

import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest
class 					A_   (      _a						):


    '''simple docstring'''






    a__     							=       (EulerAncestralDiscreteScheduler,)
    a__     							=       10


    def      lowerCAmelCase_						(self     ,      **lowercase__  )     ->      str:
        __UpperCAmelCase									=			{
            '''num_train_timesteps''': 1_100,
            '''beta_start''': 0.0001,
            '''beta_end''': 0.02,
            '''beta_schedule''': '''linear''',
        }

        config.update(**lowercase__  )
        return config


    def      lowerCAmelCase_						(self  )     ->      str:
        for timesteps in [10, 50, 100, 1_000]:
            self.check_over_configs(num_train_timesteps=lowercase__  )


    def      lowerCAmelCase_						(self  )     ->      List[str]:
        for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]     ,      [0.0002, 0.002, 0.02]  ):
            self.check_over_configs(beta_start=lowercase__     ,      beta_end=lowercase__  )


    def      lowerCAmelCase_						(self  )     ->      Optional[Any]:
        for schedule in ["linear", "scaled_linear"]:
            self.check_over_configs(beta_schedule=lowercase__  )


    def      lowerCAmelCase_						(self  )     ->      Dict:
        for prediction_type in ["epsilon", "v_prediction"]:
            self.check_over_configs(prediction_type=lowercase__  )


    def      lowerCAmelCase_						(self  )     ->      Tuple:
        __UpperCAmelCase									=			self.scheduler_classes[0]
        __UpperCAmelCase									=			self.get_scheduler_config()
        __UpperCAmelCase									=			scheduler_class(**lowercase__  )

        scheduler.set_timesteps(self.num_inference_steps  )

        __UpperCAmelCase									=			torch.manual_seed(0  )

        __UpperCAmelCase									=			self.dummy_model()
        __UpperCAmelCase									=			self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
        __UpperCAmelCase									=			sample.to(lowercase__  )

        for i, t in enumerate(scheduler.timesteps  ):
            __UpperCAmelCase									=			scheduler.scale_model_input(lowercase__     ,      lowercase__  )

            __UpperCAmelCase									=			model(lowercase__     ,      lowercase__  )

            __UpperCAmelCase									=			scheduler.step(lowercase__     ,      lowercase__     ,      lowercase__     ,      generator=lowercase__  )
            __UpperCAmelCase									=			output.prev_sample

        __UpperCAmelCase									=			torch.sum(torch.abs(lowercase__  )  )
        __UpperCAmelCase									=			torch.mean(torch.abs(lowercase__  )  )

        assert abs(result_sum.item() - 152.3192  ) < 1E-2
        assert abs(result_mean.item() - 0.1983  ) < 1E-3


    def      lowerCAmelCase_						(self  )     ->      List[str]:
        __UpperCAmelCase									=			self.scheduler_classes[0]
        __UpperCAmelCase									=			self.get_scheduler_config(prediction_type='''v_prediction'''  )
        __UpperCAmelCase									=			scheduler_class(**lowercase__  )

        scheduler.set_timesteps(self.num_inference_steps  )

        __UpperCAmelCase									=			torch.manual_seed(0  )

        __UpperCAmelCase									=			self.dummy_model()
        __UpperCAmelCase									=			self.dummy_sample_deter * scheduler.init_noise_sigma
        __UpperCAmelCase									=			sample.to(lowercase__  )

        for i, t in enumerate(scheduler.timesteps  ):
            __UpperCAmelCase									=			scheduler.scale_model_input(lowercase__     ,      lowercase__  )

            __UpperCAmelCase									=			model(lowercase__     ,      lowercase__  )

            __UpperCAmelCase									=			scheduler.step(lowercase__     ,      lowercase__     ,      lowercase__     ,      generator=lowercase__  )
            __UpperCAmelCase									=			output.prev_sample

        __UpperCAmelCase									=			torch.sum(torch.abs(lowercase__  )  )
        __UpperCAmelCase									=			torch.mean(torch.abs(lowercase__  )  )

        assert abs(result_sum.item() - 108.4439  ) < 1E-2
        assert abs(result_mean.item() - 0.1412  ) < 1E-3






    def      lowerCAmelCase_						(self  )     ->      Dict:
        __UpperCAmelCase									=			self.scheduler_classes[0]
        __UpperCAmelCase									=			self.get_scheduler_config()
        __UpperCAmelCase									=			scheduler_class(**lowercase__  )

        scheduler.set_timesteps(self.num_inference_steps     ,      device=lowercase__  )
        __UpperCAmelCase									=			torch.manual_seed(0  )

        __UpperCAmelCase									=			self.dummy_model()
        __UpperCAmelCase									=			self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
        __UpperCAmelCase									=			sample.to(lowercase__  )

        for t in scheduler.timesteps:
            __UpperCAmelCase									=			scheduler.scale_model_input(lowercase__     ,      lowercase__  )

            __UpperCAmelCase									=			model(lowercase__     ,      lowercase__  )

            __UpperCAmelCase									=			scheduler.step(lowercase__     ,      lowercase__     ,      lowercase__     ,      generator=lowercase__  )
            __UpperCAmelCase									=			output.prev_sample

        __UpperCAmelCase									=			torch.sum(torch.abs(lowercase__  )  )
        __UpperCAmelCase									=			torch.mean(torch.abs(lowercase__  )  )

        assert abs(result_sum.item() - 152.3192  ) < 1E-2
        assert abs(result_mean.item() - 0.1983  ) < 1E-3







import numpy as np


def       __a       (   SCREAMING_SNAKE_CASE      )      ->     Tuple:






    '''simple docstring'''
    __UpperCAmelCase					,     __UpperCAmelCase									=			a.shape
    __UpperCAmelCase									=			min(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE      )
    __UpperCAmelCase									=			np.eye(SCREAMING_SNAKE_CASE      )
    __UpperCAmelCase									=			a.copy()

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

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

        __UpperCAmelCase									=			q @ q_k.T
        __UpperCAmelCase									=			q_k @ r

    return q, r


if __name__ == "__main__":
       import doctest

       doctest.testmod()




