code: 
  
  
  import argparse
  import logging
  import os
  from pathlib import Path
  from typing import Any, Dict
  
  import pytorch_lightning as pl
  from pytorch_lightning.utilities import rank_zero_info
  
  from transformers import (
      AdamW,
      AutoConfig,
      AutoModel,
      AutoModelForPreTraining,
      AutoModelForQuestionAnswering,
      AutoModelForSeqaSeqLM,
      AutoModelForSequenceClassification,
      AutoModelForTokenClassification,
      AutoModelWithLMHead,
      AutoTokenizer,
      PretrainedConfig,
      PreTrainedTokenizer,
  )
  from transformers.optimization import (
      Adafactor,
      get_cosine_schedule_with_warmup,
      get_cosine_with_hard_restarts_schedule_with_warmup,
      get_linear_schedule_with_warmup,
      get_polynomial_decay_schedule_with_warmup,
  )
  from transformers.utils.versions import require_version
  
  
  lowerCAmelCase__   	=				logging.getLogger(__name__)
  
  require_version('pytorch_lightning>=1.0.4')
  
  lowerCAmelCase__   	=				{
      'base': AutoModel,
      'sequence-classification': AutoModelForSequenceClassification,
      'question-answering': AutoModelForQuestionAnswering,
      'pretraining': AutoModelForPreTraining,
      'token-classification': AutoModelForTokenClassification,
      'language-modeling': AutoModelWithLMHead,
      'summarization': AutoModelForSeqaSeqLM,
      'translation': AutoModelForSeqaSeqLM,
  }
  
  
  # update this and the import above to support new schedulers from transformers.optimization
  lowerCAmelCase__   	=				{
      'linear': get_linear_schedule_with_warmup,
      'cosine': get_cosine_schedule_with_warmup,
      'cosine_w_restarts': get_cosine_with_hard_restarts_schedule_with_warmup,
      'polynomial': get_polynomial_decay_schedule_with_warmup,
      # '': get_constant_schedule,             # not supported for now
      # '': get_constant_schedule_with_warmup, # not supported for now
  }
  lowerCAmelCase__   	=				sorted(arg_to_scheduler.keys())
  lowerCAmelCase__   	=				'{' + ', '.join(arg_to_scheduler_choices) + '}'
  
  
  
  
  
  
  
  class   lowerCAmelCase__ (       pl.LightningModule):
  
  
  
  
       '''simple docstring'''
  
  
       def __init__(		self				,		__lowerCamelCase				,		__lowerCamelCase=None				,		__lowerCamelCase="base"				,		__lowerCamelCase=None				,		__lowerCamelCase=None				,		__lowerCamelCase=None				,		**__lowerCamelCase				,		)    ->       Optional[int]:
              super().__init__()
              # TODO: move to self.save_hyperparameters()
              # self.save_hyperparameters()
              # can also expand arguments into trainer signature for easier reading
  
              self.save_hyperparameters(__lowerCamelCase)
              _A   : List[str]       					=				0
              _A   : Dict       					=				Path(self.hparams.output_dir)
              _A   : Optional[Any]       					=				self.hparams.cache_dir if self.hparams.cache_dir else None
              if config is None:
                     _A   : str       					=				AutoConfig.from_pretrained(
                         self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path				,		**({"num_labels": num_labels} if num_labels is not None else {})				,		cache_dir=__lowerCamelCase				,		**__lowerCamelCase				,		)
              else:
                     _A   : PretrainedConfig       					=				config
  
              _A   : int       					=				("encoder_layerdrop", "decoder_layerdrop", "dropout", "attention_dropout")
              for p in extra_model_params:
                     if getattr(self.hparams				,		__lowerCamelCase				,		__lowerCamelCase):
                            assert hasattr(self.config				,		__lowerCamelCase), F"model config doesn't have a `{p}` attribute"
                            setattr(self.config				,		__lowerCamelCase				,		getattr(self.hparams				,		__lowerCamelCase))
  
              if tokenizer is None:
                     _A   : Union[str, Any]       					=				AutoTokenizer.from_pretrained(
                         self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path				,		cache_dir=__lowerCamelCase				,		)
              else:
                     _A   : PreTrainedTokenizer       					=				tokenizer
              _A   : Any       					=				MODEL_MODES[mode]
              if model is None:
                     _A   : Optional[Any]       					=				self.model_type.from_pretrained(
                         self.hparams.model_name_or_path				,		from_tf=bool(".ckpt" in self.hparams.model_name_or_path)				,		config=self.config				,		cache_dir=__lowerCamelCase				,		)
              else:
                     _A   : Optional[Any]       					=				model
  
