





import os
import tempfile
import unittest

from transformers import NezhaConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import (
        MODEL_FOR_PRETRAINING_MAPPING,
        NezhaForMaskedLM,
        NezhaForMultipleChoice,
        NezhaForNextSentencePrediction,
        NezhaForPreTraining,
        NezhaForQuestionAnswering,
        NezhaForSequenceClassification,
        NezhaForTokenClassification,
        NezhaModel,
    )
    from transformers.models.nezha.modeling_nezha import NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST


class 			lowerCamelCase						:







  def __init__(     self						:   str  ,					__snake_case						:   Any  ,					__snake_case						:   List[str]=13  ,					__snake_case						:   Union[str, Any]=7  ,					__snake_case						:   str=True  ,					__snake_case						:   Optional[int]=True  ,					__snake_case						:   List[Any]=True  ,					__snake_case						:   str=True  ,					__snake_case						:   str=99  ,					__snake_case						:   Optional[Any]=32  ,					__snake_case						:   Optional[int]=5  ,					__snake_case						:   Any=4  ,					__snake_case						:   List[Any]=37  ,					__snake_case						:   str="gelu"  ,					__snake_case						:   int=0.1  ,					__snake_case						:   Optional[int]=0.1  ,					__snake_case						:   List[Any]=128  ,					__snake_case						:   str=32  ,					__snake_case						:   Any=16  ,					__snake_case						:   List[str]=2  ,					__snake_case						:   Optional[Any]=0.02  ,					__snake_case						:   Tuple=3  ,					__snake_case						:   Optional[int]=4  ,					__snake_case						:   Any=None  ,					)    ->		Tuple:
        _a							:  int				= parent
        _a							:  List[str]				= batch_size
        _a							:  Tuple				= seq_length
        _a							:  Optional[int]				= is_training
        _a							:  Union[str, Any]				= use_input_mask
        _a							:  Optional[Any]				= use_token_type_ids
        _a							:  str				= use_labels
        _a							:  str				= vocab_size
        _a							:  List[Any]				= hidden_size
        _a							:  Dict				= num_hidden_layers
        _a							:  Optional[Any]				= num_attention_heads
        _a							:  List[Any]				= intermediate_size
        _a							:  List[str]				= hidden_act
        _a							:  Dict				= hidden_dropout_prob
        _a							:  Tuple				= attention_probs_dropout_prob
        _a							:  Any				= max_position_embeddings
        _a							:  Optional[Any]				= type_vocab_size
        _a							:  Tuple				= type_sequence_label_size
        _a							:  Optional[Any]				= initializer_range
        _a							:  Optional[int]				= num_labels
        _a							:  List[str]				= num_choices
        _a							:  Dict				= scope







  def      snake_case_							(     self						:   Optional[Any]       )    ->		Any:
        _a							:  Union[str, Any]				= ids_tensor([self.batch_size, self.seq_length]  ,					self.vocab_size       )

        _a							:  List[Any]				= None
        if self.use_input_mask:
              _a							:  Union[str, Any]				= random_attention_mask([self.batch_size, self.seq_length]       )

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

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

        _a							:  int				= self.get_config()

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







  def      snake_case_							(     self						:   Union[str, Any]       )    ->		Optional[Any]:
        return NezhaConfig(
            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  ,					is_decoder=__lowerCamelCase  ,					initializer_range=self.initializer_range  ,					)







  def      snake_case_							(     self						:   Union[str, Any]       )    ->		Optional[Any]:
        (
            _a							
        )							:  Optional[int]				= self.prepare_config_and_inputs()

