
import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class    _snake_case (						UpperCAmelCase_				):
 __lowerCAmelCase	:  str	   =      (EulerDiscreteScheduler,)
 __lowerCAmelCase	:  Any	   =      10


 def   lowercase__  (       self       ,	**SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: Any     		=   {
       """num_train_timesteps""": 11_00,
       """beta_start""": 0.0_0_0_1,
       """beta_end""": 0.0_2,
       """beta_schedule""": """linear""",
   }

   config.update(**SCREAMING_SNAKE_CASE_)
   return config


 def   lowercase__  (       self):


   '''simple docstring'''




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


 def   lowercase__  (       self):


   '''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=SCREAMING_SNAKE_CASE_       ,	beta_end=SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self):


   '''simple docstring'''




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


 def   lowercase__  (       self):


   '''simple docstring'''




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


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: List[str]     		=   self.scheduler_classes[0]
   lowercase__	: Tuple     		=   self.get_scheduler_config()
   lowercase__	: Dict     		=   scheduler_class(**SCREAMING_SNAKE_CASE_)

   scheduler.set_timesteps(self.num_inference_steps)

   lowercase__	: Optional[Any]     		=   torch.manual_seed(0)

   lowercase__	: List[Any]     		=   self.dummy_model()
   lowercase__	: Optional[int]     		=   self.dummy_sample_deter * scheduler.init_noise_sigma
   lowercase__	: int     		=   sample.to(SCREAMING_SNAKE_CASE_)

   for i, t in enumerate(scheduler.timesteps):
     lowercase__	: Tuple     		=   scheduler.scale_model_input(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)

     lowercase__	: Dict     		=   model(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)

     lowercase__	: List[Any]     		=   scheduler.step(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	generator=SCREAMING_SNAKE_CASE_)
     lowercase__	: str     		=   output.prev_sample

   lowercase__	: str     		=   torch.sum(torch.abs(SCREAMING_SNAKE_CASE_))
   lowercase__	: str     		=   torch.mean(torch.abs(SCREAMING_SNAKE_CASE_))

   assert abs(result_sum.item() - 1_0.0_8_0_7) < 1E-2
   assert abs(result_mean.item() - 0.0_1_3_1) < 1E-3


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: List[str]     		=   self.scheduler_classes[0]
   lowercase__	: Tuple     		=   self.get_scheduler_config(prediction_type="""v_prediction""")
   lowercase__	: List[Any]     		=   scheduler_class(**SCREAMING_SNAKE_CASE_)

   scheduler.set_timesteps(self.num_inference_steps)

   lowercase__	: Dict     		=   torch.manual_seed(0)

   lowercase__	: Union[str, Any]     		=   self.dummy_model()
   lowercase__	: List[str]     		=   self.dummy_sample_deter * scheduler.init_noise_sigma
   lowercase__	: Optional[int]     		=   sample.to(SCREAMING_SNAKE_CASE_)

   for i, t in enumerate(scheduler.timesteps):
     lowercase__	: Tuple     		=   scheduler.scale_model_input(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)

     lowercase__	: Union[str, Any]     		=   model(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)

     lowercase__	: Optional[int]     		=   scheduler.step(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	generator=SCREAMING_SNAKE_CASE_)
     lowercase__	: Any     		=   output.prev_sample

   lowercase__	: List[str]     		=   torch.sum(torch.abs(SCREAMING_SNAKE_CASE_))
   lowercase__	: Tuple     		=   torch.mean(torch.abs(SCREAMING_SNAKE_CASE_))

   assert abs(result_sum.item() - 0.0_0_0_2) < 1E-2
   assert abs(result_mean.item() - 2.26_76E-06) < 1E-3


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Tuple     		=   self.scheduler_classes[0]
   lowercase__	: Any     		=   self.get_scheduler_config()
   lowercase__	: Dict     		=   scheduler_class(**SCREAMING_SNAKE_CASE_)

