

import argparse
import os

import torch

from transformers.utils import WEIGHTS_NAME


__A :    Union[str, Any]      		=     ['''small''', '''medium''', '''large''']

__A :    List[Any]      		=     '''lm_head.decoder.weight'''
__A :    Dict      		=     '''lm_head.weight'''



def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase					)							->     Optional[int]:



  """simple docstring"""






  _snake_case						=							torch.load(_UpperCamelCase					)
  _snake_case						=							d.pop(_UpperCamelCase					)
  os.makedirs(_UpperCamelCase			,	exist_ok=_UpperCamelCase					)
  torch.save(_UpperCamelCase			,	os.path.join(_UpperCamelCase			,	_UpperCamelCase					)					)


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




import cmath
import math



def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase					)							->     complex:



  """simple docstring"""






  _snake_case						=							math.radians(_UpperCamelCase					)
  _snake_case						=							math.radians(_UpperCamelCase					)

  # Convert voltage and current to rectangular form
  _snake_case						=							cmath.rect(_UpperCamelCase			,	_UpperCamelCase					)
  _snake_case						=							cmath.rect(_UpperCamelCase			,	_UpperCamelCase					)

  # Calculate apparent power
  return voltage_rect * current_rect


if __name__ == "__main__":
 import doctest

 doctest.testmod()

