code: 
  
  import json
  import os
  from typing import TYPE_CHECKING, List, Optional, Tuple
  
  import regex as re
  
  from ...tokenization_utils import AddedToken, PreTrainedTokenizer
  from ...utils import logging
  
  
  if TYPE_CHECKING:
         from transformers.pipelines.conversational import Conversation
  
  lowerCamelCase     =      logging.get_logger(__name__)
  
  lowerCamelCase     =      {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt'''}
  
  lowerCamelCase     =      {
      '''vocab_file''': {
          '''microsoft/deberta-base''': '''https://huggingface.co/microsoft/deberta-base/resolve/main/vocab.json''',
          '''microsoft/deberta-large''': '''https://huggingface.co/microsoft/deberta-large/resolve/main/vocab.json''',
          '''microsoft/deberta-xlarge''': '''https://huggingface.co/microsoft/deberta-xlarge/resolve/main/vocab.json''',
          '''microsoft/deberta-base-mnli''': '''https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/vocab.json''',
          '''microsoft/deberta-large-mnli''': '''https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/vocab.json''',
          '''microsoft/deberta-xlarge-mnli''': (
              '''https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/vocab.json'''
          ),
      },
      '''merges_file''': {
          '''microsoft/deberta-base''': '''https://huggingface.co/microsoft/deberta-base/resolve/main/merges.txt''',
          '''microsoft/deberta-large''': '''https://huggingface.co/microsoft/deberta-large/resolve/main/merges.txt''',
          '''microsoft/deberta-xlarge''': '''https://huggingface.co/microsoft/deberta-xlarge/resolve/main/merges.txt''',
          '''microsoft/deberta-base-mnli''': '''https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/merges.txt''',
          '''microsoft/deberta-large-mnli''': '''https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/merges.txt''',
          '''microsoft/deberta-xlarge-mnli''': (
              '''https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/merges.txt'''
          ),
      },
  }
  
  lowerCamelCase     =      {
      '''microsoft/deberta-base''': 512,
      '''microsoft/deberta-large''': 512,
      '''microsoft/deberta-xlarge''': 512,
      '''microsoft/deberta-base-mnli''': 512,
      '''microsoft/deberta-large-mnli''': 512,
      '''microsoft/deberta-xlarge-mnli''': 512,
  }
  
  lowerCamelCase     =      {
      '''microsoft/deberta-base''': {'''do_lower_case''': False},
      '''microsoft/deberta-large''': {'''do_lower_case''': False},
  }
  
  
  
  
  def     UpperCAmelCase__       (    ):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    a__						=(
        list(range(ord('''!'''							)			,   ord('''~'''							) + 1							)							) + list(range(ord('''¡'''							)			,   ord('''¬'''							) + 1							)							) + list(range(ord('''®'''							)			,   ord('''ÿ'''							) + 1							)							)
    )
    a__						=bs[:]
    a__						=0
    for b in range(2**8							):
      if b not in bs:
        bs.append(_A							)
        cs.append(2**8 + n							)
        n += 1
    a__						=[chr(_A							) for n in cs]
    return dict(zip(_A			,   _A							)							)
  
  
  
  
  def     UpperCAmelCase__       (    _A   :				Any							):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    a__						=set()
    a__						=word[0]
    for char in word[1:]:
      pairs.add((prev_char, char)							)
      a__						=char
    return pairs
  
  
  
  
  
  
  
  class 		__magic_name__    (							lowerCamelCase__							):
  
     '''simple docstring'''
  
  
  
  
     lowerCamelCase__				:			Optional[int]												=     VOCAB_FILES_NAMES
     lowerCamelCase__				:			Tuple												=     PRETRAINED_VOCAB_FILES_MAP
     lowerCamelCase__				:			Tuple												=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowerCamelCase__				:			Tuple												=     ['input_ids', 'attention_mask', 'token_type_ids']
  
