
"""simple docstring"""



import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer
def 					lowerCamelCase   (     _snake_case ):
    # 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 >= 0X4e00 and cp <= 0X9fff)
        or (cp >= 0X3400 and cp <= 0X4dbf)  #
        or (cp >= 0X2_0000 and cp <= 0X2_a6df)  #
        or (cp >= 0X2_a700 and cp <= 0X2_b73f)  #
        or (cp >= 0X2_b740 and cp <= 0X2_b81f)  #
        or (cp >= 0X2_b820 and cp <= 0X2_ceaf)  #
        or (cp >= 0Xf900 and cp <= 0Xfaff)
        or (cp >= 0X2_f800 and cp <= 0X2_fa1f)  #
    ):  #
        return True

    return False
def 					lowerCamelCase   (     _snake_case ):
    # word like '180' or '身高' or '神'
    for char in word:
        UpperCAmelCase__	:      List[Any]     							=					ord(_snake_case )
        if not _is_chinese_char(_snake_case ):
            return 0
    return 1
def 					lowerCamelCase   (     _snake_case ):
    UpperCAmelCase__	:      List[str]     							=					set()

    for token in tokens:
        UpperCAmelCase__	:      Dict     							=					len(_snake_case ) > 1 and is_chinese(_snake_case )
        if chinese_word:
            word_set.add(_snake_case )
    UpperCAmelCase__	:      Optional[Any]     							=					list(_snake_case )
    return word_list
def 					lowerCamelCase   (     _snake_case    ,_snake_case ):
    if not chinese_word_set:
        return bert_tokens
    UpperCAmelCase__	:      Union[str, Any]     							=					max([len(_snake_case ) for w in chinese_word_set] )

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

    for i in range(0    ,len(_snake_case )    ,100 ):
        UpperCAmelCase__	:      Tuple     							=					ltp_tokenizer.seg(lines[i : i + 100] )[0]
        UpperCAmelCase__	:      Union[str, Any]     							=					[get_chinese_word(_snake_case ) for r in res]
        ltp_res.extend(_snake_case )
    assert len(_snake_case ) == len(_snake_case )

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

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

    assert len(_snake_case ) == len(_snake_case )

    return ref_ids
def 					lowerCamelCase   (     _snake_case ):
    # 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:
        UpperCAmelCase__	:      Tuple     							=					f.readlines()
    UpperCAmelCase__	:      Optional[int]     							=					[line.strip() for line in data if len(_snake_case ) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
    UpperCAmelCase__	:      List[str]     							=					LTP(args.ltp )  # faster in GPU device
    UpperCAmelCase__	:      List[str]     							=					BertTokenizer.from_pretrained(args.bert )

    UpperCAmelCase__	:      Union[str, Any]     							=					prepare_ref(_snake_case    ,_snake_case    ,_snake_case )

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


if __name__ == "__main__":
    UpperCamelCase__          =	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')

    UpperCamelCase__          =	parser.parse_args()
    main(args)



"""simple docstring"""



import unittest

from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


UpperCamelCase__          =	get_tests_dir('fixtures/test_sentencepiece.model')
UpperCamelCase__          =	get_tests_dir('fixtures/test_sentencepiece_bpe.model')

UpperCamelCase__          =	'pt' if is_torch_available() else 'tf'





@require_sentencepiece
@require_tokenizers
class 				a			(					lowercase						,		unittest.TestCase      ):
      UpperCamelCase							:   Optional[int]           =     CamembertTokenizer
      UpperCamelCase							:   Optional[int]           =     CamembertTokenizerFast
      UpperCamelCase							:   str           =     True
      UpperCamelCase							:   Tuple           =     True



      def  __snake_case							(   self      ):
          super().setUp()

          # We have a SentencePiece fixture for testing
          UpperCAmelCase__	:      int     							=					CamembertTokenizer(UpperCamelCase_      )
          tokenizer.save_pretrained(self.tmpdirname      )



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      Optional[int]     							=					'<pad>'
          UpperCAmelCase__	:      Tuple     							=					1

          self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCamelCase_      )     ,	UpperCamelCase_      )
          self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCamelCase_      )     ,	UpperCamelCase_      )



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      Optional[Any]     							=					list(self.get_tokenizer().get_vocab().keys()      )

          self.assertEqual(vocab_keys[0]     ,	'<s>NOTUSED'      )
          self.assertEqual(vocab_keys[1]     ,	'<pad>'      )
          self.assertEqual(vocab_keys[-1]     ,	'<mask>'      )
          self.assertEqual(len(UpperCamelCase_      )     ,	1_004      )



      def  __snake_case							(   self      ):
          self.assertEqual(self.get_tokenizer().vocab_size     ,	1_005      )



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      Dict     							=					CamembertTokenizer(UpperCamelCase_      )
          tokenizer.save_pretrained(self.tmpdirname      )
          UpperCAmelCase__	:      str     							=					CamembertTokenizerFast.from_pretrained(self.tmpdirname      )

          UpperCAmelCase__	:      Union[str, Any]     							=					'I was born in 92000, and this is falsé.'

