






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


__a:     Tuple   					=					{'''configuration_fnet''': ['''FNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FNetConfig''']}

try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __a:     Dict   					=					['''FNetTokenizer''']

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __a:     List[str]   					=					['''FNetTokenizerFast''']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __a:     int   					=					[
      '''FNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''FNetForMaskedLM''',
      '''FNetForMultipleChoice''',
      '''FNetForNextSentencePrediction''',
      '''FNetForPreTraining''',
      '''FNetForQuestionAnswering''',
      '''FNetForSequenceClassification''',
      '''FNetForTokenClassification''',
      '''FNetLayer''',
      '''FNetModel''',
      '''FNetPreTrainedModel''',
  ]


if TYPE_CHECKING:
  from .configuration_fnet import FNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FNetConfig

  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_fnet import FNetTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_fnet_fast import FNetTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_fnet import (
        FNET_PRETRAINED_MODEL_ARCHIVE_LIST,
        FNetForMaskedLM,
        FNetForMultipleChoice,
        FNetForNextSentencePrediction,
        FNetForPreTraining,
        FNetForQuestionAnswering,
        FNetForSequenceClassification,
        FNetForTokenClassification,
        FNetLayer,
        FNetModel,
        FNetPreTrainedModel,
    )


else:
  import sys

  __a:     str   					=					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

import math





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase     ,		__lowerCAmelCase				):

    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(__lowerCAmelCase				)				) ** 2)


if __name__ == "__main__":
     import doctest

     doctest.testmod(name='''malus_law''')