        _a							:  Any				= True
        _a							:  Dict				= floats_tensor([self.batch_size, self.seq_length, self.hidden_size]       )
        _a							:  Dict				= ids_tensor([self.batch_size, self.seq_length]  ,					vocab_size=2       )

        return (
            config,
            input_ids,
            token_type_ids,
            input_mask,
            sequence_labels,
            token_labels,
            choice_labels,
            encoder_hidden_states,
            encoder_attention_mask,
        )







  def      snake_case_							(     self						:   Any  ,					__snake_case						:   int  ,					__snake_case						:   Dict  ,					__snake_case						:   int  ,					__snake_case						:   str  ,					__snake_case						:   Dict  ,					__snake_case						:   Optional[int]  ,					__snake_case						:   Tuple       )    ->		List[Any]:
        _a							:  List[Any]				= NezhaModel(config=__lowerCamelCase       )
        model.to(__lowerCamelCase       )
        model.eval()
        _a							:  Dict				= model(__lowerCamelCase  ,					attention_mask=__lowerCamelCase  ,					token_type_ids=__lowerCamelCase       )
        _a							:  int				= model(__lowerCamelCase  ,					token_type_ids=__lowerCamelCase       )
        _a							:  Tuple				= model(__lowerCamelCase       )
        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						:   int  ,					__snake_case						:   int  ,					__snake_case						:   Union[str, Any]  ,					__snake_case						:   Union[str, Any]  ,					__snake_case						:   List[str]  ,					__snake_case						:   List[str]  ,					__snake_case						:   int  ,					__snake_case						:   List[Any]  ,					__snake_case						:   Any  ,					__snake_case						:   List[str]  ,					)    ->		Optional[int]:
        _a							:  Union[str, Any]				= True
        _a							:  List[Any]				= NezhaModel(__lowerCamelCase       )
        model.to(__lowerCamelCase       )
        model.eval()
        _a							:  List[Any]				= model(
            __lowerCamelCase  ,					attention_mask=__lowerCamelCase  ,					token_type_ids=__lowerCamelCase  ,					encoder_hidden_states=__lowerCamelCase  ,					encoder_attention_mask=__lowerCamelCase  ,					)
        _a							:  Any				= model(
            __lowerCamelCase  ,					attention_mask=__lowerCamelCase  ,					token_type_ids=__lowerCamelCase  ,					encoder_hidden_states=__lowerCamelCase  ,					)
        _a							:  Tuple				= model(__lowerCamelCase  ,					attention_mask=__lowerCamelCase  ,					token_type_ids=__lowerCamelCase       )
        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						:   Any  ,					__snake_case						:   Dict  ,					__snake_case						:   str  ,					__snake_case						:   Union[str, Any]  ,					__snake_case						:   List[str]  ,					__snake_case						:   Optional[Any]  ,					__snake_case						:   Optional[Any]  ,					__snake_case						:   Tuple       )    ->		str:
        _a							:  Union[str, Any]				= NezhaForMaskedLM(config=__lowerCamelCase       )
        model.to(__lowerCamelCase       )
        model.eval()
        _a							:  List[Any]				= model(__lowerCamelCase  ,					attention_mask=__lowerCamelCase  ,					token_type_ids=__lowerCamelCase  ,					labels=__lowerCamelCase       )
        self.parent.assertEqual(result.logits.shape  ,					(self.batch_size, self.seq_length, self.vocab_size)       )







  def      snake_case_							(     self						:   Any  ,					__snake_case						:   Optional[int]  ,					__snake_case						:   List[str]  ,					__snake_case						:   List[Any]  ,					__snake_case						:   Dict  ,					__snake_case						:   Union[str, Any]  ,					__snake_case						:   int  ,					__snake_case						:   Optional[int]       )    ->		Optional[int]:
        _a							:  str				= NezhaForNextSentencePrediction(config=__lowerCamelCase       )
        model.to(__lowerCamelCase       )
        model.eval()
        _a							:  str				= model(
            __lowerCamelCase  ,					attention_mask=__lowerCamelCase  ,					token_type_ids=__lowerCamelCase  ,					labels=__lowerCamelCase  ,					)
        self.parent.assertEqual(result.logits.shape  ,					(self.batch_size, 2)       )







