


'''simple docstring'''







import inspect
import unittest

from transformers import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

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 MODEL_MAPPING, GLPNConfig, GLPNForDepthEstimation, GLPNModel
   from transformers.models.glpn.modeling_glpn import GLPN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
   from PIL import Image

   from transformers import GLPNImageProcessor







class A__	(						UpperCAmelCase__				):






      def __UpperCAmelCase	(			self      :List[Any]     )	-> int:




             '''simple docstring'''
             _a   :			Tuple					 =self.config_class(**self.inputs_dict     )
             self.parent.assertTrue(hasattr(SCREAMING_SNAKE_CASE					,						"""hidden_sizes"""     )     )
             self.parent.assertTrue(hasattr(SCREAMING_SNAKE_CASE					,						"""num_attention_heads"""     )     )
             self.parent.assertTrue(hasattr(SCREAMING_SNAKE_CASE					,						"""num_encoder_blocks"""     )     )







class A__	:






      def __init__(			self      :int					,						SCREAMING_SNAKE_CASE      :Any					,						SCREAMING_SNAKE_CASE      :List[str]=1_3					,						SCREAMING_SNAKE_CASE      :Dict=6_4					,						SCREAMING_SNAKE_CASE      :Union[str, Any]=3					,						SCREAMING_SNAKE_CASE      :List[str]=4					,						SCREAMING_SNAKE_CASE      :str=[2, 2, 2, 2]					,						SCREAMING_SNAKE_CASE      :List[str]=[8, 4, 2, 1]					,						SCREAMING_SNAKE_CASE      :Optional[Any]=[1_6, 3_2, 6_4, 1_2_8]					,						SCREAMING_SNAKE_CASE      :int=[1, 4, 8, 1_6]					,						SCREAMING_SNAKE_CASE      :int=[1, 2, 4, 8]					,						SCREAMING_SNAKE_CASE      :Tuple=True					,						SCREAMING_SNAKE_CASE      :str=True					,						SCREAMING_SNAKE_CASE      :Optional[Any]="gelu"					,						SCREAMING_SNAKE_CASE      :List[Any]=0.1					,						SCREAMING_SNAKE_CASE      :Union[str, Any]=0.1					,						SCREAMING_SNAKE_CASE      :Optional[int]=0.02					,						SCREAMING_SNAKE_CASE      :Optional[int]=1_6					,						SCREAMING_SNAKE_CASE      :Optional[int]=3					,						SCREAMING_SNAKE_CASE      :Dict=None					,						)	-> Union[str, Any]:




             '''simple docstring'''
             _a   :			Dict					 =parent
             _a   :			int					 =batch_size
             _a   :			Any					 =image_size
             _a   :			List[str]					 =num_channels
             _a   :			int					 =num_encoder_blocks
             _a   :			int					 =sr_ratios
             _a   :			Union[str, Any]					 =depths
             _a   :			Any					 =hidden_sizes
             _a   :			Dict					 =downsampling_rates
             _a   :			Optional[Any]					 =num_attention_heads
             _a   :			List[Any]					 =is_training
             _a   :			List[str]					 =use_labels
             _a   :			Any					 =hidden_act
             _a   :			List[str]					 =hidden_dropout_prob
             _a   :			Any					 =attention_probs_dropout_prob
             _a   :			Any					 =initializer_range
             _a   :			int					 =decoder_hidden_size
             _a   :			Optional[int]					 =num_labels
             _a   :			List[Any]					 =scope






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




             '''simple docstring'''
             _a   :			Tuple					 =floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

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

             _a   :			List[Any]					 =self.get_config()
             return config, pixel_values, labels






      def __UpperCAmelCase	(			self      :int     )	-> Any:




             '''simple docstring'''
             return GLPNConfig(
                 image_size=self.image_size					,						num_channels=self.num_channels					,						num_encoder_blocks=self.num_encoder_blocks					,						depths=self.depths					,						hidden_sizes=self.hidden_sizes					,						num_attention_heads=self.num_attention_heads					,						hidden_act=self.hidden_act					,						hidden_dropout_prob=self.hidden_dropout_prob					,						attention_probs_dropout_prob=self.attention_probs_dropout_prob					,						initializer_range=self.initializer_range					,						decoder_hidden_size=self.decoder_hidden_size					,						)






      def __UpperCAmelCase	(			self      :Optional[Any]					,						SCREAMING_SNAKE_CASE      :Dict					,						SCREAMING_SNAKE_CASE      :Any					,						SCREAMING_SNAKE_CASE      :List[str]     )	-> List[str]:




             '''simple docstring'''
             _a   :			Any					 =GLPNModel(config=SCREAMING_SNAKE_CASE     )
             model.to(SCREAMING_SNAKE_CASE     )
             model.eval()
             _a   :			List[str]					 =model(SCREAMING_SNAKE_CASE     )
             _a   :			List[str]					 =self.image_size // (self.downsampling_rates[-1] * 2)
             self.parent.assertEqual(
                 result.last_hidden_state.shape					,						(self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)     )






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




             '''simple docstring'''
             _a   :			Optional[Any]					 =self.num_labels
             _a   :			Optional[int]					 =GLPNForDepthEstimation(SCREAMING_SNAKE_CASE     )
             model.to(SCREAMING_SNAKE_CASE     )
             model.eval()
             _a   :			Optional[int]					 =model(SCREAMING_SNAKE_CASE     )
             self.parent.assertEqual(result.predicted_depth.shape					,						(self.batch_size, self.image_size, self.image_size)     )
             _a   :			List[Any]					 =model(SCREAMING_SNAKE_CASE					,						labels=SCREAMING_SNAKE_CASE     )
             self.parent.assertEqual(result.predicted_depth.shape					,						(self.batch_size, self.image_size, self.image_size)     )






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




             '''simple docstring'''
             _a   :			Optional[int]					 =self.prepare_config_and_inputs()
             _a ,   _a ,   _a   :			int					 =config_and_inputs
             _a   :			str					 ={"""pixel_values""": pixel_values}
             return config, inputs_dict







@require_torch
class A__	(						UpperCAmelCase__			,					UpperCAmelCase__			,					unittest.TestCase				):
      __UpperCamelCase	:  str     	=							(GLPNModel, GLPNForDepthEstimation) if is_torch_available() else ()
      __UpperCamelCase	:  int     	=							(
          {"depth-estimation": GLPNForDepthEstimation, "feature-extraction": GLPNModel} if is_torch_available() else {}
      )

      __UpperCamelCase	:  Union[str, Any]     	=							False
      __UpperCamelCase	:  int     	=							False
      __UpperCamelCase	:  str     	=							False






      def __UpperCAmelCase	(			self      :Tuple     )	-> List[Any]:




             '''simple docstring'''
             _a   :			Any					 =GLPNModelTester(self     )
             _a   :			Any					 =GLPNConfigTester(self					,						config_class=SCREAMING_SNAKE_CASE     )






      def __UpperCAmelCase	(			self      :Tuple     )	-> Dict:




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






      def __UpperCAmelCase	(			self      :str     )	-> int:




             '''simple docstring'''
             _a   :			Any					 =self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE     )






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




             '''simple docstring'''
             _a   :			Optional[Any]					 =self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_depth_estimation(*SCREAMING_SNAKE_CASE     )






      @unittest.skip("""GLPN does not use inputs_embeds"""     )
      def __UpperCAmelCase	(			self      :Optional[Any]     )	-> Optional[Any]:




             '''simple docstring'''
             pass






      @unittest.skip("""GLPN does not have get_input_embeddings method and get_output_embeddings methods"""     )
      def __UpperCAmelCase	(			self      :Tuple     )	-> str:




             '''simple docstring'''
             pass






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




             '''simple docstring'''
             _a ,   _a   :			str					 =self.model_tester.prepare_config_and_inputs_for_common()

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

                    _a   :			Dict					 =["""pixel_values"""]
                    self.assertListEqual(arg_names[:1]					,						SCREAMING_SNAKE_CASE     )






      def __UpperCAmelCase	(			self      :Tuple     )	-> Dict:




             '''simple docstring'''
             _a ,   _a   :			Optional[int]					 =self.model_tester.prepare_config_and_inputs_for_common()
             _a   :			Any					 =True

             for model_class in self.all_model_classes:
                    _a   :			Tuple					 =True
                    _a   :			int					 =False
                    _a   :			Optional[Any]					 =True
                    _a   :			Dict					 =model_class(SCREAMING_SNAKE_CASE     )
                    model.to(SCREAMING_SNAKE_CASE     )
                    model.eval()
                    with torch.no_grad():
                           _a   :			Optional[Any]					 =model(**self._prepare_for_class(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE     )     )
                    _a   :			Any					 =outputs.attentions

                    _a   :			Tuple					 =sum(self.model_tester.depths     )
                    self.assertEqual(len(SCREAMING_SNAKE_CASE     )					,						SCREAMING_SNAKE_CASE     )