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

   lowercase__	: str     		=   torch.manual_seed(0)

   lowercase__	: Dict     		=   self.dummy_model()
   lowercase__	: List[Any]     		=   self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
   lowercase__	: List[str]     		=   sample.to(SCREAMING_SNAKE_CASE_)

   for t in scheduler.timesteps:
     lowercase__	: List[Any]     		=   scheduler.scale_model_input(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)

     lowercase__	: str     		=   model(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)

     lowercase__	: Any     		=   scheduler.step(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	generator=SCREAMING_SNAKE_CASE_)
     lowercase__	: List[Any]     		=   output.prev_sample

   lowercase__	: int     		=   torch.sum(torch.abs(SCREAMING_SNAKE_CASE_))
   lowercase__	: List[Any]     		=   torch.mean(torch.abs(SCREAMING_SNAKE_CASE_))

   assert abs(result_sum.item() - 1_0.0_8_0_7) < 1E-2
   assert abs(result_mean.item() - 0.0_1_3_1) < 1E-3



 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Dict     		=   self.scheduler_classes[0]
   lowercase__	: Any     		=   self.get_scheduler_config()
   lowercase__	: Optional[Any]     		=   scheduler_class(**SCREAMING_SNAKE_CASE_       ,	use_karras_sigmas=SCREAMING_SNAKE_CASE_)

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

   lowercase__	: Any     		=   torch.manual_seed(0)

   lowercase__	: Any     		=   self.dummy_model()
   lowercase__	: Dict     		=   self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
   lowercase__	: Any     		=   sample.to(SCREAMING_SNAKE_CASE_)

   for t in scheduler.timesteps:
     lowercase__	: Union[str, Any]     		=   scheduler.scale_model_input(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)

     lowercase__	: List[str]     		=   model(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)

     lowercase__	: int     		=   scheduler.step(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	generator=SCREAMING_SNAKE_CASE_)
     lowercase__	: Optional[Any]     		=   output.prev_sample

   lowercase__	: Any     		=   torch.sum(torch.abs(SCREAMING_SNAKE_CASE_))
   lowercase__	: List[Any]     		=   torch.mean(torch.abs(SCREAMING_SNAKE_CASE_))

   assert abs(result_sum.item() - 1_2_4.5_2_2_9_9_4_9_9_5_1_1_7_1_9) < 1E-2
   assert abs(result_mean.item() - 0.1_6_2_1_3_9_3_2_6_3_3_3_9_9_9_6_3) < 1E-3








lowerCamelCase__     :		dict[tuple[int, int, int], int]        = {}

def 		UpperCamelCase						(   lowercase_			,			lowercase_			,			lowercase_ )		->						int:


  '''simple docstring'''






  if late == 3 or absent == 2:
    return 0

  # if we have no days left, and have not failed any other rules,
  # we have a prize string
  if days == 0:
    return 1

  # No easy solution, so now we need to do the recursive calculation

  # First, check if the combination is already in the cache, and
  # if yes, return the stored value from there since we already
  # know the number of possible prize strings from this point on
  lowercase__	: Tuple     		=   (days, absent, late)
  if key in cache:
    return cache[key]

  # now we calculate the three possible ways that can unfold from
  # this point on, depending on our attendance today

  # 1) if we are late (but not absent), the "absent" counter stays as
  # it is, but the "late" counter increases by one
  lowercase__	: Union[str, Any]     		=   _calculate(days - 1			,			lowercase_			,			late + 1 )

  # 2) if we are absent, the "absent" counter increases by 1, and the
  # "late" counter resets to 0
  lowercase__	: List[str]     		=   _calculate(days - 1			,			absent + 1			,			0 )

  # 3) if we are on time, this resets the "late" counter and keeps the
  # absent counter
  lowercase__	: Dict     		=   _calculate(days - 1			,			lowercase_			,			0 )

  lowercase__	: List[str]     		=   state_late + state_absent + state_ontime

  lowercase__	: List[Any]     		=   prizestrings
  return prizestrings

def 		UpperCamelCase						(   lowercase_ = 30 )		->						int:


  '''simple docstring'''






  return _calculate(lowercase_			,			absent=0			,			late=0 )


if __name__ == "__main__":
     print(solution())






