





import argparse
import csv
import logging
import os
import random

import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange

from transformers import (
    CONFIG_NAME,
    WEIGHTS_NAME,
    AdamW,
    OpenAIGPTDoubleHeadsModel,
    OpenAIGPTTokenizer,
    get_linear_schedule_with_warmup,
)


logging.basicConfig(
    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)
UpperCAmelCase_      							=     logging.getLogger(__name__)





def 						lowerCamelCase__     (     UpperCamelCase__			:       Union[str, Any]       ,    UpperCamelCase__			:       Tuple						)			-> int:



     '''simple docstring'''




     _snake_case									=				np.argmax(_lowercase       ,    axis=1						)
     return np.sum(outputs == labels						)





def 						lowerCamelCase__     (     UpperCamelCase__			:       str						)			-> Union[str, Any]:



     '''simple docstring'''




     with open(_lowercase       ,    encoding='utf_8'						) as f:
          _snake_case									=				csv.reader(_lowercase						)
          _snake_case									=				[]
          next(_lowercase						)  # skip the first line
          for line in tqdm(_lowercase						):
               output.append((' '.join(line[1:5]						), line[5], line[6], int(line[-1]						) - 1)						)
     return output





def 						lowerCamelCase__     (     UpperCamelCase__			:       Any       ,    UpperCamelCase__			:       int       ,    UpperCamelCase__			:       List[str]       ,    UpperCamelCase__			:       Tuple       ,    UpperCamelCase__			:       int       ,    UpperCamelCase__			:       int						)			-> Dict:



     '''simple docstring'''




     _snake_case									=				[]
     for dataset in encoded_datasets:
          _snake_case									=				len(_lowercase						)
          _snake_case									=				np.zeros((n_batch, 2, input_len)       ,    dtype=np.intaa						)
          _snake_case									=				np.zeros((n_batch, 2)       ,    dtype=np.intaa						)
          _snake_case									=				np.full((n_batch, 2, input_len)       ,    fill_value=-100       ,    dtype=np.intaa						)
          _snake_case									=				np.zeros((n_batch,)       ,    dtype=np.intaa						)
          for (
              i,
              (story, conta, conta, mc_label),
          ) in enumerate(_lowercase						):
               _snake_case									=				[start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
               _snake_case									=				[start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
               _snake_case									=				with_conta
               _snake_case									=				with_conta
               _snake_case									=				len(_lowercase						) - 1
               _snake_case									=				len(_lowercase						) - 1
               _snake_case									=				with_conta
               _snake_case									=				with_conta
               _snake_case									=				mc_label
          _snake_case									=				(input_ids, mc_token_ids, lm_labels, mc_labels)
          tensor_datasets.append(tuple(torch.tensor(_lowercase						) for t in all_inputs						)						)
     return tensor_datasets





def 						lowerCamelCase__     (     )			-> List[Any]:



     '''simple docstring'''




     _snake_case									=				argparse.ArgumentParser()
     parser.add_argument('--model_name'       ,    type=_lowercase       ,    default='openai-gpt'       ,    help='pretrained model name'						)
     parser.add_argument('--do_train'       ,    action='store_true'       ,    help='Whether to run training.'						)
     parser.add_argument('--do_eval'       ,    action='store_true'       ,    help='Whether to run eval on the dev set.'						)
     parser.add_argument(
         '--output_dir'       ,    default=_lowercase       ,    type=_lowercase       ,    required=_lowercase       ,    help='The output directory where the model predictions and checkpoints will be written.'       ,    )
     parser.add_argument('--train_dataset'       ,    type=_lowercase       ,    default=''						)
     parser.add_argument('--eval_dataset'       ,    type=_lowercase       ,    default=''						)
     parser.add_argument('--seed'       ,    type=_lowercase       ,    default=42						)
     parser.add_argument('--num_train_epochs'       ,    type=_lowercase       ,    default=3						)
     parser.add_argument('--train_batch_size'       ,    type=_lowercase       ,    default=8						)
     parser.add_argument('--eval_batch_size'       ,    type=_lowercase       ,    default=16						)
     parser.add_argument('--adam_epsilon'       ,    default=1e-8       ,    type=_lowercase       ,    help='Epsilon for Adam optimizer.'						)
     parser.add_argument('--max_grad_norm'       ,    type=_lowercase       ,    default=1						)
     parser.add_argument(
         '--max_steps'       ,    default=-1       ,    type=_lowercase       ,    help=(
             'If > 0: set total number of training                         steps to perform. Override num_train_epochs.'
         )       ,    )
     parser.add_argument(
         '--gradient_accumulation_steps'       ,    type=_lowercase       ,    default=1       ,    help='Number of updates steps to accumulate before                        performing a backward/update pass.'       ,    )
     parser.add_argument('--learning_rate'       ,    type=_lowercase       ,    default=6.25e-5						)
     parser.add_argument('--warmup_steps'       ,    default=0       ,    type=_lowercase       ,    help='Linear warmup over warmup_steps.'						)
     parser.add_argument('--lr_schedule'       ,    type=_lowercase       ,    default='warmup_linear'						)
     parser.add_argument('--weight_decay'       ,    type=_lowercase       ,    default=0.01						)
     parser.add_argument('--lm_coef'       ,    type=_lowercase       ,    default=0.9						)
     parser.add_argument('--n_valid'       ,    type=_lowercase       ,    default=374						)

