





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   lowerCamelCase__       (       UpperCamelCase__     :    Union[str, Any]    ,      UpperCamelCase__     :    Optional[int]  )     ->	Dict:



					'''simple docstring'''




					_snake_case													=			torch.load(__snake_case  )
					_snake_case													=			d.pop(__snake_case  )
					os.makedirs(__snake_case    ,      exist_ok=__snake_case  )
					torch.save(__snake_case    ,      os.path.join(__snake_case    ,      __snake_case  )  )


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,
										)








import random





def   lowerCamelCase__       (       UpperCamelCase__     :    int    ,      UpperCamelCase__     :    float    ,      UpperCamelCase__     :    bool = False  )     ->	dict:



					'''simple docstring'''




					_snake_case													=			{i: [] for i in range(UpperCamelCase__  )}

					# if probability is greater or equal than 1, then generate a complete graph
					if probability >= 1:
										return complete_graph(UpperCamelCase__  )
					# if probability is lower or equal than 0, then return a graph without edges
					if probability <= 0:
										return graph

					# for each couple of nodes, add an edge from u to v
					# if the number randomly generated is greater than probability probability
					for i in range(UpperCamelCase__  ):
										for j in range(i + 1    ,      UpperCamelCase__  ):
															if random.random() < probability:
																				graph[i].append(UpperCamelCase__  )
																				if not directed:
																									# if the graph is undirected, add an edge in from j to i, either
																									graph[j].append(UpperCamelCase__  )
					return graph





def   lowerCamelCase__       (       UpperCamelCase__     :    int  )     ->	dict:



					'''simple docstring'''




					return {
					    i: [j for j in range(UpperCamelCase__  ) if i != j] for i in range(UpperCamelCase__  )
					}


if __name__ == "__main__":
					import doctest

					doctest.testmod()



