






'''simple docstring'''







import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_A			:    Tuple					  =logging.get_logger(__name__)

_A			:    str					  ={
    '''facebook/wav2vec2-base-960h''': '''https://huggingface.co/facebook/wav2vec2-base-960h/resolve/main/config.json''',
    # See all Wav2Vec2 models at https://huggingface.co/models?filter=wav2vec2
}

class _lowercase			(       _lowercase	):
							a          = """wav2vec2"""




							def __init__(							self:       Union[str, Any]    ,							UpperCamelCase__:       Optional[int]=32    ,							UpperCamelCase__:       Union[str, Any]=768    ,							UpperCamelCase__:       Union[str, Any]=12    ,							UpperCamelCase__:       Any=12    ,							UpperCamelCase__:       Optional[Any]=3_072    ,							UpperCamelCase__:       Optional[Any]="gelu"    ,							UpperCamelCase__:       List[Any]=0.1    ,							UpperCamelCase__:       int=0.1    ,							UpperCamelCase__:       Tuple=0.1    ,							UpperCamelCase__:       Union[str, Any]=0.0    ,							UpperCamelCase__:       Optional[int]=0.0    ,							UpperCamelCase__:       Optional[Any]=0.1    ,							UpperCamelCase__:       Dict=0.1    ,							UpperCamelCase__:       Optional[int]=0.02    ,							UpperCamelCase__:       str=1e-5    ,							UpperCamelCase__:       int="group"    ,							UpperCamelCase__:       Optional[int]="gelu"    ,							UpperCamelCase__:       List[str]=(512, 512, 512, 512, 512, 512, 512)    ,							UpperCamelCase__:       Optional[Any]=(5, 2, 2, 2, 2, 2, 2)    ,							UpperCamelCase__:       Optional[Any]=(10, 3, 3, 3, 3, 2, 2)    ,							UpperCamelCase__:       Tuple=False    ,							UpperCamelCase__:       List[str]=128    ,							UpperCamelCase__:       Union[str, Any]=16    ,							UpperCamelCase__:       str=False    ,							UpperCamelCase__:       List[str]=True    ,							UpperCamelCase__:       List[Any]=0.05    ,							UpperCamelCase__:       Union[str, Any]=10    ,							UpperCamelCase__:       Union[str, Any]=2    ,							UpperCamelCase__:       int=0.0    ,							UpperCamelCase__:       Union[str, Any]=10    ,							UpperCamelCase__:       Optional[int]=0    ,							UpperCamelCase__:       List[str]=320    ,							UpperCamelCase__:       Tuple=2    ,							UpperCamelCase__:       int=0.1    ,							UpperCamelCase__:       Tuple=100    ,							UpperCamelCase__:       Tuple=256    ,							UpperCamelCase__:       Tuple=256    ,							UpperCamelCase__:       Tuple=0.1    ,							UpperCamelCase__:       Dict="sum"    ,							UpperCamelCase__:       str=False    ,							UpperCamelCase__:       Optional[Any]=False    ,							UpperCamelCase__:       int=256    ,							UpperCamelCase__:       List[str]=(512, 512, 512, 512, 1_500)    ,							UpperCamelCase__:       List[str]=(5, 3, 3, 1, 1)    ,							UpperCamelCase__:       Union[str, Any]=(1, 2, 3, 1, 1)    ,							UpperCamelCase__:       str=512    ,							UpperCamelCase__:       Optional[Any]=0    ,							UpperCamelCase__:       Tuple=1    ,							UpperCamelCase__:       Any=2    ,							UpperCamelCase__:       Any=False    ,							UpperCamelCase__:       Tuple=3    ,							UpperCamelCase__:       Union[str, Any]=2    ,							UpperCamelCase__:       Optional[Any]=3    ,							UpperCamelCase__:       str=None    ,							UpperCamelCase__:       Tuple=None    ,							**UpperCamelCase__:       Dict    ,							):
														super().__init__(**UpperCamelCase__    ,							pad_token_id=UpperCamelCase__    ,							bos_token_id=UpperCamelCase__    ,							eos_token_id=UpperCamelCase__  )
														lowerCamelCase__      :			Tuple 						= hidden_size
														lowerCamelCase__      :			Optional[Any] 						= feat_extract_norm
														lowerCamelCase__      :			Union[str, Any] 						= feat_extract_activation
														lowerCamelCase__      :			List[Any] 						= list(UpperCamelCase__  )
														lowerCamelCase__      :			Union[str, Any] 						= list(UpperCamelCase__  )
														lowerCamelCase__      :			Optional[Any] 						= list(UpperCamelCase__  )
														lowerCamelCase__      :			Tuple 						= conv_bias
														lowerCamelCase__      :			Optional[Any] 						= num_conv_pos_embeddings
														lowerCamelCase__      :			Dict 						= num_conv_pos_embedding_groups
														lowerCamelCase__      :			Dict 						= len(self.conv_dim  )
														lowerCamelCase__      :			List[str] 						= num_hidden_layers
														lowerCamelCase__      :			List[Any] 						= intermediate_size
														lowerCamelCase__      :			List[str] 						= hidden_act
														lowerCamelCase__      :			int 						= num_attention_heads
														lowerCamelCase__      :			List[Any] 						= hidden_dropout
														lowerCamelCase__      :			List[Any] 						= attention_dropout
														lowerCamelCase__      :			Dict 						= activation_dropout
														lowerCamelCase__      :			Optional[Any] 						= feat_proj_dropout
														lowerCamelCase__      :			List[Any] 						= final_dropout
														lowerCamelCase__      :			Optional[int] 						= layerdrop
														lowerCamelCase__      :			Dict 						= layer_norm_eps
														lowerCamelCase__      :			Any 						= initializer_range
														lowerCamelCase__      :			Dict 						= vocab_size
														lowerCamelCase__      :			Any 						= do_stable_layer_norm
														lowerCamelCase__      :			List[str] 						= use_weighted_layer_sum

