





"""simple docstring"""


import argparse
import os

import torch

from transformers.utils import WEIGHTS_NAME


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

__magic_name__       :					int						=			"""lm_head.decoder.weight"""
__magic_name__       :					Any						=			"""lm_head.weight"""

def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
						UpperCamelCase				:		Dict				   =			torch.load(SCREAMING_SNAKE_CASE     )
						UpperCamelCase				:		List[str]				   =			d.pop(SCREAMING_SNAKE_CASE     )
						os.makedirs(SCREAMING_SNAKE_CASE							,     exist_ok=SCREAMING_SNAKE_CASE     )
						torch.save(SCREAMING_SNAKE_CASE							,     os.path.join(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     )     )


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










"""simple docstring"""


from collections.abc import Callable

import numpy as np




def        lowercase__     (       lowercase_		,lowercase_		,lowercase_		,lowercase_		,lowercase_      )       ->		np.ndarray:




      """simple docstring"""

      _UpperCamelCase   :						List[str]    =							int(np.ceil((x_end - xa) / step_size      )      )
      _UpperCamelCase   :						Tuple    =							np.zeros((n + 1,)      )
      _UpperCamelCase   :						List[str]    =							ya
      _UpperCamelCase   :						List[Any]    =							xa

      for k in range(lowercase_      ):
            _UpperCamelCase   :						List[str]    =							y[k] + step_size * ode_func(lowercase_		,y[k]      )
            x += step_size

      return y


if __name__ == "__main__":
  import doctest

  doctest.testmod()