  
  
     def __init__(       self,					lowercase_,					lowercase_,					lowercase_="replace",					lowercase_="[CLS]",					lowercase_="[SEP]",					lowercase_="[SEP]",					lowercase_="[CLS]",					lowercase_="[UNK]",					lowercase_="[PAD]",					lowercase_="[MASK]",					lowercase_=False,					lowercase_=False,					**lowercase_,					)							->					Dict:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=AddedToken(lowercase_,					lstrip=lowercase_,					rstrip=lowercase_ ) if isinstance(lowercase_,					lowercase_ ) else bos_token
       a__						=AddedToken(lowercase_,					lstrip=lowercase_,					rstrip=lowercase_ ) if isinstance(lowercase_,					lowercase_ ) else eos_token
       a__						=AddedToken(lowercase_,					lstrip=lowercase_,					rstrip=lowercase_ ) if isinstance(lowercase_,					lowercase_ ) else sep_token
       a__						=AddedToken(lowercase_,					lstrip=lowercase_,					rstrip=lowercase_ ) if isinstance(lowercase_,					lowercase_ ) else cls_token
       a__						=AddedToken(lowercase_,					lstrip=lowercase_,					rstrip=lowercase_ ) if isinstance(lowercase_,					lowercase_ ) else unk_token
       a__						=AddedToken(lowercase_,					lstrip=lowercase_,					rstrip=lowercase_ ) if isinstance(lowercase_,					lowercase_ ) else pad_token
  
       # Mask token behave like a normal word, i.e. include the space before it
       a__						=AddedToken(lowercase_,					lstrip=lowercase_,					rstrip=lowercase_ ) if isinstance(lowercase_,					lowercase_ ) else mask_token
  
       super().__init__(
           errors=lowercase_,					bos_token=lowercase_,					eos_token=lowercase_,					unk_token=lowercase_,					sep_token=lowercase_,					cls_token=lowercase_,					pad_token=lowercase_,					mask_token=lowercase_,					add_prefix_space=lowercase_,					add_bos_token=lowercase_,					**lowercase_,					)
       a__						=add_bos_token
  
       with open(lowercase_,					encoding='''utf-8''' ) as vocab_handle:
         a__						=json.load(lowercase_ )
       a__						={v: k for k, v in self.encoder.items()}
       a__						=errors  # how to handle errors in decoding
       a__						=bytes_to_unicode()
       a__						={v: k for k, v in self.byte_encoder.items()}
       with open(lowercase_,					encoding='''utf-8''' ) as merges_handle:
         a__						=merges_handle.read().split('''\n''' )[1:-1]
       a__						=[tuple(merge.split() ) for merge in bpe_merges]
       a__						=dict(zip(lowercase_,					range(len(lowercase_ ) ) ) )
       a__						={}
       a__						=add_prefix_space
  
       # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
       a__						=re.compile(R'''\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+''' )
  
  
  
     @property
     # Copied from transformers.models.gpt2.tokenization_gpt2.GPT2Tokenizer.vocab_size
     def 				_UpperCAmelCase				(       self )							->					Any:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       return len(self.encoder )
  
  
  
     def 				_UpperCAmelCase				(       self )							->					List[str]:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       return dict(self.encoder,					**self.added_tokens_encoder )
  
  
  
     def 				_UpperCAmelCase				(       self,					lowercase_ )							->					Optional[Any]:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       if token in self.cache:
         return self.cache[token]
       a__						=tuple(lowercase_ )
       a__						=get_pairs(lowercase_ )
  
       if not pairs:
         return token
  
       while True:
         a__						=min(lowercase_,					key=lambda lowercase_ : self.bpe_ranks.get(lowercase_,					float('''inf''' ) ) )
         if bigram not in self.bpe_ranks:
           break
         a__,   a__						=bigram
         a__						=[]
         a__						=0
         while i < len(lowercase_ ):
           try:
             a__						=word.index(lowercase_,					lowercase_ )
           except ValueError:
             new_word.extend(word[i:] )
             break
           else:
             new_word.extend(word[i:j] )
             a__						=j
  
           if word[i] == first and i < len(lowercase_ ) - 1 and word[i + 1] == second:
             new_word.append(first + second )
             i += 2
           else:
             new_word.append(word[i] )
             i += 1
         a__						=tuple(lowercase_ )
         a__						=new_word
         if len(lowercase_ ) == 1:
           break
         else:
           a__						=get_pairs(lowercase_ )
       a__						=''' '''.join(lowercase_ )
       a__						=word
       return word
  
  
  
     def 				_UpperCAmelCase				(       self,					lowercase_,					lowercase_ = None )							->					List[int]:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       if token_ids_a is None:
         return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
       a__						=[self.cls_token_id]
       a__						=[self.sep_token_id]
       return cls + token_ids_a + sep + token_ids_a + sep
  
