
import argparse

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


logging.set_verbosity_info()





def        lowerCAmelCase       (				snake_case__   :      str				,					snake_case__   :      Union[str, Any]				,					snake_case__   :      Tuple )->			Optional[Any]:
    # Initialise PyTorch model
    A_								=					TaConfig.from_json_file(_SCREAMING_SNAKE_CASE )
    print(f'Building PyTorch model from configuration: {config}' )
    A_								=					TaForConditionalGeneration(_SCREAMING_SNAKE_CASE )

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

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


if __name__ == "__main__":
     __magic_name__  :  Any     			=   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.'
     )
     __magic_name__  :  Optional[int]     			=   parser.parse_args()
     convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)








'''simple docstring'''







def       _a		(						_SCREAMING_SNAKE_CASE		: str	):
						_SCREAMING_SNAKE_CASE					=			len(_SCREAMING_SNAKE_CASE	)
						for i in range(length - 1	):
												_SCREAMING_SNAKE_CASE					=			False
												for j in range(length - 1 - i	):
																		if collection[j] > collection[j + 1]:
																								_SCREAMING_SNAKE_CASE					=			True
																								_SCREAMING_SNAKE_CASE,				_SCREAMING_SNAKE_CASE					=			collection[j + 1], collection[j]
												if not swapped:
																		break  # Stop iteration if the collection is sorted.
						return collection


if __name__ == "__main__":
						import doctest
						import time

						doctest.testmod()

						_snake_case					: Union[str, Any]     			=					input("""Enter numbers separated by a comma:""").strip()
						_snake_case					: List[Any]     			=					[int(item) for item in user_input.split(""",""")]
						_snake_case					: Optional[int]     			=					time.process_time()
						print(*bubble_sort(unsorted), sep=""",""")
						print(F"Processing time: {(time.process_time() - start)%1E9 + 7}")