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 							_lowerCamelCase(				__snake_case						,						__snake_case						,						__snake_case	)  ->   Union[str, Any]:
  		__snake_case		       =     FairseqRobertaModel.from_pretrained(__snake_case	)
  		roberta.eval()  # disable dropout
  		__snake_case		       =     roberta.model.encoder.sentence_encoder
  		__snake_case		       =     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		       =     roberta.model.classification_heads["mnli"].out_proj.weight.shape[0]
  		print("Our BERT config:"						,						__snake_case	)
  
  		__snake_case		       =     RobertaForSequenceClassification(__snake_case	) if classification_head else RobertaForMaskedLM(__snake_case	)
  		model.eval()
  
  		# Now let's copy all the weights.
  		# Embeddings
  		__snake_case		       =     roberta_sent_encoder.embed_tokens.weight
  		__snake_case		       =     roberta_sent_encoder.embed_positions.weight
  		__snake_case		       =     torch.zeros_like(
  		    model.roberta.embeddings.token_type_embeddings.weight	)  # just zero them out b/c RoBERTa doesn't use them.
  		__snake_case		       =     roberta_sent_encoder.emb_layer_norm.weight
  		__snake_case		       =     roberta_sent_encoder.emb_layer_norm.bias
  
  		for i in range(config.num_hidden_layers	):
  				# Encoder: start of layer
  				__snake_case		       =     model.roberta.encoder.layer[i]
  				__snake_case		       =     roberta_sent_encoder.layers[i]
  
  				# self attention
  				__snake_case		       =     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		       =     roberta_layer.self_attn.q_proj.weight
  				__snake_case		       =     roberta_layer.self_attn.q_proj.bias
  				__snake_case		       =     roberta_layer.self_attn.k_proj.weight
  				__snake_case		       =     roberta_layer.self_attn.k_proj.bias
  				__snake_case		       =     roberta_layer.self_attn.v_proj.weight
  				__snake_case		       =     roberta_layer.self_attn.v_proj.bias
  
  				# self-attention output
  				__snake_case		       =     layer.attention.output
  				assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
  				__snake_case		       =     roberta_layer.self_attn.out_proj.weight
  				__snake_case		       =     roberta_layer.self_attn.out_proj.bias
  				__snake_case		       =     roberta_layer.self_attn_layer_norm.weight
  				__snake_case		       =     roberta_layer.self_attn_layer_norm.bias
  
  				# intermediate
  				__snake_case		       =     layer.intermediate
  				assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
  				__snake_case		       =     roberta_layer.fca.weight
  				__snake_case		       =     roberta_layer.fca.bias
  
  				# output
  				__snake_case		       =     layer.output
  				assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
  				__snake_case		       =     roberta_layer.fca.weight
  				__snake_case		       =     roberta_layer.fca.bias
  				__snake_case		       =     roberta_layer.final_layer_norm.weight
  				__snake_case		       =     roberta_layer.final_layer_norm.bias
  				# end of layer
  
  		if classification_head:
  				__snake_case		       =     roberta.model.classification_heads["mnli"].dense.weight
  				__snake_case		       =     roberta.model.classification_heads["mnli"].dense.bias
  				__snake_case		       =     roberta.model.classification_heads["mnli"].out_proj.weight
  				__snake_case		       =     roberta.model.classification_heads["mnli"].out_proj.bias
  		else:
  				# LM Head
  				__snake_case		       =     roberta.model.encoder.lm_head.dense.weight
  				__snake_case		       =     roberta.model.encoder.lm_head.dense.bias
  				__snake_case		       =     roberta.model.encoder.lm_head.layer_norm.weight
  				__snake_case		       =     roberta.model.encoder.lm_head.layer_norm.bias
  				__snake_case		       =     roberta.model.encoder.lm_head.weight
  				__snake_case		       =     roberta.model.encoder.lm_head.bias
  
  		# Let's check that we get the same results.
  		__snake_case		       =     roberta.encode(__snake_case	).unsqueeze(0	)  # batch of size 1
  
  		__snake_case		       =     model(__snake_case	)[0]
  		if classification_head:
  				__snake_case		       =     roberta.model.classification_heads["mnli"](roberta.extract_features(__snake_case	)	)
  		else:
  				__snake_case		       =     roberta.model(__snake_case	)[0]
  		print(our_output.shape						,						their_output.shape	)
  		__snake_case		       =     torch.max(torch.abs(our_output - their_output	)	).item()
  		print(f"""max_absolute_diff = {max_absolute_diff}"""	)  # ~ 1e-7
  		__snake_case		       =     torch.allclose(__snake_case						,						__snake_case						,						atol=1e-3	)
  		print("Do both models output the same tensors?"						,						"🔥" if success else "💩"	)
  		if not success:
  				raise Exception("Something went wRoNg"	)
  
  		pathlib.Path(__snake_case	).mkdir(parents=__snake_case						,						exist_ok=__snake_case	)
  		print(f"""Saving model to {pytorch_dump_folder_path}"""	)
  		model.save_pretrained(__snake_case	)
  
  
  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: from __future__ import annotations
  
  from collections.abc import Iterable
  from dataclasses import dataclass
  from typing import Any
  
  
  
  
  class 					UpperCamelCase	:
  					__UpperCamelCase						=   ("""_front""", """_back""", """_len""")
  
  
  
  
  					@dataclass
  					class 					UpperCamelCase	:
  										__UpperCamelCase						=   None
  										__UpperCamelCase						=   None
  										__UpperCamelCase						=   None
  