  
       def 				_lowerCamelCase				(		self				,		*__lowerCamelCase				,		**__lowerCamelCase)    ->       Dict:
              _A   : int       					=				self.model_type.from_pretrained(*__lowerCamelCase				,		**__lowerCamelCase)
  
  
       def 				_lowerCamelCase				(		self)    ->       Tuple:
              _A   : Dict       					=				arg_to_scheduler[self.hparams.lr_scheduler]
              _A   : int       					=				get_schedule_func(
                  self.opt				,		num_warmup_steps=self.hparams.warmup_steps				,		num_training_steps=self.total_steps())
              _A   : Union[str, Any]       					=				{"scheduler": scheduler, "interval": "step", "frequency": 1}
              return scheduler
  
  
       def 				_lowerCamelCase				(		self)    ->       List[str]:
              _A   : Dict       					=				self.model
              _A   : Optional[Any]       					=				["bias", "LayerNorm.weight"]
              _A   : Tuple       					=				[
                  {
                      "params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)],
                      "weight_decay": self.hparams.weight_decay,
                  },
                  {
                      "params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)],
                      "weight_decay": 0.0,
                  },
              ]
              if self.hparams.adafactor:
                     _A   : Optional[int]       					=				Adafactor(
                         __lowerCamelCase				,		lr=self.hparams.learning_rate				,		scale_parameter=__lowerCamelCase				,		relative_step=__lowerCamelCase)
  
              else:
                     _A   : str       					=				AdamW(
                         __lowerCamelCase				,		lr=self.hparams.learning_rate				,		eps=self.hparams.adam_epsilon)
              _A   : Tuple       					=				optimizer
  
              _A   : List[str]       					=				self.get_lr_scheduler()
  
              return [optimizer], [scheduler]
  
  
       def 				_lowerCamelCase				(		self				,		__lowerCamelCase				,		__lowerCamelCase)    ->       Dict:
              return self.validation_step(__lowerCamelCase				,		__lowerCamelCase)
  
  
       def 				_lowerCamelCase				(		self				,		__lowerCamelCase)    ->       Optional[int]:
              return self.validation_end(__lowerCamelCase)
  
  
       def 				_lowerCamelCase				(		self)    ->       int:
              _A   : List[Any]       					=				max(1				,		self.hparams.gpus)  # TODO: consider num_tpu_cores
              _A   : Optional[int]       					=				self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
              return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs
  
  
       def 				_lowerCamelCase				(		self				,		__lowerCamelCase)    ->       List[Any]:
              if mode == "test":
                     _A   : Tuple       					=				len(self.test_dataloader().dataset)
              else:
                     _A   : str       					=				self.get_dataloader("train"				,		self.hparams.train_batch_size				,		shuffle=__lowerCamelCase)
                     _A   : Any       					=				len(self.train_dataloader().dataset)
  
  
       def 				_lowerCamelCase				(		self				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase = False)    ->       Any:
              raise NotImplementedError("You must implement this for your task")
  
  
       def 				_lowerCamelCase				(		self)    ->       Optional[int]:
              return self.train_loader
  
  
       def 				_lowerCamelCase				(		self)    ->       Tuple:
              return self.get_dataloader("dev"				,		self.hparams.eval_batch_size				,		shuffle=__lowerCamelCase)
  
  
       def 				_lowerCamelCase				(		self)    ->       str:
              return self.get_dataloader("test"				,		self.hparams.eval_batch_size				,		shuffle=__lowerCamelCase)
  
  
       def 				_lowerCamelCase				(		self				,		__lowerCamelCase)    ->       str:
              return os.path.join(
                  self.hparams.data_dir				,		"cached_{}_{}_{}".format(
                      __lowerCamelCase				,		list(filter(__lowerCamelCase				,		self.hparams.model_name_or_path.split("/"))).pop()				,		str(self.hparams.max_seq_length)				,		)				,		)
  
  
       @pl.utilities.rank_zero_only
       def 				_lowerCamelCase				(		self				,		__lowerCamelCase)    ->       None:
              _A   : Any       					=				self.output_dir.joinpath("best_tfmr")
              _A   : str       					=				self.step_count
              self.model.save_pretrained(__lowerCamelCase)
              self.tokenizer.save_pretrained(__lowerCamelCase)
  
