'''simple docstring'''




import argparse

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


logging.set_verbosity_info()




def 							lowerCamelCase	(      UpperCAmelCase__       :					Optional[int]      ,      UpperCAmelCase__       :					Dict      ,      UpperCAmelCase__       :					Union[str, Any] )			->      str:
							# Initialise PyTorch model
							lowercase_					:			int										=       TaConfig.from_json_file(UpperCAmelCase__ )
							print(F'''Building PyTorch model from configuration: {config}''' )
							lowercase_					:			int										=       TaForConditionalGeneration(UpperCAmelCase__ )

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

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


if __name__ == "__main__":
						_lowercase						: Tuple						=						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						: Tuple						=						parser.parse_args()
						convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)






'''simple docstring'''




def 							lowerCamelCase	(      UpperCAmelCase__       :					list )			->      list:
							if any(not isinstance(UpperCAmelCase__      ,      UpperCAmelCase__ ) or x < 0 for x in sequence ):
														raise TypeError("""Sequence must be list of non-negative integers""" )
							for _ in range(len(UpperCAmelCase__ ) ):
														for i, (rod_upper, rod_lower) in enumerate(zip(UpperCAmelCase__      ,      sequence[1:] ) ):
																					if rod_upper > rod_lower:
																												sequence[i] -= rod_upper - rod_lower
																												sequence[i + 1] += rod_upper - rod_lower
							return sequence


if __name__ == "__main__":
						assert bead_sort([5, 4, 3, 2, 1]) == [1, 2, 3, 4, 5]
						assert bead_sort([7, 9, 4, 3, 5]) == [3, 4, 5, 7, 9]





