





"""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
lowerCamelCase_       =		"src/transformers"
lowerCamelCase_       =		"docs/source/en/tasks"



def 					__lowerCamelCase       (							a_					:		Tuple			,       a_					:		Any			,       a_					:		Tuple							)      ->				Tuple:





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

      __SCREAMING_SNAKE_CASE       :int		    =  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.
lowerCamelCase_       =		direct_transformers_import(TRANSFORMERS_PATH)

lowerCamelCase_       =		{
    "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_       =		{
    "summarization.md": ("nllb",),
    "translation.md": ("nllb",),
}



def 					__lowerCamelCase       (							a_					:		Optional[int]							)      ->				int:
      __SCREAMING_SNAKE_CASE       :Optional[int]		    =  TASK_GUIDE_TO_MODELS[task_guide]
      __SCREAMING_SNAKE_CASE       :List[str]		    =  SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(a_			,       set()							)
      __SCREAMING_SNAKE_CASE       :int		    =  {
          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 					__lowerCamelCase       (							a_					:		Tuple			,       a_					:		List[str]=False							)      ->				Union[str, Any]:
      __SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE       :Tuple		    =  _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-->'''			,       )

      __SCREAMING_SNAKE_CASE       :Optional[int]		    =  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__":
   lowerCamelCase_       =		argparse.ArgumentParser()
   parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
   lowerCamelCase_       =		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"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase_       =		logging.get_logger(__name__)

lowerCamelCase_       =		{
    "google/vivit-b-16x2-kinetics400": (
        "https://huggingface.co/google/vivit-b-16x2-kinetics400/resolve/main/config.json"
    ),
    # See all Vivit models at https://huggingface.co/models?filter=vivit
}






class  _SCREAMING_SNAKE_CASE(       A  ):
       SCREAMING_SNAKE_CASE_       :				Optional[Any]     						=  '''vivit'''




       def __init__(						self		,SCREAMING_SNAKE_CASE__=2_24		,SCREAMING_SNAKE_CASE__=32		,SCREAMING_SNAKE_CASE__=[2, 16, 16]		,SCREAMING_SNAKE_CASE__=3		,SCREAMING_SNAKE_CASE__=7_68		,SCREAMING_SNAKE_CASE__=12		,SCREAMING_SNAKE_CASE__=12		,SCREAMING_SNAKE_CASE__=30_72		,SCREAMING_SNAKE_CASE__="gelu_fast"		,SCREAMING_SNAKE_CASE__=0.0		,SCREAMING_SNAKE_CASE__=0.0		,SCREAMING_SNAKE_CASE__=0.0_2		,SCREAMING_SNAKE_CASE__=1E-06		,SCREAMING_SNAKE_CASE__=True		,**SCREAMING_SNAKE_CASE__		,)      ->				str:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  hidden_size
             __SCREAMING_SNAKE_CASE       :List[str]		    =  num_hidden_layers
             __SCREAMING_SNAKE_CASE       :List[str]		    =  num_attention_heads
             __SCREAMING_SNAKE_CASE       :Any		    =  intermediate_size
             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  hidden_act
             __SCREAMING_SNAKE_CASE       :int		    =  hidden_dropout_prob
             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  attention_probs_dropout_prob
             __SCREAMING_SNAKE_CASE       :Dict		    =  initializer_range
             __SCREAMING_SNAKE_CASE       :Dict		    =  layer_norm_eps

             __SCREAMING_SNAKE_CASE       :Any		    =  image_size
             __SCREAMING_SNAKE_CASE       :Any		    =  num_frames
             __SCREAMING_SNAKE_CASE       :Any		    =  tubelet_size
             __SCREAMING_SNAKE_CASE       :Tuple		    =  num_channels
             __SCREAMING_SNAKE_CASE       :List[str]		    =  qkv_bias

             super().__init__(**SCREAMING_SNAKE_CASE__							)