





'''simple docstring'''




import argparse

import torch

from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()



def 						__A						(   lowerCamelCase_       ,       lowerCamelCase_       ,       lowerCamelCase_     ):


		"""simple docstring"""



		if gpta_config_file == "":
				SCREAMING_SNAKE_CASE      :							Optional[Any]						=       GPTaConfig()
		else:
				SCREAMING_SNAKE_CASE      :							Optional[Any]						=       GPTaConfig.from_json_file(__a     )
		SCREAMING_SNAKE_CASE      :							Any						=       GPTaModel(__a     )

		# Load weights from numpy
		load_tf_weights_in_gpta(__a       ,       __a       ,       __a     )

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


if __name__ == "__main__":
							__UpperCAmelCase			    =    argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    """--gpt2_checkpoint_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(
							    """--gpt2_config_file""",
							    default="""""",
							    type=str,
							    help=(
							        """An optional config json file corresponding to the pre-trained OpenAI model. \n"""
							        """This specifies the model architecture."""
							    ),
							)
							__UpperCAmelCase			    =    parser.parse_args()
							convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)










from collections import defaultdict

from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst





def      lowerCAmelCase_ (       )     ->		Optional[int]:







  """simple docstring"""





  lowerCamelCase__  ,					lowerCamelCase__:      int			  =9, 14  # noqa: F841
  lowerCamelCase__:      List[Any]			  =[
      [0, 1, 4],
      [0, 7, 8],
      [1, 2, 8],
      [7, 8, 7],
      [7, 6, 1],
      [2, 8, 2],
      [8, 6, 6],
      [2, 3, 7],
      [2, 5, 4],
      [6, 5, 2],
      [3, 5, 14],
      [3, 4, 9],
      [5, 4, 10],
      [1, 7, 11],
  ]

  lowerCamelCase__:      List[str]			  =defaultdict(__a		)
  for nodea, nodea, cost in edges:
    adjancency[nodea].append([nodea, cost]		)
    adjancency[nodea].append([nodea, cost]		)

  lowerCamelCase__:      List[str]			  =mst(__a		)

  lowerCamelCase__:      Union[str, Any]			  =[
      [7, 6, 1],
      [2, 8, 2],
      [6, 5, 2],
      [0, 1, 4],
      [2, 5, 4],
      [2, 3, 7],
      [0, 7, 8],
      [3, 4, 9],
  ]

  for answer in expected:
    lowerCamelCase__:      Optional[int]			  =tuple(answer[:2]		)
    lowerCamelCase__:      List[Any]			  =tuple(edge[::-1]		)
    assert edge in result or reverse in result

