



"""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    a		:



  def __init__(  self	:   Union[str, Any]    ,						lowerCAmelCase	:   Union[str, Any]    ,						lowerCAmelCase	:   Dict=13    ,						lowerCAmelCase	:   str=32    ,						lowerCAmelCase	:   Dict=2    ,						lowerCAmelCase	:   Tuple=3    ,						lowerCAmelCase	:   List[str]=16    ,						lowerCAmelCase	:   List[str]=[1, 2, 1]    ,						lowerCAmelCase	:   Optional[Any]=[2, 2, 4]    ,						lowerCAmelCase	:   Tuple=2    ,						lowerCAmelCase	:   Optional[Any]=2.0    ,						lowerCAmelCase	:   List[str]=True    ,						lowerCAmelCase	:   List[str]=0.0    ,						lowerCAmelCase	:   Tuple=0.0    ,						lowerCAmelCase	:   int=0.1    ,						lowerCAmelCase	:   int="gelu"    ,						lowerCAmelCase	:   List[Any]=False    ,						lowerCAmelCase	:   List[Any]=True    ,						lowerCAmelCase	:   Union[str, Any]=0.0_2    ,						lowerCAmelCase	:   Dict=1E-5    ,						lowerCAmelCase	:   Dict=True    ,						lowerCAmelCase	:   Dict=None    ,						lowerCAmelCase	:   Optional[Any]=True    ,						lowerCAmelCase	:   List[Any]=10    ,						lowerCAmelCase	:   Any=8    ,						)       ->   Optional[int]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	Union[str, Any]            =parent
     SCREAMING_SNAKE_CASE_:	List[Any]            =batch_size
     SCREAMING_SNAKE_CASE_:	str            =image_size
     SCREAMING_SNAKE_CASE_:	Optional[int]            =patch_size
     SCREAMING_SNAKE_CASE_:	Optional[Any]            =num_channels
     SCREAMING_SNAKE_CASE_:	Optional[Any]            =embed_dim
     SCREAMING_SNAKE_CASE_:	List[Any]            =depths
     SCREAMING_SNAKE_CASE_:	List[str]            =num_heads
     SCREAMING_SNAKE_CASE_:	Dict            =window_size
     SCREAMING_SNAKE_CASE_:	List[str]            =mlp_ratio
     SCREAMING_SNAKE_CASE_:	int            =qkv_bias
     SCREAMING_SNAKE_CASE_:	List[str]            =hidden_dropout_prob
     SCREAMING_SNAKE_CASE_:	List[str]            =attention_probs_dropout_prob
     SCREAMING_SNAKE_CASE_:	List[str]            =drop_path_rate
     SCREAMING_SNAKE_CASE_:	Any            =hidden_act
     SCREAMING_SNAKE_CASE_:	List[Any]            =use_absolute_embeddings
     SCREAMING_SNAKE_CASE_:	int            =patch_norm
     SCREAMING_SNAKE_CASE_:	int            =layer_norm_eps
     SCREAMING_SNAKE_CASE_:	str            =initializer_range
     SCREAMING_SNAKE_CASE_:	List[str]            =is_training
     SCREAMING_SNAKE_CASE_:	str            =scope
     SCREAMING_SNAKE_CASE_:	str            =use_labels
     SCREAMING_SNAKE_CASE_:	Dict            =type_sequence_label_size
     SCREAMING_SNAKE_CASE_:	Optional[int]            =encoder_stride



  def 		lowerCamelCase__    (  self	:   int						)       ->   str:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	Tuple            =floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

     SCREAMING_SNAKE_CASE_:	Optional[Any]            =None
     if self.use_labels:
        SCREAMING_SNAKE_CASE_:	Optional[int]            =ids_tensor([self.batch_size]    ,						self.type_sequence_label_size						)

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

     return config, pixel_values, labels



  def 		lowerCamelCase__    (  self	:   Tuple						)       ->   Tuple:


     '''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	:   Tuple    ,						lowerCAmelCase	:   List[Any]    ,						lowerCAmelCase	:   Union[str, Any]						)       ->   List[str]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	str            =DonutSwinModel(config=lowerCAmelCase						)
     model.to(lowerCAmelCase						)
     model.eval()
     SCREAMING_SNAKE_CASE_:	Tuple            =model(lowerCAmelCase						)

