



"""simple docstring"""
from __future__ import annotations

from math import pi

# Define the Reduced Planck Constant ℏ (H bar), speed of light C, value of
# Pi and the function
_lowerCAmelCase    :Optional[int]     =					1.054_571_817E-34  # unit of ℏ : J * s

_lowerCAmelCase    :Optional[int]     =					3E8  # unit of c : m * s^-1





def  lowerCamelCase_						(UpperCamelCase__		:		float     ,       UpperCamelCase__		:		float     ,       UpperCamelCase__		:		float							):

    if (force, area, distance).count(0							) != 1:
        raise ValueError('''One and only one argument must be 0'''							)
    if force < 0:
        raise ValueError('''Magnitude of force can not be negative'''							)
    if distance < 0:
        raise ValueError('''Distance can not be negative'''							)
    if area < 0:
        raise ValueError('''Area can not be negative'''							)
    if force == 0:
        _UpperCAmelCase							:  List[str]											=    (REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (
            240 * (distance) ** 4
        )
        return {"force": force}
    elif area == 0:
        _UpperCAmelCase							:  List[Any]											=    (240 * force * (distance) ** 4) / (
            REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2
        )
        return {"area": area}
    elif distance == 0:
        _UpperCAmelCase							:  Optional[int]											=    (
            (REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (240 * force)
        ) ** (1 / 4)
        return {"distance": distance}
    raise ValueError('''One and only one argument must be 0'''							)


# Run doctest
if __name__ == "__main__":
  import doctest

  doctest.testmod()







"""simple docstring"""
import argparse
import os

import torch

from transformers.utils import WEIGHTS_NAME


_lowerCAmelCase    :int     =					['small', 'medium', 'large']

_lowerCAmelCase    :int     =					'lm_head.decoder.weight'
_lowerCAmelCase    :Dict     =					'lm_head.weight'





def  lowerCamelCase_						(UpperCamelCase__		:		str     ,       UpperCamelCase__		:		str							):
    _UpperCAmelCase							:  List[Any]											=    torch.load(UpperCamelCase__							)
    _UpperCAmelCase							:  List[str]											=    d.pop(UpperCamelCase__							)
    os.makedirs(UpperCamelCase__     ,       exist_ok=UpperCamelCase__							)
    torch.save(UpperCamelCase__     ,       os.path.join(UpperCamelCase__     ,       UpperCamelCase__							)							)


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


