAT16_OLD, {})
return DeepSpeedBF16Config(**bf16_config_dict) | def get_bfloat16_config(param_dict):
bf16_config_dict = param_dict.get(BFLOAT16, None)
if bf16_config_dict is None:
bf16_config_dict = param_dict.get(BFLOAT16_OLD, {})
return DeepSpeedBF16Config(**bf16_config_dict) | [{"test_file": "tests/unit/runtime/test_ds_config_dict.py", "test_function": "test_get_bfloat16_enabled", "test_content": "# Copyright (c) Microsoft Corporation.\n# SPDX-License-Identifier: Apache-2.0\n\n# DeepSpeed Team\n\n# A test on its own\nimport os\nimport pytest\nimport json\nimport hjson\nimport argparse\nimpor... | {"repo_url": "https://github.com/deepspeedai/DeepSpeed", "install_cmd": "pip install -e .", "commit_sha": "a41a96b19f2b5e75567c85ff9155e4bb09c8e539", "frozen_requirements": "frozen_requirements/deepspeedai_DeepSpeed.txt"} | {"body_lines": 4, "file_lines": 157, "has_docstring": false, "num_tests": 1} | {"status": "validated", "tests_run": "docker"} | repo_patch/0054 | # Copyright (c) Microsoft Corporation.
# SPDX-License-Identifier: Apache-2.0
# DeepSpeed Team
from deepspeed.runtime.config_utils import DeepSpeedConfigModel
from .fp16.loss_scaler import (
INITIAL_LOSS_SCALE,
SCALE_WINDOW,
DELAYED_SHIFT,
CONSECUTIVE_HYSTERESIS,
MIN_LOSS_SCALE,
)
################... | |
repo_patch/0055 | Aider-AI/aider | aider/waiting.py | main | main | function | null | #!/usr/bin/env python
"""
Thread-based, killable spinner utility.
Use it like:
from aider.waiting import WaitingSpinner
spinner = WaitingSpinner("Waiting for LLM")
spinner.start()
... # long task
spinner.stop()
"""
import sys
import threading
import time
from rich.console import Console
cla... | def main(): | spinner = Spinner("Running spinner...")
try:
for _ in range(100):
time.sleep(0.15)
spinner.step()
print("Success!")
except KeyboardInterrupt:
print("\nInterrupted by user.")
finally:
spinner.end() | def main():
spinner = Spinner("Running spinner...")
try:
for _ in range(100):
time.sleep(0.15)
spinner.step()
print("Success!")
except KeyboardInterrupt:
print("\nInterrupted by user.")
finally:
spinner.end() | [{"test_file": "tests/basic/test_deprecated.py", "test_function": "TestDeprecated.test_deprecated_args_show_warnings", "test_content": "import os\nfrom unittest import TestCase\nfrom unittest.mock import MagicMock, patch\n\nfrom prompt_toolkit.input import DummyInput\nfrom prompt_toolkit.output import DummyOutput\n\nfr... | {"repo_url": "https://github.com/Aider-AI/aider", "install_cmd": "pip install -e .", "commit_sha": "265d8a473b5d5bf001db321b251674a120ad75da", "frozen_requirements": "frozen_requirements/Aider-AI_aider.txt"} | {"body_lines": 10, "file_lines": 222, "has_docstring": false, "num_tests": 76} | {"status": "validated", "tests_run": "docker"} | repo_patch/0055 | #!/usr/bin/env python
"""
Thread-based, killable spinner utility.
Use it like:
from aider.waiting import WaitingSpinner
spinner = WaitingSpinner("Waiting for LLM")
spinner.start()
... # long task
spinner.stop()
"""
import sys
import threading
import time
from rich.console import Console
cla... |
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