




'''simple docstring'''


import os
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from huggingface_hub import HfFolder, delete_repo
from huggingface_hub.file_download import http_get
from requests.exceptions import HTTPError

from transformers import (
    AlbertTokenizer,
    AutoTokenizer,
    BertTokenizer,
    BertTokenizerFast,
    GPTaTokenizerFast,
    is_tokenizers_available,
)
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_tokenizers
from transformers.tokenization_utils import Trie


sys.path.append(str(Path(__file__).parent.parent / "utils"))

from test_module.custom_tokenization import CustomTokenizer  # noqa E402


if is_tokenizers_available():
  from test_module.custom_tokenization_fast import CustomTokenizerFast





class    __lowercase    (  unittest.TestCase ):







   def 		UpperCamelCase__			(	self      )				->    Tuple:
     __a			=					mock.Mock()
     __a			=					500
     __a			=					{}
     __a			=					HTTPError
     __a			=					{}

     # Download this model to make sure it's in the cache.
     __a			=					BertTokenizer.from_pretrained('hf-internal-testing/tiny-random-bert'      )

     # Under the mock environment we get a 500 error when trying to reach the tokenizer.
     with mock.patch('requests.Session.request'      ,							return_value=A_      ) as mock_head:
       __a			=					BertTokenizer.from_pretrained('hf-internal-testing/tiny-random-bert'      )
       # This check we did call the fake head request
       mock_head.assert_called()







   @require_tokenizers
   def 		UpperCamelCase__			(	self      )				->    Dict:
     __a			=					mock.Mock()
     __a			=					500
     __a			=					{}
     __a			=					HTTPError
     __a			=					{}

     # Download this model to make sure it's in the cache.
     __a			=					GPTaTokenizerFast.from_pretrained('gpt2'      )

     # Under the mock environment we get a 500 error when trying to reach the tokenizer.
     with mock.patch('requests.Session.request'      ,							return_value=A_      ) as mock_head:
       __a			=					GPTaTokenizerFast.from_pretrained('gpt2'      )
       # This check we did call the fake head request
       mock_head.assert_called()







   def 		UpperCamelCase__			(	self      )				->    Optional[Any]:
     # This test is for deprecated behavior and can be removed in v5
     try:
       __a			=					tempfile.mktemp()
       with open(A_      ,							'wb'      ) as f:
         http_get('https://huggingface.co/albert-base-v1/resolve/main/spiece.model'      ,							A_      )

       __a			=					AlbertTokenizer.from_pretrained(A_      )
     finally:
       os.remove(A_      )

     # Supporting this legacy load introduced a weird bug where the tokenizer would load local files if they are in
     # the current folder and have the right name.
     if os.path.isfile('tokenizer.json'      ):
       # We skip the test if the user has a `tokenizer.json` in this folder to avoid deleting it.
       return
     try:
       with open('tokenizer.json'      ,							'wb'      ) as f:
         http_get('https://huggingface.co/hf-internal-testing/tiny-random-bert/blob/main/tokenizer.json'      ,							A_      )
       __a			=					AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'      )
       # The tiny random BERT has a vocab size of 1024, tiny gpt2 as a vocab size of 1000
       self.assertEqual(tokenizer.vocab_size      ,							1000      )
       # Tokenizer should depend on the remote checkpoint, not the local tokenizer.json file.

     finally:
       os.remove('tokenizer.json'      )







   def 		UpperCamelCase__			(	self      )				->    Dict:
     __a			=					AlbertTokenizer.from_pretrained('https://huggingface.co/albert-base-v1/resolve/main/spiece.model'      )





@is_staging_test
class    __lowercase    (  unittest.TestCase ):
   _a							=      ["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "bla", "blou"]







   @classmethod
   def 		UpperCamelCase__			(	cls      )				->    Tuple:
     __a			=					TOKEN
     HfFolder.save_token(A_      )







   @classmethod
   def 		UpperCamelCase__			(	cls      )				->    Optional[int]:
     try:
       delete_repo(token=cls._token      ,							repo_id='test-tokenizer'      )
     except HTTPError:
       pass

     try:
       delete_repo(token=cls._token      ,							repo_id='valid_org/test-tokenizer-org'      )
     except HTTPError:
       pass

     try:
       delete_repo(token=cls._token      ,							repo_id='test-dynamic-tokenizer'      )
     except HTTPError:
       pass







   def 		UpperCamelCase__			(	self      )				->    Any:
     with tempfile.TemporaryDirectory() as tmp_dir:
       __a			=					os.path.join(A_      ,							'vocab.txt'      )
       with open(A_      ,							'w'      ,							encoding='utf-8'      ) as vocab_writer:
         vocab_writer.write(''.join([x + '\n' for x in self.vocab_tokens]      )      )
       __a			=					BertTokenizer(A_      )

     tokenizer.push_to_hub('test-tokenizer'      ,							use_auth_token=self._token      )
     __a			=					BertTokenizer.from_pretrained(f"{USER}/test-tokenizer"      )
     self.assertDictEqual(new_tokenizer.vocab      ,							tokenizer.vocab      )

