



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 							lowerCAmelCase_						(    a__				):
       def    snake_case_			(     self			)	->						Tuple:
          UpperCamelCase       :    Optional[Any]		      =      self.config_class(**self.inputs_dict			)
          self.parent.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,     'width_multiplier'			)			)






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

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

          UpperCamelCase       :    List[str]		      =      self.get_config()

          return config, pixel_values, labels, pixel_labels
       def    snake_case_			(     self			)	->						int:
          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    snake_case_			(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			)	->						Optional[int]:
          UpperCamelCase       :    Any		      =      MobileViTVaModel(config=SCREAMING_SNAKE_CASE_			)
          model.to(SCREAMING_SNAKE_CASE_			)
          model.eval()
          UpperCamelCase       :    Union[str, Any]		      =      model(SCREAMING_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    snake_case_			(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			)	->						Dict:
          UpperCamelCase       :    Optional[int]		      =      self.num_labels
          UpperCamelCase       :    Tuple		      =      MobileViTVaForImageClassification(SCREAMING_SNAKE_CASE_			)
          model.to(SCREAMING_SNAKE_CASE_			)
          model.eval()
          UpperCamelCase       :    List[str]		      =      model(SCREAMING_SNAKE_CASE_,     labels=SCREAMING_SNAKE_CASE_			)
          self.parent.assertEqual(result.logits.shape,     (self.batch_size, self.num_labels)			)
       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			)	->						Dict:
          UpperCamelCase       :    Any		      =      self.num_labels
          UpperCamelCase       :    Optional[Any]		      =      MobileViTVaForSemanticSegmentation(SCREAMING_SNAKE_CASE_			)
          model.to(SCREAMING_SNAKE_CASE_			)
          model.eval()
          UpperCamelCase       :    Optional[Any]		      =      model(SCREAMING_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,
              ),     )
          UpperCamelCase       :    List[Any]		      =      model(SCREAMING_SNAKE_CASE_,     labels=SCREAMING_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    snake_case_			(     self			)	->						List[Any]:
          UpperCamelCase       :    Union[str, Any]		      =      self.prepare_config_and_inputs()
          UpperCamelCase      ,	UpperCamelCase      ,	UpperCamelCase      ,	UpperCamelCase       :    str		      =      config_and_inputs
          UpperCamelCase       :    int		      =      {'pixel_values': pixel_values}
          return config, inputs_dict






@require_torch
class 							lowerCAmelCase_						(    a__			,       a__			,       unittest.TestCase				):
       UpperCAmelCase__							:   Tuple  	=    (
           (MobileViTVaModel, MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation)
           if is_torch_available()
           else ()
       )

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

       UpperCAmelCase__							:   Optional[int]  	=    False
       UpperCAmelCase__							:   List[str]  	=    False
       UpperCAmelCase__							:   Optional[Any]  	=    False
       UpperCAmelCase__							:   Optional[Any]  	=    False
       def    snake_case_			(     self			)	->						Optional[Any]:
          UpperCamelCase       :    Dict		      =      MobileViTVaModelTester(self			)
          UpperCamelCase       :    Optional[Any]		      =      MobileViTVaConfigTester(self,     config_class=SCREAMING_SNAKE_CASE_,     has_text_modality=SCREAMING_SNAKE_CASE_			)
       def    snake_case_			(     self			)	->						Optional[Any]:
          self.config_tester.run_common_tests()
       @unittest.skip(reason='MobileViTV2 does not use inputs_embeds'			)
       def    snake_case_			(     self			)	->						Dict:
          pass
       @unittest.skip(reason='MobileViTV2 does not support input and output embeddings'			)
       def    snake_case_			(     self			)	->						int:
          pass
       @unittest.skip(reason='MobileViTV2 does not output attentions'			)
       def    snake_case_			(     self			)	->						str:
          pass
       @require_torch_multi_gpu
       @unittest.skip(reason='Got `CUDA error: misaligned address` for tests after this one being run.'			)
       def    snake_case_			(     self			)	->						Dict:
          pass
       @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'			)
       def    snake_case_			(     self			)	->						Any:
          pass
       def    snake_case_			(     self			)	->						List[str]:
          UpperCamelCase      ,	UpperCamelCase       :    Optional[Any]		      =      self.model_tester.prepare_config_and_inputs_for_common()

