
import torch

from diffusers import KDPMaAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest







class 	A_       (      A__       ):




 '''simple docstring'''


 _UpperCamelCase					:					Union[str, Any]										=	(KDPMaAncestralDiscreteScheduler,)
 _UpperCamelCase					:					Optional[Any]										=	10





 def      SCREAMING_SNAKE_CASE__						(	self		,   **snake_case	):
  lowercase					  =   {
      '''num_train_timesteps''': 1100,
      '''beta_start''': 0.0_001,
      '''beta_end''': 0.02,
      '''beta_schedule''': '''linear''',
  }

  config.update(**__snake_case	)
  return config





 def      SCREAMING_SNAKE_CASE__						(	self	):
  for timesteps in [10, 50, 100, 1000]:
   self.check_over_configs(num_train_timesteps=__snake_case	)





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





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

  scheduler.set_timesteps(self.num_inference_steps	)

  lowercase					  =   torch.manual_seed(0	)

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

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

   lowercase					  =   model(__snake_case		,   __snake_case	)

   lowercase					  =   scheduler.step(__snake_case		,   __snake_case		,   __snake_case		,   generator=__snake_case	)
   lowercase					  =   output.prev_sample

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

  assert abs(result_sum.item() - 1_3849.3877	) < 1E-2
  assert abs(result_mean.item() - 18.0_331	) < 5E-3





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





 def      SCREAMING_SNAKE_CASE__						(	self	):
  if torch_device == "mps":
   return
  lowercase					  =   self.scheduler_classes[0]
  lowercase					  =   self.get_scheduler_config(prediction_type='v_prediction'	)
  lowercase					  =   scheduler_class(**__snake_case	)

  scheduler.set_timesteps(self.num_inference_steps	)

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

  lowercase					  =   torch.manual_seed(0	)

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

   lowercase					  =   model(__snake_case		,   __snake_case	)

   lowercase					  =   scheduler.step(__snake_case		,   __snake_case		,   __snake_case		,   generator=__snake_case	)
   lowercase					  =   output.prev_sample

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

  assert abs(result_sum.item() - 328.9970	) < 1E-2
  assert abs(result_mean.item() - 0.4_284	) < 1E-3





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

  scheduler.set_timesteps(self.num_inference_steps		,   device=__snake_case	)
  lowercase					  =   torch.manual_seed(0	)

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

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

   lowercase					  =   model(__snake_case		,   __snake_case	)

   lowercase					  =   scheduler.step(__snake_case		,   __snake_case		,   __snake_case		,   generator=__snake_case	)
   lowercase					  =   output.prev_sample

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

  assert abs(result_sum.item() - 1_3849.3818	) < 1E-1
  assert abs(result_mean.item() - 18.0_331	) < 1E-3



import unittest
from timeit import timeit






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE							):
	lowercase					  =   first_num if first_num >= second_num else second_num
	lowercase					  =   max_num
	while (common_mult % first_num > 0) or (common_mult % second_num > 0):
		common_mult += max_num
	return common_mult






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE							):
	return b if a == 0 else greatest_common_divisor(b % a			,							__SCREAMING_SNAKE_CASE							)






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE							):
	return first_num // greatest_common_divisor(__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE							) * second_num






def 							UpperCAmelCase_		(							):
	lowercase					  =   (
	    'from __main__ import least_common_multiple_slow, least_common_multiple_fast'
	)
	print(
	    'least_common_multiple_slow():'			,							timeit('least_common_multiple_slow(1000, 999)'			,							setup=__SCREAMING_SNAKE_CASE							)			,							)
	print(
	    'least_common_multiple_fast():'			,							timeit('least_common_multiple_fast(1000, 999)'			,							setup=__SCREAMING_SNAKE_CASE							)			,							)







class 	A_       (      unittest.TestCase       ):




	'''simple docstring'''


	_UpperCamelCase					:					Any										=	(
	    (10, 20),
	    (13, 15),
	    (4, 31),
	    (10, 42),
	    (43, 34),
	    (5, 12),
	    (12, 25),
	    (10, 25),
	    (6, 9),
	)
	_UpperCamelCase					:					int										=	(20, 195, 124, 210, 1462, 60, 300, 50, 18)





	def      SCREAMING_SNAKE_CASE__						(	self	):
		for i, (first_num, second_num) in enumerate(self.test_inputs	):
			lowercase					  =   least_common_multiple_slow(snake_case		,   snake_case	)
			lowercase					  =   least_common_multiple_fast(snake_case		,   snake_case	)
			with self.subTest(i=snake_case	):
				self.assertEqual(snake_case		,   self.expected_results[i]	)
				self.assertEqual(snake_case		,   self.expected_results[i]	)


if __name__ == "__main__":
	benchmark()
	unittest.main()

