



"""simple docstring"""




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       UpperCAmelCase_    :



      def __init__(	self	:      Optional[int]	,      A	:      Optional[int]	,      A	:      List[str]=1_3	,      A	:      Tuple=7	,      A	:      Optional[Any]=True	,      A	:      Optional[Any]=True	,      A	:      Optional[int]=True	,      A	:      List[Any]=True	,      A	:      str=9_9	,      A	:      Tuple=3_2	,      A	:      List[Any]=5	,      A	:      Tuple=4	,      A	:      Union[str, Any]=3_7	,      A	:      List[Any]="gelu"	,      A	:      List[str]=0.1	,      A	:      Any=0.1	,      A	:      Union[str, Any]=1_2_8	,      A	:      Union[str, Any]=3_2	,      A	:      Dict=1_6	,      A	:      Tuple=2	,      A	:      Tuple=0.02	,      A	:      Any=3	,      A	:      int=4	,      A	:      int=None	,      ):
           _UpperCAmelCase     :      int														=       parent
           _UpperCAmelCase     :      List[str]														=       batch_size
           _UpperCAmelCase     :      int														=       seq_length
           _UpperCAmelCase     :      int														=       is_training
           _UpperCAmelCase     :      List[Any]														=       use_input_mask
           _UpperCAmelCase     :      str														=       use_token_type_ids
           _UpperCAmelCase     :      Optional[Any]														=       use_labels
           _UpperCAmelCase     :      List[Any]														=       vocab_size
           _UpperCAmelCase     :      int														=       hidden_size
           _UpperCAmelCase     :      Dict														=       num_hidden_layers
           _UpperCAmelCase     :      int														=       num_attention_heads
           _UpperCAmelCase     :      List[Any]														=       intermediate_size
           _UpperCAmelCase     :      int														=       hidden_act
           _UpperCAmelCase     :      Union[str, Any]														=       hidden_dropout_prob
           _UpperCAmelCase     :      List[str]														=       attention_probs_dropout_prob
           _UpperCAmelCase     :      str														=       max_position_embeddings
           _UpperCAmelCase     :      List[Any]														=       type_vocab_size
           _UpperCAmelCase     :      Optional[Any]														=       type_sequence_label_size
           _UpperCAmelCase     :      str														=       initializer_range
           _UpperCAmelCase     :      Optional[Any]														=       num_labels
           _UpperCAmelCase     :      int														=       num_choices
           _UpperCAmelCase     :      Tuple														=       scope



      def 		snake_case_   (	self	:      List[Any]		):
           _UpperCAmelCase     :      str														=       ids_tensor([self.batch_size, self.seq_length]	,      self.vocab_size		)

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

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

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

           _UpperCAmelCase     :      Optional[Any]														=       self.get_config()

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



      def 		snake_case_   (	self	:      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=A	,      initializer_range=self.initializer_range	,      )



      def 		snake_case_   (	self	:      str		):
           (
               (
               _UpperCAmelCase
           )							,			(
               _UpperCAmelCase
           )							,			(
               _UpperCAmelCase
           )							,			(
               _UpperCAmelCase
           )							,			(
               _UpperCAmelCase
           )							,			(
               _UpperCAmelCase
           )							,			(
               _UpperCAmelCase
           )							,			
           )     :      Optional[Any]														=       self.prepare_config_and_inputs()

