'''simple docstring'''






from __future__ import annotations

from sys import maxsize
from typing import Generic, TypeVar

SCREAMING_SNAKE_CASE_	   =			TypeVar('T')






def  UpperCamelCase__					(					_lowercase    :				int     )							->	int:
  return (position - 1) // 2






def  UpperCamelCase__					(					_lowercase    :				int     )							->	int:
  return (2 * position) + 1






def  UpperCamelCase__					(					_lowercase    :				int     )							->	int:
  return (2 * position) + 2




class    a (       Generic[T]  ):



  """simple docstring"""







  def __init__(							self					):

    '''simple docstring'''







    __UpperCAmelCase:					list[tuple[T, int]]		= []
    __UpperCAmelCase:					dict[T, int]		= {}
    __UpperCAmelCase:					int		= 0







  def __len__(							self					):

    '''simple docstring'''







    return self.elements







  def __repr__(							self					):

    '''simple docstring'''







    return str(self.heap					)







  def    lowercase_  (							self					):

    '''simple docstring'''







    return self.elements == 0







  def    lowercase_  (							self   ,    snake_case_   ,    snake_case_					):

    '''simple docstring'''







    self.heap.append((elem, weight)					)
    __UpperCAmelCase:					Any		= self.elements
    self.elements += 1
    self._bubble_up(UpperCAmelCase__					)







  def    lowercase_  (							self					):

    '''simple docstring'''







    if self.elements > 1:
      self._swap_nodes(0   ,    self.elements - 1					)
    __UpperCAmelCase:					List[Any]		= self.heap.pop()
    del self.position_map[elem]
    self.elements -= 1
    if self.elements > 0:
      __UpperCAmelCase:					Optional[int]		= self.heap[0]
      self._bubble_down(UpperCAmelCase__					)
    return elem







  def    lowercase_  (							self   ,    snake_case_   ,    snake_case_					):

    '''simple docstring'''







    __UpperCAmelCase:					str		= self.position_map[elem]
    __UpperCAmelCase:					Any		= (elem, weight)
    if position > 0:
      __UpperCAmelCase:					str		= get_parent_position(UpperCAmelCase__					)
      __UpperCAmelCase:					List[Any]		= self.heap[parent_position]
      if parent_weight > weight:
        self._bubble_up(UpperCAmelCase__					)
      else:
        self._bubble_down(UpperCAmelCase__					)
    else:
      self._bubble_down(UpperCAmelCase__					)







  def    lowercase_  (							self   ,    snake_case_					):

    '''simple docstring'''







    __UpperCAmelCase:					Optional[int]		= self.position_map[elem]
    if curr_pos == 0:
      return None
    __UpperCAmelCase:					Optional[int]		= get_parent_position(UpperCAmelCase__					)
    __UpperCAmelCase:					str		= self.heap[curr_pos]
    __UpperCAmelCase:					List[str]		= self.heap[parent_position]
    if parent_weight > weight:
      self._swap_nodes(UpperCAmelCase__   ,    UpperCAmelCase__					)
      return self._bubble_up(UpperCAmelCase__					)
    return None







  def    lowercase_  (							self   ,    snake_case_					):

    '''simple docstring'''







    __UpperCAmelCase:					List[str]		= self.position_map[elem]
    __UpperCAmelCase:					Tuple		= self.heap[curr_pos]
    __UpperCAmelCase:					Union[str, Any]		= get_child_left_position(UpperCAmelCase__					)
    __UpperCAmelCase:					str		= get_child_right_position(UpperCAmelCase__					)
    if child_left_position < self.elements and child_right_position < self.elements:
      __UpperCAmelCase:					int		= self.heap[child_left_position]
      __UpperCAmelCase:					int		= self.heap[child_right_position]
      if child_right_weight < child_left_weight and child_right_weight < weight:
        self._swap_nodes(UpperCAmelCase__   ,    UpperCAmelCase__					)
        return self._bubble_down(UpperCAmelCase__					)
    if child_left_position < self.elements:
      __UpperCAmelCase:					int		= self.heap[child_left_position]
      if child_left_weight < weight:
        self._swap_nodes(UpperCAmelCase__   ,    UpperCAmelCase__					)
        return self._bubble_down(UpperCAmelCase__					)
    else:
      return None
    if child_right_position < self.elements:
      __UpperCAmelCase:					Tuple		= self.heap[child_right_position]
      if child_right_weight < weight:
        self._swap_nodes(UpperCAmelCase__   ,    UpperCAmelCase__					)
        return self._bubble_down(UpperCAmelCase__					)
    return None







  def    lowercase_  (							self   ,    snake_case_   ,    snake_case_					):