     parser.add_argument('--server_ip'       ,    type=_lowercase       ,    default=''       ,    help='Can be used for distant debugging.'						)
     parser.add_argument('--server_port'       ,    type=_lowercase       ,    default=''       ,    help='Can be used for distant debugging.'						)
     _snake_case									=				parser.parse_args()
     print(_lowercase						)

     if args.server_ip and args.server_port:
          # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
          import ptvsd

          print('Waiting for debugger attach'						)
          ptvsd.enable_attach(address=(args.server_ip, args.server_port)       ,    redirect_output=_lowercase						)
          ptvsd.wait_for_attach()

     random.seed(args.seed						)
     np.random.seed(args.seed						)
     torch.manual_seed(args.seed						)
     torch.cuda.manual_seed_all(args.seed						)

     _snake_case									=				torch.device('cuda' if torch.cuda.is_available() else 'cpu'						)
     _snake_case									=				torch.cuda.device_count()
     logger.info('device: {}, n_gpu {}'.format(_lowercase       ,    _lowercase						)						)

     if not args.do_train and not args.do_eval:
          raise ValueError('At least one of `do_train` or `do_eval` must be True.'						)

     if not os.path.exists(args.output_dir						):
          os.makedirs(args.output_dir						)

     # Load tokenizer and model
     # This loading functions also add new tokens and embeddings called `special tokens`
     # These new embeddings will be fine-tuned on the RocStories dataset
     _snake_case									=				['_start_', '_delimiter_', '_classify_']
     _snake_case									=				OpenAIGPTTokenizer.from_pretrained(args.model_name						)
     tokenizer.add_tokens(_lowercase						)
     _snake_case									=				tokenizer.convert_tokens_to_ids(_lowercase						)
     _snake_case									=				OpenAIGPTDoubleHeadsModel.from_pretrained(args.model_name						)
     model.resize_token_embeddings(len(_lowercase						)						)
     model.to(_lowercase						)

     # Load and encode the datasets
     def tokenize_and_encode(UpperCamelCase__			:       List[Any]						):
          if isinstance(_lowercase       ,    _lowercase						):
               return tokenizer.convert_tokens_to_ids(tokenizer.tokenize(_lowercase						)						)
          elif isinstance(_lowercase       ,    _lowercase						):
               return obj
          return [tokenize_and_encode(_lowercase						) for o in obj]

     logger.info('Encoding dataset...'						)
     _snake_case									=				load_rocstories_dataset(args.train_dataset						)
     _snake_case									=				load_rocstories_dataset(args.eval_dataset						)
     _snake_case									=				(train_dataset, eval_dataset)
     _snake_case									=				tokenize_and_encode(_lowercase						)

     # Compute the max input length for the Transformer
     _snake_case									=				model.config.n_positions // 2 - 2
     _snake_case									=				max(
         len(story[:max_length]						) + max(len(conta[:max_length]						)       ,    len(conta[:max_length]						)						) + 3
         for dataset in encoded_datasets
         for story, conta, conta, _ in dataset						)
     _snake_case									=				min(_lowercase       ,    model.config.n_positions						)  # Max size of input for the pre-trained model