     # Reset repo
     delete_repo(token=self._token      ,							repo_id='test-tokenizer'      )

     # Push to hub via save_pretrained
     with tempfile.TemporaryDirectory() as tmp_dir:
       tokenizer.save_pretrained(A_      ,							repo_id='test-tokenizer'      ,							push_to_hub=A_      ,							use_auth_token=self._token      )

     __a			=					BertTokenizer.from_pretrained(f"{USER}/test-tokenizer"      )
     self.assertDictEqual(new_tokenizer.vocab      ,							tokenizer.vocab      )







   def 		UpperCamelCase__			(	self      )				->    int:
     with tempfile.TemporaryDirectory() as tmp_dir:
       __a			=					os.path.join(A_      ,							'vocab.txt'      )
       with open(A_      ,							'w'      ,							encoding='utf-8'      ) as vocab_writer:
         vocab_writer.write(''.join([x + '\n' for x in self.vocab_tokens]      )      )
       __a			=					BertTokenizer(A_      )

     tokenizer.push_to_hub('valid_org/test-tokenizer-org'      ,							use_auth_token=self._token      )
     __a			=					BertTokenizer.from_pretrained('valid_org/test-tokenizer-org'      )
     self.assertDictEqual(new_tokenizer.vocab      ,							tokenizer.vocab      )

     # Reset repo
     delete_repo(token=self._token      ,							repo_id='valid_org/test-tokenizer-org'      )

     # Push to hub via save_pretrained
     with tempfile.TemporaryDirectory() as tmp_dir:
       tokenizer.save_pretrained(
           A_      ,							repo_id='valid_org/test-tokenizer-org'      ,							push_to_hub=A_      ,							use_auth_token=self._token      )

     __a			=					BertTokenizer.from_pretrained('valid_org/test-tokenizer-org'      )
     self.assertDictEqual(new_tokenizer.vocab      ,							tokenizer.vocab      )







   @require_tokenizers
   def 		UpperCamelCase__			(	self      )				->    Dict:
     CustomTokenizer.register_for_auto_class()
     with tempfile.TemporaryDirectory() as tmp_dir:
       __a			=					os.path.join(A_      ,							'vocab.txt'      )
       with open(A_      ,							'w'      ,							encoding='utf-8'      ) as vocab_writer:
         vocab_writer.write(''.join([x + '\n' for x in self.vocab_tokens]      )      )
       __a			=					CustomTokenizer(A_      )

     # No fast custom tokenizer
     tokenizer.push_to_hub('test-dynamic-tokenizer'      ,							use_auth_token=self._token      )

     __a			=					AutoTokenizer.from_pretrained(f"{USER}/test-dynamic-tokenizer"      ,							trust_remote_code=A_      )
     # Can't make an isinstance check because the new_model.config is from the CustomTokenizer class of a dynamic module
     self.assertEqual(tokenizer.__class__.__name__      ,							'CustomTokenizer'      )

     # Fast and slow custom tokenizer
     CustomTokenizerFast.register_for_auto_class()
     with tempfile.TemporaryDirectory() as tmp_dir:
       __a			=					os.path.join(A_      ,							'vocab.txt'      )
       with open(A_      ,							'w'      ,							encoding='utf-8'      ) as vocab_writer:
         vocab_writer.write(''.join([x + '\n' for x in self.vocab_tokens]      )      )

       __a			=					BertTokenizerFast.from_pretrained(A_      )
       bert_tokenizer.save_pretrained(A_      )
       __a			=					CustomTokenizerFast.from_pretrained(A_      )

     tokenizer.push_to_hub('test-dynamic-tokenizer'      ,							use_auth_token=self._token      )

     __a			=					AutoTokenizer.from_pretrained(f"{USER}/test-dynamic-tokenizer"      ,							trust_remote_code=A_      )
     # Can't make an isinstance check because the new_model.config is from the FakeConfig class of a dynamic module
     self.assertEqual(tokenizer.__class__.__name__      ,							'CustomTokenizerFast'      )
     __a			=					AutoTokenizer.from_pretrained(
         f"{USER}/test-dynamic-tokenizer"      ,							use_fast=A_      ,							trust_remote_code=A_      )
     # Can't make an isinstance check because the new_model.config is from the FakeConfig class of a dynamic module
     self.assertEqual(tokenizer.__class__.__name__      ,							'CustomTokenizer'      )





class    __lowercase    (  unittest.TestCase ):







