


"""simple docstring"""


import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest


class 			A__	(						__lowercase):


     """simple docstring"""
     snake_case__	:					Tuple													=(DDPMScheduler,)

     def 		a__						(	self:						Any     ,		**__a:						int	)->    int:
           lowerCamelCase					:					str        =	{
               """num_train_timesteps""": 1_000,
               """beta_start""": 0.00_01,
               """beta_end""": 0.02,
               """beta_schedule""": """linear""",
               """variance_type""": """fixed_small""",
               """clip_sample""": True,
           }

           config.update(**__a	)
           return config

     def 		a__						(	self:						Union[str, Any]	)->    Any:
           for timesteps in [1, 5, 100, 1_000]:
                 self.check_over_configs(num_train_timesteps=__a	)

     def 		a__						(	self:						str	)->    Tuple:
           for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]     ,		[0.0_02, 0.02, 0.2, 2]	):
                 self.check_over_configs(beta_start=__a     ,		beta_end=__a	)

     def 		a__						(	self:						Any	)->    Optional[Any]:
           for schedule in ["linear", "squaredcos_cap_v2"]:
                 self.check_over_configs(beta_schedule=__a	)

     def 		a__						(	self:						str	)->    str:
           for variance in ["fixed_small", "fixed_large", "other"]:
                 self.check_over_configs(variance_type=__a	)

     def 		a__						(	self:						Union[str, Any]	)->    List[Any]:
           for clip_sample in [True, False]:
                 self.check_over_configs(clip_sample=__a	)

     def 		a__						(	self:						Optional[Any]	)->    Union[str, Any]:
           self.check_over_configs(thresholding=__a	)
           for threshold in [0.5, 1.0, 2.0]:
                 for prediction_type in ["epsilon", "sample", "v_prediction"]:
                       self.check_over_configs(
                           thresholding=__a     ,		prediction_type=__a     ,		sample_max_value=__a     ,		)

     def 		a__						(	self:						Tuple	)->    List[Any]:
           for prediction_type in ["epsilon", "sample", "v_prediction"]:
                 self.check_over_configs(prediction_type=__a	)

     def 		a__						(	self:						Union[str, Any]	)->    Optional[int]:
           for t in [0, 500, 999]:
                 self.check_over_forward(time_step=__a	)

     def 		a__						(	self:						Optional[int]	)->    int:
           lowerCamelCase					:					Optional[Any]        =	self.scheduler_classes[0]
           lowerCamelCase					:					Optional[int]        =	self.get_scheduler_config()
           lowerCamelCase					:					Tuple        =	scheduler_class(**__a	)

           assert torch.sum(torch.abs(scheduler._get_variance(0	) - 0.0	)	) < 1e-5
           assert torch.sum(torch.abs(scheduler._get_variance(487	) - 0.0_09_79	)	) < 1e-5
           assert torch.sum(torch.abs(scheduler._get_variance(999	) - 0.02	)	) < 1e-5

     def 		a__						(	self:						str	)->    int:
           lowerCamelCase					:					Any        =	self.scheduler_classes[0]
           lowerCamelCase					:					Tuple        =	self.get_scheduler_config()
           lowerCamelCase					:					List[str]        =	scheduler_class(**__a	)

           lowerCamelCase					:					Optional[int]        =	len(__a	)

           lowerCamelCase					:					Dict        =	self.dummy_model()
           lowerCamelCase					:					List[str]        =	self.dummy_sample_deter
           lowerCamelCase					:					Optional[int]        =	torch.manual_seed(0	)

           for t in reversed(range(__a	)	):
                 # 1. predict noise residual
                 lowerCamelCase					:					Union[str, Any]        =	model(__a     ,		__a	)

                 # 2. predict previous mean of sample x_t-1
                 lowerCamelCase					:					Optional[Any]        =	scheduler.step(__a     ,		__a     ,		__a     ,		generator=__a	).prev_sample

                 # if t > 0:
                 #     noise = self.dummy_sample_deter
                 #     variance = scheduler.get_variance(t) ** (0.5) * noise
                 #
                 # sample = pred_prev_sample + variance
                 lowerCamelCase					:					Tuple        =	pred_prev_sample

           lowerCamelCase					:					List[Any]        =	torch.sum(torch.abs(__a	)	)
           lowerCamelCase					:					Union[str, Any]        =	torch.mean(torch.abs(__a	)	)

           assert abs(result_sum.item() - 258.9_606	) < 1e-2
           assert abs(result_mean.item() - 0.33_72	) < 1e-3

     def 		a__						(	self:						Any	)->    str:
           lowerCamelCase					:					Dict        =	self.scheduler_classes[0]
           lowerCamelCase					:					Optional[Any]        =	self.get_scheduler_config(prediction_type="""v_prediction"""	)
           lowerCamelCase					:					List[Any]        =	scheduler_class(**__a	)

           lowerCamelCase					:					Tuple        =	len(__a	)

           lowerCamelCase					:					Union[str, Any]        =	self.dummy_model()
           lowerCamelCase					:					int        =	self.dummy_sample_deter
           lowerCamelCase					:					Any        =	torch.manual_seed(0	)

           for t in reversed(range(__a	)	):
                 # 1. predict noise residual
                 lowerCamelCase					:					List[str]        =	model(__a     ,		__a	)

                 # 2. predict previous mean of sample x_t-1
                 lowerCamelCase					:					List[Any]        =	scheduler.step(__a     ,		__a     ,		__a     ,		generator=__a	).prev_sample

