


from .data_collator import (
    DataCollatorForLanguageModeling,
    DataCollatorForPermutationLanguageModeling,
    DataCollatorForSeqaSeq,
    DataCollatorForSOP,
    DataCollatorForTokenClassification,
    DataCollatorForWholeWordMask,
    DataCollatorWithPadding,
    DefaultDataCollator,
    default_data_collator,
)
from .metrics import glue_compute_metrics, xnli_compute_metrics
from .processors import (
    DataProcessor,
    InputExample,
    InputFeatures,
    SingleSentenceClassificationProcessor,
    SquadExample,
    SquadFeatures,
    SquadVaProcessor,
    SquadVaProcessor,
    glue_convert_examples_to_features,
    glue_output_modes,
    glue_processors,
    glue_tasks_num_labels,
    squad_convert_examples_to_features,
    xnli_output_modes,
    xnli_processors,
    xnli_tasks_num_labels,
)






from math import factorial, radians

def 			_a  (				SCREAMING_SNAKE_CASE	:				float    , SCREAMING_SNAKE_CASE	:				int = 18    , SCREAMING_SNAKE_CASE	:				int = 10     ):







 """simple docstring"""



 UpperCamelCase__					:    Dict          =   angle_in_degrees - ((angle_in_degrees // 360.0) * 360.0)

 # Converting from degrees to radians
 UpperCamelCase__					:    int          =   radians(SCREAMING_SNAKE_CASE     )

 UpperCamelCase__					:    Tuple          =   angle_in_radians
 UpperCamelCase__					:    str          =   3
 UpperCamelCase__					:    int          =   -1

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

  UpperCamelCase__					:    List[Any]          =   -b  # One positive term and the next will be negative and so on...
  a += 2  # Increased by 2 for every term.

 return round(SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE     )


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