														if (
														    (len(self.conv_stride  ) != self.num_feat_extract_layers)
														    or (len(self.conv_kernel  ) != self.num_feat_extract_layers)
														    or (len(self.conv_dim  ) != self.num_feat_extract_layers)
														):
																					raise ValueError(
																					    """Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="""
																					    """ `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="""
																					    F''' {len(self.conv_dim  )}`, `len(config.conv_stride) = {len(self.conv_stride  )}`,'''
																					    F''' `len(config.conv_kernel) = {len(self.conv_kernel  )}`.'''  )

														# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
														lowerCamelCase__      :			str 						= apply_spec_augment
														lowerCamelCase__      :			int 						= mask_time_prob
														lowerCamelCase__      :			Any 						= mask_time_length
														lowerCamelCase__      :			Optional[int] 						= mask_time_min_masks
														lowerCamelCase__      :			List[str] 						= mask_feature_prob
														lowerCamelCase__      :			List[str] 						= mask_feature_length
														lowerCamelCase__      :			Optional[int] 						= mask_feature_min_masks

														# parameters for pretraining with codevector quantized representations
														lowerCamelCase__      :			Any 						= num_codevectors_per_group
														lowerCamelCase__      :			Tuple 						= num_codevector_groups
														lowerCamelCase__      :			str 						= contrastive_logits_temperature
														lowerCamelCase__      :			Optional[Any] 						= feat_quantizer_dropout
														lowerCamelCase__      :			Union[str, Any] 						= num_negatives
														lowerCamelCase__      :			List[Any] 						= codevector_dim
														lowerCamelCase__      :			Union[str, Any] 						= proj_codevector_dim
														lowerCamelCase__      :			List[Any] 						= diversity_loss_weight

														# ctc loss
														lowerCamelCase__      :			Dict 						= ctc_loss_reduction
														lowerCamelCase__      :			Any 						= ctc_zero_infinity

