code: 
  
  
  
  
  
  import argparse
  
  import torch
  from saprl.hub import distilhubert
  
  from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging
  
  
  logging.set_verbosity_info()
  lowerCAmelCase_   =   logging.get_logger(__name__)
  
  lowerCAmelCase_   =   {
      "post_extract_proj": "feature_projection.projection",
      "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
      "self_attn.k_proj": "encoder.layers.*.attention.k_proj",
      "self_attn.v_proj": "encoder.layers.*.attention.v_proj",
      "self_attn.q_proj": "encoder.layers.*.attention.q_proj",
      "self_attn.out_proj": "encoder.layers.*.attention.out_proj",
      "self_attn_layer_norm": "encoder.layers.*.layer_norm",
      "fc1": "encoder.layers.*.feed_forward.intermediate_dense",
      "fc2": "encoder.layers.*.feed_forward.output_dense",
      "final_layer_norm": "encoder.layers.*.final_layer_norm",
      "encoder.layer_norm": "encoder.layer_norm",
      "mask_emb": "masked_spec_embed",
  }
  
  
  
  
  def 						A_			(  lowercase_      ,		lowercase_      ,		lowercase_      ,		lowercase_      ,		lowercase_  )			->						Tuple:
  
  
  
  
  
  
  
         for attribute in key.split('''.'''  ):
                _snake_case      :							List[str]     				=						getattr(_A      ,		_A  )
  
         if weight_type is not None:
                _snake_case      :							Dict     				=						getattr(_A      ,		_A  ).shape
         else:
                _snake_case      :							int     				=						hf_pointer.shape
  
         assert hf_shape == value.shape, (
             f'''Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be'''
             f''' {value.shape} for {full_name}'''
         )
  
         if weight_type == "weight":
                _snake_case      :							Optional[Any]     				=						value
         elif weight_type == "weight_g":
                _snake_case      :							Optional[Any]     				=						value
         elif weight_type == "weight_v":
                _snake_case      :							str     				=						value
         elif weight_type == "bias":
                _snake_case      :							Any     				=						value
         else:
                _snake_case      :							Optional[Any]     				=						value
  
         logger.info(f'''{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.'''  )
  
  
  
  
  def 						A_			(  lowercase_      ,		lowercase_  )			->						Optional[int]:
         _snake_case      :							Optional[int]     				=						[]
         _snake_case      :							int     				=						fairseq_model.state_dict()
  
         _snake_case      :							Tuple     				=						hf_model.feature_extractor
  
         for name, value in fairseq_dict.items():
                _snake_case      :							Any     				=						False
                if "conv_layers" in name:
                       load_conv_layer(
                           _A      ,		_A      ,		_A      ,		_A      ,		hf_model.config.feat_extract_norm == '''group'''      ,		)
                       _snake_case      :							List[str]     				=						True
                else:
                       for key, mapped_key in MAPPING.items():
                              _snake_case      :							Tuple     				=						mapped_key
  
                              if key in name:
                                     _snake_case      :							int     				=						True
                                     if "*" in mapped_key:
                                            _snake_case      :							str     				=						name.split(_A  )[0].split('''.'''  )[-2]
                                            _snake_case      :							Dict     				=						mapped_key.replace('''*'''      ,		_A  )
                                     if "weight_g" in name:
                                            _snake_case      :							List[str]     				=						'''weight_g'''
                                     elif "weight_v" in name:
                                            _snake_case      :							Tuple     				=						'''weight_v'''
                                     elif "weight" in name:
                                            _snake_case      :							Any     				=						'''weight'''
                                     elif "bias" in name:
                                            _snake_case      :							str     				=						'''bias'''
                                     else:
                                            _snake_case      :							Optional[int]     				=						None
                                     set_recursively(_A      ,		_A      ,		_A      ,		_A      ,		_A  )
                              continue
                if not is_used:
                       unused_weights.append(_A  )
  
         logger.warning(f'''Unused weights: {unused_weights}'''  )
  