           _UpperCAmelCase     :      Union[str, Any]														=       True
           _UpperCAmelCase     :      List[Any]														=       floats_tensor([self.batch_size, self.seq_length, self.hidden_size]		)
           _UpperCAmelCase     :      Tuple														=       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	:      Optional[Any]	,      A	:      Union[str, Any]	,      A	:      Optional[int]	,      A	:      Union[str, Any]	,      A	:      Dict	,      A	:      List[str]	,      A	:      Optional[int]	,      A	:      Optional[Any]		):
           _UpperCAmelCase     :      Optional[Any]														=       NezhaModel(config=A		)
           model.to(A		)
           model.eval()
           _UpperCAmelCase     :      List[Any]														=       model(A	,      attention_mask=A	,      token_type_ids=A		)
           _UpperCAmelCase     :      Optional[Any]														=       model(A	,      token_type_ids=A		)
           _UpperCAmelCase     :      Dict														=       model(A		)
           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	:      Tuple	,      A	:      int	,      A	:      Optional[Any]	,      A	:      Tuple	,      A	:      List[Any]	,      A	:      Optional[int]	,      A	:      List[str]	,      A	:      Tuple	,      A	:      Dict	,      A	:      Optional[Any]	,      ):
           _UpperCAmelCase     :      str														=       True
           _UpperCAmelCase     :      Tuple														=       NezhaModel(A		)
           model.to(A		)
           model.eval()
           _UpperCAmelCase     :      Optional[int]														=       model(
               A	,      attention_mask=A	,      token_type_ids=A	,      encoder_hidden_states=A	,      encoder_attention_mask=A	,      )
           _UpperCAmelCase     :      Union[str, Any]														=       model(
               A	,      attention_mask=A	,      token_type_ids=A	,      encoder_hidden_states=A	,      )
           _UpperCAmelCase     :      Union[str, Any]														=       model(A	,      attention_mask=A	,      token_type_ids=A		)
           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	,      A	:      Tuple	,      A	:      List[str]	,      A	:      str	,      A	:      Dict	,      A	:      str	,      A	:      Optional[Any]	,      A	:      Union[str, Any]		):
           _UpperCAmelCase     :      Optional[Any]														=       NezhaForMaskedLM(config=A		)
           model.to(A		)
           model.eval()
           _UpperCAmelCase     :      List[Any]														=       model(A	,      attention_mask=A	,      token_type_ids=A	,      labels=A		)
           self.parent.assertEqual(result.logits.shape	,      (self.batch_size, self.seq_length, self.vocab_size)		)



      def 		snake_case_   (	self	:      int	,      A	:      str	,      A	:      int	,      A	:      Union[str, Any]	,      A	:      str	,      A	:      Union[str, Any]	,      A	:      Optional[int]	,      A	:      Optional[int]		):
           _UpperCAmelCase     :      Union[str, Any]														=       NezhaForNextSentencePrediction(config=A		)
           model.to(A		)
           model.eval()
           _UpperCAmelCase     :      Tuple														=       model(
               A	,      attention_mask=A	,      token_type_ids=A	,      labels=A	,      )
           self.parent.assertEqual(result.logits.shape	,      (self.batch_size, 2)		)



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



      def 		snake_case_   (	self	:      Union[str, Any]	,      A	:      Optional[int]	,      A	:      Tuple	,      A	:      Dict	,      A	:      Optional[Any]	,      A	:      Optional[int]	,      A	:      List[Any]	,      A	:      int		):
           _UpperCAmelCase     :      Union[str, Any]														=       self.num_labels
           _UpperCAmelCase     :      Dict														=       NezhaForTokenClassification(config=A		)
           model.to(A		)
           model.eval()
           _UpperCAmelCase     :      Any														=       model(A	,      attention_mask=A	,      token_type_ids=A	,      labels=A		)
           self.parent.assertEqual(result.logits.shape	,      (self.batch_size, self.seq_length, self.num_labels)		)



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



      def 		snake_case_   (	self	:      str		):
           _UpperCAmelCase     :      str														=       self.prepare_config_and_inputs()
           (
               (
               _UpperCAmelCase
           )							,			(
               _UpperCAmelCase
           )							,			(
               _UpperCAmelCase
           )							,			(
               _UpperCAmelCase
           )							,			(
               _UpperCAmelCase
           )							,			(
               _UpperCAmelCase
           )							,			(
               _UpperCAmelCase
           )							,			
           )     :      List[str]														=       config_and_inputs
           _UpperCAmelCase     :      Dict														=       {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
           return config, inputs_dict







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



      def 		snake_case_   (	self	:      List[str]	,      A	:      List[Any]	,      A	:      str	,      A	:      Optional[int]=False		):
           _UpperCAmelCase     :      Union[str, Any]														=       super()._prepare_for_class(A	,      A	,      return_labels=A		)

           if return_labels:
                if model_class in get_values(A		):
                     _UpperCAmelCase     :      Union[str, Any]														=       torch.zeros(
                         (self.model_tester.batch_size, self.model_tester.seq_length)	,      dtype=torch.long	,      device=A		)
                     _UpperCAmelCase     :      Optional[int]														=       torch.zeros(
                         self.model_tester.batch_size	,      dtype=torch.long	,      device=A		)
           return inputs_dict



