


'''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					):

		"""simple docstring"""

		@slow
		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


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

							UpperCamelCase__   :str												= torch.Size((1, 12, 768)							)  # batch_size, sequence_length, embedding_vector_dim
							UpperCamelCase__   :Union[str, Any]												= 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():
												UpperCamelCase__   :Optional[int]												= 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     lowerCAmelCase__				(		self							):

							'''simple docstring'''


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

							UpperCamelCase__   :Union[str, Any]												= torch.Size((1, 12, 1024)							)  # batch_size, sequence_length, embedding_vector_dim
							UpperCamelCase__   :Dict												= 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():
												UpperCamelCase__   :Union[str, Any]												= 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							)							)



'''simple docstring'''
from typing import TYPE_CHECKING

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


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

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

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__snake_case							      =     [
		    '''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

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