

from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import SwinConfig
from transformers.testing_utils import require_tf, require_vision, slow, to_atuple
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
 import tensorflow as tf

 from transformers.models.swin.modeling_tf_swin import (
     TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
     TFSwinForImageClassification,
     TFSwinForMaskedImageModeling,
     TFSwinModel,
 )


if is_vision_available():
 from PIL import Image

 from transformers import AutoImageProcessor



class 		a_	:



      """simple docstring"""







      def __init__(				self					:							Dict      ,snake_case					:							str      ,snake_case					:							List[Any]=13      ,snake_case					:							Optional[Any]=32      ,snake_case					:							Tuple=2      ,snake_case					:							str=3      ,snake_case					:							List[str]=16      ,snake_case					:							List[Any]=[1, 2, 1]      ,snake_case					:							int=[2, 2, 4]      ,snake_case					:							str=2      ,snake_case					:							Union[str, Any]=2.0      ,snake_case					:							List[str]=True      ,snake_case					:							int=0.0      ,snake_case					:							str=0.0      ,snake_case					:							List[str]=0.1      ,snake_case					:							int="gelu"      ,snake_case					:							List[str]=False      ,snake_case					:							Tuple=True      ,snake_case					:							Dict=0.02      ,snake_case					:							Optional[Any]=1e-5      ,snake_case					:							Union[str, Any]=True      ,snake_case					:							str=None      ,snake_case					:							Any=True      ,snake_case					:							Optional[int]=10      ,snake_case					:							str=8      ,):
           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					:							int    ):
           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    ):
           return SwinConfig(
               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					:							Dict      ,snake_case					:							Union[str, Any]      ,snake_case					:							Tuple      ,snake_case					:							List[Any]    ):
           SCREAMING_SNAKE_CASE										=TFSwinModel(config=snake_case    )
           SCREAMING_SNAKE_CASE										=model(snake_case    )

           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					:							Tuple      ,snake_case					:							Union[str, Any]      ,snake_case					:							List[str]      ,snake_case					:							Optional[Any]    ):
           SCREAMING_SNAKE_CASE										=TFSwinForMaskedImageModeling(config=snake_case    )
           SCREAMING_SNAKE_CASE										=model(snake_case    )
           self.parent.assertEqual(
               result.logits.shape      ,(self.batch_size, self.num_channels, self.image_size, self.image_size)    )

           # test greyscale images
           SCREAMING_SNAKE_CASE										=1
           SCREAMING_SNAKE_CASE										=TFSwinForMaskedImageModeling(snake_case    )

           SCREAMING_SNAKE_CASE										=floats_tensor([self.batch_size, 1, self.image_size, self.image_size]    )
           SCREAMING_SNAKE_CASE										=model(snake_case    )
           self.parent.assertEqual(result.logits.shape      ,(self.batch_size, 1, self.image_size, self.image_size)    )
      def 	_lowerCAmelCase					(				self					:							Dict      ,snake_case					:							str      ,snake_case					:							List[str]      ,snake_case					:							List[Any]    ):
           SCREAMING_SNAKE_CASE										=self.type_sequence_label_size
           SCREAMING_SNAKE_CASE										=TFSwinForImageClassification(snake_case    )
           SCREAMING_SNAKE_CASE										=model(snake_case      ,labels=snake_case    )
           self.parent.assertEqual(result.logits.shape      ,(self.batch_size, self.type_sequence_label_size)    )

           # test greyscale images
           SCREAMING_SNAKE_CASE										=1
           SCREAMING_SNAKE_CASE										=TFSwinForImageClassification(snake_case    )
           SCREAMING_SNAKE_CASE										=floats_tensor([self.batch_size, 1, self.image_size, self.image_size]    )
           SCREAMING_SNAKE_CASE										=model(snake_case    )
           self.parent.assertEqual(result.logits.shape      ,(self.batch_size, self.type_sequence_label_size)    )


      def 	_lowerCAmelCase					(				self					:							List[str]    ):
           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_tf
class 		a_	(							lowerCamelCase_			,      lowerCamelCase_			,      unittest.TestCase					):



      """simple docstring"""







