import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()


class 						lowercase    (     unittest.TestCase     ):




							@property
							def 	__snake_case(						self   :					str      )					->				List[str]:



												'''simple docstring'''

												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")	,							)
												return model





							def 	__snake_case(						self   :					Optional[Any]      )					->				int:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.dummy_uncond_unet
												SCREAMING_SNAKE_CASE    			=       KarrasVeScheduler()

												SCREAMING_SNAKE_CASE    			=       KarrasVePipeline(unet=_UpperCamelCase	,							scheduler=_UpperCamelCase      )
												pipe.to(_UpperCamelCase      )
												pipe.set_progress_bar_config(disable=_UpperCamelCase      )

												SCREAMING_SNAKE_CASE    			=       torch.manual_seed(0      )
												SCREAMING_SNAKE_CASE    			=       pipe(num_inference_steps=2	,							generator=_UpperCamelCase	,							output_type="numpy"      ).images

												SCREAMING_SNAKE_CASE    			=       torch.manual_seed(0      )
												SCREAMING_SNAKE_CASE    			=       pipe(num_inference_steps=2	,							generator=_UpperCamelCase	,							output_type="numpy"	,							return_dict=_UpperCamelCase      )[0]

												SCREAMING_SNAKE_CASE    			=       image[0, -3:, -3:, -1]
												SCREAMING_SNAKE_CASE    			=       image_from_tuple[0, -3:, -3:, -1]

												assert image.shape == (1, 32, 32, 3)
												SCREAMING_SNAKE_CASE    			=       np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]      )

												assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-2
												assert np.abs(image_from_tuple_slice.flatten() - expected_slice      ).max() < 1e-2



@slow
@require_torch
class 						lowercase    (     unittest.TestCase     ):




							def 	__snake_case(						self   :					Tuple      )					->				Dict:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       "google/ncsnpp-celebahq-256"
												SCREAMING_SNAKE_CASE    			=       UNetaDModel.from_pretrained(_UpperCamelCase      )
												SCREAMING_SNAKE_CASE    			=       KarrasVeScheduler()

												SCREAMING_SNAKE_CASE    			=       KarrasVePipeline(unet=_UpperCamelCase	,							scheduler=_UpperCamelCase      )
												pipe.to(_UpperCamelCase      )
												pipe.set_progress_bar_config(disable=_UpperCamelCase      )

												SCREAMING_SNAKE_CASE    			=       torch.manual_seed(0      )
												SCREAMING_SNAKE_CASE    			=       pipe(num_inference_steps=20	,							generator=_UpperCamelCase	,							output_type="numpy"      ).images

												SCREAMING_SNAKE_CASE    			=       image[0, -3:, -3:, -1]
												assert image.shape == (1, 256, 256, 3)
												SCREAMING_SNAKE_CASE    			=       np.array([0.5_7_8, 0.5_8_1_1, 0.5_9_2_4, 0.5_8_0_9, 0.5_8_7, 0.5_8_8_6, 0.5_8_6_1, 0.5_8_0_2, 0.5_8_6]      )

												assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-2





import numpy as np





def 	__lowerCamelCase							(UpperCAmelCase__     :							np.ndarray      , UpperCAmelCase__     :							np.ndarray      , UpperCAmelCase__     :							float = 1e-12      , UpperCAmelCase__     :							int = 1_0_0      , ):
					assert np.shape(UpperCAmelCase__  )[0] == np.shape(UpperCAmelCase__  )[1]
					# Ensure proper dimensionality.
					assert np.shape(UpperCAmelCase__  )[0] == np.shape(UpperCAmelCase__  )[0]
					# Ensure inputs are either both complex or both real
					assert np.iscomplexobj(UpperCAmelCase__  ) == np.iscomplexobj(UpperCAmelCase__  )
					SCREAMING_SNAKE_CASE    			=       np.iscomplexobj(UpperCAmelCase__  )
					if is_complex:
										# Ensure complex input_matrix is Hermitian
										assert np.array_equal(UpperCAmelCase__      , input_matrix.conj().T  )

					# Set convergence to False. Will define convergence when we exceed max_iterations
					# or when we have small changes from one iteration to next.

					SCREAMING_SNAKE_CASE    			=       False
					SCREAMING_SNAKE_CASE    			=       0
					SCREAMING_SNAKE_CASE    			=       0
					SCREAMING_SNAKE_CASE    			=       1e12

					while not convergence:
										# Multiple matrix by the vector.
										SCREAMING_SNAKE_CASE    			=       np.dot(UpperCAmelCase__      , UpperCAmelCase__  )
										# Normalize the resulting output vector.
										SCREAMING_SNAKE_CASE    			=       w / np.linalg.norm(UpperCAmelCase__  )
										# Find rayleigh quotient
										# (faster than usual b/c we know vector is normalized already)
										SCREAMING_SNAKE_CASE    			=       vector.conj().T if is_complex else vector.T
										SCREAMING_SNAKE_CASE    			=       np.dot(UpperCAmelCase__      , np.dot(UpperCAmelCase__      , UpperCAmelCase__  )  )

										# Check convergence.
										SCREAMING_SNAKE_CASE    			=       np.abs(lambda_ - lambda_previous  ) / lambda_
										iterations += 1

										if error <= error_tol or iterations >= max_iterations:
															SCREAMING_SNAKE_CASE    			=       True

										SCREAMING_SNAKE_CASE    			=       lambda_

					if is_complex:
										SCREAMING_SNAKE_CASE    			=       np.real(lambda_  )

					return lambda_, vector





def 	__lowerCamelCase							():
					SCREAMING_SNAKE_CASE    			=       np.array([[4_1, 4, 2_0], [4, 2_6, 3_0], [2_0, 3_0, 5_0]]  )
					SCREAMING_SNAKE_CASE    			=       np.array([4_1, 4, 2_0]  )
					SCREAMING_SNAKE_CASE    			=       real_input_matrix.astype(np.complexaaa  )
					SCREAMING_SNAKE_CASE    			=       np.triu(1j * complex_input_matrix      , 1  )
					complex_input_matrix += imag_matrix
					complex_input_matrix += -1 * imag_matrix.T
					SCREAMING_SNAKE_CASE    			=       np.array([4_1, 4, 2_0]  ).astype(np.complexaaa  )

					for problem_type in ["real", "complex"]:
										if problem_type == "real":
															SCREAMING_SNAKE_CASE    			=       real_input_matrix
															SCREAMING_SNAKE_CASE    			=       real_vector
										elif problem_type == "complex":
															SCREAMING_SNAKE_CASE    			=       complex_input_matrix
															SCREAMING_SNAKE_CASE    			=       complex_vector

										# Our implementation.
										SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       power_iteration(UpperCAmelCase__      , UpperCAmelCase__  )

										# Numpy implementation.

										# Get eigenvalues and eigenvectors using built-in numpy
										# eigh (eigh used for symmetric or hermetian matrices).
										SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       np.linalg.eigh(UpperCAmelCase__  )
										# Last eigenvalue is the maximum one.
										SCREAMING_SNAKE_CASE    			=       eigen_values[-1]
										# Last column in this matrix is eigenvector corresponding to largest eigenvalue.
										SCREAMING_SNAKE_CASE    			=       eigen_vectors[:, -1]

										# Check our implementation and numpy gives close answers.
										assert np.abs(eigen_value - eigen_value_max  ) <= 1e-6
										# Take absolute values element wise of each eigenvector.
										# as they are only unique to a minus sign.
										assert np.linalg.norm(np.abs(UpperCAmelCase__  ) - np.abs(UpperCAmelCase__  )  ) <= 1e-6


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							test_power_iteration()




