


from __future__ import annotations



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_					)       ->		float:
    _lowercase			:				int 						=						sorted(numsa + numsa					)
    _lowercase						,							_lowercase			:				List[str] 						=						divmod(len(lowerCamelCase_					)				,					2					)
    if mod == 1:
        return all_numbers[div]
    else:
        return (all_numbers[div] + all_numbers[div - 1]) / 2


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 SCREAMING_SNAKE_CASE			: Union[str, Any]				   =					[float(x) for x in input("Enter the elements of first array: ").split()]
 SCREAMING_SNAKE_CASE			: Union[str, Any]				   =					[float(x) for x in input("Enter the elements of second array: ").split()]
 print(F"The median of two arrays is: {median_of_two_arrays(array_a, array_a)}")






import argparse

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


logging.set_verbosity_info()



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_					)       ->		Optional[int]:
    # Initialise PyTorch model
    _lowercase			:				Optional[int] 						=						TaConfig.from_json_file(lowerCamelCase_					)
    print(F'''Building PyTorch model from configuration: {config}'''					)
    _lowercase			:				Union[str, Any] 						=						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__":
 SCREAMING_SNAKE_CASE			: Optional[int]				   =					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."
 )
 SCREAMING_SNAKE_CASE			: Any				   =					parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)