  def      snake_case_							(     self						:   int  ,					__snake_case						:   List[str]  ,					__snake_case						:   Optional[int]  ,					__snake_case						:   int  ,					__snake_case						:   str  ,					__snake_case						:   Tuple  ,					__snake_case						:   Dict  ,					__snake_case						:   List[Any]       )    ->		Union[str, Any]:
        _a							:  str				= NezhaForPreTraining(config=__lowerCamelCase       )
        model.to(__lowerCamelCase       )
        model.eval()
        _a							:  Optional[int]				= model(
            __lowerCamelCase  ,					attention_mask=__lowerCamelCase  ,					token_type_ids=__lowerCamelCase  ,					labels=__lowerCamelCase  ,					next_sentence_label=__lowerCamelCase  ,					)
        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						:   str  ,					__snake_case						:   Any  ,					__snake_case						:   Tuple  ,					__snake_case						:   Optional[int]  ,					__snake_case						:   Optional[int]  ,					__snake_case						:   Tuple  ,					__snake_case						:   Dict  ,					__snake_case						:   Optional[Any]       )    ->		Any:
        _a							:  int				= NezhaForQuestionAnswering(config=__lowerCamelCase       )
        model.to(__lowerCamelCase       )
        model.eval()
        _a							:  Any				= model(
            __lowerCamelCase  ,					attention_mask=__lowerCamelCase  ,					token_type_ids=__lowerCamelCase  ,					start_positions=__lowerCamelCase  ,					end_positions=__lowerCamelCase  ,					)
        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						:   Any  ,					__snake_case						:   Optional[Any]  ,					__snake_case						:   List[str]  ,					__snake_case						:   int  ,					__snake_case						:   Optional[Any]  ,					__snake_case						:   List[Any]  ,					__snake_case						:   Tuple  ,					__snake_case						:   Dict       )    ->		Optional[int]:
        _a							:  Any				= self.num_labels
        _a							:  List[Any]				= NezhaForSequenceClassification(__lowerCamelCase       )
        model.to(__lowerCamelCase       )
        model.eval()
        _a							:  Dict				= model(__lowerCamelCase  ,					attention_mask=__lowerCamelCase  ,					token_type_ids=__lowerCamelCase  ,					labels=__lowerCamelCase       )
        self.parent.assertEqual(result.logits.shape  ,					(self.batch_size, self.num_labels)       )







  def      snake_case_							(     self						:   str  ,					__snake_case						:   List[str]  ,					__snake_case						:   List[str]  ,					__snake_case						:   Optional[int]  ,					__snake_case						:   List[str]  ,					__snake_case						:   List[Any]  ,					__snake_case						:   List[str]  ,					__snake_case						:   Dict       )    ->		Any:
        _a							:  str				= self.num_labels
        _a							:  List[Any]				= NezhaForTokenClassification(config=__lowerCamelCase       )
        model.to(__lowerCamelCase       )
        model.eval()
        _a							:  List[Any]				= model(__lowerCamelCase  ,					attention_mask=__lowerCamelCase  ,					token_type_ids=__lowerCamelCase  ,					labels=__lowerCamelCase       )
        self.parent.assertEqual(result.logits.shape  ,					(self.batch_size, self.seq_length, self.num_labels)       )







  def      snake_case_							(     self						:   int  ,					__snake_case						:   List[str]  ,					__snake_case						:   Optional[Any]  ,					__snake_case						:   Optional[Any]  ,					__snake_case						:   Any  ,					__snake_case						:   Tuple  ,					__snake_case						:   int  ,					__snake_case						:   Optional[int]       )    ->		Dict:
        _a							:  Tuple				= self.num_choices
        _a							:  Optional[Any]				= NezhaForMultipleChoice(config=__lowerCamelCase       )
        model.to(__lowerCamelCase       )
        model.eval()
        _a							:  List[str]				= input_ids.unsqueeze(1       ).expand(-1  ,					self.num_choices  ,					-1       ).contiguous()
        _a							:  List[str]				= token_type_ids.unsqueeze(1       ).expand(-1  ,					self.num_choices  ,					-1       ).contiguous()
        _a							:  int				= input_mask.unsqueeze(1       ).expand(-1  ,					self.num_choices  ,					-1       ).contiguous()
        _a							:  Dict				= model(
            __lowerCamelCase  ,					attention_mask=__lowerCamelCase  ,					token_type_ids=__lowerCamelCase  ,					labels=__lowerCamelCase  ,					)
        self.parent.assertEqual(result.logits.shape  ,					(self.batch_size, self.num_choices)       )







  def      snake_case_							(     self						:   int       )    ->		Optional[int]:
        _a							:  Any				= self.prepare_config_and_inputs()
        (
            _a							
        )							:  List[Any]				= config_and_inputs
        _a							:  List[Any]				= {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
        return config, inputs_dict


