




'''simple docstring'''



import collections
import inspect
import unittest

from transformers import DonutSwinConfig
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import is_torch_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch
 from torch import nn

 from transformers import DonutSwinModel
 from transformers.models.donut.modeling_donut_swin import DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST







class 	lowercase_					:


   """simple docstring"""

   def __init__(					self      :			Optional[Any]						,   __lowerCamelCase      :			Dict						,   __lowerCamelCase      :			Optional[int]=1_3						,   __lowerCamelCase      :			Union[str, Any]=3_2						,   __lowerCamelCase      :			List[str]=2						,   __lowerCamelCase      :			List[Any]=3						,   __lowerCamelCase      :			List[str]=1_6						,   __lowerCamelCase      :			Any=[1, 2, 1]						,   __lowerCamelCase      :			Any=[2, 2, 4]						,   __lowerCamelCase      :			int=2						,   __lowerCamelCase      :			Dict=2.0						,   __lowerCamelCase      :			Any=True						,   __lowerCamelCase      :			Tuple=0.0						,   __lowerCamelCase      :			int=0.0						,   __lowerCamelCase      :			Dict=0.1						,   __lowerCamelCase      :			Union[str, Any]="gelu"						,   __lowerCamelCase      :			Any=False						,   __lowerCamelCase      :			Any=True						,   __lowerCamelCase      :			Tuple=0.0_2						,   __lowerCamelCase      :			Tuple=1e-5						,   __lowerCamelCase      :			List[Any]=True						,   __lowerCamelCase      :			Optional[int]=None						,   __lowerCamelCase      :			Dict=True						,   __lowerCamelCase      :			int=1_0						,   __lowerCamelCase      :			Tuple=8						,   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   parent
      _SCREAMING_SNAKE_CASE =   batch_size
      _SCREAMING_SNAKE_CASE =   image_size
      _SCREAMING_SNAKE_CASE =   patch_size
      _SCREAMING_SNAKE_CASE =   num_channels
      _SCREAMING_SNAKE_CASE =   embed_dim
      _SCREAMING_SNAKE_CASE =   depths
      _SCREAMING_SNAKE_CASE =   num_heads
      _SCREAMING_SNAKE_CASE =   window_size
      _SCREAMING_SNAKE_CASE =   mlp_ratio
      _SCREAMING_SNAKE_CASE =   qkv_bias
      _SCREAMING_SNAKE_CASE =   hidden_dropout_prob
      _SCREAMING_SNAKE_CASE =   attention_probs_dropout_prob
      _SCREAMING_SNAKE_CASE =   drop_path_rate
      _SCREAMING_SNAKE_CASE =   hidden_act
      _SCREAMING_SNAKE_CASE =   use_absolute_embeddings
      _SCREAMING_SNAKE_CASE =   patch_norm
      _SCREAMING_SNAKE_CASE =   layer_norm_eps
      _SCREAMING_SNAKE_CASE =   initializer_range
      _SCREAMING_SNAKE_CASE =   is_training
      _SCREAMING_SNAKE_CASE =   scope
      _SCREAMING_SNAKE_CASE =   use_labels
      _SCREAMING_SNAKE_CASE =   type_sequence_label_size
      _SCREAMING_SNAKE_CASE =   encoder_stride



   def 							lowerCAmelCase_    (					self      :			Optional[Any]					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

      _SCREAMING_SNAKE_CASE =   None
      if self.use_labels:
         _SCREAMING_SNAKE_CASE =   ids_tensor([self.batch_size]						,   self.type_sequence_label_size					)

      _SCREAMING_SNAKE_CASE =   self.get_config()

      return config, pixel_values, labels



   def 							lowerCAmelCase_    (					self      :			int					):

