


'''simple docstring'''







import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

import diffusers
from diffusers import (
    AutoencoderKL,
    EulerDiscreteScheduler,
    StableDiffusionLatentUpscalePipeline,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.schedulers import KarrasDiffusionSchedulers
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()






def 			lowercase				(_A					):







       """simple docstring"""







       _lowerCAmelCase    :							Optional[int]   			=	[tensor.shape for tensor in tensor_list]
       return all(shape == shapes[0] for shape in shapes[1:]					)
class 	UpperCamelCase__					( __UpperCAmelCase    ,					__UpperCAmelCase    ,					__UpperCAmelCase    ,					unittest.TestCase	):



     """simple docstring"""






     __magic_name__    			=    StableDiffusionLatentUpscalePipeline
     __magic_name__    			=    TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
         """height""",
         """width""",
         """cross_attention_kwargs""",
         """negative_prompt_embeds""",
         """prompt_embeds""",
     }
     __magic_name__    			=    PipelineTesterMixin.required_optional_params - {"""num_images_per_prompt"""}
     __magic_name__    			=    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
     __magic_name__    			=    frozenset(
         []	)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
     __magic_name__    			=    frozenset([]	)

     __magic_name__    			=    True







     @property
     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							str   			=	1
            _lowerCAmelCase    :							Optional[int]   			=	4
            _lowerCAmelCase    :							List[str]   			=	(16, 16)

            _lowerCAmelCase    :							List[Any]   			=	floats_tensor((batch_size, num_channels) + sizes      ,	rng=random.Random(0   )   ).to(UpperCAmelCase_   )
            return image







     def a	( self   ):

            '''simple docstring'''

            torch.manual_seed(0   )
            _lowerCAmelCase    :							Any   			=	UNetaDConditionModel(
                act_fn='gelu'      ,	attention_head_dim=8      ,	norm_num_groups=UpperCAmelCase_      ,	block_out_channels=[32, 32, 64, 64]      ,	time_cond_proj_dim=160      ,	conv_in_kernel=1      ,	conv_out_kernel=1      ,	cross_attention_dim=32      ,	down_block_types=(
                    'KDownBlock2D',
                    'KCrossAttnDownBlock2D',
                    'KCrossAttnDownBlock2D',
                    'KCrossAttnDownBlock2D',
                )      ,	in_channels=8      ,	mid_block_type=UpperCAmelCase_      ,	only_cross_attention=UpperCAmelCase_      ,	out_channels=5      ,	resnet_time_scale_shift='scale_shift'      ,	time_embedding_type='fourier'      ,	timestep_post_act='gelu'      ,	up_block_types=('KCrossAttnUpBlock2D', 'KCrossAttnUpBlock2D', 'KCrossAttnUpBlock2D', 'KUpBlock2D')      ,	)
            _lowerCAmelCase    :							int   			=	AutoencoderKL(
                block_out_channels=[32, 32, 64, 64]      ,	in_channels=3      ,	out_channels=3      ,	down_block_types=[
                    'DownEncoderBlock2D',
                    'DownEncoderBlock2D',
                    'DownEncoderBlock2D',
                    'DownEncoderBlock2D',
                ]      ,	up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D', 'UpDecoderBlock2D', 'UpDecoderBlock2D']      ,	latent_channels=4      ,	)
            _lowerCAmelCase    :							Union[str, Any]   			=	EulerDiscreteScheduler(prediction_type='sample'   )
            _lowerCAmelCase    :							Optional[int]   			=	CLIPTextConfig(
                bos_token_id=0      ,	eos_token_id=2      ,	hidden_size=32      ,	intermediate_size=37      ,	layer_norm_eps=1E-05      ,	num_attention_heads=4      ,	num_hidden_layers=5      ,	pad_token_id=1      ,	vocab_size=1000      ,	hidden_act='quick_gelu'      ,	projection_dim=512      ,	)
            _lowerCAmelCase    :							Dict   			=	CLIPTextModel(UpperCAmelCase_   )
            _lowerCAmelCase    :							str   			=	CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'   )

            _lowerCAmelCase    :							List[Any]   			=	{
                'unet': model.eval(),
                'vae': vae.eval(),
                'scheduler': scheduler,
                'text_encoder': text_encoder,
                'tokenizer': tokenizer,
            }

            return components







     def a	( self      ,	snake_case__      ,	snake_case__=0   ):

