



'''simple docstring'''





from __future__ import annotations

from sys import maxsize
from typing import Generic, TypeVar

_lowerCAmelCase      						=	TypeVar('''T''')
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase			):



    """simple docstring"""





    return (position - 1) // 2
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase			):



    """simple docstring"""





    return (2 * position) + 1
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase			):



    """simple docstring"""





    return (2 * position) + 2






class        lowerCAmelCase_(				Generic[T]     ):
  '''simple docstring'''




  def __init__(    self      )    ->		None:
      lowerCAmelCase__ :  list[tuple[T, int]]							      =					[]
      lowerCAmelCase__ :  dict[T, int]							      =					{}
      lowerCAmelCase__ :  int							      =					0




  def __len__(    self      )    ->		int:
      return self.elements




  def __repr__(    self      )    ->		str:
      return str(self.heap      )




  def 							UpperCAmelCase_	(    self      )    ->		bool:
      # Check if the priority queue is empty
      return self.elements == 0




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase  ,__UpperCAmelCase      )    ->		None:
      # Add an element with given priority to the queue
      self.heap.append((elem, weight)      )
      lowerCAmelCase__ :  str							      =					self.elements
      self.elements += 1
      self._bubble_up(__UpperCAmelCase      )




  def 							UpperCAmelCase_	(    self      )    ->		T:
      # Remove and return the element with lowest weight (highest priority)
      if self.elements > 1:
          self._swap_nodes(0  ,self.elements - 1      )
      lowerCAmelCase__      ,   lowerCAmelCase__ :  Optional[int]							      =					self.heap.pop()
      del self.position_map[elem]
      self.elements -= 1
      if self.elements > 0:
          lowerCAmelCase__      ,   lowerCAmelCase__ :  str							      =					self.heap[0]
          self._bubble_down(__UpperCAmelCase      )
      return elem




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase  ,__UpperCAmelCase      )    ->		None:
      # Update the weight of the given key
      lowerCAmelCase__ :  int							      =					self.position_map[elem]
      lowerCAmelCase__ :  List[Any]							      =					(elem, weight)
      if position > 0:
          lowerCAmelCase__ :  Dict							      =					get_parent_position(__UpperCAmelCase      )
          lowerCAmelCase__      ,   lowerCAmelCase__ :  Optional[int]							      =					self.heap[parent_position]
          if parent_weight > weight:
              self._bubble_up(__UpperCAmelCase      )
          else:
              self._bubble_down(__UpperCAmelCase      )
      else:
          self._bubble_down(__UpperCAmelCase      )




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase      )    ->		None:
      # Place a node at the proper position (upward movement) [to be used internally
      # only]
      lowerCAmelCase__ :  List[str]							      =					self.position_map[elem]
      if curr_pos == 0:
          return None
      lowerCAmelCase__ :  int							      =					get_parent_position(__UpperCAmelCase      )
      lowerCAmelCase__      ,   lowerCAmelCase__ :  Any							      =					self.heap[curr_pos]
      lowerCAmelCase__      ,   lowerCAmelCase__ :  List[str]							      =					self.heap[parent_position]
      if parent_weight > weight:
          self._swap_nodes(__UpperCAmelCase  ,__UpperCAmelCase      )
          return self._bubble_up(__UpperCAmelCase      )
      return None




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase      )    ->		None:
      # Place a node at the proper position (downward movement) [to be used
      # internally only]
      lowerCAmelCase__ :  List[Any]							      =					self.position_map[elem]
      lowerCAmelCase__      ,   lowerCAmelCase__ :  Dict							      =					self.heap[curr_pos]
      lowerCAmelCase__ :  str							      =					get_child_left_position(__UpperCAmelCase      )
      lowerCAmelCase__ :  Union[str, Any]							      =					get_child_right_position(__UpperCAmelCase      )
      if child_left_position < self.elements and child_right_position < self.elements:
          lowerCAmelCase__      ,   lowerCAmelCase__ :  Dict							      =					self.heap[child_left_position]
          lowerCAmelCase__      ,   lowerCAmelCase__ :  Any							      =					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:
          lowerCAmelCase__      ,   lowerCAmelCase__ :  List[str]							      =					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:
          lowerCAmelCase__      ,   lowerCAmelCase__ :  Any							      =					self.heap[child_right_position]
          if child_right_weight < weight:
              self._swap_nodes(__UpperCAmelCase  ,__UpperCAmelCase      )
              return self._bubble_down(__UpperCAmelCase      )
      return None






