



import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest



class 		_lowercase		(       UpperCAmelCase__	):
   _UpperCAmelCase     	=			(DDPMParallelScheduler,)

   def 		A							(      self			:       Optional[int]							,   **__lowerCAmelCase			:       Union[str, Any]							)       ->				Union[str, Any]:


    """simple docstring"""






    a						=		{
        "num_train_timesteps": 1000,
        "beta_start": 0.0_0_0_1,
        "beta_end": 0.0_2,
        "beta_schedule": "linear",
        "variance_type": "fixed_small",
        "clip_sample": True,
    }

    config.update(**__lowerCAmelCase							)
    return config

   def 		A							(      self			:       Optional[int]							)       ->				Tuple:


    """simple docstring"""






    for timesteps in [1, 5, 100, 1000]:
     self.check_over_configs(num_train_timesteps=__lowerCAmelCase							)

   def 		A							(      self			:       List[str]							)       ->				int:


    """simple docstring"""






    for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]							,   [0.0_0_2, 0.0_2, 0.2, 2]							):
     self.check_over_configs(beta_start=__lowerCAmelCase							,   beta_end=__lowerCAmelCase							)

   def 		A							(      self			:       Optional[Any]							)       ->				Tuple:


    """simple docstring"""






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

   def 		A							(      self			:       Dict							)       ->				Dict:


    """simple docstring"""






    for variance in ["fixed_small", "fixed_large", "other"]:
     self.check_over_configs(variance_type=__lowerCAmelCase							)

   def 		A							(      self			:       List[str]							)       ->				Optional[int]:


    """simple docstring"""






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

   def 		A							(      self			:       Dict							)       ->				Dict:


    """simple docstring"""






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

   def 		A							(      self			:       str							)       ->				Tuple:


    """simple docstring"""






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

   def 		A							(      self			:       Dict							)       ->				Optional[int]:


    """simple docstring"""






    for t in [0, 500, 999]:
     self.check_over_forward(time_step=__lowerCAmelCase							)

   def 		A							(      self			:       Tuple							)       ->				Any:


    """simple docstring"""






    a						=		self.scheduler_classes[0]
    a						=		self.get_scheduler_config()
    a						=		scheduler_class(**__lowerCAmelCase							)

    assert torch.sum(torch.abs(scheduler._get_variance(0							) - 0.0							)							) < 1E-5
    assert torch.sum(torch.abs(scheduler._get_variance(487							) - 0.0_0_9_7_9							)							) < 1E-5
    assert torch.sum(torch.abs(scheduler._get_variance(999							) - 0.0_2							)							) < 1E-5

   def 		A							(      self			:       int							)       ->				Tuple:


    """simple docstring"""






    a						=		self.scheduler_classes[0]
    a						=		self.get_scheduler_config()
    a						=		scheduler_class(**__lowerCAmelCase							)

    a						=		len(__lowerCAmelCase							)

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

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

    a						=		model(samples.flatten(0							,   1							)							,   timesteps.flatten(0							,   1							)							)
    a						=		scheduler.batch_step_no_noise(__lowerCAmelCase							,   timesteps.flatten(0							,   1							)							,   samples.flatten(0							,   1							)							)

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

    assert abs(result_sum.item() - 1_1_5_3.1_8_3_3							) < 1E-2
    assert abs(result_mean.item() - 0.5_0_0_5							) < 1E-3

   def 		A							(      self			:       Optional[int]							)       ->				Union[str, Any]:


    """simple docstring"""






    a						=		self.scheduler_classes[0]
    a						=		self.get_scheduler_config()
    a						=		scheduler_class(**__lowerCAmelCase							)

    a						=		len(__lowerCAmelCase							)

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

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

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

     a						=		pred_prev_sample

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

    assert abs(result_sum.item() - 2_5_8.9_6_0_6							) < 1E-2
    assert abs(result_mean.item() - 0.3_3_7_2							) < 1E-3

   def 		A							(      self			:       Any							)       ->				Any:


    """simple docstring"""






    a						=		self.scheduler_classes[0]
    a						=		self.get_scheduler_config(prediction_type="v_prediction"							)
    a						=		scheduler_class(**__lowerCAmelCase							)

    a						=		len(__lowerCAmelCase							)

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

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

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

     a						=		pred_prev_sample

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

    assert abs(result_sum.item() - 2_0_2.0_2_9_6							) < 1E-2
    assert abs(result_mean.item() - 0.2_6_3_1							) < 1E-3

   def 		A							(      self			:       Dict							)       ->				str:


    """simple docstring"""






    a						=		self.scheduler_classes[0]
    a						=		self.get_scheduler_config()
    a						=		scheduler_class(**__lowerCAmelCase							)

    a						=		[100, 87, 50, 1, 0]

    scheduler.set_timesteps(timesteps=__lowerCAmelCase							)

    a						=		scheduler.timesteps

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

     a						=		scheduler.previous_timestep(__lowerCAmelCase							)
     a						=		prev_t.item()

     self.assertEqual(__lowerCAmelCase							,   __lowerCAmelCase							)

   def 		A							(      self			:       Tuple							)       ->				str:


    """simple docstring"""






    a						=		self.scheduler_classes[0]
    a						=		self.get_scheduler_config()
    a						=		scheduler_class(**__lowerCAmelCase							)

    a						=		[100, 87, 50, 51, 0]

    with self.assertRaises(__lowerCAmelCase							,   msg="`custom_timesteps` must be in descending order."							):
     scheduler.set_timesteps(timesteps=__lowerCAmelCase							)

   def 		A							(      self			:       int							)       ->				List[Any]:


    """simple docstring"""






    a						=		self.scheduler_classes[0]
    a						=		self.get_scheduler_config()
    a						=		scheduler_class(**__lowerCAmelCase							)

    a						=		[100, 87, 50, 1, 0]
    a						=		len(__lowerCAmelCase							)

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




   def 		A							(      self			:       List[Any]							)       ->				Optional[int]:


    """simple docstring"""






    a						=		self.scheduler_classes[0]
    a						=		self.get_scheduler_config()
    a						=		scheduler_class(**__lowerCAmelCase							)

    a						=		[scheduler.config.num_train_timesteps]

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






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 		_lowercase		(       UpperCAmelCase__	):
   _UpperCAmelCase     	=			42
   _UpperCAmelCase     	=			42

   def __init__(      self			:       Optional[Any]							,   __lowerCAmelCase			:       UNetaDModel							,   __lowerCAmelCase			:       ScoreSdeVeScheduler							)       ->				str:


    """simple docstring"""






    super().__init__()
    self.register_modules(unet=__lowerCAmelCase							,   scheduler=__lowerCAmelCase							)




   @torch.no_grad()
   def __call__(      self			:       int							,   __lowerCAmelCase			:       int = 1							,   __lowerCAmelCase			:       int = 2000							,   __lowerCAmelCase			:       Optional[Union[torch.Generator, List[torch.Generator]]] = None							,   __lowerCAmelCase			:       Optional[str] = "pil"							,   __lowerCAmelCase			:       bool = True							,   **__lowerCAmelCase			:       Any							,   )       ->				Union[ImagePipelineOutput, Tuple]:


    """simple docstring"""






    a						=		self.unet.config.sample_size
    a						=		(batch_size, 3, img_size, img_size)

    a						=		self.unet

    a						=		randn_tensor(__lowerCAmelCase							,   generator=__lowerCAmelCase							) * self.scheduler.init_noise_sigma
    a						=		sample.to(self.device							)

    self.scheduler.set_timesteps(__lowerCAmelCase							)
    self.scheduler.set_sigmas(__lowerCAmelCase							)

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

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

     # prediction step
     a						=		model(__lowerCAmelCase							,   __lowerCAmelCase							).sample
     a						=		self.scheduler.step_pred(__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase							,   generator=__lowerCAmelCase							)

     a		,  a						=		output.prev_sample, output.prev_sample_mean

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

    if not return_dict:
     return (sample,)

    return ImagePipelineOutput(images=__lowerCAmelCase							)