     # Prepare inputs tensors and dataloaders
     _snake_case									=				pre_process_datasets(_lowercase       ,    _lowercase       ,    _lowercase       ,    *_lowercase						)
     _snake_case									=				tensor_datasets[0], tensor_datasets[1]

     _snake_case									=				TensorDataset(*_lowercase						)
     _snake_case									=				RandomSampler(_lowercase						)
     _snake_case									=				DataLoader(_lowercase       ,    sampler=_lowercase       ,    batch_size=args.train_batch_size						)

     _snake_case									=				TensorDataset(*_lowercase						)
     _snake_case									=				SequentialSampler(_lowercase						)
     _snake_case									=				DataLoader(_lowercase       ,    sampler=_lowercase       ,    batch_size=args.eval_batch_size						)

     # Prepare optimizer
     if args.do_train:
          if args.max_steps > 0:
               _snake_case									=				args.max_steps
               _snake_case									=				args.max_steps // (len(_lowercase						) // args.gradient_accumulation_steps) + 1
          else:
               _snake_case									=				len(_lowercase						) // args.gradient_accumulation_steps * args.num_train_epochs

          _snake_case									=				list(model.named_parameters()						)
          _snake_case									=				['bias', 'LayerNorm.bias', 'LayerNorm.weight']
          _snake_case									=				[
              {
                  'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay						)],
                  'weight_decay': args.weight_decay,
              },
              {'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay						)], 'weight_decay': 0.0},
          ]
          _snake_case									=				AdamW(_lowercase       ,    lr=args.learning_rate       ,    eps=args.adam_epsilon						)
          _snake_case									=				get_linear_schedule_with_warmup(
              _lowercase       ,    num_warmup_steps=args.warmup_steps       ,    num_training_steps=_lowercase						)

     if args.do_train:
          _snake_case									=				0, 0, None
          model.train()
          for _ in trange(int(args.num_train_epochs						)       ,    desc='Epoch'						):
               _snake_case									=				0
               _snake_case									=				0
               _snake_case									=				tqdm(_lowercase       ,    desc='Training'						)
               for step, batch in enumerate(_lowercase						):
                    _snake_case									=				tuple(t.to(_lowercase						) for t in batch						)
                    _snake_case									=				batch
                    _snake_case									=				model(_lowercase       ,    mc_token_ids=_lowercase       ,    lm_labels=_lowercase       ,    mc_labels=_lowercase						)
                    _snake_case									=				args.lm_coef * losses[0] + losses[1]
                    loss.backward()
                    optimizer.step()
                    scheduler.step()
                    optimizer.zero_grad()
                    tr_loss += loss.item()
                    _snake_case									=				(
                        loss.item() if exp_average_loss is None else 0.7 * exp_average_loss + 0.3 * loss.item()
                    )
                    nb_tr_steps += 1
                    _snake_case									=				'Training loss: {:.2e} lr: {:.2e}'.format(_lowercase       ,    scheduler.get_lr()[0]						)

    # Save a trained model
     if args.do_train:
          # Save a trained model, configuration and tokenizer
          _snake_case									=				model.module if hasattr(_lowercase       ,    'module'						) else model  # Only save the model itself

          # If we save using the predefined names, we can load using `from_pretrained`
          _snake_case									=				os.path.join(args.output_dir       ,    _lowercase						)
          _snake_case									=				os.path.join(args.output_dir       ,    _lowercase						)

          torch.save(model_to_save.state_dict()       ,    _lowercase						)
          model_to_save.config.to_json_file(_lowercase						)
          tokenizer.save_vocabulary(args.output_dir						)

          # Load a trained model and vocabulary that you have fine-tuned
          _snake_case									=				OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir						)
          _snake_case									=				OpenAIGPTTokenizer.from_pretrained(args.output_dir						)
          model.to(_lowercase						)

     if args.do_eval:
          model.eval()
          _snake_case									=				0, 0
          _snake_case									=				0, 0
          for batch in tqdm(_lowercase       ,    desc='Evaluating'						):
               _snake_case									=				tuple(t.to(_lowercase						) for t in batch						)
               _snake_case									=				batch
               with torch.no_grad():
                    _snake_case									=				model(
                        _lowercase       ,    mc_token_ids=_lowercase       ,    lm_labels=_lowercase       ,    mc_labels=_lowercase						)

