

'''simple docstring'''

import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest





class 							_snake_case (       _a						):
   _A       :     Optional[Any]       =					(DDIMParallelScheduler,)
   _A       :     Union[str, Any]       =					(('''eta''', 0.0), ('''num_inference_steps''', 5_0))
   def     __UpperCamelCase						( self			:      int     ,**SCREAMING_SNAKE_CASE__			:      str							):
     SCREAMING_SNAKE_CASE:Optional[int]						      =					{
         "num_train_timesteps": 1_000,
         "beta_start": 0.0_001,
         "beta_end": 0.02,
         "beta_schedule": "linear",
         "clip_sample": True,
     }

     config.update(**SCREAMING_SNAKE_CASE__							)
     return config
   def     __UpperCamelCase						( self			:      Any     ,**SCREAMING_SNAKE_CASE__			:      int							):
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					self.scheduler_classes[0]
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					self.get_scheduler_config(**SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:List[str]						      =					scheduler_class(**SCREAMING_SNAKE_CASE__							)

     SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:List[str]						      =					10, 0.0

     SCREAMING_SNAKE_CASE:str						      =					self.dummy_model()
     SCREAMING_SNAKE_CASE:List[str]						      =					self.dummy_sample_deter

     scheduler.set_timesteps(SCREAMING_SNAKE_CASE__							)

     for t in scheduler.timesteps:
       SCREAMING_SNAKE_CASE:List[str]						      =					model(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__							)
       SCREAMING_SNAKE_CASE:Union[str, Any]						      =					scheduler.step(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__							).prev_sample

     return sample
   def     __UpperCamelCase						( self			:      Union[str, Any]							):
     for timesteps in [100, 500, 1_000]:
       self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE__							)
   def     __UpperCamelCase						( self			:      List[Any]							):
     for steps_offset in [0, 1]:
       self.check_over_configs(steps_offset=SCREAMING_SNAKE_CASE__							)

     SCREAMING_SNAKE_CASE:str						      =					self.scheduler_classes[0]
     SCREAMING_SNAKE_CASE:str						      =					self.get_scheduler_config(steps_offset=1							)
     SCREAMING_SNAKE_CASE:Tuple						      =					scheduler_class(**SCREAMING_SNAKE_CASE__							)
     scheduler.set_timesteps(5							)
     assert torch.equal(scheduler.timesteps     ,torch.LongTensor([801, 601, 401, 201, 1]							)							)
   def     __UpperCamelCase						( self			:      List[str]							):
     for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1]     ,[0.002, 0.02, 0.2, 2]							):
       self.check_over_configs(beta_start=SCREAMING_SNAKE_CASE__     ,beta_end=SCREAMING_SNAKE_CASE__							)
   def     __UpperCamelCase						( self			:      Union[str, Any]							):
     for schedule in ["linear", "squaredcos_cap_v2"]:
       self.check_over_configs(beta_schedule=SCREAMING_SNAKE_CASE__							)
   def     __UpperCamelCase						( self			:      Tuple							):
     for prediction_type in ["epsilon", "v_prediction"]:
       self.check_over_configs(prediction_type=SCREAMING_SNAKE_CASE__							)
   def     __UpperCamelCase						( self			:      Union[str, Any]							):
     for clip_sample in [True, False]:
       self.check_over_configs(clip_sample=SCREAMING_SNAKE_CASE__							)
   def     __UpperCamelCase						( self			:      Any							):
     for timestep_spacing in ["trailing", "leading"]:
       self.check_over_configs(timestep_spacing=SCREAMING_SNAKE_CASE__							)
   def     __UpperCamelCase						( self			:      List[Any]							):
     for rescale_betas_zero_snr in [True, False]:
       self.check_over_configs(rescale_betas_zero_snr=SCREAMING_SNAKE_CASE__							)
   def     __UpperCamelCase						( self			:      List[Any]							):
     self.check_over_configs(thresholding=SCREAMING_SNAKE_CASE__							)
     for threshold in [0.5, 1.0, 2.0]:
       for prediction_type in ["epsilon", "v_prediction"]:
         self.check_over_configs(
             thresholding=SCREAMING_SNAKE_CASE__     ,prediction_type=SCREAMING_SNAKE_CASE__     ,sample_max_value=SCREAMING_SNAKE_CASE__     ,)
   def     __UpperCamelCase						( self			:      int							):
     for t in [1, 10, 49]:
       self.check_over_forward(time_step=SCREAMING_SNAKE_CASE__							)
   def     __UpperCamelCase						( self			:      List[Any]							):
     for t, num_inference_steps in zip([1, 10, 50]     ,[10, 50, 500]							):
       self.check_over_forward(time_step=SCREAMING_SNAKE_CASE__     ,num_inference_steps=SCREAMING_SNAKE_CASE__							)
   def     __UpperCamelCase						( self			:      Optional[Any]							):
     for t, eta in zip([1, 10, 49]     ,[0.0, 0.5, 1.0]							):
       self.check_over_forward(time_step=SCREAMING_SNAKE_CASE__     ,eta=SCREAMING_SNAKE_CASE__							)
   def     __UpperCamelCase						( self			:      Dict							):
     SCREAMING_SNAKE_CASE:Optional[int]						      =					self.scheduler_classes[0]
     SCREAMING_SNAKE_CASE:str						      =					self.get_scheduler_config()
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					scheduler_class(**SCREAMING_SNAKE_CASE__							)