														# adapter
														lowerCamelCase__      :			str 						= add_adapter
														lowerCamelCase__      :			str 						= adapter_kernel_size
														lowerCamelCase__      :			List[str] 						= adapter_stride
														lowerCamelCase__      :			Dict 						= num_adapter_layers
														lowerCamelCase__      :			Dict 						= output_hidden_size or hidden_size
														lowerCamelCase__      :			Optional[Any] 						= adapter_attn_dim

														# SequenceClassification-specific parameter. Feel free to ignore for other classes.
														lowerCamelCase__      :			Optional[Any] 						= classifier_proj_size

														# XVector-specific parameters. Feel free to ignore for other classes.
														lowerCamelCase__      :			Optional[int] 						= list(UpperCamelCase__  )
														lowerCamelCase__      :			Optional[int] 						= list(UpperCamelCase__  )
														lowerCamelCase__      :			Optional[int] 						= list(UpperCamelCase__  )
														lowerCamelCase__      :			int 						= xvector_output_dim




							@property
							def 	lowerCamelCase_       (							self:       str  ):
														return functools.reduce(operator.mul    ,							self.conv_stride    ,							1  )







'''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 _lowercase			(       _lowercase	):




							def 	lowerCamelCase_       (							self:       Any  ):
														lowerCamelCase__      :			Dict 						= self.config_class(**self.inputs_dict  )
														self.parent.assertTrue(hasattr(UpperCamelCase__    ,							"""width_multiplier"""  )  )



class _lowercase			:




							def __init__(							self:       str    ,							UpperCamelCase__:       Optional[int]    ,							UpperCamelCase__:       str=13    ,							UpperCamelCase__:       Any=64    ,							UpperCamelCase__:       Optional[Any]=2    ,							UpperCamelCase__:       str=3    ,							UpperCamelCase__:       List[str]="swish"    ,							UpperCamelCase__:       Any=3    ,							UpperCamelCase__:       Optional[int]=32    ,							UpperCamelCase__:       Union[str, Any]=0.1    ,							UpperCamelCase__:       int=0.02    ,							UpperCamelCase__:       Dict=True    ,							UpperCamelCase__:       Dict=True    ,							UpperCamelCase__:       Any=10    ,							UpperCamelCase__:       int=None    ,							UpperCamelCase__:       List[Any]=0.25    ,							UpperCamelCase__:       str=0.0    ,							UpperCamelCase__:       Optional[int]=0.0    ,							):
														lowerCamelCase__      :			Any 						= parent
														lowerCamelCase__      :			Optional[Any] 						= batch_size
														lowerCamelCase__      :			Optional[int] 						= image_size
														lowerCamelCase__      :			str 						= patch_size
														lowerCamelCase__      :			Optional[int] 						= num_channels
														lowerCamelCase__      :			Optional[Any] 						= make_divisible(512 * width_multiplier    ,							divisor=8  )
														lowerCamelCase__      :			List[str] 						= hidden_act
														lowerCamelCase__      :			Any 						= conv_kernel_size
														lowerCamelCase__      :			Any 						= output_stride
														lowerCamelCase__      :			Union[str, Any] 						= classifier_dropout_prob
														lowerCamelCase__      :			List[str] 						= use_labels
														lowerCamelCase__      :			Optional[Any] 						= is_training
														lowerCamelCase__      :			List[str] 						= num_labels
														lowerCamelCase__      :			Dict 						= initializer_range
														lowerCamelCase__      :			List[Any] 						= scope
														lowerCamelCase__      :			Tuple 						= width_multiplier
														lowerCamelCase__      :			List[Any] 						= ffn_dropout
														lowerCamelCase__      :			Any 						= attn_dropout




							def 	lowerCamelCase_       (							self:       Dict  ):
														lowerCamelCase__      :			Dict 						= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