@require_torch
class 			lowerCamelCase						(		lowerCAmelCase_ ,					lowerCAmelCase_ ,					lowerCAmelCase_ ,					unittest.TestCase  ):
  UpperCAmelCase		:       List[str]										=     (
      (
          NezhaModel,
          NezhaForMaskedLM,
          NezhaForMultipleChoice,
          NezhaForNextSentencePrediction,
          NezhaForPreTraining,
          NezhaForQuestionAnswering,
          NezhaForSequenceClassification,
          NezhaForTokenClassification,
      )
      if is_torch_available()
      else ()
  )
  UpperCAmelCase		:       int										=     (
      {
          'feature-extraction': NezhaModel,
          'fill-mask': NezhaForMaskedLM,
          'question-answering': NezhaForQuestionAnswering,
          'text-classification': NezhaForSequenceClassification,
          'token-classification': NezhaForTokenClassification,
          'zero-shot': NezhaForSequenceClassification,
      }
      if is_torch_available()
      else {}
  )
  UpperCAmelCase		:       Any										=     True







  def      snake_case_							(     self						:   Dict  ,					__snake_case						:   int  ,					__snake_case						:   Tuple  ,					__snake_case						:   str=False       )    ->		Optional[Any]:
        _a							:  List[str]				= super()._prepare_for_class(__lowerCamelCase  ,					__lowerCamelCase  ,					return_labels=__lowerCamelCase       )

        if return_labels:
              if model_class in get_values(__lowerCamelCase       ):
                    _a							:  Optional[Any]				= torch.zeros(
                        (self.model_tester.batch_size, self.model_tester.seq_length)  ,					dtype=torch.long  ,					device=__lowerCamelCase       )
                    _a							:  Optional[Any]				= torch.zeros(
                        self.model_tester.batch_size  ,					dtype=torch.long  ,					device=__lowerCamelCase       )
        return inputs_dict







  def      snake_case_							(     self						:   int       )    ->		str:
        _a							:  Optional[Any]				= NezhaModelTester(self       )
        _a							:  int				= ConfigTester(self  ,					config_class=__lowerCamelCase  ,					hidden_size=37       )







  def      snake_case_							(     self						:   List[str]       )    ->		int:
        self.config_tester.run_common_tests()







  def      snake_case_							(     self						:   str       )    ->		Any:
        _a							:  List[str]				= self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*__lowerCamelCase       )







  def      snake_case_							(     self						:   Tuple       )    ->		Optional[Any]:
        _a							:  Union[str, Any]				= self.model_tester.prepare_config_and_inputs_for_decoder()
        self.model_tester.create_and_check_model_as_decoder(*__lowerCamelCase       )







  def      snake_case_							(     self						:   Any       )    ->		Union[str, Any]:
        # This regression test was failing with PyTorch < 1.3
        (
            _a							
        )							:  str				= self.model_tester.prepare_config_and_inputs_for_decoder()

