

"""simple docstring"""


from typing import TYPE_CHECKING

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


__A						   =			{"configuration_focalnet": ["FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "FocalNetConfig"]}


try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A						   =			[
			    "FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "FocalNetForImageClassification",
			    "FocalNetForMaskedImageModeling",
			    "FocalNetBackbone",
			    "FocalNetModel",
			    "FocalNetPreTrainedModel",
			]

if TYPE_CHECKING:
			from .configuration_focalnet import FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FocalNetConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_focalnet import (
						    FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST,
						    FocalNetBackbone,
						    FocalNetForImageClassification,
						    FocalNetForMaskedImageModeling,
						    FocalNetModel,
						    FocalNetPreTrainedModel,
						)

else:
			import sys

			__A						   =			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





"""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)