  
       @staticmethod
       def 				_lowerCamelCase				(		__lowerCamelCase				,		__lowerCamelCase)    ->       str:
              parser.add_argument(
                  "--model_name_or_path"				,		default=__lowerCamelCase				,		type=__lowerCamelCase				,		required=__lowerCamelCase				,		help="Path to pretrained model or model identifier from huggingface.co/models"				,		)
              parser.add_argument(
                  "--config_name"				,		default=""				,		type=__lowerCamelCase				,		help="Pretrained config name or path if not the same as model_name")
              parser.add_argument(
                  "--tokenizer_name"				,		default=__lowerCamelCase				,		type=__lowerCamelCase				,		help="Pretrained tokenizer name or path if not the same as model_name"				,		)
              parser.add_argument(
                  "--cache_dir"				,		default=""				,		type=__lowerCamelCase				,		help="Where do you want to store the pre-trained models downloaded from huggingface.co"				,		)
              parser.add_argument(
                  "--encoder_layerdrop"				,		type=__lowerCamelCase				,		help="Encoder layer dropout probability (Optional). Goes into model.config"				,		)
              parser.add_argument(
                  "--decoder_layerdrop"				,		type=__lowerCamelCase				,		help="Decoder layer dropout probability (Optional). Goes into model.config"				,		)
              parser.add_argument(
                  "--dropout"				,		type=__lowerCamelCase				,		help="Dropout probability (Optional). Goes into model.config"				,		)
              parser.add_argument(
                  "--attention_dropout"				,		type=__lowerCamelCase				,		help="Attention dropout probability (Optional). Goes into model.config"				,		)
              parser.add_argument("--learning_rate"				,		default=5e-5				,		type=__lowerCamelCase				,		help="The initial learning rate for Adam.")
              parser.add_argument(
                  "--lr_scheduler"				,		default="linear"				,		choices=__lowerCamelCase				,		metavar=__lowerCamelCase				,		type=__lowerCamelCase				,		help="Learning rate scheduler"				,		)
              parser.add_argument("--weight_decay"				,		default=0.0				,		type=__lowerCamelCase				,		help="Weight decay if we apply some.")
              parser.add_argument("--adam_epsilon"				,		default=1e-8				,		type=__lowerCamelCase				,		help="Epsilon for Adam optimizer.")
              parser.add_argument("--warmup_steps"				,		default=0				,		type=__lowerCamelCase				,		help="Linear warmup over warmup_steps.")
              parser.add_argument("--num_workers"				,		default=4				,		type=__lowerCamelCase				,		help="kwarg passed to DataLoader")
              parser.add_argument("--num_train_epochs"				,		dest="max_epochs"				,		default=3				,		type=__lowerCamelCase)
              parser.add_argument("--train_batch_size"				,		default=3_2				,		type=__lowerCamelCase)
              parser.add_argument("--eval_batch_size"				,		default=3_2				,		type=__lowerCamelCase)
              parser.add_argument("--adafactor"				,		action="store_true")
  
  
  
  
  
  
  
  class   lowerCAmelCase__ (       pl.Callback):
  
  
  
  
       '''simple docstring'''
  
  
       def 				_lowerCamelCase				(		self				,		__lowerCamelCase				,		__lowerCamelCase)    ->       Union[str, Any]:
              _A   : Optional[Any]       					=				trainer.lr_schedulers[0]["scheduler"]
              _A   : Tuple       					=				{F"lr_group_{i}": lr for i, lr in enumerate(lr_scheduler.get_lr())}
              pl_module.logger.log_metrics(__lowerCamelCase)
  
  
       def 				_lowerCamelCase				(		self				,		__lowerCamelCase				,		__lowerCamelCase)    ->       List[str]:
              rank_zero_info("***** Validation results *****")
              _A   : Optional[int]       					=				trainer.callback_metrics
              # Log results
              for key in sorted(__lowerCamelCase):
                     if key not in ["log", "progress_bar"]:
                            rank_zero_info("{} = {}\n".format(__lowerCamelCase				,		str(metrics[key])))
  
  
       def 				_lowerCamelCase				(		self				,		__lowerCamelCase				,		__lowerCamelCase)    ->       int:
              rank_zero_info("***** Test results *****")
              _A   : Tuple       					=				trainer.callback_metrics
              # Log and save results to file
              _A   : str       					=				os.path.join(pl_module.hparams.output_dir				,		"test_results.txt")
              with open(__lowerCamelCase				,		"w") as writer:
                     for key in sorted(__lowerCamelCase):
                            if key not in ["log", "progress_bar"]:
                                   rank_zero_info("{} = {}\n".format(__lowerCamelCase				,		str(metrics[key])))
                                   writer.write("{} = {}\n".format(__lowerCamelCase				,		str(metrics[key])))
  
