





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
__lowerCamelCase	:					Optional[Any]			  =					"""src/transformers"""
__lowerCamelCase	:					Optional[Any]			  =					"""docs/source/en/tasks"""



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

 UpperCamelCase     : int	     =       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.
__lowerCamelCase	:					Optional[int]			  =					direct_transformers_import(TRANSFORMERS_PATH)

__lowerCamelCase	:					Tuple			  =					{
    """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`).
__lowerCamelCase	:					List[Any]			  =					{
    """summarization.md""": ("""nllb""",),
    """translation.md""": ("""nllb""",),
}



def 		A_    (					_lowerCAmelCase					)						->			Optional[int]:
 UpperCamelCase     : Tuple	     =       TASK_GUIDE_TO_MODELS[task_guide]
 UpperCamelCase     : Dict	     =       SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(_lowerCAmelCase							,       set()					)
 UpperCamelCase     : 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 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase=False					)						->			List[Any]:
 UpperCamelCase     : int	     =       _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-->"							,       )

 UpperCamelCase     : 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__":
    __lowerCamelCase	:					Tuple			  =					argparse.ArgumentParser()
    parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
    __lowerCamelCase	:					Union[str, Any]			  =					parser.parse_args()

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







__lowerCamelCase	:					Tuple			  =					"""ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"""



def 		A_    (					_lowerCAmelCase					)						->			bytes:
 # Make sure the supplied data is a bytes-like object
 if not isinstance(_lowerCAmelCase							,       _lowerCAmelCase					):
  UpperCamelCase     : str	     =       F"""a bytes-like object is required, not '{data.__class__.__name__}'"""
  raise TypeError(_lowerCAmelCase					)

 UpperCamelCase     : Optional[int]	     =       "".join(bin(_lowerCAmelCase					)[2:].zfill(8					) for byte in data					)

 UpperCamelCase     : str	     =       len(_lowerCAmelCase					) % 6 != 0

 if padding_needed:
  # The padding that will be added later
  UpperCamelCase     : Optional[Any]	     =       b"=" * ((6 - len(_lowerCAmelCase					) % 6) // 2)

  # Append binary_stream with arbitrary binary digits (0's by default) to make its
  # length a multiple of 6.
  binary_stream += "0" * (6 - len(_lowerCAmelCase					) % 6)
 else:
  UpperCamelCase     : List[Any]	     =       b""

 # Encode every 6 binary digits to their corresponding Base64 character
 return (
     "".join(
         B64_CHARSET[int(binary_stream[index : index + 6]							,       2					)]
         for index in range(0							,       len(_lowerCAmelCase					)							,       6					)					).encode()
     + padding
 )



def 		A_    (					_lowerCAmelCase					)						->			bytes:
 # Make sure encoded_data is either a string or a bytes-like object
 if not isinstance(_lowerCAmelCase							,       _lowerCAmelCase					) and not isinstance(_lowerCAmelCase							,       _lowerCAmelCase					):
  UpperCamelCase     : List[Any]	     =       (
      "argument should be a bytes-like object or ASCII string, "
      F"""not '{encoded_data.__class__.__name__}'"""
  )
  raise TypeError(_lowerCAmelCase					)

 # In case encoded_data is a bytes-like object, make sure it contains only
 # ASCII characters so we convert it to a string object
 if isinstance(_lowerCAmelCase							,       _lowerCAmelCase					):
  try:
   UpperCamelCase     : Any	     =       encoded_data.decode("utf-8"					)
  except UnicodeDecodeError:
   raise ValueError("base64 encoded data should only contain ASCII characters"					)

 UpperCamelCase     : Union[str, Any]	     =       encoded_data.count("="					)

 # Check if the encoded string contains non base64 characters
 if padding:
  assert all(
      char in B64_CHARSET for char in encoded_data[:-padding]					), "Invalid base64 character(s) found."
 else:
  assert all(
      char in B64_CHARSET for char in encoded_data					), "Invalid base64 character(s) found."

 # Check the padding
 assert len(_lowerCAmelCase					) % 4 == 0 and padding < 3, "Incorrect padding"

 if padding:
  # Remove padding if there is one
  UpperCamelCase     : List[str]	     =       encoded_data[:-padding]

  UpperCamelCase     : List[Any]	     =       "".join(
      bin(B64_CHARSET.index(_lowerCAmelCase					)					)[2:].zfill(6					) for char in encoded_data					)[: -padding * 2]
 else:
  UpperCamelCase     : List[Any]	     =       "".join(
      bin(B64_CHARSET.index(_lowerCAmelCase					)					)[2:].zfill(6					) for char in encoded_data					)

 UpperCamelCase     : Any	     =       [
     int(binary_stream[index : index + 8]							,       2					)
     for index in range(0							,       len(_lowerCAmelCase					)							,       8					)
 ]

 return bytes(_lowerCAmelCase					)


if __name__ == "__main__":
    import doctest

    doctest.testmod()
