"""simple docstring"""






import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest
class        snake_case (				SCREAMING_SNAKE_CASE__       ):




     """simple docstring"""

     snake_case__       					=				(DDPMParallelScheduler,)







     def 	__lowerCAmelCase		(       self     :			str					,**lowerCamelCase__     :			Union[str, Any]    ):
         UpperCAmelCase__    =							{
             """num_train_timesteps""": 1_000,
             """beta_start""": 0.0_0_0_1,
             """beta_end""": 0.0_2,
             """beta_schedule""": """linear""",
             """variance_type""": """fixed_small""",
             """clip_sample""": True,
         }

         config.update(**_lowercase    )
         return config







     def 	__lowerCAmelCase		(       self     :			List[Any]    ):

         for timesteps in [1, 5, 100, 1_000]:
             self.check_over_configs(num_train_timesteps=_lowercase    )







     def 	__lowerCAmelCase		(       self     :			Tuple    ):

         for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]					,[0.0_0_2, 0.0_2, 0.2, 2]    ):
             self.check_over_configs(beta_start=_lowercase					,beta_end=_lowercase    )







     def 	__lowerCAmelCase		(       self     :			Any    ):

         for schedule in ["linear", "squaredcos_cap_v2"]:
             self.check_over_configs(beta_schedule=_lowercase    )







     def 	__lowerCAmelCase		(       self     :			Any    ):

         for variance in ["fixed_small", "fixed_large", "other"]:
             self.check_over_configs(variance_type=_lowercase    )







     def 	__lowerCAmelCase		(       self     :			Optional[Any]    ):

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







     def 	__lowerCAmelCase		(       self     :			Union[str, Any]    ):
         self.check_over_configs(thresholding=_lowercase    )
         for threshold in [0.5, 1.0, 2.0]:
             for prediction_type in ["epsilon", "sample", "v_prediction"]:
                 self.check_over_configs(
                     thresholding=_lowercase					,prediction_type=_lowercase					,sample_max_value=_lowercase					,)







     def 	__lowerCAmelCase		(       self     :			Union[str, Any]    ):

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







     def 	__lowerCAmelCase		(       self     :			Any    ):

         for t in [0, 500, 999]:
             self.check_over_forward(time_step=_lowercase    )







     def 	__lowerCAmelCase		(       self     :			int    ):
         UpperCAmelCase__    =							self.scheduler_classes[0]
         UpperCAmelCase__    =							self.get_scheduler_config()
         UpperCAmelCase__    =							scheduler_class(**_lowercase    )

         assert torch.sum(torch.abs(scheduler._get_variance(0    ) - 0.0    )    ) < 1e-5
         assert torch.sum(torch.abs(scheduler._get_variance(487    ) - 0.0_0_9_7_9    )    ) < 1e-5
         assert torch.sum(torch.abs(scheduler._get_variance(999    ) - 0.0_2    )    ) < 1e-5







     def 	__lowerCAmelCase		(       self     :			Dict    ):
         UpperCAmelCase__    =							self.scheduler_classes[0]
         UpperCAmelCase__    =							self.get_scheduler_config()
         UpperCAmelCase__    =							scheduler_class(**_lowercase    )

         UpperCAmelCase__    =							len(_lowercase    )

         UpperCAmelCase__    =							self.dummy_model()
         UpperCAmelCase__    =							self.dummy_sample_deter
         UpperCAmelCase__    =							self.dummy_sample_deter + 0.1
         UpperCAmelCase__    =							self.dummy_sample_deter - 0.1

         UpperCAmelCase__    =							samplea.shape[0]
         UpperCAmelCase__    =							torch.stack([samplea, samplea, samplea]					,dim=0    )
         UpperCAmelCase__    =							torch.arange(_lowercase    )[0:3, None].repeat(1					,_lowercase    )

         UpperCAmelCase__    =							model(samples.flatten(0					,1    )					,timesteps.flatten(0					,1    )    )
         UpperCAmelCase__    =							scheduler.batch_step_no_noise(_lowercase					,timesteps.flatten(0					,1    )					,samples.flatten(0					,1    )    )

         UpperCAmelCase__    =							torch.sum(torch.abs(_lowercase    )    )
         UpperCAmelCase__    =							torch.mean(torch.abs(_lowercase    )    )

         assert abs(result_sum.item() - 1_1_5_3.1_8_3_3    ) < 1e-2
         assert abs(result_mean.item() - 0.5_0_0_5    ) < 1e-3







     def 	__lowerCAmelCase		(       self     :			Any    ):
         UpperCAmelCase__    =							self.scheduler_classes[0]
         UpperCAmelCase__    =							self.get_scheduler_config()
         UpperCAmelCase__    =							scheduler_class(**_lowercase    )

         UpperCAmelCase__    =							len(_lowercase    )

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

         for t in reversed(range(_lowercase    )    ):
             # 1. predict noise residual
             UpperCAmelCase__    =							model(_lowercase					,_lowercase    )