      __UpperCAmelCase      		=				(
          (
              TFSwinModel,
              TFSwinForImageClassification,
              TFSwinForMaskedImageModeling,
          )
          if is_tf_available()
          else ()
      )
      __UpperCAmelCase      		=				(
          {'feature-extraction': TFSwinModel, 'image-classification': TFSwinForImageClassification}
          if is_tf_available()
          else {}
      )

      __UpperCAmelCase      		=				False
      __UpperCAmelCase      		=				False
      __UpperCAmelCase      		=				False
      __UpperCAmelCase      		=				False
      def 	_lowerCAmelCase					(				self					:							List[str]    ):
           SCREAMING_SNAKE_CASE										=TFSwinModelTester(self    )
           SCREAMING_SNAKE_CASE										=ConfigTester(self      ,config_class=snake_case      ,embed_dim=37    )
      def 	_lowerCAmelCase					(				self					:							Tuple    ):
           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					:							Dict    ):
           return
      def 	_lowerCAmelCase					(				self					:							str    ):
           SCREAMING_SNAKE_CASE										=self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*snake_case    )
      def 	_lowerCAmelCase					(				self					:							Dict    ):
           SCREAMING_SNAKE_CASE										=self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_masked_image_modeling(*snake_case    )
      def 	_lowerCAmelCase					(				self					:							Tuple    ):
           SCREAMING_SNAKE_CASE										=self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*snake_case    )
      @unittest.skip(reason='Swin does not use inputs_embeds'    )
      def 	_lowerCAmelCase					(				self					:							Union[str, Any]    ):
           pass
      def 	_lowerCAmelCase					(				self					:							Tuple    ):
           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(snake_case    )
                self.assertIsInstance(model.get_input_embeddings()      ,tf.keras.layers.Layer    )
                SCREAMING_SNAKE_CASE										=model.get_output_embeddings()
                self.assertTrue(x is None or isinstance(snake_case      ,tf.keras.layers.Dense    )    )
      def 	_lowerCAmelCase					(				self					:							int    ):
           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(snake_case    )
                SCREAMING_SNAKE_CASE										=inspect.signature(model.call    )
                # 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]      ,snake_case    )
      def 	_lowerCAmelCase					(				self					:							Any    ):
           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(snake_case    )
                SCREAMING_SNAKE_CASE										=model(**self._prepare_for_class(snake_case      ,snake_case    )    )
                SCREAMING_SNAKE_CASE										=outputs.attentions
                SCREAMING_SNAKE_CASE										=len(self.model_tester.depths    )
                self.assertEqual(len(snake_case    )      ,snake_case    )

                # 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(snake_case    )
                SCREAMING_SNAKE_CASE										=model(**self._prepare_for_class(snake_case      ,snake_case    )    )
                SCREAMING_SNAKE_CASE										=outputs.attentions
                self.assertEqual(len(snake_case    )      ,snake_case    )

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

                # Check attention is always last and order is fine
                SCREAMING_SNAKE_CASE										=True
                SCREAMING_SNAKE_CASE										=True
                SCREAMING_SNAKE_CASE										=model_class(snake_case    )
                SCREAMING_SNAKE_CASE										=model(**self._prepare_for_class(snake_case      ,snake_case    )    )

                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(snake_case    )    )

                SCREAMING_SNAKE_CASE										=outputs.attentions

                self.assertEqual(len(snake_case    )      ,snake_case    )

                self.assertListEqual(
                    list(self_attentions[0].shape[-3:]    )      ,[self.model_tester.num_heads[0], window_size_squared, window_size_squared]      ,)
      def 	_lowerCAmelCase					(				self					:							List[Any]      ,snake_case					:							Any      ,snake_case					:							str      ,snake_case					:							Tuple      ,snake_case					:							Union[str, Any]    ):
           SCREAMING_SNAKE_CASE										=model_class(snake_case    )
           SCREAMING_SNAKE_CASE										=model(**self._prepare_for_class(snake_case      ,snake_case    )    )
           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(snake_case    )      ,snake_case    )

           # Swin has a different seq_length
           SCREAMING_SNAKE_CASE										=to_atuple(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(snake_case    )      ,snake_case    )

           SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE										=reshaped_hidden_states[0].shape

