


import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest





class    lowerCamelCase__						(		_a    ):
      a :     Optional[int]											=		(UnCLIPScheduler,)
      def SCREAMING_SNAKE_CASE_	(		self	:	Tuple      ,       **A_	:	Tuple							):






       '''simple docstring'''


       __lowercase									=	{
           """num_train_timesteps""": 1_0_0_0,
           """variance_type""": """fixed_small_log""",
           """clip_sample""": True,
           """clip_sample_range""": 1.0,
           """prediction_type""": """epsilon""",
       }

       config.update(**A_							)
       return config
      def SCREAMING_SNAKE_CASE_	(		self	:	Optional[int]							):






       '''simple docstring'''


       for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
        self.check_over_configs(num_train_timesteps=A_							)
      def SCREAMING_SNAKE_CASE_	(		self	:	Any							):






       '''simple docstring'''


       for variance in ["fixed_small_log", "learned_range"]:
        self.check_over_configs(variance_type=A_							)
      def SCREAMING_SNAKE_CASE_	(		self	:	Optional[int]							):






       '''simple docstring'''


       for clip_sample in [True, False]:
        self.check_over_configs(clip_sample=A_							)
      def SCREAMING_SNAKE_CASE_	(		self	:	List[Any]							):






       '''simple docstring'''


       for clip_sample_range in [1, 5, 1_0, 2_0]:
        self.check_over_configs(clip_sample_range=A_							)
      def SCREAMING_SNAKE_CASE_	(		self	:	Union[str, Any]							):






       '''simple docstring'''


       for prediction_type in ["epsilon", "sample"]:
        self.check_over_configs(prediction_type=A_							)
      def SCREAMING_SNAKE_CASE_	(		self	:	int							):






       '''simple docstring'''


       for time_step in [0, 5_0_0, 9_9_9]:
        for prev_timestep in [None, 5, 1_0_0, 2_5_0, 5_0_0, 7_5_0]:
         if prev_timestep is not None and prev_timestep >= time_step:
          continue

         self.check_over_forward(time_step=A_      ,       prev_timestep=A_							)
      def SCREAMING_SNAKE_CASE_	(		self	:	int							):






       '''simple docstring'''


       __lowercase									=	self.scheduler_classes[0]
       __lowercase									=	self.get_scheduler_config(variance_type="""fixed_small_log"""							)
       __lowercase									=	scheduler_class(**A_							)

       assert torch.sum(torch.abs(scheduler._get_variance(0							) - 1.0_0_0_0e-1_0							)							) < 1e-5
       assert torch.sum(torch.abs(scheduler._get_variance(4_8_7							) - 0.0_54_96_25							)							) < 1e-5
       assert torch.sum(torch.abs(scheduler._get_variance(9_9_9							) - 0.9_99_49_87							)							) < 1e-5
      def SCREAMING_SNAKE_CASE_	(		self	:	str							):






       '''simple docstring'''


       __lowercase									=	self.scheduler_classes[0]
       __lowercase									=	self.get_scheduler_config(variance_type="""learned_range"""							)
       __lowercase									=	scheduler_class(**A_							)

       __lowercase									=	0.5

       assert scheduler._get_variance(1      ,       predicted_variance=A_							) - -10.1_71_27_90 < 1e-5
       assert scheduler._get_variance(4_8_7      ,       predicted_variance=A_							) - -5.7_99_80_52 < 1e-5
       assert scheduler._get_variance(9_9_9      ,       predicted_variance=A_							) - -0.0_01_00_11 < 1e-5
      def SCREAMING_SNAKE_CASE_	(		self	:	Dict							):






       '''simple docstring'''


       __lowercase									=	self.scheduler_classes[0]
       __lowercase									=	self.get_scheduler_config()
       __lowercase									=	scheduler_class(**A_							)

       __lowercase									=	scheduler.timesteps

       __lowercase									=	self.dummy_model()
       __lowercase									=	self.dummy_sample_deter
       __lowercase									=	torch.manual_seed(0							)

       for i, t in enumerate(A_							):
        # 1. predict noise residual
        __lowercase									=	model(A_      ,       A_							)

        # 2. predict previous mean of sample x_t-1
        __lowercase									=	scheduler.step(A_      ,       A_      ,       A_      ,       generator=A_							).prev_sample

        __lowercase									=	pred_prev_sample

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

       assert abs(result_sum.item() - 2_5_2.2_6_8_2_4_9_5							) < 1e-2
       assert abs(result_mean.item() - 0.3_28_47_43							) < 1e-3
      def SCREAMING_SNAKE_CASE_	(		self	:	List[Any]							):






       '''simple docstring'''


       __lowercase									=	self.scheduler_classes[0]
       __lowercase									=	self.get_scheduler_config()
       __lowercase									=	scheduler_class(**A_							)

       scheduler.set_timesteps(2_5							)