    '''simple docstring'''







    __UpperCAmelCase:					Optional[Any]		= self.heap[nodea_pos][0]
    __UpperCAmelCase:					str		= self.heap[nodea_pos][0]
    __UpperCAmelCase:					int		= (
        self.heap[nodea_pos],
        self.heap[nodea_pos],
    )
    __UpperCAmelCase:					Dict		= nodea_pos
    __UpperCAmelCase:					Any		= nodea_pos




class    a (       Generic[T]  ):



  """simple docstring"""







  def __init__(							self					):

    '''simple docstring'''







    __UpperCAmelCase:					dict[T, dict[T, int]]		= {}
    __UpperCAmelCase:					int		= 0







  def __repr__(							self					):

    '''simple docstring'''







    return str(self.connections					)







  def __len__(							self					):

    '''simple docstring'''







    return self.nodes







  def    lowercase_  (							self   ,    snake_case_					):

    '''simple docstring'''







    if node not in self.connections:
      __UpperCAmelCase:					Any		= {}
      self.nodes += 1







  def    lowercase_  (							self   ,    snake_case_   ,    snake_case_   ,    snake_case_					):

    '''simple docstring'''







    self.add_node(UpperCAmelCase__					)
    self.add_node(UpperCAmelCase__					)
    __UpperCAmelCase:					Optional[Any]		= weight
    __UpperCAmelCase:					List[Any]		= weight






def  UpperCamelCase__					(					_lowercase    :				GraphUndirectedWeighted[T]   ,    )							->	tuple[dict[T, int], dict[T, T | None]]:
  __UpperCAmelCase:					dict[T, int]		= {node: maxsize for node in graph.connections}
  __UpperCAmelCase:					dict[T, T | None]		= {node: None for node in graph.connections}

  __UpperCAmelCase:					MinPriorityQueue[T]		= MinPriorityQueue()
  for node, weight in dist.items():
    priority_queue.push(_lowercase   ,    _lowercase     )

  if priority_queue.is_empty():
    return dist, parent

  # initialization
  __UpperCAmelCase:					Optional[int]		= priority_queue.extract_min()
  __UpperCAmelCase:					Optional[Any]		= 0
  for neighbour in graph.connections[node]:
    if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
      __UpperCAmelCase:					Any		= dist[node] + graph.connections[node][neighbour]
      priority_queue.update_key(_lowercase   ,    dist[neighbour]     )
      __UpperCAmelCase:					List[str]		= node

    # running prim's algorithm
  while not priority_queue.is_empty():
    __UpperCAmelCase:					str		= priority_queue.extract_min()
    for neighbour in graph.connections[node]:
      if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
        __UpperCAmelCase:					Any		= dist[node] + graph.connections[node][neighbour]
        priority_queue.update_key(_lowercase   ,    dist[neighbour]     )
        __UpperCAmelCase:					Tuple		= node
  return dist, parent
'''simple docstring'''






import argparse
import os

import torch

from transformers.utils import WEIGHTS_NAME


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

SCREAMING_SNAKE_CASE_	   =			'lm_head.decoder.weight'
SCREAMING_SNAKE_CASE_	   =			'lm_head.weight'






def  UpperCamelCase__					(					_lowercase    :				str   ,    _lowercase    :				str     )							->	List[str]:
  __UpperCAmelCase:					Tuple		= torch.load(_lowercase     )
  __UpperCAmelCase:					Tuple		= d.pop(_lowercase     )
  os.makedirs(_lowercase   ,    exist_ok=_lowercase     )
  torch.save(_lowercase   ,    os.path.join(_lowercase   ,    _lowercase     )     )


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