




from typing import Optional, Union

import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class 			_UpperCamelCase      (				lowerCAmelCase	,       lowerCAmelCase		):
      @register_to_config
      def __init__(			self   :List[str] ,      lowerCamelCase   :int = 768 ,      )     ->			Tuple:
       super().__init__()

       UpperCAmelCase__							 =						nn.Parameter(torch.zeros(1 ,      lowerCamelCase      )      )
       UpperCAmelCase__							 =						nn.Parameter(torch.ones(1 ,      lowerCamelCase      )      )
      def 	UpperCAmelCase_						(			self   :Tuple ,      lowerCamelCase   :Optional[Union[str, torch.device]] = None ,      lowerCamelCase   :Optional[torch.dtype] = None ,      )     ->			Tuple:
       UpperCAmelCase__							 =						nn.Parameter(self.mean.to(lowerCamelCase      ).to(lowerCamelCase      )      )
       UpperCAmelCase__							 =						nn.Parameter(self.std.to(lowerCamelCase      ).to(lowerCamelCase      )      )
       return self
      def 	UpperCAmelCase_						(			self   :Union[str, Any] ,      lowerCamelCase   :List[str]      )     ->			List[Any]:
       UpperCAmelCase__							 =						(embeds - self.mean) * 1.0 / self.std
       return embeds







      def 	UpperCAmelCase_						(			self   :str ,      lowerCamelCase   :List[Any]      )     ->			Dict:
       UpperCAmelCase__							 =						(embeds * self.std) + self.mean
       return embeds











import argparse
import os

import torch

from transformers.utils import WEIGHTS_NAME


_lowerCAmelCase						:   List[Any]		=   ["small", "medium", "large"]

_lowerCAmelCase						:   List[Any]		=   "lm_head.decoder.weight"
_lowerCAmelCase						:   Optional[int]		=   "lm_head.weight"



def 						lowerCAmelCase		(		_lowerCAmelCase							:   str						,							_lowerCAmelCase							:   str	):




 """simple docstring"""







 UpperCAmelCase__							 =						torch.load(_lowerCAmelCase	)
 UpperCAmelCase__							 =						d.pop(_lowerCAmelCase	)
 os.makedirs(_lowerCAmelCase						,							exist_ok=_lowerCAmelCase	)
 torch.save(_lowerCAmelCase						,							os.path.join(_lowerCAmelCase						,							_lowerCAmelCase	)	)


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





