



import os
import re
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase_           = logging.get_logger(__name__)

lowerCAmelCase_           = {'vocab_file': 'spiece.model'}

lowerCAmelCase_           = {
    'vocab_file': {
        'google/bigbird-roberta-base': 'https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model',
        'google/bigbird-roberta-large': (
            'https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model'
        ),
        'google/bigbird-base-trivia-itc': (
            'https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model'
        ),
    }
}

lowerCAmelCase_           = {
    'google/bigbird-roberta-base': 4_096,
    'google/bigbird-roberta-large': 4_096,
    'google/bigbird-base-trivia-itc': 4_096,
}
class      UpperCamelCase (				snake_case__ ):







		"""simple docstring"""



		snake_case    =   VOCAB_FILES_NAMES
		snake_case    =   PRETRAINED_VOCAB_FILES_MAP
		snake_case    =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		snake_case    =   ["input_ids", "attention_mask"]
		snake_case    =   []
		def __init__(  self		:       Optional[int]						,_SCREAMING_SNAKE_CASE		:       Optional[int]						,_SCREAMING_SNAKE_CASE		:       Tuple="<unk>"						,_SCREAMING_SNAKE_CASE		:       Tuple="<s>"						,_SCREAMING_SNAKE_CASE		:       List[Any]="</s>"						,_SCREAMING_SNAKE_CASE		:       str="<pad>"						,_SCREAMING_SNAKE_CASE		:       int="[SEP]"						,_SCREAMING_SNAKE_CASE		:       Optional[int]="[MASK]"						,_SCREAMING_SNAKE_CASE		:       Optional[Any]="[CLS]"						,_SCREAMING_SNAKE_CASE		:       Optional[Dict[str, Any]] = None						,**_SCREAMING_SNAKE_CASE		:       Dict						,)    ->   None:


						'''simple docstring'''
						A	 =      AddedToken(_SCREAMING_SNAKE_CASE						,lstrip=_SCREAMING_SNAKE_CASE						,rstrip=_SCREAMING_SNAKE_CASE		) if isinstance(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		) else bos_token
						A	 =      AddedToken(_SCREAMING_SNAKE_CASE						,lstrip=_SCREAMING_SNAKE_CASE						,rstrip=_SCREAMING_SNAKE_CASE		) if isinstance(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		) else eos_token
						A	 =      AddedToken(_SCREAMING_SNAKE_CASE						,lstrip=_SCREAMING_SNAKE_CASE						,rstrip=_SCREAMING_SNAKE_CASE		) if isinstance(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		) else unk_token
						A	 =      AddedToken(_SCREAMING_SNAKE_CASE						,lstrip=_SCREAMING_SNAKE_CASE						,rstrip=_SCREAMING_SNAKE_CASE		) if isinstance(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		) else pad_token
						A	 =      AddedToken(_SCREAMING_SNAKE_CASE						,lstrip=_SCREAMING_SNAKE_CASE						,rstrip=_SCREAMING_SNAKE_CASE		) if isinstance(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		) else cls_token
						A	 =      AddedToken(_SCREAMING_SNAKE_CASE						,lstrip=_SCREAMING_SNAKE_CASE						,rstrip=_SCREAMING_SNAKE_CASE		) if isinstance(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		) else sep_token

						# Mask token behave like a normal word, i.e. include the space before it
						A	 =      AddedToken(_SCREAMING_SNAKE_CASE						,lstrip=_SCREAMING_SNAKE_CASE						,rstrip=_SCREAMING_SNAKE_CASE		) if isinstance(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		) else mask_token

						A	 =      {} if sp_model_kwargs is None else sp_model_kwargs

						super().__init__(
						    bos_token=_SCREAMING_SNAKE_CASE						,eos_token=_SCREAMING_SNAKE_CASE						,unk_token=_SCREAMING_SNAKE_CASE						,pad_token=_SCREAMING_SNAKE_CASE						,sep_token=_SCREAMING_SNAKE_CASE						,mask_token=_SCREAMING_SNAKE_CASE						,cls_token=_SCREAMING_SNAKE_CASE						,sp_model_kwargs=self.sp_model_kwargs						,**_SCREAMING_SNAKE_CASE						,)

