


import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest



class      __lowerCamelCase       (a_		):
       _lowercase											=			(UnCLIPScheduler,)







       def 						snake_case_     (       self:      str,**A_:      List[Any] ):






            '''simple docstring'''




            __UpperCamelCase      =       {
                'num_train_timesteps': 1000,
                'variance_type': 'fixed_small_log',
                'clip_sample': True,
                'clip_sample_range': 1.0,
                'prediction_type': 'epsilon',
            }

            config.update(**_A )
            return config







       def 						snake_case_     (       self:      Optional[Any] ):






            '''simple docstring'''




            for timesteps in [1, 5, 100, 1000]:
                 self.check_over_configs(num_train_timesteps=_A )







       def 						snake_case_     (       self:      Tuple ):






            '''simple docstring'''




            for variance in ["fixed_small_log", "learned_range"]:
                 self.check_over_configs(variance_type=_A )







       def 						snake_case_     (       self:      List[str] ):






            '''simple docstring'''




            for clip_sample in [True, False]:
                 self.check_over_configs(clip_sample=_A )







       def 						snake_case_     (       self:      List[str] ):






            '''simple docstring'''




            for clip_sample_range in [1, 5, 10, 20]:
                 self.check_over_configs(clip_sample_range=_A )







       def 						snake_case_     (       self:      List[str] ):






            '''simple docstring'''




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







       def 						snake_case_     (       self:      List[str] ):






            '''simple docstring'''




            for time_step in [0, 500, 999]:
                 for prev_timestep in [None, 5, 100, 250, 500, 750]:
                      if prev_timestep is not None and prev_timestep >= time_step:
                           continue

                      self.check_over_forward(time_step=_A,prev_timestep=_A )







       def 						snake_case_     (       self:      List[Any] ):






            '''simple docstring'''




            __UpperCamelCase      =       self.scheduler_classes[0]
            __UpperCamelCase      =       self.get_scheduler_config(variance_type='fixed_small_log' )
            __UpperCamelCase      =       scheduler_class(**_A )

            assert torch.sum(torch.abs(scheduler._get_variance(0 ) - 1.0_000E-10 ) ) < 1E-5
            assert torch.sum(torch.abs(scheduler._get_variance(487 ) - 0.0_5_4_9_6_2_5 ) ) < 1E-5
            assert torch.sum(torch.abs(scheduler._get_variance(999 ) - 0.9_9_9_4_9_8_7 ) ) < 1E-5







       def 						snake_case_     (       self:      int ):






            '''simple docstring'''




            __UpperCamelCase      =       self.scheduler_classes[0]
            __UpperCamelCase      =       self.get_scheduler_config(variance_type='learned_range' )
            __UpperCamelCase      =       scheduler_class(**_A )

            __UpperCamelCase      =       0.5

            assert scheduler._get_variance(1,predicted_variance=_A ) - -1_0.1_7_1_2_7_9_0 < 1E-5
            assert scheduler._get_variance(487,predicted_variance=_A ) - -5.7_9_9_8_0_5_2 < 1E-5
            assert scheduler._get_variance(999,predicted_variance=_A ) - -0.0_0_1_0_0_1_1 < 1E-5







       def 						snake_case_     (       self:      int ):






            '''simple docstring'''




            __UpperCamelCase      =       self.scheduler_classes[0]
            __UpperCamelCase      =       self.get_scheduler_config()
            __UpperCamelCase      =       scheduler_class(**_A )

            __UpperCamelCase      =       scheduler.timesteps

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

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

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

                 __UpperCamelCase      =       pred_prev_sample

            __UpperCamelCase      =       torch.sum(torch.abs(_A ) )
            __UpperCamelCase      =       torch.mean(torch.abs(_A ) )

            assert abs(result_sum.item() - 252.268_2495 ) < 1E-2
            assert abs(result_mean.item() - 0.3_2_8_4_7_4_3 ) < 1E-3







       def 						snake_case_     (       self:      List[Any] ):






            '''simple docstring'''




            __UpperCamelCase      =       self.scheduler_classes[0]
            __UpperCamelCase      =       self.get_scheduler_config()
            __UpperCamelCase      =       scheduler_class(**_A )

            scheduler.set_timesteps(25 )

            __UpperCamelCase      =       scheduler.timesteps

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

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

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

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

                 __UpperCamelCase      =       pred_prev_sample

            __UpperCamelCase      =       torch.sum(torch.abs(_A ) )
            __UpperCamelCase      =       torch.mean(torch.abs(_A ) )

            assert abs(result_sum.item() - 258.204_4983 ) < 1E-2
            assert abs(result_mean.item() - 0.3_3_6_2_0_3_8 ) < 1E-3







       def 						snake_case_     (       self:      Any ):






            '''simple docstring'''




            pass







       def 						snake_case_     (       self:      Tuple ):






            '''simple docstring'''




            pass









from __future__ import annotations

from collections.abc import Callable





def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__			,			UpperCamelCase__			,			UpperCamelCase__			,			UpperCamelCase__ = 100			,			)  ->				float:







			'''simple docstring'''

			UpperCAmelCase   		=				x_start
			UpperCAmelCase   		=				fnc(UpperCamelCase__ )
			UpperCAmelCase   		=				0.0
			for _ in range(UpperCamelCase__ ):
						# Approximates small segments of curve as linear and solve
						# for trapezoidal area
						UpperCAmelCase   		=				(x_end - x_start) / steps + xa
						UpperCAmelCase   		=				fnc(UpperCamelCase__ )
						area += abs(fxa + fxa ) * (xa - xa) / 2
						# Increment step
						UpperCAmelCase   		=				xa
						UpperCAmelCase   		=				fxa
			return area


if __name__ == "__main__":





				def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__ )  ->				str:







							'''simple docstring'''

							return x**3 + x**2

				print("f(x) = x^3 + x^2")
				print("The area between the curve, x = -5, x = 5 and the x axis is:")
				__A				:    List[Any]       =   10
				while i <= 100_000:
								print(F'with {i} steps: {trapezoidal_area(f, -5, 5, i)}')
								i *= 10



