


'''simple docstring'''
import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class 			lowerCamelCase_   (	__lowercase      ):
      lowerCAmelCase__		     =     (KDPMaDiscreteScheduler,)
      lowerCAmelCase__		     =     1_0






      def lowercase_							(					self				:    Optional[int]						,						**_A				:    Optional[int] ):


          '''simple docstring'''


          UpperCAmelCase__			:		List[Any]													=				{
              '''num_train_timesteps''': 1_100,
              '''beta_start''': 0.0_0_0_1,
              '''beta_end''': 0.0_2,
              '''beta_schedule''': '''linear''',
          }

          config.update(**snake_case_ )
          return config






      def lowercase_							(					self				:    Any ):


          '''simple docstring'''


          for timesteps in [10, 50, 100, 1_000]:
              self.check_over_configs(num_train_timesteps=snake_case_ )






      def lowercase_							(					self				:    str ):


          '''simple docstring'''


          for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]						,						[0.0_0_0_2, 0.0_0_2, 0.0_2] ):
              self.check_over_configs(beta_start=snake_case_						,						beta_end=snake_case_ )






      def lowercase_							(					self				:    str ):


          '''simple docstring'''


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






      def lowercase_							(					self				:    int ):


          '''simple docstring'''


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






      def lowercase_							(					self				:    int ):


          '''simple docstring'''


          UpperCAmelCase__			:		Union[str, Any]													=				self.scheduler_classes[0]
          UpperCAmelCase__			:		str													=				self.get_scheduler_config(prediction_type='''v_prediction''' )
          UpperCAmelCase__			:		Any													=				scheduler_class(**snake_case_ )

          scheduler.set_timesteps(self.num_inference_steps )

          UpperCAmelCase__			:		Optional[int]													=				self.dummy_model()
          UpperCAmelCase__			:		Dict													=				self.dummy_sample_deter * scheduler.init_noise_sigma
          UpperCAmelCase__			:		Optional[int]													=				sample.to(snake_case_ )

          for i, t in enumerate(scheduler.timesteps ):
              UpperCAmelCase__			:		Optional[Any]													=				scheduler.scale_model_input(snake_case_						,						snake_case_ )

              UpperCAmelCase__			:		List[Any]													=				model(snake_case_						,						snake_case_ )

              UpperCAmelCase__			:		Union[str, Any]													=				scheduler.step(snake_case_						,						snake_case_						,						snake_case_ )
              UpperCAmelCase__			:		Optional[int]													=				output.prev_sample

          UpperCAmelCase__			:		Optional[Any]													=				torch.sum(torch.abs(snake_case_ ) )
          UpperCAmelCase__			:		Dict													=				torch.mean(torch.abs(snake_case_ ) )

          if torch_device in ["cpu", "mps"]:
              assert abs(result_sum.item() - 4.69_34e-07 ) < 1e-2
              assert abs(result_mean.item() - 6.11_12e-10 ) < 1e-3
          else:
              # CUDA
              assert abs(result_sum.item() - 4.6_93_42_86_50_17_09_72e-07 ) < 1e-2
              assert abs(result_mean.item() - 0.0_0_0_2 ) < 1e-3






      def lowercase_							(					self				:    Dict ):


          '''simple docstring'''


          if torch_device == "mps":
              return
          UpperCAmelCase__			:		Dict													=				self.scheduler_classes[0]
          UpperCAmelCase__			:		Optional[int]													=				self.get_scheduler_config()
          UpperCAmelCase__			:		Tuple													=				scheduler_class(**snake_case_ )

          scheduler.set_timesteps(self.num_inference_steps )

          UpperCAmelCase__			:		str													=				self.dummy_model()
          UpperCAmelCase__			:		Optional[int]													=				self.dummy_sample_deter * scheduler.init_noise_sigma
          UpperCAmelCase__			:		Optional[int]													=				sample.to(snake_case_ )

          for i, t in enumerate(scheduler.timesteps ):
              UpperCAmelCase__			:		List[Any]													=				scheduler.scale_model_input(snake_case_						,						snake_case_ )

              UpperCAmelCase__			:		List[Any]													=				model(snake_case_						,						snake_case_ )

              UpperCAmelCase__			:		Dict													=				scheduler.step(snake_case_						,						snake_case_						,						snake_case_ )
              UpperCAmelCase__			:		List[Any]													=				output.prev_sample

