






'''simple docstring'''



import inspect
from typing import Callable, List, Optional, Union

import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextModel,
    CLIPTokenizer,
    WhisperForConditionalGeneration,
    WhisperProcessor,
)

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import logging


__UpperCamelCase   :							Tuple         =   logging.get_logger(__name__)  # pylint: disable=invalid-name
class 				__SCREAMING_SNAKE_CASE							(				_lowerCAmelCase  ):




      def __init__(	self			, lowerCamelCase			, lowerCamelCase			, lowerCamelCase			, lowerCamelCase			, lowerCamelCase			, lowerCamelCase			, lowerCamelCase			, lowerCamelCase			, lowerCamelCase			, )				->Union[str, Any]:



         '''simple docstring'''
         super().__init__()

         if safety_checker is None:
            logger.warning(
                F"""You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"""
                ' that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered'
                ' results in services or applications open to the public. Both the diffusers team and Hugging Face'
                ' strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling'
                ' it only for use-cases that involve analyzing network behavior or auditing its results. For more'
                ' information, please have a look at https://github.com/huggingface/diffusers/pull/254 .'   )

         self.register_modules(
             speech_model=lowerCamelCase			, speech_processor=lowerCamelCase			, vae=lowerCamelCase			, text_encoder=lowerCamelCase			, tokenizer=lowerCamelCase			, unet=lowerCamelCase			, scheduler=lowerCamelCase			, feature_extractor=lowerCamelCase			, )




      def 			__UpperCamelCase				(	self			, lowerCamelCase = "auto"   )				->List[Any]:



         '''simple docstring'''
         if slice_size == "auto":
            __a					  =     self.unet.config.attention_head_dim // 2
         self.unet.set_attention_slice(lowerCamelCase   )




      def 			__UpperCamelCase				(	self   )				->List[Any]:



         '''simple docstring'''
         self.enable_attention_slicing(lowerCamelCase   )






      @torch.no_grad()
      def __call__(	self			, lowerCamelCase			, lowerCamelCase=1_6000			, lowerCamelCase = 512			, lowerCamelCase = 512			, lowerCamelCase = 50			, lowerCamelCase = 7.5			, lowerCamelCase = None			, lowerCamelCase = 1			, lowerCamelCase = 0.0			, lowerCamelCase = None			, lowerCamelCase = None			, lowerCamelCase = "pil"			, lowerCamelCase = True			, lowerCamelCase = None			, lowerCamelCase = 1			, **lowerCamelCase			, )				->Optional[int]:



         '''simple docstring'''
         __a					  =     self.speech_processor.feature_extractor(
             lowerCamelCase			, return_tensors='pt'			, sampling_rate=lowerCamelCase   ).input_features.to(self.device   )
         __a					  =     self.speech_model.generate(lowerCamelCase			, max_length=48_0000   )

         __a					  =     self.speech_processor.tokenizer.batch_decode(lowerCamelCase			, skip_special_tokens=lowerCamelCase			, normalize=lowerCamelCase   )[
             0
         ]

         if isinstance(lowerCamelCase			, lowerCamelCase   ):
            __a					  =     1
         elif isinstance(lowerCamelCase			, lowerCamelCase   ):
            __a					  =     len(lowerCamelCase   )
         else:
            raise ValueError(F"""`prompt` has to be of type `str` or `list` but is {type(lowerCamelCase   )}"""   )

         if height % 8 != 0 or width % 8 != 0:
            raise ValueError(F"""`height` and `width` have to be divisible by 8 but are {height} and {width}."""   )

         if (callback_steps is None) or (
             callback_steps is not None and (not isinstance(lowerCamelCase			, lowerCamelCase   ) or callback_steps <= 0)
         ):
            raise ValueError(
                F"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
                F""" {type(lowerCamelCase   )}."""   )

         # get prompt text embeddings
         __a					  =     self.tokenizer(
             lowerCamelCase			, padding='max_length'			, max_length=self.tokenizer.model_max_length			, return_tensors='pt'			, )
         __a					  =     text_inputs.input_ids

         if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
            __a					  =     self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]   )
            logger.warning(
                'The following part of your input was truncated because CLIP can only handle sequences up to'
                F""" {self.tokenizer.model_max_length} tokens: {removed_text}"""   )
            __a					  =     text_input_ids[:, : self.tokenizer.model_max_length]
         __a					  =     self.text_encoder(text_input_ids.to(self.device   )   )[0]