     SCREAMING_SNAKE_CASE_:	Dict            =((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths						) - 1))
     SCREAMING_SNAKE_CASE_:	Dict            =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[Any]						)       ->   Optional[int]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	Tuple            =self.prepare_config_and_inputs()
     (
         (
         SCREAMING_SNAKE_CASE_
     )     ,						(
         SCREAMING_SNAKE_CASE_
     )     ,						(
         SCREAMING_SNAKE_CASE_
     )     ,						
     ):	str            =config_and_inputs
     SCREAMING_SNAKE_CASE_:	int            ={"""pixel_values""": pixel_values}
     return config, inputs_dict







@require_torch
class    a		(					UpperCAmelCase__		, UpperCAmelCase__		, unittest.TestCase    ):
  UpperCamelCase		:			Optional[Any]         =   (DonutSwinModel,) if is_torch_available() else ()
  UpperCamelCase		:			List[str]         =   {'feature-extraction': DonutSwinModel} if is_torch_available() else {}
  UpperCamelCase		:			Dict         =   True

  UpperCamelCase		:			List[str]         =   False
  UpperCamelCase		:			int         =   False
  UpperCamelCase		:			Any         =   False



  def 		lowerCamelCase__    (  self	:   str						)       ->   Optional[int]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	int            =DonutSwinModelTester(self						)
     SCREAMING_SNAKE_CASE_:	Dict            =ConfigTester(self    ,						config_class=lowerCAmelCase    ,						embed_dim=37						)



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


     '''simple docstring'''




     return



  def 		lowerCamelCase__    (  self	:   List[str]						)       ->   Optional[Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	Any            =self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*lowerCAmelCase						)



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


     '''simple docstring'''




     pass



  def 		lowerCamelCase__    (  self	:   int						)       ->   Union[str, Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_     ,						SCREAMING_SNAKE_CASE_:	Tuple            =self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
        SCREAMING_SNAKE_CASE_:	List[str]            =model_class(lowerCAmelCase						)
        self.assertIsInstance(model.get_input_embeddings()    ,						(nn.Module)						)
        SCREAMING_SNAKE_CASE_:	Union[str, Any]            =model.get_output_embeddings()
        self.assertTrue(x is None or isinstance(lowerCAmelCase    ,						nn.Linear						)						)



  def 		lowerCamelCase__    (  self	:   List[str]						)       ->   Optional[int]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_     ,						SCREAMING_SNAKE_CASE_:	Union[str, Any]            =self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
        SCREAMING_SNAKE_CASE_:	Union[str, Any]            =model_class(lowerCAmelCase						)
        SCREAMING_SNAKE_CASE_:	Dict            =inspect.signature(model.forward						)
        # signature.parameters is an OrderedDict => so arg_names order is deterministic
        SCREAMING_SNAKE_CASE_:	Optional[int]            =[*signature.parameters.keys()]

        SCREAMING_SNAKE_CASE_:	Union[str, Any]            =["""pixel_values"""]
        self.assertListEqual(arg_names[:1]    ,						lowerCAmelCase						)



  def 		lowerCamelCase__    (  self	:   List[Any]						)       ->   str:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_     ,						SCREAMING_SNAKE_CASE_:	Optional[int]            =self.model_tester.prepare_config_and_inputs_for_common()
     SCREAMING_SNAKE_CASE_:	Optional[Any]            =True

     for model_class in self.all_model_classes:
        SCREAMING_SNAKE_CASE_:	Optional[int]            =True
        SCREAMING_SNAKE_CASE_:	List[Any]            =False
        SCREAMING_SNAKE_CASE_:	List[str]            =True
        SCREAMING_SNAKE_CASE_:	List[Any]            =model_class(lowerCAmelCase						)
        model.to(lowerCAmelCase						)
        model.eval()
        with torch.no_grad():
           SCREAMING_SNAKE_CASE_:	str            =model(**self._prepare_for_class(lowerCAmelCase    ,						lowerCAmelCase						)						)
        SCREAMING_SNAKE_CASE_:	Optional[int]            =outputs.attentions
        SCREAMING_SNAKE_CASE_:	List[str]            =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_:	Tuple            =True
        SCREAMING_SNAKE_CASE_:	Union[str, Any]            =config.window_size**2
        SCREAMING_SNAKE_CASE_:	List[Any]            =model_class(lowerCAmelCase						)
        model.to(lowerCAmelCase						)
        model.eval()
        with torch.no_grad():
           SCREAMING_SNAKE_CASE_:	int            =model(**self._prepare_for_class(lowerCAmelCase    ,						lowerCAmelCase						)						)
        SCREAMING_SNAKE_CASE_:	Any            =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_:	int            =len(lowerCAmelCase						)

