


'''simple docstring'''



from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowercase	:						Tuple       =	{
    'configuration_informer': [
        'INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'InformerConfig',
    ],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowercase	:						int       =	[
	    'INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'InformerForPrediction',
	    'InformerModel',
	    'InformerPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_informer import INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, InformerConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_informer import (
		    INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    InformerForPrediction,
		    InformerModel,
		    InformerPreTrainedModel,
		)

else:
	import sys

	__lowercase	:						Optional[int]       =	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)







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 							__lowercase   ( __lowerCAmelCase							:				str					,    __lowerCAmelCase							:				Any					,    __lowerCAmelCase							:				int   ):
							# Construct model
							if gpta_config_file == "":
														a__             =     GPTaConfig()
							else:
														a__             =     GPTaConfig.from_json_file(__lowerCAmelCase   )
							a__             =     GPTaModel(__lowerCAmelCase   )

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

							# Save pytorch-model
							a__             =     pytorch_dump_folder_path + '/' + WEIGHTS_NAME
							a__             =     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__":
						snake_case							:       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.'''
						    ),
						)
						snake_case							:       Any     			=      parser.parse_args()
						convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)