           SCREAMING_SNAKE_CASE										=tf.reshape(reshaped_hidden_states[0]      ,(batch_size, num_channels, height * width)    )
           SCREAMING_SNAKE_CASE										=tf.transpose(snake_case      ,(0, 2, 1)    )

           self.assertListEqual(
               list(reshaped_hidden_states.shape[-2:]    )      ,[num_patches, self.model_tester.embed_dim]      ,)
      def 	_lowerCAmelCase					(				self					:							Optional[int]    ):
           SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE										=self.model_tester.prepare_config_and_inputs_for_common()

           SCREAMING_SNAKE_CASE										=to_atuple(self.model_tester.image_size    )

           for model_class in self.all_model_classes:
                SCREAMING_SNAKE_CASE										=True
                self.check_hidden_states_output(snake_case      ,snake_case      ,snake_case      ,snake_case    )

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

                self.check_hidden_states_output(snake_case      ,snake_case      ,snake_case      ,snake_case    )
      def 	_lowerCAmelCase					(				self					:							int    ):
           SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE										=self.model_tester.prepare_config_and_inputs_for_common()
           SCREAMING_SNAKE_CASE										=3

           SCREAMING_SNAKE_CASE										=to_atuple(self.model_tester.image_size    )
           SCREAMING_SNAKE_CASE										=to_atuple(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(snake_case      ,snake_case      ,snake_case      ,(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(snake_case      ,snake_case      ,snake_case      ,(padded_height, padded_width)    )


      @slow
      def 	_lowerCAmelCase					(				self					:							List[Any]    ):
           for model_name in TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                SCREAMING_SNAKE_CASE										=TFSwinModel.from_pretrained(snake_case    )
                self.assertIsNotNone(snake_case    )







@require_vision
@require_tf
class 		a_	(							unittest.TestCase					):



      """simple docstring"""







      @cached_property
      def 	_lowerCAmelCase					(				self					:							Any    ):
           return (
               AutoImageProcessor.from_pretrained('microsoft/swin-tiny-patch4-window7-224'    )
               if is_vision_available()
               else None
           )


      @slow
      def 	_lowerCAmelCase					(				self					:							int    ):
           SCREAMING_SNAKE_CASE										=TFSwinForImageClassification.from_pretrained('microsoft/swin-tiny-patch4-window7-224'    )
           SCREAMING_SNAKE_CASE										=self.default_image_processor

           SCREAMING_SNAKE_CASE										=Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'    )
           SCREAMING_SNAKE_CASE										=image_processor(images=snake_case      ,return_tensors='tf'    )

           # forward pass
           SCREAMING_SNAKE_CASE										=model(snake_case    )

           # verify the logits
           SCREAMING_SNAKE_CASE										=tf.TensorShape((1, 1000)    )
           self.assertEqual(outputs.logits.shape      ,snake_case    )
           SCREAMING_SNAKE_CASE										=tf.constant([-0.0_948, -0.6_454, -0.0_921]    )
           self.assertTrue(np.allclose(outputs.logits[0, :3]      ,snake_case      ,atol=1e-4    )    )




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_	(							lowerCamelCase_			,      unittest.TestCase					):



      """simple docstring"""







