'''simple docstring'''







from __future__ import annotations

import unittest

from transformers import MobileBertConfig, is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
      import tensorflow as tf

      from transformers import (
          TF_MODEL_FOR_PRETRAINING_MAPPING,
          TFMobileBertForMaskedLM,
          TFMobileBertForMultipleChoice,
          TFMobileBertForNextSentencePrediction,
          TFMobileBertForPreTraining,
          TFMobileBertForQuestionAnswering,
          TFMobileBertForSequenceClassification,
          TFMobileBertForTokenClassification,
          TFMobileBertModel,
      )


@require_tf
class 					A__      (      _snake_case						,						_snake_case						,						unittest.TestCase			):
    lowercase						=		(
        (
            TFMobileBertModel,
            TFMobileBertForMaskedLM,
            TFMobileBertForNextSentencePrediction,
            TFMobileBertForPreTraining,
            TFMobileBertForQuestionAnswering,
            TFMobileBertForSequenceClassification,
            TFMobileBertForTokenClassification,
            TFMobileBertForMultipleChoice,
        )
        if is_tf_available()
        else ()
    )
    lowercase						=		(
        {
            "feature-extraction": TFMobileBertModel,
            "fill-mask": TFMobileBertForMaskedLM,
            "question-answering": TFMobileBertForQuestionAnswering,
            "text-classification": TFMobileBertForSequenceClassification,
            "token-classification": TFMobileBertForTokenClassification,
            "zero-shot": TFMobileBertForSequenceClassification,
        }
        if is_tf_available()
        else {}
    )
    lowercase						=		False
    lowercase						=		False



    def 					snake_case_			(					self		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__=False       )						->      Any:





           '''simple docstring'''
           A_						=      super()._prepare_for_class(UpperCamelCase__		,				UpperCamelCase__		,				return_labels=UpperCamelCase__       )

           if return_labels:
                  if model_class in get_values(UpperCamelCase__       ):
                         A_						=      tf.zeros(self.model_tester.batch_size		,				dtype=tf.intaa       )

           return inputs_dict


    class 					A__      (      _snake_case			):



        def __init__(					self		,				UpperCamelCase__		,				UpperCamelCase__=13		,				UpperCamelCase__=7		,				UpperCamelCase__=True		,				UpperCamelCase__=True		,				UpperCamelCase__=True		,				UpperCamelCase__=True		,				UpperCamelCase__=99		,				UpperCamelCase__=32		,				UpperCamelCase__=32		,				UpperCamelCase__=2		,				UpperCamelCase__=4		,				UpperCamelCase__=37		,				UpperCamelCase__="gelu"		,				UpperCamelCase__=0.1		,				UpperCamelCase__=0.1		,				UpperCamelCase__=512		,				UpperCamelCase__=16		,				UpperCamelCase__=2		,				UpperCamelCase__=0.02		,				UpperCamelCase__=3		,				UpperCamelCase__=4		,				UpperCamelCase__=None		,				)						->      Optional[Any]:





               '''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_						=      embedding_size



        def 					snake_case_			(					self       )						->      List[str]:





               '''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
               if self.use_token_type_ids:
                      A_						=      ids_tensor([self.batch_size, self.seq_length]		,				self.type_vocab_size       )

               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_						=      MobileBertConfig(
                   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		,				hidden_dropout_prob=self.hidden_dropout_prob		,				attention_probs_dropout_prob=self.attention_probs_dropout_prob		,				max_position_embeddings=self.max_position_embeddings		,				type_vocab_size=self.type_vocab_size		,				initializer_range=self.initializer_range		,				embedding_size=self.embedding_size		,				)

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



        def 					snake_case_			(					self		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__       )						->      Tuple:





               '''simple docstring'''
               A_						=      TFMobileBertModel(config=UpperCamelCase__       )
               A_						=      {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
               A_						=      model(UpperCamelCase__       )

               A_						=      [input_ids, input_mask]
               A_						=      model(UpperCamelCase__       )

               A_						=      model(UpperCamelCase__       )

               self.parent.assertEqual(
                   result.last_hidden_state.shape		,				(self.batch_size, self.seq_length, self.hidden_size)       )
               self.parent.assertEqual(result.pooler_output.shape		,				(self.batch_size, self.hidden_size)       )



        def 					snake_case_			(					self		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__       )						->      Optional[Any]:





               '''simple docstring'''
               A_						=      TFMobileBertForMaskedLM(config=UpperCamelCase__       )
               A_						=      {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
               A_						=      model(UpperCamelCase__       )
               self.parent.assertEqual(result.logits.shape		,				(self.batch_size, self.seq_length, self.vocab_size)       )



        def 					snake_case_			(					self		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__       )						->      Optional[Any]:





               '''simple docstring'''
               A_						=      TFMobileBertForNextSentencePrediction(config=UpperCamelCase__       )
               A_						=      {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
               A_						=      model(UpperCamelCase__       )
               self.parent.assertEqual(result.logits.shape		,				(self.batch_size, 2)       )



        def 					snake_case_			(					self		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__       )						->      Optional[Any]:





               '''simple docstring'''
               A_						=      TFMobileBertForPreTraining(config=UpperCamelCase__       )
               A_						=      {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
               A_						=      model(UpperCamelCase__       )
               self.parent.assertEqual(
                   result.prediction_logits.shape		,				(self.batch_size, self.seq_length, self.vocab_size)       )
               self.parent.assertEqual(result.seq_relationship_logits.shape		,				(self.batch_size, 2)       )



        def 					snake_case_			(					self		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__       )						->      Dict:





               '''simple docstring'''
               A_						=      self.num_labels
               A_						=      TFMobileBertForSequenceClassification(config=UpperCamelCase__       )
               A_						=      {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
               A_						=      model(UpperCamelCase__       )
               self.parent.assertEqual(result.logits.shape		,				(self.batch_size, self.num_labels)       )



        def 					snake_case_			(					self		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__       )						->      Any:





               '''simple docstring'''
               A_						=      self.num_choices
               A_						=      TFMobileBertForMultipleChoice(config=UpperCamelCase__       )
               A_						=      tf.tile(tf.expand_dims(UpperCamelCase__		,				1       )		,				(1, self.num_choices, 1)       )
               A_						=      tf.tile(tf.expand_dims(UpperCamelCase__		,				1       )		,				(1, self.num_choices, 1)       )
               A_						=      tf.tile(tf.expand_dims(UpperCamelCase__		,				1       )		,				(1, self.num_choices, 1)       )
               A_						=      {
                   """input_ids""": multiple_choice_inputs_ids,
                   """attention_mask""": multiple_choice_input_mask,
                   """token_type_ids""": multiple_choice_token_type_ids,
               }
               A_						=      model(UpperCamelCase__       )
               self.parent.assertEqual(result.logits.shape		,				(self.batch_size, self.num_choices)       )



        def 					snake_case_			(					self		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__       )						->      Tuple:





               '''simple docstring'''
               A_						=      self.num_labels
               A_						=      TFMobileBertForTokenClassification(config=UpperCamelCase__       )
               A_						=      {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
               A_						=      model(UpperCamelCase__       )
               self.parent.assertEqual(result.logits.shape		,				(self.batch_size, self.seq_length, self.num_labels)       )



        def 					snake_case_			(					self		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__       )						->      str:





               '''simple docstring'''
               A_						=      TFMobileBertForQuestionAnswering(config=UpperCamelCase__       )
               A_						=      {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
               A_						=      model(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 					snake_case_			(					self       )						->      str:





               '''simple docstring'''
               A_						=      self.prepare_config_and_inputs()
               (
                   (
                   A_			
               )					,			(
                   A_			
               )					,			(
                   A_			
               )					,			(
                   A_			
               )					,			(
                   A_			
               )					,			(
                   A_			
               )					,			(
                   A_			
               )					,						
               )						=      config_and_inputs
               A_						=      {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
               return config, inputs_dict



    def 					snake_case_			(					self       )						->      Any:





           '''simple docstring'''
           A_						=      TFMobileBertModelTest.TFMobileBertModelTester(self       )
           A_						=      ConfigTester(self		,				config_class=UpperCamelCase__		,				hidden_size=37       )



    def 					snake_case_			(					self       )						->      str:





           '''simple docstring'''
           self.config_tester.run_common_tests()



    def 					snake_case_			(					self       )						->      int:





           '''simple docstring'''
           A_						=      self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_mobilebert_model(*UpperCamelCase__       )



    def 					snake_case_			(					self       )						->      Optional[Any]:





           '''simple docstring'''
           A_						=      self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_mobilebert_for_masked_lm(*UpperCamelCase__       )



    def 					snake_case_			(					self       )						->      Dict:





           '''simple docstring'''
           A_						=      self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_mobilebert_for_multiple_choice(*UpperCamelCase__       )



    def 					snake_case_			(					self       )						->      Optional[Any]:





           '''simple docstring'''
           A_						=      self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*UpperCamelCase__       )



    def 					snake_case_			(					self       )						->      Optional[int]:





           '''simple docstring'''
           A_						=      self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_mobilebert_for_pretraining(*UpperCamelCase__       )



    def 					snake_case_			(					self       )						->      Tuple:





           '''simple docstring'''
           A_						=      self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_mobilebert_for_question_answering(*UpperCamelCase__       )



    def 					snake_case_			(					self       )						->      Dict:





           '''simple docstring'''
           A_						=      self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_mobilebert_for_sequence_classification(*UpperCamelCase__       )



    def 					snake_case_			(					self       )						->      List[Any]:





           '''simple docstring'''
           A_						=      self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_mobilebert_for_token_classification(*UpperCamelCase__       )







    @slow
    def 					snake_case_			(					self       )						->      Union[str, Any]:





           '''simple docstring'''
           # for model_name in TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           for model_name in ["google/mobilebert-uncased"]:
                  A_						=      TFMobileBertModel.from_pretrained(UpperCamelCase__       )
                  self.assertIsNotNone(UpperCamelCase__       )


@require_tf
class 					A__      (      unittest.TestCase			):



    @slow
    def 					snake_case_			(					self       )						->      Any:





           '''simple docstring'''
           A_						=      TFMobileBertForPreTraining.from_pretrained("""google/mobilebert-uncased"""       )
           A_						=      tf.constant([[0, 1, 2, 3, 4, 5]]       )
           A_						=      model(UpperCamelCase__       )[0]

           A_						=      [1, 6, 30522]
           self.assertEqual(output.shape		,				UpperCamelCase__       )

           A_						=      tf.constant(
               [
                   [
                       [-4.5919547, -9.248295, -9.645256],
                       [-6.7306175, -6.440284, -6.6052837],
                       [-7.2743506, -6.7847915, -6.024673],
                   ]
               ]       )
           tf.debugging.assert_near(output[:, :3, :3]		,				UpperCamelCase__		,				atol=1e-4       )



'''simple docstring'''







import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer
def       UpperCAmelCase__    (      UpperCAmelCase__				)			->			Optional[int]:
       # 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 >= 0X4E_00 and cp <= 0X9F_FF)
           or (cp >= 0X34_00 and cp <= 0X4D_BF)  #
           or (cp >= 0X2_00_00 and cp <= 0X2_A6_DF)  #
           or (cp >= 0X2_A7_00 and cp <= 0X2_B7_3F)  #
           or (cp >= 0X2_B7_40 and cp <= 0X2_B8_1F)  #
           or (cp >= 0X2_B8_20 and cp <= 0X2_CE_AF)  #
           or (cp >= 0XF9_00 and cp <= 0XFA_FF)
           or (cp >= 0X2_F8_00 and cp <= 0X2_FA_1F)  #
       ):  #
              return True

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

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

       A_						=      bert_tokens
       A_					,			A_						=      0, len(UpperCAmelCase__				)
       while start < end:
              A_						=      True
              if is_chinese(bert_word[start]				):
                     A_						=      min(end - start,       UpperCAmelCase__				)
                     for i in range(UpperCAmelCase__,       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       UpperCAmelCase__    (      UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__				)			->			Dict:
       A_						=      []

       for i in range(0,       len(UpperCAmelCase__				),       1_00				):
              A_						=      ltp_tokenizer.pipeline(lines[i : i + 1_00],       tasks=["""cws"""]				).cws
              A_						=      [get_chinese_word(UpperCAmelCase__				) for r in res]
              ltp_res.extend(UpperCAmelCase__				)
       assert len(UpperCAmelCase__				) == len(UpperCAmelCase__				)

       A_						=      []
       for i in range(0,       len(UpperCAmelCase__				),       1_00				):
              A_						=      bert_tokenizer(lines[i : i + 1_00],       add_special_tokens=UpperCAmelCase__,       truncation=UpperCAmelCase__,       max_length=5_12				)
              bert_res.extend(res["""input_ids"""]				)
       assert len(UpperCAmelCase__				) == len(UpperCAmelCase__				)

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

       assert len(UpperCAmelCase__				) == len(UpperCAmelCase__				)

       return ref_ids
def       UpperCAmelCase__    (      UpperCAmelCase__				)			->			Optional[Any]:
       # 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(UpperCAmelCase__				) > 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(UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__				)

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


if __name__ == "__main__":
      __lowerCamelCase		       =    argparse.ArgumentParser(description='''prepare_chinese_ref''')
      parser.add_argument(
          '''--file_name''',
          required=False,
          type=str,
          default='''./resources/chinese-demo.txt''',
          help='''file need process, same as training data in lm''',
      )
      parser.add_argument(
          '''--ltp''',
          required=False,
          type=str,
          default='''./resources/ltp''',
          help='''resources for LTP tokenizer, usually a path''',
      )
      parser.add_argument(
          '''--bert''',
          required=False,
          type=str,
          default='''./resources/robert''',
          help='''resources for Bert tokenizer''',
      )
      parser.add_argument(
          '''--save_path''',
          required=False,
          type=str,
          default='''./resources/ref.txt''',
          help='''path to save res''',
      )

      __lowerCamelCase		       =    parser.parse_args()
      main(args)
