


'''simple docstring'''






import tempfile
import unittest

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
    is_torch_available,
    require_optimum,
    require_torch,
    slow,
)


if is_torch_available():
  import torch






@require_torch
@require_optimum
@slow
class   __snake_case			(  unittest.TestCase):







    """simple docstring"""



    def __lowercase  (  self       :    Any						)  ->    Optional[Any]:
      lowerCAmelCase_     : str     =  """hf-internal-testing/tiny-random-t5"""
      lowerCAmelCase_     : Any     =  AutoTokenizer.from_pretrained(lowerCamelCase						)
      lowerCAmelCase_     : Dict     =  AutoModelForSeqaSeqLM.from_pretrained(lowerCamelCase						)

      lowerCAmelCase_     : Dict     =  tokenizer("""This is me"""       ,		return_tensors="""pt"""						)

      lowerCAmelCase_     : Any     =  model.to_bettertransformer()

      self.assertTrue(any("""BetterTransformer""" in mod.__class__.__name__ for _, mod in model.named_modules()						)						)

      lowerCAmelCase_     : Union[str, Any]     =  model.generate(**lowerCamelCase						)

      lowerCAmelCase_     : Dict     =  model.reverse_bettertransformer()

      self.assertFalse(any("""BetterTransformer""" in mod.__class__.__name__ for _, mod in model.named_modules()						)						)

      with tempfile.TemporaryDirectory() as tmpdirname:
        model.save_pretrained(lowerCamelCase						)

        lowerCAmelCase_     : List[Any]     =  AutoModelForSeqaSeqLM.from_pretrained(lowerCamelCase						)

        self.assertFalse(
            any("""BetterTransformer""" in mod.__class__.__name__ for _, mod in model_reloaded.named_modules()						)						)

        lowerCAmelCase_     : List[Any]     =  model_reloaded.generate(**lowerCamelCase						)
        self.assertTrue(torch.allclose(lowerCamelCase       ,		lowerCamelCase						)						)





    def __lowercase  (  self       :    str						)  ->    List[str]:
      lowerCAmelCase_     : Union[str, Any]     =  """hf-internal-testing/tiny-random-t5"""
      lowerCAmelCase_     : str     =  AutoModelForSeqaSeqLM.from_pretrained(lowerCamelCase						)

      lowerCAmelCase_     : List[str]     =  model.to_bettertransformer()

      with tempfile.TemporaryDirectory() as tmpdirname:
        with self.assertRaises(lowerCamelCase						):
          model.save_pretrained(lowerCamelCase						)

        lowerCAmelCase_     : Tuple     =  model.reverse_bettertransformer()
        model.save_pretrained(lowerCamelCase						)







'''simple docstring'''






import math







def 							UpperCamelCase_     (       A__   :  float				,       A__   :  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 > 3_60:
    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(A__   )   ) ** 2)


if __name__ == "__main__":
  import doctest

  doctest.testmod(name="malus_law")