            '''simple docstring'''

            if str(UpperCAmelCase_   ).startswith('mps'   ):
                   _lowerCAmelCase    :							Any   			=	torch.manual_seed(UpperCAmelCase_   )
            else:
                   _lowerCAmelCase    :							str   			=	torch.Generator(device=UpperCAmelCase_   ).manual_seed(UpperCAmelCase_   )
            _lowerCAmelCase    :							List[str]   			=	{
                'prompt': 'A painting of a squirrel eating a burger',
                'image': self.dummy_image.cpu(),
                'generator': generator,
                'num_inference_steps': 2,
                'output_type': 'numpy',
            }
            return inputs







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							List[Any]   			=	'cpu'

            _lowerCAmelCase    :							Optional[int]   			=	self.get_dummy_components()
            _lowerCAmelCase    :							Tuple   			=	self.pipeline_class(**UpperCAmelCase_   )
            pipe.to(UpperCAmelCase_   )
            pipe.set_progress_bar_config(disable=UpperCAmelCase_   )

            _lowerCAmelCase    :							Dict   			=	self.get_dummy_inputs(UpperCAmelCase_   )
            _lowerCAmelCase    :							str   			=	pipe(**UpperCAmelCase_   ).images
            _lowerCAmelCase    :							Union[str, Any]   			=	image[0, -3:, -3:, -1]

            self.assertEqual(image.shape      ,	(1, 256, 256, 3)   )
            _lowerCAmelCase    :							Union[str, Any]   			=	np.array(
                [0.4722_2412, 0.4192_1633, 0.4471_7434, 0.4687_4192, 0.4258_8258, 0.4615_0726, 0.467_7534, 0.4558_3832, 0.4857_9055]   )
            _lowerCAmelCase    :							Union[str, Any]   			=	np.abs(image_slice.flatten() - expected_slice   ).max()
            self.assertLessEqual(UpperCAmelCase_      ,	1E-3   )







     def a	( self   ):

            '''simple docstring'''

            super().test_attention_slicing_forward_pass(expected_max_diff=7E-3   )







     def a	( self   ):

            '''simple docstring'''

            super().test_cpu_offload_forward_pass(expected_max_diff=3E-3   )







     def a	( self   ):

            '''simple docstring'''

            super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3   )







     def a	( self   ):

            '''simple docstring'''

            super().test_inference_batch_single_identical(expected_max_diff=7E-3   )







     def a	( self   ):

            '''simple docstring'''

            super().test_pt_np_pil_outputs_equivalent(expected_max_diff=3E-3   )







     def a	( self   ):

            '''simple docstring'''

            super().test_save_load_local(expected_max_difference=3E-3   )







     def a	( self   ):

            '''simple docstring'''

            super().test_save_load_optional_components(expected_max_difference=3E-3   )







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							List[Any]   			=	[
                'DDIMScheduler',
                'DDPMScheduler',
                'PNDMScheduler',
                'HeunDiscreteScheduler',
                'EulerAncestralDiscreteScheduler',
                'KDPM2DiscreteScheduler',
                'KDPM2AncestralDiscreteScheduler',
                'DPMSolverSDEScheduler',
            ]
            _lowerCAmelCase    :							List[str]   			=	self.get_dummy_components()
            _lowerCAmelCase    :							Union[str, Any]   			=	self.pipeline_class(**UpperCAmelCase_   )

            # make sure that PNDM does not need warm-up
            pipe.scheduler.register_to_config(skip_prk_steps=UpperCAmelCase_   )

            pipe.to(UpperCAmelCase_   )
            pipe.set_progress_bar_config(disable=UpperCAmelCase_   )
            _lowerCAmelCase    :							Optional[int]   			=	self.get_dummy_inputs(UpperCAmelCase_   )
            _lowerCAmelCase    :							Dict   			=	2

            _lowerCAmelCase    :							List[Any]   			=	[]
            for scheduler_enum in KarrasDiffusionSchedulers:
                   if scheduler_enum.name in skip_schedulers:
                          # no sigma schedulers are not supported
                          # no schedulers
                          continue

                   _lowerCAmelCase    :							Optional[int]   			=	getattr(UpperCAmelCase_      ,	scheduler_enum.name   )
                   _lowerCAmelCase    :							List[str]   			=	scheduler_cls.from_config(pipe.scheduler.config   )
                   _lowerCAmelCase    :							int   			=	pipe(**UpperCAmelCase_   )[0]
                   outputs.append(UpperCAmelCase_   )

            assert check_same_shape(UpperCAmelCase_   )
@require_torch_gpu
@slow
class 	UpperCamelCase__					( unittest.TestCase	):



