



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

from typing import Any
class 							_UpperCAmelCase							:





     '''simple docstring'''



     def __init__(					self       ,	A = 6		)							->					None:
         _UpperCAmelCase							:  Node | None											=    None
         _UpperCAmelCase							:  Node | None											=    None
         self.create_linked_list(A		)





     def  __lowerCAmelCase	(					self       ,	A		)							->					None:
         _UpperCAmelCase							:  int											=    Node()
         _UpperCAmelCase							:  Any											=    current_node
         _UpperCAmelCase							:  Optional[Any]											=    current_node
         _UpperCAmelCase							:  Tuple											=    current_node
         for _ in range(1       ,	A		):
             _UpperCAmelCase							:  List[str]											=    Node()
             _UpperCAmelCase							:  List[str]											=    current_node
             _UpperCAmelCase							:  List[str]											=    previous_node
             _UpperCAmelCase							:  Dict											=    current_node
         _UpperCAmelCase							:  int											=    self.front
         _UpperCAmelCase							:  Optional[int]											=    previous_node





     def  __lowerCAmelCase	(					self		)							->					bool:
         return (
             self.front == self.rear
             and self.front is not None
             and self.front.data is None
         )





     def  __lowerCAmelCase	(					self		)							->					Any | None:
         self.check_can_perform_operation()
         return self.front.data if self.front else None





     def  __lowerCAmelCase	(					self       ,	A		)							->					None:
         if self.rear is None:
             return

         self.check_is_full()
         if not self.is_empty():
             _UpperCAmelCase							:  int											=    self.rear.next
         if self.rear:
             _UpperCAmelCase							:  Dict											=    data





     def  __lowerCAmelCase	(					self		)							->					Any:
         self.check_can_perform_operation()
         if self.rear is None or self.front is None:
             return None
         if self.front == self.rear:
             _UpperCAmelCase							:  int											=    self.front.data
             _UpperCAmelCase							:  List[Any]											=    None
             return data

         _UpperCAmelCase							:  Optional[Any]											=    self.front
         _UpperCAmelCase							:  Dict											=    old_front.next
         _UpperCAmelCase							:  Dict											=    old_front.data
         _UpperCAmelCase							:  List[str]											=    None
         return data





     def  __lowerCAmelCase	(					self		)							->					None:
         if self.is_empty():
             raise Exception('''Empty Queue'''		)





     def  __lowerCAmelCase	(					self		)							->					None:
         if self.rear and self.rear.next == self.front:
             raise Exception('''Full Queue'''		)



class 							_UpperCAmelCase							:





     '''simple docstring'''



     def __init__(					self		)							->					None:
         _UpperCAmelCase							:  Any | None											=    None
         _UpperCAmelCase							:  Node | None											=    None
         _UpperCAmelCase							:  Node | None											=    None


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,
    )