  
  
     def 				_UpperCAmelCase				(       self,					lowercase_,					lowercase_ = None,					lowercase_ = False )							->					List[int]:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       if already_has_special_tokens:
         return super().get_special_tokens_mask(
             token_ids_a=lowercase_,					token_ids_a=lowercase_,					already_has_special_tokens=lowercase_ )
  
       if token_ids_a is None:
         return [1] + ([0] * len(lowercase_ )) + [1]
       return [1] + ([0] * len(lowercase_ )) + [1] + ([0] * len(lowercase_ )) + [1]
  
  
  
     def 				_UpperCAmelCase				(       self,					lowercase_,					lowercase_ = None )							->					List[int]:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=[self.sep_token_id]
       a__						=[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 ) * [0] + len(token_ids_a + sep ) * [1]
  
  
  
     def 				_UpperCAmelCase				(       self,					lowercase_ )							->					Tuple:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=[]
       for token in re.findall(self.pat,					lowercase_ ):
         a__						=''''''.join(
             self.byte_encoder[b] for b in token.encode('''utf-8''' ) )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
         bpe_tokens.extend(bpe_token for bpe_token in self.bpe(lowercase_ ).split(''' ''' ) )
       return bpe_tokens
  
  
  
     def 				_UpperCAmelCase				(       self,					lowercase_ )							->					Tuple:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       return self.encoder.get(lowercase_,					self.encoder.get(self.unk_token ) )
  
  
  
     def 				_UpperCAmelCase				(       self,					lowercase_ )							->					str:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       return self.decoder.get(lowercase_ )
  
  
  
     def 				_UpperCAmelCase				(       self,					lowercase_ )							->					Optional[Any]:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=''''''.join(lowercase_ )
       a__						=bytearray([self.byte_decoder[c] for c in text] ).decode('''utf-8''',					errors=self.errors )
       return text
  
  
  
     def 				_UpperCAmelCase				(       self,					lowercase_,					lowercase_ = None )							->					Tuple[str]:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       if not os.path.isdir(lowercase_ ):
         logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
         return
       a__						=os.path.join(
           lowercase_,					(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
       a__						=os.path.join(
           lowercase_,					(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file'''] )
  
       with open(lowercase_,					'''w''',					encoding='''utf-8''' ) as f:
         f.write(json.dumps(self.encoder,					indent=2,					sort_keys=lowercase_,					ensure_ascii=lowercase_ ) + '''\n''' )
  
       a__						=0
       with open(lowercase_,					'''w''',					encoding='''utf-8''' ) as writer:
         writer.write('''#version: 0.2\n''' )
         for bpe_tokens, token_index in sorted(self.bpe_ranks.items(),					key=lambda lowercase_ : kv[1] ):
           if index != token_index:
             logger.warning(
                 F"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
                 ''' Please check that the tokenizer is not corrupted!''' )
             a__						=token_index
           writer.write(''' '''.join(lowercase_ ) + '''\n''' )
           index += 1
  
       return vocab_file, merge_file
  
  
  
     def 				_UpperCAmelCase				(       self,					lowercase_,					lowercase_=False,					**lowercase_ )							->					Dict:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=kwargs.pop('''add_prefix_space''',					self.add_prefix_space )
       if (is_split_into_words or add_prefix_space) and (len(lowercase_ ) > 0 and not text[0].isspace()):
         a__						=''' ''' + text
       return (text, kwargs)
  
  
  
     def 				_UpperCAmelCase				(       self,					lowercase_ )							->					List[int]:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=[]
       for is_user, text in conversation.iter_texts():
         input_ids.extend(self.encode(lowercase_,					add_special_tokens=lowercase_ ) + [self.eos_token_id] )
       if len(lowercase_ ) > self.model_max_length:
         a__						=input_ids[-self.model_max_length :]
       return input_ids
  
style_context: 
  
  import unittest
  
  from transformers import ErnieMConfig, is_torch_available
  from transformers.testing_utils import require_torch, slow, torch_device
  
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
  from ...test_pipeline_mixin import PipelineTesterMixin
  
  
  if is_torch_available():
         import torch
  
         from transformers import (
             ErnieMForInformationExtraction,
             ErnieMForMultipleChoice,
             ErnieMForQuestionAnswering,
             ErnieMForSequenceClassification,
             ErnieMForTokenClassification,
             ErnieMModel,
         )
         from transformers.models.ernie_m.modeling_ernie_m import ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST
  
  
  
  
  
  
  
  class 		__magic_name__    :
  
     '''simple docstring'''
  
