





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 SCREAMING_SNAKE_CASE_						(					unittest.TestCase					):






						@slow
						def UpperCAmelCase_				(      self      :      Union[str, Any]	)							->    Any:







							"""simple docstring"""


							snake_case_		:			List[Any]											=	XLMRobertaModel.from_pretrained('xlm-roberta-base'	)
							snake_case_		:			Tuple											=	torch.tensor([[0, 581, 10269, 83, 99942, 136, 60742, 23, 70, 80583, 18276, 2]]	)
							# The dog is cute and lives in the garden house

							snake_case_		:			int											=	torch.Size((1, 12, 768)	)  # batch_size, sequence_length, embedding_vector_dim
							snake_case_		:			Dict											=	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():
								snake_case_		:			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	)	)






						@slow
						def UpperCAmelCase_				(      self      :      Tuple	)							->    Dict:







							"""simple docstring"""


							snake_case_		:			Tuple											=	XLMRobertaModel.from_pretrained('xlm-roberta-large'	)
							snake_case_		:			List[str]											=	torch.tensor([[0, 581, 10269, 83, 99942, 136, 60742, 23, 70, 80583, 18276, 2]]	)
							# The dog is cute and lives in the garden house

							snake_case_		:			Optional[Any]											=	torch.Size((1, 12, 1024)	)  # batch_size, sequence_length, embedding_vector_dim
							snake_case_		:			str											=	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():
								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 typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_SCREAMING_SNAKE_CASE      	=		{
    """configuration_lxmert""": ["""LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LxmertConfig"""],
    """tokenization_lxmert""": ["""LxmertTokenizer"""],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_SCREAMING_SNAKE_CASE      	=		["""LxmertTokenizerFast"""]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_SCREAMING_SNAKE_CASE      	=		[
						    """LxmertEncoder""",
						    """LxmertForPreTraining""",
						    """LxmertForQuestionAnswering""",
						    """LxmertModel""",
						    """LxmertPreTrainedModel""",
						    """LxmertVisualFeatureEncoder""",
						    """LxmertXLayer""",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_SCREAMING_SNAKE_CASE      	=		[
						    """TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """TFLxmertForPreTraining""",
						    """TFLxmertMainLayer""",
						    """TFLxmertModel""",
						    """TFLxmertPreTrainedModel""",
						    """TFLxmertVisualFeatureEncoder""",
						]


if TYPE_CHECKING:
						from .configuration_lxmert import LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP, LxmertConfig
						from .tokenization_lxmert import LxmertTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_lxmert_fast import LxmertTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_lxmert import (
												    LxmertEncoder,
												    LxmertForPreTraining,
												    LxmertForQuestionAnswering,
												    LxmertModel,
												    LxmertPreTrainedModel,
												    LxmertVisualFeatureEncoder,
												    LxmertXLayer,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_lxmert import (
												    TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFLxmertForPreTraining,
												    TFLxmertMainLayer,
												    TFLxmertModel,
												    TFLxmertPreTrainedModel,
												    TFLxmertVisualFeatureEncoder,
												)

else:
						import sys

						_SCREAMING_SNAKE_CASE      	=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