      """simple docstring"""
      return DonutSwinConfig(
          image_size=self.image_size						,   patch_size=self.patch_size						,   num_channels=self.num_channels						,   embed_dim=self.embed_dim						,   depths=self.depths						,   num_heads=self.num_heads						,   window_size=self.window_size						,   mlp_ratio=self.mlp_ratio						,   qkv_bias=self.qkv_bias						,   hidden_dropout_prob=self.hidden_dropout_prob						,   attention_probs_dropout_prob=self.attention_probs_dropout_prob						,   drop_path_rate=self.drop_path_rate						,   hidden_act=self.hidden_act						,   use_absolute_embeddings=self.use_absolute_embeddings						,   path_norm=self.patch_norm						,   layer_norm_eps=self.layer_norm_eps						,   initializer_range=self.initializer_range						,   encoder_stride=self.encoder_stride						,   )



   def 							lowerCAmelCase_    (					self      :			Union[str, Any]						,   __lowerCamelCase      :			List[Any]						,   __lowerCamelCase      :			Any						,   __lowerCamelCase      :			Dict					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   DonutSwinModel(config=__lowerCamelCase					)
      model.to(__lowerCamelCase					)
      model.eval()
      _SCREAMING_SNAKE_CASE =   model(__lowerCamelCase					)

      _SCREAMING_SNAKE_CASE =   ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths					) - 1))
      _SCREAMING_SNAKE_CASE =   int(config.embed_dim * 2 ** (len(config.depths					) - 1)					)

      self.parent.assertEqual(result.last_hidden_state.shape						,   (self.batch_size, expected_seq_len, expected_dim)					)



   def 							lowerCAmelCase_    (					self      :			Optional[int]					):

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







@require_torch
class 	lowercase_					(				A  ,						A  ,						unittest.TestCase     ):


   """simple docstring"""

   lowerCamelCase_       						=		(DonutSwinModel,) if is_torch_available() else ()
   lowerCamelCase_       						=		{'''feature-extraction''': DonutSwinModel} if is_torch_available() else {}
   lowerCamelCase_       						=		True

   lowerCamelCase_       						=		False
   lowerCamelCase_       						=		False
   lowerCamelCase_       						=		False



   def 							lowerCAmelCase_    (					self      :			Optional[int]					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   DonutSwinModelTester(self					)
      _SCREAMING_SNAKE_CASE =   ConfigTester(self						,   config_class=__lowerCamelCase						,   embed_dim=3_7					)



   def 							lowerCAmelCase_    (					self      :			List[Any]					):

      """simple docstring"""
      self.create_and_test_config_common_properties()
      self.config_tester.create_and_test_config_to_json_string()
      self.config_tester.create_and_test_config_to_json_file()
      self.config_tester.create_and_test_config_from_and_save_pretrained()
      self.config_tester.create_and_test_config_with_num_labels()
      self.config_tester.check_config_can_be_init_without_params()
      self.config_tester.check_config_arguments_init()



   def 							lowerCAmelCase_    (					self      :			Any					):

      """simple docstring"""
      return



   def 							lowerCAmelCase_    (					self      :			Any					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*__lowerCamelCase					)



   def 							lowerCAmelCase_    (					self      :			List[str]					):

      """simple docstring"""
      # DonutSwin does not use inputs_embeds
      pass



   def 							lowerCAmelCase_    (					self      :			List[str]					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE =   self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
         _SCREAMING_SNAKE_CASE =   model_class(__lowerCamelCase					)
         self.assertIsInstance(model.get_input_embeddings()						,   (nn.Module)					)
         _SCREAMING_SNAKE_CASE =   model.get_output_embeddings()
         self.assertTrue(x is None or isinstance(__lowerCamelCase						,   nn.Linear					)					)



   def 							lowerCAmelCase_    (					self      :			List[str]					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE =   self.model_tester.prepare_config_and_inputs_for_common()

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

         _SCREAMING_SNAKE_CASE =   ["pixel_values"]
         self.assertListEqual(arg_names[:1]						,   __lowerCamelCase					)



   def 							lowerCAmelCase_    (					self      :			Dict					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE =   self.model_tester.prepare_config_and_inputs_for_common()
      _SCREAMING_SNAKE_CASE =   True

      for model_class in self.all_model_classes:
         _SCREAMING_SNAKE_CASE =   True
         _SCREAMING_SNAKE_CASE =   False
         _SCREAMING_SNAKE_CASE =   True
         _SCREAMING_SNAKE_CASE =   model_class(__lowerCamelCase					)
         model.to(__lowerCamelCase					)
         model.eval()
         with torch.no_grad():
            _SCREAMING_SNAKE_CASE =   model(**self._prepare_for_class(__lowerCamelCase						,   __lowerCamelCase					)					)
         _SCREAMING_SNAKE_CASE =   outputs.attentions
         _SCREAMING_SNAKE_CASE =   len(self.model_tester.depths					)
         self.assertEqual(len(__lowerCamelCase					)						,   __lowerCamelCase					)

