


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
__snake_case     :   Optional[Any]            =   "src/transformers"
__snake_case     :   Optional[int]            =   "docs/source/en/tasks"






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		Optional[Any]       ,  SCREAMING_SNAKE_CASE_   :		List[str]       ,  SCREAMING_SNAKE_CASE_   :		Optional[Any]	):





			"""simple docstring"""




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

			UpperCamelCase         =					start_index
			while not lines[end_index].startswith(SCREAMING_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.
__snake_case     :   List[str]            =   direct_transformers_import(TRANSFORMERS_PATH)

__snake_case     :   Optional[int]            =   {
    "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`).
__snake_case     :   Tuple            =   {
    "summarization.md": ("nllb",),
    "translation.md": ("nllb",),
}






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		Union[str, Any]	):





			"""simple docstring"""




			UpperCamelCase         =					TASK_GUIDE_TO_MODELS[task_guide]
			UpperCamelCase         =					SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(SCREAMING_SNAKE_CASE_       ,  set()	)
			UpperCamelCase         =					{
			    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    (		SCREAMING_SNAKE_CASE_   :		Optional[Any]       ,  SCREAMING_SNAKE_CASE_   :		Dict=False	):





			"""simple docstring"""




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

			UpperCamelCase         =					get_model_list_for_task(SCREAMING_SNAKE_CASE_	)

			if current_list != new_list:
						if overwrite:
									with open(os.path.join(SCREAMING_SNAKE_CASE_       ,  SCREAMING_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__":
			__snake_case     :   Any            =   argparse.ArgumentParser()
			parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
			__snake_case     :   List[str]            =   parser.parse_args()

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








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


__snake_case            =   logging.get_logger(__name__)

__snake_case            =   {
    "microsoft/markuplm-base": "https://huggingface.co/microsoft/markuplm-base/resolve/main/config.json",
    "microsoft/markuplm-large": "https://huggingface.co/microsoft/markuplm-large/resolve/main/config.json",
}




class    UpperCAmelCase					(       __snake_case    ):
			lowercase						= """markuplm"""




			def __init__(						self							:		Optional[Any]       ,       __magic_name__							:		List[Any]=3_0_5_2_2       ,       __magic_name__							:		int=7_6_8       ,       __magic_name__							:		List[Any]=1_2       ,       __magic_name__							:		List[Any]=1_2       ,       __magic_name__							:		str=3_0_7_2       ,       __magic_name__							:		Optional[int]="gelu"       ,       __magic_name__							:		Dict=0.1       ,       __magic_name__							:		Optional[int]=0.1       ,       __magic_name__							:		Any=5_1_2       ,       __magic_name__							:		List[str]=2       ,       __magic_name__							:		Dict=0.02       ,       __magic_name__							:		List[str]=1e-12       ,       __magic_name__							:		str=0       ,       __magic_name__							:		List[str]=0       ,       __magic_name__							:		Optional[Any]=2       ,       __magic_name__							:		Dict=2_5_6       ,       __magic_name__							:		Tuple=1_0_2_4       ,       __magic_name__							:		Any=2_1_6       ,       __magic_name__							:		str=1_0_0_1       ,       __magic_name__							:		Dict=3_2       ,       __magic_name__							:		Optional[int]=5_0       ,       __magic_name__							:		List[Any]="absolute"       ,       __magic_name__							:		Any=True       ,       __magic_name__							:		Optional[Any]=None       ,       **__magic_name__							:		List[str]       ,       ):







						"""simple docstring"""







						super().__init__(
						    pad_token_id=__magic_name__       ,       bos_token_id=__magic_name__       ,       eos_token_id=__magic_name__       ,       **__magic_name__       ,       )
						UpperCamelCase         =					vocab_size
						UpperCamelCase         =					hidden_size
						UpperCamelCase         =					num_hidden_layers
						UpperCamelCase         =					num_attention_heads
						UpperCamelCase         =					hidden_act
						UpperCamelCase         =					intermediate_size
						UpperCamelCase         =					hidden_dropout_prob
						UpperCamelCase         =					attention_probs_dropout_prob
						UpperCamelCase         =					max_position_embeddings
						UpperCamelCase         =					type_vocab_size
						UpperCamelCase         =					initializer_range
						UpperCamelCase         =					layer_norm_eps
						UpperCamelCase         =					position_embedding_type
						UpperCamelCase         =					use_cache
						UpperCamelCase         =					classifier_dropout
						# additional properties
						UpperCamelCase         =					max_depth
						UpperCamelCase         =					max_xpath_tag_unit_embeddings
						UpperCamelCase         =					max_xpath_subs_unit_embeddings
						UpperCamelCase         =					tag_pad_id
						UpperCamelCase         =					subs_pad_id
						UpperCamelCase         =					xpath_unit_hidden_size




