





import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest

class      lowercase__    (							__A  ):
  __UpperCamelCase	     =  (DDIMParallelScheduler,)
  __UpperCamelCase	     =  (("""eta""", 0.0), ("""num_inference_steps""", 50))







  def 	UpperCAmelCase__		(	self    ,						**_lowercase						):
       lowerCAmelCase_					:       Tuple    =		{
           'num_train_timesteps': 1_000,
           'beta_start': 0.0001,
           'beta_end': 0.02,
           'beta_schedule': 'linear',
           'clip_sample': True,
       }

       config.update(**UpperCamelCase_						)
       return config







  def 	UpperCAmelCase__		(	self    ,						**_lowercase						):
       lowerCAmelCase_					:       List[str]    =		self.scheduler_classes[0]
       lowerCAmelCase_					:       List[str]    =		self.get_scheduler_config(**UpperCamelCase_						)
       lowerCAmelCase_					:       Optional[Any]    =		scheduler_class(**UpperCamelCase_						)

       lowerCAmelCase_					:       Dict    =		10, 0.0

       lowerCAmelCase_					:       Any    =		self.dummy_model()
       lowerCAmelCase_					:       Any    =		self.dummy_sample_deter

       scheduler.set_timesteps(UpperCamelCase_						)

       for t in scheduler.timesteps:
            lowerCAmelCase_					:       List[Any]    =		model(UpperCamelCase_    ,						UpperCamelCase_						)
            lowerCAmelCase_					:       List[Any]    =		scheduler.step(UpperCamelCase_    ,						UpperCamelCase_    ,						UpperCamelCase_    ,						UpperCamelCase_						).prev_sample

       return sample







  def 	UpperCAmelCase__		(	self						):





       for timesteps in [100, 500, 1_000]:
            self.check_over_configs(num_train_timesteps=UpperCamelCase_						)







  def 	UpperCAmelCase__		(	self						):





       for steps_offset in [0, 1]:
            self.check_over_configs(steps_offset=UpperCamelCase_						)

       lowerCAmelCase_					:       List[str]    =		self.scheduler_classes[0]
       lowerCAmelCase_					:       List[Any]    =		self.get_scheduler_config(steps_offset=1						)
       lowerCAmelCase_					:       str    =		scheduler_class(**UpperCamelCase_						)
       scheduler.set_timesteps(5						)
       assert torch.equal(scheduler.timesteps    ,						torch.LongTensor([801, 601, 401, 201, 1]						)						)







  def 	UpperCAmelCase__		(	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=UpperCamelCase_    ,						beta_end=UpperCamelCase_						)







  def 	UpperCAmelCase__		(	self						):





       for schedule in ["linear", "squaredcos_cap_v2"]:
            self.check_over_configs(beta_schedule=UpperCamelCase_						)







  def 	UpperCAmelCase__		(	self						):





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







  def 	UpperCAmelCase__		(	self						):





       for clip_sample in [True, False]:
            self.check_over_configs(clip_sample=UpperCamelCase_						)







  def 	UpperCAmelCase__		(	self						):





       for timestep_spacing in ["trailing", "leading"]:
            self.check_over_configs(timestep_spacing=UpperCamelCase_						)







  def 	UpperCAmelCase__		(	self						):





       for rescale_betas_zero_snr in [True, False]:
            self.check_over_configs(rescale_betas_zero_snr=UpperCamelCase_						)







  def 	UpperCAmelCase__		(	self						):
       self.check_over_configs(thresholding=UpperCamelCase_						)
       for threshold in [0.5, 1.0, 2.0]:
            for prediction_type in ["epsilon", "v_prediction"]:
                 self.check_over_configs(
                     thresholding=UpperCamelCase_    ,						prediction_type=UpperCamelCase_    ,						sample_max_value=UpperCamelCase_    ,						)







  def 	UpperCAmelCase__		(	self						):





       for t in [1, 10, 49]:
            self.check_over_forward(time_step=UpperCamelCase_						)







  def 	UpperCAmelCase__		(	self						):





       for t, num_inference_steps in zip([1, 10, 50]    ,						[10, 50, 500]						):
            self.check_over_forward(time_step=UpperCamelCase_    ,						num_inference_steps=UpperCamelCase_						)







  def 	UpperCAmelCase__		(	self						):





       for t, eta in zip([1, 10, 49]    ,						[0.0, 0.5, 1.0]						):
            self.check_over_forward(time_step=UpperCamelCase_    ,						eta=UpperCamelCase_						)