         # check that output_attentions also work using config
         del inputs_dict["output_attentions"]
         _SCREAMING_SNAKE_CASE =   True
         _SCREAMING_SNAKE_CASE =   config.window_size**2
         _SCREAMING_SNAKE_CASE =   model_class(__lowerCamelCase					)
         model.to(__lowerCamelCase					)
         model.eval()
         with torch.no_grad():
            _SCREAMING_SNAKE_CASE =   model(**self._prepare_for_class(__lowerCamelCase						,   __lowerCamelCase					)					)
         _SCREAMING_SNAKE_CASE =   outputs.attentions
         self.assertEqual(len(__lowerCamelCase					)						,   __lowerCamelCase					)

         self.assertListEqual(
             list(attentions[0].shape[-3:]					)						,   [self.model_tester.num_heads[0], window_size_squared, window_size_squared]						,   )
         _SCREAMING_SNAKE_CASE =   len(__lowerCamelCase					)

         # Check attention is always last and order is fine
         _SCREAMING_SNAKE_CASE =   True
         _SCREAMING_SNAKE_CASE =   True
         _SCREAMING_SNAKE_CASE =   model_class(__lowerCamelCase					)
         model.to(__lowerCamelCase					)
         model.eval()
         with torch.no_grad():
            _SCREAMING_SNAKE_CASE =   model(**self._prepare_for_class(__lowerCamelCase						,   __lowerCamelCase					)					)

         if hasattr(self.model_tester						,   "num_hidden_states_types"					):
            _SCREAMING_SNAKE_CASE =   self.model_tester.num_hidden_states_types
         else:
            # also another +1 for reshaped_hidden_states
            _SCREAMING_SNAKE_CASE =   2
         self.assertEqual(out_len + added_hidden_states						,   len(__lowerCamelCase					)					)

         _SCREAMING_SNAKE_CASE =   outputs.attentions

         self.assertEqual(len(__lowerCamelCase					)						,   __lowerCamelCase					)

         self.assertListEqual(
             list(self_attentions[0].shape[-3:]					)						,   [self.model_tester.num_heads[0], window_size_squared, window_size_squared]						,   )



   def 							lowerCAmelCase_    (					self      :			Tuple						,   __lowerCamelCase      :			Tuple						,   __lowerCamelCase      :			str						,   __lowerCamelCase      :			Tuple						,   __lowerCamelCase      :			int					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   model_class(__lowerCamelCase					)
      model.to(__lowerCamelCase					)
      model.eval()

      with torch.no_grad():
         _SCREAMING_SNAKE_CASE =   model(**self._prepare_for_class(__lowerCamelCase						,   __lowerCamelCase					)					)

      _SCREAMING_SNAKE_CASE =   outputs.hidden_states

      _SCREAMING_SNAKE_CASE =   getattr(
          self.model_tester						,   "expected_num_hidden_layers"						,   len(self.model_tester.depths					) + 1					)
      self.assertEqual(len(__lowerCamelCase					)						,   __lowerCamelCase					)

      # DonutSwin has a different seq_length
      _SCREAMING_SNAKE_CASE =   (
          config.patch_size
          if isinstance(config.patch_size						,   collections.abc.Iterable					)
          else (config.patch_size, config.patch_size)
      )

      _SCREAMING_SNAKE_CASE =   (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

      self.assertListEqual(
          list(hidden_states[0].shape[-2:]					)						,   [num_patches, self.model_tester.embed_dim]						,   )

      _SCREAMING_SNAKE_CASE =   outputs.reshaped_hidden_states
      self.assertEqual(len(__lowerCamelCase					)						,   __lowerCamelCase					)

      _SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE =   reshaped_hidden_states[0].shape
      _SCREAMING_SNAKE_CASE =   (
          reshaped_hidden_states[0].view(__lowerCamelCase						,   __lowerCamelCase						,   height * width					).permute(0						,   2						,   1					)
      )
      self.assertListEqual(
          list(reshaped_hidden_states.shape[-2:]					)						,   [num_patches, self.model_tester.embed_dim]						,   )



   def 							lowerCAmelCase_    (					self      :			Optional[int]					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE =   self.model_tester.prepare_config_and_inputs_for_common()