                    # check that output_attentions also work using config
                    del inputs_dict["output_attentions"]
                    _a   :			List[Any]					 =True
                    _a   :			Union[str, Any]					 =model_class(SCREAMING_SNAKE_CASE     )
                    model.to(SCREAMING_SNAKE_CASE     )
                    model.eval()
                    with torch.no_grad():
                           _a   :			Optional[int]					 =model(**self._prepare_for_class(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE     )     )
                    _a   :			Optional[int]					 =outputs.attentions

                    self.assertEqual(len(SCREAMING_SNAKE_CASE     )					,						SCREAMING_SNAKE_CASE     )

                    # verify the first attentions (first block, first layer)
                    _a   :			List[str]					 =(self.model_tester.image_size // 4) ** 2
                    _a   :			int					 =(self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
                    self.assertListEqual(
                        list(attentions[0].shape[-3:]     )					,						[self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]					,						)

                    # verify the last attentions (last block, last layer)
                    _a   :			Any					 =(self.model_tester.image_size // 3_2) ** 2
                    _a   :			Optional[int]					 =(self.model_tester.image_size // (3_2 * self.model_tester.sr_ratios[-1])) ** 2
                    self.assertListEqual(
                        list(attentions[-1].shape[-3:]     )					,						[self.model_tester.num_attention_heads[-1], expected_seq_len, expected_reduced_seq_len]					,						)
                    _a   :			Optional[int]					 =len(SCREAMING_SNAKE_CASE     )

                    # Check attention is always last and order is fine
                    _a   :			Any					 =True
                    _a   :			Optional[int]					 =True
                    _a   :			Optional[int]					 =model_class(SCREAMING_SNAKE_CASE     )
                    model.to(SCREAMING_SNAKE_CASE     )
                    model.eval()
                    with torch.no_grad():
                           _a   :			Optional[Any]					 =model(**self._prepare_for_class(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE     )     )

                    self.assertEqual(out_len + 1					,						len(SCREAMING_SNAKE_CASE     )     )

                    _a   :			Union[str, Any]					 =outputs.attentions

                    self.assertEqual(len(SCREAMING_SNAKE_CASE     )					,						SCREAMING_SNAKE_CASE     )
                    # verify the first attentions (first block, first layer)
                    _a   :			List[Any]					 =(self.model_tester.image_size // 4) ** 2
                    _a   :			Tuple					 =(self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
                    self.assertListEqual(
                        list(self_attentions[0].shape[-3:]     )					,						[self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]					,						)






      def __UpperCAmelCase	(			self      :List[Any]     )	-> Tuple:




             '''simple docstring'''
             def check_hidden_states_output(SCREAMING_SNAKE_CASE      :Optional[Any]					,						SCREAMING_SNAKE_CASE      :str					,						SCREAMING_SNAKE_CASE      :int     ):
                    _a   :			Optional[int]					 =model_class(SCREAMING_SNAKE_CASE     )
                    model.to(SCREAMING_SNAKE_CASE     )
                    model.eval()

                    with torch.no_grad():
                           _a   :			List[str]					 =model(**self._prepare_for_class(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE     )     )

                    _a   :			Optional[int]					 =outputs.hidden_states

                    _a   :			Tuple					 =self.model_tester.num_encoder_blocks
                    self.assertEqual(len(SCREAMING_SNAKE_CASE     )					,						SCREAMING_SNAKE_CASE     )

                    # verify the first hidden states (first block)
                    self.assertListEqual(
                        list(hidden_states[0].shape[-3:]     )					,						[
                            self.model_tester.hidden_sizes[0],
                            self.model_tester.image_size // 4,
                            self.model_tester.image_size // 4,
                        ]					,						)

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

             for model_class in self.all_model_classes:
                    _a   :			Tuple					 =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"]
                    _a   :			Any					 =True

                    check_hidden_states_output(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE     )






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




             '''simple docstring'''
             if not self.model_tester.is_training:
                    return

