

import torch

from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde

from .test_schedulers import SchedulerCommonTest





@require_torchsde
class 	snake_case_   (		__A					):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE     :   Tuple									=				(DPMSolverSDEScheduler,)
			SCREAMING_SNAKE_CASE     :   str									=				10







			def 				snake_case__(     self :				Optional[int]							,   **_UpperCamelCase :				str							)      ->Optional[Any]:
									snake_case_   =	{
									    '''num_train_timesteps''': 1_1_0_0,
									    '''beta_start''': 0.0001,
									    '''beta_end''': 0.02,
									    '''beta_schedule''': '''linear''',
									    '''noise_sampler_seed''': 0,
									}

									config.update(**_UpperCamelCase							)
									return config







			def 				snake_case__(     self :				Optional[Any]							)      ->Any:
									for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
															self.check_over_configs(num_train_timesteps=_UpperCamelCase							)







			def 				snake_case__(     self :				List[str]							)      ->int:
									for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]							,   [0.0002, 0.002, 0.02]							):
															self.check_over_configs(beta_start=_UpperCamelCase							,   beta_end=_UpperCamelCase							)







			def 				snake_case__(     self :				List[str]							)      ->Union[str, Any]:
									for schedule in ["linear", "scaled_linear"]:
															self.check_over_configs(beta_schedule=_UpperCamelCase							)







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







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

									scheduler.set_timesteps(self.num_inference_steps							)

									snake_case_   =	self.dummy_model()
									snake_case_   =	self.dummy_sample_deter * scheduler.init_noise_sigma
									snake_case_   =	sample.to(_UpperCamelCase							)

									for i, t in enumerate(scheduler.timesteps							):
															snake_case_   =	scheduler.scale_model_input(_UpperCamelCase							,   _UpperCamelCase							)

															snake_case_   =	model(_UpperCamelCase							,   _UpperCamelCase							)

															snake_case_   =	scheduler.step(_UpperCamelCase							,   _UpperCamelCase							,   _UpperCamelCase							)
															snake_case_   =	output.prev_sample

									snake_case_   =	torch.sum(torch.abs(_UpperCamelCase							)							)
									snake_case_   =	torch.mean(torch.abs(_UpperCamelCase							)							)

									if torch_device in ["mps"]:
															assert abs(result_sum.item() - 167.47821044921875							) < 1e-2
															assert abs(result_mean.item() - 0.2178705964565277							) < 1e-3
									elif torch_device in ["cuda"]:
															assert abs(result_sum.item() - 171.59352111816406							) < 1e-2
															assert abs(result_mean.item() - 0.22342906892299652							) < 1e-3
									else:
															assert abs(result_sum.item() - 162.52383422851562							) < 1e-2
															assert abs(result_mean.item() - 0.211619570851326							) < 1e-3







			def 				snake_case__(     self :				Optional[Any]							)      ->str:
									snake_case_   =	self.scheduler_classes[0]
									snake_case_   =	self.get_scheduler_config(prediction_type='''v_prediction'''							)
									snake_case_   =	scheduler_class(**_UpperCamelCase							)

									scheduler.set_timesteps(self.num_inference_steps							)

									snake_case_   =	self.dummy_model()
									snake_case_   =	self.dummy_sample_deter * scheduler.init_noise_sigma
									snake_case_   =	sample.to(_UpperCamelCase							)

									for i, t in enumerate(scheduler.timesteps							):
															snake_case_   =	scheduler.scale_model_input(_UpperCamelCase							,   _UpperCamelCase							)

															snake_case_   =	model(_UpperCamelCase							,   _UpperCamelCase							)

															snake_case_   =	scheduler.step(_UpperCamelCase							,   _UpperCamelCase							,   _UpperCamelCase							)
															snake_case_   =	output.prev_sample

									snake_case_   =	torch.sum(torch.abs(_UpperCamelCase							)							)
									snake_case_   =	torch.mean(torch.abs(_UpperCamelCase							)							)

									if torch_device in ["mps"]:
															assert abs(result_sum.item() - 124.77149200439453							) < 1e-2
															assert abs(result_mean.item() - 0.16226289014816284							) < 1e-3
									elif torch_device in ["cuda"]:
															assert abs(result_sum.item() - 128.1663360595703							) < 1e-2
															assert abs(result_mean.item() - 0.16688326001167297							) < 1e-3
									else:
															assert abs(result_sum.item() - 119.8487548828125							) < 1e-2
															assert abs(result_mean.item() - 0.1560530662536621							) < 1e-3