                 # if t > 0:
                 #     noise = self.dummy_sample_deter
                 #     variance = scheduler.get_variance(t) ** (0.5) * noise
                 #
                 # sample = pred_prev_sample + variance
                 lowerCamelCase					:					List[Any]        =	pred_prev_sample

           lowerCamelCase					:					List[str]        =	torch.sum(torch.abs(__a	)	)
           lowerCamelCase					:					List[str]        =	torch.mean(torch.abs(__a	)	)

           assert abs(result_sum.item() - 202.0_296	) < 1e-2
           assert abs(result_mean.item() - 0.26_31	) < 1e-3

     def 		a__						(	self:						Any	)->    Dict:
           lowerCamelCase					:					List[Any]        =	self.scheduler_classes[0]
           lowerCamelCase					:					Dict        =	self.get_scheduler_config()
           lowerCamelCase					:					Union[str, Any]        =	scheduler_class(**__a	)

           lowerCamelCase					:					str        =	[100, 87, 50, 1, 0]

           scheduler.set_timesteps(timesteps=__a	)

           lowerCamelCase					:					Dict        =	scheduler.timesteps

           for i, timestep in enumerate(__a	):
                 if i == len(__a	) - 1:
                       lowerCamelCase					:					str        =	-1
                 else:
                       lowerCamelCase					:					Optional[int]        =	timesteps[i + 1]

                 lowerCamelCase					:					Optional[Any]        =	scheduler.previous_timestep(__a	)
                 lowerCamelCase					:					Optional[int]        =	prev_t.item()

                 self.assertEqual(__a     ,		__a	)

     def 		a__						(	self:						Any	)->    Optional[Any]:
           lowerCamelCase					:					Union[str, Any]        =	self.scheduler_classes[0]
           lowerCamelCase					:					int        =	self.get_scheduler_config()
           lowerCamelCase					:					str        =	scheduler_class(**__a	)

           lowerCamelCase					:					str        =	[100, 87, 50, 51, 0]

           with self.assertRaises(__a     ,		msg="""`custom_timesteps` must be in descending order."""	):
                 scheduler.set_timesteps(timesteps=__a	)

     def 		a__						(	self:						Optional[Any]	)->    List[str]:
           lowerCamelCase					:					List[str]        =	self.scheduler_classes[0]
           lowerCamelCase					:					Dict        =	self.get_scheduler_config()
           lowerCamelCase					:					Optional[Any]        =	scheduler_class(**__a	)

           lowerCamelCase					:					Tuple        =	[100, 87, 50, 1, 0]
           lowerCamelCase					:					Any        =	len(__a	)

           with self.assertRaises(__a     ,		msg="""Can only pass one of `num_inference_steps` or `custom_timesteps`."""	):
                 scheduler.set_timesteps(num_inference_steps=__a     ,		timesteps=__a	)







     def 		a__						(	self:						Optional[int]	)->    Any:
           lowerCamelCase					:					List[Any]        =	self.scheduler_classes[0]
           lowerCamelCase					:					List[str]        =	self.get_scheduler_config()
           lowerCamelCase					:					Union[str, Any]        =	scheduler_class(**__a	)

           lowerCamelCase					:					Tuple        =	[scheduler.config.num_train_timesteps]

           with self.assertRaises(
               __a     ,		msg="""`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"""     ,		):
                 scheduler.set_timesteps(timesteps=__a	)










"""simple docstring"""


from __future__ import annotations

import math




def   snake_case		(     UpperCamelCase__       :						float			,				UpperCamelCase__       :						int    )   ->      float:
      lowerCamelCase					:					Dict        =	u
      for i in range(1			,				UpperCamelCase__    ):
            lowerCamelCase					:					List[str]        =	temp * (u - i)
      return temp




def   snake_case		(     )   ->      None:
      lowerCamelCase					:					List[Any]        =	int(input("""enter the numbers of values: """    )    )
      lowerCamelCase					:					list[list[float]]        =	[]
      for _ in range(UpperCamelCase__    ):
            y.append([]    )
      for i in range(UpperCamelCase__    ):
            for j in range(UpperCamelCase__    ):
                  y[i].append(UpperCamelCase__    )
                  lowerCamelCase					:					Union[str, Any]        =	0

      print("""enter the values of parameters in a list: """    )
      lowerCamelCase					:					Any        =	list(map(UpperCamelCase__			,				input().split()    )    )

      print("""enter the values of corresponding parameters: """    )
      for i in range(UpperCamelCase__    ):
            lowerCamelCase					:					int        =	float(input()    )

      lowerCamelCase					:					Dict        =	int(input("""enter the value to interpolate: """    )    )
      lowerCamelCase					:					List[Any]        =	(value - x[0]) / (x[1] - x[0])

      # for calculating forward difference table

      for i in range(1			,				UpperCamelCase__    ):
            for j in range(n - i    ):
                  lowerCamelCase					:					str        =	y[j + 1][i - 1] - y[j][i - 1]

      lowerCamelCase					:					Any        =	y[0][0]
      for i in range(1			,				UpperCamelCase__    ):
            summ += (ucal(UpperCamelCase__			,				UpperCamelCase__    ) * y[0][i]) / math.factorial(UpperCamelCase__    )

      print(F'the value at {value} is {summ}'    )


if __name__ == "__main__":
    main()