      def 		snake_case_   (	self	:      str		):
           _UpperCAmelCase     :      List[Any]														=       NezhaModelTester(self		)
           _UpperCAmelCase     :      Dict														=       ConfigTester(self	,      config_class=A	,      hidden_size=3_7		)



      def 		snake_case_   (	self	:      Tuple		):
           self.config_tester.run_common_tests()



      def 		snake_case_   (	self	:      Tuple		):
           _UpperCAmelCase     :      Any														=       self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*A		)



      def 		snake_case_   (	self	:      Any		):
           _UpperCAmelCase     :      Optional[Any]														=       self.model_tester.prepare_config_and_inputs_for_decoder()
           self.model_tester.create_and_check_model_as_decoder(*A		)



      def 		snake_case_   (	self	:      Optional[int]		):
           # This regression test was failing with PyTorch < 1.3
           (
               (
               _UpperCAmelCase
           )							,			(
               _UpperCAmelCase
           )							,			(
               _UpperCAmelCase
           )							,			(
               _UpperCAmelCase
           )							,			(
               _UpperCAmelCase
           )							,			(
               _UpperCAmelCase
           )							,			(
               _UpperCAmelCase
           )							,			(
               _UpperCAmelCase
           )							,			(
               _UpperCAmelCase
           )							,			
           )     :      str														=       self.model_tester.prepare_config_and_inputs_for_decoder()

           _UpperCAmelCase     :      Any														=       None

           self.model_tester.create_and_check_model_as_decoder(
               A	,      A	,      A	,      A	,      A	,      A	,      A	,      A	,      A	,      )



      def 		snake_case_   (	self	:      List[Any]		):
           _UpperCAmelCase     :      Tuple														=       self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_masked_lm(*A		)



      def 		snake_case_   (	self	:      Optional[int]		):
           _UpperCAmelCase     :      Optional[int]														=       self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_multiple_choice(*A		)



      def 		snake_case_   (	self	:      Union[str, Any]		):
           _UpperCAmelCase     :      List[Any]														=       self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_next_sequence_prediction(*A		)



      def 		snake_case_   (	self	:      List[str]		):
           _UpperCAmelCase     :      int														=       self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_pretraining(*A		)



      def 		snake_case_   (	self	:      Tuple		):
           _UpperCAmelCase     :      Tuple														=       self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_question_answering(*A		)



      def 		snake_case_   (	self	:      Union[str, Any]		):
           _UpperCAmelCase     :      Dict														=       self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_sequence_classification(*A		)



      def 		snake_case_   (	self	:      List[Any]		):
           _UpperCAmelCase     :      List[str]														=       self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_token_classification(*A		)



      @slow
      def 		snake_case_   (	self	:      Optional[Any]		):
           for model_name in NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                _UpperCAmelCase     :      Any														=       NezhaModel.from_pretrained(A		)
                self.assertIsNotNone(A		)



      @slow
      @require_torch_gpu
      def 		snake_case_   (	self	:      str		):
           _UpperCAmelCase							,			_UpperCAmelCase     :      str														=       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

                _UpperCAmelCase     :      List[Any]														=       True
                _UpperCAmelCase     :      Dict														=       model_class(config=A		)

                _UpperCAmelCase     :      Dict														=       self._prepare_for_class(A	,      A		)
                _UpperCAmelCase     :      Tuple														=       torch.jit.trace(
                    A	,      (inputs_dict["input_ids"].to("cpu"		), inputs_dict["attention_mask"].to("cpu"		))		)

                with tempfile.TemporaryDirectory() as tmp:
                     torch.jit.save(A	,      os.path.join(A	,      "bert.pt"		)		)
                     _UpperCAmelCase     :      Optional[int]														=       torch.jit.load(os.path.join(A	,      "bert.pt"		)	,      map_location=A		)
                     loaded(inputs_dict["input_ids"].to(A		)	,      inputs_dict["attention_mask"].to(A		)		)







@require_torch
class       UpperCAmelCase_    (							unittest.TestCase		):



      @slow
      def 		snake_case_   (	self	:      str		):
           _UpperCAmelCase     :      Union[str, Any]														=       NezhaModel.from_pretrained("sijunhe/nezha-cn-base"		)
           _UpperCAmelCase     :      Optional[int]														=       torch.tensor([[0, 1, 2, 3, 4, 5]]		)
           _UpperCAmelCase     :      str														=       torch.tensor([[0, 1, 1, 1, 1, 1]]		)
           with torch.no_grad():
                _UpperCAmelCase     :      List[Any]														=       model(A	,      attention_mask=A		)[0]
           _UpperCAmelCase     :      List[str]														=       torch.Size((1, 6, 7_6_8)		)
           self.assertEqual(output.shape	,      A		)
           _UpperCAmelCase     :      Tuple														=       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]	,      A	,      atol=1e-4		)		)



