code: 
  
  
  import os
  from shutil import copyfile
  from typing import List, Optional, Tuple
  
  from tokenizers import processors
  
  from ...tokenization_utils import AddedToken, BatchEncoding
  from ...tokenization_utils_fast import PreTrainedTokenizerFast
  from ...utils import is_sentencepiece_available, logging
  
  
  if is_sentencepiece_available():
      from .tokenization_mbartaa import MBartaaTokenizer
  else:
      lowerCAmelCase__   	=				None
  
  
  lowerCAmelCase__   	=				logging.get_logger(__name__)
  
  lowerCAmelCase__   	=				{'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}
  
  lowerCAmelCase__   	=				{
      'vocab_file': {
          'facebook/mbart-large-50-one-to-many-mmt': (
              'https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/sentencepiece.bpe.model'
          ),
      },
      'tokenizer_file': {
          'facebook/mbart-large-50-one-to-many-mmt': (
              'https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/tokenizer.json'
          ),
      },
  }
  
  lowerCAmelCase__   	=				{
      'facebook/mbart-large-50-one-to-many-mmt': 10_24,
  }
  
  # fmt: off
  lowerCAmelCase__   	=				['ar_AR', 'cs_CZ', 'de_DE', 'en_XX', 'es_XX', 'et_EE', 'fi_FI', 'fr_XX', 'gu_IN', 'hi_IN', 'it_IT', 'ja_XX', 'kk_KZ', 'ko_KR', 'lt_LT', 'lv_LV', 'my_MM', 'ne_NP', 'nl_XX', 'ro_RO', 'ru_RU', 'si_LK', 'tr_TR', 'vi_VN', 'zh_CN', 'af_ZA', 'az_AZ', 'bn_IN', 'fa_IR', 'he_IL', 'hr_HR', 'id_ID', 'ka_GE', 'km_KH', 'mk_MK', 'ml_IN', 'mn_MN', 'mr_IN', 'pl_PL', 'ps_AF', 'pt_XX', 'sv_SE', 'sw_KE', 'ta_IN', 'te_IN', 'th_TH', 'tl_XX', 'uk_UA', 'ur_PK', 'xh_ZA', 'gl_ES', 'sl_SI']
  
  
  
  
  
  
  
  class   lowerCAmelCase__ (       a):
  
  
  
  
       '''simple docstring'''
       __SCREAMING_SNAKE_CASE						    =				VOCAB_FILES_NAMES
       __SCREAMING_SNAKE_CASE						    =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       __SCREAMING_SNAKE_CASE						    =				PRETRAINED_VOCAB_FILES_MAP
       __SCREAMING_SNAKE_CASE						    =				["input_ids", "attention_mask"]
       __SCREAMING_SNAKE_CASE						    =				MBartaaTokenizer
  
       __SCREAMING_SNAKE_CASE						    =				[]
       __SCREAMING_SNAKE_CASE						    =				[]
  
  
       def __init__(		self				,		__lowerCamelCase=None				,		__lowerCamelCase=None				,		__lowerCamelCase=None				,		__lowerCamelCase=None				,		__lowerCamelCase="</s>"				,		__lowerCamelCase="</s>"				,		__lowerCamelCase="<s>"				,		__lowerCamelCase="<unk>"				,		__lowerCamelCase="<pad>"				,		__lowerCamelCase="<mask>"				,		**__lowerCamelCase				,		)    ->       str:
              # Mask token behave like a normal word, i.e. include the space before it
              _A   : List[str]       					=				AddedToken(__lowerCamelCase				,		lstrip=__lowerCamelCase				,		rstrip=__lowerCamelCase) if isinstance(__lowerCamelCase				,		__lowerCamelCase) else mask_token
  
              _A   : List[Any]       					=				kwargs.get("additional_special_tokens"				,		[])
              kwargs["additional_special_tokens"] += [
                  code for code in FAIRSEQ_LANGUAGE_CODES if code not in kwargs["additional_special_tokens"]
              ]
  
              super().__init__(
                  __lowerCamelCase				,		src_lang=__lowerCamelCase				,		tgt_lang=__lowerCamelCase				,		tokenizer_file=__lowerCamelCase				,		eos_token=__lowerCamelCase				,		sep_token=__lowerCamelCase				,		cls_token=__lowerCamelCase				,		unk_token=__lowerCamelCase				,		pad_token=__lowerCamelCase				,		mask_token=__lowerCamelCase				,		**__lowerCamelCase				,		)
  
              _A   : str       					=				vocab_file
              _A   : Any       					=				False if not self.vocab_file else True
  
              _A   : Optional[Any]       					=				{
                  lang_code: self.convert_tokens_to_ids(__lowerCamelCase) for lang_code in FAIRSEQ_LANGUAGE_CODES
              }
  