  def 	UpperCAmelCase__		(	self						):
       lowerCAmelCase_					:       Optional[Any]    =		self.scheduler_classes[0]
       lowerCAmelCase_					:       List[Any]    =		self.get_scheduler_config()
       lowerCAmelCase_					:       str    =		scheduler_class(**UpperCamelCase_						)

       assert torch.sum(torch.abs(scheduler._get_variance(0    ,						0						) - 0.0						)						) < 1e-5
       assert torch.sum(torch.abs(scheduler._get_variance(420    ,						400						) - 0.1_4771						)						) < 1e-5
       assert torch.sum(torch.abs(scheduler._get_variance(980    ,						960						) - 0.3_2460						)						) < 1e-5
       assert torch.sum(torch.abs(scheduler._get_variance(0    ,						0						) - 0.0						)						) < 1e-5
       assert torch.sum(torch.abs(scheduler._get_variance(487    ,						486						) - 0.0_0979						)						) < 1e-5
       assert torch.sum(torch.abs(scheduler._get_variance(999    ,						998						) - 0.02						)						) < 1e-5







  def 	UpperCAmelCase__		(	self						):
       lowerCAmelCase_					:       Any    =		self.scheduler_classes[0]
       lowerCAmelCase_					:       Optional[Any]    =		self.get_scheduler_config()
       lowerCAmelCase_					:       List[Any]    =		scheduler_class(**UpperCamelCase_						)

       lowerCAmelCase_					:       Tuple    =		10, 0.0
       scheduler.set_timesteps(UpperCamelCase_						)

       lowerCAmelCase_					:       List[str]    =		self.dummy_model()
       lowerCAmelCase_					:       List[str]    =		self.dummy_sample_deter
       lowerCAmelCase_					:       Optional[Any]    =		self.dummy_sample_deter + 0.1
       lowerCAmelCase_					:       str    =		self.dummy_sample_deter - 0.1

       lowerCAmelCase_					:       str    =		samplea.shape[0]
       lowerCAmelCase_					:       Tuple    =		torch.stack([samplea, samplea, samplea]    ,						dim=0						)
       lowerCAmelCase_					:       str    =		torch.arange(UpperCamelCase_						)[0:3, None].repeat(1    ,						UpperCamelCase_						)

       lowerCAmelCase_					:       int    =		model(samples.flatten(0    ,						1						)    ,						timesteps.flatten(0    ,						1						)						)
       lowerCAmelCase_					:       List[str]    =		scheduler.batch_step_no_noise(UpperCamelCase_    ,						timesteps.flatten(0    ,						1						)    ,						samples.flatten(0    ,						1						)    ,						UpperCamelCase_						)

       lowerCAmelCase_					:       Dict    =		torch.sum(torch.abs(UpperCamelCase_						)						)
       lowerCAmelCase_					:       Union[str, Any]    =		torch.mean(torch.abs(UpperCamelCase_						)						)

       assert abs(result_sum.item() - 1_147.7_904						) < 1e-2
       assert abs(result_mean.item() - 0.4982						) < 1e-3







  def 	UpperCAmelCase__		(	self						):
       lowerCAmelCase_					:       Any    =		self.full_loop()

       lowerCAmelCase_					:       Optional[Any]    =		torch.sum(torch.abs(UpperCamelCase_						)						)
       lowerCAmelCase_					:       Tuple    =		torch.mean(torch.abs(UpperCamelCase_						)						)

       assert abs(result_sum.item() - 172.0_067						) < 1e-2
       assert abs(result_mean.item() - 0.22_3967						) < 1e-3







  def 	UpperCAmelCase__		(	self						):
       lowerCAmelCase_					:       int    =		self.full_loop(prediction_type="""v_prediction"""						)

       lowerCAmelCase_					:       Tuple    =		torch.sum(torch.abs(UpperCamelCase_						)						)
       lowerCAmelCase_					:       List[Any]    =		torch.mean(torch.abs(UpperCamelCase_						)						)

       assert abs(result_sum.item() - 52.5302						) < 1e-2
       assert abs(result_mean.item() - 0.0684						) < 1e-3







  def 	UpperCAmelCase__		(	self						):
       lowerCAmelCase_					:       List[Any]    =		self.full_loop(set_alpha_to_one=UpperCamelCase_    ,						beta_start=0.01						)
       lowerCAmelCase_					:       Union[str, Any]    =		torch.sum(torch.abs(UpperCamelCase_						)						)
       lowerCAmelCase_					:       Union[str, Any]    =		torch.mean(torch.abs(UpperCamelCase_						)						)

       assert abs(result_sum.item() - 149.8_295						) < 1e-2
       assert abs(result_mean.item() - 0.1951						) < 1e-3