      @slow
      def 		snake_case_   (	self	:      List[str]		):
           _UpperCAmelCase     :      List[str]														=       NezhaForMaskedLM.from_pretrained("sijunhe/nezha-cn-base"		)
           _UpperCAmelCase     :      List[Any]														=       torch.tensor([[0, 1, 2, 3, 4, 5]]		)
           _UpperCAmelCase     :      str														=       torch.tensor([[1, 1, 1, 1, 1, 1]]		)
           with torch.no_grad():
                _UpperCAmelCase     :      Tuple														=       model(A	,      attention_mask=A		)[0]
           _UpperCAmelCase     :      List[str]														=       torch.Size((1, 6, 2_1_1_2_8)		)
           self.assertEqual(output.shape	,      A		)
           _UpperCAmelCase     :      Tuple														=       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]	,      A	,      atol=1e-4		)		)





"""simple docstring"""




from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_lowerCAmelCase							:   Optional[int]				=				logging.get_logger(__name__)

_lowerCAmelCase							:   Any				=				{
    "roberta-base": "https://huggingface.co/roberta-base/resolve/main/config.json",
    "roberta-large": "https://huggingface.co/roberta-large/resolve/main/config.json",
    "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/config.json",
    "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/config.json",
    "roberta-base-openai-detector": "https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json",
    "roberta-large-openai-detector": "https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json",
}






class       UpperCAmelCase_    (							_UpperCamelCase		):
      __SCREAMING_SNAKE_CASE		:  Optional[int]						=					'roberta'



      def __init__(	self	:      str	,      A	:      Optional[int]=5_0_2_6_5	,      A	:      Optional[int]=7_6_8	,      A	:      Tuple=1_2	,      A	:      Optional[Any]=1_2	,      A	:      Optional[int]=3_0_7_2	,      A	:      str="gelu"	,      A	:      List[Any]=0.1	,      A	:      Optional[Any]=0.1	,      A	:      Any=5_1_2	,      A	:      Dict=2	,      A	:      Union[str, Any]=0.02	,      A	:      str=1e-12	,      A	:      Optional[int]=1	,      A	:      List[str]=0	,      A	:      int=2	,      A	:      Any="absolute"	,      A	:      Optional[int]=True	,      A	:      int=None	,      **A	:      str	,      ):
           super().__init__(pad_token_id=A	,      bos_token_id=A	,      eos_token_id=A	,      **A		)

           _UpperCAmelCase     :      str														=       vocab_size
           _UpperCAmelCase     :      Dict														=       hidden_size
           _UpperCAmelCase     :      List[str]														=       num_hidden_layers
           _UpperCAmelCase     :      Any														=       num_attention_heads
           _UpperCAmelCase     :      List[str]														=       hidden_act
           _UpperCAmelCase     :      Any														=       intermediate_size
           _UpperCAmelCase     :      Union[str, Any]														=       hidden_dropout_prob
           _UpperCAmelCase     :      str														=       attention_probs_dropout_prob
           _UpperCAmelCase     :      Tuple														=       max_position_embeddings
           _UpperCAmelCase     :      Union[str, Any]														=       type_vocab_size
           _UpperCAmelCase     :      str														=       initializer_range
           _UpperCAmelCase     :      int														=       layer_norm_eps
           _UpperCAmelCase     :      Tuple														=       position_embedding_type
           _UpperCAmelCase     :      int														=       use_cache
           _UpperCAmelCase     :      Dict														=       classifier_dropout







class       UpperCAmelCase_    (							_UpperCamelCase		):



      @property
      def 		snake_case_   (	self	:      Tuple		):
           if self.task == "multiple-choice":
                _UpperCAmelCase     :      Optional[Any]														=       {0: "batch", 1: "choice", 2: "sequence"}
           else:
                _UpperCAmelCase     :      List[Any]														=       {0: "batch", 1: "sequence"}
           return OrderedDict(
               [
                   ("input_ids", dynamic_axis),
                   ("attention_mask", dynamic_axis),
               ]		)
