



'''simple docstring'''





import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


a_						:     int	     =		logging.get_logger(__name__)

a_						:     Optional[int]	     =		{
    'BridgeTower/bridgetower-base': 'https://huggingface.co/BridgeTower/bridgetower-base/blob/main/config.json',
    'BridgeTower/bridgetower-base-itm-mlm': (
        'https://huggingface.co/BridgeTower/bridgetower-base-itm-mlm/blob/main/config.json'
    ),
}





class    lowerCamelCase__  (						__A):
     """simple docstring"""



     _A       =			"""bridgetower_vision_model"""







     def __init__(self				,			__a=7_68				,			__a=12				,			__a=3				,			__a=16				,			__a=2_88				,			__a=1				,			__a=1E-05				,			__a=False				,			__a=True				,			__a=False				,			**__a				,			):






           '''simple docstring'''



           super().__init__(**UpperCamelCase__    )
           lowerCamelCase								=		hidden_size
           lowerCamelCase								=		num_hidden_layers
           lowerCamelCase								=		num_channels
           lowerCamelCase								=		patch_size
           lowerCamelCase								=		image_size
           lowerCamelCase								=		initializer_factor
           lowerCamelCase								=		layer_norm_eps
           lowerCamelCase								=		stop_gradient
           lowerCamelCase								=		share_layernorm
           lowerCamelCase								=		remove_last_layer







     @classmethod
     def 						_a      (cls				,			__a				,			**__a    ):






           '''simple docstring'''



           lowerCamelCase								=		cls.get_config_dict(UpperCamelCase__				,			**UpperCamelCase__    )

           if config_dict.get("model_type"    ) == "bridgetower":
                 lowerCamelCase								=		config_dict['text_config']

           if "model_type" in config_dict and hasattr(cls				,			"model_type"    ) and config_dict["model_type"] != cls.model_type:
                 logger.warning(
                     F"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
                     F"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""    )

           return cls.from_dict(UpperCamelCase__				,			**UpperCamelCase__    )





class    lowerCamelCase__  (						__A):
     """simple docstring"""



     _A       =			"""bridgetower_text_model"""







     def __init__(self				,			__a=5_02_65				,			__a=7_68				,			__a=12				,			__a=12				,			__a=1				,			__a=30_72				,			__a="gelu"				,			__a=0.1				,			__a=0.1				,			__a=5_14				,			__a=1				,			__a=1E-05				,			__a=1				,			__a=0				,			__a=2				,			__a="absolute"				,			__a=True				,			**__a				,			):






           '''simple docstring'''



           super().__init__(**UpperCamelCase__    )

           lowerCamelCase								=		vocab_size
           lowerCamelCase								=		hidden_size
           lowerCamelCase								=		num_hidden_layers
           lowerCamelCase								=		num_attention_heads
           lowerCamelCase								=		hidden_act
           lowerCamelCase								=		initializer_factor
           lowerCamelCase								=		intermediate_size
           lowerCamelCase								=		hidden_dropout_prob
           lowerCamelCase								=		attention_probs_dropout_prob
           lowerCamelCase								=		max_position_embeddings
           lowerCamelCase								=		type_vocab_size
           lowerCamelCase								=		layer_norm_eps
           lowerCamelCase								=		position_embedding_type
           lowerCamelCase								=		use_cache
           lowerCamelCase								=		pad_token_id
           lowerCamelCase								=		bos_token_id
           lowerCamelCase								=		eos_token_id







     @classmethod
     def 						_a      (cls				,			__a				,			**__a    ):






           '''simple docstring'''



           lowerCamelCase								=		cls.get_config_dict(UpperCamelCase__				,			**UpperCamelCase__    )

           if config_dict.get("model_type"    ) == "bridgetower":
                 lowerCamelCase								=		config_dict['text_config']

           if "model_type" in config_dict and hasattr(cls				,			"model_type"    ) and config_dict["model_type"] != cls.model_type:
                 logger.warning(
                     F"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
                     F"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""    )

           return cls.from_dict(UpperCamelCase__				,			**UpperCamelCase__    )





class    lowerCamelCase__  (						__A):
     """simple docstring"""