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

             UpperCAmelCase__    =							pred_prev_sample

         UpperCAmelCase__    =							torch.sum(torch.abs(_lowercase    )    )
         UpperCAmelCase__    =							torch.mean(torch.abs(_lowercase    )    )

         assert abs(result_sum.item() - 2_5_8.9_6_0_6    ) < 1e-2
         assert abs(result_mean.item() - 0.3_3_7_2    ) < 1e-3







     def 	__lowerCAmelCase		(       self     :			Union[str, Any]    ):
         UpperCAmelCase__    =							self.scheduler_classes[0]
         UpperCAmelCase__    =							self.get_scheduler_config(prediction_type='v_prediction'    )
         UpperCAmelCase__    =							scheduler_class(**_lowercase    )

         UpperCAmelCase__    =							len(_lowercase    )

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

         for t in reversed(range(_lowercase    )    ):
             # 1. predict noise residual
             UpperCAmelCase__    =							model(_lowercase					,_lowercase    )

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

             UpperCAmelCase__    =							pred_prev_sample

         UpperCAmelCase__    =							torch.sum(torch.abs(_lowercase    )    )
         UpperCAmelCase__    =							torch.mean(torch.abs(_lowercase    )    )

         assert abs(result_sum.item() - 2_0_2.0_2_9_6    ) < 1e-2
         assert abs(result_mean.item() - 0.2_6_3_1    ) < 1e-3







     def 	__lowerCAmelCase		(       self     :			Union[str, Any]    ):
         UpperCAmelCase__    =							self.scheduler_classes[0]
         UpperCAmelCase__    =							self.get_scheduler_config()
         UpperCAmelCase__    =							scheduler_class(**_lowercase    )

         UpperCAmelCase__    =							[100, 87, 50, 1, 0]

         scheduler.set_timesteps(timesteps=_lowercase    )

         UpperCAmelCase__    =							scheduler.timesteps

         for i, timestep in enumerate(_lowercase    ):
             if i == len(_lowercase    ) - 1:
                 UpperCAmelCase__    =							-1
             else:
                 UpperCAmelCase__    =							timesteps[i + 1]

             UpperCAmelCase__    =							scheduler.previous_timestep(_lowercase    )
             UpperCAmelCase__    =							prev_t.item()

             self.assertEqual(_lowercase					,_lowercase    )







     def 	__lowerCAmelCase		(       self     :			Any    ):
         UpperCAmelCase__    =							self.scheduler_classes[0]
         UpperCAmelCase__    =							self.get_scheduler_config()
         UpperCAmelCase__    =							scheduler_class(**_lowercase    )

         UpperCAmelCase__    =							[100, 87, 50, 51, 0]

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







     def 	__lowerCAmelCase		(       self     :			Tuple    ):
         UpperCAmelCase__    =							self.scheduler_classes[0]
         UpperCAmelCase__    =							self.get_scheduler_config()
         UpperCAmelCase__    =							scheduler_class(**_lowercase    )

         UpperCAmelCase__    =							[100, 87, 50, 1, 0]
         UpperCAmelCase__    =							len(_lowercase    )

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







     def 	__lowerCAmelCase		(       self     :			List[str]    ):
         UpperCAmelCase__    =							self.scheduler_classes[0]
         UpperCAmelCase__    =							self.get_scheduler_config()
         UpperCAmelCase__    =							scheduler_class(**_lowercase    )

         UpperCAmelCase__    =							[scheduler.config.num_train_timesteps]

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




"""simple docstring"""






import os
import sys
from contextlib import contextmanager


# Windows only
if os.name == "nt":
						import ctypes
						import msvcrt  # noqa
						class        snake_case (				ctypes.Structure       ):




											"""simple docstring"""

											snake_case__       					=				[("size", ctypes.c_int), ("visible", ctypes.c_byte)]



def 						a_		(      ):
				if os.name == "nt":
								UpperCAmelCase__    =							CursorInfo()
								UpperCAmelCase__    =							ctypes.windll.kernelaa.GetStdHandle(-1_1 )
								ctypes.windll.kernelaa.GetConsoleCursorInfo(lowerCamelCase      ,    ctypes.byref(lowerCamelCase ) )
								UpperCAmelCase__    =							False
								ctypes.windll.kernelaa.SetConsoleCursorInfo(lowerCamelCase      ,    ctypes.byref(lowerCamelCase ) )
				elif os.name == "posix":
								sys.stdout.write('\033[?25l' )
								sys.stdout.flush()



def 						a_		(      ):
				if os.name == "nt":
								UpperCAmelCase__    =							CursorInfo()
								UpperCAmelCase__    =							ctypes.windll.kernelaa.GetStdHandle(-1_1 )
								ctypes.windll.kernelaa.GetConsoleCursorInfo(lowerCamelCase      ,    ctypes.byref(lowerCamelCase ) )
								UpperCAmelCase__    =							True
								ctypes.windll.kernelaa.SetConsoleCursorInfo(lowerCamelCase      ,    ctypes.byref(lowerCamelCase ) )
				elif os.name == "posix":
								sys.stdout.write('\033[?25h' )
								sys.stdout.flush()







@contextmanager
def 						a_		(      ):
				try:
								hide_cursor()
								yield
				finally:
								show_cursor()



