
from __future__ import annotations

import unittest

from transformers import DebertaVaConfig, is_tf_available
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 (
        TFDebertaVaForMaskedLM,
        TFDebertaVaForQuestionAnswering,
        TFDebertaVaForSequenceClassification,
        TFDebertaVaForTokenClassification,
        TFDebertaVaModel,
    )




class 				_lowerCamelCase :





     """simple docstring"""







     def __init__(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE=1_3						,						__SCREAMING_SNAKE_CASE=7						,						__SCREAMING_SNAKE_CASE=True						,						__SCREAMING_SNAKE_CASE=True						,						__SCREAMING_SNAKE_CASE=True						,						__SCREAMING_SNAKE_CASE=True						,						__SCREAMING_SNAKE_CASE=9_9						,						__SCREAMING_SNAKE_CASE=3_2						,						__SCREAMING_SNAKE_CASE=2						,						__SCREAMING_SNAKE_CASE=4						,						__SCREAMING_SNAKE_CASE=3_7						,						__SCREAMING_SNAKE_CASE="gelu"						,						__SCREAMING_SNAKE_CASE=0.1						,						__SCREAMING_SNAKE_CASE=0.1						,						__SCREAMING_SNAKE_CASE=5_1_2						,						__SCREAMING_SNAKE_CASE=1_6						,						__SCREAMING_SNAKE_CASE=2						,						__SCREAMING_SNAKE_CASE=0.02						,						__SCREAMING_SNAKE_CASE=False						,						__SCREAMING_SNAKE_CASE=True						,						__SCREAMING_SNAKE_CASE="None"						,						__SCREAMING_SNAKE_CASE=3						,						__SCREAMING_SNAKE_CASE=4						,						__SCREAMING_SNAKE_CASE=None						,						)  ->   Union[str, Any]:




           """simple docstring"""







           UpperCamelCase__    :   Optional[int]					=				parent
           UpperCamelCase__    :   Any					=				batch_size
           UpperCamelCase__    :   List[str]					=				seq_length
           UpperCamelCase__    :   str					=				is_training
           UpperCamelCase__    :   Union[str, Any]					=				use_input_mask
           UpperCamelCase__    :   Union[str, Any]					=				use_token_type_ids
           UpperCamelCase__    :   Dict					=				use_labels
           UpperCamelCase__    :   Tuple					=				vocab_size
           UpperCamelCase__    :   Union[str, Any]					=				hidden_size
           UpperCamelCase__    :   int					=				num_hidden_layers
           UpperCamelCase__    :   Optional[Any]					=				num_attention_heads
           UpperCamelCase__    :   Any					=				intermediate_size
           UpperCamelCase__    :   str					=				hidden_act
           UpperCamelCase__    :   Tuple					=				hidden_dropout_prob
           UpperCamelCase__    :   Dict					=				attention_probs_dropout_prob
           UpperCamelCase__    :   Dict					=				max_position_embeddings
           UpperCamelCase__    :   Any					=				type_vocab_size
           UpperCamelCase__    :   Union[str, Any]					=				type_sequence_label_size
           UpperCamelCase__    :   Any					=				initializer_range
           UpperCamelCase__    :   Optional[int]					=				num_labels
           UpperCamelCase__    :   Optional[int]					=				num_choices
           UpperCamelCase__    :   List[str]					=				relative_attention
           UpperCamelCase__    :   Dict					=				position_biased_input
           UpperCamelCase__    :   Optional[Any]					=				pos_att_type
           UpperCamelCase__    :   Optional[int]					=				scope






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Tuple:




           """simple docstring"""







           UpperCamelCase__    :   List[Any]					=				ids_tensor([self.batch_size, self.seq_length]						,						self.vocab_size  )

           UpperCamelCase__    :   int					=				None
           if self.use_input_mask:
                 UpperCamelCase__    :   List[str]					=				random_attention_mask([self.batch_size, self.seq_length]  )

           UpperCamelCase__    :   Any					=				None
           if self.use_token_type_ids:
                 UpperCamelCase__    :   Optional[Any]					=				ids_tensor([self.batch_size, self.seq_length]						,						self.type_vocab_size  )

           UpperCamelCase__    :   int					=				None
           UpperCamelCase__    :   Tuple					=				None
           UpperCamelCase__    :   str					=				None
           if self.use_labels:
                 UpperCamelCase__    :   Union[str, Any]					=				ids_tensor([self.batch_size]						,						self.type_sequence_label_size  )
                 UpperCamelCase__    :   int					=				ids_tensor([self.batch_size, self.seq_length]						,						self.num_labels  )

           UpperCamelCase__    :   Tuple					=				DebertaVaConfig(
               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						,						relative_attention=self.relative_attention						,						position_biased_input=self.position_biased_input						,						initializer_range=self.initializer_range						,						return_dict=__SCREAMING_SNAKE_CASE						,						)