               _snake_case									=				mc_logits.detach().cpu().numpy()
               _snake_case									=				mc_labels.to('cpu'						).numpy()
               _snake_case									=				accuracy(_lowercase       ,    _lowercase						)

               eval_loss += mc_loss.mean().item()
               eval_accuracy += tmp_eval_accuracy

               nb_eval_examples += input_ids.size(0						)
               nb_eval_steps += 1

          _snake_case									=				eval_loss / nb_eval_steps
          _snake_case									=				eval_accuracy / nb_eval_examples
          _snake_case									=				tr_loss / nb_tr_steps if args.do_train else None
          _snake_case									=				{'eval_loss': eval_loss, 'eval_accuracy': eval_accuracy, 'train_loss': train_loss}

          _snake_case									=				os.path.join(args.output_dir       ,    'eval_results.txt'						)
          with open(_lowercase       ,    'w'						) as writer:
               logger.info('***** Eval results *****'						)
               for key in sorted(result.keys()						):
                    logger.info('  %s = %s'       ,    _lowercase       ,    str(result[key]						)						)
                    writer.write('%s = %s\n' % (key, str(result[key]						))						)


if __name__ == "__main__":
     main()










import argparse
import json
import os
import re
import shutil

import torch

from transformers import BioGptConfig, BioGptForCausalLM
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGHTS_NAME, logging


logging.set_verbosity_warning()

UpperCAmelCase_      							=     2
class 			UpperCamelCase_		:




					def __init__(		self				,						*,  # begin keyword-only arguments
					    lowerCAmelCase_="<s>"				,						lowerCAmelCase_="<pad>"				,						lowerCAmelCase_="</s>"				,						lowerCAmelCase_="<unk>"				,						lowerCAmelCase_=None				,						)		->							Optional[int]:
										_snake_case							,     _snake_case							,     _snake_case							,     _snake_case									=				bos, unk, pad, eos
										_snake_case									=				[]
										_snake_case									=				[]
										_snake_case									=				{}
										_snake_case									=				self.add_symbol(lowerCAmelCase_  )
										_snake_case									=				self.add_symbol(lowerCAmelCase_  )
										_snake_case									=				self.add_symbol(lowerCAmelCase_  )
										_snake_case									=				self.add_symbol(lowerCAmelCase_  )
										if extra_special_symbols:
															for s in extra_special_symbols:
																				self.add_symbol(lowerCAmelCase_  )
										_snake_case									=				len(self.symbols  )




					def __eq__(		self				,						lowerCAmelCase_  )		->							List[Any]:
										return self.indices == other.indices




					def __getitem__(		self				,						lowerCAmelCase_  )		->							Dict:
										if idx < len(self.symbols  ):
															return self.symbols[idx]
										return self.unk_word




					def __len__(		self  )		->							Optional[Any]:
										return len(self.symbols  )




					def __contains__(		self				,						lowerCAmelCase_  )		->							int:
										return sym in self.indices




					@classmethod
					def 	lowerCAmelCase			(		cls				,						lowerCAmelCase_  )		->							List[Any]:
										_snake_case									=				cls()
										d.add_from_file(lowerCAmelCase_  )
										return d




					def 	lowerCAmelCase			(		self				,						lowerCAmelCase_				,						lowerCAmelCase_=1				,						lowerCAmelCase_=False  )		->							str:
										if word in self.indices and not overwrite:
															_snake_case									=				self.indices[word]
															_snake_case									=				self.count[idx] + n
															return idx
										else:
															_snake_case									=				len(self.symbols  )
															_snake_case									=				idx
															self.symbols.append(lowerCAmelCase_  )
															self.count.append(lowerCAmelCase_  )
															return idx




					def 	lowerCAmelCase			(		self				,						lowerCAmelCase_  )		->							Optional[Any]:
										return 0






					def 	lowerCAmelCase			(		self				,						lowerCAmelCase_  )		->							Union[str, Any]:
										if isinstance(lowerCAmelCase_				,						lowerCAmelCase_  ):
															try:
																				with open(lowerCAmelCase_				,						'r'				,						encoding='utf-8'  ) as fd:
																									self.add_from_file(lowerCAmelCase_  )
															except FileNotFoundError as fnfe:
																				raise fnfe
															except UnicodeError:
																				raise Exception('Incorrect encoding detected in {}, please rebuild the dataset'.format(lowerCAmelCase_  )  )
															return

