


'''simple docstring'''

import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest





class      snake_case    (    lowercase			):





  """simple docstring"""




  _lowerCamelCase		    =  (KDPMaDiscreteScheduler,)
  _lowerCamelCase		    =  10






  def   snake_case		(    self			,  **UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_      =  {
       "num_train_timesteps": 1100,
       "beta_start": 0.0_001,
       "beta_end": 0.02,
       "beta_schedule": "linear",
   }

   config.update(**UpperCamelCase     )
   return config






  def   snake_case		(    self     ):







   """simple docstring"""
   for timesteps in [10, 50, 100, 1000]:
    self.check_over_configs(num_train_timesteps=UpperCamelCase     )






  def   snake_case		(    self     ):







   """simple docstring"""
   for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001]			,  [0.0_002, 0.002, 0.02]     ):
    self.check_over_configs(beta_start=UpperCamelCase			,  beta_end=UpperCamelCase     )






  def   snake_case		(    self     ):







   """simple docstring"""
   for schedule in ["linear", "scaled_linear"]:
    self.check_over_configs(beta_schedule=UpperCamelCase     )






  def   snake_case		(    self     ):







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






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  self.scheduler_classes[0]
   lowerCamelCase_      =  self.get_scheduler_config(prediction_type="v_prediction"     )
   lowerCamelCase_      =  scheduler_class(**UpperCamelCase     )

   scheduler.set_timesteps(self.num_inference_steps     )

   lowerCamelCase_      =  self.dummy_model()
   lowerCamelCase_      =  self.dummy_sample_deter * scheduler.init_noise_sigma
   lowerCamelCase_      =  sample.to(UpperCamelCase     )

   for i, t in enumerate(scheduler.timesteps     ):
    lowerCamelCase_      =  scheduler.scale_model_input(UpperCamelCase			,  UpperCamelCase     )

    lowerCamelCase_      =  model(UpperCamelCase			,  UpperCamelCase     )

    lowerCamelCase_      =  scheduler.step(UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase     )
    lowerCamelCase_      =  output.prev_sample

   lowerCamelCase_      =  torch.sum(torch.abs(UpperCamelCase     )     )
   lowerCamelCase_      =  torch.mean(torch.abs(UpperCamelCase     )     )

   if torch_device in ["cpu", "mps"]:
    assert abs(result_sum.item() - 4.6934e-07     ) < 1e-2
    assert abs(result_mean.item() - 6.1112e-10     ) < 1e-3
   else:
    # CUDA
    assert abs(result_sum.item() - 4.693_4286_5017_0972e-07     ) < 1e-2
    assert abs(result_mean.item() - 0.0_002     ) < 1e-3






  def   snake_case		(    self     ):







   """simple docstring"""
   if torch_device == "mps":
    return
   lowerCamelCase_      =  self.scheduler_classes[0]
   lowerCamelCase_      =  self.get_scheduler_config()
   lowerCamelCase_      =  scheduler_class(**UpperCamelCase     )

   scheduler.set_timesteps(self.num_inference_steps     )

   lowerCamelCase_      =  self.dummy_model()
   lowerCamelCase_      =  self.dummy_sample_deter * scheduler.init_noise_sigma
   lowerCamelCase_      =  sample.to(UpperCamelCase     )

   for i, t in enumerate(scheduler.timesteps     ):
    lowerCamelCase_      =  scheduler.scale_model_input(UpperCamelCase			,  UpperCamelCase     )

    lowerCamelCase_      =  model(UpperCamelCase			,  UpperCamelCase     )

    lowerCamelCase_      =  scheduler.step(UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase     )
    lowerCamelCase_      =  output.prev_sample

   lowerCamelCase_      =  torch.sum(torch.abs(UpperCamelCase     )     )
   lowerCamelCase_      =  torch.mean(torch.abs(UpperCamelCase     )     )

   if torch_device in ["cpu", "mps"]:
    assert abs(result_sum.item() - 20.4_125     ) < 1e-2
    assert abs(result_mean.item() - 0.0_266     ) < 1e-3
   else:
    # CUDA
    assert abs(result_sum.item() - 20.4_125     ) < 1e-2
    assert abs(result_mean.item() - 0.0_266     ) < 1e-3







  def   snake_case		(    self     ):







