




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      :						Dict							=  """src/transformers"""
_lowerCamelCase      :						Tuple							=  """docs/source/en/tasks"""







def SCREAMING_SNAKE_CASE							(			lowercase_					,							lowercase_					,							lowercase_		)  ->			int:



       """simple docstring"""

       with open(lowercase_					,							'''r'''					,							encoding='''utf-8'''					,							newline='''\n'''		) as f:
              A__ 					=       f.readlines()
       # Find the start prompt.
       A__ 					=       0
       while not lines[start_index].startswith(lowercase_		):
              start_index += 1
       start_index += 1

       A__ 					=       start_index
       while not lines[end_index].startswith(lowercase_		):
              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      :						Union[str, Any]							=  direct_transformers_import(TRANSFORMERS_PATH)

_lowerCamelCase      :						List[Any]							=  {
    """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[str]							=  {
    """summarization.md""": ("""nllb""",),
    """translation.md""": ("""nllb""",),
}







def SCREAMING_SNAKE_CASE							(			lowercase_		)  ->			Dict:



       """simple docstring"""

       A__ 					=       TASK_GUIDE_TO_MODELS[task_guide]
       A__ 					=       SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(lowercase_					,							set()		)
       A__ 					=       {
           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 SCREAMING_SNAKE_CASE							(			lowercase_					,							lowercase_=False		)  ->			Any:



       """simple docstring"""

       A__					,				A__					,				A__					,				A__ 					=       _find_text_in_file(
           filename=os.path.join(lowercase_					,							lowercase_		)					,							start_prompt='''<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->'''					,							end_prompt='''<!--End of the generated tip-->'''					,							)

       A__ 					=       get_model_list_for_task(lowercase_		)

       if current_list != new_list:
              if overwrite:
                     with open(os.path.join(lowercase_					,							lowercase_		)					,							'''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      :						List[Any]							=  argparse.ArgumentParser()
  parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
  _lowerCamelCase      :						Optional[Any]							=  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 warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding





class 							lowercase_			(	__lowerCAmelCase				):



 '''simple docstring'''
 UpperCAmelCase       :	Any     =      ['''image_processor''', '''tokenizer''']
 UpperCAmelCase       :	Optional[int]     =      '''ViTImageProcessor'''
 UpperCAmelCase       :	int     =      ('''CLIPTokenizer''', '''CLIPTokenizerFast''')

 def __init__(					self			:			Tuple       ,				_UpperCAmelCase			:			int=None       ,				_UpperCAmelCase			:			Tuple=None       ,				**_UpperCAmelCase			:			Dict						):
       _A     =  None
       if "feature_extractor" in kwargs:
             warnings.warn(
                 'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
                 ' instead.'       ,				_UpperCAmelCase       ,				)
             _A     =  kwargs.pop('feature_extractor'						)

       _A     =  image_processor if image_processor is not None else feature_extractor
       if image_processor is None:
             raise ValueError('You need to specify an `image_processor`.'						)
       if tokenizer is None:
             raise ValueError('You need to specify a `tokenizer`.'						)

       super().__init__(_UpperCAmelCase       ,				_UpperCAmelCase						)

 def __call__(					self			:			Optional[Any]       ,				_UpperCAmelCase			:			int=None       ,				_UpperCAmelCase			:			int=None       ,				_UpperCAmelCase			:			Optional[Any]=None       ,				_UpperCAmelCase			:			Optional[int]=None       ,				**_UpperCAmelCase			:			Union[str, Any]						):
       if text is None and visual_prompt is None and images is None:
             raise ValueError('You have to specify either text, visual prompt or images.'						)

       if text is not None and visual_prompt is not None:
             raise ValueError('You have to specify exactly one type of prompt. Either text or visual prompt.'						)

       if text is not None:
             _A     =  self.tokenizer(_UpperCAmelCase       ,				return_tensors=_UpperCAmelCase       ,				**_UpperCAmelCase						)

       if visual_prompt is not None:
             _A     =  self.image_processor(_UpperCAmelCase       ,				return_tensors=_UpperCAmelCase       ,				**_UpperCAmelCase						)

       if images is not None:
             _A     =  self.image_processor(_UpperCAmelCase       ,				return_tensors=_UpperCAmelCase       ,				**_UpperCAmelCase						)

       if visual_prompt is not None and images is not None:
             _A     =  {
                 'pixel_values': image_features.pixel_values,
                 'conditional_pixel_values': prompt_features.pixel_values,
             }
             return encoding
       elif text is not None and images is not None:
             _A     =  image_features.pixel_values
             return encoding
       elif text is not None:
             return encoding
       elif visual_prompt is not None:
             _A     =  {
                 'conditional_pixel_values': prompt_features.pixel_values,
             }
             return encoding
       else:
             return BatchEncoding(data=dict(**_UpperCAmelCase						)       ,				tensor_type=_UpperCAmelCase						)

 def      lowerCAmelCase_				(					self			:			str       ,				*_UpperCAmelCase			:			List[Any]       ,				**_UpperCAmelCase			:			Union[str, Any]						):
       return self.tokenizer.batch_decode(*_UpperCAmelCase       ,				**_UpperCAmelCase						)

 def      lowerCAmelCase_				(					self			:			Union[str, Any]       ,				*_UpperCAmelCase			:			List[str]       ,				**_UpperCAmelCase			:			Union[str, Any]						):
       return self.tokenizer.decode(*_UpperCAmelCase       ,				**_UpperCAmelCase						)

 @property
 def      lowerCAmelCase_				(					self			:			Dict						):
       warnings.warn(
           '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'       ,				_UpperCAmelCase       ,				)
       return self.image_processor_class



 @property
 def      lowerCAmelCase_				(					self			:			Tuple						):
       warnings.warn(
           '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'       ,				_UpperCAmelCase       ,				)
       return self.image_processor

