






import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      Optional[Any]     )    ->   Tuple:





    """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       _SCREAMING_SNAKE_CASE    (			__lowercase :      str     )    ->   List[str]:





    """simple docstring"""

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



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      List[str]     )    ->   Tuple:





    """simple docstring"""

    __A       =						set()

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



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      List[str]      ,					__lowercase :      set()     )    ->   Optional[int]:





    """simple docstring"""

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

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





    """simple docstring"""

    __A       =						[]

    for i in range(0      ,					len(__lowercase     )      ,					1_0_0     ):
        __A       =						ltp_tokenizer.seg(lines[i : i + 1_0_0]     )[0]
        __A       =						[get_chinese_word(__lowercase     ) for r in res]
        ltp_res.extend(__lowercase     )
    assert len(__lowercase     ) == len(__lowercase     )

    __A       =						[]
    for i in range(0      ,					len(__lowercase     )      ,					1_0_0     ):
        __A       =						bert_tokenizer(lines[i : i + 1_0_0]      ,					add_special_tokens=__lowercase      ,					truncation=__lowercase      ,					max_length=5_1_2     )
        bert_res.extend(res["""input_ids"""]     )
    assert len(__lowercase     ) == len(__lowercase     )

    __A       =						[]
    for input_ids, chinese_word in zip(__lowercase      ,					__lowercase     ):
        __A       =						[]
        for id in input_ids:
            __A       =						bert_tokenizer._convert_id_to_token(__lowercase     )
            input_tokens.append(__lowercase     )
        __A       =						add_sub_symbol(__lowercase      ,					__lowercase     )
        __A       =						[]
        # 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] == "##":
                __A       =						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       _SCREAMING_SNAKE_CASE    (			__lowercase :      List[str]     )    ->   List[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(__lowercase     ) > 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(__lowercase      ,					__lowercase      ,					__lowercase     )

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


if __name__ == "__main__":
  __a   :							str						= 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")

  __a   :							Optional[int]						= parser.parse_args()
  main(args)












import unittest

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

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers import (
      SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      SqueezeBertForMaskedLM,
      SqueezeBertForMultipleChoice,
      SqueezeBertForQuestionAnswering,
      SqueezeBertForSequenceClassification,
      SqueezeBertForTokenClassification,
      SqueezeBertModel,
  )


class    __lowercase   (						lowercase_	):







     '''simple docstring'''




     def __init__(					self							:      Optional[int]			,						UpperCamelCase_							:      Tuple			,						UpperCamelCase_							:      List[str]=13			,						UpperCamelCase_							:      int=7			,						UpperCamelCase_							:      Dict=True			,						UpperCamelCase_							:      int=True			,						UpperCamelCase_							:      str=False			,						UpperCamelCase_							:      Dict=True			,						UpperCamelCase_							:      Union[str, Any]=99			,						UpperCamelCase_							:      Any=32			,						UpperCamelCase_							:      Union[str, Any]=5			,						UpperCamelCase_							:      List[str]=4			,						UpperCamelCase_							:      Union[str, Any]=64			,						UpperCamelCase_							:      Any="gelu"			,						UpperCamelCase_							:      str=0.1			,						UpperCamelCase_							:      List[Any]=0.1			,						UpperCamelCase_							:      int=512			,						UpperCamelCase_							:      Dict=16			,						UpperCamelCase_							:      List[str]=2			,						UpperCamelCase_							:      int=0.02			,						UpperCamelCase_							:      Dict=3			,						UpperCamelCase_							:      List[Any]=4			,						UpperCamelCase_							:      int=None			,						UpperCamelCase_							:      Any=2			,						UpperCamelCase_							:      Any=2			,						UpperCamelCase_							:      int=2			,						UpperCamelCase_							:      Optional[Any]=2			,						UpperCamelCase_							:      str=4			,						UpperCamelCase_							:      List[str]=1			,						):


         """simple docstring"""