             _a ,   _a   :			List[str]					 =self.model_tester.prepare_config_and_inputs_for_common()
             _a   :			Any					 =True

             for model_class in self.all_model_classes:
                    if model_class in get_values(SCREAMING_SNAKE_CASE     ):
                           continue
                    # TODO: remove the following 3 lines once we have a MODEL_FOR_DEPTH_ESTIMATION_MAPPING
                    # this can then be incorporated into _prepare_for_class in test_modeling_common.py
                    if model_class.__name__ == "GLPNForDepthEstimation":
                           _a ,   _a ,   _a ,   _a   :			Optional[Any]					 =inputs_dict["""pixel_values"""].shape
                           _a   :			Optional[int]					 =torch.zeros(
                               [self.model_tester.batch_size, height, width]					,						device=SCREAMING_SNAKE_CASE     ).long()
                    _a   :			Dict					 =model_class(SCREAMING_SNAKE_CASE     )
                    model.to(SCREAMING_SNAKE_CASE     )
                    model.train()
                    _a   :			int					 =self._prepare_for_class(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						return_labels=SCREAMING_SNAKE_CASE     )
                    _a   :			str					 =model(**SCREAMING_SNAKE_CASE     ).loss
                    loss.backward()






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




             '''simple docstring'''
             for model_name in GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                    _a   :			Union[str, Any]					 =GLPNModel.from_pretrained(SCREAMING_SNAKE_CASE     )
                    self.assertIsNotNone(SCREAMING_SNAKE_CASE     )


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







@require_torch
@require_vision
@slow
class A__	(						unittest.TestCase				):






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




             '''simple docstring'''
             _a   :			Union[str, Any]					 =GLPNImageProcessor.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]     )
             _a   :			int					 =GLPNForDepthEstimation.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]     ).to(SCREAMING_SNAKE_CASE     )

             _a   :			Dict					 =prepare_img()
             _a   :			List[Any]					 =image_processor(images=SCREAMING_SNAKE_CASE					,						return_tensors="""pt"""     ).to(SCREAMING_SNAKE_CASE     )

             # forward pass
             with torch.no_grad():
                    _a   :			Any					 =model(**SCREAMING_SNAKE_CASE     )

             # verify the predicted depth
             _a   :			str					 =torch.Size([1, 4_8_0, 6_4_0]     )
             self.assertEqual(outputs.predicted_depth.shape					,						SCREAMING_SNAKE_CASE     )

             _a   :			Any					 =torch.tensor(
                 [[3.4_291, 2.7_865, 2.5_151], [3.2_841, 2.7_021, 2.3_502], [3.1_147, 2.4_625, 2.2_481]]     ).to(SCREAMING_SNAKE_CASE     )

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






'''simple docstring'''







import gc
import unittest

import numpy as np
import torch

from diffusers import RePaintPipeline, RePaintScheduler, UNetaDModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_image,
    load_numpy,
    nightly,
    require_torch_gpu,
    skip_mps,
    torch_device,
)

from ..pipeline_params import IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()







class A__	(						UpperCAmelCase__			,					unittest.TestCase				):
      __UpperCamelCase	:  str     	=							RePaintPipeline
      __UpperCamelCase	:  Dict     	=							IMAGE_INPAINTING_PARAMS - {"width", "height", "guidance_scale"}
      __UpperCamelCase	:  List[str]     	=							PipelineTesterMixin.required_optional_params - {
          "latents",
          "num_images_per_prompt",
          "callback",
          "callback_steps",
      }
      __UpperCamelCase	:  List[Any]     	=							IMAGE_INPAINTING_BATCH_PARAMS
      __UpperCamelCase	:  Tuple     	=							False






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




             '''simple docstring'''
             torch.manual_seed(0     )
             torch.manual_seed(0     )
             _a   :			Tuple					 =UNetaDModel(
                 block_out_channels=(3_2, 6_4)					,						layers_per_block=2					,						sample_size=3_2					,						in_channels=3					,						out_channels=3					,						down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")					,						up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")					,						)
             _a   :			Any					 =RePaintScheduler()
             _a   :			Optional[Any]					 ={"""unet""": unet, """scheduler""": scheduler}
             return components






      def __UpperCAmelCase	(			self      :List[Any]					,						SCREAMING_SNAKE_CASE      :str					,						SCREAMING_SNAKE_CASE      :Optional[Any]=0     )	-> Any:




             '''simple docstring'''
             if str(SCREAMING_SNAKE_CASE     ).startswith("""mps"""     ):
                    _a   :			Optional[Any]					 =torch.manual_seed(SCREAMING_SNAKE_CASE     )
             else:
                    _a   :			Any					 =torch.Generator(device=SCREAMING_SNAKE_CASE     ).manual_seed(SCREAMING_SNAKE_CASE     )
             _a   :			int					 =np.random.RandomState(SCREAMING_SNAKE_CASE     ).standard_normal((1, 3, 3_2, 3_2)     )
             _a   :			Union[str, Any]					 =torch.from_numpy(SCREAMING_SNAKE_CASE     ).to(device=SCREAMING_SNAKE_CASE					,						dtype=torch.floataa     )
             _a   :			List[Any]					 =(image > 0).to(device=SCREAMING_SNAKE_CASE					,						dtype=torch.floataa     )
             _a   :			str					 ={
                 """image""": image,
                 """mask_image""": mask,
                 """generator""": generator,
                 """num_inference_steps""": 5,
                 """eta""": 0.0,
                 """jump_length""": 2,
                 """jump_n_sample""": 2,
                 """output_type""": """numpy""",
             }
             return inputs






      def __UpperCAmelCase	(			self      :Dict     )	-> Dict:




             '''simple docstring'''
             _a   :			Tuple					 ="""cpu"""  # ensure determinism for the device-dependent torch.Generator
             _a   :			Tuple					 =self.get_dummy_components()
             _a   :			str					 =RePaintPipeline(**SCREAMING_SNAKE_CASE     )
             _a   :			int					 =sd_pipe.to(SCREAMING_SNAKE_CASE     )
             sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE     )

             _a   :			Optional[int]					 =self.get_dummy_inputs(SCREAMING_SNAKE_CASE     )
             _a   :			Optional[Any]					 =sd_pipe(**SCREAMING_SNAKE_CASE     ).images
             _a   :			Any					 =image[0, -3:, -3:, -1]

             assert image.shape == (1, 3_2, 3_2, 3)
             _a   :			str					 =np.array([1.0_000, 0.5_426, 0.5_497, 0.2_200, 1.0_000, 1.0_000, 0.5_623, 1.0_000, 0.6_274]     )

             assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1e-3






      @skip_mps
      def __UpperCAmelCase	(			self      :Union[str, Any]     )	-> Optional[int]:




             '''simple docstring'''
             return super().test_save_load_local()






      @unittest.skip("""non-deterministic pipeline"""     )
      def __UpperCAmelCase	(			self      :Tuple     )	-> int:




             '''simple docstring'''
             return super().test_inference_batch_single_identical()






      @skip_mps
      def __UpperCAmelCase	(			self      :Any     )	-> Tuple:




             '''simple docstring'''
             return super().test_dict_tuple_outputs_equivalent()






      @skip_mps
      def __UpperCAmelCase	(			self      :str     )	-> Optional[Any]:




             '''simple docstring'''
             return super().test_save_load_optional_components()






      @skip_mps
      def __UpperCAmelCase	(			self      :Dict     )	-> List[str]:




             '''simple docstring'''
             return super().test_attention_slicing_forward_pass()







@nightly
@require_torch_gpu
class A__	(						unittest.TestCase				):






      def __UpperCAmelCase	(			self      :Tuple     )	-> Optional[int]:




             '''simple docstring'''
             super().tearDown()
             gc.collect()
             torch.cuda.empty_cache()






      def __UpperCAmelCase	(			self      :str     )	-> int:




             '''simple docstring'''
             _a   :			Optional[int]					 =load_image(
                 """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"""
                 """repaint/celeba_hq_256.png"""     )
             _a   :			Optional[Any]					 =load_image(
                 """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png"""     )
             _a   :			Union[str, Any]					 =load_numpy(
                 """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"""
                 """repaint/celeba_hq_256_result.npy"""     )

             _a   :			str					 ="""google/ddpm-ema-celebahq-256"""
             _a   :			Union[str, Any]					 =UNetaDModel.from_pretrained(SCREAMING_SNAKE_CASE     )
             _a   :			Dict					 =RePaintScheduler.from_pretrained(SCREAMING_SNAKE_CASE     )

             _a   :			str					 =RePaintPipeline(unet=SCREAMING_SNAKE_CASE					,						scheduler=SCREAMING_SNAKE_CASE     ).to(SCREAMING_SNAKE_CASE     )
             repaint.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE     )
             repaint.enable_attention_slicing()

             _a   :			Dict					 =torch.manual_seed(0     )
             _a   :			Any					 =repaint(
                 SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						num_inference_steps=2_5_0					,						eta=0.0					,						jump_length=1_0					,						jump_n_sample=1_0					,						generator=SCREAMING_SNAKE_CASE					,						output_type="""np"""					,						)
             _a   :			Any					 =output.images[0]

             assert image.shape == (2_5_6, 2_5_6, 3)
             assert np.abs(expected_image - image     ).mean() < 1e-2