						A	 =      vocab_file

						A	 =      spm.SentencePieceProcessor(**self.sp_model_kwargs		)
						self.sp_model.Load(_SCREAMING_SNAKE_CASE		)
		@property
		def       A(  self		:       List[Any]		)    ->   Union[str, Any]:


						'''simple docstring'''
						return self.sp_model.get_piece_size()
		def       A(  self		:       Tuple		)    ->   Any:


						'''simple docstring'''
						A	 =      {self.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE		): i for i in range(self.vocab_size		)}
						vocab.update(self.added_tokens_encoder		)
						return vocab
		def __getstate__(  self		:       Dict		)    ->   Optional[Any]:


						'''simple docstring'''
						A	 =      self.__dict__.copy()
						A	 =      None
						return state
		def __setstate__(  self		:       Tuple						,_SCREAMING_SNAKE_CASE		:       Optional[int]		)    ->   List[Any]:


						'''simple docstring'''
						A	 =      d

						# for backward compatibility
						if not hasattr(self						,'sp_model_kwargs'		):
										A	 =      {}

						A	 =      spm.SentencePieceProcessor(**self.sp_model_kwargs		)
						self.sp_model.Load(self.vocab_file		)
		def       A(  self		:       Optional[Any]						,_SCREAMING_SNAKE_CASE		:       str		)    ->   List[str]:


						'''simple docstring'''
						return self.sp_model.encode(_SCREAMING_SNAKE_CASE						,out_type=_SCREAMING_SNAKE_CASE		)
		def       A(  self		:       Dict						,_SCREAMING_SNAKE_CASE		:       Optional[Any]		)    ->   List[Any]:


						'''simple docstring'''
						return self.sp_model.piece_to_id(_SCREAMING_SNAKE_CASE		)
		def       A(  self		:       int						,_SCREAMING_SNAKE_CASE		:       Any		)    ->   int:


						'''simple docstring'''
						A	 =      self.sp_model.IdToPiece(_SCREAMING_SNAKE_CASE		)
						return token
		def       A(  self		:       List[Any]						,_SCREAMING_SNAKE_CASE		:       Any		)    ->   str:


						'''simple docstring'''
						A	 =      []
						A	 =      ''
						A	 =      False
						for token in tokens:
										# make sure that special tokens are not decoded using sentencepiece model
										if token in self.all_special_tokens:
														if not prev_is_special:
																		out_string += " "
														out_string += self.sp_model.decode(_SCREAMING_SNAKE_CASE		) + token
														A	 =      True
														A	 =      []
										else:
														current_sub_tokens.append(_SCREAMING_SNAKE_CASE		)
														A	 =      False
						out_string += self.sp_model.decode(_SCREAMING_SNAKE_CASE		)
						return out_string.strip()
		def       A(  self		:       Dict						,_SCREAMING_SNAKE_CASE		:       List[int]						,_SCREAMING_SNAKE_CASE		:       bool = False						,_SCREAMING_SNAKE_CASE		:       bool = None						,_SCREAMING_SNAKE_CASE		:       bool = True						,**_SCREAMING_SNAKE_CASE		:       int						,)    ->   str:


						'''simple docstring'''
						A	 =      kwargs.pop('use_source_tokenizer'						,_SCREAMING_SNAKE_CASE		)

						A	 =      self.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE						,skip_special_tokens=_SCREAMING_SNAKE_CASE		)

						# To avoid mixing byte-level and unicode for byte-level BPT
						# we need to build string separately for added tokens and byte-level tokens
						# cf. https://github.com/huggingface/transformers/issues/1133
						A	 =      []
						A	 =      []
						for token in filtered_tokens:
										if skip_special_tokens and token in self.all_special_ids:
														continue
										if token in self.added_tokens_encoder:
														if current_sub_text:
																		sub_texts.append(self.convert_tokens_to_string(_SCREAMING_SNAKE_CASE		)		)
																		A	 =      []
														sub_texts.append(_SCREAMING_SNAKE_CASE		)
										else:
														current_sub_text.append(_SCREAMING_SNAKE_CASE		)
						if current_sub_text:
										sub_texts.append(self.convert_tokens_to_string(_SCREAMING_SNAKE_CASE		)		)

