
from collections import OrderedDict
from typing import Mapping

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


__a         =   logging.get_logger(__name__)

__a         =   {
    'xlm-mlm-en-2048': 'https://huggingface.co/xlm-mlm-en-2048/resolve/main/config.json',
    'xlm-mlm-ende-1024': 'https://huggingface.co/xlm-mlm-ende-1024/resolve/main/config.json',
    'xlm-mlm-enfr-1024': 'https://huggingface.co/xlm-mlm-enfr-1024/resolve/main/config.json',
    'xlm-mlm-enro-1024': 'https://huggingface.co/xlm-mlm-enro-1024/resolve/main/config.json',
    'xlm-mlm-tlm-xnli15-1024': 'https://huggingface.co/xlm-mlm-tlm-xnli15-1024/resolve/main/config.json',
    'xlm-mlm-xnli15-1024': 'https://huggingface.co/xlm-mlm-xnli15-1024/resolve/main/config.json',
    'xlm-clm-enfr-1024': 'https://huggingface.co/xlm-clm-enfr-1024/resolve/main/config.json',
    'xlm-clm-ende-1024': 'https://huggingface.co/xlm-clm-ende-1024/resolve/main/config.json',
    'xlm-mlm-17-1280': 'https://huggingface.co/xlm-mlm-17-1280/resolve/main/config.json',
    'xlm-mlm-100-1280': 'https://huggingface.co/xlm-mlm-100-1280/resolve/main/config.json',
}
class 					lowercase__(							UpperCAmelCase				):



	"""simple docstring"""




	a     :List[Any]       	=      'xlm'
	a     :Dict       	=      {
	    'hidden_size': 'emb_dim',
	    'num_attention_heads': 'n_heads',
	    'num_hidden_layers': 'n_layers',
	    'n_words': 'vocab_size',  # For backward compatibility
	}
	def __init__(			self    :     List[Any]			,				SCREAMING_SNAKE_CASE_    :     Dict=3_0_1_4_5			,				SCREAMING_SNAKE_CASE_    :     Dict=2_0_4_8			,				SCREAMING_SNAKE_CASE_    :     Optional[int]=1_2			,				SCREAMING_SNAKE_CASE_    :     Optional[int]=1_6			,				SCREAMING_SNAKE_CASE_    :     Dict=0.1			,				SCREAMING_SNAKE_CASE_    :     str=0.1			,				SCREAMING_SNAKE_CASE_    :     Any=True			,				SCREAMING_SNAKE_CASE_    :     Any=False			,				SCREAMING_SNAKE_CASE_    :     str=False			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]=False			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]=1			,				SCREAMING_SNAKE_CASE_    :     Dict=True			,				SCREAMING_SNAKE_CASE_    :     Dict=5_1_2			,				SCREAMING_SNAKE_CASE_    :     Optional[int]=2_0_4_8**-0.5			,				SCREAMING_SNAKE_CASE_    :     Dict=1e-12			,				SCREAMING_SNAKE_CASE_    :     Any=0.02			,				SCREAMING_SNAKE_CASE_    :     str=0			,				SCREAMING_SNAKE_CASE_    :     List[Any]=1			,				SCREAMING_SNAKE_CASE_    :     List[Any]=2			,				SCREAMING_SNAKE_CASE_    :     int=3			,				SCREAMING_SNAKE_CASE_    :     Dict=5			,				SCREAMING_SNAKE_CASE_    :     int=True			,				SCREAMING_SNAKE_CASE_    :     List[Any]="first"			,				SCREAMING_SNAKE_CASE_    :     Any=True			,				SCREAMING_SNAKE_CASE_    :     List[Any]=None			,				SCREAMING_SNAKE_CASE_    :     str=True			,				SCREAMING_SNAKE_CASE_    :     Union[str, Any]=0.1			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]=5			,				SCREAMING_SNAKE_CASE_    :     List[str]=5			,				SCREAMING_SNAKE_CASE_    :     Any=0			,				SCREAMING_SNAKE_CASE_    :     str=0			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]=2			,				SCREAMING_SNAKE_CASE_    :     Tuple=0			,				**SCREAMING_SNAKE_CASE_    :     Dict			,				)			->	List[Any]:
					lowercase_	    =   vocab_size
					lowercase_	    =   emb_dim
					lowercase_	    =   n_layers
					lowercase_	    =   n_heads
					lowercase_	    =   dropout
					lowercase_	    =   attention_dropout
					lowercase_	    =   gelu_activation
					lowercase_	    =   sinusoidal_embeddings
					lowercase_	    =   causal
					lowercase_	    =   asm
					lowercase_	    =   n_langs
					lowercase_	    =   use_lang_emb
					lowercase_	    =   layer_norm_eps
					lowercase_	    =   bos_index
					lowercase_	    =   eos_index
					lowercase_	    =   pad_index
					lowercase_	    =   unk_index
					lowercase_	    =   mask_index
					lowercase_	    =   is_encoder
					lowercase_	    =   max_position_embeddings
					lowercase_	    =   embed_init_std
					lowercase_	    =   init_std
					lowercase_	    =   summary_type
					lowercase_	    =   summary_use_proj
					lowercase_	    =   summary_activation
					lowercase_	    =   summary_proj_to_labels
					lowercase_	    =   summary_first_dropout
					lowercase_	    =   start_n_top
					lowercase_	    =   end_n_top
					lowercase_	    =   mask_token_id
					lowercase_	    =   lang_id

					if "n_words" in kwargs:
									lowercase_	    =   kwargs['''n_words''']

					super().__init__(pad_token_id=SCREAMING_SNAKE_CASE_			,				bos_token_id=SCREAMING_SNAKE_CASE_			,				**SCREAMING_SNAKE_CASE_			)

class 					lowercase__(							UpperCAmelCase				):



	"""simple docstring"""




	@property
	def 		_lowercase    (			self    :     int			)			->	Mapping[str, Mapping[int, str]]:
					if self.task == "multiple-choice":
									lowercase_	    =   {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
					else:
									lowercase_	    =   {0: '''batch''', 1: '''sequence'''}
					return OrderedDict(
					    [
					        ('''input_ids''', dynamic_axis),
					        ('''attention_mask''', dynamic_axis),
					        ('''token_type_ids''', dynamic_axis),
					    ]			)






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 							a		(				snake_case__:   Optional[int]		,  snake_case__:   Union[str, Any]		,  snake_case__:   Any   ):


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

				lowercase_	    =   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.
__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 							a		(				snake_case__:   Tuple   ):


				'''simple docstring'''
				lowercase_	    =   TASK_GUIDE_TO_MODELS[task_guide]
				lowercase_	    =   SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(snake_case__		,  set()   )
				lowercase_	    =   {
				    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 							a		(				snake_case__:   List[Any]		,  snake_case__:   Optional[int]=False   ):


				'''simple docstring'''
				lowercase_    ,      lowercase_    ,      lowercase_    ,      lowercase_	    =   _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-->'''		,  )

				lowercase_	    =   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__":
						__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)




