


'''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				:					Tuple       ,						__UpperCamelCase				:					str       ,						__UpperCamelCase				:					int		)				->  Optional[int]:







     """simple docstring"""
     _A				=       TaConfig.from_json_file(__A		)
     print(F'Building PyTorch model from configuration: {config}'		)
     _A				=       TaForConditionalGeneration(__A		)

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

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


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





'''simple docstring'''
from __future__ import annotations

def 				lowerCamelCase_   (   __UpperCamelCase				:					list[int | float]		)				->  int | float:







     """simple docstring"""
     if len(__UpperCamelCase		) == 0:
          raise ValueError('find_max() arg is an empty sequence'		)
     _A				=       nums[0]
     for x in nums:
          if x > max_num:
               _A				=       x
     return max_num


if __name__ == "__main__":
    import doctest

    doctest.testmod(verbose=True)