     assert torch.sum(torch.abs(scheduler._get_variance(0     ,0							) - 0.0							)							) < 1e-5
     assert torch.sum(torch.abs(scheduler._get_variance(420     ,400							) - 0.14_771							)							) < 1e-5
     assert torch.sum(torch.abs(scheduler._get_variance(980     ,960							) - 0.32_460							)							) < 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							) - 0.00_979							)							) < 1e-5
     assert torch.sum(torch.abs(scheduler._get_variance(999     ,998							) - 0.02							)							) < 1e-5
   def     __UpperCamelCase						( self			:      Tuple							):
     SCREAMING_SNAKE_CASE:List[str]						      =					self.scheduler_classes[0]
     SCREAMING_SNAKE_CASE:Dict						      =					self.get_scheduler_config()
     SCREAMING_SNAKE_CASE:Dict						      =					scheduler_class(**SCREAMING_SNAKE_CASE__							)

     SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:Tuple						      =					10, 0.0
     scheduler.set_timesteps(SCREAMING_SNAKE_CASE__							)

     SCREAMING_SNAKE_CASE:List[Any]						      =					self.dummy_model()
     SCREAMING_SNAKE_CASE:Tuple						      =					self.dummy_sample_deter
     SCREAMING_SNAKE_CASE:Dict						      =					self.dummy_sample_deter + 0.1
     SCREAMING_SNAKE_CASE:List[Any]						      =					self.dummy_sample_deter - 0.1

     SCREAMING_SNAKE_CASE:Dict						      =					samplea.shape[0]
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					torch.stack([samplea, samplea, samplea]     ,dim=0							)
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					torch.arange(SCREAMING_SNAKE_CASE__							)[0:3, None].repeat(1     ,SCREAMING_SNAKE_CASE__							)

     SCREAMING_SNAKE_CASE:Any						      =					model(samples.flatten(0     ,1							)     ,timesteps.flatten(0     ,1							)							)
     SCREAMING_SNAKE_CASE:Any						      =					scheduler.batch_step_no_noise(SCREAMING_SNAKE_CASE__     ,timesteps.flatten(0     ,1							)     ,samples.flatten(0     ,1							)     ,SCREAMING_SNAKE_CASE__							)

     SCREAMING_SNAKE_CASE:List[str]						      =					torch.sum(torch.abs(SCREAMING_SNAKE_CASE__							)							)
     SCREAMING_SNAKE_CASE:List[Any]						      =					torch.mean(torch.abs(SCREAMING_SNAKE_CASE__							)							)

     assert abs(result_sum.item() - 1_147.7_904							) < 1e-2
     assert abs(result_mean.item() - 0.4_982							) < 1e-3
   def     __UpperCamelCase						( self			:      Dict							):
     SCREAMING_SNAKE_CASE:Any						      =					self.full_loop()