       __lowercase									=	scheduler.timesteps

       __lowercase									=	self.dummy_model()
       __lowercase									=	self.dummy_sample_deter
       __lowercase									=	torch.manual_seed(0							)

       for i, t in enumerate(A_							):
        # 1. predict noise residual
        __lowercase									=	model(A_      ,       A_							)

        if i + 1 == timesteps.shape[0]:
         __lowercase									=	None
        else:
         __lowercase									=	timesteps[i + 1]

        # 2. predict previous mean of sample x_t-1
        __lowercase									=	scheduler.step(
            A_      ,       A_      ,       A_      ,       prev_timestep=A_      ,       generator=A_							).prev_sample

        __lowercase									=	pred_prev_sample

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

       assert abs(result_sum.item() - 2_5_8.2_0_4_4_9_8_3							) < 1e-2
       assert abs(result_mean.item() - 0.3_36_20_38							) < 1e-3
      def SCREAMING_SNAKE_CASE_	(		self	:	str							):






       '''simple docstring'''


       pass






      def SCREAMING_SNAKE_CASE_	(		self	:	Optional[int]							):






       '''simple docstring'''


       pass




"""simple docstring"""

def lowerCAmelCase_      (				UpperCamelCase__	: int				,   UpperCamelCase__	: list ):






 """simple docstring"""







 _enforce_args(UpperCamelCase__				,   UpperCamelCase__ )
 if n == 0:
  return 0
 __lowercase									=	float("""-inf""" )
 for i in range(1				,   n + 1 ):
  __lowercase									=	max(
      UpperCamelCase__				,   prices[i - 1] + naive_cut_rod_recursive(n - i				,   UpperCamelCase__ ) )

 return max_revue


def lowerCAmelCase_      (				UpperCamelCase__	: int				,   UpperCamelCase__	: list ):






 """simple docstring"""







 _enforce_args(UpperCamelCase__				,   UpperCamelCase__ )
 __lowercase									=	[float("""-inf""" ) for _ in range(n + 1 )]
 return _top_down_cut_rod_recursive(UpperCamelCase__				,   UpperCamelCase__				,   UpperCamelCase__ )


def lowerCAmelCase_      (				UpperCamelCase__	: int				,   UpperCamelCase__	: list				,   UpperCamelCase__	: list ):






 """simple docstring"""







 if max_rev[n] >= 0:
  return max_rev[n]
 elif n == 0:
  return 0
 else:
  __lowercase									=	float("""-inf""" )
  for i in range(1				,   n + 1 ):
   __lowercase									=	max(
       UpperCamelCase__				,   prices[i - 1] + _top_down_cut_rod_recursive(n - i				,   UpperCamelCase__				,   UpperCamelCase__ )				,   )

  __lowercase									=	max_revenue

 return max_rev[n]


def lowerCAmelCase_      (				UpperCamelCase__	: int				,   UpperCamelCase__	: list ):






 """simple docstring"""







 _enforce_args(UpperCamelCase__				,   UpperCamelCase__ )

 # length(max_rev) = n + 1, to accommodate for the revenue obtainable from a rod of
 # length 0.
 __lowercase									=	[float("""-inf""" ) for _ in range(n + 1 )]
 __lowercase									=	0

 for i in range(1				,   n + 1 ):
  __lowercase									=	max_rev[i]
  for j in range(1				,   i + 1 ):
   __lowercase									=	max(UpperCamelCase__				,   prices[j - 1] + max_rev[i - j] )

  __lowercase									=	max_revenue_i

 return max_rev[n]


def lowerCAmelCase_      (				UpperCamelCase__	: int				,   UpperCamelCase__	: list ):






 """simple docstring"""







 if n < 0:
  __lowercase									=	f'''n must be greater than or equal to 0. Got n = {n}'''
  raise ValueError(UpperCamelCase__ )

 if n > len(UpperCamelCase__ ):
  __lowercase									=	(
      """Each integral piece of rod must have a corresponding price. """
      f'''Got n = {n} but length of prices = {len(UpperCamelCase__ )}'''
  )
  raise ValueError(UpperCamelCase__ )


def lowerCAmelCase_      (				):






 """simple docstring"""







 __lowercase									=	[6, 10, 12, 15, 20, 23]
 __lowercase									=	len(UpperCamelCase__ )

 # the best revenue comes from cutting the rod into 6 pieces, each
 # of length 1 resulting in a revenue of 6 * 6 = 36.
 __lowercase									=	36

 __lowercase									=	top_down_cut_rod(UpperCamelCase__				,   UpperCamelCase__ )
 __lowercase									=	bottom_up_cut_rod(UpperCamelCase__				,   UpperCamelCase__ )
 __lowercase									=	naive_cut_rod_recursive(UpperCamelCase__				,   UpperCamelCase__ )

 assert expected_max_revenue == max_rev_top_down
 assert max_rev_top_down == max_rev_bottom_up
 assert max_rev_bottom_up == max_rev_naive


if __name__ == "__main__":
       main()
