
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  ):




	'''simple docstring'''





	@slow
	def 				UpperCamelCase_  (       self					: Dict ):
					__A   			= XLMRobertaModel.from_pretrained("xlm-roberta-base" )
					__A   			= 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

					__A   			= torch.Size((1, 12, 7_68) )  # batch_size, sequence_length, embedding_vector_dim
					__A   			= 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():
									__A   			= 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					: Optional[Any] ):
					__A   			= XLMRobertaModel.from_pretrained("xlm-roberta-large" )
					__A   			= 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

					__A   			= torch.Size((1, 12, 10_24) )  # batch_size, sequence_length, embedding_vector_dim
					__A   			= 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():
									__A   			= 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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


SCREAMING_SNAKE_CASE							:List[Any]							 =				{}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE							:List[str]							 =				['BartphoTokenizer']

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

else:
					import sys

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