     """simple docstring"""






     def a	( self   ):

            '''simple docstring'''

            super().tearDown()
            gc.collect()
            torch.cuda.empty_cache()







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							int   			=	torch.manual_seed(33   )

            _lowerCAmelCase    :							Tuple   			=	StableDiffusionPipeline.from_pretrained('CompVis/stable-diffusion-v1-4'      ,	torch_dtype=torch.floataa   )
            pipe.to('cuda'   )

            _lowerCAmelCase    :							Tuple   			=	StableDiffusionLatentUpscalePipeline.from_pretrained(
                'stabilityai/sd-x2-latent-upscaler'      ,	torch_dtype=torch.floataa   )
            upscaler.to('cuda'   )

            _lowerCAmelCase    :							Union[str, Any]   			=	'a photo of an astronaut high resolution, unreal engine, ultra realistic'

            _lowerCAmelCase    :							str   			=	pipe(UpperCAmelCase_      ,	generator=UpperCAmelCase_      ,	output_type='latent'   ).images

            _lowerCAmelCase    :							int   			=	upscaler(
                prompt=UpperCAmelCase_      ,	image=UpperCAmelCase_      ,	num_inference_steps=20      ,	guidance_scale=0      ,	generator=UpperCAmelCase_      ,	output_type='np'      ,	).images[0]

            _lowerCAmelCase    :							Optional[int]   			=	load_numpy(
                'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/astronaut_1024.npy'   )
            assert np.abs((expected_image - image).mean()   ) < 5E-2







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Optional[int]   			=	torch.manual_seed(33   )

            _lowerCAmelCase    :							List[str]   			=	StableDiffusionLatentUpscalePipeline.from_pretrained(
                'stabilityai/sd-x2-latent-upscaler'      ,	torch_dtype=torch.floataa   )
            upscaler.to('cuda'   )

            _lowerCAmelCase    :							Union[str, Any]   			=	'the temple of fire by Ross Tran and Gerardo Dottori, oil on canvas'

            _lowerCAmelCase    :							Dict   			=	load_image(
                'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_512.png'   )

            _lowerCAmelCase    :							int   			=	upscaler(
                prompt=UpperCAmelCase_      ,	image=UpperCAmelCase_      ,	num_inference_steps=20      ,	guidance_scale=0      ,	generator=UpperCAmelCase_      ,	output_type='np'      ,	).images[0]

            _lowerCAmelCase    :							Optional[Any]   			=	load_numpy(
                'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_1024.npy'   )
            assert np.abs((expected_image - image).max()   ) < 5E-2





'''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 	UpperCamelCase__					( SCREAMING_SNAKE_CASE_	):



     """simple docstring"""






     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							int   			=       self.config_class(**self.inputs_dict   )
            self.parent.assertTrue(hasattr(snake_case__      ,	'width_multiplier'   )   )
class 	UpperCamelCase__					:



     """simple docstring"""






     def __init__( self      ,	snake_case__      ,	snake_case__=13      ,	snake_case__=64      ,	snake_case__=2      ,	snake_case__=3      ,	snake_case__="swish"      ,	snake_case__=3      ,	snake_case__=32      ,	snake_case__=0.1      ,	snake_case__=0.02      ,	snake_case__=True      ,	snake_case__=True      ,	snake_case__=10      ,	snake_case__=None      ,	snake_case__=0.25      ,	snake_case__=0.0      ,	snake_case__=0.0      ,	):

            '''simple docstring'''