        # Check attention is always last and order is fine
        SCREAMING_SNAKE_CASE_:	Dict            =True
        SCREAMING_SNAKE_CASE_:	List[Any]            =True
        SCREAMING_SNAKE_CASE_:	str            =model_class(lowerCAmelCase						)
        model.to(lowerCAmelCase						)
        model.eval()
        with torch.no_grad():
           SCREAMING_SNAKE_CASE_:	Dict            =model(**self._prepare_for_class(lowerCAmelCase    ,						lowerCAmelCase						)						)

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

        SCREAMING_SNAKE_CASE_:	Dict            =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	:   Optional[Any]    ,						lowerCAmelCase	:   Any    ,						lowerCAmelCase	:   int    ,						lowerCAmelCase	:   Union[str, Any]						)       ->   str:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	str            =model_class(lowerCAmelCase						)
     model.to(lowerCAmelCase						)
     model.eval()

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

     SCREAMING_SNAKE_CASE_:	Optional[Any]            =outputs.hidden_states

     SCREAMING_SNAKE_CASE_:	Optional[Any]            =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_:	Tuple            =(
         config.patch_size
         if isinstance(config.patch_size    ,						collections.abc.Iterable						)
         else (config.patch_size, config.patch_size)
     )

     SCREAMING_SNAKE_CASE_:	List[str]            =(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_:	Optional[int]            =outputs.reshaped_hidden_states
     self.assertEqual(len(lowerCAmelCase						)    ,						lowerCAmelCase						)

     SCREAMING_SNAKE_CASE_     ,						SCREAMING_SNAKE_CASE_     ,						SCREAMING_SNAKE_CASE_     ,						SCREAMING_SNAKE_CASE_:	List[str]            =reshaped_hidden_states[0].shape
     SCREAMING_SNAKE_CASE_:	Optional[Any]            =(
         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	:   Any						)       ->   List[str]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_     ,						SCREAMING_SNAKE_CASE_:	Tuple            =self.model_tester.prepare_config_and_inputs_for_common()

     SCREAMING_SNAKE_CASE_:	Optional[int]            =(
         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_:	Dict            =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_:	str            =True

        self.check_hidden_states_output(lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase						)



  def 		lowerCamelCase__    (  self	:   Tuple						)       ->   Optional[Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_     ,						SCREAMING_SNAKE_CASE_:	Optional[int]            =self.model_tester.prepare_config_and_inputs_for_common()
     SCREAMING_SNAKE_CASE_:	Dict            =3

     SCREAMING_SNAKE_CASE_:	Any            =(
         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_:	List[Any]            =(
         config.patch_size
         if isinstance(config.patch_size    ,						collections.abc.Iterable						)
         else (config.patch_size, config.patch_size)
     )

     SCREAMING_SNAKE_CASE_:	Optional[int]            =image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
     SCREAMING_SNAKE_CASE_:	Optional[Any]            =image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

     for model_class in self.all_model_classes:
        SCREAMING_SNAKE_CASE_:	str            =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_:	Dict            =True
        self.check_hidden_states_output(lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase    ,						(padded_height, padded_width)						)



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


     '''simple docstring'''




     for model_name in DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        SCREAMING_SNAKE_CASE_:	Tuple            =DonutSwinModel.from_pretrained(lowerCAmelCase						)
        self.assertIsNotNone(lowerCAmelCase						)





  def 		lowerCamelCase__    (  self	:   Dict						)       ->   Tuple:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_     ,						SCREAMING_SNAKE_CASE_:	Optional[Any]            =self.model_tester.prepare_config_and_inputs_for_common()