  
  
  
  def 							_UpperCAmelCase       (UpperCamelCase__  :						Any       ,							UpperCamelCase__  :						List[Any]		):
         #  To allow all pl args uncomment the following line
         #  parser = pl.Trainer.add_argparse_args(parser)
         parser.add_argument(
             "--output_dir"       ,							default=UpperCamelCase__       ,							type=UpperCamelCase__       ,							required=UpperCamelCase__       ,							help="The output directory where the model predictions and checkpoints will be written."       ,							)
         parser.add_argument(
             "--fp16"       ,							action="store_true"       ,							help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit"       ,							)
  
         parser.add_argument(
             "--fp16_opt_level"       ,							type=UpperCamelCase__       ,							default="O2"       ,							help=(
                 "For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
                 "See details at https://nvidia.github.io/apex/amp.html"
             )       ,							)
         parser.add_argument("--n_tpu_cores"       ,							dest="tpu_cores"       ,							type=UpperCamelCase__		)
         parser.add_argument("--max_grad_norm"       ,							dest="gradient_clip_val"       ,							default=1.0       ,							type=UpperCamelCase__       ,							help="Max gradient norm"		)
         parser.add_argument("--do_train"       ,							action="store_true"       ,							help="Whether to run training."		)
         parser.add_argument("--do_predict"       ,							action="store_true"       ,							help="Whether to run predictions on the test set."		)
         parser.add_argument(
             "--gradient_accumulation_steps"       ,							dest="accumulate_grad_batches"       ,							type=UpperCamelCase__       ,							default=1       ,							help="Number of updates steps to accumulate before performing a backward/update pass."       ,							)
         parser.add_argument("--seed"       ,							type=UpperCamelCase__       ,							default=42       ,							help="random seed for initialization"		)
         parser.add_argument(
             "--data_dir"       ,							default=UpperCamelCase__       ,							type=UpperCamelCase__       ,							required=UpperCamelCase__       ,							help="The input data dir. Should contain the training files for the CoNLL-2003 NER task."       ,							)
  
  
  
  
  
  
  def 							_UpperCAmelCase       (UpperCamelCase__  :						BaseTransformer       ,							UpperCamelCase__  :						argparse.Namespace       ,							UpperCamelCase__  :						List[Any]=None       ,							UpperCamelCase__  :						List[str]=True       ,							UpperCamelCase__  :						int=[]       ,							UpperCamelCase__  :						Dict=None       ,							UpperCamelCase__  :						Dict=None       ,							**UpperCamelCase__  :						Union[str, Any]       ,							):
         pl.seed_everything(args.seed		)
  
         # init model
         _A   : Dict       					=				Path(model.hparams.output_dir		)
         odir.mkdir(exist_ok=UpperCamelCase__		)
  
         # add custom checkpoints
         if checkpoint_callback is None:
                _A   : Optional[int]       					=				pl.callbacks.ModelCheckpoint(
                    filepath=args.output_dir       ,							prefix="checkpoint"       ,							monitor="val_loss"       ,							mode="min"       ,							save_top_k=1		)
         if early_stopping_callback:
                extra_callbacks.append(UpperCamelCase__		)
         if logging_callback is None:
                _A   : List[Any]       					=				LoggingCallback()
  
         _A   : str       					=				{}
  
         # TODO: remove with PyTorch 1.6 since pl uses native amp
         if args.fpaa:
                _A   : str       					=				16
                _A   : List[Any]       					=				args.fpaa_opt_level
  
         if args.gpus > 1:
                _A   : int       					=				"ddp"
  
         _A   : Optional[int]       					=				args.accumulate_grad_batches
         _A   : Optional[Any]       					=				extra_train_kwargs.get("accelerator"       ,							UpperCamelCase__		)
         _A   : List[str]       					=				extra_train_kwargs.get("profiler"       ,							UpperCamelCase__		)
  
         _A   : List[Any]       					=				pl.Trainer.from_argparse_args(
             UpperCamelCase__       ,							weights_summary=UpperCamelCase__       ,							callbacks=[logging_callback] + extra_callbacks       ,							logger=UpperCamelCase__       ,							checkpoint_callback=UpperCamelCase__       ,							**UpperCamelCase__       ,							)
  
         if args.do_train:
                trainer.fit(UpperCamelCase__		)
  
         return trainer
  
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
      )
  