code: 
  
  
  
  
  
  import argparse
  import pathlib
  
  import fairseq
  import torch
  from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
  from fairseq.modules import TransformerSentenceEncoderLayer
  from packaging import version
  
  from transformers import RobertaConfig, RobertaForMaskedLM, RobertaForSequenceClassification
  from transformers.models.bert.modeling_bert import (
      BertIntermediate,
      BertLayer,
      BertOutput,
      BertSelfAttention,
      BertSelfOutput,
  )
  from transformers.utils import logging
  
  
  if version.parse(fairseq.__version__) < version.parse("0.9.0"):
         raise Exception("requires fairseq >= 0.9.0")
  
  
  logging.set_verbosity_info()
  lowerCAmelCase_   =   logging.get_logger(__name__)
  
  lowerCAmelCase_   =   "Hello world! cécé herlolip"
  
  
  
  
  def 						A_			(  lowercase_      ,		lowercase_      ,		lowercase_  )			->						List[Any]:
         _snake_case      :							Union[str, Any]     				=						FairseqRobertaModel.from_pretrained(lowercase_  )
         roberta.eval()  # disable dropout
         _snake_case      :							List[Any]     				=						roberta.model.encoder.sentence_encoder
         _snake_case      :							List[Any]     				=						RobertaConfig(
             vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings      ,		hidden_size=roberta.args.encoder_embed_dim      ,		num_hidden_layers=roberta.args.encoder_layers      ,		num_attention_heads=roberta.args.encoder_attention_heads      ,		intermediate_size=roberta.args.encoder_ffn_embed_dim      ,		max_position_embeddings=514      ,		type_vocab_size=1      ,		layer_norm_eps=1E-5      ,		)
         if classification_head:
                _snake_case      :							int     				=						roberta.model.classification_heads['''mnli'''].out_proj.weight.shape[0]
         print('''Our BERT config:'''      ,		lowercase_  )
  
         _snake_case      :							Tuple     				=						RobertaForSequenceClassification(lowercase_  ) if classification_head else RobertaForMaskedLM(lowercase_  )
         model.eval()
  
         # Now let's copy all the weights.
         # Embeddings
         _snake_case      :							Optional[Any]     				=						roberta_sent_encoder.embed_tokens.weight
         _snake_case      :							Any     				=						roberta_sent_encoder.embed_positions.weight
         _snake_case      :							List[str]     				=						torch.zeros_like(
             model.roberta.embeddings.token_type_embeddings.weight  )  # just zero them out b/c RoBERTa doesn't use them.
         _snake_case      :							Tuple     				=						roberta_sent_encoder.emb_layer_norm.weight
         _snake_case      :							Tuple     				=						roberta_sent_encoder.emb_layer_norm.bias
  
         for i in range(config.num_hidden_layers  ):
                # Encoder: start of layer
                _snake_case      :							int     				=						model.roberta.encoder.layer[i]
                _snake_case      :							List[Any]     				=						roberta_sent_encoder.layers[i]
  
                # self attention
                _snake_case      :							List[str]     				=						layer.attention.self
                assert (
                    roberta_layer.self_attn.k_proj.weight.data.shape
                    == roberta_layer.self_attn.q_proj.weight.data.shape
                    == roberta_layer.self_attn.v_proj.weight.data.shape
                    == torch.Size((config.hidden_size, config.hidden_size)  )
                )
  
                _snake_case      :							str     				=						roberta_layer.self_attn.q_proj.weight
                _snake_case      :							List[Any]     				=						roberta_layer.self_attn.q_proj.bias
                _snake_case      :							Any     				=						roberta_layer.self_attn.k_proj.weight
                _snake_case      :							List[str]     				=						roberta_layer.self_attn.k_proj.bias
                _snake_case      :							int     				=						roberta_layer.self_attn.v_proj.weight
                _snake_case      :							Any     				=						roberta_layer.self_attn.v_proj.bias
  