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

														lowerCamelCase__      :			Union[str, Any] 						= self.get_config()

														return config, pixel_values, labels, pixel_labels




							def 	lowerCamelCase_       (							self:       List[Any]  ):
														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 	lowerCamelCase_       (							self:       Union[str, Any]    ,							UpperCamelCase__:       Optional[Any]    ,							UpperCamelCase__:       int    ,							UpperCamelCase__:       Tuple    ,							UpperCamelCase__:       Optional[int]  ):
														lowerCamelCase__      :			Union[str, Any] 						= MobileViTVaModel(config=UpperCamelCase__  )
														model.to(UpperCamelCase__  )
														model.eval()
														lowerCamelCase__      :			str 						= model(UpperCamelCase__  )
														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 	lowerCamelCase_       (							self:       Union[str, Any]    ,							UpperCamelCase__:       List[Any]    ,							UpperCamelCase__:       Union[str, Any]    ,							UpperCamelCase__:       List[Any]    ,							UpperCamelCase__:       Tuple  ):
														lowerCamelCase__      :			Tuple 						= self.num_labels
														lowerCamelCase__      :			Dict 						= MobileViTVaForImageClassification(UpperCamelCase__  )
														model.to(UpperCamelCase__  )
														model.eval()
														lowerCamelCase__      :			int 						= model(UpperCamelCase__    ,							labels=UpperCamelCase__  )
														self.parent.assertEqual(result.logits.shape    ,							(self.batch_size, self.num_labels)  )




							def 	lowerCamelCase_       (							self:       List[Any]    ,							UpperCamelCase__:       List[Any]    ,							UpperCamelCase__:       Any    ,							UpperCamelCase__:       Optional[Any]    ,							UpperCamelCase__:       str  ):
														lowerCamelCase__      :			List[str] 						= self.num_labels
														lowerCamelCase__      :			Union[str, Any] 						= MobileViTVaForSemanticSegmentation(UpperCamelCase__  )
														model.to(UpperCamelCase__  )
														model.eval()
														lowerCamelCase__      :			Tuple 						= model(UpperCamelCase__  )
														self.parent.assertEqual(
														    result.logits.shape    ,							(
														        self.batch_size,
														        self.num_labels,
														        self.image_size // self.output_stride,
														        self.image_size // self.output_stride,
														    )    ,							)
														lowerCamelCase__      :			List[Any] 						= model(UpperCamelCase__    ,							labels=UpperCamelCase__  )
														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 	lowerCamelCase_       (							self:       Tuple  ):
														lowerCamelCase__      :			Any 						= self.prepare_config_and_inputs()
														lowerCamelCase__						,	lowerCamelCase__						,	lowerCamelCase__						,	lowerCamelCase__      :			Union[str, Any] 						= config_and_inputs
														lowerCamelCase__      :			Optional[Any] 						= {"""pixel_values""": pixel_values}
														return config, inputs_dict



@require_torch
class _lowercase			(       _lowercase       ,   _lowercase       ,   unittest.TestCase	):
							a          = (
							    (MobileViTVaModel, MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation)
							    if is_torch_available()
							    else ()
							)

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

							a          = False
							a          = False
							a          = False
							a          = False




							def 	lowerCamelCase_       (							self:       Optional[int]  ):
														lowerCamelCase__      :			Tuple 						= MobileViTVaModelTester(self  )
														lowerCamelCase__      :			List[str] 						= MobileViTVaConfigTester(self    ,							config_class=UpperCamelCase__    ,							has_text_modality=UpperCamelCase__  )




							def 	lowerCamelCase_       (							self:       Tuple  ):
														self.config_tester.run_common_tests()




							@unittest.skip(reason="""MobileViTV2 does not use inputs_embeds"""  )
							def 	lowerCamelCase_       (							self:       int  ):
														pass




							@unittest.skip(reason="""MobileViTV2 does not support input and output embeddings"""  )
							def 	lowerCamelCase_       (							self:       List[str]  ):
														pass




							@unittest.skip(reason="""MobileViTV2 does not output attentions"""  )
							def 	lowerCamelCase_       (							self:       Union[str, Any]  ):
														pass