						# Mimic the behavior of the Rust tokenizer:
						# No space before [MASK] and [SEP]
						if spaces_between_special_tokens:
										A	 =      re.sub(R' (\[(MASK|SEP)\])'						,R'\1'						,' '.join(_SCREAMING_SNAKE_CASE		)		)
						else:
										A	 =      ''.join(_SCREAMING_SNAKE_CASE		)

						A	 =      (
						    clean_up_tokenization_spaces
						    if clean_up_tokenization_spaces is not None
						    else self.clean_up_tokenization_spaces
						)
						if clean_up_tokenization_spaces:
										A	 =      self.clean_up_tokenization(_SCREAMING_SNAKE_CASE		)
										return clean_text
						else:
										return text
		def       A(  self		:       Optional[Any]						,_SCREAMING_SNAKE_CASE		:       str						,_SCREAMING_SNAKE_CASE		:       Optional[str] = None		)    ->   Tuple[str]:


						'''simple docstring'''
						if not os.path.isdir(_SCREAMING_SNAKE_CASE		):
										logger.error(f'Vocabulary path ({save_directory}) should be a directory'		)
										return
						A	 =      os.path.join(
						    _SCREAMING_SNAKE_CASE						,(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']		)

						if os.path.abspath(self.vocab_file		) != os.path.abspath(_SCREAMING_SNAKE_CASE		) and os.path.isfile(self.vocab_file		):
										copyfile(self.vocab_file						,_SCREAMING_SNAKE_CASE		)
						elif not os.path.isfile(self.vocab_file		):
										with open(_SCREAMING_SNAKE_CASE						,'wb'		) as fi:
														A	 =      self.sp_model.serialized_model_proto()
														fi.write(_SCREAMING_SNAKE_CASE		)

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


						'''simple docstring'''
						if token_ids_a is None:
										return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
						A	 =      [self.cls_token_id]
						A	 =      [self.sep_token_id]
						return cls + token_ids_a + sep + token_ids_a + sep
		def       A(  self		:       Optional[int]						,_SCREAMING_SNAKE_CASE		:       List[int]						,_SCREAMING_SNAKE_CASE		:       Optional[List[int]] = None						,_SCREAMING_SNAKE_CASE		:       bool = False		)    ->   List[int]:


						'''simple docstring'''
						if already_has_special_tokens:
										return super().get_special_tokens_mask(
										    token_ids_a=_SCREAMING_SNAKE_CASE						,token_ids_a=_SCREAMING_SNAKE_CASE						,already_has_special_tokens=_SCREAMING_SNAKE_CASE		)

						if token_ids_a is None:
										return [1] + ([0] * len(_SCREAMING_SNAKE_CASE		)) + [1]
						return [1] + ([0] * len(_SCREAMING_SNAKE_CASE		)) + [1] + ([0] * len(_SCREAMING_SNAKE_CASE		)) + [1]







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


						'''simple docstring'''
						A	 =      [self.sep_token_id]
						A	 =      [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]













import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer







def      snake_case		(							UpperCAmelCase	:       List[str]   ):
				# This defines a "chinese character" as anything in the CJK Unicode block:
				#   https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
				#
				# Note that the CJK Unicode block is NOT all Japanese and Korean characters,
				# despite its name. The modern Korean Hangul alphabet is a different block,
				# as is Japanese Hiragana and Katakana. Those alphabets are used to write
				# space-separated words, so they are not treated specially and handled
				# like the all of the other languages.
				if (
				    (cp >= 0x4_E_0_0 and cp <= 0x9_F_F_F)
				    or (cp >= 0x3_4_0_0 and cp <= 0x4_D_B_F)  #
				    or (cp >= 0x2_0_0_0_0 and cp <= 0x2_A_6_D_F)  #
				    or (cp >= 0x2_A_7_0_0 and cp <= 0x2_B_7_3_F)  #
				    or (cp >= 0x2_B_7_4_0 and cp <= 0x2_B_8_1_F)  #
				    or (cp >= 0x2_B_8_2_0 and cp <= 0x2_C_E_A_F)  #
				    or (cp >= 0xF_9_0_0 and cp <= 0xF_A_F_F)
				    or (cp >= 0x2_F_8_0_0 and cp <= 0x2_F_A_1_F)  #
				):  #
								return True