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

									scheduler.set_timesteps(self.num_inference_steps							,   device=_UpperCamelCase							)

									snake_case_   =	self.dummy_model()
									snake_case_   =	self.dummy_sample_deter.to(_UpperCamelCase							) * scheduler.init_noise_sigma

									for t in scheduler.timesteps:
															snake_case_   =	scheduler.scale_model_input(_UpperCamelCase							,   _UpperCamelCase							)

															snake_case_   =	model(_UpperCamelCase							,   _UpperCamelCase							)

															snake_case_   =	scheduler.step(_UpperCamelCase							,   _UpperCamelCase							,   _UpperCamelCase							)
															snake_case_   =	output.prev_sample

									snake_case_   =	torch.sum(torch.abs(_UpperCamelCase							)							)
									snake_case_   =	torch.mean(torch.abs(_UpperCamelCase							)							)

									if torch_device in ["mps"]:
															assert abs(result_sum.item() - 167.46957397460938							) < 1e-2
															assert abs(result_mean.item() - 0.21805934607982635							) < 1e-3
									elif torch_device in ["cuda"]:
															assert abs(result_sum.item() - 171.59353637695312							) < 1e-2
															assert abs(result_mean.item() - 0.22342908382415771							) < 1e-3
									else:
															assert abs(result_sum.item() - 162.52383422851562							) < 1e-2
															assert abs(result_mean.item() - 0.211619570851326							) < 1e-3







			def 				snake_case__(     self :				Optional[Any]							)      ->int:
									snake_case_   =	self.scheduler_classes[0]
									snake_case_   =	self.get_scheduler_config()
									snake_case_   =	scheduler_class(**_UpperCamelCase							,   use_karras_sigmas=_UpperCamelCase							)

									scheduler.set_timesteps(self.num_inference_steps							,   device=_UpperCamelCase							)

									snake_case_   =	self.dummy_model()
									snake_case_   =	self.dummy_sample_deter.to(_UpperCamelCase							) * scheduler.init_noise_sigma
									snake_case_   =	sample.to(_UpperCamelCase							)

									for t in scheduler.timesteps:
															snake_case_   =	scheduler.scale_model_input(_UpperCamelCase							,   _UpperCamelCase							)

															snake_case_   =	model(_UpperCamelCase							,   _UpperCamelCase							)

															snake_case_   =	scheduler.step(_UpperCamelCase							,   _UpperCamelCase							,   _UpperCamelCase							)
															snake_case_   =	output.prev_sample

									snake_case_   =	torch.sum(torch.abs(_UpperCamelCase							)							)
									snake_case_   =	torch.mean(torch.abs(_UpperCamelCase							)							)

									if torch_device in ["mps"]:
															assert abs(result_sum.item() - 176.66974135742188							) < 1e-2
															assert abs(result_mean.item() - 0.23003872730981811							) < 1e-2
									elif torch_device in ["cuda"]:
															assert abs(result_sum.item() - 177.63653564453125							) < 1e-2
															assert abs(result_mean.item() - 0.23003872730981811							) < 1e-2
									else:
															assert abs(result_sum.item() - 170.3135223388672							) < 1e-2
															assert abs(result_mean.item() - 0.23003872730981811							) < 1e-2


from typing import TYPE_CHECKING

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


lowerCAmelCase_       	=	{'''configuration_dpt''': ['''DPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DPTConfig''']}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase_       	=	['''DPTFeatureExtractor''']
					lowerCAmelCase_       	=	['''DPTImageProcessor''']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase_       	=	[
					    '''DPT_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''DPTForDepthEstimation''',
					    '''DPTForSemanticSegmentation''',
					    '''DPTModel''',
					    '''DPTPreTrainedModel''',
					]


if TYPE_CHECKING:
					from .configuration_dpt import DPT_PRETRAINED_CONFIG_ARCHIVE_MAP, DPTConfig

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_dpt import DPTFeatureExtractor
										from .image_processing_dpt import DPTImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_dpt import (
										    DPT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    DPTForDepthEstimation,
										    DPTForSemanticSegmentation,
										    DPTModel,
										    DPTPreTrainedModel,
										)


else:
					import sys

					lowerCAmelCase_       	=	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)