

'''simple docstring'''


import unittest

from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
					from PIL import Image
else:




					class     a			:






											"""simple docstring"""




											@staticmethod
											def       __magic_name__							(							*snake_case_					:      Optional[Any]			,			**snake_case_					:      int  ):



													'''simple docstring'''



													pass




@is_pipeline_test
@require_vision
class     a			(				unittest.TestCase       ):






						"""simple docstring"""




						@require_torch
						def       __magic_name__							(							self					:      List[str]  ):



								'''simple docstring'''



								snake_case__						:       Any       						=  pipeline(
								    model='''hf-internal-testing/tiny-random-clip-zero-shot-image-classification'''			,			)
								snake_case__						:       Tuple       						=  Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''  )
								snake_case__						:       Union[str, Any]       						=  image_classifier(snake_case_			,			candidate_labels=['''a''', '''b''', '''c''']  )

								# The floating scores are so close, we enter floating error approximation and the order is not guaranteed across
								# python and torch versions.
								self.assertIn(
								    nested_simplify(snake_case_  )			,			[
								        [{'''score''': 0.3_3_3, '''label''': '''a'''}, {'''score''': 0.3_3_3, '''label''': '''b'''}, {'''score''': 0.3_3_3, '''label''': '''c'''}],
								        [{'''score''': 0.3_3_3, '''label''': '''a'''}, {'''score''': 0.3_3_3, '''label''': '''c'''}, {'''score''': 0.3_3_3, '''label''': '''b'''}],
								    ]			,			)

								snake_case__						:       str       						=  image_classifier([image] * 5			,			candidate_labels=['''A''', '''B''', '''C''']			,			batch_size=2  )
								self.assertEqual(
								    nested_simplify(snake_case_  )			,			[
								        [
								            {'''score''': 0.3_3_3, '''label''': ANY(snake_case_  )},
								            {'''score''': 0.3_3_3, '''label''': ANY(snake_case_  )},
								            {'''score''': 0.3_3_3, '''label''': ANY(snake_case_  )},
								        ],
								        [
								            {'''score''': 0.3_3_3, '''label''': ANY(snake_case_  )},
								            {'''score''': 0.3_3_3, '''label''': ANY(snake_case_  )},
								            {'''score''': 0.3_3_3, '''label''': ANY(snake_case_  )},
								        ],
								        [
								            {'''score''': 0.3_3_3, '''label''': ANY(snake_case_  )},
								            {'''score''': 0.3_3_3, '''label''': ANY(snake_case_  )},
								            {'''score''': 0.3_3_3, '''label''': ANY(snake_case_  )},
								        ],
								        [
								            {'''score''': 0.3_3_3, '''label''': ANY(snake_case_  )},
								            {'''score''': 0.3_3_3, '''label''': ANY(snake_case_  )},
								            {'''score''': 0.3_3_3, '''label''': ANY(snake_case_  )},
								        ],
								        [
								            {'''score''': 0.3_3_3, '''label''': ANY(snake_case_  )},
								            {'''score''': 0.3_3_3, '''label''': ANY(snake_case_  )},
								            {'''score''': 0.3_3_3, '''label''': ANY(snake_case_  )},
								        ],
								    ]			,			)







						@require_tf
						def       __magic_name__							(							self					:      Dict  ):



								'''simple docstring'''



								snake_case__						:       Union[str, Any]       						=  pipeline(
								    model='''hf-internal-testing/tiny-random-clip-zero-shot-image-classification'''			,			framework='''tf'''  )
								snake_case__						:       Any       						=  Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''  )
								snake_case__						:       Dict       						=  image_classifier(snake_case_			,			candidate_labels=['''a''', '''b''', '''c''']  )

								self.assertEqual(
								    nested_simplify(snake_case_  )			,			[{'''score''': 0.3_3_3, '''label''': '''a'''}, {'''score''': 0.3_3_3, '''label''': '''b'''}, {'''score''': 0.3_3_3, '''label''': '''c'''}]			,			)

