

"""simple docstring"""


class     snake_case	:

				def __init__(	self							:				Optional[Any])->   str:



						'''simple docstring'''


						__lowerCAmelCase:       Union[str, Any]          =			{}

				def 							lowercase_     (	self							:				str)->   None:



						'''simple docstring'''


						print(self.vertex)
						for i in self.vertex:
								print(UpperCamelCase__     ,  " -> "     ,  " -> ".join([str(UpperCamelCase__) for j in self.vertex[i]]))

				def 							lowercase_     (	self							:				str     ,  UpperCamelCase__							:				int     ,  UpperCamelCase__							:				int)->   None:



						'''simple docstring'''


						if from_vertex in self.vertex:
								self.vertex[from_vertex].append(UpperCamelCase__)
						else:
								# else make a new vertex
								__lowerCAmelCase:       Any          =			[to_vertex]

				def 							lowercase_     (	self							:				Dict)->   None:



						'''simple docstring'''


						__lowerCAmelCase:       str          =			[False] * len(self.vertex)

						# call the recursive helper function
						for i in range(len(self.vertex)):
								if not visited[i]:
										self.dfs_recursive(UpperCamelCase__     ,  UpperCamelCase__)




				def 							lowercase_     (	self							:				Any     ,  UpperCamelCase__							:				int     ,  UpperCamelCase__							:				list)->   None:



						'''simple docstring'''


						__lowerCAmelCase:       Union[str, Any]          =			True

						print(UpperCamelCase__     ,  end=" ")

						# Recur for all the vertices that are adjacent to this node
						for i in self.vertex:
								if not visited[i]:
										self.dfs_recursive(UpperCamelCase__     ,  UpperCamelCase__)


if __name__ == "__main__":
			__A						   =			Graph()
			g.add_edge(0, 1)
			g.add_edge(0, 2)
			g.add_edge(1, 2)
			g.add_edge(2, 0)
			g.add_edge(2, 3)
			g.add_edge(3, 3)

			g.print_graph()
			print("DFS:")
			g.dfs()

			# OUTPUT:
			# 0  ->  1 -> 2
			# 1  ->  2
			# 2  ->  0 -> 3
			# 3  ->  3
			# DFS:
			#  0 1 2 3





"""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__    (    __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE )	->	Optional[int]:
		# Construct model
		if gpta_config_file == "":
				__lowerCAmelCase:       Optional[int]          =			GPTaConfig()
		else:
				__lowerCAmelCase:       List[str]          =			GPTaConfig.from_json_file(__SCREAMING_SNAKE_CASE )
		__lowerCAmelCase:       Optional[int]          =			GPTaModel(__SCREAMING_SNAKE_CASE )

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

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


if __name__ == "__main__":
			__A						   =			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."
			    ),
			)
			__A						   =			parser.parse_args()
			convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)