         __A       =						parent
         __A       =						batch_size
         __A       =						seq_length
         __A       =						is_training
         __A       =						use_input_mask
         __A       =						use_token_type_ids
         __A       =						use_labels
         __A       =						vocab_size
         __A       =						hidden_size
         __A       =						num_hidden_layers
         __A       =						num_attention_heads
         __A       =						intermediate_size
         __A       =						hidden_act
         __A       =						hidden_dropout_prob
         __A       =						attention_probs_dropout_prob
         __A       =						max_position_embeddings
         __A       =						type_vocab_size
         __A       =						type_sequence_label_size
         __A       =						initializer_range
         __A       =						num_labels
         __A       =						num_choices
         __A       =						scope
         __A       =						q_groups
         __A       =						k_groups
         __A       =						v_groups
         __A       =						post_attention_groups
         __A       =						intermediate_groups
         __A       =						output_groups




     def 		lowerCAmelCase_  (					self							:      Dict					):


         """simple docstring"""



         __A       =						ids_tensor([self.batch_size, self.seq_length]			,						self.vocab_size					)

         __A       =						None
         if self.use_input_mask:
             __A       =						random_attention_mask([self.batch_size, self.seq_length]					)

         __A       =						None
         __A       =						None
         __A       =						None
         if self.use_labels:
             __A       =						ids_tensor([self.batch_size]			,						self.type_sequence_label_size					)
             __A       =						ids_tensor([self.batch_size, self.seq_length]			,						self.num_labels					)
             __A       =						ids_tensor([self.batch_size]			,						self.num_choices					)

         __A       =						self.get_config()

         return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels




     def 		lowerCAmelCase_  (					self							:      Any					):


         """simple docstring"""



         return SqueezeBertConfig(
             embedding_size=self.hidden_size			,						vocab_size=self.vocab_size			,						hidden_size=self.hidden_size			,						num_hidden_layers=self.num_hidden_layers			,						num_attention_heads=self.num_attention_heads			,						intermediate_size=self.intermediate_size			,						hidden_act=self.hidden_act			,						attention_probs_dropout_prob=self.hidden_dropout_prob			,						attention_dropout=self.attention_probs_dropout_prob			,						max_position_embeddings=self.max_position_embeddings			,						initializer_range=self.initializer_range			,						q_groups=self.q_groups			,						k_groups=self.k_groups			,						v_groups=self.v_groups			,						post_attention_groups=self.post_attention_groups			,						intermediate_groups=self.intermediate_groups			,						output_groups=self.output_groups			,						)




     def 		lowerCAmelCase_  (					self							:      Tuple			,						UpperCamelCase_							:      List[Any]			,						UpperCamelCase_							:      int			,						UpperCamelCase_							:      int			,						UpperCamelCase_							:      List[Any]			,						UpperCamelCase_							:      int			,						UpperCamelCase_							:      Dict					):


         """simple docstring"""



         __A       =						SqueezeBertModel(config=UpperCamelCase_					)
         model.to(UpperCamelCase_					)
         model.eval()
         __A       =						model(UpperCamelCase_			,						UpperCamelCase_					)
         __A       =						model(UpperCamelCase_					)
         self.parent.assertEqual(result.last_hidden_state.shape			,						(self.batch_size, self.seq_length, self.hidden_size)					)




     def 		lowerCAmelCase_  (					self							:      List[str]			,						UpperCamelCase_							:      Optional[int]			,						UpperCamelCase_							:      str			,						UpperCamelCase_							:      Optional[int]			,						UpperCamelCase_							:      List[str]			,						UpperCamelCase_							:      Union[str, Any]			,						UpperCamelCase_							:      Optional[int]					):


         """simple docstring"""