  
  
  
  def 						A_			(  lowercase_      ,		lowercase_      ,		lowercase_      ,		lowercase_      ,		lowercase_  )			->						List[Any]:
         _snake_case      :							Tuple     				=						full_name.split('''conv_layers.'''  )[-1]
         _snake_case      :							Optional[int]     				=						name.split('''.'''  )
         _snake_case      :							List[Any]     				=						int(items[0]  )
         _snake_case      :							int     				=						int(items[1]  )
  
         if type_id == 0:
                if "bias" in name:
                       assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
                           f'''{full_name} has size {value.shape}, but'''
                           f''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''
                       )
                       _snake_case      :							Optional[int]     				=						value
                       logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''  )
                elif "weight" in name:
                       assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
                           f'''{full_name} has size {value.shape}, but'''
                           f''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''
                       )
                       _snake_case      :							Any     				=						value
                       logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''  )
         elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
                if "bias" in name:
                       assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
                           f'''{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'''
                           " found."
                       )
                       _snake_case      :							int     				=						value
                       logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''  )
                elif "weight" in name:
                       assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
                           f'''{full_name} has size {value.shape}, but'''
                           f''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'''
                       )
                       _snake_case      :							Optional[Any]     				=						value
                       logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''  )
         else:
                unused_weights.append(_A  )
  
  
  
  
  def 						A_			(  lowercase_  )			->						Optional[Any]:
         _snake_case      :							Optional[Any]     				=						HubertConfig()
         _snake_case      :							str     				=						model.config
  
         _snake_case      :							List[Any]     				=						fs_config.activation_dropout
         _snake_case      :							Optional[Any]     				=						False
         _snake_case      :							int     				=						fs_config.attention_dropout
         _snake_case      :							int     				=						False
         _snake_case      :							Dict     				=						eval(fs_config.extractor_conv_feature_layers  )
         _snake_case      :							Optional[Any]     				=						[x[0] for x in conv_layers]
         _snake_case      :							Optional[int]     				=						[x[1] for x in conv_layers]
         _snake_case      :							Optional[Any]     				=						[x[2] for x in conv_layers]
         _snake_case      :							List[Any]     				=						'''gelu'''
         _snake_case      :							List[str]     				=						'''layer''' if fs_config.extractor_mode == '''layer_norm''' else '''group'''
         _snake_case      :							List[Any]     				=						False
         _snake_case      :							List[Any]     				=						0.0
         _snake_case      :							str     				=						0.0
         _snake_case      :							Union[str, Any]     				=						fs_config.activation_fn
         _snake_case      :							Union[str, Any]     				=						fs_config.dropout
         _snake_case      :							Dict     				=						fs_config.encoder_embed_dim
         _snake_case      :							List[Any]     				=						0.02
         _snake_case      :							List[Any]     				=						fs_config.encoder_ffn_embed_dim
         _snake_case      :							List[Any]     				=						1E-5
         _snake_case      :							int     				=						0.0
         _snake_case      :							Tuple     				=						fs_config.encoder_attention_heads
         _snake_case      :							List[Any]     				=						fs_config.conv_pos_groups
         _snake_case      :							Tuple     				=						fs_config.conv_pos
         _snake_case      :							int     				=						len(_A  )
         _snake_case      :							Optional[int]     				=						fs_config.encoder_layers
  
         return config
  
  
  
  
  @torch.no_grad()
  def 						A_			(  lowercase_      ,		lowercase_=None  )			->						int:
         _snake_case      :							Optional[int]     				=						distilhubert().model.model
  
         if config_path is not None:
                _snake_case      :							int     				=						HubertConfig.from_pretrained(_A  )
         else:
                _snake_case      :							str     				=						convert_config(_A  )
         _snake_case      :							List[str]     				=						model.eval()
  
         _snake_case      :							List[str]     				=						WavaVecaFeatureExtractor(
             feature_size=1      ,		sampling_rate=16000      ,		padding_value=0      ,		do_normalize=_A      ,		return_attention_mask=_A      ,		)
         _snake_case      :							str     				=						HubertModel(_A  )
  
         recursively_load_weights(_A      ,		_A  )
  
         feature_extractor.save_pretrained(_A  )
         hf_model.save_pretrained(_A  )
  
  
  if __name__ == "__main__":
         lowerCAmelCase_   =   argparse.ArgumentParser()
         parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
         parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
         lowerCAmelCase_   =   parser.parse_args()
         convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  import unittest
  
  from transformers import MarkupLMConfig, is_torch_available
  from transformers.testing_utils import require_torch, slow, torch_device
  from transformers.utils import cached_property
  
