



'''simple docstring'''






import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest

class 				SCREAMING_SNAKE_CASE(							A__			):







	"""simple docstring"""






	lowerCamelCase__			 =       (PNDMScheduler,)
	lowerCamelCase__			 =       (("""num_inference_steps""", 50),)







	def      A	(						self			:     str		,			**__snake_case			:     Tuple		)			->		Any:
								UpperCAmelCase :       Optional[int] 							=		{
								    '''num_train_timesteps''': 1000,
								    '''beta_start''': 0.00_01,
								    '''beta_end''': 0.02,
								    '''beta_schedule''': '''linear''',
								}

								config.update(**__snake_case		)
								return config







	def      A	(						self			:     Optional[Any]		,			__snake_case			:     Optional[Any]=0		,			**__snake_case			:     Tuple		)			->		Union[str, Any]:
								UpperCAmelCase :       List[str] 							=		dict(self.forward_default_kwargs		)
								UpperCAmelCase :       Tuple 							=		kwargs.pop('''num_inference_steps'''		,			__snake_case		)
								UpperCAmelCase :       Tuple 							=		self.dummy_sample
								UpperCAmelCase :       Union[str, Any] 							=		0.1 * sample
								UpperCAmelCase :       Any 							=		[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

								for scheduler_class in self.scheduler_classes:
															UpperCAmelCase :       Union[str, Any] 							=		self.get_scheduler_config(**__snake_case		)
															UpperCAmelCase :       Dict 							=		scheduler_class(**__snake_case		)
															scheduler.set_timesteps(__snake_case		)
															# copy over dummy past residuals
															UpperCAmelCase :       str 							=		dummy_past_residuals[:]

															with tempfile.TemporaryDirectory() as tmpdirname:
																						scheduler.save_config(__snake_case		)
																						UpperCAmelCase :       List[str] 							=		scheduler_class.from_pretrained(__snake_case		)
																						new_scheduler.set_timesteps(__snake_case		)
																						# copy over dummy past residuals
																						UpperCAmelCase :       Optional[Any] 							=		dummy_past_residuals[:]

															UpperCAmelCase :       int 							=		scheduler.step_prk(__snake_case		,			__snake_case		,			__snake_case		,			**__snake_case		).prev_sample
															UpperCAmelCase :       Dict 							=		new_scheduler.step_prk(__snake_case		,			__snake_case		,			__snake_case		,			**__snake_case		).prev_sample

															assert torch.sum(torch.abs(output - new_output		)		) < 1E-5, "Scheduler outputs are not identical"

															UpperCAmelCase :       Optional[Any] 							=		scheduler.step_plms(__snake_case		,			__snake_case		,			__snake_case		,			**__snake_case		).prev_sample
															UpperCAmelCase :       List[str] 							=		new_scheduler.step_plms(__snake_case		,			__snake_case		,			__snake_case		,			**__snake_case		).prev_sample

															assert torch.sum(torch.abs(output - new_output		)		) < 1E-5, "Scheduler outputs are not identical"







	def      A	(						self			:     Tuple		)			->		Tuple:
								pass







	def      A	(						self			:     int		,			__snake_case			:     List[Any]=0		,			**__snake_case			:     List[Any]		)			->		Union[str, Any]:
								UpperCAmelCase :       List[Any] 							=		dict(self.forward_default_kwargs		)
								UpperCAmelCase :       Tuple 							=		kwargs.pop('''num_inference_steps'''		,			__snake_case		)
								UpperCAmelCase :       Dict 							=		self.dummy_sample
								UpperCAmelCase :       List[Any] 							=		0.1 * sample
								UpperCAmelCase :       int 							=		[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

								for scheduler_class in self.scheduler_classes:
															UpperCAmelCase :       Any 							=		self.get_scheduler_config()
															UpperCAmelCase :       Tuple 							=		scheduler_class(**__snake_case		)
															scheduler.set_timesteps(__snake_case		)

															# copy over dummy past residuals (must be after setting timesteps)
															UpperCAmelCase :       Union[str, Any] 							=		dummy_past_residuals[:]

															with tempfile.TemporaryDirectory() as tmpdirname:
																						scheduler.save_config(__snake_case		)
																						UpperCAmelCase :       List[str] 							=		scheduler_class.from_pretrained(__snake_case		)
																						# copy over dummy past residuals
																						new_scheduler.set_timesteps(__snake_case		)

																						# copy over dummy past residual (must be after setting timesteps)
																						UpperCAmelCase :       Dict 							=		dummy_past_residuals[:]

															UpperCAmelCase :       Optional[int] 							=		scheduler.step_prk(__snake_case		,			__snake_case		,			__snake_case		,			**__snake_case		).prev_sample
															UpperCAmelCase :       List[Any] 							=		new_scheduler.step_prk(__snake_case		,			__snake_case		,			__snake_case		,			**__snake_case		).prev_sample

															assert torch.sum(torch.abs(output - new_output		)		) < 1E-5, "Scheduler outputs are not identical"