        _a							:  int				= None

        self.model_tester.create_and_check_model_as_decoder(
            __lowerCamelCase  ,					__lowerCamelCase  ,					__lowerCamelCase  ,					__lowerCamelCase  ,					__lowerCamelCase  ,					__lowerCamelCase  ,					__lowerCamelCase  ,					__lowerCamelCase  ,					__lowerCamelCase  ,					)







  def      snake_case_							(     self						:   Union[str, Any]       )    ->		Union[str, Any]:
        _a							:  str				= self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_lm(*__lowerCamelCase       )







  def      snake_case_							(     self						:   Dict       )    ->		List[str]:
        _a							:  Tuple				= self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_multiple_choice(*__lowerCamelCase       )







  def      snake_case_							(     self						:   int       )    ->		List[str]:
        _a							:  Dict				= self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_next_sequence_prediction(*__lowerCamelCase       )







  def      snake_case_							(     self						:   Any       )    ->		Dict:
        _a							:  Any				= self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_pretraining(*__lowerCamelCase       )







  def      snake_case_							(     self						:   Any       )    ->		Dict:
        _a							:  Optional[int]				= self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_question_answering(*__lowerCamelCase       )







  def      snake_case_							(     self						:   Optional[int]       )    ->		int:
        _a							:  str				= self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_sequence_classification(*__lowerCamelCase       )







  def      snake_case_							(     self						:   List[str]       )    ->		str:
        _a							:  List[str]				= self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_token_classification(*__lowerCamelCase       )







  @slow
  def      snake_case_							(     self						:   Optional[int]       )    ->		Union[str, Any]:
        for model_name in NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              _a							:  List[str]				= NezhaModel.from_pretrained(__lowerCamelCase       )
              self.assertIsNotNone(__lowerCamelCase       )







  @slow
  @require_torch_gpu
  def      snake_case_							(     self						:   Tuple       )    ->		Tuple:
        _a							:  int				= self.model_tester.prepare_config_and_inputs_for_common()
        for model_class in self.all_model_classes:
              # NezhaForMultipleChoice behaves incorrectly in JIT environments.
              if model_class == NezhaForMultipleChoice:
                    return

              _a							:  int				= True
              _a							:  Tuple				= model_class(config=__lowerCamelCase       )

              _a							:  str				= self._prepare_for_class(__lowerCamelCase  ,					__lowerCamelCase       )
              _a							:  str				= torch.jit.trace(
                  __lowerCamelCase  ,					(inputs_dict['''input_ids'''].to('''cpu'''       ), inputs_dict['''attention_mask'''].to('''cpu'''       ))       )

              with tempfile.TemporaryDirectory() as tmp:
                    torch.jit.save(__lowerCamelCase  ,					os.path.join(__lowerCamelCase  ,					'''bert.pt'''       )       )
                    _a							:  str				= torch.jit.load(os.path.join(__lowerCamelCase  ,					'''bert.pt'''       )  ,					map_location=__lowerCamelCase       )
                    loaded(inputs_dict['''input_ids'''].to(__lowerCamelCase       )  ,					inputs_dict['''attention_mask'''].to(__lowerCamelCase       )       )


@require_torch
class 			lowerCamelCase						(		unittest.TestCase  ):







  @slow
  def      snake_case_							(     self						:   List[Any]       )    ->		Union[str, Any]:
        _a							:  Optional[Any]				= NezhaModel.from_pretrained('''sijunhe/nezha-cn-base'''       )
        _a							:  Dict				= torch.tensor([[0, 1, 2, 3, 4, 5]]       )
        _a							:  Optional[Any]				= torch.tensor([[0, 1, 1, 1, 1, 1]]       )
        with torch.no_grad():
              _a							:  int				= model(__lowerCamelCase  ,					attention_mask=__lowerCamelCase       )[0]
        _a							:  Dict				= torch.Size((1, 6, 768)       )
        self.assertEqual(output.shape  ,					__lowerCamelCase       )
        _a							:  Any				= torch.tensor([[[0.0_685, 0.2_441, 0.1_102], [0.0_600, 0.1_906, 0.1_349], [0.0_221, 0.0_819, 0.0_586]]]       )

        self.assertTrue(torch.allclose(output[:, 1:4, 1:4]  ,					__lowerCamelCase  ,					atol=1E-4       )       )