     _A       =			"""bridgetower"""







     def __init__(self				,			__a=True				,			__a="gelu"				,			__a=7_68				,			__a=1				,			__a=1E-05				,			__a=False				,			__a="add"				,			__a=12				,			__a=6				,			__a=False				,			__a=False				,			__a=None				,			__a=None				,			**__a				,			):






           '''simple docstring'''



           lowerCamelCase								=		kwargs.pop("text_config_dict"				,			UpperCamelCase__    )
           lowerCamelCase								=		kwargs.pop("vision_config_dict"				,			UpperCamelCase__    )

           super().__init__(**UpperCamelCase__    )
           lowerCamelCase								=		share_cross_modal_transformer_layers
           lowerCamelCase								=		hidden_act
           lowerCamelCase								=		hidden_size
           lowerCamelCase								=		initializer_factor
           lowerCamelCase								=		layer_norm_eps
           lowerCamelCase								=		share_link_tower_layers
           lowerCamelCase								=		link_tower_type
           lowerCamelCase								=		num_attention_heads
           lowerCamelCase								=		num_hidden_layers
           lowerCamelCase								=		tie_word_embeddings
           lowerCamelCase								=		init_layernorm_from_vision_encoder

           if text_config is None:
                 lowerCamelCase								=		{}
                 logger.info("`text_config` is `None`. Initializing the `BridgeTowerTextConfig` with default values."    )

           if vision_config is None:
                 lowerCamelCase								=		{}
                 logger.info("`vision_config` is `None`. Initializing the `BridgeTowerVisionConfig` with default values."    )

           lowerCamelCase								=		BridgeTowerTextConfig(**UpperCamelCase__    )
           lowerCamelCase								=		BridgeTowerVisionConfig(**UpperCamelCase__    )







     @classmethod
     def 						_a      (cls				,			__a				,			__a				,			**__a    ):






           '''simple docstring'''



           return cls(text_config=text_config.to_dict()				,			vision_config=vision_config.to_dict()				,			**UpperCamelCase__    )







     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		copy.deepcopy(self.__dict__    )
           lowerCamelCase								=		self.text_config.to_dict()
           lowerCamelCase								=		self.vision_config.to_dict()
           lowerCamelCase								=		self.__class__.model_type
           return output




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_						:     Tuple	     =		'src/transformers'
a_						:     Any	     =		'docs/source/en/tasks'

def     __lowercase(	UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__ ):


      """simple docstring"""



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

      lowerCamelCase								=		start_index
      while not lines[end_index].startswith(UpperCAmelCase__ ):
            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_						:     Union[str, Any]	     =		direct_transformers_import(TRANSFORMERS_PATH)

a_						:     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`).
a_						:     Union[str, Any]	     =		{
    'summarization.md': ('nllb',),
    'translation.md': ('nllb',),
}

def     __lowercase(	UpperCAmelCase__ ):


      """simple docstring"""



      lowerCamelCase								=		TASK_GUIDE_TO_MODELS[task_guide]
      lowerCamelCase								=		SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(UpperCAmelCase__   ,   set() )
      lowerCamelCase								=		{
          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     __lowercase(	UpperCAmelCase__   ,   UpperCAmelCase__=False ):


      """simple docstring"""



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

      lowerCamelCase								=		get_model_list_for_task(UpperCAmelCase__ )

      if current_list != new_list:
            if overwrite:
                  with open(os.path.join(UpperCAmelCase__   ,   UpperCAmelCase__ )   ,   "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_						:     str	     =		argparse.ArgumentParser()
     parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
     a_						:     str	     =		parser.parse_args()

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