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()
  snake_case_    						=				logging.get_logger(__name__)
  
  snake_case_    						=				'''Hello world! cécé herlolip'''
  
  def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					str			,  SCREAMING_SNAKE_CASE_	:					str			,  SCREAMING_SNAKE_CASE_	:					bool    ):
  
  
  
       '''simple docstring'''
  
  
  
  
  
       lowercase__					:     Tuple   = FairseqRobertaModel.from_pretrained(SCREAMING_SNAKE_CASE_    )
       roberta.eval()  # disable dropout
       lowercase__					:     str   = roberta.model.encoder.sentence_encoder
       lowercase__					:     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:
            lowercase__					:     str   = roberta.model.classification_heads['mnli'].out_proj.weight.shape[0]
       print('Our BERT config:'			,  SCREAMING_SNAKE_CASE_    )
  
       lowercase__					:     List[str]   = RobertaForSequenceClassification(SCREAMING_SNAKE_CASE_    ) if classification_head else RobertaForMaskedLM(SCREAMING_SNAKE_CASE_    )
       model.eval()
  
       # Now let's copy all the weights.
       # Embeddings
       lowercase__					:     Optional[Any]   = roberta_sent_encoder.embed_tokens.weight
       lowercase__					:     Union[str, Any]   = roberta_sent_encoder.embed_positions.weight
       lowercase__					:     List[Any]   = torch.zeros_like(
           model.roberta.embeddings.token_type_embeddings.weight    )  # just zero them out b/c RoBERTa doesn't use them.
       lowercase__					:     Optional[int]   = roberta_sent_encoder.emb_layer_norm.weight
       lowercase__					:     List[Any]   = roberta_sent_encoder.emb_layer_norm.bias
  
       for i in range(config.num_hidden_layers    ):
            # Encoder: start of layer
            lowercase__					:     BertLayer   = model.roberta.encoder.layer[i]
            lowercase__					:     TransformerSentenceEncoderLayer   = roberta_sent_encoder.layers[i]
  
            # self attention
            lowercase__					:     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)    )
            )
  
            lowercase__					:     Union[str, Any]   = roberta_layer.self_attn.q_proj.weight
            lowercase__					:     Dict   = roberta_layer.self_attn.q_proj.bias
            lowercase__					:     List[str]   = roberta_layer.self_attn.k_proj.weight
            lowercase__					:     Any   = roberta_layer.self_attn.k_proj.bias
            lowercase__					:     Optional[int]   = roberta_layer.self_attn.v_proj.weight
            lowercase__					:     Optional[int]   = roberta_layer.self_attn.v_proj.bias
  
            # self-attention output
            lowercase__					:     BertSelfOutput   = layer.attention.output
            assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
            lowercase__					:     str   = roberta_layer.self_attn.out_proj.weight
            lowercase__					:     Optional[int]   = roberta_layer.self_attn.out_proj.bias
            lowercase__					:     Optional[int]   = roberta_layer.self_attn_layer_norm.weight
            lowercase__					:     str   = roberta_layer.self_attn_layer_norm.bias
  
            # intermediate
            lowercase__					:     BertIntermediate   = layer.intermediate
            assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
            lowercase__					:     int   = roberta_layer.fca.weight
            lowercase__					:     int   = roberta_layer.fca.bias
  
            # output
            lowercase__					:     BertOutput   = layer.output
            assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
            lowercase__					:     int   = roberta_layer.fca.weight
            lowercase__					:     int   = roberta_layer.fca.bias
            lowercase__					:     Optional[Any]   = roberta_layer.final_layer_norm.weight
            lowercase__					:     Union[str, Any]   = roberta_layer.final_layer_norm.bias
            # end of layer
  
       if classification_head:
            lowercase__					:     Optional[int]   = roberta.model.classification_heads['mnli'].dense.weight
            lowercase__					:     Optional[int]   = roberta.model.classification_heads['mnli'].dense.bias
            lowercase__					:     int   = roberta.model.classification_heads['mnli'].out_proj.weight
            lowercase__					:     int   = roberta.model.classification_heads['mnli'].out_proj.bias
       else:
            # LM Head
            lowercase__					:     Optional[Any]   = roberta.model.encoder.lm_head.dense.weight
            lowercase__					:     Tuple   = roberta.model.encoder.lm_head.dense.bias
            lowercase__					:     Dict   = roberta.model.encoder.lm_head.layer_norm.weight
            lowercase__					:     int   = roberta.model.encoder.lm_head.layer_norm.bias
            lowercase__					:     Optional[Any]   = roberta.model.encoder.lm_head.weight
            lowercase__					:     Union[str, Any]   = roberta.model.encoder.lm_head.bias
  