								snake_case__						:       Any       						=  image_classifier([image] * 5			,			candidate_labels=['''A''', '''B''', '''C''']			,			batch_size=2  )
								self.assertEqual(
								    nested_simplify(snake_case_  )			,			[
								        [
								            {'''score''': 0.3_3_3, '''label''': ANY(snake_case_  )},
								            {'''score''': 0.3_3_3, '''label''': ANY(snake_case_  )},
								            {'''score''': 0.3_3_3, '''label''': ANY(snake_case_  )},
								        ],
								        [
								            {'''score''': 0.3_3_3, '''label''': ANY(snake_case_  )},
								            {'''score''': 0.3_3_3, '''label''': ANY(snake_case_  )},
								            {'''score''': 0.3_3_3, '''label''': ANY(snake_case_  )},
								        ],
								        [
								            {'''score''': 0.3_3_3, '''label''': ANY(snake_case_  )},
								            {'''score''': 0.3_3_3, '''label''': ANY(snake_case_  )},
								            {'''score''': 0.3_3_3, '''label''': ANY(snake_case_  )},
								        ],
								        [
								            {'''score''': 0.3_3_3, '''label''': ANY(snake_case_  )},
								            {'''score''': 0.3_3_3, '''label''': ANY(snake_case_  )},
								            {'''score''': 0.3_3_3, '''label''': ANY(snake_case_  )},
								        ],
								        [
								            {'''score''': 0.3_3_3, '''label''': ANY(snake_case_  )},
								            {'''score''': 0.3_3_3, '''label''': ANY(snake_case_  )},
								            {'''score''': 0.3_3_3, '''label''': ANY(snake_case_  )},
								        ],
								    ]			,			)







						@slow
						@require_torch
						def       __magic_name__							(							self					:      Any  ):



								'''simple docstring'''



								snake_case__						:       List[str]       						=  pipeline(
								    task='''zero-shot-image-classification'''			,			model='''openai/clip-vit-base-patch32'''			,			)
								# This is an image of 2 cats with remotes and no planes
								snake_case__						:       Tuple       						=  Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''  )
								snake_case__						:       Tuple       						=  image_classifier(snake_case_			,			candidate_labels=['''cat''', '''plane''', '''remote''']  )

								self.assertEqual(
								    nested_simplify(snake_case_  )			,			[
								        {'''score''': 0.5_1_1, '''label''': '''remote'''},
								        {'''score''': 0.4_8_5, '''label''': '''cat'''},
								        {'''score''': 0.0_0_4, '''label''': '''plane'''},
								    ]			,			)

								snake_case__						:       Union[str, Any]       						=  image_classifier([image] * 5			,			candidate_labels=['''cat''', '''plane''', '''remote''']			,			batch_size=2  )
								self.assertEqual(
								    nested_simplify(snake_case_  )			,			[
								        [
								            {'''score''': 0.5_1_1, '''label''': '''remote'''},
								            {'''score''': 0.4_8_5, '''label''': '''cat'''},
								            {'''score''': 0.0_0_4, '''label''': '''plane'''},
								        ],
								    ]
								    * 5			,			)







						@slow
						@require_tf
						def       __magic_name__							(							self					:      Union[str, Any]  ):



								'''simple docstring'''



								snake_case__						:       Tuple       						=  pipeline(
								    task='''zero-shot-image-classification'''			,			model='''openai/clip-vit-base-patch32'''			,			framework='''tf'''  )
								# This is an image of 2 cats with remotes and no planes
								snake_case__						:       int       						=  Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''  )
								snake_case__						:       Any       						=  image_classifier(snake_case_			,			candidate_labels=['''cat''', '''plane''', '''remote''']  )
								self.assertEqual(
								    nested_simplify(snake_case_  )			,			[
								        {'''score''': 0.5_1_1, '''label''': '''remote'''},
								        {'''score''': 0.4_8_5, '''label''': '''cat'''},
								        {'''score''': 0.0_0_4, '''label''': '''plane'''},
								    ]			,			)

								snake_case__						:       Optional[int]       						=  image_classifier([image] * 5			,			candidate_labels=['''cat''', '''plane''', '''remote''']			,			batch_size=2  )
								self.assertEqual(
								    nested_simplify(snake_case_  )			,			[
								        [
								            {'''score''': 0.5_1_1, '''label''': '''remote'''},
								            {'''score''': 0.4_8_5, '''label''': '''cat'''},
								            {'''score''': 0.0_0_4, '''label''': '''plane'''},
								        ],
								    ]
								    * 5			,			)






