
'''simple docstring'''





from typing import Any

import numpy as np





def 							snake_case				(		UpperCAmelCase						)->      bool:







      """simple docstring"""
      assert np.shape(UpperCAmelCase						)[0] == np.shape(UpperCAmelCase						)[1]

      # If matrix not symmetric, exit right away.
      if np.allclose(UpperCAmelCase   ,	matrix.T						) is False:
            return False

      # Get eigenvalues and eignevectors for a symmetric matrix.
      __A						,   __A													=      np.linalg.eigh(UpperCAmelCase						)

      # Check sign of all eigenvalues.
      # np.all returns a value of type np.bool_
      return bool(np.all(eigen_values > 0						)						)





def 							snake_case				(		UpperCAmelCase						)->      Any:







      """simple docstring"""
      __A													=      np.random.randn(UpperCAmelCase   ,	UpperCAmelCase						)
      __A													=      np.dot(UpperCAmelCase   ,	random_matrix.T						)
      assert _is_matrix_spd(UpperCAmelCase						)
      return spd_matrix





def 							snake_case				(		UpperCAmelCase   ,	UpperCAmelCase   ,	UpperCAmelCase = 1_0_0_0   ,	UpperCAmelCase = 1e-8   ,	)->      Any:







      """simple docstring"""
      assert np.shape(UpperCAmelCase						)[0] == np.shape(UpperCAmelCase						)[1]
      assert np.shape(UpperCAmelCase						)[0] == np.shape(UpperCAmelCase						)[0]
      assert _is_matrix_spd(UpperCAmelCase						)

      # Initialize solution guess, residual, search direction.
      __A													=      np.zeros((np.shape(UpperCAmelCase						)[0], 1)						)
      __A													=      np.copy(UpperCAmelCase						)
      __A													=      np.copy(UpperCAmelCase						)

      # Set initial errors in solution guess and residual.
      __A													=      1e9
      __A													=      1e9
      __A													=      1e9

      # Set iteration counter to threshold number of iterations.
      __A													=      0

      while error > tol:
            # Save this value so we only calculate the matrix-vector product once.
            __A													=      np.dot(UpperCAmelCase   ,	UpperCAmelCase						)

            # The main algorithm.

            # Update search direction magnitude.
            __A													=      np.dot(ra.T   ,	UpperCAmelCase						) / np.dot(pa.T   ,	UpperCAmelCase						)
            # Update solution guess.
            __A													=      xa + alpha * pa
            # Calculate new residual.
            __A													=      ra - alpha * w
            # Calculate new Krylov subspace scale.
            __A													=      np.dot(r.T   ,	UpperCAmelCase						) / np.dot(ra.T   ,	UpperCAmelCase						)
            # Calculate new A conjuage search direction.
            __A													=      r + beta * pa

            # Calculate errors.
            __A													=      np.linalg.norm(r - ra						)
            __A													=      np.linalg.norm(x - xa						)
            __A													=      np.maximum(UpperCAmelCase   ,	UpperCAmelCase						)

            # Update variables.
            __A													=      np.copy(UpperCAmelCase						)
            __A													=      np.copy(UpperCAmelCase						)
            __A													=      np.copy(UpperCAmelCase						)

            # Update number of iterations.
            iterations += 1
            if iterations > max_iterations:
                  break

      return x





def 							snake_case				(		)->      None:







      """simple docstring"""
      __A													=      3
      __A													=      _create_spd_matrix(UpperCAmelCase						)
      __A													=      np.random.randn(UpperCAmelCase   ,	1						)
      __A													=      np.dot(UpperCAmelCase   ,	UpperCAmelCase						)

      # Numpy solution.
      __A													=      np.linalg.solve(UpperCAmelCase   ,	UpperCAmelCase						)

      # Our implementation.
      __A													=      conjugate_gradient(UpperCAmelCase   ,	UpperCAmelCase						)

      # Ensure both solutions are close to x_true (and therefore one another).
      assert np.linalg.norm(x_numpy - x_true						) <= 1e-6
      assert np.linalg.norm(x_conjugate_gradient - x_true						) <= 1e-6


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 test_conjugate_gradient()








'''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 			UpperCamelCase__	(     SCREAMING_SNAKE_CASE   ,  unittest.TestCase):
 UpperCAmelCase__     :     Union[str, Any]    					=       RePaintPipeline
 UpperCAmelCase__     :     Any    					=       IMAGE_INPAINTING_PARAMS - {'width', 'height', 'guidance_scale'}
 UpperCAmelCase__     :     Optional[int]    					=       PipelineTesterMixin.required_optional_params - {
     'latents',
     'num_images_per_prompt',
     'callback',
     'callback_steps',
 }
 UpperCAmelCase__     :     Optional[int]    					=       IMAGE_INPAINTING_BATCH_PARAMS
 UpperCAmelCase__     :     Any    					=       False






 def 						lowercase_    (      self	:Any      )					->      int:







       '''simple docstring'''



       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 						lowercase_    (      self	:Any						,					_A	:List[Any]						,					_A	:Tuple=0      )					->      List[str]:







       '''simple docstring'''



       if str(_A      ).startswith('mps'      ):
             __A													=      torch.manual_seed(_A      )
       else:
             __A													=      torch.Generator(device=_A      ).manual_seed(_A      )
       __A													=      np.random.RandomState(_A      ).standard_normal((1, 3, 32, 32)      )
       __A													=      torch.from_numpy(_A      ).to(device=_A						,					dtype=torch.floataa      )
       __A													=      (image > 0).to(device=_A						,					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 						lowercase_    (      self	:Union[str, Any]      )					->      Optional[int]:







       '''simple docstring'''



       __A													=      'cpu'  # ensure determinism for the device-dependent torch.Generator
       __A													=      self.get_dummy_components()
       __A													=      RePaintPipeline(**_A      )
       __A													=      sd_pipe.to(_A      )
       sd_pipe.set_progress_bar_config(disable=_A      )

       __A													=      self.get_dummy_inputs(_A      )
       __A													=      sd_pipe(**_A      ).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 						lowercase_    (      self	:Dict      )					->      Optional[int]:







       '''simple docstring'''



       return super().test_save_load_local()






 @unittest.skip('non-deterministic pipeline'      )
 def 						lowercase_    (      self	:Union[str, Any]      )					->      int:







       '''simple docstring'''



       return super().test_inference_batch_single_identical()






 @skip_mps
 def 						lowercase_    (      self	:Dict      )					->      Union[str, Any]:







       '''simple docstring'''



       return super().test_dict_tuple_outputs_equivalent()






 @skip_mps
 def 						lowercase_    (      self	:List[str]      )					->      int:







       '''simple docstring'''



       return super().test_save_load_optional_components()






 @skip_mps
 def 						lowercase_    (      self	:Optional[int]      )					->      Optional[Any]:







       '''simple docstring'''



       return super().test_attention_slicing_forward_pass()


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






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







       '''simple docstring'''



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






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







       '''simple docstring'''



       __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(_A      )
       __A													=      RePaintScheduler.from_pretrained(_A      )

       __A													=      RePaintPipeline(unet=_A						,					scheduler=_A      ).to(_A      )
       repaint.set_progress_bar_config(disable=_A      )
       repaint.enable_attention_slicing()

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

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






