"""simple docstring"""




import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest
class     UpperCamelCase__(			__A ):
						lowerCAmelCase__  :						Optional[Any]													=		(DDPMParallelScheduler,)





						def 				snake_case__	(       self		,**__UpperCAmelCase     )					->				Any:
													A__	     =							{
													    'num_train_timesteps': 10_00,
													    'beta_start': 0.0_0_0_1,
													    'beta_end': 0.0_2,
													    'beta_schedule': 'linear',
													    'variance_type': 'fixed_small',
													    'clip_sample': True,
													}

													config.update(**__UpperCAmelCase     )
													return config





						def 				snake_case__	(       self     )					->				Optional[int]:
													for timesteps in [1, 5, 1_00, 10_00]:
																				self.check_over_configs(num_train_timesteps=__UpperCAmelCase     )





						def 				snake_case__	(       self     )					->				Optional[Any]:
													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=__UpperCAmelCase		,beta_end=__UpperCAmelCase     )





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





						def 				snake_case__	(       self     )					->				Tuple:
													for variance in ["fixed_small", "fixed_large", "other"]:
																				self.check_over_configs(variance_type=__UpperCAmelCase     )





						def 				snake_case__	(       self     )					->				List[str]:
													for clip_sample in [True, False]:
																				self.check_over_configs(clip_sample=__UpperCAmelCase     )





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





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





						def 				snake_case__	(       self     )					->				Tuple:
													for t in [0, 5_00, 9_99]:
																				self.check_over_forward(time_step=__UpperCAmelCase     )





						def 				snake_case__	(       self     )					->				str:
													A__	     =							self.scheduler_classes[0]
													A__	     =							self.get_scheduler_config()
													A__	     =							scheduler_class(**__UpperCAmelCase     )

													assert torch.sum(torch.abs(scheduler._get_variance(0     ) - 0.0     )     ) < 1e-5
													assert torch.sum(torch.abs(scheduler._get_variance(4_87     ) - 0.0_0_9_7_9     )     ) < 1e-5
													assert torch.sum(torch.abs(scheduler._get_variance(9_99     ) - 0.0_2     )     ) < 1e-5





						def 				snake_case__	(       self     )					->				Optional[int]:
													A__	     =							self.scheduler_classes[0]
													A__	     =							self.get_scheduler_config()
													A__	     =							scheduler_class(**__UpperCAmelCase     )

													A__	     =							len(__UpperCAmelCase     )

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

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

													A__	     =							model(samples.flatten(0		,1     )		,timesteps.flatten(0		,1     )     )
													A__	     =							scheduler.batch_step_no_noise(__UpperCAmelCase		,timesteps.flatten(0		,1     )		,samples.flatten(0		,1     )     )

													A__	     =							torch.sum(torch.abs(__UpperCAmelCase     )     )
													A__	     =							torch.mean(torch.abs(__UpperCAmelCase     )     )

													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 				snake_case__	(       self     )					->				Union[str, Any]:
													A__	     =							self.scheduler_classes[0]
													A__	     =							self.get_scheduler_config()
													A__	     =							scheduler_class(**__UpperCAmelCase     )

													A__	     =							len(__UpperCAmelCase     )

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

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

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

																				A__	     =							pred_prev_sample

													A__	     =							torch.sum(torch.abs(__UpperCAmelCase     )     )
													A__	     =							torch.mean(torch.abs(__UpperCAmelCase     )     )

													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 				snake_case__	(       self     )					->				Any:
													A__	     =							self.scheduler_classes[0]
													A__	     =							self.get_scheduler_config(prediction_type='v_prediction'     )
													A__	     =							scheduler_class(**__UpperCAmelCase     )

													A__	     =							len(__UpperCAmelCase     )

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

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

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

																				A__	     =							pred_prev_sample

													A__	     =							torch.sum(torch.abs(__UpperCAmelCase     )     )
													A__	     =							torch.mean(torch.abs(__UpperCAmelCase     )     )

													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 				snake_case__	(       self     )					->				Optional[Any]:
													A__	     =							self.scheduler_classes[0]
													A__	     =							self.get_scheduler_config()
													A__	     =							scheduler_class(**__UpperCAmelCase     )

													A__	     =							[1_00, 87, 50, 1, 0]

													scheduler.set_timesteps(timesteps=__UpperCAmelCase     )

													A__	     =							scheduler.timesteps

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

																				A__	     =							scheduler.previous_timestep(__UpperCAmelCase     )
																				A__	     =							prev_t.item()

																				self.assertEqual(__UpperCAmelCase		,__UpperCAmelCase     )





						def 				snake_case__	(       self     )					->				List[str]:
													A__	     =							self.scheduler_classes[0]
													A__	     =							self.get_scheduler_config()
													A__	     =							scheduler_class(**__UpperCAmelCase     )

													A__	     =							[1_00, 87, 50, 51, 0]

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





						def 				snake_case__	(       self     )					->				Union[str, Any]:
													A__	     =							self.scheduler_classes[0]
													A__	     =							self.get_scheduler_config()
													A__	     =							scheduler_class(**__UpperCAmelCase     )

													A__	     =							[1_00, 87, 50, 1, 0]
													A__	     =							len(__UpperCAmelCase     )

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





						def 				snake_case__	(       self     )					->				Union[str, Any]:
													A__	     =							self.scheduler_classes[0]
													A__	     =							self.get_scheduler_config()
													A__	     =							scheduler_class(**__UpperCAmelCase     )

													A__	     =							[scheduler.config.num_train_timesteps]

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






"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__lowerCamelCase											=  {"configuration_glpn": ["GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP", "GLPNConfig"]}

try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowerCamelCase											=  ["GLPNFeatureExtractor"]
				__lowerCamelCase											=  ["GLPNImageProcessor"]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowerCamelCase											=  [
				    "GLPN_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "GLPNForDepthEstimation",
				    "GLPNLayer",
				    "GLPNModel",
				    "GLPNPreTrainedModel",
				]


if TYPE_CHECKING:
				from .configuration_glpn import GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP, GLPNConfig

				try:
								if not is_vision_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .feature_extraction_glpn import GLPNFeatureExtractor
								from .image_processing_glpn import GLPNImageProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_glpn import (
								    GLPN_PRETRAINED_MODEL_ARCHIVE_LIST,
								    GLPNForDepthEstimation,
								    GLPNLayer,
								    GLPNModel,
								    GLPNPreTrainedModel,
								)


else:
				import sys

				__lowerCamelCase											=  _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





