






'''simple docstring'''
def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase				):


   """simple docstring"""




   lowercase_						:     Optional[int]							     =  word.split()

   def justify(_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase				) -> str:
      lowercase_						:     Tuple							     =  max_width - width
      lowercase_						:     int							     =  len(_UpperCamelCase				)
      if len(_UpperCamelCase				) == 1:
         # if there is only word in line
         # just insert overall_spaces_count for the remainder of line
         return line[0] + " " * overall_spaces_count
      else:
         lowercase_						:     Tuple							     =  words_count - 1
         # num_spaces_between_words_list[i] : tells you to insert
         # num_spaces_between_words_list[i] spaces
         # after word on line[i]
         lowercase_						:     int							     =  spaces_to_insert_between_words * [
             overall_spaces_count // spaces_to_insert_between_words
         ]
         lowercase_						:     Any							     =  (
             overall_spaces_count % spaces_to_insert_between_words
         )
         # distribute spaces via round robin to the left words
         for i in range(_UpperCamelCase				):
            num_spaces_between_words_list[i] += 1
         lowercase_						:     List[Any]							     =  []
         for i in range(_UpperCamelCase				):
            # add the word
            aligned_words_list.append(line[i]				)
            # add the spaces to insert
            aligned_words_list.append(num_spaces_between_words_list[i] * " "				)
         # just add the last word to the sentence
         aligned_words_list.append(line[-1]				)
         # join the aligned words list to form a justified line
         return "".join(_UpperCamelCase				)

   lowercase_						:     Union[str, Any]							     =  []
   lowercase_						:     list[str]							     =  []
   lowercase_						:     str							     =  0
   for word in words:
      if width + len(_UpperCamelCase				) + len(_UpperCamelCase				) <= max_width:
         # keep adding words until we can fill out max_width
         # width = sum of length of all words (without overall_spaces_count)
         # len(word) = length of current word
         # len(line) = number of overall_spaces_count to insert between words
         line.append(_UpperCamelCase				)
         width += len(_UpperCamelCase				)
      else:
         # justify the line and add it to result
         answer.append(justify(_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase				)				)
         # reset new line and new width
         lowercase_     ,		lowercase_						:     List[str]							     =  [word], len(_UpperCamelCase				)
   lowercase_						:     Any							     =  max_width - width - len(_UpperCamelCase				)
   answer.append(" ".join(_UpperCamelCase				) + (remaining_spaces + 1) * " "				)
   return answer


if __name__ == "__main__":
 from doctest import testmod

 testmod()












'''simple docstring'''
import argparse
import os

import torch

from transformers.utils import WEIGHTS_NAME


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

UpperCamelCase__			=      'lm_head.decoder.weight'
UpperCamelCase__			=      'lm_head.weight'




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase				):


   """simple docstring"""




   lowercase_						:     Optional[Any]							     =  torch.load(_UpperCamelCase				)
   lowercase_						:     List[Any]							     =  d.pop(_UpperCamelCase				)
   os.makedirs(_UpperCamelCase    ,		exist_ok=_UpperCamelCase				)
   torch.save(_UpperCamelCase    ,		os.path.join(_UpperCamelCase    ,		_UpperCamelCase				)				)


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




