



'''simple docstring'''



import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer


def 							lowercase_				(    __A :       Tuple			)       ->      Union[str, Any]:



 """simple docstring"""




 if (
     (cp >= 0X4_e00 and cp <= 0X9_fff)
     or (cp >= 0X3_400 and cp <= 0X4_dbf)  #
     or (cp >= 0X20_000 and cp <= 0X2a_6df)  #
     or (cp >= 0X2a_700 and cp <= 0X2b_73f)  #
     or (cp >= 0X2b_740 and cp <= 0X2b_81f)  #
     or (cp >= 0X2b_820 and cp <= 0X2c_eaf)  #
     or (cp >= 0Xf_900 and cp <= 0Xf_aff)
     or (cp >= 0X2f_800 and cp <= 0X2f_a1f)  #
 ):  #
  return True

 return False


def 							lowercase_				(    __A :       str			)       ->      Union[str, Any]:



 """simple docstring"""




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


def 							lowercase_				(    __A :       Optional[Any]			)       ->      Optional[int]:



 """simple docstring"""




 lowercase					:   Optional[int]				  =set()

 for token in tokens:
  lowercase					:   Dict				  =len(__SCREAMING_SNAKE_CASE			) > 1 and is_chinese(__SCREAMING_SNAKE_CASE			)
  if chinese_word:
   word_set.add(__SCREAMING_SNAKE_CASE			)
 lowercase					:   int				  =list(__SCREAMING_SNAKE_CASE			)
 return word_list


def 							lowercase_				(    __A :       Any					,      __A :       str			)       ->      List[Any]:



 """simple docstring"""




 if not chinese_word_set:
  return bert_tokens
 lowercase					:   Dict				  =max([len(__SCREAMING_SNAKE_CASE			) for w in chinese_word_set]			)

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


def 							lowercase_				(    __A :       Dict					,      __A :       Optional[Any]					,      __A :       List[Any]			)       ->      Dict:



 """simple docstring"""




 lowercase					:   Union[str, Any]				  =[]

 for i in range(0					,      len(__SCREAMING_SNAKE_CASE			)					,      1_0_0			):
  lowercase					:   Tuple				  =ltp_tokenizer.seg(lines[i : i + 1_0_0]			)[0]
  lowercase					:   Optional[int]				  =[get_chinese_word(__SCREAMING_SNAKE_CASE			) for r in res]
  ltp_res.extend(__SCREAMING_SNAKE_CASE			)
 assert len(__SCREAMING_SNAKE_CASE			) == len(__SCREAMING_SNAKE_CASE			)

 lowercase					:   Optional[Any]				  =[]
 for i in range(0					,      len(__SCREAMING_SNAKE_CASE			)					,      1_0_0			):
  lowercase					:   str				  =bert_tokenizer(lines[i : i + 1_0_0]					,      add_special_tokens=__SCREAMING_SNAKE_CASE					,      truncation=__SCREAMING_SNAKE_CASE					,      max_length=5_1_2			)
  bert_res.extend(res['''input_ids''']			)
 assert len(__SCREAMING_SNAKE_CASE			) == len(__SCREAMING_SNAKE_CASE			)

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

 assert len(__SCREAMING_SNAKE_CASE			) == len(__SCREAMING_SNAKE_CASE			)

 return ref_ids


def 							lowercase_				(    __A :       Union[str, Any]			)       ->      Any:



 """simple docstring"""




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

 lowercase					:   List[str]				  =prepare_ref(__SCREAMING_SNAKE_CASE					,      __SCREAMING_SNAKE_CASE					,      __SCREAMING_SNAKE_CASE			)

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


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

      SCREAMING_SNAKE_CASE						=	parser.parse_args()
      main(args)











'''simple docstring'''



import subprocess
import sys

from transformers import BertConfig, BertModel, BertTokenizer, pipeline
from transformers.testing_utils import TestCasePlus, require_torch
class UpperCAmelCase_					(    __A				):




      """simple docstring"""




      @require_torch
      def 				A__      ( self			:		Tuple		)  ->  Optional[int]:


       '''simple docstring'''

       lowercase					:   Any				  ='''
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
        '''

       lowercase					:   Optional[int]				  ='''
mname = "hf-internal-testing/tiny-random-bert"
BertConfig.from_pretrained(mname)
BertModel.from_pretrained(mname)
BertTokenizer.from_pretrained(mname)
pipe = pipeline(task="fill-mask", model=mname)
print("success")
        '''

       lowercase					:   Any				  ='''
import socket
def offline_socket(*args, **kwargs): raise RuntimeError("Offline mode is enabled, we shouldn\'t access internet")
socket.socket = offline_socket
        '''

       # Force fetching the files so that we can use the cache
       lowercase					:   Tuple				  ='''hf-internal-testing/tiny-random-bert'''
       BertConfig.from_pretrained(UpperCAmelCase		)
       BertModel.from_pretrained(UpperCAmelCase		)
       BertTokenizer.from_pretrained(UpperCAmelCase		)
       pipeline(task='''fill-mask'''     ,      model=UpperCAmelCase		)

       # baseline - just load from_pretrained with normal network
       lowercase					:   List[str]				  =[sys.executable, '''-c''', '''\n'''.join([load, run, mock]		)]

       # should succeed
       lowercase					:   Tuple				  =self.get_env()
       # should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
       lowercase					:   Optional[Any]				  ='''1'''
       lowercase					:   Any				  =subprocess.run(UpperCAmelCase     ,      env=UpperCAmelCase     ,      check=UpperCAmelCase     ,      capture_output=UpperCAmelCase		)
       self.assertEqual(result.returncode     ,      0     ,      result.stderr		)
       self.assertIn('''success'''     ,      result.stdout.decode()		)






      @require_torch
      def 				A__      ( self			:		str		)  ->  List[str]:


       '''simple docstring'''

       lowercase					:   str				  ='''
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
        '''

       lowercase					:   Optional[Any]				  ='''
mname = "hf-internal-testing/tiny-random-bert"
BertConfig.from_pretrained(mname)
BertModel.from_pretrained(mname)
BertTokenizer.from_pretrained(mname)
pipe = pipeline(task="fill-mask", model=mname)
print("success")
        '''

       lowercase					:   Optional[int]				  ='''
import socket
def offline_socket(*args, **kwargs): raise socket.error("Faking flaky internet")
socket.socket = offline_socket
        '''

       # Force fetching the files so that we can use the cache
       lowercase					:   Optional[Any]				  ='''hf-internal-testing/tiny-random-bert'''
       BertConfig.from_pretrained(UpperCAmelCase		)
       BertModel.from_pretrained(UpperCAmelCase		)
       BertTokenizer.from_pretrained(UpperCAmelCase		)
       pipeline(task='''fill-mask'''     ,      model=UpperCAmelCase		)

       # baseline - just load from_pretrained with normal network
       lowercase					:   Optional[Any]				  =[sys.executable, '''-c''', '''\n'''.join([load, run, mock]		)]

       # should succeed
       lowercase					:   str				  =self.get_env()
       lowercase					:   Any				  =subprocess.run(UpperCAmelCase     ,      env=UpperCAmelCase     ,      check=UpperCAmelCase     ,      capture_output=UpperCAmelCase		)
       self.assertEqual(result.returncode     ,      0     ,      result.stderr		)
       self.assertIn('''success'''     ,      result.stdout.decode()		)






      @require_torch
      def 				A__      ( self			:		Any		)  ->  Optional[Any]:


       '''simple docstring'''

       lowercase					:   Optional[Any]				  ='''
from transformers import BertConfig, BertModel, BertTokenizer
        '''

       lowercase					:   List[Any]				  ='''
mname = "hf-internal-testing/tiny-random-bert-sharded"
BertConfig.from_pretrained(mname)
BertModel.from_pretrained(mname)
print("success")
        '''

       lowercase					:   int				  ='''
import socket
def offline_socket(*args, **kwargs): raise ValueError("Offline mode is enabled")
socket.socket = offline_socket
        '''

