'''simple docstring'''

import random
import sys

import numpy as np
from matplotlib import pyplot as plt
from matplotlib.colors import ListedColormap

A_     :						Tuple    							='Usage of script: script_name <size_of_canvas:int>'

A_     :						Union[str, Any]    							=[0] * 1_00 + [1] * 10
random.shuffle(choice)



def snake_case_      (					__snake_case  :							int)			->					list[list[bool]]:
   lowerCAmelCase_			=      [[False for i in range(_UpperCAmelCase)] for j in range(_UpperCAmelCase)]
   return canvas



def snake_case_      (					__snake_case  :							list[list[bool]])			->					None:


   for i, row in enumerate(_UpperCAmelCase):
      for j, _ in enumerate(_UpperCAmelCase):
         lowerCAmelCase_			=      bool(random.getrandbits(1))



def snake_case_      (					__snake_case  :							list[list[bool]])			->					list[list[bool]]:
   lowerCAmelCase_			=      np.array(_UpperCAmelCase)
   lowerCAmelCase_			=      np.array(create_canvas(current_canvas.shape[0]))
   for r, row in enumerate(_UpperCAmelCase):
      for c, pt in enumerate(_UpperCAmelCase):
         lowerCAmelCase_			=      __judge_point(
             _UpperCAmelCase	,   current_canvas[r - 1 : r + 2, c - 1 : c + 2])

   lowerCAmelCase_			=      next_gen_canvas
   del next_gen_canvas  # cleaning memory as we move on.
   lowerCAmelCase_			=      current_canvas.tolist()
   return return_canvas



def snake_case_      (					__snake_case  :							bool	,   __snake_case  :							list[list[bool]])			->					bool:
   lowerCAmelCase_			=      0
   lowerCAmelCase_			=      0
   # finding dead or alive neighbours count.
   for i in neighbours:
      for status in i:
         if status:
            alive += 1
         else:
            dead += 1

    # handling duplicate entry for focus pt.
   if pt:
      alive -= 1
   else:
      dead -= 1

   # running the rules of game here.
   lowerCAmelCase_			=      pt
   if pt:
      if alive < 2:
         lowerCAmelCase_			=      False
      elif alive == 2 or alive == 3:
         lowerCAmelCase_			=      True
      elif alive > 3:
         lowerCAmelCase_			=      False
   else:
      if alive == 3:
         lowerCAmelCase_			=      True

   return state


if __name__ == "__main__":
 if len(sys.argv) != 2:
  raise Exception(usage_doc)

 A_     :						str    							=int(sys.argv[1])
 # main working structure of this module.
 A_     :						str    							=create_canvas(canvas_size)
 seed(c)
 A_     :						str    							=plt.subplots()
 fig.show()
 A_     :						int    							=ListedColormap(['''w''', '''k'''])
 try:
  while True:
   A_     :						str    							=run(c)
   ax.matshow(c, cmap=cmap)
   fig.canvas.draw()
   ax.cla()
 except KeyboardInterrupt:
  # do nothing.
  pass












import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest





class UpperCAmelCase_				( _SCREAMING_SNAKE_CASE ):
  __lowerCAmelCase   :						int							=   (KDPMaDiscreteScheduler,)
  __lowerCAmelCase   :						Union[str, Any]							=   10


  def 						__UpperCAmelCase     (							self ,**__snake_case ):



       """simple docstring"""





       A_          =       {
           '''num_train_timesteps''': 1_1_0_0,
           '''beta_start''': 0.0001,
           '''beta_end''': 0.02,
           '''beta_schedule''': '''linear''',
       }

       config.update(**__snake_case )
       return config


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
            self.check_over_configs(num_train_timesteps=__snake_case )


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001] ,[0.0002, 0.002, 0.02] ):
            self.check_over_configs(beta_start=__snake_case ,beta_end=__snake_case )


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





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


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





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


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       self.scheduler_classes[0]
       A_          =       self.get_scheduler_config(prediction_type='''v_prediction''' )
       A_          =       scheduler_class(**__snake_case )

       scheduler.set_timesteps(self.num_inference_steps )

       A_          =       self.dummy_model()
       A_          =       self.dummy_sample_deter * scheduler.init_noise_sigma
       A_          =       sample.to(__snake_case )

       for i, t in enumerate(scheduler.timesteps ):
            A_          =       scheduler.scale_model_input(__snake_case ,__snake_case )

            A_          =       model(__snake_case ,__snake_case )

            A_          =       scheduler.step(__snake_case ,__snake_case ,__snake_case )
            A_          =       output.prev_sample

       A_          =       torch.sum(torch.abs(__snake_case ) )
       A_          =       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() - 0.0002 ) < 1E-3


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       if torch_device == "mps":
            return
       A_          =       self.scheduler_classes[0]
       A_          =       self.get_scheduler_config()
       A_          =       scheduler_class(**__snake_case )

       scheduler.set_timesteps(self.num_inference_steps )

       A_          =       self.dummy_model()
       A_          =       self.dummy_sample_deter * scheduler.init_noise_sigma
       A_          =       sample.to(__snake_case )

       for i, t in enumerate(scheduler.timesteps ):
            A_          =       scheduler.scale_model_input(__snake_case ,__snake_case )

            A_          =       model(__snake_case ,__snake_case )

            A_          =       scheduler.step(__snake_case ,__snake_case ,__snake_case )
            A_          =       output.prev_sample

       A_          =       torch.sum(torch.abs(__snake_case ) )
       A_          =       torch.mean(torch.abs(__snake_case ) )

       if torch_device in ["cpu", "mps"]:
            assert abs(result_sum.item() - 20.4125 ) < 1E-2
            assert abs(result_mean.item() - 0.0266 ) < 1E-3
       else:
            # CUDA
            assert abs(result_sum.item() - 20.4125 ) < 1E-2
            assert abs(result_mean.item() - 0.0266 ) < 1E-3




  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       if torch_device == "mps":
            return
       A_          =       self.scheduler_classes[0]
       A_          =       self.get_scheduler_config()
       A_          =       scheduler_class(**__snake_case )

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

       A_          =       self.dummy_model()
       A_          =       self.dummy_sample_deter.to(__snake_case ) * scheduler.init_noise_sigma

       for t in scheduler.timesteps:
            A_          =       scheduler.scale_model_input(__snake_case ,__snake_case )

            A_          =       model(__snake_case ,__snake_case )

            A_          =       scheduler.step(__snake_case ,__snake_case ,__snake_case )
            A_          =       output.prev_sample

       A_          =       torch.sum(torch.abs(__snake_case ) )
       A_          =       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() - 20.4125 ) < 1E-2
            assert abs(result_mean.item() - 0.0266 ) < 1E-3
       else:
            # CUDA
            assert abs(result_sum.item() - 20.4125 ) < 1E-2
            assert abs(result_mean.item() - 0.0266 ) < 1E-3



