import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest

class 	lowerCamelCase_    ( _A						):






						'''simple docstring'''
						a__   =  (DDPMParallelScheduler,)







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[str]	,	**__lowerCamelCase      :			List[Any] )      ->				Optional[Any]:
									A							:					int              =						{
									    "num_train_timesteps": 10_00,
									    "beta_start": 0.0001,
									    "beta_end": 0.02,
									    "beta_schedule": "linear",
									    "variance_type": "fixed_small",
									    "clip_sample": True,
									}

									config.update(**__lowerCamelCase )
									return config







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Tuple )      ->				str:
									for timesteps in [1, 5, 1_00, 10_00]:
												self.check_over_configs(num_train_timesteps=__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Tuple )      ->				List[str]:
									for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]	,	[0.002, 0.02, 0.2, 2] ):
												self.check_over_configs(beta_start=__lowerCamelCase	,	beta_end=__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[Any] )      ->				Union[str, Any]:
									for schedule in ["linear", "squaredcos_cap_v2"]:
												self.check_over_configs(beta_schedule=__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			str )      ->				List[Any]:
									for variance in ["fixed_small", "fixed_large", "other"]:
												self.check_over_configs(variance_type=__lowerCamelCase )







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







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[str] )      ->				List[str]:
									self.check_over_configs(thresholding=__lowerCamelCase )
									for threshold in [0.5, 1.0, 2.0]:
												for prediction_type in ["epsilon", "sample", "v_prediction"]:
															self.check_over_configs(
															    thresholding=__lowerCamelCase	,	prediction_type=__lowerCamelCase	,	sample_max_value=__lowerCamelCase	,	)







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







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Any )      ->				List[str]:
									for t in [0, 5_00, 9_99]:
												self.check_over_forward(time_step=__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[Any] )      ->				int:
									A							:					int              =						self.scheduler_classes[0]
									A							:					str              =						self.get_scheduler_config()
									A							:					int              =						scheduler_class(**__lowerCamelCase )

									assert torch.sum(torch.abs(scheduler._get_variance(0 ) - 0.0 ) ) < 1e-5
									assert torch.sum(torch.abs(scheduler._get_variance(4_87 ) - 0.00979 ) ) < 1e-5
									assert torch.sum(torch.abs(scheduler._get_variance(9_99 ) - 0.02 ) ) < 1e-5







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Union[str, Any] )      ->				Tuple:
									A							:					Optional[Any]              =						self.scheduler_classes[0]
									A							:					Union[str, Any]              =						self.get_scheduler_config()
									A							:					Any              =						scheduler_class(**__lowerCamelCase )

									A							:					Dict              =						len(__lowerCamelCase )

									A							:					List[Any]              =						self.dummy_model()
									A							:					Optional[int]              =						self.dummy_sample_deter
									A							:					List[Any]              =						self.dummy_sample_deter + 0.1
									A							:					Dict              =						self.dummy_sample_deter - 0.1

									A							:					Optional[int]              =						samplea.shape[0]
									A							:					List[str]              =						torch.stack([samplea, samplea, samplea]	,	dim=0 )
									A							:					int              =						torch.arange(__lowerCamelCase )[0:3, None].repeat(1	,	__lowerCamelCase )

									A							:					Optional[int]              =						model(samples.flatten(0	,	1 )	,	timesteps.flatten(0	,	1 ) )
									A							:					List[str]              =						scheduler.batch_step_no_noise(__lowerCamelCase	,	timesteps.flatten(0	,	1 )	,	samples.flatten(0	,	1 ) )

									A							:					Optional[Any]              =						torch.sum(torch.abs(__lowerCamelCase ) )
									A							:					Optional[int]              =						torch.mean(torch.abs(__lowerCamelCase ) )

									assert abs(result_sum.item() - 1153.1833 ) < 1e-2
									assert abs(result_mean.item() - 0.5005 ) < 1e-3







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Tuple )      ->				Optional[int]:
									A							:					Optional[Any]              =						self.scheduler_classes[0]
									A							:					Union[str, Any]              =						self.get_scheduler_config()
									A							:					Tuple              =						scheduler_class(**__lowerCamelCase )

									A							:					Any              =						len(__lowerCamelCase )

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

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

												# 2. predict previous mean of sample x_t-1
												A							:					Tuple              =						scheduler.step(__lowerCamelCase	,	__lowerCamelCase	,	__lowerCamelCase	,	generator=__lowerCamelCase ).prev_sample