'''simple docstring'''


import inspect
import unittest

from transformers import MobileViTVaConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch

					from transformers import MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation, MobileViTVaModel
					from transformers.models.mobilevitva.modeling_mobilevitva import (
					    MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
					    make_divisible,
					)


if is_vision_available():
					from PIL import Image

					from transformers import MobileViTImageProcessor




class     a			(				SCREAMING_SNAKE_CASE       ):






						"""simple docstring"""




						def       __magic_name__							(							self					:      Optional[Any]  ):



								'''simple docstring'''



								snake_case__						:       str       						=  self.config_class(**self.inputs_dict  )
								self.parent.assertTrue(hasattr(snake_case_			,			'''width_multiplier'''  )  )





class     a			:






						"""simple docstring"""




						def __init__(							self					:      List[str]			,			snake_case_					:      Optional[int]			,			snake_case_					:      Dict=1_3			,			snake_case_					:      Any=6_4			,			snake_case_					:      Dict=2			,			snake_case_					:      Optional[int]=3			,			snake_case_					:      str="swish"			,			snake_case_					:      str=3			,			snake_case_					:      Union[str, Any]=3_2			,			snake_case_					:      Optional[Any]=0.1			,			snake_case_					:      Any=0.0_2			,			snake_case_					:      int=True			,			snake_case_					:      Tuple=True			,			snake_case_					:      Dict=1_0			,			snake_case_					:      Optional[int]=None			,			snake_case_					:      str=0.2_5			,			snake_case_					:      List[Any]=0.0			,			snake_case_					:      Optional[Any]=0.0			,			):



								'''simple docstring'''



								snake_case__						:       List[Any]       						=  parent
								snake_case__						:       Dict       						=  batch_size
								snake_case__						:       Dict       						=  image_size
								snake_case__						:       Tuple       						=  patch_size
								snake_case__						:       Tuple       						=  num_channels
								snake_case__						:       Tuple       						=  make_divisible(5_1_2 * width_multiplier			,			divisor=8  )
								snake_case__						:       Optional[int]       						=  hidden_act
								snake_case__						:       int       						=  conv_kernel_size
								snake_case__						:       Optional[int]       						=  output_stride
								snake_case__						:       List[Any]       						=  classifier_dropout_prob
								snake_case__						:       int       						=  use_labels
								snake_case__						:       Optional[Any]       						=  is_training
								snake_case__						:       int       						=  num_labels
								snake_case__						:       str       						=  initializer_range
								snake_case__						:       Dict       						=  scope
								snake_case__						:       Tuple       						=  width_multiplier
								snake_case__						:       Optional[Any]       						=  ffn_dropout
								snake_case__						:       Dict       						=  attn_dropout







						def       __magic_name__							(							self					:      str  ):



								'''simple docstring'''



								snake_case__						:       int       						=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

								snake_case__						:       List[str]       						=  None
								snake_case__						:       Union[str, Any]       						=  None
								if self.use_labels:
										snake_case__						:       Tuple       						=  ids_tensor([self.batch_size]			,			self.num_labels  )
										snake_case__						:       Optional[Any]       						=  ids_tensor([self.batch_size, self.image_size, self.image_size]			,			self.num_labels  )

								snake_case__						:       Optional[int]       						=  self.get_config()

								return config, pixel_values, labels, pixel_labels







						def       __magic_name__							(							self					:      List[str]  ):



								'''simple docstring'''



								return MobileViTVaConfig(
								    image_size=self.image_size			,			patch_size=self.patch_size			,			num_channels=self.num_channels			,			hidden_act=self.hidden_act			,			conv_kernel_size=self.conv_kernel_size			,			output_stride=self.output_stride			,			classifier_dropout_prob=self.classifier_dropout_prob			,			initializer_range=self.initializer_range			,			width_multiplier=self.width_multiplier			,			ffn_dropout=self.ffn_dropout_prob			,			attn_dropout=self.attn_dropout_prob			,			)







						def       __magic_name__							(							self					:      Optional[Any]			,			snake_case_					:      Optional[int]			,			snake_case_					:      Optional[int]			,			snake_case_					:      Optional[Any]			,			snake_case_					:      Optional[Any]  ):