         # duplicate text embeddings for each generation per prompt, using mps friendly method
         __a  ,      __a  ,      __a					  =     text_embeddings.shape
         __a					  =     text_embeddings.repeat(1			, lowerCamelCase			, 1   )
         __a					  =     text_embeddings.view(bs_embed * num_images_per_prompt			, lowerCamelCase			, -1   )

         # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
         # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
         # corresponds to doing no classifier free guidance.
         __a					  =     guidance_scale > 1.0
         # get unconditional embeddings for classifier free guidance
         if do_classifier_free_guidance:
            __a					  =     42
            if negative_prompt is None:
               __a					  =     [''] * batch_size
            elif type(lowerCamelCase   ) is not type(lowerCamelCase   ):
               raise TypeError(
                   F"""`negative_prompt` should be the same type to `prompt`, but got {type(lowerCamelCase   )} !="""
                   F""" {type(lowerCamelCase   )}."""   )
            elif isinstance(lowerCamelCase			, lowerCamelCase   ):
               __a					  =     [negative_prompt]
            elif batch_size != len(lowerCamelCase   ):
               raise ValueError(
                   F"""`negative_prompt`: {negative_prompt} has batch size {len(lowerCamelCase   )}, but `prompt`:"""
                   F""" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"""
                   ' the batch size of `prompt`.'   )
            else:
               __a					  =     negative_prompt

            __a					  =     text_input_ids.shape[-1]
            __a					  =     self.tokenizer(
                lowerCamelCase			, padding='max_length'			, max_length=lowerCamelCase			, truncation=lowerCamelCase			, return_tensors='pt'			, )
            __a					  =     self.text_encoder(uncond_input.input_ids.to(self.device   )   )[0]

            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
            __a					  =     uncond_embeddings.shape[1]
            __a					  =     uncond_embeddings.repeat(1			, lowerCamelCase			, 1   )
            __a					  =     uncond_embeddings.view(batch_size * num_images_per_prompt			, lowerCamelCase			, -1   )

            # For classifier free guidance, we need to do two forward passes.
            # Here we concatenate the unconditional and text embeddings into a single batch
            # to avoid doing two forward passes
            __a					  =     torch.cat([uncond_embeddings, text_embeddings]   )

         # get the initial random noise unless the user supplied it

         # Unlike in other pipelines, latents need to be generated in the target device
         # for 1-to-1 results reproducibility with the CompVis implementation.
         # However this currently doesn't work in `mps`.
         __a					  =     (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
         __a					  =     text_embeddings.dtype
         if latents is None:
            if self.device.type == "mps":
               # randn does not exist on mps
               __a					  =     torch.randn(lowerCamelCase			, generator=lowerCamelCase			, device='cpu'			, dtype=lowerCamelCase   ).to(
                   self.device   )
            else:
               __a					  =     torch.randn(lowerCamelCase			, generator=lowerCamelCase			, device=self.device			, dtype=lowerCamelCase   )
         else:
            if latents.shape != latents_shape:
               raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}"""   )
            __a					  =     latents.to(self.device   )

         # set timesteps
         self.scheduler.set_timesteps(lowerCamelCase   )

         # Some schedulers like PNDM have timesteps as arrays
         # It's more optimized to move all timesteps to correct device beforehand
         __a					  =     self.scheduler.timesteps.to(self.device   )

         # scale the initial noise by the standard deviation required by the scheduler
         __a					  =     latents * self.scheduler.init_noise_sigma

         # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
         # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
         # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
         # and should be between [0, 1]
         __a					  =     'eta' in set(inspect.signature(self.scheduler.step   ).parameters.keys()   )
         __a					  =     {}
         if accepts_eta:
            __a					  =     eta

         for i, t in enumerate(self.progress_bar(lowerCamelCase   )   ):
            # expand the latents if we are doing classifier free guidance
            __a					  =     torch.cat([latents] * 2   ) if do_classifier_free_guidance else latents
            __a					  =     self.scheduler.scale_model_input(lowerCamelCase			, lowerCamelCase   )

