






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





def 	lowerCAmelCase_		(lowercase__						:       Optional[Any]     ,					lowercase__						:       List[str]     ,					lowercase__						:       List[str]       )		->    int:


				'''simple docstring'''




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

				lowerCAmelCase__      			=						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.
_UpperCAmelCase			:		List[str]   				=    direct_transformers_import(TRANSFORMERS_PATH)

_UpperCAmelCase			:		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`).
_UpperCAmelCase			:		str   				=    {
    "summarization.md": ("nllb",),
    "translation.md": ("nllb",),
}





def 	lowerCAmelCase_		(lowercase__						:       int       )		->    int:


				'''simple docstring'''




				lowerCAmelCase__      			=						TASK_GUIDE_TO_MODELS[task_guide]
				lowerCAmelCase__      			=						SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(lowercase__     ,					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_		(lowercase__						:       int     ,					lowercase__						:       Dict=False       )		->    Tuple:


				'''simple docstring'''




				lowerCAmelCase__			,		lowerCAmelCase__			,		lowerCAmelCase__			,		lowerCAmelCase__      			=						_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-->'''     ,					)

				lowerCAmelCase__      			=						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__":
			_UpperCAmelCase			:		Optional[int]   				=    argparse.ArgumentParser()
			parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
			_UpperCAmelCase			:		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)













import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class  lowerCAmelCase_       (				snake_case__					):
							UpperCamelCase_		:Tuple		      =    ['image_processor', 'tokenizer']
							UpperCamelCase_		:Tuple		      =    'ViTImageProcessor'
							UpperCamelCase_		:Dict		      =    ('CLIPTokenizer', 'CLIPTokenizerFast')




							def __init__(   self     :   Union[str, Any] ,							SCREAMING_SNAKE_CASE_     :   Dict=None ,							SCREAMING_SNAKE_CASE_     :   Tuple=None ,							**SCREAMING_SNAKE_CASE_     :   Union[str, Any]	):
											lowerCAmelCase__      			=						None
											if "feature_extractor" in kwargs:
															warnings.warn(
															    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
															    ''' instead.''' ,							SCREAMING_SNAKE_CASE_ ,							)
															lowerCAmelCase__      			=						kwargs.pop('''feature_extractor'''	)

											lowerCAmelCase__      			=						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__(SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_	)




							def __call__(   self     :   Dict ,							SCREAMING_SNAKE_CASE_     :   Any=None ,							SCREAMING_SNAKE_CASE_     :   Tuple=None ,							SCREAMING_SNAKE_CASE_     :   Dict=None ,							SCREAMING_SNAKE_CASE_     :   Optional[int]=None ,							**SCREAMING_SNAKE_CASE_     :   Optional[int]	):
											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:
															lowerCAmelCase__      			=						self.tokenizer(SCREAMING_SNAKE_CASE_ ,							return_tensors=SCREAMING_SNAKE_CASE_ ,							**SCREAMING_SNAKE_CASE_	)

											if visual_prompt is not None:
															lowerCAmelCase__      			=						self.image_processor(SCREAMING_SNAKE_CASE_ ,							return_tensors=SCREAMING_SNAKE_CASE_ ,							**SCREAMING_SNAKE_CASE_	)

											if images is not None:
															lowerCAmelCase__      			=						self.image_processor(SCREAMING_SNAKE_CASE_ ,							return_tensors=SCREAMING_SNAKE_CASE_ ,							**SCREAMING_SNAKE_CASE_	)

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




							def 						__snake_case					(   self     :   Union[str, Any] ,							*SCREAMING_SNAKE_CASE_     :   Union[str, Any] ,							**SCREAMING_SNAKE_CASE_     :   str	):
											return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE_ ,							**SCREAMING_SNAKE_CASE_	)




							def 						__snake_case					(   self     :   Optional[int] ,							*SCREAMING_SNAKE_CASE_     :   Union[str, Any] ,							**SCREAMING_SNAKE_CASE_     :   Tuple	):
											return self.tokenizer.decode(*SCREAMING_SNAKE_CASE_ ,							**SCREAMING_SNAKE_CASE_	)




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




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