  
  
  
  					class 					UpperCamelCase	:
  										__UpperCamelCase						=   ("""_cur""",)
  
  
  
  
  										def __init__( self :					Dict      ,_lowerCAmelCase :					Deque._Node | None						):
  
  
  												"""simple docstring"""
  
  
  
  
  												__snake_case		       =     cur
  
  
  
  
  										def __iter__( self :					Dict						):
  
  
  												"""simple docstring"""
  
  
  
  
  												return self
  
  
  
  
  
  
  
  										def 							UpperCamelCase_						( self :					Optional[Any]						):
  
  
  												"""simple docstring"""
  
  
  
  
  												if self._cur is None:
  														# finished iterating
  														raise StopIteration
  												__snake_case		       =     self._cur.val
  												__snake_case		       =     self._cur.next_node
  
  												return val
  
  
  
  
  					def __init__( self :					Optional[int]      ,_lowerCAmelCase :					Iterable[Any] | None = None						):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     None
  							__snake_case		       =     None
  							__snake_case		       =     0
  
  							if iterable is not None:
  									# append every value to the deque
  									for val in iterable:
  											self.append(_lowerCAmelCase						)
  
  
  
  
  					def 							UpperCamelCase_						( self :					int      ,_lowerCAmelCase :					Any						):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     self._Node(_lowerCAmelCase      ,_lowerCAmelCase      ,_lowerCAmelCase						)
  							if self.is_empty():
  									# front = back
  									__snake_case		       =     __snake_case		       =     node
  									__snake_case		       =     1
  							else:
  									# connect nodes
  									__snake_case		       =     node
  									__snake_case		       =     self._back
  									__snake_case		       =     node  # assign new back to the new node
  
  									self._len += 1
  
  									# make sure there were no errors
  									assert not self.is_empty(), "Error on appending value."
  
  
  
  
  					def 							UpperCamelCase_						( self :					Any      ,_lowerCAmelCase :					Any						):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     self._Node(_lowerCAmelCase      ,_lowerCAmelCase      ,_lowerCAmelCase						)
  							if self.is_empty():
  									# front = back
  									__snake_case		       =     __snake_case		       =     node
  									__snake_case		       =     1
  							else:
  									# connect nodes
  									__snake_case		       =     self._front
  									__snake_case		       =     node
  									__snake_case		       =     node  # assign new front to the new node
  
  									self._len += 1
  
  									# make sure there were no errors
  									assert not self.is_empty(), "Error on appending value."
  
  
  
  
  					def 							UpperCamelCase_						( self :					int      ,_lowerCAmelCase :					Iterable[Any]						):
  
  
  							"""simple docstring"""
  
  
  
  
  							for val in iterable:
  									self.append(_lowerCAmelCase						)
  
  
  
  
  					def 							UpperCamelCase_						( self :					Optional[int]      ,_lowerCAmelCase :					Iterable[Any]						):
  
  
  							"""simple docstring"""
  
  
  
  
  							for val in iterable:
  									self.appendleft(_lowerCAmelCase						)
  
  
  
  
  					def 							UpperCamelCase_						( self :					List[Any]						):
  
  
  							"""simple docstring"""
  
  
  
  
  							assert not self.is_empty(), "Deque is empty."
  
  							__snake_case		       =     self._back
  							__snake_case		       =     self._back.prev_node  # set new back
  							# drop the last node - python will deallocate memory automatically
  							__snake_case		       =     None
  
  							self._len -= 1
  
  							return topop.val
  
  
  
  
  					def 							UpperCamelCase_						( self :					List[str]						):
  
  
  							"""simple docstring"""
  
  
  
  
  							assert not self.is_empty(), "Deque is empty."
  
  							__snake_case		       =     self._front
  							__snake_case		       =     self._front.next_node  # set new front and drop the first node
  							__snake_case		       =     None
  
  							self._len -= 1
  
  							return topop.val
  
  
  
  
  					def 							UpperCamelCase_						( self :					int						):
  
  
  							"""simple docstring"""
  
  
  
  
  							return self._front is None
  
  
  
  
  					def __len__( self :					Tuple						):
  
  
  							"""simple docstring"""
  
  
  
  
  							return self._len
  
  
  
  
  					def __eq__( self :					Union[str, Any]      ,_lowerCAmelCase :					object						):
  
  
  							"""simple docstring"""
  
  
  
  
  							if not isinstance(_lowerCAmelCase      ,_lowerCAmelCase						):
  									return NotImplemented
  
  							__snake_case		       =     self._front
  							__snake_case		       =     other._front
  
  							# if the length of the dequeues are not the same, they are not equal
  							if len(self						) != len(_lowerCAmelCase						):
  									return False
  
  							while me is not None and oth is not None:
  									# compare every value
  									if me.val != oth.val:
  											return False
  									__snake_case		       =     me.next_node
  									__snake_case		       =     oth.next_node
  
  							return True
  
  
  
  
  					def __iter__( self :					Tuple						):
  
  
  							"""simple docstring"""
  
  
  
  
  							return Deque._Iterator(self._front						)
  
  
  
  
  
  
  
  					def __repr__( self :					List[Any]						):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     []
  							__snake_case		       =     self._front
  							while aux is not None:
  									# append the values in a list to display
  									values_list.append(aux.val						)
  									__snake_case		       =     aux.next_node
  
  							return F"""[{", ".join(repr(_lowerCAmelCase						) for val in values_list						)}]"""
  
  
  if __name__ == "__main__":
  		import doctest
  
  		doctest.testmod()
  
  
  
  
  
  