
'''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
snake_case									=    """src/transformers"""
snake_case									=    """docs/source/en/tasks"""


def        UpperCAmelCase_ (  lowerCamelCase_      ,			lowerCamelCase_      ,			lowerCamelCase_			):







 """simple docstring"""


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

 lowerCAmelCase__     :   List[Any]     		=	start_index
 while not lines[end_index].startswith(__A			):
  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.
snake_case									=    direct_transformers_import(TRANSFORMERS_PATH)

snake_case									=    {
    """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`).
snake_case									=    {
    """summarization.md""": ("""nllb""",),
    """translation.md""": ("""nllb""",),
}


def        UpperCAmelCase_ (  lowerCamelCase_			):







 """simple docstring"""


 lowerCAmelCase__     :   Dict     		=	TASK_GUIDE_TO_MODELS[task_guide]
 lowerCAmelCase__     :   Tuple     		=	SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(__A      ,			set()			)
 lowerCAmelCase__     :   Optional[Any]     		=	{
     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        UpperCAmelCase_ (  lowerCamelCase_      ,			lowerCamelCase_=False			):







 """simple docstring"""


 lowerCAmelCase__     :   List[str]     		=	_find_text_in_file(
     filename=os.path.join(__A      ,			__A			)      ,			start_prompt="<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->"      ,			end_prompt="<!--End of the generated tip-->"      ,			)

 lowerCAmelCase__     :   Dict     		=	get_model_list_for_task(__A			)

 if current_list != new_list:
  if overwrite:
   with open(os.path.join(__A      ,			__A			)      ,			"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__":
     snake_case									=    argparse.ArgumentParser()
     parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
     snake_case									=    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 argparse
from collections import defaultdict


def 				lowercase_					(     __A			:							Optional[Any]       ,   __A			:							Optional[int]       ,   __A			:							str       ,   __A			:							Tuple       ,   __A			:							int      )							->   Dict:



 """simple docstring"""




 lowercase       : int						=F'{file}_{class_name}_{test_name}'
 done_test[_id] += 1

 with open(__A       ,   '''r'''      ) as f:
  lowercase       : List[Any]						=f.readlines()

 lowercase       : Tuple						=F'class {class_name}('
 lowercase       : Optional[Any]						=F'{4 * " "}def {test_name}('
 lowercase       : Union[str, Any]						=F'{8 * " "}{correct_line.split()[0]}'
 lowercase       : List[Any]						=F'{1_6 * " "}{correct_line.split()[0]}'
 lowercase       : int						=False
 lowercase       : Dict						=False
 lowercase       : Optional[Any]						=False
 lowercase       : str						=False
 lowercase       : str						=0
 lowercase       : Optional[Any]						=0

 lowercase       : List[Any]						=[]
 for line in lines:
  if line.startswith(__A      ):
   lowercase       : List[str]						=True
  elif in_class and line.startswith(__A      ):
   lowercase       : int						=True
  elif in_class and in_func and (line.startswith(__A      ) or line.startswith(__A      )):
   lowercase       : str						=len(line.split(correct_line.split()[0]      )[0]      )
   count += 1

   if count == done_test[_id]:
    lowercase       : Dict						=True

  if in_class and in_func and in_line:
   if ")" not in line:
    continue
   else:
    lowercase       : Any						=True

  if in_class and in_func and in_line and insert_line:
   new_lines.append(F'{spaces * " "}{correct_line}'      )
   lowercase       : List[str]						=False
  else:
   new_lines.append(__A      )

 with open(__A       ,   '''w'''      ) as f:
  for line in new_lines:
   f.write(__A      )


def 				lowercase_					(     __A			:							Any       ,   __A			:							List[Any]=None      )							->   str:



 """simple docstring"""




 if fail is not None:
  with open(__A       ,   '''r'''      ) as f:
   lowercase       : Optional[Any]						={l.strip() for l in f.readlines()}
 else:
  lowercase       : int						=None

 with open(__A       ,   '''r'''      ) as f:
  lowercase       : Union[str, Any]						=f.readlines()

 lowercase       : Union[str, Any]						=defaultdict(__A      )
 for line in correct_lines:
  lowercase	,		lowercase	,		lowercase	,		lowercase       : Optional[int]						=line.split(''';'''      )
  if test_failures is None or "::".join([file, class_name, test_name]      ) in test_failures:
   overwrite_file(__A       ,   __A       ,   __A       ,   __A       ,   __A      )


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE							=						argparse.ArgumentParser()
      parser.add_argument('--correct_filename', help='filename of tests with expected result')
      parser.add_argument('--fail_filename', help='filename of test failures', type=str, default=None)
      SCREAMING_SNAKE_CASE							=						parser.parse_args()

      main(args.correct_filename, args.fail_filename)



