

"""simple docstring"""






import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()






def   snake_case__     (	__lowerCamelCase       :					int							,	__lowerCamelCase       :					Any							,	__lowerCamelCase       :					List[Any]			):
			"""simple docstring"""
			lowerCamelCase__	:	Any				=TaConfig.from_json_file(__lowerCamelCase			)
			print(f'''Building PyTorch model from configuration: {config}'''			)
			lowerCamelCase__	:	Optional[int]				=TaForConditionalGeneration(__lowerCamelCase			)

			# Load weights from tf checkpoint
			load_tf_weights_in_ta(__lowerCamelCase							,	__lowerCamelCase							,	__lowerCamelCase			)

			# Save pytorch-model
			print(f'''Save PyTorch model to {pytorch_dump_path}'''			)
			model.save_pretrained(__lowerCamelCase			)


if __name__ == "__main__":
		_lowercase				:				Dict   		=		argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
		)
		parser.add_argument(
		    "--config_file",
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        "The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture."
		    ),
		)
		parser.add_argument(
		    "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
		)
		_lowercase				:				int   		=		parser.parse_args()
		convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)









"""simple docstring"""






def   snake_case__     (	__lowerCamelCase       :					int = 10							,	__lowerCamelCase       :					int = 22			):
			"""simple docstring"""
			lowerCamelCase__	:	Optional[Any]				=range(1							,	__lowerCamelCase			)
			lowerCamelCase__	:	str				=range(1							,	__lowerCamelCase			)
			return sum(
			    1 for power in powers for base in bases if len(str(base**power			)			) == power			)


if __name__ == "__main__":
		print(f'{solution(1_0, 2_2) = }')




