

'''simple docstring'''





import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow


if is_torch_available():
							import torch

							from transformers import XLMRobertaModel
@require_sentencepiece
@require_tokenizers
@require_torch
class        A  (	unittest.TestCase ):





						@slow
						def __lowerCAmelCase   (	self			)			->							Dict:
										_a        =   XLMRobertaModel.from_pretrained("xlm-roberta-base"			)
										_a        =   torch.tensor([[0, 5_8_1, 1_0_2_6_9, 8_3, 9_9_9_4_2, 1_3_6, 6_0_7_4_2, 2_3, 7_0, 8_0_5_8_3, 1_8_2_7_6, 2]]			)
										# The dog is cute and lives in the garden house

										_a        =   torch.Size((1, 1_2, 7_6_8)			)  # batch_size, sequence_length, embedding_vector_dim
										_a        =   torch.tensor(
										    [[-0.0_101, 0.1_218, -0.0_803, 0.0_801, 0.1_327, 0.0_776, -0.1_215, 0.2_383, 0.3_338, 0.3_106, 0.0_300, 0.0_252]]			)
										#  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')
										#  xlmr.eval()
										#  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
										with torch.no_grad():
														_a        =   model(_lowerCamelCase			)['last_hidden_state'].detach()
										self.assertEqual(output.shape	,    _lowerCamelCase			)
										# compare the actual values for a slice of last dim
										self.assertTrue(torch.allclose(output[:, :, -1]	,    _lowerCamelCase	,    atol=1E-3			)			)





						@slow
						def __lowerCAmelCase   (	self			)			->							Optional[Any]:
										_a        =   XLMRobertaModel.from_pretrained("xlm-roberta-large"			)
										_a        =   torch.tensor([[0, 5_8_1, 1_0_2_6_9, 8_3, 9_9_9_4_2, 1_3_6, 6_0_7_4_2, 2_3, 7_0, 8_0_5_8_3, 1_8_2_7_6, 2]]			)
										# The dog is cute and lives in the garden house

										_a        =   torch.Size((1, 1_2, 1_0_2_4)			)  # batch_size, sequence_length, embedding_vector_dim
										_a        =   torch.tensor(
										    [[-0.0_699, -0.0_318, 0.0_705, -0.1_241, 0.0_999, -0.0_520, 0.1_004, -0.1_838, -0.4_704, 0.1_437, 0.0_821, 0.0_126]]			)
										#  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.large')
										#  xlmr.eval()
										#  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
										with torch.no_grad():
														_a        =   model(_lowerCamelCase			)['last_hidden_state'].detach()
										self.assertEqual(output.shape	,    _lowerCamelCase			)
										# compare the actual values for a slice of last dim
										self.assertTrue(torch.allclose(output[:, :, -1]	,    _lowerCamelCase	,    atol=1E-3			)			)







"""simple docstring"""






import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer




def       A_  (				__lowercase	):
 # 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       A_  (				__lowercase	):
 # word like '180' or '身高' or '神'
 for char in word:
  UpperCamelCase_     :					Union[str, Any]   	=ord(__lowercase	)
  if not _is_chinese_char(__lowercase	):
   return 0
 return 1




def       A_  (				__lowercase	):
 UpperCamelCase_     :					List[str]   	=set()

 for token in tokens:
  UpperCamelCase_     :					Optional[int]   	=len(__lowercase	) > 1 and is_chinese(__lowercase	)
  if chinese_word:
   word_set.add(__lowercase	)
 UpperCamelCase_     :					Tuple   	=list(__lowercase	)
 return word_list




def       A_  (				__lowercase						,	__lowercase	):
 if not chinese_word_set:
  return bert_tokens
 UpperCamelCase_     :					List[str]   	=max([len(__lowercase	) for w in chinese_word_set]	)

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




def       A_  (				__lowercase						,	__lowercase						,	__lowercase	):
 UpperCamelCase_     :					Tuple   	=[]

 for i in range(0						,	len(__lowercase	)						,	1_00	):
  UpperCamelCase_     :					Union[str, Any]   	=ltp_tokenizer.seg(lines[i : i + 1_00]	)[0]
  UpperCamelCase_     :					int   	=[get_chinese_word(__lowercase	) for r in res]
  ltp_res.extend(__lowercase	)
 assert len(__lowercase	) == len(__lowercase	)

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

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

 assert len(__lowercase	) == len(__lowercase	)

 return ref_ids




def       A_  (				__lowercase	):
 # 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(__lowercase	) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
 UpperCamelCase_     :					Optional[Any]   	=LTP(args.ltp	)  # faster in GPU device
 UpperCamelCase_     :					Dict   	=BertTokenizer.from_pretrained(args.bert	)

 UpperCamelCase_     :					int   	=prepare_ref(__lowercase						,	__lowercase						,	__lowercase	)

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


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)