     SCREAMING_SNAKE_CASE:str						      =					torch.sum(torch.abs(SCREAMING_SNAKE_CASE__							)							)
     SCREAMING_SNAKE_CASE:List[str]						      =					torch.mean(torch.abs(SCREAMING_SNAKE_CASE__							)							)

     assert abs(result_sum.item() - 172.0_067							) < 1e-2
     assert abs(result_mean.item() - 0.223_967							) < 1e-3
   def     __UpperCamelCase						( self			:      str							):
     SCREAMING_SNAKE_CASE:List[str]						      =					self.full_loop(prediction_type="v_prediction"							)

     SCREAMING_SNAKE_CASE:Optional[int]						      =					torch.sum(torch.abs(SCREAMING_SNAKE_CASE__							)							)
     SCREAMING_SNAKE_CASE:Tuple						      =					torch.mean(torch.abs(SCREAMING_SNAKE_CASE__							)							)

     assert abs(result_sum.item() - 52.5_302							) < 1e-2
     assert abs(result_mean.item() - 0.0_684							) < 1e-3
   def     __UpperCamelCase						( self			:      Any							):
     # We specify different beta, so that the first alpha is 0.99
     SCREAMING_SNAKE_CASE:str						      =					self.full_loop(set_alpha_to_one=SCREAMING_SNAKE_CASE__     ,beta_start=0.01							)
     SCREAMING_SNAKE_CASE:str						      =					torch.sum(torch.abs(SCREAMING_SNAKE_CASE__							)							)
     SCREAMING_SNAKE_CASE:List[str]						      =					torch.mean(torch.abs(SCREAMING_SNAKE_CASE__							)							)

     assert abs(result_sum.item() - 149.8_295							) < 1e-2
     assert abs(result_mean.item() - 0.1_951							) < 1e-3







   def     __UpperCamelCase						( self			:      Tuple							):
     # We specify different beta, so that the first alpha is 0.99
     SCREAMING_SNAKE_CASE:Any						      =					self.full_loop(set_alpha_to_one=SCREAMING_SNAKE_CASE__     ,beta_start=0.01							)
     SCREAMING_SNAKE_CASE:Optional[int]						      =					torch.sum(torch.abs(SCREAMING_SNAKE_CASE__							)							)
     SCREAMING_SNAKE_CASE:int						      =					torch.mean(torch.abs(SCREAMING_SNAKE_CASE__							)							)

     assert abs(result_sum.item() - 149.0_784							) < 1e-2
     assert abs(result_mean.item() - 0.1_941							) < 1e-3









'''simple docstring'''

import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image


if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("9.1.0"):
    A_       						=							{
        "linear": PIL.Image.Resampling.BILINEAR,
        "bilinear": PIL.Image.Resampling.BILINEAR,
        "bicubic": PIL.Image.Resampling.BICUBIC,
        "lanczos": PIL.Image.Resampling.LANCZOS,
        "nearest": PIL.Image.Resampling.NEAREST,
    }
else:
    A_       						=							{
        "linear": PIL.Image.LINEAR,
        "bilinear": PIL.Image.BILINEAR,
        "bicubic": PIL.Image.BICUBIC,
        "lanczos": PIL.Image.LANCZOS,
        "nearest": PIL.Image.NEAREST,
    }





def    A_  ( snake_case ):
  SCREAMING_SNAKE_CASE:Any						      =					(images / 2 + 0.5).clamp(0      ,       1 )
  SCREAMING_SNAKE_CASE:Optional[Any]						      =					images.cpu().permute(0      ,       2      ,       3      ,       1 ).float().numpy()
  SCREAMING_SNAKE_CASE:str						      =					numpy_to_pil(snake_case )
  return images





def    A_  ( snake_case ):
  if images.ndim == 3:
    SCREAMING_SNAKE_CASE:List[str]						      =					images[None, ...]
  SCREAMING_SNAKE_CASE:List[Any]						      =					(images * 255).round().astype("uint8" )
  if images.shape[-1] == 1:
    # special case for grayscale (single channel) images
    SCREAMING_SNAKE_CASE:Any						      =					[Image.fromarray(image.squeeze()      ,       mode="L" ) for image in images]
  else:
    SCREAMING_SNAKE_CASE:int						      =					[Image.fromarray(snake_case ) for image in images]

  return pil_images