            _lowerCAmelCase    :							Tuple   			=       parent
            _lowerCAmelCase    :							Optional[int]   			=       batch_size
            _lowerCAmelCase    :							List[Any]   			=       image_size
            _lowerCAmelCase    :							List[Any]   			=       patch_size
            _lowerCAmelCase    :							List[str]   			=       num_channels
            _lowerCAmelCase    :							Optional[Any]   			=       make_divisible(512 * width_multiplier      ,	divisor=8   )
            _lowerCAmelCase    :							Optional[Any]   			=       hidden_act
            _lowerCAmelCase    :							List[Any]   			=       conv_kernel_size
            _lowerCAmelCase    :							Optional[Any]   			=       output_stride
            _lowerCAmelCase    :							List[Any]   			=       classifier_dropout_prob
            _lowerCAmelCase    :							str   			=       use_labels
            _lowerCAmelCase    :							List[str]   			=       is_training
            _lowerCAmelCase    :							Optional[int]   			=       num_labels
            _lowerCAmelCase    :							List[str]   			=       initializer_range
            _lowerCAmelCase    :							str   			=       scope
            _lowerCAmelCase    :							Any   			=       width_multiplier
            _lowerCAmelCase    :							Union[str, Any]   			=       ffn_dropout
            _lowerCAmelCase    :							Optional[int]   			=       attn_dropout







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Optional[Any]   			=       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

            _lowerCAmelCase    :							Optional[Any]   			=       None
            _lowerCAmelCase    :							Dict   			=       None
            if self.use_labels:
                   _lowerCAmelCase    :							Optional[Any]   			=       ids_tensor([self.batch_size]      ,	self.num_labels   )
                   _lowerCAmelCase    :							Optional[Any]   			=       ids_tensor([self.batch_size, self.image_size, self.image_size]      ,	self.num_labels   )

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

            return config, pixel_values, labels, pixel_labels







     def a	( self   ):

            '''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 a	( self      ,	snake_case__      ,	snake_case__      ,	snake_case__      ,	snake_case__   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Any   			=       MobileViTVaModel(config=snake_case__   )
            model.to(snake_case__   )
            model.eval()
            _lowerCAmelCase    :							str   			=       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 a	( self      ,	snake_case__      ,	snake_case__      ,	snake_case__      ,	snake_case__   ):

            '''simple docstring'''

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







     def a	( self      ,	snake_case__      ,	snake_case__      ,	snake_case__      ,	snake_case__   ):

            '''simple docstring'''

            _lowerCAmelCase    :							List[Any]   			=       self.num_labels
            _lowerCAmelCase    :							Optional[int]   			=       MobileViTVaForSemanticSegmentation(snake_case__   )
            model.to(snake_case__   )
            model.eval()
            _lowerCAmelCase    :							Dict   			=       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,
                )      ,	)
            _lowerCAmelCase    :							Any   			=       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 a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							int   			=       self.prepare_config_and_inputs()
            _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase    :							Optional[int]   			=       config_and_inputs
            _lowerCAmelCase    :							Tuple   			=       {'pixel_values': pixel_values}
            return config, inputs_dict
@require_torch
class 	UpperCamelCase__					( SCREAMING_SNAKE_CASE_    ,					SCREAMING_SNAKE_CASE_    ,					unittest.TestCase	):



     """simple docstring"""






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

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

     __magic_name__    			=    False
     __magic_name__    			=    False
     __magic_name__    			=    False
     __magic_name__    			=    False







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							int   			=       MobileViTVaModelTester(self   )
            _lowerCAmelCase    :							Dict   			=       MobileViTVaConfigTester(self      ,	config_class=snake_case__      ,	has_text_modality=snake_case__   )







     def a	( self   ):

            '''simple docstring'''

            self.config_tester.run_common_tests()







     @unittest.skip(reason='MobileViTV2 does not use inputs_embeds'   )
     def a	( self   ):

            '''simple docstring'''

            pass







     @unittest.skip(reason='MobileViTV2 does not support input and output embeddings'   )
     def a	( self   ):

            '''simple docstring'''

            pass







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

            '''simple docstring'''

            pass







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

            '''simple docstring'''

            pass







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

            '''simple docstring'''

            pass







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase			,     _lowerCAmelCase    :							Optional[int]   			=       self.model_tester.prepare_config_and_inputs_for_common()

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

                   _lowerCAmelCase    :							Any   			=       ['pixel_values']
                   self.assertListEqual(arg_names[:1]      ,	snake_case__   )







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Optional[int]   			=       self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*snake_case__   )







     def a	( self   ):

            '''simple docstring'''

            def check_hidden_states_output(snake_case__      ,	snake_case__      ,	snake_case__   ):
                   _lowerCAmelCase    :							Dict   			=       model_class(snake_case__   )
                   model.to(snake_case__   )
                   model.eval()

                   with torch.no_grad():
                          _lowerCAmelCase    :							Optional[Any]   			=       model(**self._prepare_for_class(snake_case__      ,	snake_case__   )   )