															UpperCAmelCase :       str 							=		scheduler.step_plms(__snake_case		,			__snake_case		,			__snake_case		,			**__snake_case		).prev_sample
															UpperCAmelCase :       Optional[int] 							=		new_scheduler.step_plms(__snake_case		,			__snake_case		,			__snake_case		,			**__snake_case		).prev_sample

															assert torch.sum(torch.abs(output - new_output		)		) < 1E-5, "Scheduler outputs are not identical"







	def      A	(						self			:     Tuple		,			**__snake_case			:     Tuple		)			->		Any:
								UpperCAmelCase :       Dict 							=		self.scheduler_classes[0]
								UpperCAmelCase :       Tuple 							=		self.get_scheduler_config(**__snake_case		)
								UpperCAmelCase :       Tuple 							=		scheduler_class(**__snake_case		)

								UpperCAmelCase :       Any 							=		10
								UpperCAmelCase :       Optional[Any] 							=		self.dummy_model()
								UpperCAmelCase :       int 							=		self.dummy_sample_deter
								scheduler.set_timesteps(__snake_case		)

								for i, t in enumerate(scheduler.prk_timesteps		):
															UpperCAmelCase :       Tuple 							=		model(__snake_case		,			__snake_case		)
															UpperCAmelCase :       int 							=		scheduler.step_prk(__snake_case		,			__snake_case		,			__snake_case		).prev_sample

								for i, t in enumerate(scheduler.plms_timesteps		):
															UpperCAmelCase :       Any 							=		model(__snake_case		,			__snake_case		)
															UpperCAmelCase :       Any 							=		scheduler.step_plms(__snake_case		,			__snake_case		,			__snake_case		).prev_sample

								return sample







	def      A	(						self			:     Optional[int]		)			->		Optional[int]:
								UpperCAmelCase :       Union[str, Any] 							=		dict(self.forward_default_kwargs		)

								UpperCAmelCase :       Dict 							=		kwargs.pop('''num_inference_steps'''		,			__snake_case		)

								for scheduler_class in self.scheduler_classes:
															UpperCAmelCase :       List[Any] 							=		self.get_scheduler_config()
															UpperCAmelCase :       int 							=		scheduler_class(**__snake_case		)

															UpperCAmelCase :       List[str] 							=		self.dummy_sample
															UpperCAmelCase :       Any 							=		0.1 * sample

															if num_inference_steps is not None and hasattr(__snake_case		,			'''set_timesteps'''		):
																						scheduler.set_timesteps(__snake_case		)
															elif num_inference_steps is not None and not hasattr(__snake_case		,			'''set_timesteps'''		):
																						UpperCAmelCase :       List[Any] 							=		num_inference_steps

															# copy over dummy past residuals (must be done after set_timesteps)
															UpperCAmelCase :       Optional[int] 							=		[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
															UpperCAmelCase :       Optional[int] 							=		dummy_past_residuals[:]

															UpperCAmelCase :       List[Any] 							=		scheduler.step_prk(__snake_case		,			0		,			__snake_case		,			**__snake_case		).prev_sample
															UpperCAmelCase :       Optional[Any] 							=		scheduler.step_prk(__snake_case		,			1		,			__snake_case		,			**__snake_case		).prev_sample

															self.assertEqual(output_a.shape		,			sample.shape		)
															self.assertEqual(output_a.shape		,			output_a.shape		)

															UpperCAmelCase :       str 							=		scheduler.step_plms(__snake_case		,			0		,			__snake_case		,			**__snake_case		).prev_sample
															UpperCAmelCase :       List[str] 							=		scheduler.step_plms(__snake_case		,			1		,			__snake_case		,			**__snake_case		).prev_sample

															self.assertEqual(output_a.shape		,			sample.shape		)
															self.assertEqual(output_a.shape		,			output_a.shape		)







	def      A	(						self			:     List[str]		)			->		List[Any]:
								for timesteps in [100, 1000]:
															self.check_over_configs(num_train_timesteps=__snake_case		)







	def      A	(						self			:     Any		)			->		Dict:
								for steps_offset in [0, 1]:
															self.check_over_configs(steps_offset=__snake_case		)

								UpperCAmelCase :       Union[str, Any] 							=		self.scheduler_classes[0]
								UpperCAmelCase :       str 							=		self.get_scheduler_config(steps_offset=1		)
								UpperCAmelCase :       Tuple 							=		scheduler_class(**__snake_case		)
								scheduler.set_timesteps(10		)
								assert torch.equal(
								    scheduler.timesteps		,			torch.LongTensor(
								        [901, 851, 851, 801, 801, 751, 751, 701, 701, 651, 651, 601, 601, 501, 401, 301, 201, 101, 1]		)		,			)







	def      A	(						self			:     int		)			->		Optional[Any]:
								for beta_start, beta_end in zip([0.00_01, 0.0_01]		,			[0.0_02, 0.02]		):
															self.check_over_configs(beta_start=__snake_case		,			beta_end=__snake_case		)







