






'''simple docstring'''



import inspect
import unittest

from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, 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 MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation, MobileNetVaModel
 from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import MobileNetVaImageProcessor
class 							__SCREAMING_SNAKE_CASE       (							_lowerCAmelCase     ):




      def  __UpperCamelCase							(  self      )							->int:



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







class 							__SCREAMING_SNAKE_CASE       :




      def __init__(  self ,   lowerCamelCase ,   lowerCamelCase=13 ,   lowerCamelCase=3 ,   lowerCamelCase=32 ,   lowerCamelCase=0.25 ,   lowerCamelCase=8 ,   lowerCamelCase=8 ,   lowerCamelCase=6 ,   lowerCamelCase=32 ,   lowerCamelCase=True ,   lowerCamelCase=True ,   lowerCamelCase=True ,   lowerCamelCase="relu6" ,   lowerCamelCase=1280 ,   lowerCamelCase=0.1 ,   lowerCamelCase=0.02 ,   lowerCamelCase=True ,   lowerCamelCase=True ,   lowerCamelCase=10 ,   lowerCamelCase=None ,   )							->Optional[int]:



         '''simple docstring'''
         __a				     =    parent
         __a				     =    batch_size
         __a				     =    num_channels
         __a				     =    image_size
         __a				     =    depth_multiplier
         __a				     =    depth_divisible_by
         __a				     =    min_depth
         __a				     =    expand_ratio
         __a				     =    tf_padding
         __a				     =    output_stride
         __a				     =    first_layer_is_expansion
         __a				     =    finegrained_output
         __a				     =    hidden_act
         __a				     =    last_hidden_size if finegrained_output else int(last_hidden_size * depth_multiplier      )
         __a				     =    classifier_dropout_prob
         __a				     =    use_labels
         __a				     =    is_training
         __a				     =    num_labels
         __a				     =    initializer_range
         __a				     =    scope




      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 MobileNetVaConfig(
             num_channels=self.num_channels ,   image_size=self.image_size ,   depth_multiplier=self.depth_multiplier ,   depth_divisible_by=self.depth_divisible_by ,   min_depth=self.min_depth ,   expand_ratio=self.expand_ratio ,   output_stride=self.output_stride ,   first_layer_is_expansion=self.first_layer_is_expansion ,   finegrained_output=self.finegrained_output ,   hidden_act=self.hidden_act ,   tf_padding=self.tf_padding ,   classifier_dropout_prob=self.classifier_dropout_prob ,   initializer_range=self.initializer_range ,   )




      def  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase      )							->Tuple:



         '''simple docstring'''
         __a				     =    MobileNetVaModel(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,
             ) ,   )
         self.parent.assertEqual(
             result.pooler_output.shape ,   (self.batch_size, self.last_hidden_size) ,   )




      def  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase      )							->Dict:



         '''simple docstring'''
         __a				     =    self.num_labels
         __a				     =    MobileNetVaForImageClassification(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				     =    MobileNetVaForSemanticSegmentation(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      )							->Any:



         '''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  					=(
          (MobileNetVaModel, MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation)
          if is_torch_available()
          else ()
      )
      __a  					=(
          {
              "feature-extraction": MobileNetVaModel,
              "image-classification": MobileNetVaForImageClassification,
              "image-segmentation": MobileNetVaForSemanticSegmentation,
          }
          if is_torch_available()
          else {}
      )

      __a  					=False
      __a  					=False
      __a  					=False
      __a  					=False




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



         '''simple docstring'''
         __a				     =    MobileNetVaModelTester(self      )
         __a				     =    MobileNetVaConfigTester(self ,   config_class=lowerCamelCase ,   has_text_modality=lowerCamelCase      )




      def  __UpperCamelCase							(  self      )							->str:



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




      @unittest.skip(reason='MobileNetV2 does not use inputs_embeds'      )
      def  __UpperCamelCase							(  self      )							->Any:



         '''simple docstring'''
         pass




      @unittest.skip(reason='MobileNetV2 does not support input and output embeddings'      )
      def  __UpperCamelCase							(  self      )							->int:



         '''simple docstring'''
         pass




      @unittest.skip(reason='MobileNetV2 does not output attentions'      )
      def  __UpperCamelCase							(  self      )							->Any:



         '''simple docstring'''
         pass




      def  __UpperCamelCase							(  self      )							->str:



         '''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      )							->Optional[Any]:



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




      def  __UpperCamelCase							(  self      )							->Dict:



         '''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				     =    16
            self.assertEqual(len(lowerCamelCase      ) ,   lowerCamelCase      )

         __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      )							->Union[str, Any]:



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




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



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






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



         '''simple docstring'''
         for model_name in MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            __a				     =    MobileNetVaModel.from_pretrained(lowerCamelCase      )
            self.assertIsNotNone(lowerCamelCase      )







def    __UpperCAmelCase	(				)  ->			Union[str, 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      )							->Optional[Any]:



         '''simple docstring'''
         return (
             MobileNetVaImageProcessor.from_pretrained('google/mobilenet_v2_1.0_224'      ) if is_vision_available() else None
         )




      @slow
      def  __UpperCamelCase							(  self      )							->Any:



         '''simple docstring'''
         __a				     =    MobileNetVaForImageClassification.from_pretrained('google/mobilenet_v2_1.0_224'      ).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, 1001)      )
         self.assertEqual(outputs.logits.shape ,   lowerCamelCase      )

         __a				     =    torch.tensor([0.2445, -1.1993, 0.1905]      ).to(lowerCamelCase      )

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






      @slow
      def  __UpperCamelCase							(  self      )							->int:



         '''simple docstring'''
         __a				     =    MobileNetVaForSemanticSegmentation.from_pretrained('google/deeplabv3_mobilenet_v2_1.0_513'      )
         __a				     =    model.to(lowerCamelCase      )

         __a				     =    MobileNetVaImageProcessor.from_pretrained('google/deeplabv3_mobilenet_v2_1.0_513'      )

         __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, 65, 65)      )
         self.assertEqual(logits.shape ,   lowerCamelCase      )

         __a				     =    torch.tensor(
             [
                 [[17.5790, 17.7581, 18.3355], [18.3257, 18.4230, 18.8973], [18.6169, 18.8650, 19.2187]],
                 [[-2.1595, -2.0977, -2.3741], [-2.4226, -2.3028, -2.6835], [-2.7819, -2.5991, -2.7706]],
                 [[4.2058, 4.8317, 4.7638], [4.4136, 5.0361, 4.9383], [4.5028, 4.9644, 4.8734]],
             ] ,   device=lowerCamelCase ,   )

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







'''simple docstring'''



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

import numpy as np
import PIL
import torch
import torch.utils.checkpoint

from ...models import UNetaDModel, VQModel
from ...schedulers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput



def    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	List[Any]    )  ->			Dict:

   """simple docstring"""
   __a     ,			__a				     =    image.size
   __a     ,			__a				     =    (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
   __a				     =    image.resize((w, h),					resample=PIL_INTERPOLATION['lanczos']    )
   __a				     =    np.array(SCREAMING_SNAKE_CASE__    ).astype(np.floataa    ) / 2_5_5.0
   __a				     =    image[None].transpose(0,					3,					1,					2    )
   __a				     =    torch.from_numpy(SCREAMING_SNAKE_CASE__    )
   return 2.0 * image - 1.0



class 							__SCREAMING_SNAKE_CASE       (							_lowerCAmelCase     ):




      def __init__(  self ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   )							->Dict:



         '''simple docstring'''
         super().__init__()
         self.register_modules(vqvae=lowerCamelCase ,   unet=lowerCamelCase ,   scheduler=lowerCamelCase      )






      @torch.no_grad()
      def __call__(  self ,   lowerCamelCase = None ,   lowerCamelCase = 1 ,   lowerCamelCase = 100 ,   lowerCamelCase = 0.0 ,   lowerCamelCase = None ,   lowerCamelCase = "pil" ,   lowerCamelCase = True ,   )							->Union[Tuple, ImagePipelineOutput]:



         '''simple docstring'''
         if isinstance(lowerCamelCase ,   PIL.Image.Image      ):
            __a				     =    1
         elif isinstance(lowerCamelCase ,   torch.Tensor      ):
            __a				     =    image.shape[0]
         else:
            raise ValueError(F"""`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(lowerCamelCase      )}"""      )

         if isinstance(lowerCamelCase ,   PIL.Image.Image      ):
            __a				     =    preprocess(lowerCamelCase      )

         __a     ,			__a				     =    image.shape[-2:]

         # in_channels should be 6: 3 for latents, 3 for low resolution image
         __a				     =    (batch_size, self.unet.config.in_channels // 2, height, width)
         __a				     =    next(self.unet.parameters()      ).dtype

         __a				     =    randn_tensor(lowerCamelCase ,   generator=lowerCamelCase ,   device=self.device ,   dtype=lowerCamelCase      )

         __a				     =    image.to(device=self.device ,   dtype=lowerCamelCase      )

         # set timesteps and move to the correct device
         self.scheduler.set_timesteps(lowerCamelCase ,   device=self.device      )
         __a				     =    self.scheduler.timesteps

         # 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 t in self.progress_bar(lowerCamelCase      ):
            # concat latents and low resolution image in the channel dimension.
            __a				     =    torch.cat([latents, image] ,   dim=1      )
            __a				     =    self.scheduler.scale_model_input(lowerCamelCase ,   lowerCamelCase      )
            # predict the noise residual
            __a				     =    self.unet(lowerCamelCase ,   lowerCamelCase      ).sample
            # compute the previous noisy sample x_t -> x_t-1
            __a				     =    self.scheduler.step(lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   **lowerCamelCase      ).prev_sample

         # decode the image latents with the VQVAE
         __a				     =    self.vqvae.decode(lowerCamelCase      ).sample
         __a				     =    torch.clamp(lowerCamelCase ,   -1.0 ,   1.0      )
         __a				     =    image / 2 + 0.5
         __a				     =    image.cpu().permute(0 ,   2 ,   3 ,   1      ).numpy()

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

         if not return_dict:
            return (image,)

         return ImagePipelineOutput(images=lowerCamelCase      )