  @slow
  def      snake_case_							(     self						:   Union[str, Any]       )    ->		Optional[int]:
        _a							:  Dict				= NezhaForMaskedLM.from_pretrained('''sijunhe/nezha-cn-base'''       )
        _a							:  Any				= torch.tensor([[0, 1, 2, 3, 4, 5]]       )
        _a							:  Dict				= torch.tensor([[1, 1, 1, 1, 1, 1]]       )
        with torch.no_grad():
              _a							:  List[str]				= model(__lowerCamelCase  ,					attention_mask=__lowerCamelCase       )[0]
        _a							:  int				= torch.Size((1, 6, 21128)       )
        self.assertEqual(output.shape  ,					__lowerCamelCase       )
        _a							:  List[str]				= torch.tensor(
            [[-2.7_939, -1.7_902, -2.2_189], [-2.8_585, -1.8_908, -2.3_723], [-2.6_499, -1.7_750, -2.2_558]]       )

        self.assertTrue(torch.allclose(output[:, 1:4, 1:4]  ,					__lowerCamelCase  ,					atol=1E-4       )       )











'''simple docstring'''






import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mvp import MvpTokenizer


A_			      =		logging.get_logger(__name__)


A_			      =		{"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

# See all MVP models at https://huggingface.co/models?filter=mvp
A_			      =		{
    "vocab_file": {
        "RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json",
    },
    "added_tokens.json": {
        "RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json",
    },
    "merges_file": {
        "RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt",
    },
    "tokenizer_file": {
        "RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/tokenizer.json",
    },
}

A_			      =		{
    "RUCAIBox/mvp": 1024,
}






class        lowercase_      (						lowerCAmelCase_							):
     A_											=			VOCAB_FILES_NAMES
     A_											=			PRETRAINED_VOCAB_FILES_MAP
     A_											=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     A_											=			["input_ids", "attention_mask"]
     A_											=			MvpTokenizer





     def __init__(     self     :					Optional[int]     ,							__lowerCamelCase     :					Tuple=None     ,							__lowerCamelCase     :					Tuple=None     ,							__lowerCamelCase     :					List[str]=None     ,							__lowerCamelCase     :					Optional[int]="replace"     ,							__lowerCamelCase     :					int="<s>"     ,							__lowerCamelCase     :					Dict="</s>"     ,							__lowerCamelCase     :					int="</s>"     ,							__lowerCamelCase     :					int="<s>"     ,							__lowerCamelCase     :					int="<unk>"     ,							__lowerCamelCase     :					List[str]="<pad>"     ,							__lowerCamelCase     :					int="<mask>"     ,							__lowerCamelCase     :					int=False     ,							__lowerCamelCase     :					List[str]=True     ,							**__lowerCamelCase     :					Dict     ,							):
           super().__init__(
               __lowerCamelCase     ,							__lowerCamelCase     ,							tokenizer_file=__lowerCamelCase     ,							errors=__lowerCamelCase     ,							bos_token=__lowerCamelCase     ,							eos_token=__lowerCamelCase     ,							sep_token=__lowerCamelCase     ,							cls_token=__lowerCamelCase     ,							unk_token=__lowerCamelCase     ,							pad_token=__lowerCamelCase     ,							mask_token=__lowerCamelCase     ,							add_prefix_space=__lowerCamelCase     ,							trim_offsets=__lowerCamelCase     ,							**__lowerCamelCase     ,							)

           snake_case__       :   Any	      =    json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()			)
           if pre_tok_state.get('add_prefix_space'     ,							__lowerCamelCase			) != add_prefix_space:
                 snake_case__       :   str	      =    getattr(__lowerCamelCase     ,							pre_tok_state.pop('type'			)			)
                 snake_case__       :   int	      =    add_prefix_space
                 snake_case__       :   List[str]	      =    pre_tok_class(**__lowerCamelCase			)

           snake_case__       :   Tuple	      =    add_prefix_space

           # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
           snake_case__       :   Dict	      =    'post_processor'
           snake_case__       :   Optional[int]	      =    getattr(self.backend_tokenizer     ,							__lowerCamelCase     ,							__lowerCamelCase			)
           if tokenizer_component_instance:
                 snake_case__       :   List[Any]	      =    json.loads(tokenizer_component_instance.__getstate__()			)

