code: 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 					UpperCAmelCase_				(							_a     ,     unittest.TestCase    ):
      UpperCAmelCase_			:       str						 =							RePaintPipeline
      UpperCAmelCase_			:       List[Any]						 =							IMAGE_INPAINTING_PARAMS - {'width', 'height', 'guidance_scale'}
      UpperCAmelCase_			:       Optional[int]						 =							PipelineTesterMixin.required_optional_params - {
          'latents',
          'num_images_per_prompt',
          'callback',
          'callback_steps',
      }
      UpperCAmelCase_			:       str						 =							IMAGE_INPAINTING_BATCH_PARAMS
      UpperCAmelCase_			:       List[str]						 =							False
  
  
  
  
      def 						__lowerCamelCase (					self  :   List[Any]   )	-> Optional[int]:
        torch.manual_seed(0   )
        torch.manual_seed(0   )
        a__     		=     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")      ,      )
        a__     		=     RePaintScheduler()
        a__     		=     {"unet": unet, "scheduler": scheduler}
        return components
  
  
  
  
      def 						__lowerCamelCase (					self  :   Dict      ,      lowerCAmelCase  :   Optional[int]      ,      lowerCAmelCase  :   Optional[int]=0   )	-> List[str]:
  
  
  
  
  
  
  
        if str(lowerCAmelCase   ).startswith("mps"   ):
          a__     		=     torch.manual_seed(lowerCAmelCase   )
        else:
          a__     		=     torch.Generator(device=lowerCAmelCase   ).manual_seed(lowerCAmelCase   )
        a__     		=     np.random.RandomState(lowerCAmelCase   ).standard_normal((1, 3, 32, 32)   )
        a__     		=     torch.from_numpy(lowerCAmelCase   ).to(device=lowerCAmelCase      ,      dtype=torch.floataa   )
        a__     		=     (image > 0).to(device=lowerCAmelCase      ,      dtype=torch.floataa   )
        a__     		=     {
            "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  :   Tuple   )	-> Union[str, Any]:
        a__     		=     "cpu"  # ensure determinism for the device-dependent torch.Generator
        a__     		=     self.get_dummy_components()
        a__     		=     RePaintPipeline(**lowerCAmelCase   )
        a__     		=     sd_pipe.to(lowerCAmelCase   )
        sd_pipe.set_progress_bar_config(disable=lowerCAmelCase   )
  
        a__     		=     self.get_dummy_inputs(lowerCAmelCase   )
        a__     		=     sd_pipe(**lowerCAmelCase   ).images
        a__     		=     image[0, -3:, -3:, -1]
  
        assert image.shape == (1, 32, 32, 3)
        a__     		=     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[Any]   )	-> Union[str, Any]:
        return super().test_save_load_local()
  
  
  
  
      @unittest.skip("non-deterministic pipeline"   )
      def 						__lowerCamelCase (					self  :   int   )	-> Optional[Any]:
        return super().test_inference_batch_single_identical()
  
  
  
  
      @skip_mps
      def 						__lowerCamelCase (					self  :   Optional[Any]   )	-> Optional[Any]:
        return super().test_dict_tuple_outputs_equivalent()
  
  
  
  
      @skip_mps
      def 						__lowerCamelCase (					self  :   str   )	-> Union[str, Any]:
        return super().test_save_load_optional_components()
  
  
  
  
      @skip_mps
      def 						__lowerCamelCase (					self  :   List[str]   )	-> List[Any]:
        return super().test_attention_slicing_forward_pass()
  
  
  
  
  
  
  
  @nightly
  @require_torch_gpu
  class 					UpperCAmelCase_				(							unittest.TestCase    ):
  
  
  
  
      def 						__lowerCamelCase (					self  :   Union[str, Any]   )	-> List[str]:
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()
  
  
  
  
      def 						__lowerCamelCase (					self  :   Tuple   )	-> List[str]:
        a__     		=     load_image(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"
            "repaint/celeba_hq_256.png"   )
        a__     		=     load_image(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png"   )
        a__     		=     load_numpy(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"
            "repaint/celeba_hq_256_result.npy"   )
  
        a__     		=     "google/ddpm-ema-celebahq-256"
        a__     		=     UNetaDModel.from_pretrained(lowerCAmelCase   )
        a__     		=     RePaintScheduler.from_pretrained(lowerCAmelCase   )
  
        a__     		=     RePaintPipeline(unet=lowerCAmelCase      ,      scheduler=lowerCAmelCase   ).to(lowerCAmelCase   )
        repaint.set_progress_bar_config(disable=lowerCAmelCase   )
        repaint.enable_attention_slicing()
  
        a__     		=     torch.manual_seed(0   )
        a__     		=     repaint(
            lowerCAmelCase      ,      lowerCAmelCase      ,      num_inference_steps=250      ,      eta=0.0      ,      jump_length=10      ,      jump_n_sample=10      ,      generator=lowerCAmelCase      ,      output_type="np"      ,      )
        a__     		=     output.images[0]
  
        assert image.shape == (256, 256, 3)
        assert np.abs(expected_image - image   ).mean() < 1e-2
  
  
style_context: import random
  
  
  
  
  
  
  
  class 					UpperCAmelCase_				:
  
