



import json
from typing import List, Optional, Tuple

from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoFormerTokenizer
from .tokenization_utils import JiebaPreTokenizer


UpperCAmelCase__       =	logging.get_logger(__name__)

UpperCAmelCase__       =	{"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

UpperCAmelCase__       =	{
    "vocab_file": {
        "junnyu/roformer_chinese_small": "https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/vocab.txt",
        "junnyu/roformer_chinese_base": "https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/vocab.txt",
        "junnyu/roformer_chinese_char_small": (
            "https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/vocab.txt"
        ),
        "junnyu/roformer_chinese_char_base": (
            "https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/vocab.txt"
        ),
        "junnyu/roformer_small_discriminator": (
            "https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/vocab.txt"
        ),
        "junnyu/roformer_small_generator": (
            "https://huggingface.co/junnyu/roformer_small_generator/resolve/main/vocab.txt"
        ),
    }
}

UpperCAmelCase__       =	{
    "junnyu/roformer_chinese_small": 1536,
    "junnyu/roformer_chinese_base": 1536,
    "junnyu/roformer_chinese_char_small": 512,
    "junnyu/roformer_chinese_char_base": 512,
    "junnyu/roformer_small_discriminator": 128,
    "junnyu/roformer_small_generator": 128,
}


UpperCAmelCase__       =	{
    "junnyu/roformer_chinese_small": {"do_lower_case": True},
    "junnyu/roformer_chinese_base": {"do_lower_case": True},
    "junnyu/roformer_chinese_char_small": {"do_lower_case": True},
    "junnyu/roformer_chinese_char_base": {"do_lower_case": True},
    "junnyu/roformer_small_discriminator": {"do_lower_case": True},
    "junnyu/roformer_small_generator": {"do_lower_case": True},
}







class       __lowerCAmelCase   (      A						):
			UpperCamelCase        =					VOCAB_FILES_NAMES
			UpperCamelCase        =					PRETRAINED_VOCAB_FILES_MAP
			UpperCamelCase        =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			UpperCamelCase        =					PRETRAINED_INIT_CONFIGURATION
			UpperCamelCase        =					RoFormerTokenizer



			def __init__(						self       :  Dict			,		A       :  Any=None			,		A       :  Optional[Any]=None			,		A       :  Union[str, Any]=True			,		A       :  List[str]="[UNK]"			,		A       :  List[str]="[SEP]"			,		A       :  Union[str, Any]="[PAD]"			,		A       :  Any="[CLS]"			,		A       :  str="[MASK]"			,		A       :  Optional[int]=True			,		A       :  str=None			,		**A       :  Dict			,		)       ->   Union[str, Any]:



						"""simple docstring"""






						super().__init__(
						    A			,		tokenizer_file=A			,		do_lower_case=A			,		unk_token=A			,		sep_token=A			,		pad_token=A			,		cls_token=A			,		mask_token=A			,		tokenize_chinese_chars=A			,		strip_accents=A			,		**A			,		)

						_UpperCAmelCase			=       json.loads(self.backend_tokenizer.normalizer.__getstate__())
						if (
						    pre_tok_state.get('lowercase'			,		A) != do_lower_case
						    or pre_tok_state.get('strip_accents'			,		A) != strip_accents
						):
									_UpperCAmelCase			=       getattr(A			,		pre_tok_state.pop('type'))
									_UpperCAmelCase			=       do_lower_case
									_UpperCAmelCase			=       strip_accents
									_UpperCAmelCase			=       pre_tok_class(**A)

						_UpperCAmelCase			=       do_lower_case



			def __getstate__(						self       :  Optional[Any])       ->   Optional[Any]:



						"""simple docstring"""






						_UpperCAmelCase			=       self.__dict__.copy()
						_UpperCAmelCase			=       BertPreTokenizer()
						return state



			def __setstate__(						self       :  List[Any]			,		A       :  Optional[Any])       ->   List[str]:



						"""simple docstring"""






						_UpperCAmelCase			=       d
						_UpperCAmelCase			=       self.__dict__['_tokenizer'].get_vocab()
						_UpperCAmelCase			=       PreTokenizer.custom(JiebaPreTokenizer(A))



			def    _lowerCamelCase	(						self       :  List[Any]			,		A       :  Optional[int]			,		A       :  List[Any]=None)       ->   Optional[Any]:



						"""simple docstring"""






						_UpperCAmelCase			=       [self.cls_token_id] + token_ids_a + [self.sep_token_id]

						if token_ids_a:
									output += token_ids_a + [self.sep_token_id]

						return output



			def    _lowerCamelCase	(						self       :  List[str]			,		A       :  List[int]			,		A       :  Optional[List[int]] = None)       ->   List[int]:



						"""simple docstring"""






						_UpperCAmelCase			=       [self.sep_token_id]
						_UpperCAmelCase			=       [self.cls_token_id]
						if token_ids_a is None:
									return len(cls + token_ids_a + sep) * [0]
						return len(cls + token_ids_a + sep) * [0] + len(token_ids_a + sep) * [1]



			def    _lowerCamelCase	(						self       :  Union[str, Any]			,		A       :  str			,		A       :  Optional[str] = None)       ->   Tuple[str]:



						"""simple docstring"""






						_UpperCAmelCase			=       self._tokenizer.model.save(A			,		name=A)
						return tuple(A)



			def    _lowerCamelCase	(						self       :  List[Any]			,		A       :  Tuple			,		A       :  str=None			,		A       :  Union[str, Any]=None			,		A       :  Union[str, Any]=False			,		**A       :  Tuple			,		)       ->   str:



						"""simple docstring"""






						_UpperCAmelCase			=       BertPreTokenizer()
						return super().save_pretrained(A			,		A			,		A			,		A			,		**A)





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__       =	"src/transformers"
UpperCAmelCase__       =	"docs/source/en/tasks"





def 							A     (					_UpperCAmelCase    :       List[Any]							,       _UpperCAmelCase    :       Optional[int]							,       _UpperCAmelCase    :       Any   )    ->			int:







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

			_UpperCAmelCase			=       start_index
			while not lines[end_index].startswith(_UpperCAmelCase   ):
						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__       =	direct_transformers_import(TRANSFORMERS_PATH)

UpperCAmelCase__       =	{
    "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__       =	{
    "summarization.md": ("nllb",),
    "translation.md": ("nllb",),
}





def 							A     (					_UpperCAmelCase    :       List[Any]   )    ->			int:







			'''simple docstring'''
			_UpperCAmelCase			=       TASK_GUIDE_TO_MODELS[task_guide]
			_UpperCAmelCase			=       SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(_UpperCAmelCase							,       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 							A     (					_UpperCAmelCase    :       Tuple							,       _UpperCAmelCase    :       Dict=False   )    ->			List[Any]:







			'''simple docstring'''
			_UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase			=       _find_text_in_file(
			    filename=os.path.join(_UpperCAmelCase							,       _UpperCAmelCase   )							,       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(_UpperCAmelCase   )

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

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