     SCREAMING_SNAKE_CASE_:	List[Any]            =_config_zero_init(lowerCAmelCase						)
     for model_class in self.all_model_classes:
        SCREAMING_SNAKE_CASE_:	Tuple            =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    a		(					UpperCAmelCase__		, unittest.TestCase    ):
  UpperCamelCase		:			Dict         =   PaintByExamplePipeline
  UpperCamelCase		:			str         =   IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
  UpperCamelCase		:			Tuple         =   IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
  UpperCamelCase		:			List[str]         =   frozenset([]    )  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess



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


     '''simple docstring'''




     torch.manual_seed(0						)
     SCREAMING_SNAKE_CASE_:	Any            =UNetaDConditionModel(
         block_out_channels=(32, 64)    ,						layers_per_block=2    ,						sample_size=32    ,						in_channels=9    ,						out_channels=4    ,						down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")    ,						up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")    ,						cross_attention_dim=32    ,						)
     SCREAMING_SNAKE_CASE_:	int            =PNDMScheduler(skip_prk_steps=lowerCAmelCase						)
     torch.manual_seed(0						)
     SCREAMING_SNAKE_CASE_:	str            =AutoencoderKL(
         block_out_channels=[32, 64]    ,						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_:	Tuple            =CLIPVisionConfig(
         hidden_size=32    ,						projection_dim=32    ,						intermediate_size=37    ,						layer_norm_eps=1E-05    ,						num_attention_heads=4    ,						num_hidden_layers=5    ,						image_size=32    ,						patch_size=4    ,						)
     SCREAMING_SNAKE_CASE_:	List[Any]            =PaintByExampleImageEncoder(lowerCAmelCase    ,						proj_size=32						)
     SCREAMING_SNAKE_CASE_:	Tuple            =CLIPImageProcessor(crop_size=32    ,						size=32						)

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



  def 		lowerCamelCase__    (  self	:   str    ,						lowerCAmelCase	:   Dict						)       ->   Union[str, Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	Union[str, Any]            =np.array(image.convert("""RGB"""						)						)
     SCREAMING_SNAKE_CASE_:	Optional[int]            =image[None].transpose(0    ,						3    ,						1    ,						2						)
     SCREAMING_SNAKE_CASE_:	Optional[Any]            =torch.from_numpy(lowerCAmelCase						).to(dtype=torch.floataa						) / 1_2_7.5 - 1.0
     return image



  def 		lowerCamelCase__    (  self	:   Union[str, Any]    ,						lowerCAmelCase	:   int="cpu"    ,						lowerCAmelCase	:   Dict=0						)       ->   Any:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	str            =floats_tensor((1, 3, 32, 32)    ,						rng=random.Random(lowerCAmelCase						)						).to(lowerCAmelCase						)
     SCREAMING_SNAKE_CASE_:	Tuple            =image.cpu().permute(0    ,						2    ,						3    ,						1						)[0]
     SCREAMING_SNAKE_CASE_:	Any            =Image.fromarray(np.uinta(lowerCAmelCase						)						).convert("""RGB"""						).resize((64, 64)						)
     SCREAMING_SNAKE_CASE_:	Tuple            =Image.fromarray(np.uinta(image + 4						)						).convert("""RGB"""						).resize((64, 64)						)
     SCREAMING_SNAKE_CASE_:	List[str]            =Image.fromarray(np.uinta(lowerCAmelCase						)						).convert("""RGB"""						).resize((32, 32)						)

     if str(lowerCAmelCase						).startswith("""mps"""						):
        SCREAMING_SNAKE_CASE_:	Dict            =torch.manual_seed(lowerCAmelCase						)
     else:
        SCREAMING_SNAKE_CASE_:	Optional[Any]            =torch.Generator(device=lowerCAmelCase						).manual_seed(lowerCAmelCase						)
     SCREAMING_SNAKE_CASE_:	str            ={
         """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[Any]						)       ->   Tuple:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	Tuple            =self.get_dummy_components()

     # make sure here that pndm scheduler skips prk
     SCREAMING_SNAKE_CASE_:	Union[str, Any]            =PaintByExamplePipeline(**lowerCAmelCase						)
     SCREAMING_SNAKE_CASE_:	Any            =pipe.to("""cpu"""						)
     pipe.set_progress_bar_config(disable=lowerCAmelCase						)

