

'''simple docstring'''






import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest






class      lowercase_      (lowerCamelCase__ ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE       :    Dict							     =     (DDPMScheduler,)






  def    SCREAMING_SNAKE_CASE ( self  :	List[Any]   ,**lowercase__  :	int     ):
   __lowercase				    =					{
       '''num_train_timesteps''': 1_0_0_0,
       '''beta_start''': 0.0_0_0_1,
       '''beta_end''': 0.0_2,
       '''beta_schedule''': '''linear''',
       '''variance_type''': '''fixed_small''',
       '''clip_sample''': True,
   }

   config.update(**lowercase__     )
   return config






  def    SCREAMING_SNAKE_CASE ( self  :	Any     ):
   for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
    self.check_over_configs(num_train_timesteps=lowercase__     )






  def    SCREAMING_SNAKE_CASE ( self  :	Optional[int]     ):
   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=lowercase__   ,beta_end=lowercase__     )






  def    SCREAMING_SNAKE_CASE ( self  :	Optional[Any]     ):
   for schedule in ["linear", "squaredcos_cap_v2"]:
    self.check_over_configs(beta_schedule=lowercase__     )






  def    SCREAMING_SNAKE_CASE ( self  :	List[str]     ):
   for variance in ["fixed_small", "fixed_large", "other"]:
    self.check_over_configs(variance_type=lowercase__     )






  def    SCREAMING_SNAKE_CASE ( self  :	Union[str, Any]     ):
   for clip_sample in [True, False]:
    self.check_over_configs(clip_sample=lowercase__     )






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






  def    SCREAMING_SNAKE_CASE ( self  :	Optional[Any]     ):
   for prediction_type in ["epsilon", "sample", "v_prediction"]:
    self.check_over_configs(prediction_type=lowercase__     )






  def    SCREAMING_SNAKE_CASE ( self  :	Optional[Any]     ):
   for t in [0, 5_0_0, 9_9_9]:
    self.check_over_forward(time_step=lowercase__     )






  def    SCREAMING_SNAKE_CASE ( self  :	List[Any]     ):
   __lowercase				    =					self.scheduler_classes[0]
   __lowercase				    =					self.get_scheduler_config()
   __lowercase				    =					scheduler_class(**lowercase__     )

   assert torch.sum(torch.abs(scheduler._get_variance(0     ) - 0.0     )     ) < 1e-5
   assert torch.sum(torch.abs(scheduler._get_variance(4_8_7     ) - 0.0_0_9_7_9     )     ) < 1e-5
   assert torch.sum(torch.abs(scheduler._get_variance(9_9_9     ) - 0.0_2     )     ) < 1e-5






  def    SCREAMING_SNAKE_CASE ( self  :	Union[str, Any]     ):
   __lowercase				    =					self.scheduler_classes[0]
   __lowercase				    =					self.get_scheduler_config()
   __lowercase				    =					scheduler_class(**lowercase__     )

   __lowercase				    =					len(lowercase__     )

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

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

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

    # if t > 0:
    #     noise = self.dummy_sample_deter
    #     variance = scheduler.get_variance(t) ** (0.5) * noise
    #
    # sample = pred_prev_sample + variance
    __lowercase				    =					pred_prev_sample

   __lowercase				    =					torch.sum(torch.abs(lowercase__     )     )
   __lowercase				    =					torch.mean(torch.abs(lowercase__     )     )

   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    SCREAMING_SNAKE_CASE ( self  :	Optional[Any]     ):
   __lowercase				    =					self.scheduler_classes[0]
   __lowercase				    =					self.get_scheduler_config(prediction_type='''v_prediction'''     )
   __lowercase				    =					scheduler_class(**lowercase__     )

   __lowercase				    =					len(lowercase__     )

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

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

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

    # if t > 0:
    #     noise = self.dummy_sample_deter
    #     variance = scheduler.get_variance(t) ** (0.5) * noise
    #
    # sample = pred_prev_sample + variance
    __lowercase				    =					pred_prev_sample

   __lowercase				    =					torch.sum(torch.abs(lowercase__     )     )
   __lowercase				    =					torch.mean(torch.abs(lowercase__     )     )

   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    SCREAMING_SNAKE_CASE ( self  :	Optional[Any]     ):
   __lowercase				    =					self.scheduler_classes[0]
   __lowercase				    =					self.get_scheduler_config()
   __lowercase				    =					scheduler_class(**lowercase__     )

