






import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer



def 					_lowerCamelCase(			lowerCAmelCase__				:		str      ):





				'''simple docstring'''







				if (
				    (cp >= 0X4e_00 and cp <= 0X9f_ff)
				    or (cp >= 0X34_00 and cp <= 0X4d_bf)  #
				    or (cp >= 0X2_00_00 and cp <= 0X2_a6_df)  #
				    or (cp >= 0X2_a7_00 and cp <= 0X2_b7_3f)  #
				    or (cp >= 0X2_b7_40 and cp <= 0X2_b8_1f)  #
				    or (cp >= 0X2_b8_20 and cp <= 0X2_ce_af)  #
				    or (cp >= 0Xf9_00 and cp <= 0Xfa_ff)
				    or (cp >= 0X2_f8_00 and cp <= 0X2_fa_1f)  #
				):  #
								return True

				return False



def 					_lowerCamelCase(			lowerCAmelCase__				:		str      ):





				'''simple docstring'''







				for char in word:
								SCREAMING_SNAKE_CASE_   :	Tuple													=   ord(lowerCAmelCase__      )
								if not _is_chinese_char(lowerCAmelCase__      ):
												return 0
				return 1



def 					_lowerCamelCase(			lowerCAmelCase__				:		List[str]      ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	int													=   set()

				for token in tokens:
								SCREAMING_SNAKE_CASE_   :	List[Any]													=   len(lowerCAmelCase__      ) > 1 and is_chinese(lowerCAmelCase__      )
								if chinese_word:
												word_set.add(lowerCAmelCase__      )
				SCREAMING_SNAKE_CASE_   :	List[Any]													=   list(lowerCAmelCase__      )
				return word_list



def 					_lowerCamelCase(			lowerCAmelCase__				:		List[str]    ,    lowerCAmelCase__				:		set()      ):





				'''simple docstring'''







				if not chinese_word_set:
								return bert_tokens
				SCREAMING_SNAKE_CASE_   :	Any													=   max([len(lowerCAmelCase__      ) for w in chinese_word_set]      )

				SCREAMING_SNAKE_CASE_   :	List[str]													=   bert_tokens
				SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_   :	int													=   0, len(lowerCAmelCase__      )
				while start < end:
								SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   True
								if is_chinese(bert_word[start]      ):
												SCREAMING_SNAKE_CASE_   :	int													=   min(end - start    ,    lowerCAmelCase__      )
												for i in range(lowerCAmelCase__    ,    1    ,    -1      ):
																SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   ''.join(bert_word[start : start + i]      )
																if whole_word in chinese_word_set:
																				for j in range(start + 1    ,    start + i      ):
																								SCREAMING_SNAKE_CASE_   :	int													=   '##' + bert_word[j]
																				SCREAMING_SNAKE_CASE_   :	Tuple													=   start + i
																				SCREAMING_SNAKE_CASE_   :	Tuple													=   False
																				break
								if single_word:
												start += 1
				return bert_word



def 					_lowerCamelCase(			lowerCAmelCase__				:		List[str]    ,    lowerCAmelCase__				:		LTP    ,    lowerCAmelCase__				:		BertTokenizer      ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   []

				for i in range(0    ,    len(lowerCAmelCase__      )    ,    100      ):
								SCREAMING_SNAKE_CASE_   :	Optional[int]													=   ltp_tokenizer.pipeline(lines[i : i + 100]    ,    tasks=['cws']      ).cws
								SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   [get_chinese_word(lowerCAmelCase__      ) for r in res]
								ltp_res.extend(lowerCAmelCase__      )
				assert len(lowerCAmelCase__      ) == len(lowerCAmelCase__      )

				SCREAMING_SNAKE_CASE_   :	Any													=   []
				for i in range(0    ,    len(lowerCAmelCase__      )    ,    100      ):
								SCREAMING_SNAKE_CASE_   :	List[str]													=   bert_tokenizer(lines[i : i + 100]    ,    add_special_tokens=lowerCAmelCase__    ,    truncation=lowerCAmelCase__    ,    max_length=512      )
								bert_res.extend(res['input_ids']      )
				assert len(lowerCAmelCase__      ) == len(lowerCAmelCase__      )

