





'''simple docstring'''


from math import factorial, radians






def  UpperCAmelCase_       (		lowercase__       ,	lowercase__ = 1_8       ,	lowercase__ = 1_0       ):


     '''simple docstring'''







     a_       		=angle_in_degrees - ((angle_in_degrees // 360.0) * 360.0)

     # Converting from degrees to radians
     a_       		=radians(lowercase__       )

     a_       		=angle_in_radians
     a_       		=3
     a_       		=-1

     for _ in range(lowercase__       ):
          result += (b * (angle_in_radians**a)) / factorial(lowercase__       )

          a_       		=-b  # One positive term and the next will be negative and so on...
          a += 2  # Increased by 2 for every term.

     return round(lowercase__       ,	lowercase__       )


if __name__ == "__main__":
  __import__('''doctest''').testmod()







'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


lowercase							       =			{
    '''configuration_rag''': ['''RagConfig'''],
    '''retrieval_rag''': ['''RagRetriever'''],
    '''tokenization_rag''': ['''RagTokenizer'''],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase							       =			[
      '''RagModel''',
      '''RagPreTrainedModel''',
      '''RagSequenceForGeneration''',
      '''RagTokenForGeneration''',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase							       =			[
      '''TFRagModel''',
      '''TFRagPreTrainedModel''',
      '''TFRagSequenceForGeneration''',
      '''TFRagTokenForGeneration''',
  ]


if TYPE_CHECKING:
  from .configuration_rag import RagConfig
  from .retrieval_rag import RagRetriever
  from .tokenization_rag import RagTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_rag import (
        TFRagModel,
        TFRagPreTrainedModel,
        TFRagSequenceForGeneration,
        TFRagTokenForGeneration,
    )

else:
  import sys

  lowercase							       =			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