				return False







def      snake_case		(							UpperCAmelCase	:       str   ):
				# word like '180' or '身高' or '神'
				for char in word:
								A	 =      ord(UpperCAmelCase   )
								if not _is_chinese_char(UpperCAmelCase   ):
												return 0
				return 1







def      snake_case		(							UpperCAmelCase	:       List[str]   ):
				A	 =      set()

				for token in tokens:
								A	 =      len(UpperCAmelCase   ) > 1 and is_chinese(UpperCAmelCase   )
								if chinese_word:
												word_set.add(UpperCAmelCase   )
				A	 =      list(UpperCAmelCase   )
				return word_list







def      snake_case		(							UpperCAmelCase	:       List[str],						UpperCAmelCase	:       set()   ):
				if not chinese_word_set:
								return bert_tokens
				A	 =      max([len(UpperCAmelCase   ) for w in chinese_word_set]   )

				A	 =      bert_tokens
				A ,		A	 =      0, len(UpperCAmelCase   )
				while start < end:
								A	 =      True
								if is_chinese(bert_word[start]   ):
												A	 =      min(end - start,						UpperCAmelCase   )
												for i in range(UpperCAmelCase,						1,						-1   ):
																A	 =      ''.join(bert_word[start : start + i]   )
																if whole_word in chinese_word_set:
																				for j in range(start + 1,						start + i   ):
																								A	 =      '##' + bert_word[j]
																				A	 =      start + i
																				A	 =      False
																				break
								if single_word:
												start += 1
				return bert_word







def      snake_case		(							UpperCAmelCase	:       List[str],						UpperCAmelCase	:       LTP,						UpperCAmelCase	:       BertTokenizer   ):
				A	 =      []

				for i in range(0,						len(UpperCAmelCase   ),						1_00   ):
								A	 =      ltp_tokenizer.seg(lines[i : i + 1_00]   )[0]
								A	 =      [get_chinese_word(UpperCAmelCase   ) for r in res]
								ltp_res.extend(UpperCAmelCase   )
				assert len(UpperCAmelCase   ) == len(UpperCAmelCase   )

				A	 =      []
				for i in range(0,						len(UpperCAmelCase   ),						1_00   ):
								A	 =      bert_tokenizer(lines[i : i + 1_00],						add_special_tokens=UpperCAmelCase,						truncation=UpperCAmelCase,						max_length=5_12   )
								bert_res.extend(res['input_ids']   )
				assert len(UpperCAmelCase   ) == len(UpperCAmelCase   )

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

				assert len(UpperCAmelCase   ) == len(UpperCAmelCase   )

				return ref_ids







def      snake_case		(							UpperCAmelCase	:       List[Any]   ):
				# For Chinese (Ro)Bert, the best result is from : RoBERTa-wwm-ext (https://github.com/ymcui/Chinese-BERT-wwm)
				# If we want to fine-tune these model, we have to use same tokenizer : LTP (https://github.com/HIT-SCIR/ltp)
				with open(args.file_name,						'r',						encoding='utf-8'   ) as f:
								A	 =      f.readlines()
				A	 =      [line.strip() for line in data if len(UpperCAmelCase   ) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
				A	 =      LTP(args.ltp   )  # faster in GPU device
				A	 =      BertTokenizer.from_pretrained(args.bert   )

				A	 =      prepare_ref(UpperCAmelCase,						UpperCAmelCase,						UpperCAmelCase   )

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


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

					lowerCAmelCase_           = parser.parse_args()
					main(args)