       # baseline - just load from_pretrained with normal network
       lowercase					:   Tuple				  =[sys.executable, '''-c''', '''\n'''.join([load, run]		)]

       # should succeed
       lowercase					:   Optional[Any]				  =self.get_env()
       lowercase					:   List[Any]				  =subprocess.run(UpperCAmelCase     ,      env=UpperCAmelCase     ,      check=UpperCAmelCase     ,      capture_output=UpperCAmelCase		)
       self.assertEqual(result.returncode     ,      0     ,      result.stderr		)
       self.assertIn('''success'''     ,      result.stdout.decode()		)

       # next emulate no network
       lowercase					:   Tuple				  =[sys.executable, '''-c''', '''\n'''.join([load, mock, run]		)]

       # Doesn't fail anymore since the model is in the cache due to other tests, so commenting this.
       # env["TRANSFORMERS_OFFLINE"] = "0"
       # result = subprocess.run(cmd, env=env, check=False, capture_output=True)
       # self.assertEqual(result.returncode, 1, result.stderr)

       # should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
       lowercase					:   Any				  ='''1'''
       lowercase					:   Optional[Any]				  =subprocess.run(UpperCAmelCase     ,      env=UpperCAmelCase     ,      check=UpperCAmelCase     ,      capture_output=UpperCAmelCase		)
       self.assertEqual(result.returncode     ,      0     ,      result.stderr		)
       self.assertIn('''success'''     ,      result.stdout.decode()		)






      @require_torch
      def 				A__      ( self			:		Tuple		)  ->  Optional[int]:


       '''simple docstring'''

       lowercase					:   Optional[int]				  ='''
from transformers import pipeline
        '''
       lowercase					:   List[Any]				  ='''
mname = "hf-internal-testing/tiny-random-bert"
pipe = pipeline(model=mname)
        '''

       lowercase					:   Tuple				  ='''
import socket
def offline_socket(*args, **kwargs): raise socket.error("Offline mode is enabled")
socket.socket = offline_socket
        '''
       lowercase					:   Tuple				  =self.get_env()
       lowercase					:   Optional[int]				  ='''1'''
       lowercase					:   Union[str, Any]				  =[sys.executable, '''-c''', '''\n'''.join([load, mock, run]		)]
       lowercase					:   Dict				  =subprocess.run(UpperCAmelCase     ,      env=UpperCAmelCase     ,      check=UpperCAmelCase     ,      capture_output=UpperCAmelCase		)
       self.assertEqual(result.returncode     ,      1     ,      result.stderr		)
       self.assertIn(
           '''You cannot infer task automatically within `pipeline` when using offline mode'''     ,      result.stderr.decode().replace('''\n'''     ,      ''''''		)     ,      )






      @require_torch
      def 				A__      ( self			:		int		)  ->  Optional[int]:


       '''simple docstring'''

       lowercase					:   List[str]				  ='''
from transformers import AutoModel
        '''
       lowercase					:   Dict				  ='''
mname = "hf-internal-testing/test_dynamic_model"
AutoModel.from_pretrained(mname, trust_remote_code=True)
print("success")
        '''

       # baseline - just load from_pretrained with normal network
       lowercase					:   Dict				  =[sys.executable, '''-c''', '''\n'''.join([load, run]		)]

       # should succeed
       lowercase					:   Optional[Any]				  =self.get_env()
       lowercase					:   int				  =subprocess.run(UpperCAmelCase     ,      env=UpperCAmelCase     ,      check=UpperCAmelCase     ,      capture_output=UpperCAmelCase		)
       self.assertEqual(result.returncode     ,      0     ,      result.stderr		)
       self.assertIn('''success'''     ,      result.stdout.decode()		)

       # should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
       lowercase					:   List[str]				  ='''1'''
       lowercase					:   List[Any]				  =subprocess.run(UpperCAmelCase     ,      env=UpperCAmelCase     ,      check=UpperCAmelCase     ,      capture_output=UpperCAmelCase		)
       self.assertEqual(result.returncode     ,      0     ,      result.stderr		)
       self.assertIn('''success'''     ,      result.stdout.decode()		)