     SCREAMING_SNAKE_CASE_:	Optional[int]            =self.get_dummy_inputs()
     SCREAMING_SNAKE_CASE_:	int            =pipe(**lowerCAmelCase						)
     SCREAMING_SNAKE_CASE_:	int            =output.images

     SCREAMING_SNAKE_CASE_:	List[str]            =image[0, -3:, -3:, -1]

     assert image.shape == (1, 64, 64, 3)
     SCREAMING_SNAKE_CASE_:	Optional[Any]            =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	:   Any						)       ->   Union[str, Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	Optional[Any]            ="""cpu"""
     SCREAMING_SNAKE_CASE_:	Any            =self.get_dummy_inputs()
     inputs.pop("""mask_image"""						)
     SCREAMING_SNAKE_CASE_:	List[str]            =self.convert_to_pt(inputs.pop("""image"""						)						)
     SCREAMING_SNAKE_CASE_:	int            =image.clamp(0    ,						1						) / 2

     # make sure here that pndm scheduler skips prk
     SCREAMING_SNAKE_CASE_:	Optional[Any]            =PaintByExamplePipeline(**self.get_dummy_components()						)
     SCREAMING_SNAKE_CASE_:	List[Any]            =pipe.to(lowerCAmelCase						)
     pipe.set_progress_bar_config(disable=lowerCAmelCase						)

     SCREAMING_SNAKE_CASE_:	int            =pipe(image=lowerCAmelCase    ,						mask_image=mask_image[:, 0]    ,						**lowerCAmelCase						)
     SCREAMING_SNAKE_CASE_:	int            =output.images

     SCREAMING_SNAKE_CASE_:	str            =image.cpu().permute(0    ,						2    ,						3    ,						1						)[0]
     SCREAMING_SNAKE_CASE_:	List[Any]            =mask_image.cpu().permute(0    ,						2    ,						3    ,						1						)[0]

     SCREAMING_SNAKE_CASE_:	str            =Image.fromarray(np.uinta(lowerCAmelCase						)						).convert("""RGB"""						)
     SCREAMING_SNAKE_CASE_:	Tuple            =Image.fromarray(np.uinta(lowerCAmelCase						)						).convert("""RGB"""						)

     SCREAMING_SNAKE_CASE_:	int            =pipe(**self.get_dummy_inputs()						)
     SCREAMING_SNAKE_CASE_:	Optional[int]            =output.images

     assert out_a.shape == (1, 64, 64, 3)
     assert np.abs(out_a.flatten() - out_a.flatten()						).max() < 5E-2





  def 		lowerCamelCase__    (  self	:   Optional[Any]						)       ->   Dict:


     '''simple docstring'''




     super().test_inference_batch_single_identical(expected_max_diff=3E-3						)







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



  def 		lowerCamelCase__    (  self	:   Dict						)       ->   Any:


     '''simple docstring'''




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





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


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	List[str]            =load_image(
         """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
         """/paint_by_example/dog_in_bucket.png"""						)
     SCREAMING_SNAKE_CASE_:	Tuple            =load_image(
         """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
         """/paint_by_example/mask.png"""						)
     SCREAMING_SNAKE_CASE_:	List[str]            =load_image(
         """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
         """/paint_by_example/panda.jpg"""						)

     SCREAMING_SNAKE_CASE_:	Tuple            =PaintByExamplePipeline.from_pretrained("""Fantasy-Studio/Paint-by-Example"""						)
     SCREAMING_SNAKE_CASE_:	Optional[int]            =pipe.to(lowerCAmelCase						)
     pipe.set_progress_bar_config(disable=lowerCAmelCase						)

     SCREAMING_SNAKE_CASE_:	str            =torch.manual_seed(321						)
     SCREAMING_SNAKE_CASE_:	Optional[Any]            =pipe(
         image=lowerCAmelCase    ,						mask_image=lowerCAmelCase    ,						example_image=lowerCAmelCase    ,						generator=lowerCAmelCase    ,						guidance_scale=5.0    ,						num_inference_steps=50    ,						output_type="""np"""    ,						)

     SCREAMING_SNAKE_CASE_:	Union[str, Any]            =output.images

     SCREAMING_SNAKE_CASE_:	Any            =image[0, -3:, -3:, -1]

     assert image.shape == (1, 512, 512, 3)
     SCREAMING_SNAKE_CASE_:	Any            =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