      _SCREAMING_SNAKE_CASE =   (
          self.model_tester.image_size
          if isinstance(self.model_tester.image_size						,   collections.abc.Iterable					)
          else (self.model_tester.image_size, self.model_tester.image_size)
      )

      for model_class in self.all_model_classes:
         _SCREAMING_SNAKE_CASE =   True
         self.check_hidden_states_output(__lowerCamelCase						,   __lowerCamelCase						,   __lowerCamelCase						,   __lowerCamelCase					)

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

         self.check_hidden_states_output(__lowerCamelCase						,   __lowerCamelCase						,   __lowerCamelCase						,   __lowerCamelCase					)



   def 							lowerCAmelCase_    (					self      :			Union[str, Any]					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE =   self.model_tester.prepare_config_and_inputs_for_common()
      _SCREAMING_SNAKE_CASE =   3

      _SCREAMING_SNAKE_CASE =   (
          self.model_tester.image_size
          if isinstance(self.model_tester.image_size						,   collections.abc.Iterable					)
          else (self.model_tester.image_size, self.model_tester.image_size)
      )
      _SCREAMING_SNAKE_CASE =   (
          config.patch_size
          if isinstance(config.patch_size						,   collections.abc.Iterable					)
          else (config.patch_size, config.patch_size)
      )

      _SCREAMING_SNAKE_CASE =   image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
      _SCREAMING_SNAKE_CASE =   image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

      for model_class in self.all_model_classes:
         _SCREAMING_SNAKE_CASE =   True
         self.check_hidden_states_output(__lowerCamelCase						,   __lowerCamelCase						,   __lowerCamelCase						,   (padded_height, padded_width)					)

         # check that output_hidden_states also work using config
         del inputs_dict["output_hidden_states"]
         _SCREAMING_SNAKE_CASE =   True
         self.check_hidden_states_output(__lowerCamelCase						,   __lowerCamelCase						,   __lowerCamelCase						,   (padded_height, padded_width)					)



   @slow
   def 							lowerCAmelCase_    (					self      :			Optional[Any]					):

      """simple docstring"""
      for model_name in DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         _SCREAMING_SNAKE_CASE =   DonutSwinModel.from_pretrained(__lowerCamelCase					)
         self.assertIsNotNone(__lowerCamelCase					)



   def 							lowerCAmelCase_    (					self      :			Optional[Any]					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE =   self.model_tester.prepare_config_and_inputs_for_common()

      _SCREAMING_SNAKE_CASE =   _config_zero_init(__lowerCamelCase					)
      for model_class in self.all_model_classes:
         _SCREAMING_SNAKE_CASE =   model_class(config=__lowerCamelCase					)
         for name, param in model.named_parameters():
            if "embeddings" not in name and param.requires_grad:
               self.assertIn(
                   ((param.data.mean() * 1e9).round() / 1e9).item()						,   [0.0, 1.0]						,   msg=F"""Parameter {name} of model {model_class} seems not properly initialized"""						,   )







'''simple docstring'''



import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPImageProcessor, CLIPVisionConfig

from diffusers import AutoencoderKL, PaintByExamplePipeline, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.paint_by_example import PaintByExampleImageEncoder
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()







class 	lowercase_					(				A  ,						unittest.TestCase     ):


   """simple docstring"""

   lowerCamelCase_       						=		PaintByExamplePipeline
   lowerCamelCase_       						=		IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
   lowerCamelCase_       						=		IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
   lowerCamelCase_       						=		frozenset([]     )  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess



   def 							lowerCAmelCase_    (					self      :			Optional[Any]					):