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




							@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""  )
							def 	lowerCamelCase_       (							self:       Tuple  ):
														pass




							def 	lowerCamelCase_       (							self:       Tuple  ):
														lowerCamelCase__						,	lowerCamelCase__      :			int 						= self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					lowerCamelCase__      :			Union[str, Any] 						= model_class(UpperCamelCase__  )
																					lowerCamelCase__      :			int 						= inspect.signature(model.forward  )
																					# signature.parameters is an OrderedDict => so arg_names order is deterministic
																					lowerCamelCase__      :			Tuple 						= [*signature.parameters.keys()]

																					lowerCamelCase__      :			str 						= ["""pixel_values"""]
																					self.assertListEqual(arg_names[:1]    ,							UpperCamelCase__  )




							def 	lowerCamelCase_       (							self:       str  ):
														lowerCamelCase__      :			int 						= self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model(*UpperCamelCase__  )




							def 	lowerCamelCase_       (							self:       List[str]  ):
														def check_hidden_states_output(UpperCamelCase__:       Union[str, Any]    ,							UpperCamelCase__:       List[str]    ,							UpperCamelCase__:       Optional[Any]  ):
																					lowerCamelCase__      :			List[Any] 						= model_class(UpperCamelCase__  )
																					model.to(UpperCamelCase__  )
																					model.eval()

																					with torch.no_grad():
																												lowerCamelCase__      :			Tuple 						= model(**self._prepare_for_class(UpperCamelCase__    ,							UpperCamelCase__  )  )

																					lowerCamelCase__      :			Optional[int] 						= outputs.hidden_states

																					lowerCamelCase__      :			List[Any] 						= 5
																					self.assertEqual(len(UpperCamelCase__  )    ,							UpperCamelCase__  )

																					# MobileViTV2's feature maps are of shape (batch_size, num_channels, height, width)
																					# with the width and height being successively divided by 2.
																					lowerCamelCase__      :			int 						= 2
																					for i in range(len(UpperCamelCase__  )  ):
																												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  )

														lowerCamelCase__						,	lowerCamelCase__      :			Tuple 						= self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					lowerCamelCase__      :			int 						= True
																					check_hidden_states_output(UpperCamelCase__    ,							UpperCamelCase__    ,							UpperCamelCase__  )

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

																					check_hidden_states_output(UpperCamelCase__    ,							UpperCamelCase__    ,							UpperCamelCase__  )




							def 	lowerCamelCase_       (							self:       Any  ):
														lowerCamelCase__      :			int 						= self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_image_classification(*UpperCamelCase__  )




							def 	lowerCamelCase_       (							self:       Dict  ):
														lowerCamelCase__      :			str 						= self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_semantic_segmentation(*UpperCamelCase__  )




							@slow
							def 	lowerCamelCase_       (							self:       Union[str, Any]  ):
														for model_name in MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																					lowerCamelCase__      :			Union[str, Any] 						= MobileViTVaModel.from_pretrained(UpperCamelCase__  )
																					self.assertIsNotNone(UpperCamelCase__  )





def 				SCREAMING_SNAKE_CASE_ ()   -> Optional[int]:
							lowerCamelCase__      :			str 						= Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""   )
							return image



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




							@cached_property
							def 	lowerCamelCase_       (							self:       Tuple  ):
														return (
														    MobileViTImageProcessor.from_pretrained("""apple/mobilevitv2-1.0-imagenet1k-256"""  )
														    if is_vision_available()
														    else None
														)




							@slow
							def 	lowerCamelCase_       (							self:       Any  ):
														lowerCamelCase__      :			Optional[Any] 						= MobileViTVaForImageClassification.from_pretrained("""apple/mobilevitv2-1.0-imagenet1k-256"""  ).to(
														    UpperCamelCase__  )

														lowerCamelCase__      :			Union[str, Any] 						= self.default_image_processor
														lowerCamelCase__      :			List[Any] 						= prepare_img()
														lowerCamelCase__      :			Any 						= image_processor(images=UpperCamelCase__    ,							return_tensors="""pt"""  ).to(UpperCamelCase__  )

														# forward pass
														with torch.no_grad():
																					lowerCamelCase__      :			int 						= model(**UpperCamelCase__  )

