
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 			lowercase_		(      unittest.TestCase       ):


       @slow
       def 	_snake_case				(    self				)						->    int:
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=XLMRobertaModel.from_pretrained('''xlm-roberta-base'''				)
              SCREAMING_SNAKE_CASE_							:				Any		=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_							:				Tuple		=torch.Size((1, 12, 768)				)  # batch_size, sequence_length, embedding_vector_dim
              SCREAMING_SNAKE_CASE_							:				int		=torch.tensor(
                  [[-0.0_101, 0.1_218, -0.0_803, 0.0_801, 0.1_327, 0.0_776, -0.1_215, 0.2_383, 0.3_338, 0.3_106, 0.0_300, 0.0_252]]				)
              #  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_							:				Tuple		=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 	_snake_case				(    self				)						->    int:
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=XLMRobertaModel.from_pretrained('''xlm-roberta-large'''				)
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=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_							:				List[Any]		=torch.Size((1, 12, 1_024)				)  # batch_size, sequence_length, embedding_vector_dim
              SCREAMING_SNAKE_CASE_							:				List[str]		=torch.tensor(
                  [[-0.0_699, -0.0_318, 0.0_705, -0.1_241, 0.0_999, -0.0_520, 0.1_004, -0.1_838, -0.4_704, 0.1_437, 0.0_821, 0.0_126]]				)
              #  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_							:				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 ceil, sqrt
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     int = 1_0_0_0_0_0_0				)			->						int:
       SCREAMING_SNAKE_CASE_							:				List[Any]		=0

       for outer_width in range(3			,							(limit // 4) + 2				):
              if outer_width**2 > limit:
                     SCREAMING_SNAKE_CASE_							:				List[str]		=max(ceil(sqrt(outer_width**2 - limit				)				)			,							1				)
              else:
                     SCREAMING_SNAKE_CASE_							:				List[str]		=1
              if (outer_width - hole_width_lower_bound) % 2:
                     hole_width_lower_bound += 1

              answer += (outer_width - hole_width_lower_bound - 2) // 2 + 1

       return answer


if __name__ == "__main__":
 print(F"{solution() = }")
