


import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest

class   A__ (   snake_case__     ):







    """simple docstring"""

    __magic_name__        =		(DDPMParallelScheduler,)
    def 		a_      (	self      , **__snake_case   ):
         snake_case					=    {
             '''num_train_timesteps''': 1_0_0_0,
             '''beta_start''': 0.0001,
             '''beta_end''': 0.02,
             '''beta_schedule''': '''linear''',
             '''variance_type''': '''fixed_small''',
             '''clip_sample''': True,
         }

         config.update(**__snake_case   )
         return config
    def 		a_      (	self   ):
         for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
              self.check_over_configs(num_train_timesteps=__snake_case   )
    def 		a_      (	self   ):
         for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]      , [0.002, 0.02, 0.2, 2]   ):
              self.check_over_configs(beta_start=__snake_case      , beta_end=__snake_case   )
    def 		a_      (	self   ):
         for schedule in ["linear", "squaredcos_cap_v2"]:
              self.check_over_configs(beta_schedule=__snake_case   )
    def 		a_      (	self   ):
         for variance in ["fixed_small", "fixed_large", "other"]:
              self.check_over_configs(variance_type=__snake_case   )
    def 		a_      (	self   ):
         for clip_sample in [True, False]:
              self.check_over_configs(clip_sample=__snake_case   )
    def 		a_      (	self   ):
         self.check_over_configs(thresholding=__snake_case   )
         for threshold in [0.5, 1.0, 2.0]:
              for prediction_type in ["epsilon", "sample", "v_prediction"]:
                   self.check_over_configs(
                       thresholding=__snake_case      , prediction_type=__snake_case      , sample_max_value=__snake_case      , )
    def 		a_      (	self   ):
         for prediction_type in ["epsilon", "sample", "v_prediction"]:
              self.check_over_configs(prediction_type=__snake_case   )
    def 		a_      (	self   ):
         for t in [0, 5_0_0, 9_9_9]:
              self.check_over_forward(time_step=__snake_case   )
    def 		a_      (	self   ):
         snake_case					=    self.scheduler_classes[0]
         snake_case					=    self.get_scheduler_config()
         snake_case					=    scheduler_class(**__snake_case   )

         assert torch.sum(torch.abs(scheduler._get_variance(0   ) - 0.0   )   ) < 1E-5
         assert torch.sum(torch.abs(scheduler._get_variance(4_8_7   ) - 0.0_0979   )   ) < 1E-5
         assert torch.sum(torch.abs(scheduler._get_variance(9_9_9   ) - 0.02   )   ) < 1E-5
    def 		a_      (	self   ):
         snake_case					=    self.scheduler_classes[0]
         snake_case					=    self.get_scheduler_config()
         snake_case					=    scheduler_class(**__snake_case   )

         snake_case					=    len(__snake_case   )

         snake_case					=    self.dummy_model()
         snake_case					=    self.dummy_sample_deter
         snake_case					=    self.dummy_sample_deter + 0.1
         snake_case					=    self.dummy_sample_deter - 0.1

         snake_case					=    samplea.shape[0]
         snake_case					=    torch.stack([samplea, samplea, samplea]      , dim=0   )
         snake_case					=    torch.arange(__snake_case   )[0:3, None].repeat(1      , __snake_case   )

         snake_case					=    model(samples.flatten(0      , 1   )      , timesteps.flatten(0      , 1   )   )
         snake_case					=    scheduler.batch_step_no_noise(__snake_case      , timesteps.flatten(0      , 1   )      , samples.flatten(0      , 1   )   )

         snake_case					=    torch.sum(torch.abs(__snake_case   )   )
         snake_case					=    torch.mean(torch.abs(__snake_case   )   )

         assert abs(result_sum.item() - 1153.1833   ) < 1E-2
         assert abs(result_mean.item() - 0.5005   ) < 1E-3
    def 		a_      (	self   ):
         snake_case					=    self.scheduler_classes[0]
         snake_case					=    self.get_scheduler_config()
         snake_case					=    scheduler_class(**__snake_case   )

         snake_case					=    len(__snake_case   )

         snake_case					=    self.dummy_model()
         snake_case					=    self.dummy_sample_deter
         snake_case					=    torch.manual_seed(0   )

         for t in reversed(range(__snake_case   )   ):
              # 1. predict noise residual
              snake_case					=    model(__snake_case      , __snake_case   )

              # 2. predict previous mean of sample x_t-1
              snake_case					=    scheduler.step(__snake_case      , __snake_case      , __snake_case      , generator=__snake_case   ).prev_sample

              snake_case					=    pred_prev_sample

         snake_case					=    torch.sum(torch.abs(__snake_case   )   )
         snake_case					=    torch.mean(torch.abs(__snake_case   )   )

         assert abs(result_sum.item() - 258.9606   ) < 1E-2
         assert abs(result_mean.item() - 0.3372   ) < 1E-3
    def 		a_      (	self   ):
         snake_case					=    self.scheduler_classes[0]
         snake_case					=    self.get_scheduler_config(prediction_type='''v_prediction'''   )
         snake_case					=    scheduler_class(**__snake_case   )

         snake_case					=    len(__snake_case   )

         snake_case					=    self.dummy_model()
         snake_case					=    self.dummy_sample_deter
         snake_case					=    torch.manual_seed(0   )

         for t in reversed(range(__snake_case   )   ):
              # 1. predict noise residual
              snake_case					=    model(__snake_case      , __snake_case   )