								'''simple docstring'''



								snake_case__						:       Optional[Any]       						=  MobileViTVaModel(config=snake_case_  )
								model.to(snake_case_  )
								model.eval()
								snake_case__						:       Dict       						=  model(snake_case_  )
								self.parent.assertEqual(
								    result.last_hidden_state.shape			,			(
								        self.batch_size,
								        self.last_hidden_size,
								        self.image_size // self.output_stride,
								        self.image_size // self.output_stride,
								    )			,			)







						def       __magic_name__							(							self					:      Union[str, Any]			,			snake_case_					:      Optional[int]			,			snake_case_					:      List[Any]			,			snake_case_					:      Tuple			,			snake_case_					:      Optional[Any]  ):



								'''simple docstring'''



								snake_case__						:       Optional[int]       						=  self.num_labels
								snake_case__						:       Optional[Any]       						=  MobileViTVaForImageClassification(snake_case_  )
								model.to(snake_case_  )
								model.eval()
								snake_case__						:       int       						=  model(snake_case_			,			labels=snake_case_  )
								self.parent.assertEqual(result.logits.shape			,			(self.batch_size, self.num_labels)  )







						def       __magic_name__							(							self					:      int			,			snake_case_					:      List[Any]			,			snake_case_					:      Dict			,			snake_case_					:      Any			,			snake_case_					:      List[str]  ):



								'''simple docstring'''



								snake_case__						:       Dict       						=  self.num_labels
								snake_case__						:       Any       						=  MobileViTVaForSemanticSegmentation(snake_case_  )
								model.to(snake_case_  )
								model.eval()
								snake_case__						:       str       						=  model(snake_case_  )
								self.parent.assertEqual(
								    result.logits.shape			,			(
								        self.batch_size,
								        self.num_labels,
								        self.image_size // self.output_stride,
								        self.image_size // self.output_stride,
								    )			,			)
								snake_case__						:       Optional[int]       						=  model(snake_case_			,			labels=snake_case_  )
								self.parent.assertEqual(
								    result.logits.shape			,			(
								        self.batch_size,
								        self.num_labels,
								        self.image_size // self.output_stride,
								        self.image_size // self.output_stride,
								    )			,			)







						def       __magic_name__							(							self					:      str  ):



								'''simple docstring'''



								snake_case__						:       Dict       						=  self.prepare_config_and_inputs()
								snake_case__						,       snake_case__						,       snake_case__						,       snake_case__						:       List[str]       						=  config_and_inputs
								snake_case__						:       List[str]       						=  {'''pixel_values''': pixel_values}
								return config, inputs_dict





@require_torch
class     a			(				SCREAMING_SNAKE_CASE			,			SCREAMING_SNAKE_CASE			,			unittest.TestCase       ):






						"""simple docstring"""




						__UpperCAmelCase			     =    (
						    (MobileViTVaModel, MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation)
						    if is_torch_available()
						    else ()
						)

						__UpperCAmelCase			     =    (
						    {
						        """feature-extraction""": MobileViTVaModel,
						        """image-classification""": MobileViTVaForImageClassification,
						        """image-segmentation""": MobileViTVaForSemanticSegmentation,
						    }
						    if is_torch_available()
						    else {}
						)

						__UpperCAmelCase			     =    False
						__UpperCAmelCase			     =    False
						__UpperCAmelCase			     =    False
						__UpperCAmelCase			     =    False







						def       __magic_name__							(							self					:      int  ):



								'''simple docstring'''



								snake_case__						:       str       						=  MobileViTVaModelTester(self  )
								snake_case__						:       Union[str, Any]       						=  MobileViTVaConfigTester(self			,			config_class=snake_case_			,			has_text_modality=snake_case_  )







						def       __magic_name__							(							self					:      List[str]  ):



								'''simple docstring'''



								self.config_tester.run_common_tests()







						@unittest.skip(reason='''MobileViTV2 does not use inputs_embeds'''  )
						def       __magic_name__							(							self					:      Optional[int]  ):



								'''simple docstring'''



								pass







						@unittest.skip(reason='''MobileViTV2 does not support input and output embeddings'''  )
						def       __magic_name__							(							self					:      Optional[int]  ):



								'''simple docstring'''



								pass







						@unittest.skip(reason='''MobileViTV2 does not output attentions'''  )
						def       __magic_name__							(							self					:      int  ):



								'''simple docstring'''



								pass







						@require_torch_multi_gpu
						@unittest.skip(reason='''Got `CUDA error: misaligned address` for tests after this one being run.'''  )
						def       __magic_name__							(							self					:      int  ):