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

             UpperCamelCase       :    Optional[int]		      =      ['pixel_values']
             self.assertListEqual(arg_names[:1],     SCREAMING_SNAKE_CASE_			)
       def    snake_case_			(     self			)	->						Optional[int]:
          UpperCamelCase       :    Optional[int]		      =      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_			)
       def    snake_case_			(     self			)	->						Tuple:
          def check_hidden_states_output(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			):
             UpperCamelCase       :    Optional[Any]		      =      model_class(SCREAMING_SNAKE_CASE_			)
             model.to(SCREAMING_SNAKE_CASE_			)
             model.eval()

             with torch.no_grad():
                UpperCamelCase       :    List[Any]		      =      model(**self._prepare_for_class(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			)			)

             UpperCamelCase       :    Tuple		      =      outputs.hidden_states

             UpperCamelCase       :    Dict		      =      5
             self.assertEqual(len(SCREAMING_SNAKE_CASE_			),     SCREAMING_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.
             UpperCamelCase       :    Any		      =      2
             for i in range(len(SCREAMING_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			)

          UpperCamelCase      ,	UpperCamelCase       :    Any		      =      self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
             UpperCamelCase       :    Union[str, Any]		      =      True
             check_hidden_states_output(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			)

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

             check_hidden_states_output(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			)
       def    snake_case_			(     self			)	->						Optional[int]:
          UpperCamelCase       :    List[str]		      =      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE_			)
       def    snake_case_			(     self			)	->						str:
          UpperCamelCase       :    int		      =      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_semantic_segmentation(*SCREAMING_SNAKE_CASE_			)






       @slow
       def    snake_case_			(     self			)	->						Optional[Any]:
          for model_name in MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             UpperCamelCase       :    str		      =      MobileViTVaModel.from_pretrained(SCREAMING_SNAKE_CASE_			)
             self.assertIsNotNone(SCREAMING_SNAKE_CASE_			)






def 						UpperCamelCase      (  )      ->	Tuple:
   UpperCamelCase       :    Any		      =      Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'      )
   return image



@require_torch
@require_vision
class 							lowerCAmelCase_						(    unittest.TestCase				):
       @cached_property
       def    snake_case_			(     self			)	->						str:
          return (
              MobileViTImageProcessor.from_pretrained('apple/mobilevitv2-1.0-imagenet1k-256'			)
              if is_vision_available()
              else None
          )
       @slow
       def    snake_case_			(     self			)	->						Optional[Any]:
          UpperCamelCase       :    Any		      =      MobileViTVaForImageClassification.from_pretrained('apple/mobilevitv2-1.0-imagenet1k-256'			).to(
              SCREAMING_SNAKE_CASE_			)

          UpperCamelCase       :    Union[str, Any]		      =      self.default_image_processor
          UpperCamelCase       :    Any		      =      prepare_img()
          UpperCamelCase       :    Tuple		      =      image_processor(images=SCREAMING_SNAKE_CASE_,     return_tensors='pt'			).to(SCREAMING_SNAKE_CASE_			)

          # forward pass
          with torch.no_grad():
             UpperCamelCase       :    Tuple		      =      model(**SCREAMING_SNAKE_CASE_			)

          # verify the logits
          UpperCamelCase       :    Union[str, Any]		      =      torch.Size((1, 1000)			)
          self.assertEqual(outputs.logits.shape,     SCREAMING_SNAKE_CASE_			)

          UpperCamelCase       :    Tuple		      =      torch.tensor([-1.6336e00, -7.3204e-02, -5.1883e-01]			).to(SCREAMING_SNAKE_CASE_			)

          self.assertTrue(torch.allclose(outputs.logits[0, :3],     SCREAMING_SNAKE_CASE_,     atol=1e-4			)			)
       @slow
       def    snake_case_			(     self			)	->						Union[str, Any]:
          UpperCamelCase       :    Optional[int]		      =      MobileViTVaForSemanticSegmentation.from_pretrained('shehan97/mobilevitv2-1.0-voc-deeplabv3'			)
          UpperCamelCase       :    List[str]		      =      model.to(SCREAMING_SNAKE_CASE_			)

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

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

          # forward pass
          with torch.no_grad():
             UpperCamelCase       :    Tuple		      =      model(**SCREAMING_SNAKE_CASE_			)
          UpperCamelCase       :    str		      =      outputs.logits

          # verify the logits
          UpperCamelCase       :    Dict		      =      torch.Size((1, 21, 32, 32)			)
          self.assertEqual(logits.shape,     SCREAMING_SNAKE_CASE_			)