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, ids_tensor
  from ...test_pipeline_mixin import PipelineTesterMixin
  
  
  if is_torch_available():
     import torch
  
     from transformers import (
         MarkupLMForQuestionAnswering,
         MarkupLMForSequenceClassification,
         MarkupLMForTokenClassification,
         MarkupLMModel,
     )
  
  # TODO check dependencies
  from transformers import MarkupLMFeatureExtractor, MarkupLMProcessor, MarkupLMTokenizer
  
  class 				A    :
  
  
  
        def __init__(					self     ,  lowercase_     ,  lowercase_=13     ,  lowercase_=7     ,  lowercase_=True     ,  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_=None     ,  lowercase_=20     ,  lowercase_=30     ,  lowercase_=2     ,  lowercase_=2     ,  lowercase_=10     ,  )      ->   Optional[int]:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Union[str, Any]     				=						parent
               _snake_case      :							List[str]     				=						batch_size
               _snake_case      :							str     				=						seq_length
               _snake_case      :							str     				=						is_training
               _snake_case      :							Optional[Any]     				=						use_input_mask
               _snake_case      :							Any     				=						use_token_type_ids
               _snake_case      :							str     				=						use_labels
               _snake_case      :							int     				=						vocab_size
               _snake_case      :							Tuple     				=						hidden_size
               _snake_case      :							str     				=						num_hidden_layers
               _snake_case      :							str     				=						num_attention_heads
               _snake_case      :							Optional[int]     				=						intermediate_size
               _snake_case      :							Tuple     				=						hidden_act
               _snake_case      :							Any     				=						hidden_dropout_prob
               _snake_case      :							Tuple     				=						attention_probs_dropout_prob
               _snake_case      :							str     				=						max_position_embeddings
               _snake_case      :							List[Any]     				=						type_vocab_size
               _snake_case      :							str     				=						type_sequence_label_size
               _snake_case      :							Any     				=						initializer_range
               _snake_case      :							Optional[Any]     				=						num_labels
               _snake_case      :							Union[str, Any]     				=						scope
               _snake_case      :							int     				=						max_xpath_tag_unit_embeddings
               _snake_case      :							Any     				=						max_xpath_subs_unit_embeddings
               _snake_case      :							Union[str, Any]     				=						tag_pad_id
               _snake_case      :							Union[str, Any]     				=						subs_pad_id
               _snake_case      :							int     				=						max_depth
  
  
  
        def    __a (					self					)      ->   Tuple:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							List[Any]     				=						ids_tensor([self.batch_size, self.seq_length]     ,  self.vocab_size					)
  
               _snake_case      :							Any     				=						ids_tensor(
                   [self.batch_size, self.seq_length, self.max_depth]     ,  self.max_xpath_tag_unit_embeddings					)
  
               _snake_case      :							Tuple     				=						ids_tensor(
                   [self.batch_size, self.seq_length, self.max_depth]     ,  self.max_xpath_subs_unit_embeddings					)
  
               _snake_case      :							Optional[int]     				=						None
               if self.use_input_mask:
                      _snake_case      :							Optional[int]     				=						ids_tensor([self.batch_size, self.seq_length]     ,  vocab_size=2					)
  
               _snake_case      :							int     				=						None
               if self.use_token_type_ids:
                      _snake_case      :							List[Any]     				=						ids_tensor([self.batch_size, self.seq_length]     ,  self.type_vocab_size					)
  
               _snake_case      :							List[Any]     				=						None
               _snake_case      :							int     				=						None
               if self.use_labels:
                      _snake_case      :							Optional[int]     				=						ids_tensor([self.batch_size]     ,  self.type_sequence_label_size					)
                      _snake_case      :							Optional[int]     				=						ids_tensor([self.batch_size, self.seq_length]     ,  self.num_labels					)
  