   """simple docstring"""
   if torch_device == "mps":
    return
   lowerCamelCase_      =  self.scheduler_classes[0]
   lowerCamelCase_      =  self.get_scheduler_config()
   lowerCamelCase_      =  scheduler_class(**UpperCamelCase     )

   scheduler.set_timesteps(self.num_inference_steps			,  device=UpperCamelCase     )

   lowerCamelCase_      =  self.dummy_model()
   lowerCamelCase_      =  self.dummy_sample_deter.to(UpperCamelCase     ) * scheduler.init_noise_sigma

   for t in scheduler.timesteps:
    lowerCamelCase_      =  scheduler.scale_model_input(UpperCamelCase			,  UpperCamelCase     )

    lowerCamelCase_      =  model(UpperCamelCase			,  UpperCamelCase     )

    lowerCamelCase_      =  scheduler.step(UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase     )
    lowerCamelCase_      =  output.prev_sample

   lowerCamelCase_      =  torch.sum(torch.abs(UpperCamelCase     )     )
   lowerCamelCase_      =  torch.mean(torch.abs(UpperCamelCase     )     )

   if str(UpperCamelCase     ).startswith("cpu"     ):
    # The following sum varies between 148 and 156 on mps. Why?
    assert abs(result_sum.item() - 20.4_125     ) < 1e-2
    assert abs(result_mean.item() - 0.0_266     ) < 1e-3
   else:
    # CUDA
    assert abs(result_sum.item() - 20.4_125     ) < 1e-2
    assert abs(result_mean.item() - 0.0_266     ) < 1e-3







'''simple docstring'''

from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput





class      snake_case    (    lowercase			):





  """simple docstring"""




  _lowerCamelCase		    =  42
  _lowerCamelCase		    =  42






  def __init__(    self			,  UpperCamelCase			,  UpperCamelCase     ):







   """simple docstring"""
   super().__init__()
   self.register_modules(unet=UpperCamelCase			,  scheduler=UpperCamelCase     )







  @torch.no_grad()
  def __call__(    self			,  UpperCamelCase = 1			,  UpperCamelCase = 2000			,  UpperCamelCase = None			,  UpperCamelCase = "pil"			,  UpperCamelCase = True			,  **UpperCamelCase			,  ):







   """simple docstring"""
   lowerCamelCase_      =  self.unet.config.sample_size
   lowerCamelCase_      =  (batch_size, 3, img_size, img_size)

   lowerCamelCase_      =  self.unet

   lowerCamelCase_      =  randn_tensor(UpperCamelCase			,  generator=UpperCamelCase     ) * self.scheduler.init_noise_sigma
   lowerCamelCase_      =  sample.to(self.device     )

   self.scheduler.set_timesteps(UpperCamelCase     )
   self.scheduler.set_sigmas(UpperCamelCase     )

   for i, t in enumerate(self.progress_bar(self.scheduler.timesteps     )     ):
    lowerCamelCase_      =  self.scheduler.sigmas[i] * torch.ones(shape[0]			,  device=self.device     )

    # correction step
    for _ in range(self.scheduler.config.correct_steps     ):
     lowerCamelCase_      =  self.unet(UpperCamelCase			,  UpperCamelCase     ).sample
     lowerCamelCase_      =  self.scheduler.step_correct(UpperCamelCase			,  UpperCamelCase			,  generator=UpperCamelCase     ).prev_sample

    # prediction step
    lowerCamelCase_      =  model(UpperCamelCase			,  UpperCamelCase     ).sample
    lowerCamelCase_      =  self.scheduler.step_pred(UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  generator=UpperCamelCase     )

    lowerCamelCase_ ,lowerCamelCase_      =  output.prev_sample, output.prev_sample_mean

   lowerCamelCase_      =  sample_mean.clamp(0			,  1     )
   lowerCamelCase_      =  sample.cpu().permute(0			,  2			,  3			,  1     ).numpy()
   if output_type == "pil":
    lowerCamelCase_      =  self.numpy_to_pil(UpperCamelCase     )

   if not return_dict:
    return (sample,)

   return ImagePipelineOutput(images=UpperCamelCase     )



