


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




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
    )