                   _lowerCAmelCase    :							List[str]   			=       outputs.hidden_states

                   _lowerCAmelCase    :							List[str]   			=       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.
                   _lowerCAmelCase    :							List[Any]   			=       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   )

            _lowerCAmelCase			,     _lowerCAmelCase    :							Optional[int]   			=       self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                   _lowerCAmelCase    :							Optional[int]   			=       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"]
                   _lowerCAmelCase    :							Any   			=       True

                   check_hidden_states_output(snake_case__      ,	snake_case__      ,	snake_case__   )







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Dict   			=       self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_image_classification(*snake_case__   )







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Any   			=       self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_semantic_segmentation(*snake_case__   )







     @slow
     def a	( self   ):

            '''simple docstring'''

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






def 			lowercase				():







       """simple docstring"""







       _lowerCAmelCase    :							Any   			=       Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'					)
       return image
@require_torch
@require_vision
class 	UpperCamelCase__					( unittest.TestCase	):



     """simple docstring"""






     @cached_property
     def a	( self   ):

            '''simple docstring'''

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







     @slow
     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Union[str, Any]   			=       MobileViTVaForImageClassification.from_pretrained('apple/mobilevitv2-1.0-imagenet1k-256'   ).to(
                snake_case__   )

            _lowerCAmelCase    :							str   			=       self.default_image_processor
            _lowerCAmelCase    :							Any   			=       prepare_img()
            _lowerCAmelCase    :							Optional[int]   			=       image_processor(images=snake_case__      ,	return_tensors='pt'   ).to(snake_case__   )

            # forward pass
            with torch.no_grad():
                   _lowerCAmelCase    :							Tuple   			=       model(**snake_case__   )

            # verify the logits
            _lowerCAmelCase    :							Optional[Any]   			=       torch.Size((1, 1000)   )
            self.assertEqual(outputs.logits.shape      ,	snake_case__   )

            _lowerCAmelCase    :							Tuple   			=       torch.tensor([-1.6336E00, -7.3204E-02, -5.1883E-01]   ).to(snake_case__   )

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







     @slow
     def a	( self   ):

            '''simple docstring'''

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

            _lowerCAmelCase    :							int   			=       MobileViTImageProcessor.from_pretrained('shehan97/mobilevitv2-1.0-voc-deeplabv3'   )

            _lowerCAmelCase    :							Optional[int]   			=       prepare_img()
            _lowerCAmelCase    :							Dict   			=       image_processor(images=snake_case__      ,	return_tensors='pt'   ).to(snake_case__   )

            # forward pass
            with torch.no_grad():
                   _lowerCAmelCase    :							int   			=       model(**snake_case__   )
            _lowerCAmelCase    :							Dict   			=       outputs.logits

            # verify the logits
            _lowerCAmelCase    :							str   			=       torch.Size((1, 21, 32, 32)   )
            self.assertEqual(logits.shape      ,	snake_case__   )

            _lowerCAmelCase    :							Any   			=       torch.tensor(
                [
                    [[7.0863, 7.1525, 6.8201], [6.6931, 6.8770, 6.8933], [6.2978, 7.0366, 6.9636]],
                    [[-3.7134, -3.6712, -3.6675], [-3.5825, -3.3549, -3.4777], [-3.3435, -3.3979, -3.2857]],
                    [[-2.9329, -2.8003, -2.7369], [-3.0564, -2.4780, -2.0207], [-2.6889, -1.9298, -1.7640]],
                ]      ,	device=snake_case__      ,	)

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







     @slow
     def a	( self   ):

            '''simple docstring'''

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

            _lowerCAmelCase    :							str   			=       MobileViTImageProcessor.from_pretrained('shehan97/mobilevitv2-1.0-voc-deeplabv3'   )

            _lowerCAmelCase    :							Tuple   			=       prepare_img()
            _lowerCAmelCase    :							List[str]   			=       image_processor(images=snake_case__      ,	return_tensors='pt'   ).to(snake_case__   )

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

            _lowerCAmelCase    :							Optional[Any]   			=       outputs.logits.detach().cpu()

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

            _lowerCAmelCase    :							List[str]   			=       image_processor.post_process_semantic_segmentation(outputs=snake_case__   )
            _lowerCAmelCase    :							Tuple   			=       torch.Size((32, 32)   )
            self.assertEqual(segmentation[0].shape      ,	snake_case__   )