                # self-attention output
                _snake_case      :							Any     				=						layer.attention.output
                assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
                _snake_case      :							int     				=						roberta_layer.self_attn.out_proj.weight
                _snake_case      :							Union[str, Any]     				=						roberta_layer.self_attn.out_proj.bias
                _snake_case      :							List[Any]     				=						roberta_layer.self_attn_layer_norm.weight
                _snake_case      :							List[Any]     				=						roberta_layer.self_attn_layer_norm.bias
  
                # intermediate
                _snake_case      :							List[str]     				=						layer.intermediate
                assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
                _snake_case      :							Union[str, Any]     				=						roberta_layer.fca.weight
                _snake_case      :							List[str]     				=						roberta_layer.fca.bias
  
                # output
                _snake_case      :							str     				=						layer.output
                assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
                _snake_case      :							int     				=						roberta_layer.fca.weight
                _snake_case      :							List[Any]     				=						roberta_layer.fca.bias
                _snake_case      :							int     				=						roberta_layer.final_layer_norm.weight
                _snake_case      :							Optional[int]     				=						roberta_layer.final_layer_norm.bias
                # end of layer
  
         if classification_head:
                _snake_case      :							List[str]     				=						roberta.model.classification_heads['''mnli'''].dense.weight
                _snake_case      :							Union[str, Any]     				=						roberta.model.classification_heads['''mnli'''].dense.bias
                _snake_case      :							str     				=						roberta.model.classification_heads['''mnli'''].out_proj.weight
                _snake_case      :							List[Any]     				=						roberta.model.classification_heads['''mnli'''].out_proj.bias
         else:
                # LM Head
                _snake_case      :							List[Any]     				=						roberta.model.encoder.lm_head.dense.weight
                _snake_case      :							Optional[Any]     				=						roberta.model.encoder.lm_head.dense.bias
                _snake_case      :							Optional[int]     				=						roberta.model.encoder.lm_head.layer_norm.weight
                _snake_case      :							Dict     				=						roberta.model.encoder.lm_head.layer_norm.bias
                _snake_case      :							Union[str, Any]     				=						roberta.model.encoder.lm_head.weight
                _snake_case      :							Dict     				=						roberta.model.encoder.lm_head.bias
  
         # Let's check that we get the same results.
         _snake_case      :							Optional[Any]     				=						roberta.encode(lowercase_  ).unsqueeze(0  )  # batch of size 1
  
         _snake_case      :							Optional[int]     				=						model(lowercase_  )[0]
         if classification_head:
                _snake_case      :							int     				=						roberta.model.classification_heads['''mnli'''](roberta.extract_features(lowercase_  )  )
         else:
                _snake_case      :							str     				=						roberta.model(lowercase_  )[0]
         print(our_output.shape      ,		their_output.shape  )
         _snake_case      :							Tuple     				=						torch.max(torch.abs(our_output - their_output  )  ).item()
         print(f'''max_absolute_diff = {max_absolute_diff}'''  )  # ~ 1e-7
         _snake_case      :							Optional[int]     				=						torch.allclose(lowercase_      ,		lowercase_      ,		atol=1E-3  )
         print('''Do both models output the same tensors?'''      ,		'''🔥''' if success else '''💩'''  )
         if not success:
                raise Exception('''Something went wRoNg'''  )
  
         pathlib.Path(lowercase_  ).mkdir(parents=lowercase_      ,		exist_ok=lowercase_  )
         print(f'''Saving model to {pytorch_dump_folder_path}'''  )
         model.save_pretrained(lowercase_  )
  
  
  if __name__ == "__main__":
         lowerCAmelCase_   =   argparse.ArgumentParser()
         # Required parameters
         parser.add_argument(
             "--roberta_checkpoint_path", default=None, type=str, required=True, help="Path the official PyTorch dump."
         )
         parser.add_argument(
             "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
         )
         parser.add_argument(
             "--classification_head", action="store_true", help="Whether to convert a final classification head."
         )
         lowerCAmelCase_   =   parser.parse_args()
         convert_roberta_checkpoint_to_pytorch(
             args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
         )
  
  
  
  
  
  
  
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					)					)
  
  
  
  
  
  
  