


"""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 _lowerCamelCase			(	_UpperCamelCase					,				_UpperCamelCase					,				_UpperCamelCase   ):

			'''simple docstring'''




			if gpta_config_file == "":
						__lowerCAmelCase       					=    GPTaConfig()
			else:
						__lowerCAmelCase       					=    GPTaConfig.from_json_file(_UpperCamelCase   )
			__lowerCAmelCase       					=    GPTaModel(_UpperCamelCase   )

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

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


if __name__ == "__main__":
	A  :     Tuple    					=    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  :     Union[str, Any]    					=    parser.parse_args()
	convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)








"""simple docstring"""


def _lowerCamelCase			(	_UpperCamelCase					,				_UpperCamelCase   ):

			'''simple docstring'''




			__lowerCAmelCase       					=    len(_UpperCamelCase   )
			print("The following activities are selected:"   )

			# The first activity is always selected
			__lowerCAmelCase       					=    0
			print(_UpperCamelCase					,				end=","   )

			# Consider rest of the activities
			for j in range(_UpperCamelCase   ):
						# If this activity has start time greater than
						# or equal to the finish time of previously
						# selected activity, then select it
						if start[j] >= finish[i]:
									print(_UpperCamelCase					,				end=","   )
									__lowerCAmelCase       					=    j


if __name__ == "__main__":
	import doctest

	doctest.testmod()

	A  :     Dict    					=    [1, 3, 0, 5, 8, 5]
	A  :     int    					=    [2, 4, 6, 7, 9, 9]
	print_max_activities(start, finish)