														# verify the logits
														lowerCamelCase__      :			str 						= torch.Size((1, 1_000)  )
														self.assertEqual(outputs.logits.shape    ,							UpperCamelCase__  )

														lowerCamelCase__      :			Optional[int] 						= torch.tensor([-1.6_336e00, -7.3_204e-02, -5.1_883e-01]  ).to(UpperCamelCase__  )

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




							@slow
							def 	lowerCamelCase_       (							self:       Union[str, Any]  ):
														lowerCamelCase__      :			int 						= MobileViTVaForSemanticSegmentation.from_pretrained("""shehan97/mobilevitv2-1.0-voc-deeplabv3"""  )
														lowerCamelCase__      :			Optional[Any] 						= model.to(UpperCamelCase__  )

														lowerCamelCase__      :			Any 						= MobileViTImageProcessor.from_pretrained("""shehan97/mobilevitv2-1.0-voc-deeplabv3"""  )

														lowerCamelCase__      :			Union[str, Any] 						= prepare_img()
														lowerCamelCase__      :			Dict 						= image_processor(images=UpperCamelCase__    ,							return_tensors="""pt"""  ).to(UpperCamelCase__  )

														# forward pass
														with torch.no_grad():
																					lowerCamelCase__      :			Optional[Any] 						= model(**UpperCamelCase__  )
														lowerCamelCase__      :			str 						= outputs.logits

														# verify the logits
														lowerCamelCase__      :			List[str] 						= torch.Size((1, 21, 32, 32)  )
														self.assertEqual(logits.shape    ,							UpperCamelCase__  )

														lowerCamelCase__      :			Any 						= torch.tensor(
														    [
														        [[7.0_863, 7.1_525, 6.8_201], [6.6_931, 6.8_770, 6.8_933], [6.2_978, 7.0_366, 6.9_636]],
														        [[-3.7_134, -3.6_712, -3.6_675], [-3.5_825, -3.3_549, -3.4_777], [-3.3_435, -3.3_979, -3.2_857]],
														        [[-2.9_329, -2.8_003, -2.7_369], [-3.0_564, -2.4_780, -2.0_207], [-2.6_889, -1.9_298, -1.7_640]],
														    ]    ,							device=UpperCamelCase__    ,							)

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




							@slow
							def 	lowerCamelCase_       (							self:       Optional[int]  ):
														lowerCamelCase__      :			Optional[Any] 						= MobileViTVaForSemanticSegmentation.from_pretrained("""shehan97/mobilevitv2-1.0-voc-deeplabv3"""  )
														lowerCamelCase__      :			List[Any] 						= model.to(UpperCamelCase__  )

														lowerCamelCase__      :			Union[str, Any] 						= MobileViTImageProcessor.from_pretrained("""shehan97/mobilevitv2-1.0-voc-deeplabv3"""  )

														lowerCamelCase__      :			Optional[Any] 						= prepare_img()
														lowerCamelCase__      :			Dict 						= image_processor(images=UpperCamelCase__    ,							return_tensors="""pt"""  ).to(UpperCamelCase__  )

														# forward pass
														with torch.no_grad():
																					lowerCamelCase__      :			Dict 						= model(**UpperCamelCase__  )

														lowerCamelCase__      :			List[str] 						= outputs.logits.detach().cpu()

														lowerCamelCase__      :			List[Any] 						= image_processor.post_process_semantic_segmentation(outputs=UpperCamelCase__    ,							target_sizes=[(50, 60)]  )
														lowerCamelCase__      :			int 						= torch.Size((50, 60)  )
														self.assertEqual(segmentation[0].shape    ,							UpperCamelCase__  )

														lowerCamelCase__      :			Optional[Any] 						= image_processor.post_process_semantic_segmentation(outputs=UpperCamelCase__  )
														lowerCamelCase__      :			int 						= torch.Size((32, 32)  )
														self.assertEqual(segmentation[0].shape    ,							UpperCamelCase__  )

