




'''simple docstring'''







import torch
import torch.nn.functional as F

from diffusers import VQDiffusionScheduler

from .test_schedulers import SchedulerCommonTest





class 	lowerCamelCase_				(			UpperCAmelCase_				):




					'''simple docstring'''






					lowercase__       =					(VQDiffusionScheduler,)






					def 							__SCREAMING_SNAKE_CASE      (       self,    **__UpperCamelCase					):
							_snake_case      			=			{
							    "num_vec_classes": 4_097,
							    "num_train_timesteps": 100,
							}

							config.update(**__UpperCamelCase					)
							return config






					def 							__SCREAMING_SNAKE_CASE      (       self,    __UpperCamelCase					):
							_snake_case      			=			4
							_snake_case      			=			8
							_snake_case      			=			8

							_snake_case      			=			torch.randint(0,    __UpperCamelCase,    (batch_size, height * width)					)

							return sample






					@property
					def 							__SCREAMING_SNAKE_CASE      (       self					):
							assert False






					def 							__SCREAMING_SNAKE_CASE      (       self,    __UpperCamelCase					):
							def model(__UpperCamelCase,    __UpperCamelCase,    *__UpperCamelCase					):
									_snake_case			,       _snake_case      			=			sample.shape
									_snake_case      			=			torch.rand((batch_size, num_vec_classes - 1, num_latent_pixels)					)
									_snake_case      			=			F.log_softmax(logits.double(),    dim=1					).float()
									return return_value

							return model






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							for timesteps in [2, 5, 100, 1_000]:
									self.check_over_configs(num_train_timesteps=__UpperCamelCase					)






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							for num_vec_classes in [5, 100, 1_000, 4_000]:
									self.check_over_configs(num_vec_classes=__UpperCamelCase					)






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							for t in [0, 50, 99]:
									self.check_over_forward(time_step=__UpperCamelCase					)






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							pass











'''simple docstring'''







import numpy as np






def 						_lowercase						(      lowercase_    ):



		'''simple docstring'''







		_snake_case			,       _snake_case      			=			a.shape
		_snake_case      			=			min(lowercase_		,				lowercase_    )
		_snake_case      			=			np.eye(lowercase_    )
		_snake_case      			=			a.copy()

		for k in range(t - 1    ):
				# select a column of modified matrix A':
				_snake_case      			=			r[k:, [k]]
				# construct first basis vector
				_snake_case      			=			np.zeros_like(lowercase_    )
				_snake_case      			=			1.0
				# determine scaling factor
				_snake_case      			=			np.linalg.norm(lowercase_    )
				# construct vector v for Householder reflection
				_snake_case      			=			x + np.sign(x[0]    ) * alpha * ea
				v /= np.linalg.norm(lowercase_    )

				# construct the Householder matrix
				_snake_case      			=			np.eye(m - k    ) - 2.0 * v @ v.T
				# pad with ones and zeros as necessary
				_snake_case      			=			np.block([[np.eye(lowercase_    ), np.zeros((k, m - k)    )], [np.zeros((m - k, k)    ), q_k]]    )

				_snake_case      			=			q @ q_k.T
				_snake_case      			=			q_k @ r

		return q, r


if __name__ == "__main__":
						import doctest

						doctest.testmod()





