
'''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 	__lowercase				(  unittest.TestCase    ):

			@slow
			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      Optional[Any]     =		XLMRobertaModel.from_pretrained('''xlm-roberta-base'''      )
								lowerCamelCase_					:      Union[str, Any]     =		torch.tensor([[0, 5_8_1, 1_0_2_6_9, 8_3, 9_9_9_4_2, 1_3_6, 6_0_7_4_2, 2_3, 7_0, 8_0_5_8_3, 1_8_2_7_6, 2]]      )
								# The dog is cute and lives in the garden house

								lowerCamelCase_					:      Optional[int]     =		torch.Size((1, 1_2, 7_6_8)      )  # batch_size, sequence_length, embedding_vector_dim
								lowerCamelCase_					:      Optional[int]     =		torch.tensor(
								    [[-0.01_01, 0.12_18, -0.08_03, 0.08_01, 0.13_27, 0.07_76, -0.12_15, 0.23_83, 0.33_38, 0.31_06, 0.03_00, 0.02_52]]      )
								#  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_					:      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        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      Any     =		XLMRobertaModel.from_pretrained('''xlm-roberta-large'''      )
								lowerCamelCase_					:      Any     =		torch.tensor([[0, 5_8_1, 1_0_2_6_9, 8_3, 9_9_9_4_2, 1_3_6, 6_0_7_4_2, 2_3, 7_0, 8_0_5_8_3, 1_8_2_7_6, 2]]      )
								# The dog is cute and lives in the garden house

								lowerCamelCase_					:      Optional[int]     =		torch.Size((1, 1_2, 1_0_2_4)      )  # batch_size, sequence_length, embedding_vector_dim
								lowerCamelCase_					:      Optional[Any]     =		torch.tensor(
								    [[-0.06_99, -0.03_18, 0.07_05, -0.12_41, 0.09_99, -0.05_20, 0.10_04, -0.18_38, -0.47_04, 0.14_37, 0.08_21, 0.01_26]]      )
								#  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_					:      List[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      )      )




'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


__lowercase							:   int					=     {}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__lowercase							:   Any					=     ['''BartphoTokenizer''']

if TYPE_CHECKING:
					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_bartpho import BartphoTokenizer

else:
					import sys

					__lowercase							:   Union[str, Any]					=     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