      """simple docstring"""
      torch.manual_seed(0					)
      _SCREAMING_SNAKE_CASE =   UNetaDConditionModel(
          block_out_channels=(3_2, 6_4)						,   layers_per_block=2						,   sample_size=3_2						,   in_channels=9						,   out_channels=4						,   down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")						,   up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")						,   cross_attention_dim=3_2						,   )
      _SCREAMING_SNAKE_CASE =   PNDMScheduler(skip_prk_steps=__lowerCamelCase					)
      torch.manual_seed(0					)
      _SCREAMING_SNAKE_CASE =   AutoencoderKL(
          block_out_channels=[3_2, 6_4]						,   in_channels=3						,   out_channels=3						,   down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]						,   up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]						,   latent_channels=4						,   )
      torch.manual_seed(0					)
      _SCREAMING_SNAKE_CASE =   CLIPVisionConfig(
          hidden_size=3_2						,   projection_dim=3_2						,   intermediate_size=3_7						,   layer_norm_eps=1e-05						,   num_attention_heads=4						,   num_hidden_layers=5						,   image_size=3_2						,   patch_size=4						,   )
      _SCREAMING_SNAKE_CASE =   PaintByExampleImageEncoder(__lowerCamelCase						,   proj_size=3_2					)
      _SCREAMING_SNAKE_CASE =   CLIPImageProcessor(crop_size=3_2						,   size=3_2					)

      _SCREAMING_SNAKE_CASE =   {
          "unet": unet,
          "scheduler": scheduler,
          "vae": vae,
          "image_encoder": image_encoder,
          "safety_checker": None,
          "feature_extractor": feature_extractor,
      }
      return components



   def 							lowerCAmelCase_    (					self      :			Any						,   __lowerCamelCase      :			str					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   np.array(image.convert("RGB"					)					)
      _SCREAMING_SNAKE_CASE =   image[None].transpose(0						,   3						,   1						,   2					)
      _SCREAMING_SNAKE_CASE =   torch.from_numpy(__lowerCamelCase					).to(dtype=torch.floataa					) / 1_2_7.5 - 1.0
      return image



   def 							lowerCAmelCase_    (					self      :			List[str]						,   __lowerCamelCase      :			Optional[int]="cpu"						,   __lowerCamelCase      :			Union[str, Any]=0					):

      """simple docstring"""
      # TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
      _SCREAMING_SNAKE_CASE =   floats_tensor((1, 3, 3_2, 3_2)						,   rng=random.Random(__lowerCamelCase					)					).to(__lowerCamelCase					)
      _SCREAMING_SNAKE_CASE =   image.cpu().permute(0						,   2						,   3						,   1					)[0]
      _SCREAMING_SNAKE_CASE =   Image.fromarray(np.uinta(__lowerCamelCase					)					).convert("RGB"					).resize((6_4, 6_4)					)
      _SCREAMING_SNAKE_CASE =   Image.fromarray(np.uinta(image + 4					)					).convert("RGB"					).resize((6_4, 6_4)					)
      _SCREAMING_SNAKE_CASE =   Image.fromarray(np.uinta(__lowerCamelCase					)					).convert("RGB"					).resize((3_2, 3_2)					)

      if str(__lowerCamelCase					).startswith("mps"					):
         _SCREAMING_SNAKE_CASE =   torch.manual_seed(__lowerCamelCase					)
      else:
         _SCREAMING_SNAKE_CASE =   torch.Generator(device=__lowerCamelCase					).manual_seed(__lowerCamelCase					)
      _SCREAMING_SNAKE_CASE =   {
          "example_image": example_image,
          "image": init_image,
          "mask_image": mask_image,
          "generator": generator,
          "num_inference_steps": 2,
          "guidance_scale": 6.0,
          "output_type": "numpy",
      }
      return inputs



   def 							lowerCAmelCase_    (					self      :			List[str]					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   self.get_dummy_components()

      # make sure here that pndm scheduler skips prk
      _SCREAMING_SNAKE_CASE =   PaintByExamplePipeline(**__lowerCamelCase					)
      _SCREAMING_SNAKE_CASE =   pipe.to("cpu"					)
      pipe.set_progress_bar_config(disable=__lowerCamelCase					)

      _SCREAMING_SNAKE_CASE =   self.get_dummy_inputs()
      _SCREAMING_SNAKE_CASE =   pipe(**__lowerCamelCase					)
      _SCREAMING_SNAKE_CASE =   output.images