         __A       =						SqueezeBertForMaskedLM(config=UpperCamelCase_					)
         model.to(UpperCamelCase_					)
         model.eval()
         __A       =						model(UpperCamelCase_			,						attention_mask=UpperCamelCase_			,						labels=UpperCamelCase_					)
         self.parent.assertEqual(result.logits.shape			,						(self.batch_size, self.seq_length, self.vocab_size)					)




     def 		lowerCAmelCase_  (					self							:      Optional[int]			,						UpperCamelCase_							:      int			,						UpperCamelCase_							:      Optional[Any]			,						UpperCamelCase_							:      Optional[Any]			,						UpperCamelCase_							:      Optional[Any]			,						UpperCamelCase_							:      Union[str, Any]			,						UpperCamelCase_							:      Optional[Any]					):


         """simple docstring"""



         __A       =						SqueezeBertForQuestionAnswering(config=UpperCamelCase_					)
         model.to(UpperCamelCase_					)
         model.eval()
         __A       =						model(
             UpperCamelCase_			,						attention_mask=UpperCamelCase_			,						start_positions=UpperCamelCase_			,						end_positions=UpperCamelCase_					)
         self.parent.assertEqual(result.start_logits.shape			,						(self.batch_size, self.seq_length)					)
         self.parent.assertEqual(result.end_logits.shape			,						(self.batch_size, self.seq_length)					)




     def 		lowerCAmelCase_  (					self							:      str			,						UpperCamelCase_							:      Tuple			,						UpperCamelCase_							:      List[Any]			,						UpperCamelCase_							:      Optional[int]			,						UpperCamelCase_							:      Any			,						UpperCamelCase_							:      List[Any]			,						UpperCamelCase_							:      Any					):


         """simple docstring"""



         __A       =						self.num_labels
         __A       =						SqueezeBertForSequenceClassification(UpperCamelCase_					)
         model.to(UpperCamelCase_					)
         model.eval()
         __A       =						model(UpperCamelCase_			,						attention_mask=UpperCamelCase_			,						labels=UpperCamelCase_					)
         self.parent.assertEqual(result.logits.shape			,						(self.batch_size, self.num_labels)					)




     def 		lowerCAmelCase_  (					self							:      Any			,						UpperCamelCase_							:      Optional[Any]			,						UpperCamelCase_							:      str			,						UpperCamelCase_							:      Dict			,						UpperCamelCase_							:      List[Any]			,						UpperCamelCase_							:      Dict			,						UpperCamelCase_							:      int					):


         """simple docstring"""



         __A       =						self.num_labels
         __A       =						SqueezeBertForTokenClassification(config=UpperCamelCase_					)
         model.to(UpperCamelCase_					)
         model.eval()

         __A       =						model(UpperCamelCase_			,						attention_mask=UpperCamelCase_			,						labels=UpperCamelCase_					)
         self.parent.assertEqual(result.logits.shape			,						(self.batch_size, self.seq_length, self.num_labels)					)




     def 		lowerCAmelCase_  (					self							:      Tuple			,						UpperCamelCase_							:      List[str]			,						UpperCamelCase_							:      List[Any]			,						UpperCamelCase_							:      List[Any]			,						UpperCamelCase_							:      Tuple			,						UpperCamelCase_							:      Optional[Any]			,						UpperCamelCase_							:      Dict					):


         """simple docstring"""



         __A       =						self.num_choices
         __A       =						SqueezeBertForMultipleChoice(config=UpperCamelCase_					)
         model.to(UpperCamelCase_					)
         model.eval()
         __A       =						input_ids.unsqueeze(1					).expand(-1			,						self.num_choices			,						-1					).contiguous()
         __A       =						input_mask.unsqueeze(1					).expand(-1			,						self.num_choices			,						-1					).contiguous()
         __A       =						model(
             UpperCamelCase_			,						attention_mask=UpperCamelCase_			,						labels=UpperCamelCase_			,						)
         self.parent.assertEqual(result.logits.shape			,						(self.batch_size, self.num_choices)					)





     def 		lowerCAmelCase_  (					self							:      int					):


         """simple docstring"""