								'''simple docstring'''



								pass







						@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''  )
						def       __magic_name__							(							self					:      Optional[Any]  ):



								'''simple docstring'''



								pass







						def       __magic_name__							(							self					:      Optional[Any]  ):



								'''simple docstring'''



								snake_case__						,       snake_case__						:       Union[str, Any]       						=  self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
										snake_case__						:       Optional[int]       						=  model_class(snake_case_  )
										snake_case__						:       Tuple       						=  inspect.signature(model.forward  )
										# signature.parameters is an OrderedDict => so arg_names order is deterministic
										snake_case__						:       Any       						=  [*signature.parameters.keys()]

										snake_case__						:       Optional[int]       						=  ['''pixel_values''']
										self.assertListEqual(arg_names[:1]			,			snake_case_  )







						def       __magic_name__							(							self					:      List[str]  ):



								'''simple docstring'''



								snake_case__						:       Union[str, Any]       						=  self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*snake_case_  )







						def       __magic_name__							(							self					:      List[str]  ):



								'''simple docstring'''



								def check_hidden_states_output(snake_case_					:      Tuple			,			snake_case_					:      Optional[Any]			,			snake_case_					:      Tuple  ):
										snake_case__						:       Optional[Any]       						=  model_class(snake_case_  )
										model.to(snake_case_  )
										model.eval()

										with torch.no_grad():
												snake_case__						:       Tuple       						=  model(**self._prepare_for_class(snake_case_			,			snake_case_  )  )

										snake_case__						:       Union[str, Any]       						=  outputs.hidden_states

										snake_case__						:       Any       						=  5
										self.assertEqual(len(snake_case_  )			,			snake_case_  )

										# MobileViTV2's feature maps are of shape (batch_size, num_channels, height, width)
										# with the width and height being successively divided by 2.
										snake_case__						:       Dict       						=  2
										for i in range(len(snake_case_  )  ):
												self.assertListEqual(
												    list(hidden_states[i].shape[-2:]  )			,			[self.model_tester.image_size // divisor, self.model_tester.image_size // divisor]			,			)
												divisor *= 2

										self.assertEqual(self.model_tester.output_stride			,			divisor // 2  )

								snake_case__						,       snake_case__						:       Union[str, Any]       						=  self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
										snake_case__						:       List[str]       						=  True
										check_hidden_states_output(snake_case_			,			snake_case_			,			snake_case_  )

										# check that output_hidden_states also work using config
										del inputs_dict["output_hidden_states"]
										snake_case__						:       int       						=  True

										check_hidden_states_output(snake_case_			,			snake_case_			,			snake_case_  )







						def       __magic_name__							(							self					:      int  ):



								'''simple docstring'''



								snake_case__						:       Union[str, Any]       						=  self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*snake_case_  )







						def       __magic_name__							(							self					:      Any  ):



								'''simple docstring'''



								snake_case__						:       Dict       						=  self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_semantic_segmentation(*snake_case_  )







						@slow
						def       __magic_name__							(							self					:      List[Any]  ):



								'''simple docstring'''



								for model_name in MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										snake_case__						:       Any       						=  MobileViTVaModel.from_pretrained(snake_case_  )
										self.assertIsNotNone(snake_case_  )





def 	_a       (				):

		"""simple docstring"""

		snake_case__						:       Optional[Any]       						=  Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''     )
		return image





@require_torch
@require_vision
class     a			(				unittest.TestCase       ):






						"""simple docstring"""




						@cached_property
						def       __magic_name__							(							self					:      Optional[int]  ):



								'''simple docstring'''



								return (
								    MobileViTImageProcessor.from_pretrained('''apple/mobilevitv2-1.0-imagenet1k-256'''  )
								    if is_vision_available()
								    else None
								)







						@slow
						def       __magic_name__							(							self					:      Tuple  ):



								'''simple docstring'''



								snake_case__						:       Tuple       						=  MobileViTVaForImageClassification.from_pretrained('''apple/mobilevitv2-1.0-imagenet1k-256'''  ).to(
								    snake_case_  )

								snake_case__						:       Any       						=  self.default_image_processor
								snake_case__						:       Tuple       						=  prepare_img()
								snake_case__						:       str       						=  image_processor(images=snake_case_			,			return_tensors='''pt'''  ).to(snake_case_  )