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






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  )  ->   List[Any]:




           """simple docstring"""







           UpperCamelCase__    :   str					=				TFDebertaVaModel(config=__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   Dict					=				{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}

           UpperCamelCase__    :   Any					=				[input_ids, input_mask]
           UpperCamelCase__    :   List[str]					=				model(__SCREAMING_SNAKE_CASE  )

           UpperCamelCase__    :   Optional[int]					=				model(__SCREAMING_SNAKE_CASE  )

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






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  )  ->   str:




           """simple docstring"""







           UpperCamelCase__    :   Optional[int]					=				TFDebertaVaForMaskedLM(config=__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   Optional[int]					=				{
               '''input_ids''': input_ids,
               '''attention_mask''': input_mask,
               '''token_type_ids''': token_type_ids,
           }
           UpperCamelCase__    :   Optional[Any]					=				model(__SCREAMING_SNAKE_CASE  )
           self.parent.assertEqual(result.logits.shape						,						(self.batch_size, self.seq_length, self.vocab_size)  )






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  )  ->   Tuple:




           """simple docstring"""







           UpperCamelCase__    :   Tuple					=				self.num_labels
           UpperCamelCase__    :   List[str]					=				TFDebertaVaForSequenceClassification(config=__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   Dict					=				{
               '''input_ids''': input_ids,
               '''attention_mask''': input_mask,
               '''token_type_ids''': token_type_ids,
           }

           UpperCamelCase__    :   Optional[int]					=				model(__SCREAMING_SNAKE_CASE  )
           self.parent.assertEqual(result.logits.shape						,						(self.batch_size, self.num_labels)  )






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  )  ->   Dict:




           """simple docstring"""







           UpperCamelCase__    :   str					=				self.num_labels
           UpperCamelCase__    :   int					=				TFDebertaVaForTokenClassification(config=__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   Union[str, Any]					=				{
               '''input_ids''': input_ids,
               '''attention_mask''': input_mask,
               '''token_type_ids''': token_type_ids,
           }
           UpperCamelCase__    :   Dict					=				model(__SCREAMING_SNAKE_CASE  )
           self.parent.assertEqual(result.logits.shape						,						(self.batch_size, self.seq_length, self.num_labels)  )






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  )  ->   List[str]:




           """simple docstring"""







           UpperCamelCase__    :   str					=				TFDebertaVaForQuestionAnswering(config=__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   Dict					=				{
               '''input_ids''': input_ids,
               '''attention_mask''': input_mask,
               '''token_type_ids''': token_type_ids,
           }

           UpperCamelCase__    :   Union[str, Any]					=				model(__SCREAMING_SNAKE_CASE  )
           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    __SCREAMING_SNAKE_CASE			(							self  )  ->   List[Any]:




           """simple docstring"""







           UpperCamelCase__    :   Dict					=				self.prepare_config_and_inputs()
           (
               (
               UpperCamelCase__
           )				,(
               UpperCamelCase__
           )				,(
               UpperCamelCase__
           )				,(
               UpperCamelCase__
           )				,(
               UpperCamelCase__
           )				,(
               UpperCamelCase__
           )				,(
               UpperCamelCase__
           )				,
           )    :   Any					=				config_and_inputs
           UpperCamelCase__    :   Union[str, Any]					=				{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
           return config, inputs_dict




@require_tf
class 				_lowerCamelCase (      UpperCamelCase_	,							UpperCamelCase_	,							unittest.TestCase ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		   =  (
         (
             TFDebertaVaModel,
             TFDebertaVaForMaskedLM,
             TFDebertaVaForQuestionAnswering,
             TFDebertaVaForSequenceClassification,
             TFDebertaVaForTokenClassification,
         )
         if is_tf_available()
         else ()
     )
     SCREAMING_SNAKE_CASE_		   =  (
         {
             '''feature-extraction''': TFDebertaVaModel,
             '''fill-mask''': TFDebertaVaForMaskedLM,
             '''question-answering''': TFDebertaVaForQuestionAnswering,
             '''text-classification''': TFDebertaVaForSequenceClassification,
             '''token-classification''': TFDebertaVaForTokenClassification,
             '''zero-shot''': TFDebertaVaForSequenceClassification,
         }
         if is_tf_available()
         else {}
     )

     SCREAMING_SNAKE_CASE_		   =  False
     SCREAMING_SNAKE_CASE_		   =  False






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   str:




           """simple docstring"""







           UpperCamelCase__    :   Dict					=				TFDebertaVaModelTester(self  )
           UpperCamelCase__    :   Optional[int]					=				ConfigTester(self						,						config_class=__SCREAMING_SNAKE_CASE						,						hidden_size=3_7  )