  
  
  
     def __init__(       self,					lowercase_,					lowercase_=13,					lowercase_=7,					lowercase_=True,					lowercase_=True,					lowercase_=True,					lowercase_=99,					lowercase_=32,					lowercase_=5,					lowercase_=4,					lowercase_=37,					lowercase_="gelu",					lowercase_=0.1,					lowercase_=0.1,					lowercase_=512,					lowercase_=16,					lowercase_=2,					lowercase_=0.02,					lowercase_=3,					lowercase_=4,					lowercase_=None,					)							->					List[Any]:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=parent
       a__						=batch_size
       a__						=seq_length
       a__						=is_training
       a__						=use_input_mask
       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 )							->					List[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
       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, input_mask, sequence_labels, token_labels, choice_labels
  
  
  
     def 				_UpperCAmelCase				(       self )							->					Optional[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__						=self.get_config()
  
       return config, input_ids, input_mask
  
  
  
     def 				_UpperCAmelCase				(       self )							->					Optional[int]:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       return ErnieMConfig(
           vocab_size=self.vocab_size,					hidden_size=self.hidden_size,					num_hidden_layers=self.num_hidden_layers,					num_attention_heads=self.num_attention_heads,					intermediate_size=self.intermediate_size,					hidden_act=self.hidden_act,					hidden_dropout_prob=self.hidden_dropout_prob,					attention_probs_dropout_prob=self.attention_probs_dropout_prob,					max_position_embeddings=self.max_position_embeddings,					type_vocab_size=self.type_vocab_size,					initializer_range=self.initializer_range,					)
  
  
  
     def 				_UpperCAmelCase				(       self,					lowercase_,					lowercase_,					lowercase_,					lowercase_,					lowercase_,					lowercase_ )							->					Union[str, Any]:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=ErnieMModel(config=lowercase_ )
       model.to(lowercase_ )
       model.eval()
       a__						=model(lowercase_,					return_dict=lowercase_ )
       self.parent.assertEqual(result.last_hidden_state.shape,					(self.batch_size, self.seq_length, self.hidden_size) )
  
  
  
     def 				_UpperCAmelCase				(       self,					lowercase_,					lowercase_,					lowercase_,					lowercase_,					lowercase_,					lowercase_ )							->					List[str]:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=ErnieMForQuestionAnswering(config=lowercase_ )
       model.to(lowercase_ )
       model.eval()
       a__						=model(
           lowercase_,					attention_mask=lowercase_,					start_positions=lowercase_,					end_positions=lowercase_,					)
       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,					lowercase_,					lowercase_,					lowercase_,					lowercase_,					lowercase_,					lowercase_ )							->					Dict:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=ErnieMForInformationExtraction(config=lowercase_ )
       model.to(lowercase_ )
       model.eval()
       a__						=torch.ones_like(lowercase_,					dtype=torch.floataa )
       a__						=model(
           lowercase_,					attention_mask=lowercase_,					start_positions=lowercase_,					end_positions=lowercase_,					)
       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,					lowercase_,					lowercase_,					lowercase_,					lowercase_,					lowercase_,					lowercase_ )							->					Union[str, Any]:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=self.num_labels
       a__						=ErnieMForSequenceClassification(lowercase_ )
       model.to(lowercase_ )
       model.eval()
       a__						=model(lowercase_,					attention_mask=lowercase_,					labels=lowercase_ )
       self.parent.assertEqual(result.logits.shape,					(self.batch_size, self.num_labels) )
  
  
  
     def 				_UpperCAmelCase				(       self,					lowercase_,					lowercase_,					lowercase_,					lowercase_,					lowercase_,					lowercase_ )							->					Any:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=self.num_labels
       a__						=ErnieMForTokenClassification(config=lowercase_ )
       model.to(lowercase_ )
       model.eval()
       input_ids.to(lowercase_ )
       input_mask.to(lowercase_ )
       token_labels.to(lowercase_ )
  
       a__						=model(lowercase_,					attention_mask=lowercase_,					labels=lowercase_ )
  
       self.parent.assertEqual(result.logits.shape,					(self.batch_size, self.seq_length, self.num_labels) )
  
  
  
     def 				_UpperCAmelCase				(       self,					lowercase_,					lowercase_,					lowercase_,					lowercase_,					lowercase_,					lowercase_ )							->					Optional[int]:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=self.num_choices
       a__						=ErnieMForMultipleChoice(config=lowercase_ )
       model.to(lowercase_ )
       model.eval()
       a__						=input_ids.unsqueeze(1 ).expand(-1,					self.num_choices,					-1 ).contiguous()
       a__						=input_mask.unsqueeze(1 ).expand(-1,					self.num_choices,					-1 ).contiguous()
       a__						=model(
           lowercase_,					attention_mask=lowercase_,					labels=lowercase_,					)
       self.parent.assertEqual(result.logits.shape,					(self.batch_size, self.num_choices) )
  