               _snake_case      :							Tuple     				=						self.get_config()
  
               return (
                   config,
                   input_ids,
                   xpath_tags_seq,
                   xpath_subs_seq,
                   token_type_ids,
                   input_mask,
                   sequence_labels,
                   token_labels,
               )
  
  
  
        def    __a (					self					)      ->   Tuple:
  
  
  
               '''simple docstring'''
  
               return MarkupLMConfig(
                   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     ,  max_xpath_tag_unit_embeddings=self.max_xpath_tag_unit_embeddings     ,  max_xpath_subs_unit_embeddings=self.max_xpath_subs_unit_embeddings     ,  tag_pad_id=self.tag_pad_id     ,  subs_pad_id=self.subs_pad_id     ,  max_depth=self.max_depth     ,  )
  
  
  
        def    __a (					self     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  )      ->   Optional[Any]:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Optional[Any]     				=						MarkupLMModel(config=lowercase_					)
               model.to(lowercase_					)
               model.eval()
               print('''Configs:'''     ,  model.config.tag_pad_id     ,  model.config.subs_pad_id					)
               _snake_case      :							int     				=						model(lowercase_     ,  attention_mask=lowercase_     ,  token_type_ids=lowercase_					)
               _snake_case      :							Dict     				=						model(lowercase_     ,  token_type_ids=lowercase_					)
               _snake_case      :							str     				=						model(lowercase_					)
               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    __a (					self     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  )      ->   Any:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Optional[Any]     				=						self.num_labels
               _snake_case      :							List[str]     				=						MarkupLMForSequenceClassification(lowercase_					)
               model.to(lowercase_					)
               model.eval()
               _snake_case      :							Optional[int]     				=						model(
                   lowercase_     ,  xpath_tags_seq=lowercase_     ,  xpath_subs_seq=lowercase_     ,  attention_mask=lowercase_     ,  token_type_ids=lowercase_     ,  labels=lowercase_     ,  )
               self.parent.assertEqual(result.logits.shape     ,  (self.batch_size, self.num_labels)					)
  
  
  
        def    __a (					self     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  )      ->   Optional[Any]:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							List[Any]     				=						self.num_labels
               _snake_case      :							Dict     				=						MarkupLMForTokenClassification(config=lowercase_					)
               model.to(lowercase_					)
               model.eval()
               _snake_case      :							List[str]     				=						model(
                   lowercase_     ,  xpath_tags_seq=lowercase_     ,  xpath_subs_seq=lowercase_     ,  attention_mask=lowercase_     ,  token_type_ids=lowercase_     ,  labels=lowercase_     ,  )
               self.parent.assertEqual(result.logits.shape     ,  (self.batch_size, self.seq_length, self.num_labels)					)
  
  
  
        def    __a (					self     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  )      ->   Tuple:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Optional[int]     				=						MarkupLMForQuestionAnswering(config=lowercase_					)
               model.to(lowercase_					)
               model.eval()
               _snake_case      :							Optional[int]     				=						model(
                   lowercase_     ,  xpath_tags_seq=lowercase_     ,  xpath_subs_seq=lowercase_     ,  attention_mask=lowercase_     ,  token_type_ids=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    __a (					self					)      ->   Optional[Any]:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Optional[int]     				=						self.prepare_config_and_inputs()
               (
                   (
                   _snake_case
               )				,    (
                   _snake_case
               )				,    (
                   _snake_case
               )				,    (
                   _snake_case
               )				,    (
                   _snake_case
               )				,    (
                   _snake_case
               )				,    (
                   _snake_case
               )				,    (
                   _snake_case
               )				,    
               )      :							Tuple     				=						config_and_inputs
               _snake_case      :							str     				=						{
                   '''input_ids''': input_ids,
                   '''xpath_tags_seq''': xpath_tags_seq,
                   '''xpath_subs_seq''': xpath_subs_seq,
                   '''token_type_ids''': token_type_ids,
                   '''attention_mask''': input_mask,
               }
               return config, inputs_dict
  