												A							:					Union[str, Any]              =						pred_prev_sample

									A							:					int              =						torch.sum(torch.abs(__lowerCamelCase ) )
									A							:					Union[str, Any]              =						torch.mean(torch.abs(__lowerCamelCase ) )

									assert abs(result_sum.item() - 258.9606 ) < 1e-2
									assert abs(result_mean.item() - 0.3372 ) < 1e-3







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[Any] )      ->				str:
									A							:					Tuple              =						self.scheduler_classes[0]
									A							:					Tuple              =						self.get_scheduler_config(prediction_type="v_prediction" )
									A							:					Optional[int]              =						scheduler_class(**__lowerCamelCase )

									A							:					Union[str, Any]              =						len(__lowerCamelCase )

									A							:					str              =						self.dummy_model()
									A							:					Tuple              =						self.dummy_sample_deter
									A							:					int              =						torch.manual_seed(0 )

									for t in reversed(range(__lowerCamelCase ) ):
												# 1. predict noise residual
												A							:					int              =						model(__lowerCamelCase	,	__lowerCamelCase )

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

												A							:					List[str]              =						pred_prev_sample

									A							:					Optional[Any]              =						torch.sum(torch.abs(__lowerCamelCase ) )
									A							:					Optional[int]              =						torch.mean(torch.abs(__lowerCamelCase ) )

									assert abs(result_sum.item() - 202.0296 ) < 1e-2
									assert abs(result_mean.item() - 0.2631 ) < 1e-3







						def 			SCREAMING_SNAKE_CASE__		(	self      :			int )      ->				Dict:
									A							:					Any              =						self.scheduler_classes[0]
									A							:					Optional[int]              =						self.get_scheduler_config()
									A							:					Tuple              =						scheduler_class(**__lowerCamelCase )

									A							:					Dict              =						[1_00, 87, 50, 1, 0]

									scheduler.set_timesteps(timesteps=__lowerCamelCase )

									A							:					Optional[Any]              =						scheduler.timesteps

									for i, timestep in enumerate(__lowerCamelCase ):
												if i == len(__lowerCamelCase ) - 1:
															A							:					List[Any]              =						-1
												else:
															A							:					Optional[int]              =						timesteps[i + 1]

												A							:					Optional[Any]              =						scheduler.previous_timestep(__lowerCamelCase )
												A							:					Optional[int]              =						prev_t.item()

												self.assertEqual(__lowerCamelCase	,	__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[str] )      ->				int:
									A							:					Union[str, Any]              =						self.scheduler_classes[0]
									A							:					Dict              =						self.get_scheduler_config()
									A							:					str              =						scheduler_class(**__lowerCamelCase )

									A							:					Dict              =						[1_00, 87, 50, 51, 0]

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







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[Any] )      ->				List[str]:
									A							:					List[str]              =						self.scheduler_classes[0]
									A							:					Tuple              =						self.get_scheduler_config()
									A							:					Tuple              =						scheduler_class(**__lowerCamelCase )

									A							:					Any              =						[1_00, 87, 50, 1, 0]
									A							:					Optional[Any]              =						len(__lowerCamelCase )

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







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[str] )      ->				List[Any]:
									A							:					List[Any]              =						self.scheduler_classes[0]
									A							:					Dict              =						self.get_scheduler_config()
									A							:					int              =						scheduler_class(**__lowerCamelCase )

									A							:					List[Any]              =						[scheduler.config.num_train_timesteps]

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




import os


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_doctest_list.py
__SCREAMING_SNAKE_CASE           =  """."""


if __name__ == "__main__":
							__SCREAMING_SNAKE_CASE           =  os.path.join(REPO_PATH, """utils/documentation_tests.txt""")
							__SCREAMING_SNAKE_CASE           =  []
							__SCREAMING_SNAKE_CASE           =  []
							with open(doctest_file_path) as fp:
														for line in fp:
																					__SCREAMING_SNAKE_CASE           =  line.strip()
																					__SCREAMING_SNAKE_CASE           =  os.path.join(REPO_PATH, line)
																					if not (os.path.isfile(path) or os.path.isdir(path)):
																												non_existent_paths.append(line)
																					all_paths.append(path)
							if len(non_existent_paths) > 0:
														__SCREAMING_SNAKE_CASE           =  """\n""".join(non_existent_paths)
														raise ValueError(F"""`utils/documentation_tests.txt` contains non-existent paths:\n{non_existent_paths}""")
							if all_paths != sorted(all_paths):
														raise ValueError("""Files in `utils/documentation_tests.txt` are not in alphabetical order.""")