          UpperCAmelCase__	:      Optional[Any]     							=					tokenizer.encode(UpperCamelCase_      )
          UpperCAmelCase__	:      Dict     							=					rust_tokenizer.encode(UpperCamelCase_      )
          self.assertListEqual(UpperCamelCase_     ,	UpperCamelCase_      )

          UpperCAmelCase__	:      str     							=					tokenizer.encode(UpperCamelCase_     ,	add_special_tokens=UpperCamelCase_      )
          UpperCAmelCase__	:      Union[str, Any]     							=					rust_tokenizer.encode(UpperCamelCase_     ,	add_special_tokens=UpperCamelCase_      )
          self.assertListEqual(UpperCamelCase_     ,	UpperCamelCase_      )

          # <unk> tokens are not the same for `rust` than for `slow`.
          # Because spm gives back raw token instead of `unk` in EncodeAsPieces
          # tokens = tokenizer.tokenize(sequence)
          UpperCAmelCase__	:      List[Any]     							=					tokenizer.convert_ids_to_tokens(UpperCamelCase_      )
          UpperCAmelCase__	:      str     							=					rust_tokenizer.tokenize(UpperCamelCase_      )
          self.assertListEqual(UpperCamelCase_     ,	UpperCamelCase_      )



      def  __snake_case							(   self      ):
          if not self.test_rust_tokenizer:
              return

          UpperCAmelCase__	:      Any     							=					self.get_tokenizer()
          UpperCAmelCase__	:      List[str]     							=					self.get_rust_tokenizer()

          UpperCAmelCase__	:      List[str]     							=					'I was born in 92000, and this is falsé.'

          UpperCAmelCase__	:      List[str]     							=					tokenizer.tokenize(UpperCamelCase_      )
          UpperCAmelCase__	:      str     							=					rust_tokenizer.tokenize(UpperCamelCase_      )
          self.assertListEqual(UpperCamelCase_     ,	UpperCamelCase_      )

          UpperCAmelCase__	:      Optional[Any]     							=					tokenizer.encode(UpperCamelCase_     ,	add_special_tokens=UpperCamelCase_      )
          UpperCAmelCase__	:      Union[str, Any]     							=					rust_tokenizer.encode(UpperCamelCase_     ,	add_special_tokens=UpperCamelCase_      )
          self.assertListEqual(UpperCamelCase_     ,	UpperCamelCase_      )

          UpperCAmelCase__	:      str     							=					self.get_rust_tokenizer()
          UpperCAmelCase__	:      Union[str, Any]     							=					tokenizer.encode(UpperCamelCase_      )
          UpperCAmelCase__	:      List[str]     							=					rust_tokenizer.encode(UpperCamelCase_      )
          self.assertListEqual(UpperCamelCase_     ,	UpperCamelCase_      )



      @slow
      def  __snake_case							(   self      ):
          # fmt: off
          UpperCAmelCase__	:      Any     							=					{'input_ids': [[5, 54, 7_196, 297, 30, 23, 776, 18, 11, 3_215, 3_705, 8_252, 22, 3_164, 1_181, 2_116, 29, 16, 813, 25, 791, 3_314, 20, 3_446, 38, 27_575, 120, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 468, 17, 11, 9_088, 20, 1_517, 8, 22_804, 18_818, 10, 38, 629, 607, 607, 142, 19, 7_196, 867, 56, 10_326, 24, 2_267, 20, 416, 5_072, 15_612, 233, 734, 7, 2_399, 27, 16, 3_015, 1_649, 7, 24, 20, 4_338, 2_399, 27, 13, 3_400, 14, 13, 6_189, 8, 930, 9, 6]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
          # fmt: on

          # camembert is a french model. So we also use french texts.
          UpperCAmelCase__	:      Union[str, Any]     							=					[
              'Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '
              'utilisé principalement dans le domaine du traitement automatique des langues (TAL).',
              'À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '
              'pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '
              'telles que la traduction et la synthèse de texte.',
          ]

          self.tokenizer_integration_test_util(
              expected_encoding=UpperCamelCase_     ,	model_name='camembert-base'     ,	revision='3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf'     ,	sequences=UpperCamelCase_     ,	)