				SCREAMING_SNAKE_CASE_   :	str													=   []
				for input_ids, chinese_word in zip(lowerCAmelCase__    ,    lowerCAmelCase__      ):
								SCREAMING_SNAKE_CASE_   :	int													=   []
								for id in input_ids:
												SCREAMING_SNAKE_CASE_   :	List[str]													=   bert_tokenizer._convert_id_to_token(lowerCAmelCase__      )
												input_tokens.append(lowerCAmelCase__      )
								SCREAMING_SNAKE_CASE_   :	Dict													=   add_sub_symbol(lowerCAmelCase__    ,    lowerCAmelCase__      )
								SCREAMING_SNAKE_CASE_   :	Any													=   []
								# We only save pos of chinese subwords start with ##, which mean is part of a whole word.
								for i, token in enumerate(lowerCAmelCase__      ):
												if token[:2] == "##":
																SCREAMING_SNAKE_CASE_   :	int													=   token[2:]
																# save chinese tokens' pos
																if len(lowerCAmelCase__      ) == 1 and _is_chinese_char(ord(lowerCAmelCase__      )      ):
																				ref_id.append(lowerCAmelCase__      )
								ref_ids.append(lowerCAmelCase__      )

				assert len(lowerCAmelCase__      ) == len(lowerCAmelCase__      )

				return ref_ids



def 					_lowerCamelCase(			lowerCAmelCase__				:		Optional[Any]      ):





				'''simple docstring'''







				with open(args.file_name    ,    'r'    ,    encoding='utf-8'      ) as f:
								SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   f.readlines()
				SCREAMING_SNAKE_CASE_   :	Tuple													=   [line.strip() for line in data if len(lowerCAmelCase__      ) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
				SCREAMING_SNAKE_CASE_   :	Dict													=   LTP(args.ltp      )  # faster in GPU device
				SCREAMING_SNAKE_CASE_   :	List[Any]													=   BertTokenizer.from_pretrained(args.bert      )

				SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   prepare_ref(lowerCAmelCase__    ,    lowerCAmelCase__    ,    lowerCAmelCase__      )

				with open(args.save_path    ,    'w'    ,    encoding='utf-8'      ) as f:
								SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   [json.dumps(lowerCAmelCase__      ) + '\n' for ref in ref_ids]
								f.writelines(lowerCAmelCase__      )


if __name__ == "__main__":
				A        =			argparse.ArgumentParser(description='prepare_chinese_ref')
				parser.add_argument(
				    '--file_name',
				    required=False,
				    type=str,
				    default='./resources/chinese-demo.txt',
				    help='file need process, same as training data in lm',
				)
				parser.add_argument(
				    '--ltp',
				    required=False,
				    type=str,
				    default='./resources/ltp',
				    help='resources for LTP tokenizer, usually a path',
				)
				parser.add_argument(
				    '--bert',
				    required=False,
				    type=str,
				    default='./resources/robert',
				    help='resources for Bert tokenizer',
				)
				parser.add_argument(
				    '--save_path',
				    required=False,
				    type=str,
				    default='./resources/ref.txt',
				    help='path to save res',
				)

				A        =			parser.parse_args()
				main(args)







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(			lowerCAmelCase__				:		Tuple    ,    lowerCAmelCase__				:		Optional[Any]    ,    lowerCAmelCase__				:		Union[str, Any]      ):





				'''simple docstring'''







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

				SCREAMING_SNAKE_CASE_   :	int													=   start_index
				while not lines[end_index].startswith(lowerCAmelCase__      ):
								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(			lowerCAmelCase__				:		Dict      ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	List[str]													=   TASK_GUIDE_TO_MODELS[task_guide]
				SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(lowerCAmelCase__    ,    set()      )
				SCREAMING_SNAKE_CASE_   :	Optional[int]													=   {
				    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(			lowerCAmelCase__				:		List[Any]    ,    lowerCAmelCase__				:		List[str]=False      ):





				'''simple docstring'''







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

				SCREAMING_SNAKE_CASE_   :	Dict													=   get_model_list_for_task(lowerCAmelCase__      )

				if current_list != new_list:
								if overwrite:
												with open(os.path.join(lowerCAmelCase__    ,    lowerCAmelCase__      )    ,    '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)