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
_lowercase:	List[Any]       			=		'''src/transformers'''
_lowercase:	Union[str, Any]       			=		'''docs/source/en/tasks'''






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

							_lowerCAmelCase							=							start_index
							while not lines[end_index].startswith(snake_case  ):
														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.
_lowercase:	List[str]       			=		direct_transformers_import(TRANSFORMERS_PATH)

_lowercase:	str       			=		{
    '''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`).
_lowercase:	List[str]       			=		{
    '''summarization.md''': ('''nllb''',),
    '''translation.md''': ('''nllb''',),
}






def _lowerCamelCase  (		snake_case  ):
							_lowerCAmelCase							=							TASK_GUIDE_TO_MODELS[task_guide]
							_lowerCAmelCase							=							SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(snake_case     , 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  ):
							_lowerCAmelCase			,		_lowerCAmelCase			,		_lowerCAmelCase			,		_lowerCAmelCase							=							_find_text_in_file(
							    filename=os.path.join(snake_case     , snake_case  )     , 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(snake_case  )

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

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







from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_lowercase:	Optional[Any]       			=		logging.get_logger(__name__)

_lowercase:	Tuple       			=		{
    '''andreasmadsen/efficient_mlm_m0.40''': (
        '''https://huggingface.co/andreasmadsen/efficient_mlm_m0.40/resolve/main/config.json'''
    ),
}



class        lowerCamelCase__				(   UpperCAmelCase				):
			UpperCamelCase__    							="roberta-prelayernorm"





			def __init__(       self	:	int  ,  lowercase__	:	int=5_02_65  ,  lowercase__	:	str=7_68  ,  lowercase__	:	List[Any]=12  ,  lowercase__	:	Any=12  ,  lowercase__	:	Optional[Any]=30_72  ,  lowercase__	:	List[Any]="gelu"  ,  lowercase__	:	str=0.1  ,  lowercase__	:	List[Any]=0.1  ,  lowercase__	:	Tuple=5_12  ,  lowercase__	:	Optional[int]=2  ,  lowercase__	:	Dict=0.0_2  ,  lowercase__	:	Tuple=1e-12  ,  lowercase__	:	Optional[int]=1  ,  lowercase__	:	Optional[int]=0  ,  lowercase__	:	Union[str, Any]=2  ,  lowercase__	:	Any="absolute"  ,  lowercase__	:	Any=True  ,  lowercase__	:	Any=None  ,  **lowercase__	:	Optional[int]  ,  ):
										super().__init__(pad_token_id=lowercase__  ,  bos_token_id=lowercase__  ,  eos_token_id=lowercase__  ,  **lowercase__						)

										_lowerCAmelCase							=							vocab_size
										_lowerCAmelCase							=							hidden_size
										_lowerCAmelCase							=							num_hidden_layers
										_lowerCAmelCase							=							num_attention_heads
										_lowerCAmelCase							=							hidden_act
										_lowerCAmelCase							=							intermediate_size
										_lowerCAmelCase							=							hidden_dropout_prob
										_lowerCAmelCase							=							attention_probs_dropout_prob
										_lowerCAmelCase							=							max_position_embeddings
										_lowerCAmelCase							=							type_vocab_size
										_lowerCAmelCase							=							initializer_range
										_lowerCAmelCase							=							layer_norm_eps
										_lowerCAmelCase							=							position_embedding_type
										_lowerCAmelCase							=							use_cache
										_lowerCAmelCase							=							classifier_dropout







class        lowerCamelCase__				(   UpperCAmelCase				):





			@property
			def   SCREAMING_SNAKE_CASE__     (       self	:	List[str]						):
										if self.task == "multiple-choice":
																	_lowerCAmelCase							=							{0: 'batch', 1: 'choice', 2: 'sequence'}
										else:
																	_lowerCAmelCase							=							{0: 'batch', 1: 'sequence'}
										return OrderedDict(
										    [
										        ('input_ids', dynamic_axis),
										        ('attention_mask', dynamic_axis),
										    ]						)






