





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							= 'src/transformers'
_a							= 'docs/source/en/tasks'





def lowerCAmelCase__(__snake_case			,__snake_case			,__snake_case				)							-> List[Any]:







	'''simple docstring'''







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

	lowerCamelCase__														=						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.
_a							= direct_transformers_import(TRANSFORMERS_PATH)

_a							= {
    '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							= {
    'summarization.md': ('nllb',),
    'translation.md': ('nllb',),
}





def lowerCAmelCase__(__snake_case				)							-> Dict:







	'''simple docstring'''







	lowerCamelCase__														=						TASK_GUIDE_TO_MODELS[task_guide]
	lowerCamelCase__														=						SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(A__			,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 lowerCAmelCase__(__snake_case			,__snake_case=False				)							-> Union[str, Any]:







	'''simple docstring'''







	lowerCamelCase__														=						_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__														=						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__":
			_a							= argparse.ArgumentParser()
			parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
			_a							= parser.parse_args()

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







import copy
import os
from typing import Union

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


lowerCAmelCase__			:      Tuple								=logging.get_logger(__name__)

lowerCAmelCase__			:      Optional[int]								={
    'microsoft/git-base': 'https://huggingface.co/microsoft/git-base/resolve/main/config.json',
}







class 	__lowercase    (__SCREAMING_SNAKE_CASE  ):





							"""simple docstring"""







							_UpperCAmelCase      =						"""git_vision_model"""






							def __init__(      self			,						lowerCAmelCase__=7_6_8			,						lowerCAmelCase__=3_0_7_2			,						lowerCAmelCase__=1_2			,						lowerCAmelCase__=1_2			,						lowerCAmelCase__=3			,						lowerCAmelCase__=2_2_4			,						lowerCAmelCase__=1_6			,						lowerCAmelCase__="quick_gelu"			,						lowerCAmelCase__=1E-5			,						lowerCAmelCase__=0.0			,						lowerCAmelCase__=0.02			,						**lowerCAmelCase__			,						):





										"""simple docstring"""




										super().__init__(**lowerCAmelCase__       )

										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					hidden_size
										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					intermediate_size
										SCREAMING_SNAKE_CASE_		:     Any					=					num_hidden_layers
										SCREAMING_SNAKE_CASE_		:     Dict					=					num_attention_heads
										SCREAMING_SNAKE_CASE_		:     Tuple					=					num_channels
										SCREAMING_SNAKE_CASE_		:     Tuple					=					patch_size
										SCREAMING_SNAKE_CASE_		:     Any					=					image_size
										SCREAMING_SNAKE_CASE_		:     Dict					=					initializer_range
										SCREAMING_SNAKE_CASE_		:     List[str]					=					attention_dropout
										SCREAMING_SNAKE_CASE_		:     Any					=					layer_norm_eps
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					hidden_act






							@classmethod
							def 					UpperCamelCase__     (      cls			,						lowerCAmelCase__			,						**lowerCAmelCase__       ):





										"""simple docstring"""




										cls._set_token_in_kwargs(lowerCAmelCase__       )

										SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		:     List[str]					=					cls.get_config_dict(lowerCAmelCase__			,						**lowerCAmelCase__       )

										# get the vision config dict if we are loading from GITConfig
										if config_dict.get('model_type'       ) == "git":
													SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					config_dict['vision_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(lowerCAmelCase__			,						**lowerCAmelCase__       )







class 	__lowercase    (__SCREAMING_SNAKE_CASE  ):





							"""simple docstring"""







							_UpperCAmelCase      =						"""git"""






							def __init__(      self			,						lowerCAmelCase__=None			,						lowerCAmelCase__=3_0_5_2_2			,						lowerCAmelCase__=7_6_8			,						lowerCAmelCase__=6			,						lowerCAmelCase__=1_2			,						lowerCAmelCase__=3_0_7_2			,						lowerCAmelCase__="gelu"			,						lowerCAmelCase__=0.1			,						lowerCAmelCase__=0.1			,						lowerCAmelCase__=1_0_2_4			,						lowerCAmelCase__=0.02			,						lowerCAmelCase__=1E-12			,						lowerCAmelCase__=0			,						lowerCAmelCase__="absolute"			,						lowerCAmelCase__=True			,						lowerCAmelCase__=False			,						lowerCAmelCase__=1_0_1			,						lowerCAmelCase__=1_0_2			,						lowerCAmelCase__=None			,						**lowerCAmelCase__			,						):





										"""simple docstring"""




										super().__init__(bos_token_id=lowerCAmelCase__			,						eos_token_id=lowerCAmelCase__			,						pad_token_id=lowerCAmelCase__			,						**lowerCAmelCase__       )

										if vision_config is None:
													SCREAMING_SNAKE_CASE_		:     Dict					=					{}
													logger.info('vision_config is None. initializing the GitVisionConfig with default values.'       )

										SCREAMING_SNAKE_CASE_		:     str					=					GitVisionConfig(**lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     Dict					=					vocab_size
										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					hidden_size
										SCREAMING_SNAKE_CASE_		:     Tuple					=					num_hidden_layers
										SCREAMING_SNAKE_CASE_		:     Any					=					num_attention_heads
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					hidden_act
										SCREAMING_SNAKE_CASE_		:     Any					=					intermediate_size
										SCREAMING_SNAKE_CASE_		:     Tuple					=					hidden_dropout_prob
										SCREAMING_SNAKE_CASE_		:     str					=					attention_probs_dropout_prob
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					max_position_embeddings
										SCREAMING_SNAKE_CASE_		:     int					=					initializer_range
										SCREAMING_SNAKE_CASE_		:     int					=					layer_norm_eps
										SCREAMING_SNAKE_CASE_		:     List[str]					=					position_embedding_type
										SCREAMING_SNAKE_CASE_		:     List[str]					=					use_cache
										SCREAMING_SNAKE_CASE_		:     Any					=					tie_word_embeddings
										SCREAMING_SNAKE_CASE_		:     Dict					=					num_image_with_embedding

										SCREAMING_SNAKE_CASE_		:     List[str]					=					bos_token_id
										SCREAMING_SNAKE_CASE_		:     str					=					eos_token_id






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					copy.deepcopy(self.__dict__       )
										SCREAMING_SNAKE_CASE_		:     str					=					self.vision_config.to_dict()
										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					self.__class__.model_type
										return output