          UpperCamelCase       :    List[str]		      =      torch.tensor(
              [
                  [[7.08_63, 7.15_25, 6.82_01], [6.69_31, 6.87_70, 6.89_33], [6.29_78, 7.03_66, 6.96_36]],
                  [[-3.71_34, -3.67_12, -3.66_75], [-3.58_25, -3.35_49, -3.47_77], [-3.34_35, -3.39_79, -3.28_57]],
                  [[-2.93_29, -2.80_03, -2.73_69], [-3.05_64, -2.47_80, -2.02_07], [-2.68_89, -1.92_98, -1.76_40]],
              ],     device=SCREAMING_SNAKE_CASE_,     )

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






       @slow
       def    snake_case_			(     self			)	->						Union[str, Any]:
          UpperCamelCase       :    str		      =      MobileViTVaForSemanticSegmentation.from_pretrained('shehan97/mobilevitv2-1.0-voc-deeplabv3'			)
          UpperCamelCase       :    Optional[int]		      =      model.to(SCREAMING_SNAKE_CASE_			)

          UpperCamelCase       :    Any		      =      MobileViTImageProcessor.from_pretrained('shehan97/mobilevitv2-1.0-voc-deeplabv3'			)

          UpperCamelCase       :    Tuple		      =      prepare_img()
          UpperCamelCase       :    int		      =      image_processor(images=SCREAMING_SNAKE_CASE_,     return_tensors='pt'			).to(SCREAMING_SNAKE_CASE_			)

          # forward pass
          with torch.no_grad():
             UpperCamelCase       :    str		      =      model(**SCREAMING_SNAKE_CASE_			)

          UpperCamelCase       :    List[Any]		      =      outputs.logits.detach().cpu()

          UpperCamelCase       :    int		      =      image_processor.post_process_semantic_segmentation(outputs=SCREAMING_SNAKE_CASE_,     target_sizes=[(50, 60)]			)
          UpperCamelCase       :    Optional[int]		      =      torch.Size((50, 60)			)
          self.assertEqual(segmentation[0].shape,     SCREAMING_SNAKE_CASE_			)

          UpperCamelCase       :    Union[str, Any]		      =      image_processor.post_process_semantic_segmentation(outputs=SCREAMING_SNAKE_CASE_			)
          UpperCamelCase       :    List[Any]		      =      torch.Size((32, 32)			)
          self.assertEqual(segmentation[0].shape,     SCREAMING_SNAKE_CASE_			)










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

import torch

from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class       __A							(   lowerCamelCase__	):




      """simple docstring"""







      def __init__(		self   ,				a__   ,				a__   ,				a__):



             """simple docstring"""





             super().__init__()
             self.register_modules(vqvae=a__   ,				unet=a__   ,				scheduler=a__)



      @torch.no_grad()
      def __call__(		self   ,				a__ = 1   ,				a__ = None   ,				a__ = 0.0   ,				a__ = 50   ,				a__ = "pil"   ,				a__ = True   ,				**a__   ,				):



             """simple docstring"""





             _lowerCamelCase				:  Tuple						=		randn_tensor(
                 (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)   ,				generator=a__   ,				)
             _lowerCamelCase				:  Optional[int]						=		latents.to(self.device)

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

             self.scheduler.set_timesteps(a__)

             # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
             _lowerCamelCase				:  Optional[int]						=		'''eta''' in set(inspect.signature(self.scheduler.step).parameters.keys())

             _lowerCamelCase				:  Union[str, Any]						=		{}
             if accepts_eta:
                    _lowerCamelCase				:  List[Any]						=		eta

             for t in self.progress_bar(self.scheduler.timesteps):
                    _lowerCamelCase				:  List[Any]						=		self.scheduler.scale_model_input(a__   ,				a__)
                    # predict the noise residual
                    _lowerCamelCase				:  Optional[int]						=		self.unet(a__   ,				a__).sample
                    # compute the previous noisy sample x_t -> x_t-1
                    _lowerCamelCase				:  List[str]						=		self.scheduler.step(a__   ,				a__   ,				a__   ,				**a__).prev_sample

             # decode the image latents with the VAE
             _lowerCamelCase				:  Optional[int]						=		self.vqvae.decode(a__).sample

             _lowerCamelCase				:  Dict						=		(image / 2 + 0.5).clamp(0   ,				1)
             _lowerCamelCase				:  List[str]						=		image.cpu().permute(0   ,				2   ,				3   ,				1).numpy()
             if output_type == "pil":
                    _lowerCamelCase				:  List[Any]						=		self.numpy_to_pil(a__)

             if not return_dict:
                    return (image,)

             return ImagePipelineOutput(images=a__)