  
  
  
      def __init__(					self  :   Optional[int]      ,      lowerCAmelCase  :   list[list[float]]      ,      lowerCAmelCase  :   list[int]      ,      lowerCAmelCase  :   float = 0.01      ,      lowerCAmelCase  :   int = 1000      ,      lowerCAmelCase  :   float = -1      ,      )	-> None:
        a__     		=     sample
        if len(self.sample   ) == 0:
          raise ValueError("Sample data can not be empty"   )
        a__     		=     target
        if len(self.target   ) == 0:
          raise ValueError("Target data can not be empty"   )
        if len(self.sample   ) != len(self.target   ):
          raise ValueError("Sample data and Target data do not have matching lengths"   )
        a__     		=     learning_rate
        a__     		=     epoch_number
        a__     		=     bias
        a__     		=     len(lowerCAmelCase   )
        a__     		=     len(sample[0]   )  # number of columns in dataset
        a__     		=     []
  
  
  
  
      def 						__lowerCamelCase (					self  :   Optional[Any]   )	-> None:
        for sample in self.sample:
          sample.insert(0      ,      self.bias   )
  
        for _ in range(self.col_sample   ):
          self.weight.append(random.random()   )
  
        self.weight.insert(0      ,      self.bias   )
  
        a__     		=     0
  
        while True:
          a__     		=     False
          for i in range(self.number_sample   ):
            a__     		=     0
            for j in range(self.col_sample + 1   ):
              a__     		=     u + self.weight[j] * self.sample[i][j]
            a__     		=     self.sign(lowerCAmelCase   )
            if y != self.target[i]:
              for j in range(self.col_sample + 1   ):
                a__     		=     (
                    self.weight[j]
                    + self.learning_rate
                    * (self.target[i] - y)
                    * self.sample[i][j]
                )
              a__     		=     True
              # print('Epoch: \n',epoch_count)
          a__     		=     epoch_count + 1
          # if you want control the epoch or just by error
          if not has_misclassified:
            print(("\nEpoch:\n", epoch_count)   )
            print("------------------------\n"   )
            # if epoch_count > self.epoch_number or not error:
            break
  
  
  
  
      def 						__lowerCamelCase (					self  :   List[str]      ,      lowerCAmelCase  :   list[float]   )	-> None:
        if len(self.sample   ) == 0:
          raise ValueError("Sample data can not be empty"   )
        sample.insert(0      ,      self.bias   )
        a__     		=     0
        for i in range(self.col_sample + 1   ):
          a__     		=     u + self.weight[i] * sample[i]
  
        a__     		=     self.sign(lowerCAmelCase   )
  
        if y == -1:
          print(("Sample: ", sample)   )
          print("classification: P1"   )
        else:
          print(("Sample: ", sample)   )
          print("classification: P2"   )
  
  
  
  
      def 						__lowerCamelCase (					self  :   int      ,      lowerCAmelCase  :   float   )	-> int:
        return 1 if u >= 0 else -1
  
  _SCREAMING_SNAKE_CASE   						=    [
      [-0.6508, 0.1097, 4.0009],
      [-1.4492, 0.8896, 4.4005],
      [2.0850, 0.6876, 12.0710],
      [0.2626, 1.1476, 7.7985],
      [0.6418, 1.0234, 7.0427],
      [0.2569, 0.6730, 8.3265],
      [1.1155, 0.6043, 7.4446],
      [0.0914, 0.3399, 7.0677],
      [0.0121, 0.5256, 4.6316],
      [-0.0429, 0.4660, 5.4323],
      [0.4340, 0.6870, 8.2287],
      [0.2735, 1.0287, 7.1934],
      [0.4839, 0.4851, 7.4850],
      [0.4089, -0.1267, 5.5019],
      [1.4391, 0.1614, 8.5843],
      [-0.9115, -0.1973, 2.1962],
      [0.3654, 1.0475, 7.4858],
      [0.2144, 0.7515, 7.1699],
      [0.2013, 1.0014, 6.5489],
      [0.6483, 0.2183, 5.8991],
      [-0.1147, 0.2242, 7.2435],
      [-0.7970, 0.8795, 3.8762],
      [-1.0625, 0.6366, 2.4707],
      [0.5307, 0.1285, 5.6883],
      [-1.2200, 0.7777, 1.7252],
      [0.3957, 0.1076, 5.6623],
      [-0.1013, 0.5989, 7.1812],
      [2.4482, 0.9455, 11.2095],
      [2.0149, 0.6192, 10.9263],
      [0.2012, 0.2611, 5.4631],
  ]
  
  _SCREAMING_SNAKE_CASE   						=    [
      -1,
      -1,
      -1,
      1,
      1,
      -1,
      1,
      -1,
      1,
      1,
      -1,
      1,
      -1,
      -1,
      -1,
      -1,
      1,
      1,
      1,
      1,
      -1,
      1,
      1,
      1,
      1,
      -1,
      -1,
      1,
      -1,
      1,
  ]
  
  
  if __name__ == "__main__":
       import doctest
  
       doctest.testmod()
  
       _SCREAMING_SNAKE_CASE   						=    Perceptron(
           sample=samples, target=target, learning_rate=0.01, epoch_number=1000, bias=-1
       )
       network.training()
       print("""Finished training perceptron""")
       print("""Enter values to predict or q to exit""")
       while True:
            _SCREAMING_SNAKE_CASE   						=    []
            for i in range(len(samples[0])):
                 _SCREAMING_SNAKE_CASE   						=    input("""value: """).strip()
                 if user_input == "q":
                      break
                 _SCREAMING_SNAKE_CASE   						=    float(user_input)
                 sample.insert(i, observation)
            network.sort(sample)
  
  