'''simple docstring'''







import argparse

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


logging.set_verbosity_info()




def        __lowercase						(      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)				->					str:
     """simple docstring"""




     __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__":
 SCREAMING_SNAKE_CASE_      =			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_      =			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        __lowercase						(      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  )				->					tuple[str, float]:
     """simple docstring"""




     if (stress, tangential_force, area).count(0		) != 1:
          raise ValueError("""You cannot supply more or less than 2 values"""		)
     elif stress < 0:
          raise ValueError("""Stress cannot be negative"""		)
     elif tangential_force < 0:
          raise ValueError("""Tangential Force cannot be negative"""		)
     elif area < 0:
          raise ValueError("""Area cannot be negative"""		)
     elif stress == 0:
          return (
              "stress",
              tangential_force / area,
          )
     elif tangential_force == 0:
          return (
              "tangential_force",
              stress * area,
          )
     else:
          return (
              "area",
              tangential_force / stress,
          )


if __name__ == "__main__":
 import doctest

 doctest.testmod()