										_snake_case									=				f.readlines()
										_snake_case									=				self._load_meta(lowerCAmelCase_  )

										for line in lines[indices_start_line:]:
															try:
																				_snake_case							,     _snake_case									=				line.rstrip().rsplit(' '				,						1  )
																				if field == "#fairseq:overwrite":
																									_snake_case									=				True
																									_snake_case							,     _snake_case									=				line.rsplit(' '				,						1  )
																				else:
																									_snake_case									=				False
																				_snake_case									=				int(lowerCAmelCase_  )
																				_snake_case									=				line
																				if word in self and not overwrite:
																									raise RuntimeError(
																									    'Duplicate word found when loading Dictionary: \'{}\'. '
																									    'Duplicate words can overwrite earlier ones by adding the '
																									    '#fairseq:overwrite flag at the end of the corresponding row '
																									    'in the dictionary file. If using the Camembert model, please '
																									    'download an updated copy of the model file.'.format(lowerCAmelCase_  )  )
																				self.add_symbol(lowerCAmelCase_				,						n=lowerCAmelCase_				,						overwrite=lowerCAmelCase_  )
															except ValueError:
																				raise ValueError('Incorrect dictionary format, expected \'<token> <cnt> [flags]\''  )





def 						lowerCamelCase__     (     UpperCamelCase__			:       Union[str, Any]						)			-> Tuple:



					'''simple docstring'''




					_snake_case									=				dict((re.sub(R'@@$'       ,    ''       ,    UpperCamelCase__						), v) if k.endswith('@@'						) else (re.sub(R'$'       ,    '</w>'       ,    UpperCamelCase__						), v) for k, v in d.items()						)
					_snake_case									=				'<s> <pad> </s> <unk>'.split()
					# restore the special tokens
					for k in keep_keys:
										del da[F'''{k}</w>''']
										_snake_case									=				d[k]  # restore
					return da





def 						lowerCamelCase__     (     UpperCamelCase__			:       Optional[Any]       ,    UpperCamelCase__			:       Dict						)			-> List[str]:



					'''simple docstring'''




					if not os.path.exists(UpperCamelCase__						):
										raise ValueError(F'''path {biogpt_checkpoint_path} does not exist!'''						)
					os.makedirs(UpperCamelCase__       ,    exist_ok=UpperCamelCase__						)
					print(F'''Writing results to {pytorch_dump_folder_path}'''						)

					# handle various types of models

					_snake_case									=				os.path.join(UpperCamelCase__       ,    'checkpoint.pt'						)
					if not os.path.isfile(UpperCamelCase__						):
										raise ValueError(F'''path to the file {checkpoint_file} does not exist!'''						)
					_snake_case									=				torch.load(UpperCamelCase__       ,    map_location='cpu'						)

					_snake_case									=				chkpt['cfg']['model']

					# dicts
					_snake_case									=				os.path.join(UpperCamelCase__       ,    'dict.txt'						)
					if not os.path.isfile(UpperCamelCase__						):
										raise ValueError(F'''path to the file {dict_file} does not exist!'''						)
					_snake_case									=				Dictionary.load(UpperCamelCase__						)
					_snake_case									=				rewrite_dict_keys(src_dict.indices						)
					_snake_case									=				len(UpperCamelCase__						)
					_snake_case									=				os.path.join(UpperCamelCase__       ,    VOCAB_FILES_NAMES['vocab_file']						)
					print(F'''Generating {src_vocab_file} of {src_vocab_size} records'''						)
					with open(UpperCamelCase__       ,    'w'       ,    encoding='utf-8'						) as f:
										f.write(json.dumps(UpperCamelCase__       ,    ensure_ascii=UpperCamelCase__       ,    indent=UpperCamelCase__						)						)

