code: 
  
  
  import csv
  import dataclasses
  import json
  from dataclasses import dataclass
  from typing import List, Optional, Union
  
  from ...utils import is_tf_available, is_torch_available, logging
  
  
  lowerCAmelCase__   	=				logging.get_logger(__name__)
  
  
  
  
  
  
  
  @dataclass
  class   lowerCAmelCase__ :
  
  
  
  
       '''simple docstring'''
       __SCREAMING_SNAKE_CASE						    =				42
       __SCREAMING_SNAKE_CASE						    =				42
       __SCREAMING_SNAKE_CASE						    =				None
       __SCREAMING_SNAKE_CASE						    =				None
  
  
       def 				_lowerCamelCase				(		self)    ->       Union[str, Any]:
              return json.dumps(dataclasses.asdict(self)				,		indent=2) + "\n"
  
  
  
  
  
  
  
  @dataclass(frozen=a)
  class   lowerCAmelCase__ :
  
  
  
  
       '''simple docstring'''
       __SCREAMING_SNAKE_CASE						    =				42
       __SCREAMING_SNAKE_CASE						    =				None
       __SCREAMING_SNAKE_CASE						    =				None
       __SCREAMING_SNAKE_CASE						    =				None
  
  
       def 				_lowerCamelCase				(		self)    ->       Optional[int]:
              return json.dumps(dataclasses.asdict(self)) + "\n"
  
  
  
  
  
  
  
  class   lowerCAmelCase__ :
  
  
  
  
       '''simple docstring'''
  
  
       def 				_lowerCamelCase				(		self				,		__lowerCamelCase)    ->       Union[str, Any]:
              raise NotImplementedError()
  
  
       def 				_lowerCamelCase				(		self				,		__lowerCamelCase)    ->       Tuple:
              raise NotImplementedError()
  
  
       def 				_lowerCamelCase				(		self				,		__lowerCamelCase)    ->       Union[str, Any]:
              raise NotImplementedError()
  
  
       def 				_lowerCamelCase				(		self				,		__lowerCamelCase)    ->       int:
              raise NotImplementedError()
  
  
       def 				_lowerCamelCase				(		self)    ->       Any:
              raise NotImplementedError()
  
  
       def 				_lowerCamelCase				(		self				,		__lowerCamelCase)    ->       List[Any]:
              if len(self.get_labels()) > 1:
                     _A   : Dict       					=				self.get_labels()[int(example.label)]
              return example
  
  
       @classmethod
       def 				_lowerCamelCase				(		cls				,		__lowerCamelCase				,		__lowerCamelCase=None)    ->       Optional[Any]:
              with open(__lowerCamelCase				,		"r"				,		encoding="utf-8-sig") as f:
                     return list(csv.reader(__lowerCamelCase				,		delimiter="\t"				,		quotechar=__lowerCamelCase))
  
  
  
  
  
  
  
  class   lowerCAmelCase__ (       a):
  
  
  
  
       '''simple docstring'''
  
  
       def __init__(		self				,		__lowerCamelCase=None				,		__lowerCamelCase=None				,		__lowerCamelCase="classification"				,		__lowerCamelCase=False)    ->       Dict:
              _A   : Optional[Any]       					=				[] if labels is None else labels
              _A   : Dict       					=				[] if examples is None else examples
              _A   : Any       					=				mode
              _A   : Optional[int]       					=				verbose
  
  
       def __len__(		self)    ->       Any:
              return len(self.examples)
  
  
       def __getitem__(		self				,		__lowerCamelCase)    ->       Optional[int]:
              if isinstance(__lowerCamelCase				,		__lowerCamelCase):
                     return SingleSentenceClassificationProcessor(labels=self.labels				,		examples=self.examples[idx])
              return self.examples[idx]
  
  
       @classmethod
       def 				_lowerCamelCase				(		cls				,		__lowerCamelCase				,		__lowerCamelCase=""				,		__lowerCamelCase=0				,		__lowerCamelCase=1				,		__lowerCamelCase=None				,		__lowerCamelCase=False				,		**__lowerCamelCase)    ->       int:
              _A   : Optional[int]       					=				cls(**__lowerCamelCase)
              processor.add_examples_from_csv(
                  __lowerCamelCase				,		split_name=__lowerCamelCase				,		column_label=__lowerCamelCase				,		column_text=__lowerCamelCase				,		column_id=__lowerCamelCase				,		skip_first_row=__lowerCamelCase				,		overwrite_labels=__lowerCamelCase				,		overwrite_examples=__lowerCamelCase				,		)
              return processor
  
