



from __future__ import annotations

import random






class 		lowercase						:






	"""simple docstring"""


	a__      :					Dict		   =							6




	def __init__(	self	:						int     )			-> Optional[Any]:
							UpperCAmelCase_=   list(range(1						,  Dice.NUM_SIDES + 1     )     )




	def   _SCREAMING_SNAKE_CASE		(	self	:						List[str]     )			-> Optional[int]:
							return random.choice(self.sides     )



def        __a     (    lowerCAmelCase_   :					int			,lowerCAmelCase_   :					int = 2				)      -> Optional[int]:






						'''simple docstring'''



						UpperCAmelCase_=   [Dice() for i in range(_UpperCAmelCase				)]
						UpperCAmelCase_=   [0] * (len(_UpperCAmelCase				) * Dice.NUM_SIDES + 1)
						for _ in range(_UpperCAmelCase				):
												count_of_sum[sum(dice.roll() for dice in dices				)] += 1
						UpperCAmelCase_=   [round((count * 1_00) / num_throws			,2				) for count in count_of_sum]
						return probability[num_dice:]  # remove probability of sums that never appear


if __name__ == "__main__":
	import doctest

	doctest.testmod()











"""simple docstring"""
import torch

from diffusers import HeunDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest







class 	a				(					a__   ):
 snake_case__    	=    (HeunDiscreteScheduler,)
 snake_case__    	=    1_0



 def 					UpperCamelCase__   (			self							,    **_snake_case			):







  """simple docstring"""






  lowerCAmelCase  							=      {
      'num_train_timesteps': 11_00,
      'beta_start': 0.0_001,
      'beta_end': 0.02,
      'beta_schedule': 'linear',
  }

  config.update(**_snake_case			)
  return config



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  for timesteps in [10, 50, 1_00, 10_00]:
   self.check_over_configs(num_train_timesteps=_snake_case			)



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001]							,    [0.0_002, 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", "exp"]:
   self.check_over_configs(beta_schedule=_snake_case			)



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  for clip_sample_range in [1.0, 2.0, 3.0]:
   self.check_over_configs(clip_sample_range=_snake_case							,    clip_sample=_snake_case			)



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






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



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  lowerCAmelCase  							=      self.scheduler_classes[0]
  lowerCAmelCase  							=      self.get_scheduler_config()
  lowerCAmelCase  							=      scheduler_class(**_snake_case			)

  scheduler.set_timesteps(self.num_inference_steps			)

  lowerCAmelCase  							=      self.dummy_model()
  lowerCAmelCase  							=      self.dummy_sample_deter * scheduler.init_noise_sigma
  lowerCAmelCase  							=      sample.to(_snake_case			)

  for i, t in enumerate(scheduler.timesteps			):
   lowerCAmelCase  							=      scheduler.scale_model_input(_snake_case							,    _snake_case			)

   lowerCAmelCase  							=      model(_snake_case							,    _snake_case			)

   lowerCAmelCase  							=      scheduler.step(_snake_case							,    _snake_case							,    _snake_case			)
   lowerCAmelCase  							=      output.prev_sample

  lowerCAmelCase  							=      torch.sum(torch.abs(_snake_case			)			)
  lowerCAmelCase  							=      torch.mean(torch.abs(_snake_case			)			)

  if torch_device in ["cpu", "mps"]:
   assert abs(result_sum.item() - 0.1_233			) < 1E-2
   assert abs(result_mean.item() - 0.0_002			) < 1E-3
  else:
   # CUDA
   assert abs(result_sum.item() - 0.1_233			) < 1E-2
   assert abs(result_mean.item() - 0.0_002			) < 1E-3



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  lowerCAmelCase  							=      self.scheduler_classes[0]
  lowerCAmelCase  							=      self.get_scheduler_config(prediction_type='v_prediction'			)
  lowerCAmelCase  							=      scheduler_class(**_snake_case			)

  scheduler.set_timesteps(self.num_inference_steps			)

  lowerCAmelCase  							=      self.dummy_model()
  lowerCAmelCase  							=      self.dummy_sample_deter * scheduler.init_noise_sigma
  lowerCAmelCase  							=      sample.to(_snake_case			)

  for i, t in enumerate(scheduler.timesteps			):
   lowerCAmelCase  							=      scheduler.scale_model_input(_snake_case							,    _snake_case			)

   lowerCAmelCase  							=      model(_snake_case							,    _snake_case			)

   lowerCAmelCase  							=      scheduler.step(_snake_case							,    _snake_case							,    _snake_case			)
   lowerCAmelCase  							=      output.prev_sample

  lowerCAmelCase  							=      torch.sum(torch.abs(_snake_case			)			)
  lowerCAmelCase  							=      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_002			) < 1E-3



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  lowerCAmelCase  							=      self.scheduler_classes[0]
  lowerCAmelCase  							=      self.get_scheduler_config()
  lowerCAmelCase  							=      scheduler_class(**_snake_case			)

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

  lowerCAmelCase  							=      self.dummy_model()
  lowerCAmelCase  							=      self.dummy_sample_deter.to(_snake_case			) * scheduler.init_noise_sigma

  for t in scheduler.timesteps:
   lowerCAmelCase  							=      scheduler.scale_model_input(_snake_case							,    _snake_case			)

   lowerCAmelCase  							=      model(_snake_case							,    _snake_case			)

   lowerCAmelCase  							=      scheduler.step(_snake_case							,    _snake_case							,    _snake_case			)
   lowerCAmelCase  							=      output.prev_sample

  lowerCAmelCase  							=      torch.sum(torch.abs(_snake_case			)			)
  lowerCAmelCase  							=      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() - 0.1_233			) < 1E-2
   assert abs(result_mean.item() - 0.0_002			) < 1E-3
  elif str(_snake_case			).startswith('mps'			):
   # Larger tolerance on mps
   assert abs(result_mean.item() - 0.0_002			) < 1E-2
  else:
   # CUDA
   assert abs(result_sum.item() - 0.1_233			) < 1E-2
   assert abs(result_mean.item() - 0.0_002			) < 1E-3



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  lowerCAmelCase  							=      self.scheduler_classes[0]
  lowerCAmelCase  							=      self.get_scheduler_config()
  lowerCAmelCase  							=      scheduler_class(**_snake_case							,    use_karras_sigmas=_snake_case			)

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

  lowerCAmelCase  							=      self.dummy_model()
  lowerCAmelCase  							=      self.dummy_sample_deter.to(_snake_case			) * scheduler.init_noise_sigma
  lowerCAmelCase  							=      sample.to(_snake_case			)

  for t in scheduler.timesteps:
   lowerCAmelCase  							=      scheduler.scale_model_input(_snake_case							,    _snake_case			)

   lowerCAmelCase  							=      model(_snake_case							,    _snake_case			)

   lowerCAmelCase  							=      scheduler.step(_snake_case							,    _snake_case							,    _snake_case			)
   lowerCAmelCase  							=      output.prev_sample

  lowerCAmelCase  							=      torch.sum(torch.abs(_snake_case			)			)
  lowerCAmelCase  							=      torch.mean(torch.abs(_snake_case			)			)

  assert abs(result_sum.item() - 0.00_015			) < 1E-2
  assert abs(result_mean.item() - 1.98_69_55_45_35_03_46_95E-07			) < 1E-2





