






'''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 							__SCREAMING_SNAKE_CASE       :




      def __init__(  self ,   lowerCamelCase ,   lowerCamelCase=13 ,   lowerCamelCase=7 ,   lowerCamelCase=True ,   lowerCamelCase=True ,   lowerCamelCase=True ,   lowerCamelCase=True ,   lowerCamelCase=99 ,   lowerCamelCase=32 ,   lowerCamelCase=5 ,   lowerCamelCase=4 ,   lowerCamelCase=37 ,   lowerCamelCase="gelu" ,   lowerCamelCase=0.1 ,   lowerCamelCase=0.1 ,   lowerCamelCase=128 ,   lowerCamelCase=32 ,   lowerCamelCase=16 ,   lowerCamelCase=2 ,   lowerCamelCase=0.02 ,   lowerCamelCase=3 ,   lowerCamelCase=4 ,   lowerCamelCase=None ,   )							->int:



         '''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




      def  __UpperCamelCase							(  self      )							->Any:



         '''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				     =    self.get_config()

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




      def  __UpperCamelCase							(  self      )							->Dict:



         '''simple docstring'''
         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  __UpperCamelCase							(  self      )							->Dict:



         '''simple docstring'''
         (
             (
             __a
         )     ,			(
             __a
         )     ,			(
             __a
         )     ,			(
             __a
         )     ,			(
             __a
         )     ,			(
             __a
         )     ,			(
             __a
         )     ,			
         )				     =    self.prepare_config_and_inputs()

         __a				     =    True
         __a				     =    floats_tensor([self.batch_size, self.seq_length, self.hidden_size]      )
         __a				     =    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  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase      )							->int:



         '''simple docstring'''
         __a				     =    NezhaModel(config=lowerCamelCase      )
         model.to(lowerCamelCase      )
         model.eval()
         __a				     =    model(lowerCamelCase ,   attention_mask=lowerCamelCase ,   token_type_ids=lowerCamelCase      )
         __a				     =    model(lowerCamelCase ,   token_type_ids=lowerCamelCase      )
         __a				     =    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  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   )							->int:



         '''simple docstring'''
         __a				     =    True
         __a				     =    NezhaModel(lowerCamelCase      )
         model.to(lowerCamelCase      )
         model.eval()
         __a				     =    model(
             lowerCamelCase ,   attention_mask=lowerCamelCase ,   token_type_ids=lowerCamelCase ,   encoder_hidden_states=lowerCamelCase ,   encoder_attention_mask=lowerCamelCase ,   )
         __a				     =    model(
             lowerCamelCase ,   attention_mask=lowerCamelCase ,   token_type_ids=lowerCamelCase ,   encoder_hidden_states=lowerCamelCase ,   )
         __a				     =    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  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase      )							->Tuple:



         '''simple docstring'''
         __a				     =    NezhaForMaskedLM(config=lowerCamelCase      )
         model.to(lowerCamelCase      )
         model.eval()
         __a				     =    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  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase      )							->List[str]:



         '''simple docstring'''
         __a				     =    NezhaForNextSentencePrediction(config=lowerCamelCase      )
         model.to(lowerCamelCase      )
         model.eval()
         __a				     =    model(
             lowerCamelCase ,   attention_mask=lowerCamelCase ,   token_type_ids=lowerCamelCase ,   labels=lowerCamelCase ,   )
         self.parent.assertEqual(result.logits.shape ,   (self.batch_size, 2)      )




      def  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase      )							->Dict:



         '''simple docstring'''
         __a				     =    NezhaForPreTraining(config=lowerCamelCase      )
         model.to(lowerCamelCase      )
         model.eval()
         __a				     =    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  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase      )							->Any:



         '''simple docstring'''
         __a				     =    NezhaForQuestionAnswering(config=lowerCamelCase      )
         model.to(lowerCamelCase      )
         model.eval()
         __a				     =    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  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase      )							->List[Any]:



         '''simple docstring'''
         __a				     =    self.num_labels
         __a				     =    NezhaForSequenceClassification(lowerCamelCase      )
         model.to(lowerCamelCase      )
         model.eval()
         __a				     =    model(lowerCamelCase ,   attention_mask=lowerCamelCase ,   token_type_ids=lowerCamelCase ,   labels=lowerCamelCase      )
         self.parent.assertEqual(result.logits.shape ,   (self.batch_size, self.num_labels)      )




      def  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase      )							->Union[str, Any]:



         '''simple docstring'''
         __a				     =    self.num_labels
         __a				     =    NezhaForTokenClassification(config=lowerCamelCase      )
         model.to(lowerCamelCase      )
         model.eval()
         __a				     =    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  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase      )							->Optional[int]:



         '''simple docstring'''
         __a				     =    self.num_choices
         __a				     =    NezhaForMultipleChoice(config=lowerCamelCase      )
         model.to(lowerCamelCase      )
         model.eval()
         __a				     =    input_ids.unsqueeze(1      ).expand(-1 ,   self.num_choices ,   -1      ).contiguous()
         __a				     =    token_type_ids.unsqueeze(1      ).expand(-1 ,   self.num_choices ,   -1      ).contiguous()
         __a				     =    input_mask.unsqueeze(1      ).expand(-1 ,   self.num_choices ,   -1      ).contiguous()
         __a				     =    model(
             lowerCamelCase ,   attention_mask=lowerCamelCase ,   token_type_ids=lowerCamelCase ,   labels=lowerCamelCase ,   )
         self.parent.assertEqual(result.logits.shape ,   (self.batch_size, self.num_choices)      )






      def  __UpperCamelCase							(  self      )							->Any:



         '''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







@require_torch
class 							__SCREAMING_SNAKE_CASE       (							_lowerCAmelCase      ,      _lowerCAmelCase      ,      _lowerCAmelCase      ,      unittest.TestCase     ):
      __a  					=(
          (
              NezhaModel,
              NezhaForMaskedLM,
              NezhaForMultipleChoice,
              NezhaForNextSentencePrediction,
              NezhaForPreTraining,
              NezhaForQuestionAnswering,
              NezhaForSequenceClassification,
              NezhaForTokenClassification,
          )
          if is_torch_available()
          else ()
      )
      __a  					=(
          {
              "feature-extraction": NezhaModel,
              "fill-mask": NezhaForMaskedLM,
              "question-answering": NezhaForQuestionAnswering,
              "text-classification": NezhaForSequenceClassification,
              "token-classification": NezhaForTokenClassification,
              "zero-shot": NezhaForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )
      __a  					=True




      def  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase=False      )							->Any:



         '''simple docstring'''
         __a				     =    super()._prepare_for_class(lowerCamelCase ,   lowerCamelCase ,   return_labels=lowerCamelCase      )

         if return_labels:
            if model_class in get_values(lowerCamelCase      ):
               __a				     =    torch.zeros(
                   (self.model_tester.batch_size, self.model_tester.seq_length) ,   dtype=torch.long ,   device=lowerCamelCase      )
               __a				     =    torch.zeros(
                   self.model_tester.batch_size ,   dtype=torch.long ,   device=lowerCamelCase      )
         return inputs_dict




      def  __UpperCamelCase							(  self      )							->Dict:



         '''simple docstring'''
         __a				     =    NezhaModelTester(self      )
         __a				     =    ConfigTester(self ,   config_class=lowerCamelCase ,   hidden_size=37      )




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



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




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



         '''simple docstring'''
         __a				     =    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*lowerCamelCase      )




      def  __UpperCamelCase							(  self      )							->str:



         '''simple docstring'''
         __a				     =    self.model_tester.prepare_config_and_inputs_for_decoder()
         self.model_tester.create_and_check_model_as_decoder(*lowerCamelCase      )




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



         '''simple docstring'''
         # This regression test was failing with PyTorch < 1.3
         (
             (
             __a
         )     ,			(
             __a
         )     ,			(
             __a
         )     ,			(
             __a
         )     ,			(
             __a
         )     ,			(
             __a
         )     ,			(
             __a
         )     ,			(
             __a
         )     ,			(
             __a
         )     ,			
         )				     =    self.model_tester.prepare_config_and_inputs_for_decoder()

         __a				     =    None

         self.model_tester.create_and_check_model_as_decoder(
             lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   )




      def  __UpperCamelCase							(  self      )							->str:



         '''simple docstring'''
         __a				     =    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_masked_lm(*lowerCamelCase      )




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



         '''simple docstring'''
         __a				     =    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_multiple_choice(*lowerCamelCase      )




      def  __UpperCamelCase							(  self      )							->Dict:



         '''simple docstring'''
         __a				     =    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_next_sequence_prediction(*lowerCamelCase      )




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



         '''simple docstring'''
         __a				     =    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_pretraining(*lowerCamelCase      )




      def  __UpperCamelCase							(  self      )							->int:



         '''simple docstring'''
         __a				     =    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_question_answering(*lowerCamelCase      )




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



         '''simple docstring'''
         __a				     =    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_sequence_classification(*lowerCamelCase      )




      def  __UpperCamelCase							(  self      )							->Union[str, Any]:



         '''simple docstring'''
         __a				     =    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_token_classification(*lowerCamelCase      )




      @slow
      def  __UpperCamelCase							(  self      )							->Optional[int]:



         '''simple docstring'''
         for model_name in NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            __a				     =    NezhaModel.from_pretrained(lowerCamelCase      )
            self.assertIsNotNone(lowerCamelCase      )






      @slow
      @require_torch_gpu
      def  __UpperCamelCase							(  self      )							->List[str]:



         '''simple docstring'''
         __a     ,			__a				     =    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				     =    True
            __a				     =    model_class(config=lowerCamelCase      )

            __a				     =    self._prepare_for_class(lowerCamelCase ,   lowerCamelCase      )
            __a				     =    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				     =    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 							__SCREAMING_SNAKE_CASE       (							unittest.TestCase     ):




      @slow
      def  __UpperCamelCase							(  self      )							->List[str]:



         '''simple docstring'''
         __a				     =    NezhaModel.from_pretrained('sijunhe/nezha-cn-base'      )
         __a				     =    torch.tensor([[0, 1, 2, 3, 4, 5]]      )
         __a				     =    torch.tensor([[0, 1, 1, 1, 1, 1]]      )
         with torch.no_grad():
            __a				     =    model(lowerCamelCase ,   attention_mask=lowerCamelCase      )[0]
         __a				     =    torch.Size((1, 6, 768)      )
         self.assertEqual(output.shape ,   lowerCamelCase      )
         __a				     =    torch.tensor([[[0.0685, 0.2441, 0.1102], [0.0600, 0.1906, 0.1349], [0.0221, 0.0819, 0.0586]]]      )

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






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



         '''simple docstring'''
         __a				     =    NezhaForMaskedLM.from_pretrained('sijunhe/nezha-cn-base'      )
         __a				     =    torch.tensor([[0, 1, 2, 3, 4, 5]]      )
         __a				     =    torch.tensor([[1, 1, 1, 1, 1, 1]]      )
         with torch.no_grad():
            __a				     =    model(lowerCamelCase ,   attention_mask=lowerCamelCase      )[0]
         __a				     =    torch.Size((1, 6, 2_1128)      )
         self.assertEqual(output.shape ,   lowerCamelCase      )
         __a				     =    torch.tensor(
             [[-2.7939, -1.7902, -2.2189], [-2.8585, -1.8908, -2.3723], [-2.6499, -1.7750, -2.2558]]      )

         self.assertTrue(torch.allclose(output[:, 1:4, 1:4] ,   lowerCamelCase ,   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


__UpperCamelCase      :       Dict      			=      logging.get_logger(__name__)

__UpperCamelCase      :       Optional[int]      			=      {
    """facebook/xmod-base""": """https://huggingface.co/facebook/xmod-base/resolve/main/config.json""",
    """facebook/xmod-large-prenorm""": """https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json""",
    """facebook/xmod-base-13-125k""": """https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json""",
    """facebook/xmod-base-30-125k""": """https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json""",
    """facebook/xmod-base-30-195k""": """https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json""",
    """facebook/xmod-base-60-125k""": """https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json""",
    """facebook/xmod-base-60-265k""": """https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json""",
    """facebook/xmod-base-75-125k""": """https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json""",
    """facebook/xmod-base-75-269k""": """https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json""",
}
class 							__SCREAMING_SNAKE_CASE       (							_lowerCAmelCase     ):
      __a  					="xmod"




      def __init__(  self ,   lowerCamelCase=3_0522 ,   lowerCamelCase=768 ,   lowerCamelCase=12 ,   lowerCamelCase=12 ,   lowerCamelCase=3072 ,   lowerCamelCase="gelu" ,   lowerCamelCase=0.1 ,   lowerCamelCase=0.1 ,   lowerCamelCase=512 ,   lowerCamelCase=2 ,   lowerCamelCase=0.02 ,   lowerCamelCase=1e-12 ,   lowerCamelCase=1 ,   lowerCamelCase=0 ,   lowerCamelCase=2 ,   lowerCamelCase="absolute" ,   lowerCamelCase=True ,   lowerCamelCase=None ,   lowerCamelCase=False ,   lowerCamelCase=2 ,   lowerCamelCase=False ,   lowerCamelCase=True ,   lowerCamelCase=True ,   lowerCamelCase=("en_XX",) ,   lowerCamelCase=None ,   **lowerCamelCase ,   )							->Any:



         '''simple docstring'''
         super().__init__(pad_token_id=lowerCamelCase ,   bos_token_id=lowerCamelCase ,   eos_token_id=lowerCamelCase ,   **lowerCamelCase      )

         __a				     =    vocab_size
         __a				     =    hidden_size
         __a				     =    num_hidden_layers
         __a				     =    num_attention_heads
         __a				     =    hidden_act
         __a				     =    intermediate_size
         __a				     =    hidden_dropout_prob
         __a				     =    attention_probs_dropout_prob
         __a				     =    max_position_embeddings
         __a				     =    type_vocab_size
         __a				     =    initializer_range
         __a				     =    layer_norm_eps
         __a				     =    position_embedding_type
         __a				     =    use_cache
         __a				     =    classifier_dropout
         __a				     =    pre_norm
         __a				     =    adapter_reduction_factor
         __a				     =    adapter_layer_norm
         __a				     =    adapter_reuse_layer_norm
         __a				     =    ln_before_adapter
         __a				     =    list(lowerCamelCase      )
         __a				     =    default_language







class 							__SCREAMING_SNAKE_CASE       (							_lowerCAmelCase     ):




      @property
      def  __UpperCamelCase							(  self      )							->Mapping[str, Mapping[int, str]]:



         '''simple docstring'''
         if self.task == "multiple-choice":
            __a				     =    {0: 'batch', 1: 'choice', 2: 'sequence'}
         else:
            __a				     =    {0: 'batch', 1: 'sequence'}
         return OrderedDict(
             [
                 ('input_ids', dynamic_axis),
                 ('attention_mask', dynamic_axis),
             ]      )