            # predict the noise residual
            __a					  =     self.unet(lowerCamelCase			, lowerCamelCase			, encoder_hidden_states=lowerCamelCase   ).sample

            # perform guidance
            if do_classifier_free_guidance:
               __a  ,      __a					  =     noise_pred.chunk(2   )
               __a					  =     noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

            # compute the previous noisy sample x_t -> x_t-1
            __a					  =     self.scheduler.step(lowerCamelCase			, lowerCamelCase			, lowerCamelCase			, **lowerCamelCase   ).prev_sample

            # call the callback, if provided
            if callback is not None and i % callback_steps == 0:
               callback(lowerCamelCase			, lowerCamelCase			, lowerCamelCase   )

         __a					  =     1 / 0.18215 * latents
         __a					  =     self.vae.decode(lowerCamelCase   ).sample

         __a					  =     (image / 2 + 0.5).clamp(0			, 1   )

         # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
         __a					  =     image.cpu().permute(0			, 2			, 3			, 1   ).float().numpy()

         if output_type == "pil":
            __a					  =     self.numpy_to_pil(lowerCamelCase   )

         if not return_dict:
            return image

         return StableDiffusionPipelineOutput(images=lowerCamelCase			, nsfw_content_detected=lowerCamelCase   )







'''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 				__SCREAMING_SNAKE_CASE							(				_lowerCAmelCase  ):




      def 			__UpperCamelCase				(	self   )				->List[Any]:



         '''simple docstring'''
         __a					  =     self.config_class(**self.inputs_dict   )
         self.parent.assertTrue(hasattr(lowerCamelCase			, 'width_multiplier'   )   )







class 				__SCREAMING_SNAKE_CASE							:




      def __init__(	self			, lowerCamelCase			, lowerCamelCase=13			, lowerCamelCase=64			, lowerCamelCase=2			, lowerCamelCase=3			, lowerCamelCase="swish"			, lowerCamelCase=3			, lowerCamelCase=32			, lowerCamelCase=0.1			, lowerCamelCase=0.02			, lowerCamelCase=True			, lowerCamelCase=True			, lowerCamelCase=10			, lowerCamelCase=None			, lowerCamelCase=0.25			, lowerCamelCase=0.0			, lowerCamelCase=0.0			, )				->List[str]:



         '''simple docstring'''
         __a					  =     parent
         __a					  =     batch_size
         __a					  =     image_size
         __a					  =     patch_size
         __a					  =     num_channels
         __a					  =     make_divisible(512 * width_multiplier			, divisor=8   )
         __a					  =     hidden_act
         __a					  =     conv_kernel_size
         __a					  =     output_stride
         __a					  =     classifier_dropout_prob
         __a					  =     use_labels
         __a					  =     is_training
         __a					  =     num_labels
         __a					  =     initializer_range
         __a					  =     scope
         __a					  =     width_multiplier
         __a					  =     ffn_dropout
         __a					  =     attn_dropout




      def 			__UpperCamelCase				(	self   )				->Optional[Any]:



         '''simple docstring'''
         __a					  =     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

         __a					  =     None
         __a					  =     None
         if self.use_labels:
            __a					  =     ids_tensor([self.batch_size]			, self.num_labels   )
            __a					  =     ids_tensor([self.batch_size, self.image_size, self.image_size]			, self.num_labels   )

         __a					  =     self.get_config()

         return config, pixel_values, labels, pixel_labels




      def 			__UpperCamelCase				(	self   )				->Dict:



         '''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 			__UpperCamelCase				(	self			, lowerCamelCase			, lowerCamelCase			, lowerCamelCase			, lowerCamelCase   )				->str:



         '''simple docstring'''
         __a					  =     MobileViTVaModel(config=lowerCamelCase   )
         model.to(lowerCamelCase   )
         model.eval()
         __a					  =     model(lowerCamelCase   )
         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 			__UpperCamelCase				(	self			, lowerCamelCase			, lowerCamelCase			, lowerCamelCase			, lowerCamelCase   )				->Any:



         '''simple docstring'''
         __a					  =     self.num_labels
         __a					  =     MobileViTVaForImageClassification(lowerCamelCase   )
         model.to(lowerCamelCase   )
         model.eval()
         __a					  =     model(lowerCamelCase			, labels=lowerCamelCase   )
         self.parent.assertEqual(result.logits.shape			, (self.batch_size, self.num_labels)   )




      def 			__UpperCamelCase				(	self			, lowerCamelCase			, lowerCamelCase			, lowerCamelCase			, lowerCamelCase   )				->Optional[int]:



         '''simple docstring'''
         __a					  =     self.num_labels
         __a					  =     MobileViTVaForSemanticSegmentation(lowerCamelCase   )
         model.to(lowerCamelCase   )
         model.eval()
         __a					  =     model(lowerCamelCase   )
         self.parent.assertEqual(
             result.logits.shape			, (
                 self.batch_size,
                 self.num_labels,
                 self.image_size // self.output_stride,
                 self.image_size // self.output_stride,
             )			, )
         __a					  =     model(lowerCamelCase			, labels=lowerCamelCase   )
         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 			__UpperCamelCase				(	self   )				->Optional[int]:



         '''simple docstring'''
         __a					  =     self.prepare_config_and_inputs()
         __a  ,      __a  ,      __a  ,      __a					  =     config_and_inputs
         __a					  =     {'pixel_values': pixel_values}
         return config, inputs_dict







@require_torch
class 				__SCREAMING_SNAKE_CASE							(				_lowerCAmelCase   ,   _lowerCAmelCase   ,   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 			__UpperCamelCase				(	self   )				->Dict:



         '''simple docstring'''
         __a					  =     MobileViTVaModelTester(self   )
         __a					  =     MobileViTVaConfigTester(self			, config_class=lowerCamelCase			, has_text_modality=lowerCamelCase   )




      def 			__UpperCamelCase				(	self   )				->Tuple:



         '''simple docstring'''
         self.config_tester.run_common_tests()




      @unittest.skip(reason='MobileViTV2 does not use inputs_embeds'   )
      def 			__UpperCamelCase				(	self   )				->Tuple:



         '''simple docstring'''
         pass




      @unittest.skip(reason='MobileViTV2 does not support input and output embeddings'   )
      def 			__UpperCamelCase				(	self   )				->Optional[Any]:



         '''simple docstring'''
         pass




      @unittest.skip(reason='MobileViTV2 does not output attentions'   )
      def 			__UpperCamelCase				(	self   )				->Optional[Any]:



         '''simple docstring'''
         pass




      @require_torch_multi_gpu
      @unittest.skip(reason='Got `CUDA error: misaligned address` for tests after this one being run.'   )
      def 			__UpperCamelCase				(	self   )				->List[str]:



         '''simple docstring'''
         pass




      @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'   )
      def 			__UpperCamelCase				(	self   )				->Union[str, Any]:



         '''simple docstring'''
         pass




      def 			__UpperCamelCase				(	self   )				->Dict:



         '''simple docstring'''
         __a  ,      __a					  =     self.model_tester.prepare_config_and_inputs_for_common()

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

            __a					  =     ['pixel_values']
            self.assertListEqual(arg_names[:1]			, lowerCamelCase   )




      def 			__UpperCamelCase				(	self   )				->List[str]:



         '''simple docstring'''
         __a					  =     self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*lowerCamelCase   )




      def 			__UpperCamelCase				(	self   )				->Optional[Any]:



         '''simple docstring'''
         def check_hidden_states_output(lowerCamelCase			, lowerCamelCase			, lowerCamelCase   ):
            __a					  =     model_class(lowerCamelCase   )
            model.to(lowerCamelCase   )
            model.eval()

            with torch.no_grad():
               __a					  =     model(**self._prepare_for_class(lowerCamelCase			, lowerCamelCase   )   )

            __a					  =     outputs.hidden_states

            __a					  =     5
            self.assertEqual(len(lowerCamelCase   )			, lowerCamelCase   )

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

         __a  ,      __a					  =     self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
            __a					  =     True
            check_hidden_states_output(lowerCamelCase			, lowerCamelCase			, lowerCamelCase   )