              _A   : List[str]       					=				src_lang if src_lang is not None else "en_XX"
              _A   : int       					=				tgt_lang
              _A   : Any       					=				self.lang_code_to_id[self._src_lang]
              self.set_src_lang_special_tokens(self._src_lang)
  
  
       @property
       def 				_lowerCamelCase				(		self)    ->       str:
              return self._src_lang
  
  
       @src_lang.setter
       def 				_lowerCamelCase				(		self				,		__lowerCamelCase)    ->       None:
              _A   : Optional[Any]       					=				new_src_lang
              self.set_src_lang_special_tokens(self._src_lang)
  
  
       def 				_lowerCamelCase				(		self				,		__lowerCamelCase				,		__lowerCamelCase = None)    ->       List[int]:
              if token_ids_a is None:
                     return self.prefix_tokens + token_ids_a + self.suffix_tokens
              # We don't expect to process pairs, but leave the pair logic for API consistency
              return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens
  
  
       def 				_lowerCamelCase				(		self				,		__lowerCamelCase				,		__lowerCamelCase = "en_XX"				,		__lowerCamelCase = None				,		__lowerCamelCase = "ro_RO"				,		**__lowerCamelCase				,		)    ->       BatchEncoding:
              _A   : int       					=				src_lang
              _A   : List[str]       					=				tgt_lang
              return super().prepare_seqaseq_batch(__lowerCamelCase				,		__lowerCamelCase				,		**__lowerCamelCase)
  
  
       def 				_lowerCamelCase				(		self)    ->       List[str]:
              return self.set_src_lang_special_tokens(self.src_lang)
  
  
       def 				_lowerCamelCase				(		self)    ->       Optional[int]:
              return self.set_tgt_lang_special_tokens(self.tgt_lang)
  
  
       def 				_lowerCamelCase				(		self				,		__lowerCamelCase)    ->       None:
              _A   : List[Any]       					=				self.convert_tokens_to_ids(__lowerCamelCase)
              _A   : Union[str, Any]       					=				[self.cur_lang_code_id]
              _A   : List[str]       					=				[self.eos_token_id]
  
              _A   : Tuple       					=				self.convert_ids_to_tokens(self.prefix_tokens)
              _A   : List[str]       					=				self.convert_ids_to_tokens(self.suffix_tokens)
  
              _A   : List[str]       					=				processors.TemplateProcessing(
                  single=prefix_tokens_str + ["$A"] + suffix_tokens_str				,		pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str				,		special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str				,		self.prefix_tokens + self.suffix_tokens))				,		)
  
  
       def 				_lowerCamelCase				(		self				,		__lowerCamelCase)    ->       None:
              _A   : Union[str, Any]       					=				self.convert_tokens_to_ids(__lowerCamelCase)
              _A   : Dict       					=				[self.cur_lang_code_id]
              _A   : Optional[int]       					=				[self.eos_token_id]
  
              _A   : List[str]       					=				self.convert_ids_to_tokens(self.prefix_tokens)
              _A   : Tuple       					=				self.convert_ids_to_tokens(self.suffix_tokens)
  
              _A   : int       					=				processors.TemplateProcessing(
                  single=prefix_tokens_str + ["$A"] + suffix_tokens_str				,		pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str				,		special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str				,		self.prefix_tokens + self.suffix_tokens))				,		)
  
  
       def 				_lowerCamelCase				(		self				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase				,		**__lowerCamelCase)    ->       int:
              if src_lang is None or tgt_lang is None:
                     raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model")
              _A   : Optional[int]       					=				src_lang
              _A   : Union[str, Any]       					=				self(__lowerCamelCase				,		add_special_tokens=__lowerCamelCase				,		return_tensors=__lowerCamelCase				,		**__lowerCamelCase)
              _A   : int       					=				self.convert_tokens_to_ids(__lowerCamelCase)
              _A   : List[Any]       					=				tgt_lang_id
              return inputs
  
  
       def 				_lowerCamelCase				(		self				,		__lowerCamelCase				,		__lowerCamelCase = None)    ->       Tuple[str]:
              if not self.can_save_slow_tokenizer:
                     raise ValueError(
                         "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
                         "tokenizer.")
  
              if not os.path.isdir(__lowerCamelCase):
                     logger.error(F"Vocabulary path ({save_directory}) should be a directory")
                     return
              _A   : List[str]       					=				os.path.join(
                  __lowerCamelCase				,		(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"])
  
              if os.path.abspath(self.vocab_file) != os.path.abspath(__lowerCamelCase):
                     copyfile(self.vocab_file				,		__lowerCamelCase)
  
              return (out_vocab_file,)
  
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
      )
  