



"""simple docstring"""
import argparse

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


logging.set_verbosity_info()
def    A_  (			_lowercase,					_lowercase,					_lowercase	):







						'''simple docstring'''


						snake_case_ :str						=			TaConfig.from_json_file(_lowercase	)
						print(f"""Building PyTorch model from configuration: {config}"""	)
						snake_case_ :Optional[Any]						=			TaForConditionalGeneration(_lowercase	)

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

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


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








def      lowercase__      (     __snake_case    :							list  ):


					'''simple docstring'''


					for i in range(len(__snake_case  ) - 1     ,       0     ,       -1  ):
										UpperCAmelCase_    :      Dict            =						False

										for j in range(__snake_case     ,       0     ,       -1  ):
															if unsorted[j] < unsorted[j - 1]:
																				UpperCAmelCase_       ,  UpperCAmelCase_    :      Any            =						unsorted[j - 1], unsorted[j]
																				UpperCAmelCase_    :      int            =						True

										for j in range(__snake_case  ):
															if unsorted[j] > unsorted[j + 1]:
																				UpperCAmelCase_       ,  UpperCAmelCase_    :      Optional[int]            =						unsorted[j + 1], unsorted[j]
																				UpperCAmelCase_    :      Any            =						True

										if not swapped:
															break
					return unsorted


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		__UpperCAmelCase								=						input('Enter numbers separated by a comma:\n').strip()
		__UpperCAmelCase								=						[int(item) for item in user_input.split(',')]
		print(F'{cocktail_shaker_sort(unsorted) = }')