   __lowercase				    =					[1_0_0, 8_7, 5_0, 1, 0]

   scheduler.set_timesteps(timesteps=lowercase__     )

   __lowercase				    =					scheduler.timesteps

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

    __lowercase				    =					scheduler.previous_timestep(lowercase__     )
    __lowercase				    =					prev_t.item()

    self.assertEqual(lowercase__   ,lowercase__     )






  def    SCREAMING_SNAKE_CASE ( self  :	str     ):
   __lowercase				    =					self.scheduler_classes[0]
   __lowercase				    =					self.get_scheduler_config()
   __lowercase				    =					scheduler_class(**lowercase__     )

   __lowercase				    =					[1_0_0, 8_7, 5_0, 5_1, 0]

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






  def    SCREAMING_SNAKE_CASE ( self  :	Tuple     ):
   __lowercase				    =					self.scheduler_classes[0]
   __lowercase				    =					self.get_scheduler_config()
   __lowercase				    =					scheduler_class(**lowercase__     )

   __lowercase				    =					[1_0_0, 8_7, 5_0, 1, 0]
   __lowercase				    =					len(lowercase__     )

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






  def    SCREAMING_SNAKE_CASE ( self  :	Union[str, Any]     ):
   __lowercase				    =					self.scheduler_classes[0]
   __lowercase				    =					self.get_scheduler_config()
   __lowercase				    =					scheduler_class(**lowercase__     )

   __lowercase				    =					[scheduler.config.num_train_timesteps]

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









import colorsys

from PIL import Image  # type: ignore






def 	UpperCamelCase	(							snake_case__  : float						,snake_case__  : float						,snake_case__  : int	):




     '''simple docstring'''






     __snake_case      :List[Any] 							=    x
     __snake_case      :str 							=    y
     for step in range(snake_case__	):  # noqa: B007
          __snake_case      :Tuple 							=    a * a - b * b + x
          __snake_case      :Dict 							=    2 * a * b + y
          __snake_case      :str 							=    a_new

          # divergence happens for all complex number with an absolute value
          # greater than 4
          if a * a + b * b > 4:
               break
     return step / (max_step - 1)






def 	UpperCamelCase	(							snake_case__  : float	):




     '''simple docstring'''






     if distance == 1:
          return (0, 0, 0)
     else:
          return (255, 255, 255)






def 	UpperCamelCase	(							snake_case__  : float	):




     '''simple docstring'''






     if distance == 1:
          return (0, 0, 0)
     else:
          return tuple(round(i * 255	) for i in colorsys.hsv_to_rgb(snake_case__						,1						,1	)	)






def 	UpperCamelCase	(							snake_case__  : int = 800						,snake_case__  : int = 600						,snake_case__  : float = -0.6						,snake_case__  : float = 0						,snake_case__  : float = 3.2						,snake_case__  : int = 50						,snake_case__  : bool = True						,):




     '''simple docstring'''






     __snake_case      :Tuple 							=    Image.new("""RGB"""						,(image_width, image_height)	)
     __snake_case      :Dict 							=    img.load()

     # loop through the image-coordinates
     for image_x in range(snake_case__	):
          for image_y in range(snake_case__	):
               # determine the figure-coordinates based on the image-coordinates
               __snake_case      :Dict 							=    figure_width / image_width * image_height
               __snake_case      :Any 							=    figure_center_x + (image_x / image_width - 0.5) * figure_width
               __snake_case      :List[str] 							=    figure_center_y + (image_y / image_height - 0.5) * figure_height

               __snake_case      :Tuple 							=    get_distance(snake_case__						,snake_case__						,snake_case__	)

               # color the corresponding pixel based on the selected coloring-function
               if use_distance_color_coding:
                    __snake_case      :Optional[int] 							=    get_color_coded_rgb(snake_case__	)
               else:
                    __snake_case      :Optional[int] 							=    get_black_and_white_rgb(snake_case__	)

     return img


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     # colored version, full figure
     lowerCamelCase__       		=  get_image()

     # uncomment for colored version, different section, zoomed in
     # img = get_image(figure_center_x = -0.6, figure_center_y = -0.4,
     # figure_width = 0.8)

     # uncomment for black and white version, full figure
     # img = get_image(use_distance_color_coding = False)

     # uncomment to save the image
     # img.save("mandelbrot.png")

     img.show()


