


'''simple docstring'''







import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    PNDMScheduler,
    StableDiffusionLDMaDPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS


enable_full_determinism()
class 	UpperCamelCase__					( unittest.TestCase	):



     """simple docstring"""






     __magic_name__    			=    StableDiffusionLDMaDPipeline
     __magic_name__    			=    TEXT_TO_IMAGE_PARAMS
     __magic_name__    			=    TEXT_TO_IMAGE_BATCH_PARAMS
     __magic_name__    			=    TEXT_TO_IMAGE_IMAGE_PARAMS







     def a	( self   ):

            '''simple docstring'''

            torch.manual_seed(0   )
            _lowerCAmelCase    :							Dict   			=       UNetaDConditionModel(
                block_out_channels=(32, 64)      ,	layers_per_block=2      ,	sample_size=32      ,	in_channels=4      ,	out_channels=4      ,	down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')      ,	up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')      ,	cross_attention_dim=32      ,	)
            _lowerCAmelCase    :							Union[str, Any]   			=       DDIMScheduler(
                beta_start=0.0_0085      ,	beta_end=0.012      ,	beta_schedule='scaled_linear'      ,	clip_sample=snake_case__      ,	set_alpha_to_one=snake_case__      ,	)
            torch.manual_seed(0   )
            _lowerCAmelCase    :							Union[str, Any]   			=       AutoencoderKL(
                block_out_channels=[32, 64]      ,	in_channels=6      ,	out_channels=6      ,	down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']      ,	up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']      ,	latent_channels=4      ,	)
            torch.manual_seed(0   )
            _lowerCAmelCase    :							List[Any]   			=       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      ,	)
            _lowerCAmelCase    :							Any   			=       CLIPTextModel(snake_case__   )
            _lowerCAmelCase    :							Tuple   			=       CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'   )

            _lowerCAmelCase    :							Any   			=       {
                'unet': unet,
                'scheduler': scheduler,
                'vae': vae,
                'text_encoder': text_encoder,
                'tokenizer': tokenizer,
                'safety_checker': None,
                'feature_extractor': None,
            }
            return components







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

            '''simple docstring'''

            if str(snake_case__   ).startswith('mps'   ):
                   _lowerCAmelCase    :							Dict   			=       torch.manual_seed(snake_case__   )
            else:
                   _lowerCAmelCase    :							Union[str, Any]   			=       torch.Generator(device=snake_case__   ).manual_seed(snake_case__   )
            _lowerCAmelCase    :							Tuple   			=       {
                'prompt': 'A painting of a squirrel eating a burger',
                'generator': generator,
                'num_inference_steps': 2,
                'guidance_scale': 6.0,
                'output_type': 'numpy',
            }
            return inputs







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Optional[Any]   			=       'cpu'  # ensure determinism for the device-dependent torch.Generator

            _lowerCAmelCase    :							List[str]   			=       self.get_dummy_components()
            _lowerCAmelCase    :							Optional[int]   			=       StableDiffusionLDMaDPipeline(**snake_case__   )
            _lowerCAmelCase    :							int   			=       ldmad_pipe.to(snake_case__   )
            ldmad_pipe.set_progress_bar_config(disable=snake_case__   )

            _lowerCAmelCase    :							str   			=       self.get_dummy_inputs(snake_case__   )
            _lowerCAmelCase    :							Union[str, Any]   			=       ldmad_pipe(**snake_case__   )
            _lowerCAmelCase			,     _lowerCAmelCase    :							Any   			=       output.rgb, output.depth

            _lowerCAmelCase    :							Dict   			=       rgb[0, -3:, -3:, -1]
            _lowerCAmelCase    :							Dict   			=       depth[0, -3:, -1]

            assert rgb.shape == (1, 64, 64, 3)
            assert depth.shape == (1, 64, 64)

            _lowerCAmelCase    :							Optional[int]   			=       np.array(
                [0.3733_8176, 0.7_0247, 0.7420_3193, 0.5164_3604, 0.5825_6793, 0.6093_2136, 0.418_1095, 0.4835_5877, 0.4653_5262]   )
            _lowerCAmelCase    :							Tuple   			=       np.array([103.4_6727, 85.81_2004, 87.84_9236]   )

            assert np.abs(image_slice_rgb.flatten() - expected_slice_rgb   ).max() < 1E-2
            assert np.abs(image_slice_depth.flatten() - expected_slice_depth   ).max() < 1E-2







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Any   			=       self.get_dummy_components()
            _lowerCAmelCase    :							Tuple   			=       StableDiffusionLDMaDPipeline(**snake_case__   )
            _lowerCAmelCase    :							int   			=       ldmad_pipe.to(snake_case__   )
            ldmad_pipe.set_progress_bar_config(disable=snake_case__   )

            _lowerCAmelCase    :							Optional[int]   			=       self.get_dummy_inputs(snake_case__   )
            _lowerCAmelCase    :							str   			=       3 * [inputs['prompt']]