      __UpperCAmelCase      		=				RePaintPipeline
      __UpperCAmelCase      		=				IMAGE_INPAINTING_PARAMS - {'width', 'height', 'guidance_scale'}
      __UpperCAmelCase      		=				PipelineTesterMixin.required_optional_params - {
          'latents',
          'num_images_per_prompt',
          'callback',
          'callback_steps',
      }
      __UpperCAmelCase      		=				IMAGE_INPAINTING_BATCH_PARAMS
      __UpperCAmelCase      		=				False
      def 	_lowerCAmelCase					(				self					:							Tuple    ):
           torch.manual_seed(0    )
           torch.manual_seed(0    )
           SCREAMING_SNAKE_CASE										=UNetaDModel(
               block_out_channels=(32, 64)      ,layers_per_block=2      ,sample_size=32      ,in_channels=3      ,out_channels=3      ,down_block_types=('DownBlock2D', 'AttnDownBlock2D')      ,up_block_types=('AttnUpBlock2D', 'UpBlock2D')      ,)
           SCREAMING_SNAKE_CASE										=RePaintScheduler()
           SCREAMING_SNAKE_CASE										={'unet': unet, 'scheduler': scheduler}
           return components
      def 	_lowerCAmelCase					(				self					:							Tuple      ,snake_case					:							Optional[int]      ,snake_case					:							Dict=0    ):
           if str(snake_case    ).startswith('mps'    ):
                SCREAMING_SNAKE_CASE										=torch.manual_seed(snake_case    )
           else:
                SCREAMING_SNAKE_CASE										=torch.Generator(device=snake_case    ).manual_seed(snake_case    )
           SCREAMING_SNAKE_CASE										=np.random.RandomState(snake_case    ).standard_normal((1, 3, 32, 32)    )
           SCREAMING_SNAKE_CASE										=torch.from_numpy(snake_case    ).to(device=snake_case      ,dtype=torch.floataa    )
           SCREAMING_SNAKE_CASE										=(image > 0).to(device=snake_case      ,dtype=torch.floataa    )
           SCREAMING_SNAKE_CASE										={
               '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 	_lowerCAmelCase					(				self					:							Optional[Any]    ):
           SCREAMING_SNAKE_CASE										='cpu'  # ensure determinism for the device-dependent torch.Generator
           SCREAMING_SNAKE_CASE										=self.get_dummy_components()
           SCREAMING_SNAKE_CASE										=RePaintPipeline(**snake_case    )
           SCREAMING_SNAKE_CASE										=sd_pipe.to(snake_case    )
           sd_pipe.set_progress_bar_config(disable=snake_case    )

           SCREAMING_SNAKE_CASE										=self.get_dummy_inputs(snake_case    )
           SCREAMING_SNAKE_CASE										=sd_pipe(**snake_case    ).images
           SCREAMING_SNAKE_CASE										=image[0, -3:, -3:, -1]

           assert image.shape == (1, 32, 32, 3)
           SCREAMING_SNAKE_CASE										=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 	_lowerCAmelCase					(				self					:							Optional[int]    ):
           return super().test_save_load_local()
      @unittest.skip('non-deterministic pipeline'    )
      def 	_lowerCAmelCase					(				self					:							Tuple    ):
           return super().test_inference_batch_single_identical()
      @skip_mps
      def 	_lowerCAmelCase					(				self					:							Optional[Any]    ):
           return super().test_dict_tuple_outputs_equivalent()
      @skip_mps
      def 	_lowerCAmelCase					(				self					:							Optional[Any]    ):
           return super().test_save_load_optional_components()


      @skip_mps
      def 	_lowerCAmelCase					(				self					:							Any    ):
           return super().test_attention_slicing_forward_pass()







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



      """simple docstring"""







      def 	_lowerCAmelCase					(				self					:							Union[str, Any]    ):
           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()


      def 	_lowerCAmelCase					(				self					:							Tuple    ):
           SCREAMING_SNAKE_CASE										=load_image(
               'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'
               'repaint/celeba_hq_256.png'    )
           SCREAMING_SNAKE_CASE										=load_image(
               'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png'    )
           SCREAMING_SNAKE_CASE										=load_numpy(
               'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'
               'repaint/celeba_hq_256_result.npy'    )

           SCREAMING_SNAKE_CASE										='google/ddpm-ema-celebahq-256'
           SCREAMING_SNAKE_CASE										=UNetaDModel.from_pretrained(snake_case    )
           SCREAMING_SNAKE_CASE										=RePaintScheduler.from_pretrained(snake_case    )

           SCREAMING_SNAKE_CASE										=RePaintPipeline(unet=snake_case      ,scheduler=snake_case    ).to(snake_case    )
           repaint.set_progress_bar_config(disable=snake_case    )
           repaint.enable_attention_slicing()

           SCREAMING_SNAKE_CASE										=torch.manual_seed(0    )
           SCREAMING_SNAKE_CASE										=repaint(
               snake_case      ,snake_case      ,num_inference_steps=250      ,eta=0.0      ,jump_length=10      ,jump_n_sample=10      ,generator=snake_case      ,output_type='np'      ,)
           SCREAMING_SNAKE_CASE										=output.images[0]

           assert image.shape == (256, 256, 3)
           assert np.abs(expected_image - image    ).mean() < 1e-2

