



'''simple docstring'''





import argparse
import os

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
__a          =		"src/transformers"
__a          =		"docs/source/en/tasks"





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase		)		-> Optional[Any]:
      with open(_lowerCAmelCase     ,   """r"""     ,   encoding="""utf-8"""     ,   newline="""\n"""		) as f:
            snake_case__  :    str    						=      f.readlines()
      # Find the start prompt.
      snake_case__  :    int    						=      0
      while not lines[start_index].startswith(_lowerCAmelCase		):
            start_index += 1
      start_index += 1

      snake_case__  :    str    						=      start_index
      while not lines[end_index].startswith(_lowerCAmelCase		):
            end_index += 1
      end_index -= 1

      while len(lines[start_index]		) <= 1:
            start_index += 1
      while len(lines[end_index]		) <= 1:
            end_index -= 1
      end_index += 1
      return "".join(lines[start_index:end_index]		), start_index, end_index, lines


# This is to make sure the transformers module imported is the one in the repo.
__a          =		direct_transformers_import(TRANSFORMERS_PATH)

__a          =		{
    "asr.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_CTC_MAPPING_NAMES,
    "audio_classification.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES,
    "language_modeling.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_CAUSAL_LM_MAPPING_NAMES,
    "image_classification.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES,
    "masked_language_modeling.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_MASKED_LM_MAPPING_NAMES,
    "multiple_choice.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES,
    "object_detection.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES,
    "question_answering.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES,
    "semantic_segmentation.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES,
    "sequence_classification.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES,
    "summarization.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
    "token_classification.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES,
    "translation.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
    "video_classification.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES,
    "document_question_answering.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES,
    "monocular_depth_estimation.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES,
}

# This list contains model types used in some task guides that are not in `CONFIG_MAPPING_NAMES` (therefore not in any
# `MODEL_MAPPING_NAMES` or any `MODEL_FOR_XXX_MAPPING_NAMES`).
__a          =		{
    "summarization.md": ("nllb",),
    "translation.md": ("nllb",),
}





def 						__snake_case(				_lowerCAmelCase		)		-> Dict:
      snake_case__  :    Tuple    						=      TASK_GUIDE_TO_MODELS[task_guide]
      snake_case__  :    Optional[int]    						=      SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(_lowerCAmelCase     ,   set()		)
      snake_case__  :    List[str]    						=      {
          code: name
          for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
          if (code in model_maping_names or code in special_model_types)
      }
      return ", ".join([f"[{name}](../model_doc/{code})" for code, name in model_names.items()]		) + "\n"





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase=False		)		-> Optional[Any]:
      snake_case__						,				snake_case__						,				snake_case__						,				snake_case__  :    Tuple    						=      _find_text_in_file(
          filename=os.path.join(_lowerCAmelCase     ,   _lowerCAmelCase		)     ,   start_prompt="""<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->"""     ,   end_prompt="""<!--End of the generated tip-->"""     ,   )

      snake_case__  :    str    						=      get_model_list_for_task(_lowerCAmelCase		)

      if current_list != new_list:
            if overwrite:
                  with open(os.path.join(_lowerCAmelCase     ,   _lowerCAmelCase		)     ,   """w"""     ,   encoding="""utf-8"""     ,   newline="""\n"""		) as f:
                        f.writelines(lines[:start_index] + [new_list] + lines[end_index:]		)
            else:
                  raise ValueError(
                      f"The list of models that can be used in the {task_guide} guide needs an update. Run `make fix-copies`"
                      """ to fix this."""		)


if __name__ == "__main__":
       __a          =		argparse.ArgumentParser()
       parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
       __a          =		parser.parse_args()

       for task_guide in TASK_GUIDE_TO_MODELS.keys():
              check_model_list_for_task(task_guide, args.fix_and_overwrite)





'''simple docstring'''





import os
import time

import pytest

from datasets.utils.filelock import FileLock, Timeout





def 						__snake_case(				_lowerCAmelCase		)		-> Optional[int]:
      snake_case__  :    int    						=      FileLock(str(tmpdir / """foo.lock"""		)		)
      snake_case__  :    Dict    						=      FileLock(str(tmpdir / """foo.lock"""		)		)
      snake_case__  :    List[str]    						=      0.01
      with locka.acquire():
            with pytest.raises(_lowerCAmelCase		):
                  snake_case__  :    str    						=      time.time()
                  locka.acquire(_lowerCAmelCase		)
                  assert time.time() - _start > timeout





def 						__snake_case(				_lowerCAmelCase		)		-> Tuple:
      snake_case__  :    Dict    						=      """a""" * 1_000 + """.lock"""
      snake_case__  :    int    						=      FileLock(str(tmpdir / filename		)		)
      assert locka._lock_file.endswith(""".lock"""		)
      assert not locka._lock_file.endswith(_lowerCAmelCase		)
      assert len(os.path.basename(locka._lock_file		)		) <= 255
      snake_case__  :    Dict    						=      FileLock(tmpdir / filename		)
      with locka.acquire():
            with pytest.raises(_lowerCAmelCase		):
                  locka.acquire(0		)