  def 	UpperCAmelCase__		(	self						):
       lowerCAmelCase_					:       Optional[int]    =		self.full_loop(set_alpha_to_one=UpperCamelCase_    ,						beta_start=0.01						)
       lowerCAmelCase_					:       str    =		torch.sum(torch.abs(UpperCamelCase_						)						)
       lowerCAmelCase_					:       Tuple    =		torch.mean(torch.abs(UpperCamelCase_						)						)

       assert abs(result_sum.item() - 149.0_784						) < 1e-2
       assert abs(result_mean.item() - 0.1941						) < 1e-3







from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin





@dataclass
class      lowercase__    (							__A  ):
  __UpperCamelCase	     =  42
  __UpperCamelCase	     =  42
  __UpperCamelCase	     =  None





class      lowercase__    (							__A     ,					__A  ):
  __UpperCamelCase	     =  2







  @register_to_config
  def __init__(	self    ,						_lowercase = 0.02    ,						_lowercase = 100    ,						_lowercase = 1.007    ,						_lowercase = 80    ,						_lowercase = 0.05    ,						_lowercase = 50    ,						):
       # standard deviation of the initial noise distribution
       lowerCAmelCase_					:       int    =		sigma_max

       # setable values
       lowerCAmelCase_					:       int    =		None
       lowerCAmelCase_					:       np.IntTensor    =		None
       lowerCAmelCase_					:       torch.FloatTensor    =		None  # sigma(t_i)







  def 	UpperCAmelCase__		(	self    ,						_lowercase    ,						_lowercase = None						):
       return sample







  def 	UpperCAmelCase__		(	self    ,						_lowercase    ,						_lowercase = None						):
       lowerCAmelCase_					:       int    =		num_inference_steps
       lowerCAmelCase_					:       int    =		np.arange(0    ,						self.num_inference_steps						)[::-1].copy()
       lowerCAmelCase_					:       List[Any]    =		torch.from_numpy(_lowercase						).to(_lowercase						)
       lowerCAmelCase_					:       List[str]    =		[
           (
               self.config.sigma_max**2
               * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
           )
           for i in self.timesteps
       ]
       lowerCAmelCase_					:       Optional[int]    =		torch.tensor(_lowercase    ,						dtype=torch.floataa    ,						device=_lowercase						)







  def 	UpperCAmelCase__		(	self    ,						_lowercase    ,						_lowercase    ,						_lowercase = None						):
       if self.config.s_min <= sigma <= self.config.s_max:
            lowerCAmelCase_					:       List[Any]    =		min(self.config.s_churn / self.num_inference_steps    ,						2**0.5 - 1						)
       else:
            lowerCAmelCase_					:       List[str]    =		0

       # sample eps ~ N(0, S_noise^2 * I)
       lowerCAmelCase_					:       List[Any]    =		self.config.s_noise * randn_tensor(sample.shape    ,						generator=_lowercase						).to(sample.device						)
       lowerCAmelCase_					:       Optional[int]    =		sigma + gamma * sigma
       lowerCAmelCase_					:       int    =		sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

       return sample_hat, sigma_hat







  def 	UpperCAmelCase__		(	self    ,						_lowercase    ,						_lowercase    ,						_lowercase    ,						_lowercase    ,						_lowercase = True    ,						):
       lowerCAmelCase_					:       Union[str, Any]    =		sample_hat + sigma_hat * model_output
       lowerCAmelCase_					:       Dict    =		(sample_hat - pred_original_sample) / sigma_hat
       lowerCAmelCase_					:       List[Any]    =		sample_hat + (sigma_prev - sigma_hat) * derivative

       if not return_dict:
            return (sample_prev, derivative)

       return KarrasVeOutput(
           prev_sample=_lowercase    ,						derivative=_lowercase    ,						pred_original_sample=_lowercase						)







  def 	UpperCAmelCase__		(	self    ,						_lowercase    ,						_lowercase    ,						_lowercase    ,						_lowercase    ,						_lowercase    ,						_lowercase    ,						_lowercase = True    ,						):
       lowerCAmelCase_					:       str    =		sample_prev + sigma_prev * model_output
       lowerCAmelCase_					:       List[Any]    =		(sample_prev - pred_original_sample) / sigma_prev
       lowerCAmelCase_					:       Tuple    =		sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

       if not return_dict:
            return (sample_prev, derivative)

       return KarrasVeOutput(
           prev_sample=_lowercase    ,						derivative=_lowercase    ,						pred_original_sample=_lowercase						)







  def 	UpperCAmelCase__		(	self    ,						_lowercase    ,						_lowercase    ,						_lowercase						):
       raise NotImplementedError()
