

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 	snake_case_   (		unittest.TestCase					):







			'''simple docstring'''

			@slow
			def 				snake_case__(     self :				Optional[int]							)      ->int:
									snake_case_   =	XLMRobertaModel.from_pretrained('''xlm-roberta-base'''							)
									snake_case_   =	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

									snake_case_   =	torch.Size((1, 1_2, 7_6_8)							)  # batch_size, sequence_length, embedding_vector_dim
									snake_case_   =	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():
															snake_case_   =	model(_UpperCamelCase							)['''last_hidden_state'''].detach()
									self.assertEqual(output.shape							,   _UpperCamelCase							)
									# compare the actual values for a slice of last dim
									self.assertTrue(torch.allclose(output[:, :, -1]							,   _UpperCamelCase							,   atol=1e-3							)							)







			@slow
			def 				snake_case__(     self :				Union[str, Any]							)      ->Tuple:
									snake_case_   =	XLMRobertaModel.from_pretrained('''xlm-roberta-large'''							)
									snake_case_   =	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

									snake_case_   =	torch.Size((1, 1_2, 1_0_2_4)							)  # batch_size, sequence_length, embedding_vector_dim
									snake_case_   =	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():
															snake_case_   =	model(_UpperCamelCase							)['''last_hidden_state'''].detach()
									self.assertEqual(output.shape							,   _UpperCamelCase							)
									# compare the actual values for a slice of last dim
									self.assertTrue(torch.allclose(output[:, :, -1]							,   _UpperCamelCase							,   atol=1e-3							)							)


from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable


lowerCAmelCase_       	=	{'''configuration_dpt''': ['''DPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DPTConfig''']}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase_       	=	['''DPTFeatureExtractor''']
					lowerCAmelCase_       	=	['''DPTImageProcessor''']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase_       	=	[
					    '''DPT_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''DPTForDepthEstimation''',
					    '''DPTForSemanticSegmentation''',
					    '''DPTModel''',
					    '''DPTPreTrainedModel''',
					]


if TYPE_CHECKING:
					from .configuration_dpt import DPT_PRETRAINED_CONFIG_ARCHIVE_MAP, DPTConfig

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_dpt import DPTFeatureExtractor
										from .image_processing_dpt import DPTImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_dpt import (
										    DPT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    DPTForDepthEstimation,
										    DPTForSemanticSegmentation,
										    DPTModel,
										    DPTPreTrainedModel,
										)


else:
					import sys

					lowerCAmelCase_       	=	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)