            # check that output_hidden_states also work using config
            del inputs_dict["output_hidden_states"]
            __a					  =     True

            check_hidden_states_output(lowerCamelCase			, lowerCamelCase			, lowerCamelCase   )




      def 			__UpperCamelCase				(	self   )				->Optional[int]:



         '''simple docstring'''
         __a					  =     self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*lowerCamelCase   )




      def 			__UpperCamelCase				(	self   )				->Dict:



         '''simple docstring'''
         __a					  =     self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_semantic_segmentation(*lowerCamelCase   )






      @slow
      def 			__UpperCamelCase				(	self   )				->List[str]:



         '''simple docstring'''
         for model_name in MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            __a					  =     MobileViTVaModel.from_pretrained(lowerCamelCase   )
            self.assertIsNotNone(lowerCamelCase   )







def  __UpperCAmelCase						(					)	->      List[Any]:

   """simple docstring"""
   __a					  =     Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'     )
   return image



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




      @cached_property
      def 			__UpperCamelCase				(	self   )				->int:



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




      @slow
      def 			__UpperCamelCase				(	self   )				->List[Any]:



         '''simple docstring'''
         __a					  =     MobileViTVaForImageClassification.from_pretrained('apple/mobilevitv2-1.0-imagenet1k-256'   ).to(
             lowerCamelCase   )

         __a					  =     self.default_image_processor
         __a					  =     prepare_img()
         __a					  =     image_processor(images=lowerCamelCase			, return_tensors='pt'   ).to(lowerCamelCase   )

         # forward pass
         with torch.no_grad():
            __a					  =     model(**lowerCamelCase   )

         # verify the logits
         __a					  =     torch.Size((1, 1000)   )
         self.assertEqual(outputs.logits.shape			, lowerCamelCase   )

         __a					  =     torch.tensor([-1.6336e00, -7.3204e-02, -5.1883e-01]   ).to(lowerCamelCase   )

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




      @slow
      def 			__UpperCamelCase				(	self   )				->List[str]:



         '''simple docstring'''
         __a					  =     MobileViTVaForSemanticSegmentation.from_pretrained('shehan97/mobilevitv2-1.0-voc-deeplabv3'   )
         __a					  =     model.to(lowerCamelCase   )

         __a					  =     MobileViTImageProcessor.from_pretrained('shehan97/mobilevitv2-1.0-voc-deeplabv3'   )

         __a					  =     prepare_img()
         __a					  =     image_processor(images=lowerCamelCase			, return_tensors='pt'   ).to(lowerCamelCase   )

         # forward pass
         with torch.no_grad():
            __a					  =     model(**lowerCamelCase   )
         __a					  =     outputs.logits

         # verify the logits
         __a					  =     torch.Size((1, 21, 32, 32)   )
         self.assertEqual(logits.shape			, lowerCamelCase   )

         __a					  =     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=lowerCamelCase			, )

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






      @slow
      def 			__UpperCamelCase				(	self   )				->Union[str, Any]:



         '''simple docstring'''
         __a					  =     MobileViTVaForSemanticSegmentation.from_pretrained('shehan97/mobilevitv2-1.0-voc-deeplabv3'   )
         __a					  =     model.to(lowerCamelCase   )

         __a					  =     MobileViTImageProcessor.from_pretrained('shehan97/mobilevitv2-1.0-voc-deeplabv3'   )

         __a					  =     prepare_img()
         __a					  =     image_processor(images=lowerCamelCase			, return_tensors='pt'   ).to(lowerCamelCase   )

         # forward pass
         with torch.no_grad():
            __a					  =     model(**lowerCamelCase   )

         __a					  =     outputs.logits.detach().cpu()

         __a					  =     image_processor.post_process_semantic_segmentation(outputs=lowerCamelCase			, target_sizes=[(50, 60)]   )
         __a					  =     torch.Size((50, 60)   )
         self.assertEqual(segmentation[0].shape			, lowerCamelCase   )

         __a					  =     image_processor.post_process_semantic_segmentation(outputs=lowerCamelCase   )
         __a					  =     torch.Size((32, 32)   )
         self.assertEqual(segmentation[0].shape			, lowerCamelCase   )