            # forward
            _lowerCAmelCase    :							List[str]   			=       ldmad_pipe(**snake_case__   )
            _lowerCAmelCase			,     _lowerCAmelCase    :							Any   			=       output.rgb, output.depth
            _lowerCAmelCase    :							str   			=       rgb_slice_a[0, -3:, -3:, -1]
            _lowerCAmelCase    :							int   			=       depth_slice_a[0, -3:, -1]

            _lowerCAmelCase    :							int   			=       self.get_dummy_inputs(snake_case__   )
            _lowerCAmelCase    :							Optional[Any]   			=       3 * [inputs.pop('prompt'   )]

            _lowerCAmelCase    :							Dict   			=       ldmad_pipe.tokenizer(
                snake_case__      ,	padding='max_length'      ,	max_length=ldmad_pipe.tokenizer.model_max_length      ,	truncation=snake_case__      ,	return_tensors='pt'      ,	)
            _lowerCAmelCase    :							int   			=       text_inputs['input_ids'].to(snake_case__   )

            _lowerCAmelCase    :							str   			=       ldmad_pipe.text_encoder(snake_case__   )[0]

            _lowerCAmelCase    :							List[Any]   			=       prompt_embeds

            # forward
            _lowerCAmelCase    :							Optional[int]   			=       ldmad_pipe(**snake_case__   )
            _lowerCAmelCase			,     _lowerCAmelCase    :							str   			=       output.rgb, output.depth
            _lowerCAmelCase    :							str   			=       rgb_slice_a[0, -3:, -3:, -1]
            _lowerCAmelCase    :							Any   			=       depth_slice_a[0, -3:, -1]

            assert np.abs(rgb_slice_a.flatten() - rgb_slice_a.flatten()   ).max() < 1E-4
            assert np.abs(depth_slice_a.flatten() - depth_slice_a.flatten()   ).max() < 1E-4







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							str   			=       'cpu'  # ensure determinism for the device-dependent torch.Generator
            _lowerCAmelCase    :							List[Any]   			=       self.get_dummy_components()
            _lowerCAmelCase    :							Optional[Any]   			=       PNDMScheduler(skip_prk_steps=snake_case__   )
            _lowerCAmelCase    :							Tuple   			=       StableDiffusionLDMaDPipeline(**snake_case__   )
            _lowerCAmelCase    :							int   			=       ldmad_pipe.to(snake_case__   )
            ldmad_pipe.set_progress_bar_config(disable=snake_case__   )

            _lowerCAmelCase    :							int   			=       self.get_dummy_inputs(snake_case__   )
            _lowerCAmelCase    :							Dict   			=       'french fries'
            _lowerCAmelCase    :							Optional[Any]   			=       ldmad_pipe(**snake_case__      ,	negative_prompt=snake_case__   )

            _lowerCAmelCase			,     _lowerCAmelCase    :							Dict   			=       output.rgb, output.depth
            _lowerCAmelCase    :							Union[str, Any]   			=       rgb[0, -3:, -3:, -1]
            _lowerCAmelCase    :							Dict   			=       depth[0, -3:, -1]

            assert rgb.shape == (1, 64, 64, 3)
            assert depth.shape == (1, 64, 64)

            _lowerCAmelCase    :							Optional[Any]   			=       np.array(
                [0.3_7044, 0.7181_1503, 0.722_3251, 0.4860_3675, 0.563_8391, 0.636_4948, 0.4283_3704, 0.490_1315, 0.4792_6217]   )
            _lowerCAmelCase    :							Dict   			=       np.array([107.8_4738, 84.6_2802, 89.96_2135]   )
            assert np.abs(rgb_slice.flatten() - expected_slice_rgb   ).max() < 1E-2
            assert np.abs(depth_slice.flatten() - expected_slice_depth   ).max() < 1E-2
@slow
@require_torch_gpu
class 	UpperCamelCase__					( unittest.TestCase	):



     """simple docstring"""






     def a	( self   ):

            '''simple docstring'''

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







     def a	( self      ,	snake_case__      ,	snake_case__="cpu"      ,	snake_case__=torch.floataa      ,	snake_case__=0   ):

            '''simple docstring'''

            _lowerCAmelCase    :							List[Any]   			=       torch.Generator(device=snake_case__   ).manual_seed(snake_case__   )
            _lowerCAmelCase    :							Dict   			=       np.random.RandomState(snake_case__   ).standard_normal((1, 4, 64, 64)   )
            _lowerCAmelCase    :							Any   			=       torch.from_numpy(snake_case__   ).to(device=snake_case__      ,	dtype=snake_case__   )
            _lowerCAmelCase    :							str   			=       {
                'prompt': 'a photograph of an astronaut riding a horse',
                'latents': latents,
                'generator': generator,
                'num_inference_steps': 3,
                'guidance_scale': 7.5,
                'output_type': 'numpy',
            }
            return inputs