      _SCREAMING_SNAKE_CASE =   image[0, -3:, -3:, -1]

      assert image.shape == (1, 6_4, 6_4, 3)
      _SCREAMING_SNAKE_CASE =   np.array([0.4_6_8_6, 0.5_6_8_7, 0.4_0_0_7, 0.5_2_1_8, 0.5_7_4_1, 0.4_4_8_2, 0.4_9_4_0, 0.4_6_2_9, 0.4_5_0_3]					)

      assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-2



   def 							lowerCAmelCase_    (					self      :			Tuple					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   "cpu"
      _SCREAMING_SNAKE_CASE =   self.get_dummy_inputs()
      inputs.pop("mask_image"					)
      _SCREAMING_SNAKE_CASE =   self.convert_to_pt(inputs.pop("image"					)					)
      _SCREAMING_SNAKE_CASE =   image.clamp(0						,   1					) / 2

      # make sure here that pndm scheduler skips prk
      _SCREAMING_SNAKE_CASE =   PaintByExamplePipeline(**self.get_dummy_components()					)
      _SCREAMING_SNAKE_CASE =   pipe.to(__lowerCamelCase					)
      pipe.set_progress_bar_config(disable=__lowerCamelCase					)

      _SCREAMING_SNAKE_CASE =   pipe(image=__lowerCamelCase						,   mask_image=mask_image[:, 0]						,   **__lowerCamelCase					)
      _SCREAMING_SNAKE_CASE =   output.images

      _SCREAMING_SNAKE_CASE =   image.cpu().permute(0						,   2						,   3						,   1					)[0]
      _SCREAMING_SNAKE_CASE =   mask_image.cpu().permute(0						,   2						,   3						,   1					)[0]

      _SCREAMING_SNAKE_CASE =   Image.fromarray(np.uinta(__lowerCamelCase					)					).convert("RGB"					)
      _SCREAMING_SNAKE_CASE =   Image.fromarray(np.uinta(__lowerCamelCase					)					).convert("RGB"					)

      _SCREAMING_SNAKE_CASE =   pipe(**self.get_dummy_inputs()					)
      _SCREAMING_SNAKE_CASE =   output.images

      assert out_a.shape == (1, 6_4, 6_4, 3)
      assert np.abs(out_a.flatten() - out_a.flatten()					).max() < 5e-2



   def 							lowerCAmelCase_    (					self      :			List[Any]					):

      """simple docstring"""
      super().test_inference_batch_single_identical(expected_max_diff=3e-3					)







@slow
@require_torch_gpu
class 	lowercase_					(				unittest.TestCase     ):


   """simple docstring"""

   def 							lowerCAmelCase_    (					self      :			Optional[int]					):

      """simple docstring"""
      # clean up the VRAM after each test
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()



   def 							lowerCAmelCase_    (					self      :			Optional[Any]					):

      """simple docstring"""
      # make sure here that pndm scheduler skips prk
      _SCREAMING_SNAKE_CASE =   load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
          "/paint_by_example/dog_in_bucket.png"					)
      _SCREAMING_SNAKE_CASE =   load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
          "/paint_by_example/mask.png"					)
      _SCREAMING_SNAKE_CASE =   load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
          "/paint_by_example/panda.jpg"					)

      _SCREAMING_SNAKE_CASE =   PaintByExamplePipeline.from_pretrained("Fantasy-Studio/Paint-by-Example"					)
      _SCREAMING_SNAKE_CASE =   pipe.to(__lowerCamelCase					)
      pipe.set_progress_bar_config(disable=__lowerCamelCase					)

      _SCREAMING_SNAKE_CASE =   torch.manual_seed(3_2_1					)
      _SCREAMING_SNAKE_CASE =   pipe(
          image=__lowerCamelCase						,   mask_image=__lowerCamelCase						,   example_image=__lowerCamelCase						,   generator=__lowerCamelCase						,   guidance_scale=5.0						,   num_inference_steps=5_0						,   output_type="np"						,   )

      _SCREAMING_SNAKE_CASE =   output.images

      _SCREAMING_SNAKE_CASE =   image[0, -3:, -3:, -1]

      assert image.shape == (1, 5_1_2, 5_1_2, 3)
      _SCREAMING_SNAKE_CASE =   np.array([0.4_8_3_4, 0.4_8_1_1, 0.4_8_7_4, 0.5_1_2_2, 0.5_0_8_1, 0.5_1_4_4, 0.5_2_9_1, 0.5_2_9_0, 0.5_3_7_4]					)

      assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-2

