





import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest



class        _lowerCAmelCase    (			UpperCAmelCase_							):


  '''simple docstring'''
  a_							:       Optional[int]      					=(CMStochasticIterativeScheduler,)
  a_							:       Any      					=10







  def UpperCamelCase_ (      self      :					Union[str, Any]      ,				**UpperCamelCase      :					Any							):


    '''simple docstring'''





    _snake_case   :				Optional[Any]														=	{
        'num_train_timesteps': 2_01,
        'sigma_min': 0.0_02,
        'sigma_max': 80.0,
    }

    config.update(**UpperCamelCase							)
    return config







  def UpperCamelCase_ (      self      :					str							):


    '''simple docstring'''





    _snake_case   :				List[str]														=	10

    _snake_case   :				Tuple														=	self.get_scheduler_config()
    _snake_case   :				Optional[Any]														=	self.scheduler_classes[0](**UpperCamelCase							)

    scheduler.set_timesteps(UpperCamelCase							)

    _snake_case   :				List[str]														=	scheduler.timesteps[0]
    _snake_case   :				Union[str, Any]														=	scheduler.timesteps[1]

    _snake_case   :				Any														=	self.dummy_sample
    _snake_case   :				List[str]														=	0.1 * sample

    _snake_case   :				Tuple														=	scheduler.step(UpperCamelCase      ,				UpperCamelCase      ,				UpperCamelCase							).prev_sample
    _snake_case   :				Union[str, Any]														=	scheduler.step(UpperCamelCase      ,				UpperCamelCase      ,				UpperCamelCase							).prev_sample

    self.assertEqual(output_a.shape      ,				sample.shape							)
    self.assertEqual(output_a.shape      ,				output_a.shape							)







  def UpperCamelCase_ (      self      :					List[str]							):


    '''simple docstring'''





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







  def UpperCamelCase_ (      self      :					Union[str, Any]							):


    '''simple docstring'''





    for clip_denoised in [True, False]:
      self.check_over_configs(clip_denoised=UpperCamelCase							)







  def UpperCamelCase_ (      self      :					Dict							):


    '''simple docstring'''





    _snake_case   :				Optional[int]														=	self.scheduler_classes[0]
    _snake_case   :				Dict														=	self.get_scheduler_config()
    _snake_case   :				List[Any]														=	scheduler_class(**UpperCamelCase							)

    _snake_case   :				Tuple														=	1
    scheduler.set_timesteps(UpperCamelCase							)
    _snake_case   :				Any														=	scheduler.timesteps

    _snake_case   :				Dict														=	torch.manual_seed(0							)

    _snake_case   :				Optional[int]														=	self.dummy_model()
    _snake_case   :				Any														=	self.dummy_sample_deter * scheduler.init_noise_sigma

    for i, t in enumerate(UpperCamelCase							):
      # 1. scale model input
      _snake_case   :				Optional[int]														=	scheduler.scale_model_input(UpperCamelCase      ,				UpperCamelCase							)

      # 2. predict noise residual
      _snake_case   :				Optional[Any]														=	model(UpperCamelCase      ,				UpperCamelCase							)

      # 3. predict previous sample x_t-1
      _snake_case   :				Any														=	scheduler.step(UpperCamelCase      ,				UpperCamelCase      ,				UpperCamelCase      ,				generator=UpperCamelCase							).prev_sample

      _snake_case   :				Tuple														=	pred_prev_sample

    _snake_case   :				List[str]														=	torch.sum(torch.abs(UpperCamelCase							)							)
    _snake_case   :				Optional[Any]														=	torch.mean(torch.abs(UpperCamelCase							)							)

    assert abs(result_sum.item() - 1_92.76_14							) < 1e-2
    assert abs(result_mean.item() - 0.25_10							) < 1e-3







  def UpperCamelCase_ (      self      :					Optional[Any]							):


    '''simple docstring'''





    _snake_case   :				str														=	self.scheduler_classes[0]
    _snake_case   :				str														=	self.get_scheduler_config()
    _snake_case   :				Tuple														=	scheduler_class(**UpperCamelCase							)

