'''simple docstring'''






from ...configuration_utils import PretrainedConfig




class    a (       __lowerCAmelCase  ):



  """simple docstring"""
  __lowerCAmelCase				      =					"""bert-generation"""







  def __init__(							self   ,    snake_case_=5_0358   ,    snake_case_=1024   ,    snake_case_=24   ,    snake_case_=16   ,    snake_case_=4096   ,    snake_case_="gelu"   ,    snake_case_=0.1   ,    snake_case_=0.1   ,    snake_case_=512   ,    snake_case_=0.0_2   ,    snake_case_=1e-1_2   ,    snake_case_=0   ,    snake_case_=2   ,    snake_case_=1   ,    snake_case_="absolute"   ,    snake_case_=True   ,    **snake_case_   ,    ):

    '''simple docstring'''







    super().__init__(pad_token_id=snake_case_   ,    bos_token_id=snake_case_   ,    eos_token_id=snake_case_   ,    **snake_case_					)

    __UpperCAmelCase:					str		= vocab_size
    __UpperCAmelCase:					Optional[Any]		= hidden_size
    __UpperCAmelCase:					str		= num_hidden_layers
    __UpperCAmelCase:					Optional[int]		= num_attention_heads
    __UpperCAmelCase:					Optional[int]		= hidden_act
    __UpperCAmelCase:					Tuple		= intermediate_size
    __UpperCAmelCase:					Optional[Any]		= hidden_dropout_prob
    __UpperCAmelCase:					Any		= attention_probs_dropout_prob
    __UpperCAmelCase:					Optional[Any]		= max_position_embeddings
    __UpperCAmelCase:					int		= initializer_range
    __UpperCAmelCase:					int		= layer_norm_eps
    __UpperCAmelCase:					Optional[Any]		= position_embedding_type
    __UpperCAmelCase:					Any		= use_cache
'''simple docstring'''






import argparse
import os

import torch

from transformers.utils import WEIGHTS_NAME


SCREAMING_SNAKE_CASE_	   =			['small', 'medium', 'large']

SCREAMING_SNAKE_CASE_	   =			'lm_head.decoder.weight'
SCREAMING_SNAKE_CASE_	   =			'lm_head.weight'






def  UpperCamelCase__					(					_lowercase    :				str   ,    _lowercase    :				str     )							->	List[str]:
  __UpperCAmelCase:					Tuple		= torch.load(_lowercase     )
  __UpperCAmelCase:					Tuple		= d.pop(_lowercase     )
  os.makedirs(_lowercase   ,    exist_ok=_lowercase     )
  torch.save(_lowercase   ,    os.path.join(_lowercase   ,    _lowercase     )     )


if __name__ == "__main__":
    SCREAMING_SNAKE_CASE_	   =			argparse.ArgumentParser()
    parser.add_argument('--dialogpt_path', default='.', type=str)
    SCREAMING_SNAKE_CASE_	   =			parser.parse_args()
    for MODEL in DIALOGPT_MODELS:
        SCREAMING_SNAKE_CASE_	   =			os.path.join(args.dialogpt_path, F"""{MODEL}_ft.pkl""")
        SCREAMING_SNAKE_CASE_	   =			F"""./DialoGPT-{MODEL}"""
        convert_dialogpt_checkpoint(
            checkpoint_path,
            pytorch_dump_folder_path,
        )