





'''simple docstring'''

import argparse

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


logging.set_verbosity_info()







def  a__    (    lowercase :					Any, lowercase :					Optional[int], lowercase :					Any				)		->		Dict:





     """simple docstring"""

     _UpperCamelCase       =	TaConfig.from_json_file(__a				)
     print(F"""Building PyTorch model from configuration: {config}"""				)
     _UpperCamelCase       =	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__":
   lowercase__						:      List[str]	=    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__						:      Optional[Any]	=    parser.parse_args()
   convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)










'''simple docstring'''

import math







def  a__    (    lowercase :					float, lowercase :					float				)		->		float:





     """simple docstring"""

     if initial_intensity < 0:
          raise ValueError('''The value of intensity cannot be negative'''				)
          # handling of negative values of initial intensity
     if angle < 0 or angle > 360:
          raise ValueError('''In Malus Law, the angle is in the range 0-360 degrees'''				)
          # handling of values out of allowed range
     return initial_intensity * (math.cos(math.radians(lowercase				)				) ** 2)


if __name__ == "__main__":
   import doctest

   doctest.testmod(name='malus_law')