  
  @require_torch
  class 				A    (__UpperCAmelCase       ,__UpperCAmelCase       ,unittest.TestCase				):
        _SCREAMING_SNAKE_CASE					=							(
            (
                MarkupLMModel,
                MarkupLMForSequenceClassification,
                MarkupLMForTokenClassification,
                MarkupLMForQuestionAnswering,
            )
            if is_torch_available()
            else None
        )
        _SCREAMING_SNAKE_CASE					=							(
            {
                """feature-extraction""": MarkupLMModel,
                """question-answering""": MarkupLMForQuestionAnswering,
                """text-classification""": MarkupLMForSequenceClassification,
                """token-classification""": MarkupLMForTokenClassification,
                """zero-shot""": MarkupLMForSequenceClassification,
            }
            if is_torch_available()
            else {}
        )
  
  
  
        def    __a (					self     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_					)      ->   List[str]:
  
  
  
               '''simple docstring'''
  
               return True
  
  
  
        def    __a (					self					)      ->   Optional[Any]:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Any     				=						MarkupLMModelTester(self					)
               _snake_case      :							Dict     				=						ConfigTester(self     ,  config_class=lowercase_     ,  hidden_size=37					)
  
  
  
        def    __a (					self					)      ->   Optional[int]:
  
  
  
               '''simple docstring'''
  
               self.config_tester.run_common_tests()
  
  
  
        def    __a (					self					)      ->   List[Any]:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Union[str, Any]     				=						self.model_tester.prepare_config_and_inputs()
               self.model_tester.create_and_check_model(*lowercase_					)
  
  
  
        def    __a (					self					)      ->   Optional[Any]:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Optional[int]     				=						self.model_tester.prepare_config_and_inputs()
               self.model_tester.create_and_check_for_sequence_classification(*lowercase_					)
  
  
  
        def    __a (					self					)      ->   Any:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Optional[Any]     				=						self.model_tester.prepare_config_and_inputs()
               self.model_tester.create_and_check_for_token_classification(*lowercase_					)
  
  
  
        def    __a (					self					)      ->   str:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Optional[int]     				=						self.model_tester.prepare_config_and_inputs()
               self.model_tester.create_and_check_for_question_answering(*lowercase_					)
  
  
  
  
  def 						A_			(  )			->						Any:
         _snake_case      :							Tuple     				=						'''
      <!DOCTYPE html>
      <html>
      <head>
      <title>Page Title</title>
      </head>
      <body>
  
      <h1>This is a Heading</h1>
      <p>This is a paragraph.</p>
  
      </body>
      </html>
      '''
  
         return html_string
  
  @require_torch
  class 				A    (unittest.TestCase				):
  
  
  
        @cached_property
        def    __a (					self					)      ->   int:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							List[str]     				=						MarkupLMFeatureExtractor()
               _snake_case      :							Any     				=						MarkupLMTokenizer.from_pretrained('''microsoft/markuplm-base'''					)
  
               return MarkupLMProcessor(lowercase_     ,  lowercase_					)
  
  
  
        @slow
        def    __a (					self					)      ->   Optional[int]:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Any     				=						MarkupLMModel.from_pretrained('''microsoft/markuplm-base'''					).to(lowercase_					)
  
               _snake_case      :							Tuple     				=						self.default_processor
  
               _snake_case      :							Union[str, Any]     				=						processor(prepare_html_string()     ,  return_tensors='''pt'''					)
               _snake_case      :							List[Any]     				=						inputs.to(lowercase_					)
  
               # forward pass
               with torch.no_grad():
                      _snake_case      :							List[str]     				=						model(**lowercase_					)
  
               # verify the last hidden states
               _snake_case      :							Union[str, Any]     				=						torch.Size([1, 14, 768]					)
               self.assertEqual(outputs.last_hidden_state.shape     ,  lowercase_					)
  
               _snake_case      :							List[str]     				=						torch.tensor(
                   [[0.0675, -0.0052, 0.5001], [-0.2281, 0.0802, 0.2192], [-0.0583, -0.3311, 0.1185]]					).to(lowercase_					)
  
               self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]     ,  lowercase_     ,  atol=1E-4					)					)
  
  
  
  
  
  
  