         __A       =						self.prepare_config_and_inputs()
         ((__A)     ,						(__A)     ,						(__A)     ,						(__A)     ,						(__A)     ,						(__A))       =						config_and_inputs
         __A       =						{"""input_ids""": input_ids, """attention_mask""": input_mask}
         return config, inputs_dict


@require_torch
class    __lowercase   (						lowercase_      ,   lowercase_      ,   unittest.TestCase	):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE     =					(
         (
             SqueezeBertModel,
             SqueezeBertForMaskedLM,
             SqueezeBertForMultipleChoice,
             SqueezeBertForQuestionAnswering,
             SqueezeBertForSequenceClassification,
             SqueezeBertForTokenClassification,
         )
         if is_torch_available()
         else None
     )
     SCREAMING_SNAKE_CASE     =					(
         {
             "feature-extraction": SqueezeBertModel,
             "fill-mask": SqueezeBertForMaskedLM,
             "question-answering": SqueezeBertForQuestionAnswering,
             "text-classification": SqueezeBertForSequenceClassification,
             "token-classification": SqueezeBertForTokenClassification,
             "zero-shot": SqueezeBertForSequenceClassification,
         }
         if is_torch_available()
         else {}
     )
     SCREAMING_SNAKE_CASE     =					False
     SCREAMING_SNAKE_CASE     =					True
     SCREAMING_SNAKE_CASE     =					False




     def 		lowerCAmelCase_  (					self							:      Tuple					):


         """simple docstring"""



         __A       =						SqueezeBertModelTester(self					)
         __A       =						ConfigTester(self			,						config_class=UpperCamelCase_			,						dim=37					)




     def 		lowerCAmelCase_  (					self							:      Optional[Any]					):


         """simple docstring"""



         self.config_tester.run_common_tests()




     def 		lowerCAmelCase_  (					self							:      str					):


         """simple docstring"""



         __A       =						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_squeezebert_model(*UpperCamelCase_					)




     def 		lowerCAmelCase_  (					self							:      Union[str, Any]					):


         """simple docstring"""



         __A       =						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_squeezebert_for_masked_lm(*UpperCamelCase_					)




     def 		lowerCAmelCase_  (					self							:      Union[str, Any]					):


         """simple docstring"""



         __A       =						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_squeezebert_for_question_answering(*UpperCamelCase_					)




     def 		lowerCAmelCase_  (					self							:      Any					):


         """simple docstring"""



         __A       =						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_squeezebert_for_sequence_classification(*UpperCamelCase_					)




     def 		lowerCAmelCase_  (					self							:      Union[str, Any]					):


         """simple docstring"""



         __A       =						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_squeezebert_for_token_classification(*UpperCamelCase_					)




     def 		lowerCAmelCase_  (					self							:      Dict					):


         """simple docstring"""



         __A       =						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_squeezebert_for_multiple_choice(*UpperCamelCase_					)





     @slow
     def 		lowerCAmelCase_  (					self							:      int					):


         """simple docstring"""



         for model_name in SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             __A       =						SqueezeBertModel.from_pretrained(UpperCamelCase_					)
             self.assertIsNotNone(UpperCamelCase_					)


@require_sentencepiece
@require_tokenizers
@require_torch
class    __lowercase   (						unittest.TestCase	):







     '''simple docstring'''




     @slow
     def 		lowerCAmelCase_  (					self							:      Optional[int]					):


         """simple docstring"""



         __A       =						SqueezeBertForSequenceClassification.from_pretrained("""squeezebert/squeezebert-mnli"""					)

         __A       =						torch.tensor([[1, 29_414, 232, 328, 740, 1_140, 12_695, 69, 13, 1_588, 2]]					)
         __A       =						model(UpperCamelCase_					)[0]
         __A       =						torch.Size((1, 3)					)
         self.assertEqual(output.shape			,						UpperCamelCase_					)
         __A       =						torch.tensor([[0.6401, -0.0349, -0.6041]]					)
         self.assertTrue(torch.allclose(UpperCamelCase_			,						UpperCamelCase_			,						atol=1e-4					)					)




