





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							)				->      int:







	'''simple docstring'''







	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.
_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							)				->      List[Any]:







	'''simple docstring'''







	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							)				->      Union[str, Any]:







	'''simple docstring'''







	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__":
			_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)










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 						=      {
    "camembert-base": "https://huggingface.co/camembert-base/resolve/main/config.json",
    "umberto-commoncrawl-cased-v1": (
        "https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json"
    ),
    "umberto-wikipedia-uncased-v1": (
        "https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json"
    ),
}






class 					__A    (    lowerCAmelCase ):

			'''simple docstring'''







			lowerCAmelCase_							     =						"""camembert"""





			def __init__(    self       ,  __lowerCAmelCase=3_0_5_2_2       ,  __lowerCAmelCase=7_6_8       ,  __lowerCAmelCase=1_2       ,  __lowerCAmelCase=1_2       ,  __lowerCAmelCase=3_0_7_2       ,  __lowerCAmelCase="gelu"       ,  __lowerCAmelCase=0.1       ,  __lowerCAmelCase=0.1       ,  __lowerCAmelCase=5_1_2       ,  __lowerCAmelCase=2       ,  __lowerCAmelCase=0.02       ,  __lowerCAmelCase=1E-12       ,  __lowerCAmelCase=1       ,  __lowerCAmelCase=0       ,  __lowerCAmelCase=2       ,  __lowerCAmelCase="absolute"       ,  __lowerCAmelCase=True       ,  __lowerCAmelCase=None       ,  **__lowerCAmelCase       ,  ):



				'''simple docstring'''







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

				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 					__A    (    lowerCAmelCase ):

			'''simple docstring'''







			@property
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				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),
				    ]							)