    _snake_case   :				Optional[int]														=	[1_06, 0]
    scheduler.set_timesteps(timesteps=UpperCamelCase							)
    _snake_case   :				Optional[Any]														=	scheduler.timesteps

    _snake_case   :				Any														=	torch.manual_seed(0							)

    _snake_case   :				Union[str, Any]														=	self.dummy_model()
    _snake_case   :				Dict														=	self.dummy_sample_deter * scheduler.init_noise_sigma

    for t in timesteps:
      # 1. scale model input
      _snake_case   :				int														=	scheduler.scale_model_input(UpperCamelCase      ,				UpperCamelCase							)

      # 2. predict noise residual
      _snake_case   :				Union[str, Any]														=	model(UpperCamelCase      ,				UpperCamelCase							)

      # 3. predict previous sample x_t-1
      _snake_case   :				Optional[int]														=	scheduler.step(UpperCamelCase      ,				UpperCamelCase      ,				UpperCamelCase      ,				generator=UpperCamelCase							).prev_sample

      _snake_case   :				Union[str, Any]														=	pred_prev_sample

    _snake_case   :				Union[str, Any]														=	torch.sum(torch.abs(UpperCamelCase							)							)
    _snake_case   :				Tuple														=	torch.mean(torch.abs(UpperCamelCase							)							)

    assert abs(result_sum.item() - 3_47.63_57							) < 1e-2
    assert abs(result_mean.item() - 0.45_27							) < 1e-3







  def UpperCamelCase_ (      self      :					List[Any]							):


    '''simple docstring'''





    _snake_case   :				Any														=	self.scheduler_classes[0]
    _snake_case   :				Optional[Any]														=	self.get_scheduler_config()
    _snake_case   :				Optional[int]														=	scheduler_class(**UpperCamelCase							)

    _snake_case   :				Optional[Any]														=	[39, 30, 12, 15, 0]

    with self.assertRaises(UpperCamelCase      ,				msg='`timesteps` must be in descending order.'							):
      scheduler.set_timesteps(timesteps=UpperCamelCase							)







  def UpperCamelCase_ (      self      :					Tuple							):


    '''simple docstring'''





    _snake_case   :				Optional[int]														=	self.scheduler_classes[0]
    _snake_case   :				List[Any]														=	self.get_scheduler_config()
    _snake_case   :				int														=	scheduler_class(**UpperCamelCase							)

    _snake_case   :				Dict														=	[39, 30, 12, 1, 0]
    _snake_case   :				Union[str, Any]														=	len(UpperCamelCase							)

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







  def UpperCamelCase_ (      self      :					Optional[Any]							):


    '''simple docstring'''





    _snake_case   :				Dict														=	self.scheduler_classes[0]
    _snake_case   :				str														=	self.get_scheduler_config()
    _snake_case   :				int														=	scheduler_class(**UpperCamelCase							)

    _snake_case   :				Dict														=	[scheduler.config.num_train_timesteps]

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











import copy
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union



@dataclass
class        _lowerCAmelCase    :


  '''simple docstring'''
  a_							:       Optional[Union[str, Path]]      					=None
  a_							:       bool      					=False
  a_							:       bool      					=False
  a_							:       bool      					=False
  a_							:       Optional[Dict]      					=None
  a_							:       Optional[str]      					=None
  a_							:       bool      					=False
  a_							:       bool      					=False
  a_							:       bool      					=False
  a_							:       bool      					=True
  a_							:       Optional[int]      					=None
  a_							:       int      					=1
  a_							:       Optional[Union[str, bool]]      					=None
  a_							:       bool      					=False
  a_							:       Optional[Dict]      					=None
  a_							:       Optional[str]      					=None







  def UpperCamelCase_ (      self      :					Dict							):


    '''simple docstring'''





    return self.__class__(**{k: copy.deepcopy(UpperCamelCase							) for k, v in self.__dict__.items()}							)