          UpperCAmelCase__			:		Optional[int]													=				torch.sum(torch.abs(snake_case_ ) )
          UpperCAmelCase__			:		Union[str, Any]													=				torch.mean(torch.abs(snake_case_ ) )

          if torch_device in ["cpu", "mps"]:
              assert abs(result_sum.item() - 2_0.4_1_2_5 ) < 1e-2
              assert abs(result_mean.item() - 0.0_2_6_6 ) < 1e-3
          else:
              # CUDA
              assert abs(result_sum.item() - 2_0.4_1_2_5 ) < 1e-2
              assert abs(result_mean.item() - 0.0_2_6_6 ) < 1e-3






      def lowercase_							(					self				:    Tuple ):


          '''simple docstring'''


          if torch_device == "mps":
              return
          UpperCAmelCase__			:		Tuple													=				self.scheduler_classes[0]
          UpperCAmelCase__			:		Dict													=				self.get_scheduler_config()
          UpperCAmelCase__			:		List[str]													=				scheduler_class(**snake_case_ )

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

          UpperCAmelCase__			:		Dict													=				self.dummy_model()
          UpperCAmelCase__			:		Optional[int]													=				self.dummy_sample_deter.to(snake_case_ ) * scheduler.init_noise_sigma

          for t in scheduler.timesteps:
              UpperCAmelCase__			:		Tuple													=				scheduler.scale_model_input(snake_case_						,						snake_case_ )

              UpperCAmelCase__			:		Union[str, Any]													=				model(snake_case_						,						snake_case_ )

              UpperCAmelCase__			:		str													=				scheduler.step(snake_case_						,						snake_case_						,						snake_case_ )
              UpperCAmelCase__			:		Dict													=				output.prev_sample

          UpperCAmelCase__			:		Union[str, Any]													=				torch.sum(torch.abs(snake_case_ ) )
          UpperCAmelCase__			:		Tuple													=				torch.mean(torch.abs(snake_case_ ) )

          if str(snake_case_ ).startswith('''cpu''' ):
              # The following sum varies between 148 and 156 on mps. Why?
              assert abs(result_sum.item() - 2_0.4_1_2_5 ) < 1e-2
              assert abs(result_mean.item() - 0.0_2_6_6 ) < 1e-3
          else:
              # CUDA
              assert abs(result_sum.item() - 2_0.4_1_2_5 ) < 1e-2
              assert abs(result_mean.item() - 0.0_2_6_6 ) < 1e-3


import colorsys

from PIL import Image  # type: ignore




def    __UpperCamelCase     (				lowerCAmelCase__							:				float  ,   lowerCAmelCase__							:				float  ,   lowerCAmelCase__							:				int  ):
 __a :  Any						=  x
 __a :  List[Any]						=  y
 for step in range(lowerCAmelCase__  ):  # noqa: B007
  __a :  List[Any]						=  a * a - b * b + x
  __a :  Tuple						=  2 * a * b + y
  __a :  Optional[int]						=  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     (				lowerCAmelCase__							:				float  ):
 if distance == 1:
  return (0, 0, 0)
 else:
  return (2_5_5, 2_5_5, 2_5_5)




def    __UpperCamelCase     (				lowerCAmelCase__							:				float  ):
 if distance == 1:
  return (0, 0, 0)
 else:
  return tuple(round(i * 2_5_5  ) for i in colorsys.hsv_to_rgb(lowerCAmelCase__  ,   1  ,   1  )  )




def    __UpperCamelCase     (				lowerCAmelCase__							:				int = 8_0_0  ,   lowerCAmelCase__							:				int = 6_0_0  ,   lowerCAmelCase__							:				float = -0.6  ,   lowerCAmelCase__							:				float = 0  ,   lowerCAmelCase__							:				float = 3.2  ,   lowerCAmelCase__							:				int = 5_0  ,   lowerCAmelCase__							:				bool = True  ,   ):
 __a :  int						=  Image.new('''RGB'''  ,   (image_width, image_height)  )
 __a :  Dict						=  img.load()

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

   __a :  Tuple						=  get_distance(lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  )

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

 return img


if __name__ == "__main__":
      import doctest

      doctest.testmod()

      # colored version, full figure
      lowercase__       =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()