  
       @classmethod
       def 				_lowerCamelCase				(		cls				,		__lowerCamelCase				,		__lowerCamelCase=None				,		**__lowerCamelCase)    ->       str:
              _A   : List[Any]       					=				cls(**__lowerCamelCase)
              processor.add_examples(__lowerCamelCase				,		labels=__lowerCamelCase)
              return processor
  
  
       def 				_lowerCamelCase				(		self				,		__lowerCamelCase				,		__lowerCamelCase=""				,		__lowerCamelCase=0				,		__lowerCamelCase=1				,		__lowerCamelCase=None				,		__lowerCamelCase=False				,		__lowerCamelCase=False				,		__lowerCamelCase=False				,		)    ->       Dict:
              _A   : int       					=				self._read_tsv(__lowerCamelCase)
              if skip_first_row:
                     _A   : int       					=				lines[1:]
              _A   : Optional[Any]       					=				[]
              _A   : List[str]       					=				[]
              _A   : Dict       					=				[]
              for i, line in enumerate(__lowerCamelCase):
                     texts.append(line[column_text])
                     labels.append(line[column_label])
                     if column_id is not None:
                            ids.append(line[column_id])
                     else:
                            _A   : str       					=				F"{split_name}-{i}" if split_name else str(__lowerCamelCase)
                            ids.append(__lowerCamelCase)
  
              return self.add_examples(
                  __lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase				,		overwrite_labels=__lowerCamelCase				,		overwrite_examples=__lowerCamelCase)
  
  
       def 				_lowerCamelCase				(		self				,		__lowerCamelCase				,		__lowerCamelCase=None				,		__lowerCamelCase=None				,		__lowerCamelCase=False				,		__lowerCamelCase=False)    ->       Tuple:
              if labels is not None and len(__lowerCamelCase) != len(__lowerCamelCase):
                     raise ValueError(
                         F"Text and labels have mismatched lengths {len(__lowerCamelCase)} and {len(__lowerCamelCase)}")
              if ids is not None and len(__lowerCamelCase) != len(__lowerCamelCase):
                     raise ValueError(F"Text and ids have mismatched lengths {len(__lowerCamelCase)} and {len(__lowerCamelCase)}")
              if ids is None:
                     _A   : Optional[Any]       					=				[None] * len(__lowerCamelCase)
              if labels is None:
                     _A   : Union[str, Any]       					=				[None] * len(__lowerCamelCase)
              _A   : Optional[int]       					=				[]
              _A   : List[Any]       					=				set()
              for text_or_text_and_label, label, guid in zip(__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase):
                     if isinstance(__lowerCamelCase				,		(tuple, list)) and label is None:
                            _A					,    _A   : List[str]       					=				text_or_text_and_label
                     else:
                            _A   : Union[str, Any]       					=				text_or_text_and_label
                     added_labels.add(__lowerCamelCase)
                     examples.append(InputExample(guid=__lowerCamelCase				,		text_a=__lowerCamelCase				,		text_b=__lowerCamelCase				,		label=__lowerCamelCase))
  
              # Update examples
              if overwrite_examples:
                     _A   : Optional[Any]       					=				examples
              else:
                     self.examples.extend(__lowerCamelCase)
  
              # Update labels
              if overwrite_labels:
                     _A   : Optional[Any]       					=				list(__lowerCamelCase)
              else:
                     _A   : Any       					=				list(set(self.labels).union(__lowerCamelCase))
  
              return self.examples
  
  
       def 				_lowerCamelCase				(		self				,		__lowerCamelCase				,		__lowerCamelCase=None				,		__lowerCamelCase=False				,		__lowerCamelCase=0				,		__lowerCamelCase=True				,		__lowerCamelCase=None				,		)    ->       Tuple:
              if max_length is None:
                     _A   : Optional[int]       					=				tokenizer.max_len
  
              _A   : Any       					=				{label: i for i, label in enumerate(self.labels)}
  
              _A   : Optional[Any]       					=				[]
              for ex_index, example in enumerate(self.examples):
                     if ex_index % 1_0_0_0_0 == 0:
                            logger.info(F"Tokenizing example {ex_index}")
  
                     _A   : Any       					=				tokenizer.encode(
                         example.text_a				,		add_special_tokens=__lowerCamelCase				,		max_length=min(__lowerCamelCase				,		tokenizer.max_len)				,		)
                     all_input_ids.append(__lowerCamelCase)
  
              _A   : List[str]       					=				max(len(__lowerCamelCase) for input_ids in all_input_ids)
  