              # 2. predict previous mean of sample x_t-1
              snake_case					=    scheduler.step(__snake_case      , __snake_case      , __snake_case      , generator=__snake_case   ).prev_sample

              snake_case					=    pred_prev_sample

         snake_case					=    torch.sum(torch.abs(__snake_case   )   )
         snake_case					=    torch.mean(torch.abs(__snake_case   )   )

         assert abs(result_sum.item() - 202.0296   ) < 1E-2
         assert abs(result_mean.item() - 0.2631   ) < 1E-3
    def 		a_      (	self   ):
         snake_case					=    self.scheduler_classes[0]
         snake_case					=    self.get_scheduler_config()
         snake_case					=    scheduler_class(**__snake_case   )

         snake_case					=    [1_0_0, 8_7, 5_0, 1, 0]

         scheduler.set_timesteps(timesteps=__snake_case   )

         snake_case					=    scheduler.timesteps

         for i, timestep in enumerate(__snake_case   ):
              if i == len(__snake_case   ) - 1:
                   snake_case					=    -1
              else:
                   snake_case					=    timesteps[i + 1]

              snake_case					=    scheduler.previous_timestep(__snake_case   )
              snake_case					=    prev_t.item()

              self.assertEqual(__snake_case      , __snake_case   )
    def 		a_      (	self   ):
         snake_case					=    self.scheduler_classes[0]
         snake_case					=    self.get_scheduler_config()
         snake_case					=    scheduler_class(**__snake_case   )

         snake_case					=    [1_0_0, 8_7, 5_0, 5_1, 0]

         with self.assertRaises(__snake_case      , msg='''`custom_timesteps` must be in descending order.'''   ):
              scheduler.set_timesteps(timesteps=__snake_case   )
    def 		a_      (	self   ):
         snake_case					=    self.scheduler_classes[0]
         snake_case					=    self.get_scheduler_config()
         snake_case					=    scheduler_class(**__snake_case   )

         snake_case					=    [1_0_0, 8_7, 5_0, 1, 0]
         snake_case					=    len(__snake_case   )

         with self.assertRaises(__snake_case      , msg='''Can only pass one of `num_inference_steps` or `custom_timesteps`.'''   ):
              scheduler.set_timesteps(num_inference_steps=__snake_case      , timesteps=__snake_case   )




    def 		a_      (	self   ):
         snake_case					=    self.scheduler_classes[0]
         snake_case					=    self.get_scheduler_config()
         snake_case					=    scheduler_class(**__snake_case   )

         snake_case					=    [scheduler.config.num_train_timesteps]

         with self.assertRaises(
             __snake_case      , msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'''      , ):
              scheduler.set_timesteps(timesteps=__snake_case   )








from __future__ import annotations

from scipy.special import comb  # type: ignore

class   A__ :







    """simple docstring"""

    def __init__(	self      , __snake_case   ):
         snake_case					=    list_of_points
         # Degree determines the flexibility of the curve.
         # Degree = 1 will produce a straight line.
         snake_case					=    len(__snake_case   ) - 1
    def 		a_      (	self      , __snake_case   ):
         assert 0 <= t <= 1, "Time t must be between 0 and 1."
         snake_case					=    []
         for i in range(len(self.list_of_points   )   ):
              # basis function for each i
              output_values.append(
                  comb(self.degree      , __snake_case   ) * ((1 - t) ** (self.degree - i)) * (t**i)   )
         # the basis must sum up to 1 for it to produce a valid Bezier curve.
         assert round(sum(__snake_case   )      , 5   ) == 1
         return output_values
    def 		a_      (	self      , __snake_case   ):
         assert 0 <= t <= 1, "Time t must be between 0 and 1."

         snake_case					=    self.basis_function(__snake_case   )
         snake_case					=    0.0
         snake_case					=    0.0
         for i in range(len(self.list_of_points   )   ):
              # For all points, sum up the product of i-th basis function and i-th point.
              x += basis_function[i] * self.list_of_points[i][0]
              y += basis_function[i] * self.list_of_points[i][1]
         return (x, y)




    def 		a_      (	self      , __snake_case = 0.01   ):
         from matplotlib import pyplot as plt  # type: ignore

         snake_case					=    []  # x coordinates of points to plot
         snake_case					=    []  # y coordinates of points to plot

         snake_case					=    0.0
         while t <= 1:
              snake_case					=    self.bezier_curve_function(__snake_case   )
              to_plot_x.append(value[0]   )
              to_plot_y.append(value[1]   )
              t += step_size

         snake_case					=    [i[0] for i in self.list_of_points]
         snake_case					=    [i[1] for i in self.list_of_points]

         plt.plot(
             __snake_case      , __snake_case      , color='''blue'''      , label='''Curve of Degree ''' + str(self.degree   )      , )
         plt.scatter(__snake_case      , __snake_case      , color='''red'''      , label='''Control Points'''   )
         plt.legend()
         plt.show()


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 BezierCurve([(1, 2), (3, 5)]).plot_curve()  # degree 1
 BezierCurve([(0, 0), (5, 5), (5, 0)]).plot_curve()  # degree 2
 BezierCurve([(0, 0), (5, 5), (5, 0), (2.5, -2.5)]).plot_curve()  # degree 3




