
'''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_	(   _lowercase			,       _lowercase			,       _lowercase				)     ->							int:
     '''simple docstring'''

     lowerCamelCase_					:      Union[str, Any]     =		TaConfig.from_json_file(_lowercase				)
     print(F"""Building PyTorch model from configuration: {config}"""				)
     lowerCamelCase_					:      List[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__":
     __lowercase							:   Optional[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.'''
     )
     __lowercase							:   Tuple					=     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

from math import pi, sqrt




def lowercase_	(   _lowercase			,       _lowercase				)     ->							tuple:
					'''simple docstring'''

					if inductance <= 0:
										raise ValueError('''Inductance cannot be 0 or negative'''				)

					elif capacitance <= 0:
										raise ValueError('''Capacitance cannot be 0 or negative'''				)

					else:
										return (
										    "Resonant frequency",
										    float(1 / (2 * pi * (sqrt(inductance * capacitance				)))				),
										)


if __name__ == "__main__":
					import doctest

					doctest.testmod()


