





import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer



def _a				(	lowerCamelCase:   Optional[int] )	->						str:







      '''simple docstring'''






      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 _a				(	lowerCamelCase:   str )	->						str:







      '''simple docstring'''






      for char in word:
            __A          =				ord(lowerCamelCase )
            if not _is_chinese_char(lowerCamelCase ):
                  return 0
      return 1



def _a				(	lowerCamelCase:   List[str] )	->						Union[str, Any]:







      '''simple docstring'''






      __A          =				set()

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



def _a				(	lowerCamelCase:   List[str]					,				lowerCamelCase:   set() )	->						Tuple:







      '''simple docstring'''






      if not chinese_word_set:
            return bert_tokens
      __A          =				max([len(lowerCamelCase ) for w in chinese_word_set] )

      __A          =				bert_tokens
      __A   ,						__A          =				0, len(lowerCamelCase )
      while start < end:
            __A          =				True
            if is_chinese(bert_word[start] ):
                  __A          =				min(end - start					,				lowerCamelCase )
                  for i in range(lowerCamelCase					,				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 _a				(	lowerCamelCase:   List[str]					,				lowerCamelCase:   LTP					,				lowerCamelCase:   BertTokenizer )	->						Optional[int]:







      '''simple docstring'''






      __A          =				[]

      for i in range(0					,				len(lowerCamelCase )					,				1_00 ):
            __A          =				ltp_tokenizer.pipeline(lines[i : i + 1_00]					,				tasks=['''cws'''] ).cws
            __A          =				[get_chinese_word(lowerCamelCase ) for r in res]
            ltp_res.extend(lowerCamelCase )
      assert len(lowerCamelCase ) == len(lowerCamelCase )

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

      __A          =				[]
      for input_ids, chinese_word in zip(lowerCamelCase					,				lowerCamelCase ):
            __A          =				[]
            for id in input_ids:
                  __A          =				bert_tokenizer._convert_id_to_token(lowerCamelCase )
                  input_tokens.append(lowerCamelCase )
            __A          =				add_sub_symbol(lowerCamelCase					,				lowerCamelCase )
            __A          =				[]
            # 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] == "##":
                        __A          =				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 _a				(	lowerCamelCase:   Optional[int] )	->						Optional[Any]:







      '''simple docstring'''






      with open(args.file_name					,				'''r'''					,				encoding='''utf-8''' ) as f:
            __A          =				f.readlines()
      __A          =				[line.strip() for line in data if len(lowerCamelCase ) > 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(lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase )

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


if __name__ == "__main__":
       snake_case__					:							Dict													=     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',
       )

       snake_case__					:							Tuple													=     parser.parse_args()
       main(args)












import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


snake_case__					:							Union[str, Any]													=     500000

snake_case__						,    snake_case__					:							Optional[Any]													=     os.path.split(__file__)
snake_case__					:							List[Any]													=     os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))



@get_duration
def _a				(	lowerCamelCase:   datasets.Dataset					,				**lowerCamelCase:   Optional[int] )	->						str:







      '''simple docstring'''






      __A          =				dataset.map(**lowerCamelCase )



@get_duration
def _a				(	lowerCamelCase:   datasets.Dataset					,				**lowerCamelCase:   Optional[int] )	->						str:







      '''simple docstring'''






      __A          =				dataset.filter(**lowerCamelCase )



def _a				(	)	->						List[Any]:







      '''simple docstring'''






      __A          =				{'''num examples''': SPEED_TEST_N_EXAMPLES}
      with tempfile.TemporaryDirectory() as tmp_dir:
            __A          =				datasets.Features({'''text''': datasets.Value('''string''' ), '''numbers''': datasets.Value('''float32''' )} )
            __A          =				generate_example_dataset(
                os.path.join(lowerCamelCase					,				'''dataset.arrow''' )					,				lowerCamelCase					,				num_examples=lowerCamelCase )

            __A          =				transformers.AutoTokenizer.from_pretrained('''bert-base-cased'''					,				use_fast=lowerCamelCase )

            def tokenize(lowerCamelCase:   List[str] ):
                  return tokenizer(examples['''text'''] )

            __A          =				map(lowerCamelCase )

            __A          =				map(lowerCamelCase					,				batched=lowerCamelCase )

            __A          =				map(lowerCamelCase					,				function=lambda lowerCamelCase : None					,				batched=lowerCamelCase )

            with dataset.formatted_as(type='''numpy''' ):
                  __A          =				map(lowerCamelCase					,				function=lambda lowerCamelCase : None					,				batched=lowerCamelCase )

            with dataset.formatted_as(type='''pandas''' ):
                  __A          =				map(lowerCamelCase					,				function=lambda lowerCamelCase : None					,				batched=lowerCamelCase )

            with dataset.formatted_as(type='''torch'''					,				columns='''numbers''' ):
                  __A          =				map(lowerCamelCase					,				function=lambda lowerCamelCase : None					,				batched=lowerCamelCase )

            with dataset.formatted_as(type='''tensorflow'''					,				columns='''numbers''' ):
                  __A          =				map(lowerCamelCase					,				function=lambda lowerCamelCase : None					,				batched=lowerCamelCase )

            __A          =				map(lowerCamelCase					,				function=lowerCamelCase					,				batched=lowerCamelCase )

            __A          =				filter(lowerCamelCase )

            # Activate later when tokenizer support batched inputs
            # with dataset.formatted_as(type='numpy'):
            #     times[func.__name__ + " fast-tokenizer batched numpy"] = func(dataset, function=tokenize, batched=True)

      with open(lowerCamelCase					,				'''wb''' ) as f:
            f.write(json.dumps(lowerCamelCase ).encode('''utf-8''' ) )


if __name__ == "__main__":  # useful to run the profiler
       benchmark_map_filter()