  
  
     def 				_UpperCAmelCase				(       self )							->					Optional[int]:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=self.prepare_config_and_inputs()
       (
           (
           a__
       ),   (
           a__
       ),   (
           a__
       ),   (
           a__
       ),   (
           a__
       ),   (
           a__
       ),   
       )						=config_and_inputs
       a__						={'''input_ids''': input_ids, '''attention_mask''': input_mask}
       return config, inputs_dict
  
  
  
  
  
  
  
  @require_torch
  class 		__magic_name__    (							lowerCamelCase__	, lowerCamelCase__	, unittest.TestCase							):
  
     '''simple docstring'''
  
  
  
  
     lowerCamelCase__				:			List[Any]												=     (
         (
             ErnieMModel,
             ErnieMForMultipleChoice,
             ErnieMForQuestionAnswering,
             ErnieMForSequenceClassification,
             ErnieMForTokenClassification,
         )
         if is_torch_available()
         else ()
     )
     lowerCamelCase__				:			Union[str, Any]												=     ()
     lowerCamelCase__				:			Union[str, Any]												=     (
         {
             'feature-extraction': ErnieMModel,
             'question-answering': ErnieMForQuestionAnswering,
             'text-classification': ErnieMForSequenceClassification,
             'token-classification': ErnieMForTokenClassification,
             'zero-shot': ErnieMForSequenceClassification,
         }
         if is_torch_available()
         else {}
     )
     lowerCamelCase__				:			Any												=     False
  
  
  
     def 				_UpperCAmelCase				(       self,					lowercase_,					lowercase_,					lowercase_,					lowercase_,					lowercase_ )							->					Optional[Any]:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       if pipeline_test_casse_name == "QAPipelineTests":
         return True
  
       return False
  
  
  
     def 				_UpperCAmelCase				(       self )							->					int:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=ErnieMModelTester(self )
       a__						=ConfigTester(self,					config_class=lowercase_,					hidden_size=37 )
  
  
  
     def 				_UpperCAmelCase				(       self )							->					Any:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       self.config_tester.run_common_tests()
  
  
  
     def 				_UpperCAmelCase				(       self )							->					Any:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*lowercase_ )
  
  
  
     def 				_UpperCAmelCase				(       self )							->					Union[str, Any]:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=self.model_tester.prepare_config_and_inputs()
       for type in ["absolute", "relative_key", "relative_key_query"]:
         a__						=type
         self.model_tester.create_and_check_model(*lowercase_ )
  
  
  
     def 				_UpperCAmelCase				(       self )							->					Dict:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_multiple_choice(*lowercase_ )
  
  
  
     def 				_UpperCAmelCase				(       self )							->					Tuple:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_question_answering(*lowercase_ )
  
  
  
     def 				_UpperCAmelCase				(       self )							->					Any:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_sequence_classification(*lowercase_ )
  
  
  
     def 				_UpperCAmelCase				(       self )							->					str:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_information_extraction(*lowercase_ )
  
  
  
     def 				_UpperCAmelCase				(       self )							->					Any:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_token_classification(*lowercase_ )
  
  
  
     @slow
     def 				_UpperCAmelCase				(       self )							->					str:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       for model_name in ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         a__						=ErnieMModel.from_pretrained(lowercase_ )
         self.assertIsNotNone(lowercase_ )
  
  
  
  
  
  
  
  @require_torch
  class 		__magic_name__    (							unittest.TestCase							):
  
     '''simple docstring'''
  
  
  
  
     @slow
     def 				_UpperCAmelCase				(       self )							->					str:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=ErnieMModel.from_pretrained('''susnato/ernie-m-base_pytorch''' )
       model.eval()
       a__						=torch.tensor([[0, 1, 2, 3, 4, 5]] )
       a__						=model(lowercase_ )[0]
  
       # TODO Replace vocab size
       a__						=768
  
       a__						=torch.Size((1, 6, hidden_size) )
       self.assertEqual(output.shape,					lowercase_ )
  
       a__						=torch.tensor(
           [[[-0.0012, 0.1245, -0.0214], [-0.0742, 0.0244, -0.0771], [-0.0333, 0.1164, -0.1554]]] )
  
       self.assertTrue(torch.allclose(output[:, :3, :3],					lowercase_,					atol=1E-3 ) )
  