
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   __a		(      self  )       ->   Dict:
							lowerCAmelCase_      			=			XLMRobertaModel.from_pretrained("xlm-roberta-base"  )
							lowerCAmelCase_      			=			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

							lowerCAmelCase_      			=			torch.Size((1, 12, 768)  )  # batch_size, sequence_length, embedding_vector_dim
							lowerCAmelCase_      			=			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():
										lowerCAmelCase_      			=			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]:
							lowerCAmelCase_      			=			XLMRobertaModel.from_pretrained("xlm-roberta-large"  )
							lowerCAmelCase_      			=			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

							lowerCAmelCase_      			=			torch.Size((1, 12, 1_024)  )  # batch_size, sequence_length, embedding_vector_dim
							lowerCAmelCase_      			=			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():
										lowerCAmelCase_      			=			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  )  )





def      lowerCamelCase__       (       __lowerCAmelCase						:						list[list[int]]					,							__lowerCAmelCase						:						int					,							__lowerCAmelCase						:						int					,							__lowerCAmelCase						:						set       ):




			"""simple docstring"""


			lowerCAmelCase_			,						lowerCAmelCase_      			=			len(__lowerCAmelCase       ), len(grid[0]       )
			if (
			    min(__lowerCAmelCase					,							__lowerCAmelCase       ) < 0
			    or row == row_length
			    or col == col_length
			    or (row, col) in visit
			    or grid[row][col] == 1
			):
						return 0
			if row == row_length - 1 and col == col_length - 1:
						return 1

			visit.add((row, col)       )

			lowerCAmelCase_      			=			0
			count += depth_first_search(__lowerCAmelCase					,							row + 1					,							__lowerCAmelCase					,							__lowerCAmelCase       )
			count += depth_first_search(__lowerCAmelCase					,							row - 1					,							__lowerCAmelCase					,							__lowerCAmelCase       )
			count += depth_first_search(__lowerCAmelCase					,							__lowerCAmelCase					,							col + 1					,							__lowerCAmelCase       )
			count += depth_first_search(__lowerCAmelCase					,							__lowerCAmelCase					,							col - 1					,							__lowerCAmelCase       )

			visit.remove((row, col)       )
			return count


if __name__ == "__main__":
			import doctest

			doctest.testmod()