   def 		UpperCamelCase__			(	self      )				->    Optional[int]:
     __a			=					Trie()
     trie.add('Hello 友達'      )
     self.assertEqual(trie.data      ,							{'H': {'e': {'l': {'l': {'o': {' ': {'友': {'達': {'': 1}}}}}}}}}      )
     trie.add('Hello'      )
     trie.data
     self.assertEqual(trie.data      ,							{'H': {'e': {'l': {'l': {'o': {'': 1, ' ': {'友': {'達': {'': 1}}}}}}}}}      )







   def 		UpperCamelCase__			(	self      )				->    str:
     __a			=					Trie()
     self.assertEqual(trie.split('[CLS] This is a extra_id_100'      )      ,							['[CLS] This is a extra_id_100']      )
     trie.add('[CLS]'      )
     trie.add('extra_id_1'      )
     trie.add('extra_id_100'      )
     self.assertEqual(trie.split('[CLS] This is a extra_id_100'      )      ,							['[CLS]', ' This is a ', 'extra_id_100']      )







   def 		UpperCamelCase__			(	self      )				->    List[Any]:
     __a			=					Trie()
     trie.add('A'      )
     self.assertEqual(trie.split('ABC'      )      ,							['A', 'BC']      )
     self.assertEqual(trie.split('BCA'      )      ,							['BC', 'A']      )







   def 		UpperCamelCase__			(	self      )				->    Tuple:
     __a			=					Trie()
     trie.add('TOKEN]'      )
     trie.add('[SPECIAL_TOKEN]'      )
     self.assertEqual(trie.split('This is something [SPECIAL_TOKEN]'      )      ,							['This is something ', '[SPECIAL_TOKEN]']      )







   def 		UpperCamelCase__			(	self      )				->    Dict:
     __a			=					Trie()
     trie.add('A'      )
     trie.add('P'      )
     trie.add('[SPECIAL_TOKEN]'      )
     self.assertEqual(trie.split('This is something [SPECIAL_TOKEN]'      )      ,							['This is something ', '[SPECIAL_TOKEN]']      )







   def 		UpperCamelCase__			(	self      )				->    Optional[Any]:
     __a			=					Trie()
     trie.add('AB'      )
     trie.add('B'      )
     trie.add('C'      )
     self.assertEqual(trie.split('ABC'      )      ,							['AB', 'C']      )







   def 		UpperCamelCase__			(	self      )				->    Tuple:
     __a			=					Trie()
     trie.add('ABC'      )
     trie.add('B'      )
     trie.add('CD'      )
     self.assertEqual(trie.split('ABCD'      )      ,							['ABC', 'D']      )







   def 		UpperCamelCase__			(	self      )				->    int:
     __a			=					Trie()
     __a			=					trie.cut_text('ABC'      ,							[0, 0, 2, 1, 2, 3]      )
     self.assertEqual(A_      ,							['AB', 'C']      )









'''simple docstring'''


import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer


def 						SCREAMING_SNAKE_CASE       (      a_	:							Tuple			):
  # 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 >= 0X20000 and cp <= 0X2a6df)  #
      or (cp >= 0X2a700 and cp <= 0X2b73f)  #
      or (cp >= 0X2b740 and cp <= 0X2b81f)  #
      or (cp >= 0X2b820 and cp <= 0X2ceaf)  #
      or (cp >= 0Xf900 and cp <= 0Xfaff)
      or (cp >= 0X2f800 and cp <= 0X2fa1f)  #
  ):  #
    return True

  return False


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


def 						SCREAMING_SNAKE_CASE       (      a_	:							List[str]			):
  __a			=					set()

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


def 						SCREAMING_SNAKE_CASE       (      a_	:							List[str]				,					a_	:							set()			):
  if not chinese_word_set:
    return bert_tokens
  __a			=					max([len(a_			) for w in chinese_word_set]			)

  __a			=					bert_tokens
  __a	,       __a			=					0, len(a_			)
  while start < end:
    __a			=					True
    if is_chinese(bert_word[start]			):
      __a			=					min(end - start				,					a_			)
      for i in range(a_				,					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 						SCREAMING_SNAKE_CASE       (      a_	:							List[str]				,					a_	:							LTP				,					a_	:							BertTokenizer			):
  __a			=					[]

  for i in range(0				,					len(a_			)				,					100			):
    __a			=					ltp_tokenizer.seg(lines[i : i + 100]			)[0]
    __a			=					[get_chinese_word(a_			) for r in res]
    ltp_res.extend(a_			)
  assert len(a_			) == len(a_			)

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

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

  assert len(a_			) == len(a_			)

  return ref_ids


def 						SCREAMING_SNAKE_CASE       (      a_	:							str			):
  # 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(a_			) > 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(a_				,					a_				,					a_			)

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


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)