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Union[str, Any]   			=       StableDiffusionLDMaDPipeline.from_pretrained('Intel/ldm3d'   )
            _lowerCAmelCase    :							List[str]   			=       ldmad_pipe.to(snake_case__   )
            ldmad_pipe.set_progress_bar_config(disable=snake_case__   )

            _lowerCAmelCase    :							Dict   			=       self.get_inputs(snake_case__   )
            _lowerCAmelCase    :							Optional[Any]   			=       ldmad_pipe(**snake_case__   )
            _lowerCAmelCase			,     _lowerCAmelCase    :							str   			=       output.rgb, output.depth
            _lowerCAmelCase    :							int   			=       rgb[0, -3:, -3:, -1].flatten()
            _lowerCAmelCase    :							List[Any]   			=       rgb[0, -3:, -1].flatten()

            assert rgb.shape == (1, 512, 512, 3)
            assert depth.shape == (1, 512, 512)

            _lowerCAmelCase    :							List[str]   			=       np.array(
                [0.5380_5465, 0.5670_7305, 0.548_6515, 0.5701_2236, 0.581_4511, 0.5625_3487, 0.5484_3014, 0.5509_2263, 0.645_9706]   )
            _lowerCAmelCase    :							str   			=       np.array(
                [0.926_3781, 0.667_8672, 0.548_6515, 0.9220_2145, 0.6783_1135, 0.5625_3487, 0.924_1694, 0.755_1478, 0.645_9706]   )
            assert np.abs(rgb_slice - expected_slice_rgb   ).max() < 3E-3
            assert np.abs(depth_slice - expected_slice_depth   ).max() < 3E-3
@nightly
@require_torch_gpu
class 	UpperCamelCase__					( unittest.TestCase	):



     """simple docstring"""






     def a	( self   ):

            '''simple docstring'''

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







     def a	( self      ,	snake_case__      ,	snake_case__="cpu"      ,	snake_case__=torch.floataa      ,	snake_case__=0   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Any   			=       torch.Generator(device=snake_case__   ).manual_seed(snake_case__   )
            _lowerCAmelCase    :							Optional[Any]   			=       np.random.RandomState(snake_case__   ).standard_normal((1, 4, 64, 64)   )
            _lowerCAmelCase    :							List[Any]   			=       torch.from_numpy(snake_case__   ).to(device=snake_case__      ,	dtype=snake_case__   )
            _lowerCAmelCase    :							Union[str, Any]   			=       {
                'prompt': 'a photograph of an astronaut riding a horse',
                'latents': latents,
                'generator': generator,
                'num_inference_steps': 50,
                'guidance_scale': 7.5,
                'output_type': 'numpy',
            }
            return inputs







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Any   			=       StableDiffusionLDMaDPipeline.from_pretrained('Intel/ldm3d'   ).to(snake_case__   )
            ldmad_pipe.set_progress_bar_config(disable=snake_case__   )

            _lowerCAmelCase    :							Tuple   			=       self.get_inputs(snake_case__   )
            _lowerCAmelCase    :							int   			=       ldmad_pipe(**snake_case__   )
            _lowerCAmelCase			,     _lowerCAmelCase    :							str   			=       output.rgb, output.depth

            _lowerCAmelCase    :							List[str]   			=       0.49_5586
            _lowerCAmelCase    :							int   			=       0.3379_5515
            _lowerCAmelCase    :							int   			=       112.4_8518
            _lowerCAmelCase    :							Optional[int]   			=       98.48_9746
            assert np.abs(expected_rgb_mean - rgb.mean()   ) < 1E-3
            assert np.abs(expected_rgb_std - rgb.std()   ) < 1E-3
            assert np.abs(expected_depth_mean - depth.mean()   ) < 1E-3
            assert np.abs(expected_depth_std - depth.std()   ) < 1E-3







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Optional[int]   			=       StableDiffusionLDMaDPipeline.from_pretrained('Intel/ldm3d-4c'   ).to(snake_case__   )
            ldmad_pipe.set_progress_bar_config(disable=snake_case__   )

            _lowerCAmelCase    :							List[Any]   			=       self.get_inputs(snake_case__   )
            _lowerCAmelCase    :							Union[str, Any]   			=       ldmad_pipe(**snake_case__   )
            _lowerCAmelCase			,     _lowerCAmelCase    :							List[str]   			=       output.rgb, output.depth

            _lowerCAmelCase    :							str   			=       0.419_4127
            _lowerCAmelCase    :							str   			=       0.3537_5586
            _lowerCAmelCase    :							Optional[int]   			=       0.563_8502
            _lowerCAmelCase    :							List[str]   			=       0.3468_6103

            assert rgb.shape == (1, 512, 512, 3)
            assert depth.shape == (1, 512, 512, 1)
            assert np.abs(expected_rgb_mean - rgb.mean()   ) < 1E-3
            assert np.abs(expected_rgb_std - rgb.std()   ) < 1E-3
            assert np.abs(expected_depth_mean - depth.mean()   ) < 1E-3
            assert np.abs(expected_depth_std - depth.std()   ) < 1E-3





'''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__   )