              _A   : Any       					=				[]
              for ex_index, (input_ids, example) in enumerate(zip(__lowerCamelCase				,		self.examples)):
                     if ex_index % 1_0_0_0_0 == 0:
                            logger.info(F"Writing example {ex_index}/{len(self.examples)}")
                     # The mask has 1 for real tokens and 0 for padding tokens. Only real
                     # tokens are attended to.
                     _A   : Optional[int]       					=				[1 if mask_padding_with_zero else 0] * len(__lowerCamelCase)
  
                     # Zero-pad up to the sequence length.
                     _A   : Optional[Any]       					=				batch_length - len(__lowerCamelCase)
                     if pad_on_left:
                            _A   : List[str]       					=				([pad_token] * padding_length) + input_ids
                            _A   : Dict       					=				([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
                     else:
                            _A   : Dict       					=				input_ids + ([pad_token] * padding_length)
                            _A   : int       					=				attention_mask + ([0 if mask_padding_with_zero else 1] * padding_length)
  
                     if len(__lowerCamelCase) != batch_length:
                            raise ValueError(F"Error with input length {len(__lowerCamelCase)} vs {batch_length}")
                     if len(__lowerCamelCase) != batch_length:
                            raise ValueError(F"Error with input length {len(__lowerCamelCase)} vs {batch_length}")
  
                     if self.mode == "classification":
                            _A   : Tuple       					=				label_map[example.label]
                     elif self.mode == "regression":
                            _A   : Optional[int]       					=				float(example.label)
                     else:
                            raise ValueError(self.mode)
  
                     if ex_index < 5 and self.verbose:
                            logger.info("*** Example ***")
                            logger.info(F"guid: {example.guid}")
                            logger.info(F"input_ids: {' '.join([str(__lowerCamelCase) for x in input_ids])}")
                            logger.info(F"attention_mask: {' '.join([str(__lowerCamelCase) for x in attention_mask])}")
                            logger.info(F"label: {example.label} (id = {label})")
  
                     features.append(InputFeatures(input_ids=__lowerCamelCase				,		attention_mask=__lowerCamelCase				,		label=__lowerCamelCase))
  
              if return_tensors is None:
                     return features
              elif return_tensors == "tf":
                     if not is_tf_available():
                            raise RuntimeError("return_tensors set to 'tf' but TensorFlow 2.0 can't be imported")
                     import tensorflow as tf
  
                     def gen():
                            for ex in features:
                                   yield ({"input_ids": ex.input_ids, "attention_mask": ex.attention_mask}, ex.label)
  
                     _A   : Tuple       					=				tf.data.Dataset.from_generator(
                         __lowerCamelCase				,		({"input_ids": tf.intaa, "attention_mask": tf.intaa}, tf.intaa)				,		({"input_ids": tf.TensorShape([None]), "attention_mask": tf.TensorShape([None])}, tf.TensorShape([]))				,		)
                     return dataset
              elif return_tensors == "pt":
                     if not is_torch_available():
                            raise RuntimeError("return_tensors set to 'pt' but PyTorch can't be imported")
                     import torch
                     from torch.utils.data import TensorDataset
  
                     _A   : Dict       					=				torch.tensor([f.input_ids for f in features]				,		dtype=torch.long)
                     _A   : Any       					=				torch.tensor([f.attention_mask for f in features]				,		dtype=torch.long)
                     if self.mode == "classification":
                            _A   : int       					=				torch.tensor([f.label for f in features]				,		dtype=torch.long)
                     elif self.mode == "regression":
                            _A   : int       					=				torch.tensor([f.label for f in features]				,		dtype=torch.float)
  
                     _A   : Dict       					=				TensorDataset(__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase)
                     return dataset
              else:
                     raise ValueError("return_tensors should be one of 'tf' or 'pt'")
  
style_context: 
  
  
  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 							_UpperCAmelCase       (UpperCamelCase__  :						str       ,							UpperCamelCase__  :						str       ,							UpperCamelCase__  :						bool		):
         _A   : Union[str, Any]       					=				FairseqRobertaModel.from_pretrained(UpperCamelCase__		)
         roberta.eval()  # disable dropout
         _A   : Dict       					=				roberta.model.encoder.sentence_encoder
         _A   : int       					=				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:
                _A   : List[Any]       					=				roberta.model.classification_heads["mnli"].out_proj.weight.shape[0]
         print("Our BERT config:"       ,							UpperCamelCase__		)
  
         _A   : Dict       					=				RobertaForSequenceClassification(UpperCamelCase__		) if classification_head else RobertaForMaskedLM(UpperCamelCase__		)
         model.eval()
  