								# forward pass
								with torch.no_grad():
										snake_case__						:       Any       						=  model(**snake_case_  )

								# verify the logits
								snake_case__						:       Dict       						=  torch.Size((1, 1_0_0_0)  )
								self.assertEqual(outputs.logits.shape			,			snake_case_  )

								snake_case__						:       Tuple       						=  torch.tensor([-1.6_336e00, -7.3_204e-02, -5.1_883e-01]  ).to(snake_case_  )

								self.assertTrue(torch.allclose(outputs.logits[0, :3]			,			snake_case_			,			atol=1e-4  )  )







						@slow
						def       __magic_name__							(							self					:      Tuple  ):



								'''simple docstring'''



								snake_case__						:       List[Any]       						=  MobileViTVaForSemanticSegmentation.from_pretrained('''shehan97/mobilevitv2-1.0-voc-deeplabv3'''  )
								snake_case__						:       Any       						=  model.to(snake_case_  )

								snake_case__						:       Tuple       						=  MobileViTImageProcessor.from_pretrained('''shehan97/mobilevitv2-1.0-voc-deeplabv3'''  )

								snake_case__						:       str       						=  prepare_img()
								snake_case__						:       Union[str, Any]       						=  image_processor(images=snake_case_			,			return_tensors='''pt'''  ).to(snake_case_  )

								# forward pass
								with torch.no_grad():
										snake_case__						:       Union[str, Any]       						=  model(**snake_case_  )
								snake_case__						:       Tuple       						=  outputs.logits

								# verify the logits
								snake_case__						:       Optional[Any]       						=  torch.Size((1, 2_1, 3_2, 3_2)  )
								self.assertEqual(logits.shape			,			snake_case_  )

								snake_case__						:       List[Any]       						=  torch.tensor(
								    [
								        [[7.0_8_6_3, 7.1_5_2_5, 6.8_2_0_1], [6.6_9_3_1, 6.8_7_7_0, 6.8_9_3_3], [6.2_9_7_8, 7.0_3_6_6, 6.9_6_3_6]],
								        [[-3.7_1_3_4, -3.6_7_1_2, -3.6_6_7_5], [-3.5_8_2_5, -3.3_5_4_9, -3.4_7_7_7], [-3.3_4_3_5, -3.3_9_7_9, -3.2_8_5_7]],
								        [[-2.9_3_2_9, -2.8_0_0_3, -2.7_3_6_9], [-3.0_5_6_4, -2.4_7_8_0, -2.0_2_0_7], [-2.6_8_8_9, -1.9_2_9_8, -1.7_6_4_0]],
								    ]			,			device=snake_case_			,			)

								self.assertTrue(torch.allclose(logits[0, :3, :3, :3]			,			snake_case_			,			atol=1e-4  )  )







						@slow
						def       __magic_name__							(							self					:      List[Any]  ):



								'''simple docstring'''



								snake_case__						:       Optional[int]       						=  MobileViTVaForSemanticSegmentation.from_pretrained('''shehan97/mobilevitv2-1.0-voc-deeplabv3'''  )
								snake_case__						:       List[str]       						=  model.to(snake_case_  )

								snake_case__						:       Optional[int]       						=  MobileViTImageProcessor.from_pretrained('''shehan97/mobilevitv2-1.0-voc-deeplabv3'''  )

								snake_case__						:       str       						=  prepare_img()
								snake_case__						:       str       						=  image_processor(images=snake_case_			,			return_tensors='''pt'''  ).to(snake_case_  )

								# forward pass
								with torch.no_grad():
										snake_case__						:       Any       						=  model(**snake_case_  )

								snake_case__						:       str       						=  outputs.logits.detach().cpu()

								snake_case__						:       int       						=  image_processor.post_process_semantic_segmentation(outputs=snake_case_			,			target_sizes=[(5_0, 6_0)]  )
								snake_case__						:       int       						=  torch.Size((5_0, 6_0)  )
								self.assertEqual(segmentation[0].shape			,			snake_case_  )

								snake_case__						:       str       						=  image_processor.post_process_semantic_segmentation(outputs=snake_case_  )
								snake_case__						:       Any       						=  torch.Size((3_2, 3_2)  )
								self.assertEqual(segmentation[0].shape			,			snake_case_  )



