


'''simple docstring'''







import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest
class    lowercase_	(       a__  ):
    __UpperCAmelCase       =      (EulerAncestralDiscreteScheduler,)
    __UpperCAmelCase       =      10


    def   __a				(	self    ,   **a       ):
          UpperCamelCase__      =							{
              "num_train_timesteps": 11_00,
              "beta_start": 0.0001,
              "beta_end": 0.02,
              "beta_schedule": "linear",
          }

          config.update(**a       )
          return config


    def   __a				(	self       ):
          for timesteps in [10, 50, 1_00, 10_00]:
                self.check_over_configs(num_train_timesteps=a       )


    def   __a				(	self       ):
          for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]    ,   [0.0002, 0.002, 0.02]       ):
                self.check_over_configs(beta_start=a    ,   beta_end=a       )


    def   __a				(	self       ):
          for schedule in ["linear", "scaled_linear"]:
                self.check_over_configs(beta_schedule=a       )


    def   __a				(	self       ):
          for prediction_type in ["epsilon", "v_prediction"]:
                self.check_over_configs(prediction_type=a       )


    def   __a				(	self       ):
          UpperCamelCase__      =							self.scheduler_classes[0]
          UpperCamelCase__      =							self.get_scheduler_config()
          UpperCamelCase__      =							scheduler_class(**a       )

          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(a       )

          for i, t in enumerate(scheduler.timesteps       ):
                UpperCamelCase__      =							scheduler.scale_model_input(a    ,   a       )

                UpperCamelCase__      =							model(a    ,   a       )

                UpperCamelCase__      =							scheduler.step(a    ,   a    ,   a    ,   generator=a       )
                UpperCamelCase__      =							output.prev_sample

          UpperCamelCase__      =							torch.sum(torch.abs(a       )       )
          UpperCamelCase__      =							torch.mean(torch.abs(a       )       )

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


    def   __a				(	self       ):
          UpperCamelCase__      =							self.scheduler_classes[0]
          UpperCamelCase__      =							self.get_scheduler_config(prediction_type="v_prediction"       )
          UpperCamelCase__      =							scheduler_class(**a       )

          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(a       )

          for i, t in enumerate(scheduler.timesteps       ):
                UpperCamelCase__      =							scheduler.scale_model_input(a    ,   a       )

                UpperCamelCase__      =							model(a    ,   a       )

                UpperCamelCase__      =							scheduler.step(a    ,   a    ,   a    ,   generator=a       )
                UpperCamelCase__      =							output.prev_sample

          UpperCamelCase__      =							torch.sum(torch.abs(a       )       )
          UpperCamelCase__      =							torch.mean(torch.abs(a       )       )

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


    def   __a				(	self       ):
          UpperCamelCase__      =							self.scheduler_classes[0]
          UpperCamelCase__      =							self.get_scheduler_config()
          UpperCamelCase__      =							scheduler_class(**a       )

          scheduler.set_timesteps(self.num_inference_steps    ,   device=a       )
          UpperCamelCase__      =							torch.manual_seed(0       )

          UpperCamelCase__      =							self.dummy_model()
          UpperCamelCase__      =							self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
          UpperCamelCase__      =							sample.to(a       )

          for t in scheduler.timesteps:
                UpperCamelCase__      =							scheduler.scale_model_input(a    ,   a       )

                UpperCamelCase__      =							model(a    ,   a       )

                UpperCamelCase__      =							scheduler.step(a    ,   a    ,   a    ,   generator=a       )
                UpperCamelCase__      =							output.prev_sample

          UpperCamelCase__      =							torch.sum(torch.abs(a       )       )
          UpperCamelCase__      =							torch.mean(torch.abs(a       )       )

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









'''simple docstring'''







from __future__ import annotations

from enum import Enum, unique
from typing import TypeVar

# Create a generic variable that can be 'Enum', or any subclass.
a__    :   Optional[Any]								=			TypeVar('T', bound='Enum')
@unique
class    lowercase_	(       a__  ):
    __UpperCAmelCase       =      80
    __UpperCAmelCase       =      70
    __UpperCAmelCase       =      60
    __UpperCAmelCase       =      50
    __UpperCAmelCase       =      40
    __UpperCAmelCase       =      30
    __UpperCAmelCase       =      20
    __UpperCAmelCase       =      10

@unique
class    lowercase_	(       a__  ):
    __UpperCAmelCase       =      24
    __UpperCAmelCase       =      21
    __UpperCAmelCase       =      18
    __UpperCAmelCase       =      15
    __UpperCAmelCase       =      12
    __UpperCAmelCase       =      9
    __UpperCAmelCase       =      6
    __UpperCAmelCase       =      3
    __UpperCAmelCase       =      2
    __UpperCAmelCase       =      1
    __UpperCAmelCase       =      -1
    __UpperCAmelCase       =      -2
    __UpperCAmelCase       =      -3
    __UpperCAmelCase       =      -6
    __UpperCAmelCase       =      -9
    __UpperCAmelCase       =      -12
    __UpperCAmelCase       =      -15
    __UpperCAmelCase       =      -18
    __UpperCAmelCase       =      -21
    __UpperCAmelCase       =      -24


    @classmethod
    def   __a				(	cls       ):
          return {unit.name: unit.value for unit in cls if unit.value > 0}


    @classmethod
    def   __a				(	cls       ):
          return {unit.name: unit.value for unit in cls if unit.value < 0}







def _UpperCamelCase			(	__A       )				->						str:


      '''simple docstring'''



      UpperCamelCase__      =							SIUnit.get_positive() if value > 0 else SIUnit.get_negative()
      for name_prefix, value_prefix in prefixes.items():
            UpperCamelCase__      =							value / (10**value_prefix)
            if numerical_part > 1:
                  return F'''{numerical_part!s} {name_prefix}'''
      return str(__A       )







def _UpperCamelCase			(	__A       )				->						str:


      '''simple docstring'''



      for prefix in BinaryUnit:
            UpperCamelCase__      =							value / (2**prefix.value)
            if numerical_part > 1:
                  return F'''{numerical_part!s} {prefix.name}'''
      return str(__A       )


if __name__ == "__main__":
  import doctest

  doctest.testmod()