         # Now let's copy all the weights.
         # Embeddings
         _A   : Optional[int]       					=				roberta_sent_encoder.embed_tokens.weight
         _A   : List[str]       					=				roberta_sent_encoder.embed_positions.weight
         _A   : Optional[int]       					=				torch.zeros_like(
             model.roberta.embeddings.token_type_embeddings.weight		)  # just zero them out b/c RoBERTa doesn't use them.
         _A   : List[Any]       					=				roberta_sent_encoder.emb_layer_norm.weight
         _A   : Tuple       					=				roberta_sent_encoder.emb_layer_norm.bias
  
         for i in range(config.num_hidden_layers		):
                # Encoder: start of layer
                _A   : BertLayer       					=				model.roberta.encoder.layer[i]
                _A   : TransformerSentenceEncoderLayer       					=				roberta_sent_encoder.layers[i]
  
                # self attention
                _A   : BertSelfAttention       					=				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)		)
                )
  
                _A   : List[str]       					=				roberta_layer.self_attn.q_proj.weight
                _A   : Optional[Any]       					=				roberta_layer.self_attn.q_proj.bias
                _A   : Union[str, Any]       					=				roberta_layer.self_attn.k_proj.weight
                _A   : Tuple       					=				roberta_layer.self_attn.k_proj.bias
                _A   : List[str]       					=				roberta_layer.self_attn.v_proj.weight
                _A   : Optional[Any]       					=				roberta_layer.self_attn.v_proj.bias
  
                # self-attention output
                _A   : BertSelfOutput       					=				layer.attention.output
                assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
                _A   : Tuple       					=				roberta_layer.self_attn.out_proj.weight
                _A   : List[str]       					=				roberta_layer.self_attn.out_proj.bias
                _A   : Dict       					=				roberta_layer.self_attn_layer_norm.weight
                _A   : Optional[int]       					=				roberta_layer.self_attn_layer_norm.bias
  
                # intermediate
                _A   : BertIntermediate       					=				layer.intermediate
                assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
                _A   : Tuple       					=				roberta_layer.fca.weight
                _A   : List[str]       					=				roberta_layer.fca.bias
  
                # output
                _A   : BertOutput       					=				layer.output
                assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
                _A   : int       					=				roberta_layer.fca.weight
                _A   : List[str]       					=				roberta_layer.fca.bias
                _A   : List[str]       					=				roberta_layer.final_layer_norm.weight
                _A   : Any       					=				roberta_layer.final_layer_norm.bias
                # end of layer
  
         if classification_head:
                _A   : int       					=				roberta.model.classification_heads["mnli"].dense.weight
                _A   : List[Any]       					=				roberta.model.classification_heads["mnli"].dense.bias
                _A   : Optional[Any]       					=				roberta.model.classification_heads["mnli"].out_proj.weight
                _A   : Optional[int]       					=				roberta.model.classification_heads["mnli"].out_proj.bias
         else:
                # LM Head
                _A   : Any       					=				roberta.model.encoder.lm_head.dense.weight
                _A   : Dict       					=				roberta.model.encoder.lm_head.dense.bias
                _A   : List[str]       					=				roberta.model.encoder.lm_head.layer_norm.weight
                _A   : List[str]       					=				roberta.model.encoder.lm_head.layer_norm.bias
                _A   : List[Any]       					=				roberta.model.encoder.lm_head.weight
                _A   : Optional[Any]       					=				roberta.model.encoder.lm_head.bias
  
         # Let's check that we get the same results.
         _A   : torch.Tensor       					=				roberta.encode(UpperCamelCase__		).unsqueeze(0		)  # batch of size 1
  
         _A   : int       					=				model(UpperCamelCase__		)[0]
         if classification_head:
                _A   : str       					=				roberta.model.classification_heads["mnli"](roberta.extract_features(UpperCamelCase__		)		)
         else:
                _A   : int       					=				roberta.model(UpperCamelCase__		)[0]
         print(our_output.shape       ,							their_output.shape		)
         _A   : List[Any]       					=				torch.max(torch.abs(our_output - their_output		)		).item()
         print(f"max_absolute_diff = {max_absolute_diff}"		)  # ~ 1e-7
         _A   : List[Any]       					=				torch.allclose(UpperCamelCase__       ,							UpperCamelCase__       ,							atol=1E-3		)
         print("Do both models output the same tensors?"       ,							"🔥" if success else "💩"		)
         if not success:
                raise Exception("Something went wRoNg"		)
  
         pathlib.Path(UpperCamelCase__		).mkdir(parents=UpperCamelCase__       ,							exist_ok=UpperCamelCase__		)
         print(f"Saving model to {pytorch_dump_folder_path}"		)
         model.save_pretrained(UpperCamelCase__		)
  
  
  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
      )
  