

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	):




	@slow
	def        lowerCAmelCase_       (     self						:     Optional[Any]							):
		__A :			Union[str, Any]		    =			XLMRobertaModel.from_pretrained("""xlm-roberta-base"""							)
		__A :			Optional[Any]		    =			torch.tensor([[0, 581, 1_0269, 83, 9_9942, 136, 6_0742, 23, 70, 8_0583, 1_8276, 2]]							)
		# The dog is cute and lives in the garden house

		__A :			List[str]		    =			torch.Size((1, 12, 768)							)  # batch_size, sequence_length, embedding_vector_dim
		__A :			int		    =			torch.tensor(
		    [[-0.0_1_0_1, 0.1_2_1_8, -0.0_8_0_3, 0.0_8_0_1, 0.1_3_2_7, 0.0_7_7_6, -0.1_2_1_5, 0.2_3_8_3, 0.3_3_3_8, 0.3_1_0_6, 0.0_3_0_0, 0.0_2_5_2]]							)
		#  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():
			__A :			Dict		    =			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        lowerCAmelCase_       (     self						:     List[str]							):
		__A :			Any		    =			XLMRobertaModel.from_pretrained("""xlm-roberta-large"""							)
		__A :			int		    =			torch.tensor([[0, 581, 1_0269, 83, 9_9942, 136, 6_0742, 23, 70, 8_0583, 1_8276, 2]]							)
		# The dog is cute and lives in the garden house

		__A :			Optional[Any]		    =			torch.Size((1, 12, 1024)							)  # batch_size, sequence_length, embedding_vector_dim
		__A :			Dict		    =			torch.tensor(
		    [[-0.0_6_9_9, -0.0_3_1_8, 0.0_7_0_5, -0.1_2_4_1, 0.0_9_9_9, -0.0_5_2_0, 0.1_0_0_4, -0.1_8_3_8, -0.4_7_0_4, 0.1_4_3_7, 0.0_8_2_1, 0.0_1_2_6]]							)
		#  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():
			__A :			Dict		    =			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 sqrt
def 							__SCREAMING_SNAKE_CASE     (   a__    :   int = 1000000       )					->				int:
	__A :			int		    =			0
	__A :			int		    =			0
	__A :			int

	while num_cuboids <= limit:
		max_cuboid_size += 1
		for sum_shortest_sides in range(2			,2 * max_cuboid_size + 1       ):
			if sqrt(sum_shortest_sides**2 + max_cuboid_size**2       ).is_integer():
				num_cuboids += (
				    min(a__			,sum_shortest_sides // 2       )
				    - max(1			,sum_shortest_sides - max_cuboid_size       )
				    + 1
				)

	return max_cuboid_size


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





