
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 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			str						,  _lowerCamelCase :			Any						,  _lowerCamelCase :			int)					->    Union[str, Any]:






							'''simple docstring'''





							if gpta_config_file == "":
														__UpperCamelCase	:   Any			   =					GPTaConfig()
							else:
														__UpperCamelCase	:   List[Any]			   =					GPTaConfig.from_json_file(__lowerCAmelCase)
							__UpperCamelCase	:   Optional[Any]			   =					GPTaModel(__lowerCAmelCase)

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

							# Save pytorch-model
							__UpperCamelCase	:   List[str]			   =					pytorch_dump_folder_path + "/" + WEIGHTS_NAME
							__UpperCamelCase	:   str			   =					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  :					List[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)



from argparse import ArgumentParser

from .env import EnvironmentCommand






def 							__lowercase   ( ):
							a__             =     ArgumentParser('Diffusers CLI tool'					,    usage='diffusers-cli <command> [<args>]'   )
							a__             =     parser.add_subparsers(help='diffusers-cli command helpers'   )

							# Register commands
							EnvironmentCommand.register_subcommand(__lowerCAmelCase   )

							# Let's go
							a__             =     parser.parse_args()

							if not hasattr(__lowerCAmelCase					,    'func'   ):
														parser.print_help()
														exit(1   )

							# Run
							a__             =     args.func(__lowerCAmelCase   )
							service.run()


if __name__ == "__main__":
						main()