  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase  ,__UpperCAmelCase      )    ->		None:
      # Swap the nodes at the given positions
      lowerCAmelCase__ :  str							      =					self.heap[nodea_pos][0]
      lowerCAmelCase__ :  Dict							      =					self.heap[nodea_pos][0]
      lowerCAmelCase__      ,   lowerCAmelCase__ :  Tuple							      =					(
          self.heap[nodea_pos],
          self.heap[nodea_pos],
      )
      lowerCAmelCase__ :  int							      =					nodea_pos
      lowerCAmelCase__ :  int							      =					nodea_pos







class        lowerCAmelCase_(				Generic[T]     ):
  '''simple docstring'''




  def __init__(    self      )    ->		None:
      lowerCAmelCase__ :  dict[T, dict[T, int]]							      =					{}
      lowerCAmelCase__ :  int							      =					0




  def __repr__(    self      )    ->		str:
      return str(self.connections      )




  def __len__(    self      )    ->		int:
      return self.nodes




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase      )    ->		None:
      # Add a node in the graph if it is not in the graph
      if node not in self.connections:
          lowerCAmelCase__ :  Optional[int]							      =					{}
          self.nodes += 1






  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase      )    ->		None:
      # Add an edge between 2 nodes in the graph
      self.add_node(__UpperCAmelCase      )
      self.add_node(__UpperCAmelCase      )
      lowerCAmelCase__ :  Any							      =					weight
      lowerCAmelCase__ :  Tuple							      =					weight







def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    ):



    """simple docstring"""





    lowerCAmelCase__ :  dict[T, int]							      =					{node: maxsize for node in graph.connections}
    lowerCAmelCase__ :  dict[T, T | None]							      =					{node: None for node in graph.connections}

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

    if priority_queue.is_empty():
        return dist, parent

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

    # running prim's algorithm
    while not priority_queue.is_empty():
        lowerCAmelCase__ :  Any							      =					priority_queue.extract_min()
        for neighbour in graph.connections[node]:
            if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
                lowerCAmelCase__ :  Optional[int]							      =					dist[node] + graph.connections[node][neighbour]
                priority_queue.update_key(UpperCamelCase							,    dist[neighbour]			)
                lowerCAmelCase__ :  Optional[int]							      =					node
    return dist, parent





import argparse

import torch

from transformers import OpenAIGPTConfig, OpenAIGPTModel, load_tf_weights_in_openai_gpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()






def        UpperCamelCase_(							_snake_case   :  Dict       ,     _snake_case   :  Optional[int]       ,     _snake_case   :  str	):




			"""simple docstring"""






			if openai_config_file == "":
						__a        =OpenAIGPTConfig()
			else:
						__a        =OpenAIGPTConfig.from_json_file(_snake_case	)
			__a        =OpenAIGPTModel(_snake_case	)

			# Load weights from numpy
			load_tf_weights_in_openai_gpt(_snake_case       ,     _snake_case       ,     _snake_case	)

			# Save pytorch-model
			__a        =pytorch_dump_folder_path + '/' + WEIGHTS_NAME
			__a        =pytorch_dump_folder_path + '/' + CONFIG_NAME
			print(F'Save PyTorch model to {pytorch_weights_dump_path}'	)
			torch.save(model.state_dict()       ,     _snake_case	)
			print(F'Save configuration file to {pytorch_config_dump_path}'	)
			with open(_snake_case       ,     'w'       ,     encoding='utf-8'	) as f:
						f.write(config.to_json_string()	)


if __name__ == "__main__":
						_lowerCAmelCase					:			Tuple  				=			argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    "--openai_checkpoint_folder_path",
						    default=None,
						    type=str,
						    required=True,
						    help="Path to the TensorFlow checkpoint path.",
						)
						parser.add_argument(
						    "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
						)
						parser.add_argument(
						    "--openai_config_file",
						    default="",
						    type=str,
						    help=(
						        "An optional config json file corresponding to the pre-trained OpenAI model. \n"
						        "This specifies the model architecture."
						    ),
						)
						_lowerCAmelCase					:			int  				=			parser.parse_args()
						convert_openai_checkpoint_to_pytorch(
						    args.openai_checkpoint_folder_path, args.openai_config_file, args.pytorch_dump_folder_path
						)






