code: 
  
  
  import os
  import sys
  from pathlib import Path
  
  import torch
  
  from transformers.testing_utils import TestCasePlus, execute_subprocess_async, require_torch_multi_gpu
  from utils import load_json
  
  
  lowerCAmelCase__   	=				torch.cuda.is_available()
  lowerCAmelCase__   	=				[" Sam ate lunch today.", "Sams lunch ingredients."]
  lowerCAmelCase__   	=				["A very interesting story about what I ate for lunch.", "Avocado, celery, turkey, coffee"]
  lowerCAmelCase__   	=				{
      "max_tokens_per_batch": None,
      "supervise_forward": True,
      "normalize_hidden": True,
      "label_smoothing": 0.2,
      "eval_max_gen_length": None,
      "eval_beams": 1,
      "val_metric": "loss",
      "save_top_k": 1,
      "adafactor": True,
      "early_stopping_patience": 2,
      "logger_name": "default",
      "length_penalty": 0.5,
      "cache_dir": "",
      "task": "summarization",
      "num_workers": 2,
      "alpha_hid": 0,
      "freeze_embeds": True,
      "enc_only": False,
      "tgt_suffix": "",
      "resume_from_checkpoint": None,
      "sortish_sampler": True,
      "student_decoder_layers": 1,
      "val_check_interval": 1.0,
      "output_dir": "",
      "fp16": False,  # TODO(SS): set this to CUDA_AVAILABLE if ci installs apex or start using native amp
      "no_teacher": False,
      "fp16_opt_level": "O1",
      "gpus": 1 if CUDA_AVAILABLE else 0,
      "n_tpu_cores": 0,
      "max_grad_norm": 1.0,
      "do_train": True,
      "do_predict": True,
      "accumulate_grad_batches": 1,
      "server_ip": "",
      "server_port": "",
      "seed": 42,
      "model_name_or_path": "sshleifer/bart-tiny-random",
      "config_name": "",
      "tokenizer_name": "facebook/bart-large",
      "do_lower_case": False,
      "learning_rate": 0.3,
      "lr_scheduler": "linear",
      "weight_decay": 0.0,
      "adam_epsilon": 1E-08,
      "warmup_steps": 0,
      "max_epochs": 1,
      "train_batch_size": 2,
      "eval_batch_size": 2,
      "max_source_length": 12,
      "max_target_length": 12,
      "val_max_target_length": 12,
      "test_max_target_length": 12,
      "fast_dev_run": False,
      "no_cache": False,
      "n_train": -1,
      "n_val": -1,
      "n_test": -1,
      "student_encoder_layers": 1,
      "freeze_encoder": False,
      "auto_scale_batch_size": False,
      "overwrite_output_dir": False,
      "student": None,
  }
  def 							_UpperCAmelCase       (UpperCamelCase__  :						Path       ,							UpperCamelCase__  :						list		):
         _A   : str       					=				"\n".join(_UpperCamelCase		)
         Path(_UpperCamelCase		).open("w"		).writelines(_UpperCamelCase		)
  def 							_UpperCAmelCase       (UpperCamelCase__  :						Optional[Any]		):
  
  
  
  
  
  
  
         for split in ["train", "val", "test"]:
                _dump_articles(os.path.join(_UpperCamelCase       ,							f"{split}.source"		)       ,							_UpperCamelCase		)
                _dump_articles(os.path.join(_UpperCamelCase       ,							f"{split}.target"		)       ,							_UpperCamelCase		)
         return tmp_dir
  
  
  
  
  
  
  
  class   lowerCAmelCase__ (       SCREAMING_SNAKE_CASE__):
  
  
  
  
       '''simple docstring'''
  
  
       @classmethod
       def 				_lowerCamelCase				(		cls)    ->       List[Any]:
              return cls
  
  
       @require_torch_multi_gpu
       def 				_lowerCamelCase				(		self)    ->       Optional[int]:
              _A   : List[str]       					=				{
                  "no_teacher": True,
                  "freeze_encoder": True,
                  "gpus": 2,
                  "overwrite_output_dir": True,
                  "sortish_sampler": True,
              }
              self._test_distiller_cli_fork(a_				,		check_contents=a_)
  
  
       def 				_lowerCamelCase				(		self				,		__lowerCamelCase				,		__lowerCamelCase=True)    ->       Optional[int]:
              _A   : Dict       					=				{
                  "label_smoothing": 0.0,
                  "early_stopping_patience": -1,
                  "train_batch_size": 1,
                  "eval_batch_size": 2,
                  "max_epochs": 2,
                  "alpha_mlm": 0.2,
                  "alpha_ce": 0.8,
                  "do_predict": True,
                  "model_name_or_path": "sshleifer/tinier_bart",
                  "teacher": CHEAP_ARGS["model_name_or_path"],
                  "val_check_interval": 0.5,
              }
              default_updates.update(a_)
              _A   : List[Any]       					=				CHEAP_ARGS.copy()
              _A   : List[str]       					=				make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())
              _A   : Dict       					=				self.get_auto_remove_tmp_dir()
              args_d.update(data_dir=a_				,		output_dir=a_				,		**a_)
  
              def convert(__lowerCamelCase				,		__lowerCamelCase):
                     if k in ["tgt_suffix", "server_ip", "server_port", "out", "n_tpu_cores"]:
                            return ""
                     if v is False or v is None:
                            return ""
                     if v is True:  # or len(str(v))==0:
                            return F"--{k}"
                     return F"--{k}={v}"
  
              _A   : List[Any]       					=				[x for x in (convert(a_				,		a_) for k, v in args_d.items()) if len(a_)]
              _A   : Union[str, Any]       					=				[sys.executable, F"{self.test_file_dir}/distillation.py"] + cli_args
              execute_subprocess_async(a_				,		env=self.get_env())
  
              _A   : Dict       					=				os.listdir(a_)
              _A   : Tuple       					=				{os.path.basename(a_) for p in contents}
              _A   : List[str]       					=				[p for p in contents if p.endswith("ckpt")]
              assert len(a_) > 0
  
              self.assertIn("test_generations.txt"				,		a_)
              self.assertIn("test_results.txt"				,		a_)
  
              # get the following from the module, (we don't have access to `model` here)
              _A   : List[str]       					=				os.path.join(a_				,		"metrics.json")
              _A   : Union[str, Any]       					=				"rouge2"
  
              _A   : str       					=				load_json(a_)
              # {'test': [{'test_avg_loss': 10.63731575012207, 'test_avg_rouge1': 0.0, 'test_avg_rouge2': 0.0, 'test_avg_rougeL': 0.0, 'test_avg_gen_time': 0.1822289228439331, 'test_avg_gen_len': 142.0, 'step_count': 1}]}
              print(a_)
              _A   : str       					=				metrics["val"][-1]
              self.assertGreaterEqual(last_step_stats["val_avg_gen_time"]				,		0.0_1)
              self.assertIsInstance(last_step_stats[F"val_avg_{val_metric}"]				,		a_)
              self.assertEqual(len(metrics["test"])				,		1)
              _A   : Any       					=				int(args_d["max_epochs"] * (1 / args_d["val_check_interval"]) / 2 + 1)
              self.assertEqual(len(metrics["val"])				,		a_)
  
  
  
  
  
  
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
      )
  