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Any:




           """simple docstring"""







           self.config_tester.run_common_tests()






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Tuple:




           """simple docstring"""







           UpperCamelCase__    :   str					=				self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE  )






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   int:




           """simple docstring"""







           UpperCamelCase__    :   Union[str, Any]					=				self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_masked_lm(*__SCREAMING_SNAKE_CASE  )






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




           """simple docstring"""







           UpperCamelCase__    :   int					=				self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_question_answering(*__SCREAMING_SNAKE_CASE  )






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




           """simple docstring"""







           UpperCamelCase__    :   Union[str, Any]					=				self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_sequence_classification(*__SCREAMING_SNAKE_CASE  )






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




           """simple docstring"""







           UpperCamelCase__    :   Dict					=				self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_token_classification(*__SCREAMING_SNAKE_CASE  )






     @slow
     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   List[Any]:




           """simple docstring"""







           UpperCamelCase__    :   Optional[int]					=				TFDebertaVaModel.from_pretrained('''kamalkraj/deberta-v2-xlarge'''  )
           self.assertIsNotNone(__SCREAMING_SNAKE_CASE  )




@require_tf
class 				_lowerCamelCase (      unittest.TestCase ):





     """simple docstring"""







     @unittest.skip(reason='''Model not available yet'''  )
     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Tuple:




           """simple docstring"""







           pass






     @slow
     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   List[Any]:




           """simple docstring"""







           UpperCamelCase__    :   List[str]					=				TFDebertaVaModel.from_pretrained('''kamalkraj/deberta-v2-xlarge'''  )
           UpperCamelCase__    :   str					=				tf.constant([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]  )
           UpperCamelCase__    :   str					=				tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]  )
           UpperCamelCase__    :   Optional[Any]					=				model(__SCREAMING_SNAKE_CASE						,						attention_mask=__SCREAMING_SNAKE_CASE  )[0]

           UpperCamelCase__    :   str					=				tf.constant(
               [[[0.2356, 0.1948, 0.0369], [-0.1063, 0.3586, -0.5152], [-0.6399, -0.0259, -0.2525]]]  )
           tf.debugging.assert_near(output[:, 1:4, 1:4]						,						__SCREAMING_SNAKE_CASE						,						atol=1e-4  )




import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__		):
      # 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 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__		):
      # word like '180' or '身高' or '神'
      for char in word:
            UpperCamelCase__    :   Optional[Any]					=				ord(UpperCamelCase__		)
            if not _is_chinese_char(UpperCamelCase__		):
                  return 0
      return 1


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__		):
      UpperCamelCase__    :   Dict					=				set()

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


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__		):
      if not chinese_word_set:
            return bert_tokens
      UpperCamelCase__    :   str					=				max([len(UpperCamelCase__		) for w in chinese_word_set]		)

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


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__		):
      UpperCamelCase__    :   str					=				[]

      for i in range(0 ,		len(UpperCamelCase__		) ,		1_0_0		):
            UpperCamelCase__    :   Optional[Any]					=				ltp_tokenizer.pipeline(lines[i : i + 1_0_0] ,		tasks=['''cws''']		).cws
            UpperCamelCase__    :   List[str]					=				[get_chinese_word(UpperCamelCase__		) for r in res]
            ltp_res.extend(UpperCamelCase__		)
      assert len(UpperCamelCase__		) == len(UpperCamelCase__		)

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

      UpperCamelCase__    :   Any					=				[]
      for input_ids, chinese_word in zip(UpperCamelCase__ ,		UpperCamelCase__		):
            UpperCamelCase__    :   Union[str, Any]					=				[]
            for id in input_ids:
                  UpperCamelCase__    :   List[str]					=				bert_tokenizer._convert_id_to_token(UpperCamelCase__		)
                  input_tokens.append(UpperCamelCase__		)
            UpperCamelCase__    :   Optional[Any]					=				add_sub_symbol(UpperCamelCase__ ,		UpperCamelCase__		)
            UpperCamelCase__    :   Any					=				[]
            # 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] == "##":
                        UpperCamelCase__    :   Union[str, Any]					=				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 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__		):
      # 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__    :   List[Any]					=				f.readlines()
      UpperCamelCase__    :   Tuple					=				[line.strip() for line in data if len(UpperCamelCase__		) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
      UpperCamelCase__    :   int					=				LTP(args.ltp		)  # faster in GPU device
      UpperCamelCase__    :   int					=				BertTokenizer.from_pretrained(args.bert		)

      UpperCamelCase__    :   Dict					=				prepare_ref(UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__		)

      with open(args.save_path ,		'''w''' ,		encoding='''utf-8'''		) as f:
            UpperCamelCase__    :   Tuple					=				[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)


