from math import factorial, pi


def      a__ (		__UpperCamelCase     ,      __UpperCamelCase = 3_0				):

      if not isinstance(__UpperCamelCase     ,      (int, float)				):
            raise ValueError("maclaurin_sin() requires either an int or float for theta"				)

      if not isinstance(__UpperCamelCase     ,      __UpperCamelCase				) or accuracy <= 0:
            raise ValueError("maclaurin_sin() requires a positive int for accuracy"				)

      SCREAMING_SNAKE_CASE_          =  float(__UpperCamelCase				)
      SCREAMING_SNAKE_CASE_          =  theta // (2 * pi)
      theta -= 2 * div * pi
      return sum(
          (-1) ** r * theta ** (2 * r + 1) / factorial(2 * r + 1				) for r in range(__UpperCamelCase				)				)


def      a__ (		__UpperCamelCase     ,      __UpperCamelCase = 3_0				):

      if not isinstance(__UpperCamelCase     ,      (int, float)				):
            raise ValueError("maclaurin_cos() requires either an int or float for theta"				)

      if not isinstance(__UpperCamelCase     ,      __UpperCamelCase				) or accuracy <= 0:
            raise ValueError("maclaurin_cos() requires a positive int for accuracy"				)

      SCREAMING_SNAKE_CASE_          =  float(__UpperCamelCase				)
      SCREAMING_SNAKE_CASE_          =  theta // (2 * pi)
      theta -= 2 * div * pi
      return sum((-1) ** r * theta ** (2 * r) / factorial(2 * r				) for r in range(__UpperCamelCase				)				)


if __name__ == "__main__":
      import doctest

      doctest.testmod()

      print(maclaurin_sin(10))
      print(maclaurin_sin(-10))
      print(maclaurin_sin(10, 15))
      print(maclaurin_sin(-10, 15))

      print(maclaurin_cos(5))
      print(maclaurin_cos(-5))
      print(maclaurin_cos(10, 15))
      print(maclaurin_cos(-10, 15))



import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow


if is_torch_available():
      import torch

      from transformers import XLMRobertaModel


@require_sentencepiece
@require_tokenizers
@require_torch
class   lowerCamelCase					(unittest.TestCase					):
  """simple docstring"""







  @slow
  def    __A     (							self :       Dict   )  ->	Any:
        SCREAMING_SNAKE_CASE_          =  XLMRobertaModel.from_pretrained("xlm-roberta-base"   )
        SCREAMING_SNAKE_CASE_          =  torch.tensor([[0, 581, 10_269, 83, 99_942, 136, 60_742, 23, 70, 80_583, 18_276, 2]]   )
        # The dog is cute and lives in the garden house

        SCREAMING_SNAKE_CASE_          =  torch.Size((1, 12, 768)   )  # batch_size, sequence_length, embedding_vector_dim
        SCREAMING_SNAKE_CASE_          =  torch.tensor(
            [[-0.0101, 0.1218, -0.0803, 0.0801, 0.1327, 0.0776, -0.1215, 0.2383, 0.3338, 0.3106, 0.0300, 0.0252]]   )
        #  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')
        #  xlmr.eval()
        #  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
        with torch.no_grad():
              SCREAMING_SNAKE_CASE_          =  model(__magic_name__   )["last_hidden_state"].detach()
        self.assertEqual(output.shape  ,     __magic_name__   )
        # compare the actual values for a slice of last dim
        self.assertTrue(torch.allclose(output[:, :, -1]  ,     __magic_name__  ,     atol=1e-3   )   )







  @slow
  def    __A     (							self :       Tuple   )  ->	List[str]:
        SCREAMING_SNAKE_CASE_          =  XLMRobertaModel.from_pretrained("xlm-roberta-large"   )
        SCREAMING_SNAKE_CASE_          =  torch.tensor([[0, 581, 10_269, 83, 99_942, 136, 60_742, 23, 70, 80_583, 18_276, 2]]   )
        # The dog is cute and lives in the garden house

        SCREAMING_SNAKE_CASE_          =  torch.Size((1, 12, 1_024)   )  # batch_size, sequence_length, embedding_vector_dim
        SCREAMING_SNAKE_CASE_          =  torch.tensor(
            [[-0.0699, -0.0318, 0.0705, -0.1241, 0.0999, -0.0520, 0.1004, -0.1838, -0.4704, 0.1437, 0.0821, 0.0126]]   )
        #  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.large')
        #  xlmr.eval()
        #  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
        with torch.no_grad():
              SCREAMING_SNAKE_CASE_          =  model(__magic_name__   )["last_hidden_state"].detach()
        self.assertEqual(output.shape  ,     __magic_name__   )
        # compare the actual values for a slice of last dim
        self.assertTrue(torch.allclose(output[:, :, -1]  ,     __magic_name__  ,     atol=1e-3   )   )