                 # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
                 if "sep" in state:
                       snake_case__       :   int	      =    tuple(state['sep']			)
                 if "cls" in state:
                       snake_case__       :   Union[str, Any]	      =    tuple(state['cls']			)

                 snake_case__       :   Dict	      =    False

                 if state.get('add_prefix_space'     ,							__lowerCamelCase			) != add_prefix_space:
                       snake_case__       :   List[Any]	      =    add_prefix_space
                       snake_case__       :   List[str]	      =    True

                 if state.get('trim_offsets'     ,							__lowerCamelCase			) != trim_offsets:
                       snake_case__       :   str	      =    trim_offsets
                       snake_case__       :   List[Any]	      =    True

                 if changes_to_apply:
                       snake_case__       :   Optional[int]	      =    getattr(__lowerCamelCase     ,							state.pop('type'			)			)
                       snake_case__       :   str	      =    component_class(**__lowerCamelCase			)
                       setattr(self.backend_tokenizer     ,							__lowerCamelCase     ,							__lowerCamelCase			)





     @property
     def 			_lowerCAmelCase      (     self     :					str			):
           if self._mask_token is None:
                 if self.verbose:
                       logger.error('Using mask_token, but it is not set yet.'			)
                 return None
           return str(self._mask_token			)





     @mask_token.setter
     def 			_lowerCAmelCase      (     self     :					Tuple     ,							__lowerCamelCase     :					Optional[Any]			):
           snake_case__       :   str	      =    AddedToken(__lowerCamelCase     ,							lstrip=__lowerCamelCase     ,							rstrip=__lowerCamelCase			) if isinstance(__lowerCamelCase     ,							__lowerCamelCase			) else value
           snake_case__       :   Optional[int]	      =    value





     def 			_lowerCAmelCase      (     self     :					List[str]     ,							*__lowerCamelCase     :					List[str]     ,							**__lowerCamelCase     :					List[Any]			):
           snake_case__       :   Optional[int]	      =    kwargs.get('is_split_into_words'     ,							__lowerCamelCase			)

           if is_split_into_words and not self.add_prefix_space:
                 raise ValueError(
                     F"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
                     'to use it with pretokenized inputs.'			)

           return super()._batch_encode_plus(*__lowerCamelCase     ,							**__lowerCamelCase			)





     def 			_lowerCAmelCase      (     self     :					List[str]     ,							*__lowerCamelCase     :					Optional[Any]     ,							**__lowerCamelCase     :					Dict			):
           snake_case__       :   Tuple	      =    kwargs.get('is_split_into_words'     ,							__lowerCamelCase			)

           if is_split_into_words and not self.add_prefix_space:
                 raise ValueError(
                     F"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
                     'to use it with pretokenized inputs.'			)

           return super()._encode_plus(*__lowerCamelCase     ,							**__lowerCamelCase			)





     def 			_lowerCAmelCase      (     self     :					Union[str, Any]     ,							__lowerCamelCase     :					str     ,							__lowerCamelCase     :					Optional[str] = None			):
           snake_case__       :   List[str]	      =    self._tokenizer.model.save(__lowerCamelCase     ,							name=__lowerCamelCase			)
           return tuple(__lowerCamelCase			)





     def 			_lowerCAmelCase      (     self     :					List[str]     ,							__lowerCamelCase     :					Tuple     ,							__lowerCamelCase     :					Optional[int]=None			):
           snake_case__       :   Optional[Any]	      =    [self.bos_token_id] + token_ids_a + [self.eos_token_id]
           if token_ids_a is None:
                 return output

           return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]





     def 			_lowerCAmelCase      (     self     :					Dict     ,							__lowerCamelCase     :					List[int]     ,							__lowerCamelCase     :					Optional[List[int]] = None			):
           snake_case__       :   Dict	      =    [self.sep_token_id]
           snake_case__       :   List[Any]	      =    [self.cls_token_id]

           if token_ids_a is None:
                 return len(cls + token_ids_a + sep			) * [0]
           return len(cls + token_ids_a + sep + sep + token_ids_a + sep			) * [0]