					# merges_file (bpecodes)
					_snake_case									=				os.path.join(UpperCamelCase__       ,    'bpecodes'						)
					if not os.path.isfile(UpperCamelCase__						):
										raise ValueError(F'''path to the file {bpecodes_file} does not exist!'''						)

					_snake_case									=				os.path.join(UpperCamelCase__       ,    VOCAB_FILES_NAMES['merges_file']						)
					shutil.copyfile(UpperCamelCase__       ,    UpperCamelCase__						)

					# model config
					_snake_case									=				os.path.join(UpperCamelCase__       ,    'config.json'						)

					_snake_case									=				{
					    'activation_dropout': args['activation_dropout'],
					    'architectures': ['BioGptForCausalLM'],
					    'attention_probs_dropout_prob': args['attention_dropout'],
					    'bos_token_id': 0,
					    'eos_token_id': 2,
					    'hidden_act': args['activation_fn'],
					    'hidden_dropout_prob': args['dropout'],
					    'hidden_size': args['decoder_embed_dim'],
					    'initializer_range': 0.02,
					    'intermediate_size': args['decoder_ffn_embed_dim'],
					    'layer_norm_eps': 1e-12,
					    'layerdrop': args['decoder_layerdrop'],
					    'max_position_embeddings': args['max_target_positions'],
					    'model_type': 'biogpt',
					    'num_attention_heads': args['decoder_attention_heads'],
					    'num_hidden_layers': args['decoder_layers'],
					    'pad_token_id': 1,
					    'scale_embedding': not args['no_scale_embedding'],
					    'tie_word_embeddings': args['share_decoder_input_output_embed'],
					    'vocab_size': src_vocab_size,
					}

					# good hparam defaults to start with

					print(F'''Generating {biogpt_model_config_file}'''						)
					with open(UpperCamelCase__       ,    'w'       ,    encoding='utf-8'						) as f:
										f.write(json.dumps(UpperCamelCase__       ,    ensure_ascii=UpperCamelCase__       ,    indent=UpperCamelCase__						)						)

					# tokenizer config
					_snake_case									=				os.path.join(UpperCamelCase__       ,    UpperCamelCase__						)

					_snake_case									=				{
					    'bos_token': '<s>',
					    'eos_token': '</s>',
					    'model_max_length': 1_024,
					    'pad_token': '<pad>',
					    'special_tokens_map_file': None,
					    'tokenizer_class': 'BioGptTokenizer',
					    'unk_token': '<unk>',
					}

					print(F'''Generating {biogpt_tokenizer_config_file}'''						)
					with open(UpperCamelCase__       ,    'w'       ,    encoding='utf-8'						) as f:
										f.write(json.dumps(UpperCamelCase__       ,    ensure_ascii=UpperCamelCase__       ,    indent=UpperCamelCase__						)						)

					# model
					_snake_case									=				chkpt['model']

					# remove unneeded keys
					_snake_case									=				[
					    'decoder.version',
					]
					for k in ignore_keys:
										model_state_dict.pop(UpperCamelCase__       ,    UpperCamelCase__						)

					_snake_case									=				list(model_state_dict.keys()						)
					for layer_name in layer_names:
										if layer_name.endswith('output_projection.weight'						):
															_snake_case									=				model_state_dict.pop(UpperCamelCase__						)
										else:
															_snake_case									=				model_state_dict.pop(UpperCamelCase__						)

					_snake_case									=				BioGptConfig.from_pretrained(UpperCamelCase__						)
					_snake_case									=				BioGptForCausalLM(UpperCamelCase__						)

					# check that it loads ok
					model_new.load_state_dict(UpperCamelCase__						)

					# save
					_snake_case									=				os.path.join(UpperCamelCase__       ,    UpperCamelCase__						)
					print(F'''Generating {pytorch_weights_dump_path}'''						)
					torch.save(UpperCamelCase__       ,    UpperCamelCase__						)

					print('Conversion is done!'						)


if __name__ == "__main__":
					UpperCAmelCase_      							=     argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--biogpt_checkpoint_path""",
					    default=None,
					    type=str,
					    required=True,
					    help=(
					        """Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts,"""
					        """ bpecodes, etc."""
					    ),
					)
					parser.add_argument(
					    """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
					)
					UpperCAmelCase_      							=     parser.parse_args()
					convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)