       # Let's check that we get the same results.
       lowercase__					:     torch.Tensor   = roberta.encode(SCREAMING_SNAKE_CASE_    ).unsqueeze(0    )  # batch of size 1
  
       lowercase__					:     Any   = model(SCREAMING_SNAKE_CASE_    )[0]
       if classification_head:
            lowercase__					:     List[str]   = roberta.model.classification_heads['mnli'](roberta.extract_features(SCREAMING_SNAKE_CASE_    )    )
       else:
            lowercase__					:     Tuple   = roberta.model(SCREAMING_SNAKE_CASE_    )[0]
       print(our_output.shape			,  their_output.shape    )
       lowercase__					:     Tuple   = torch.max(torch.abs(our_output - their_output    )    ).item()
       print(f"""max_absolute_diff = {max_absolute_diff}"""    )  # ~ 1e-7
       lowercase__					:     Optional[Any]   = torch.allclose(SCREAMING_SNAKE_CASE_			,  SCREAMING_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(SCREAMING_SNAKE_CASE_    ).mkdir(parents=SCREAMING_SNAKE_CASE_			,  exist_ok=SCREAMING_SNAKE_CASE_    )
       print(f"""Saving model to {pytorch_dump_folder_path}"""    )
       model.save_pretrained(SCREAMING_SNAKE_CASE_    )
  
  
  if __name__ == "__main__":
         snake_case_    						=				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.'''
         )
         snake_case_    						=				parser.parse_args()
         convert_roberta_checkpoint_to_pytorch(
             args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
         )
  
  
style_context: 
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_flax_available,
      is_tf_available,
      is_torch_available,
  )
  
  
  snake_case_    						=				{'''configuration_gptj''': ['''GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTJConfig''', '''GPTJOnnxConfig''']}
  
  try:
         if not is_torch_available():
                raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
         pass
  else:
         snake_case_    						=				[
             '''GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST''',
             '''GPTJForCausalLM''',
             '''GPTJForQuestionAnswering''',
             '''GPTJForSequenceClassification''',
             '''GPTJModel''',
             '''GPTJPreTrainedModel''',
         ]
  
  try:
         if not is_tf_available():
                raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
         pass
  else:
         snake_case_    						=				[
             '''TFGPTJForCausalLM''',
             '''TFGPTJForQuestionAnswering''',
             '''TFGPTJForSequenceClassification''',
             '''TFGPTJModel''',
             '''TFGPTJPreTrainedModel''',
         ]
  
  try:
         if not is_flax_available():
                raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
         pass
  else:
         snake_case_    						=				[
             '''FlaxGPTJForCausalLM''',
             '''FlaxGPTJModel''',
             '''FlaxGPTJPreTrainedModel''',
         ]
  
  
  if TYPE_CHECKING:
         from .configuration_gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig, GPTJOnnxConfig
  
         try:
                if not is_torch_available():
                       raise OptionalDependencyNotAvailable()
         except OptionalDependencyNotAvailable:
                pass
         else:
                from .modeling_gptj import (
                    GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
                    GPTJForCausalLM,
                    GPTJForQuestionAnswering,
                    GPTJForSequenceClassification,
                    GPTJModel,
                    GPTJPreTrainedModel,
                )
  
         try:
                if not is_tf_available():
                       raise OptionalDependencyNotAvailable()
         except OptionalDependencyNotAvailable:
                pass
         else:
                from .modeling_tf_gptj import (
                    TFGPTJForCausalLM,
                    TFGPTJForQuestionAnswering,
                    TFGPTJForSequenceClassification,
                    TFGPTJModel,
                    TFGPTJPreTrainedModel,
                )
  
         try:
                if not is_flax_available():
                       raise OptionalDependencyNotAvailable()
         except OptionalDependencyNotAvailable:
                pass
         else:
                from .modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel, FlaxGPTJPreTrainedModel
  
  else:
         import sys
  
         snake_case_    						=				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
  
  