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







	def      A	(						self			:     int		)			->		Optional[Any]:
								for prediction_type in ["epsilon", "v_prediction"]:
															self.check_over_configs(prediction_type=__snake_case		)







	def      A	(						self			:     Any		)			->		Any:
								for t in [1, 5, 10]:
															self.check_over_forward(time_step=__snake_case		)







	def      A	(						self			:     Dict		)			->		int:
								for t, num_inference_steps in zip([1, 5, 10]		,			[10, 50, 100]		):
															self.check_over_forward(num_inference_steps=__snake_case		)







	def      A	(						self			:     List[Any]		)			->		int:
								# earlier version of set_timesteps() caused an error indexing alpha's with inference steps as power of 3
								UpperCAmelCase :       str 							=		27

								for scheduler_class in self.scheduler_classes:
															UpperCAmelCase :       Tuple 							=		self.dummy_sample
															UpperCAmelCase :       List[Any] 							=		0.1 * sample

															UpperCAmelCase :       Optional[int] 							=		self.get_scheduler_config()
															UpperCAmelCase :       int 							=		scheduler_class(**__snake_case		)

															scheduler.set_timesteps(__snake_case		)

															# before power of 3 fix, would error on first step, so we only need to do two
															for i, t in enumerate(scheduler.prk_timesteps[:2]		):
																						UpperCAmelCase :       str 							=		scheduler.step_prk(__snake_case		,			__snake_case		,			__snake_case		).prev_sample







	def      A	(						self			:     Optional[int]		)			->		Optional[int]:
								with self.assertRaises(__snake_case		):
															UpperCAmelCase :       Optional[int] 							=		self.scheduler_classes[0]
															UpperCAmelCase :       List[str] 							=		self.get_scheduler_config()
															UpperCAmelCase :       List[str] 							=		scheduler_class(**__snake_case		)

															scheduler.step_plms(self.dummy_sample		,			1		,			self.dummy_sample		).prev_sample







	def      A	(						self			:     Tuple		)			->		List[str]:
								UpperCAmelCase :       Union[str, Any] 							=		self.full_loop()
								UpperCAmelCase :       str 							=		torch.sum(torch.abs(__snake_case		)		)
								UpperCAmelCase :       List[str] 							=		torch.mean(torch.abs(__snake_case		)		)

								assert abs(result_sum.item() - 1_98.13_18		) < 1E-2
								assert abs(result_mean.item() - 0.25_80		) < 1E-3







	def      A	(						self			:     Union[str, Any]		)			->		Dict:
								UpperCAmelCase :       int 							=		self.full_loop(prediction_type='''v_prediction'''		)
								UpperCAmelCase :       List[Any] 							=		torch.sum(torch.abs(__snake_case		)		)
								UpperCAmelCase :       Dict 							=		torch.mean(torch.abs(__snake_case		)		)

								assert abs(result_sum.item() - 67.39_86		) < 1E-2
								assert abs(result_mean.item() - 0.08_78		) < 1E-3







	def      A	(						self			:     Any		)			->		List[str]:
								# We specify different beta, so that the first alpha is 0.99
								UpperCAmelCase :       int 							=		self.full_loop(set_alpha_to_one=__snake_case		,			beta_start=0.01		)
								UpperCAmelCase :       Optional[int] 							=		torch.sum(torch.abs(__snake_case		)		)
								UpperCAmelCase :       str 							=		torch.mean(torch.abs(__snake_case		)		)

								assert abs(result_sum.item() - 2_30.03_99		) < 1E-2
								assert abs(result_mean.item() - 0.29_95		) < 1E-3







	def      A	(						self			:     List[Any]		)			->		str:
								# We specify different beta, so that the first alpha is 0.99
								UpperCAmelCase :       List[str] 							=		self.full_loop(set_alpha_to_one=__snake_case		,			beta_start=0.01		)
								UpperCAmelCase :       Union[str, Any] 							=		torch.sum(torch.abs(__snake_case		)		)
								UpperCAmelCase :       List[str] 							=		torch.mean(torch.abs(__snake_case		)		)

								assert abs(result_sum.item() - 1_86.94_82		) < 1E-2
								assert abs(result_mean.item() - 0.24_34		) < 1E-3










'''simple docstring'''




import warnings

from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline  # noqa F401


warnings.warn(
    '''The `inpainting.py` script is outdated. Please use directly `from diffusers import'''
    ''' StableDiffusionInpaintPipeline` instead.'''
)