






"""simple docstring"""



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 					_UpperCAmelCase       ( unittest.TestCase							):




					@slow
					def   A  (				self     :						Union[str, Any]   )  ->  Dict:
							lowercase_ : List[str]							       =   XLMRobertaModel.from_pretrained('''xlm-roberta-base'''   )
							lowercase_ : List[str]							       =   torch.tensor([[0, 5_81, 1_02_69, 83, 9_99_42, 1_36, 6_07_42, 23, 70, 8_05_83, 1_82_76, 2]]   )
							# The dog is cute and lives in the garden house

							lowercase_ : Dict							       =   torch.Size((1, 12, 7_68)   )  # batch_size, sequence_length, embedding_vector_dim
							lowercase_ : int							       =   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():
									lowercase_ : str							       =   model(A   )['''last_hidden_state'''].detach()
							self.assertEqual(output.shape    ,						A   )
							# compare the actual values for a slice of last dim
							self.assertTrue(torch.allclose(output[:, :, -1]    ,						A    ,						atol=1e-3   )   )






					@slow
					def   A  (				self     :						Optional[int]   )  ->  List[str]:
							lowercase_ : List[Any]							       =   XLMRobertaModel.from_pretrained('''xlm-roberta-large'''   )
							lowercase_ : Union[str, Any]							       =   torch.tensor([[0, 5_81, 1_02_69, 83, 9_99_42, 1_36, 6_07_42, 23, 70, 8_05_83, 1_82_76, 2]]   )
							# The dog is cute and lives in the garden house

							lowercase_ : Optional[Any]							       =   torch.Size((1, 12, 10_24)   )  # batch_size, sequence_length, embedding_vector_dim
							lowercase_ : List[str]							       =   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():
									lowercase_ : Optional[int]							       =   model(A   )['''last_hidden_state'''].detach()
							self.assertEqual(output.shape    ,						A   )
							# compare the actual values for a slice of last dim
							self.assertTrue(torch.allclose(output[:, :, -1]    ,						A    ,						atol=1e-3   )   )










from math import pi






def lowerCamelCase__    (							snake_case_	:  int       ,      snake_case_	:  int       )      ->				float:
   return 2 * pi * radius * (angle / 360)


if __name__ == "__main__":
    print(arc_length(90, 10))

