



"""simple docstring"""






_lowercase : int      						=						{0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}

_lowercase : Dict      						=						{0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}






def      snake_case__   (		__lowerCamelCase				:    dict[int, list[int]]							,		__lowerCamelCase				:    int							,		__lowerCamelCase				:    list[bool]	):
			"""simple docstring"""
			lowerCamelCase__		:		str			=True
			lowerCamelCase__		:		Optional[int]			=[]

			for neighbour in graph[vert]:
						if not visited[neighbour]:
									order += topology_sort(__lowerCamelCase							,		__lowerCamelCase							,		__lowerCamelCase	)

			order.append(__lowerCamelCase	)

			return order






def      snake_case__   (		__lowerCamelCase				:    dict[int, list[int]]							,		__lowerCamelCase				:    int							,		__lowerCamelCase				:    list[bool]	):
			"""simple docstring"""
			lowerCamelCase__		:		Tuple			=True
			lowerCamelCase__		:		Optional[Any]			=[vert]

			for neighbour in reversed_graph[vert]:
						if not visited[neighbour]:
									component += find_components(__lowerCamelCase							,		__lowerCamelCase							,		__lowerCamelCase	)

			return component






def      snake_case__   (		__lowerCamelCase				:    dict[int, list[int]]	):
			"""simple docstring"""
			lowerCamelCase__		:		int			=len(__lowerCamelCase	) * [False]
			lowerCamelCase__		:		dict[int, list[int]]			={vert: [] for vert in range(len(__lowerCamelCase	)	)}

			for vert, neighbours in graph.items():
						for neighbour in neighbours:
									reversed_graph[neighbour].append(__lowerCamelCase	)

			lowerCamelCase__		:		Any			=[]
			for i, was_visited in enumerate(__lowerCamelCase	):
						if not was_visited:
									order += topology_sort(__lowerCamelCase							,		__lowerCamelCase							,		__lowerCamelCase	)

			lowerCamelCase__		:		Dict			=[]
			lowerCamelCase__		:		Dict			=len(__lowerCamelCase	) * [False]

			for i in range(len(__lowerCamelCase	)	):
						lowerCamelCase__		:		List[Any]			=order[len(__lowerCamelCase	) - i - 1]
						if not visited[vert]:
									lowerCamelCase__		:		List[Any]			=find_components(__lowerCamelCase							,		__lowerCamelCase							,		__lowerCamelCase	)
									components_list.append(__lowerCamelCase	)

			return components_list









"""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      snake_case__   (		__lowerCamelCase				:    List[Any]							,		__lowerCamelCase				:    List[str]							,		__lowerCamelCase				:    List[Any]	):
			"""simple docstring"""
			# Construct model
			if gpta_config_file == "":
						lowerCamelCase__		:		Dict			=GPTaConfig()
			else:
						lowerCamelCase__		:		Tuple			=GPTaConfig.from_json_file(__lowerCamelCase	)
			lowerCamelCase__		:		Optional[Any]			=GPTaModel(__lowerCamelCase	)

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

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


if __name__ == "__main__":
		_lowercase : Optional[Any]      						=						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."
		    ),
		)
		_lowercase : Any      						=						parser.parse_args()
		convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)




