


"""simple docstring"""


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      (  _UpperCamelCase):
		__UpperCamelCase           =			(DPMSolverSDEScheduler,)
		__UpperCamelCase           =			10







		def  a_ (					self      :       int    ,   **a__      :       Optional[int]		)       ->						Dict:





						'''simple docstring'''







						_A					  =			{
						    "num_train_timesteps": 11_00,
						    "beta_start": 0.0_0_0_1,
						    "beta_end": 0.0_2,
						    "beta_schedule": "linear",
						    "noise_sampler_seed": 0,
						}

						config.update(**a__		)
						return config







		def  a_ (					self      :       Dict		)       ->						Dict:





						'''simple docstring'''







						for timesteps in [10, 50, 1_00, 10_00]:
										self.check_over_configs(num_train_timesteps=a__		)







		def  a_ (					self      :       Dict		)       ->						Tuple:





						'''simple docstring'''







						for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]    ,   [0.0_0_0_2, 0.0_0_2, 0.0_2]		):
										self.check_over_configs(beta_start=a__    ,   beta_end=a__		)







		def  a_ (					self      :       Optional[Any]		)       ->						List[Any]:





						'''simple docstring'''







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







		def  a_ (					self      :       Optional[Any]		)       ->						List[Any]:





						'''simple docstring'''







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







		def  a_ (					self      :       Union[str, Any]		)       ->						List[str]:





						'''simple docstring'''







						_A					  =			self.scheduler_classes[0]
						_A					  =			self.get_scheduler_config()
						_A					  =			scheduler_class(**a__		)

						scheduler.set_timesteps(self.num_inference_steps		)

						_A					  =			self.dummy_model()
						_A					  =			self.dummy_sample_deter * scheduler.init_noise_sigma
						_A					  =			sample.to(a__		)

						for i, t in enumerate(scheduler.timesteps		):
										_A					  =			scheduler.scale_model_input(a__    ,   a__		)

										_A					  =			model(a__    ,   a__		)

										_A					  =			scheduler.step(a__    ,   a__    ,   a__		)
										_A					  =			output.prev_sample

						_A					  =			torch.sum(torch.abs(a__		)		)
						_A					  =			torch.mean(torch.abs(a__		)		)

						if torch_device in ["mps"]:
										assert abs(result_sum.item() - 1_67.47_82_10_44_92_18_75		) < 1E-2
										assert abs(result_mean.item() - 0.2_1_7_8_7_0_5_9_6_4_5_6_5_2_7_7		) < 1E-3
						elif torch_device in ["cuda"]:
										assert abs(result_sum.item() - 1_71.59_35_21_11_81_64_06		) < 1E-2
										assert abs(result_mean.item() - 0.2_2_3_4_2_9_0_6_8_9_2_2_9_9_6_5_2		) < 1E-3
						else:
										assert abs(result_sum.item() - 1_62.52_38_34_22_85_15_62		) < 1E-2
										assert abs(result_mean.item() - 0.2_1_1_6_1_9_5_7_0_8_5_1_3_2_6		) < 1E-3







		def  a_ (					self      :       Optional[int]		)       ->						str:





						'''simple docstring'''







						_A					  =			self.scheduler_classes[0]
						_A					  =			self.get_scheduler_config(prediction_type="v_prediction"		)
						_A					  =			scheduler_class(**a__		)

						scheduler.set_timesteps(self.num_inference_steps		)

						_A					  =			self.dummy_model()
						_A					  =			self.dummy_sample_deter * scheduler.init_noise_sigma
						_A					  =			sample.to(a__		)

						for i, t in enumerate(scheduler.timesteps		):
										_A					  =			scheduler.scale_model_input(a__    ,   a__		)

										_A					  =			model(a__    ,   a__		)

										_A					  =			scheduler.step(a__    ,   a__    ,   a__		)
										_A					  =			output.prev_sample

						_A					  =			torch.sum(torch.abs(a__		)		)
						_A					  =			torch.mean(torch.abs(a__		)		)

						if torch_device in ["mps"]:
										assert abs(result_sum.item() - 1_24.77_14_92_00_43_94_53		) < 1E-2
										assert abs(result_mean.item() - 0.1_6_2_2_6_2_8_9_0_1_4_8_1_6_2_8_4		) < 1E-3
						elif torch_device in ["cuda"]:
										assert abs(result_sum.item() - 1_28.1_66_33_60_59_57_03		) < 1E-2
										assert abs(result_mean.item() - 0.1_6_6_8_8_3_2_6_0_0_1_1_6_7_2_9_7		) < 1E-3
						else:
										assert abs(result_sum.item() - 1_19.8_48_75_48_82_81_25		) < 1E-2
										assert abs(result_mean.item() - 0.1_5_6_0_5_3_0_6_6_2_5_3_6_6_2_1		) < 1E-3







		def  a_ (					self      :       List[str]		)       ->						Any:





						'''simple docstring'''







						_A					  =			self.scheduler_classes[0]
						_A					  =			self.get_scheduler_config()
						_A					  =			scheduler_class(**a__		)

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

						_A					  =			self.dummy_model()
						_A					  =			self.dummy_sample_deter.to(a__		) * scheduler.init_noise_sigma

						for t in scheduler.timesteps:
										_A					  =			scheduler.scale_model_input(a__    ,   a__		)

										_A					  =			model(a__    ,   a__		)

										_A					  =			scheduler.step(a__    ,   a__    ,   a__		)
										_A					  =			output.prev_sample

						_A					  =			torch.sum(torch.abs(a__		)		)
						_A					  =			torch.mean(torch.abs(a__		)		)

						if torch_device in ["mps"]:
										assert abs(result_sum.item() - 1_67.46_95_73_97_46_09_38		) < 1E-2
										assert abs(result_mean.item() - 0.2_1_8_0_5_9_3_4_6_0_7_9_8_2_6_3_5		) < 1E-3
						elif torch_device in ["cuda"]:
										assert abs(result_sum.item() - 1_71.59_35_36_37_69_53_12		) < 1E-2
										assert abs(result_mean.item() - 0.2_2_3_4_2_9_0_8_3_8_2_4_1_5_7_7_1		) < 1E-3
						else:
										assert abs(result_sum.item() - 1_62.52_38_34_22_85_15_62		) < 1E-2
										assert abs(result_mean.item() - 0.2_1_1_6_1_9_5_7_0_8_5_1_3_2_6		) < 1E-3







		def  a_ (					self      :       int		)       ->						Optional[int]:





						'''simple docstring'''







						_A					  =			self.scheduler_classes[0]
						_A					  =			self.get_scheduler_config()
						_A					  =			scheduler_class(**a__    ,   use_karras_sigmas=a__		)

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

						_A					  =			self.dummy_model()
						_A					  =			self.dummy_sample_deter.to(a__		) * scheduler.init_noise_sigma
						_A					  =			sample.to(a__		)

						for t in scheduler.timesteps:
										_A					  =			scheduler.scale_model_input(a__    ,   a__		)

										_A					  =			model(a__    ,   a__		)

										_A					  =			scheduler.step(a__    ,   a__    ,   a__		)
										_A					  =			output.prev_sample

						_A					  =			torch.sum(torch.abs(a__		)		)
						_A					  =			torch.mean(torch.abs(a__		)		)

						if torch_device in ["mps"]:
										assert abs(result_sum.item() - 1_76.66_97_41_35_74_21_88		) < 1E-2
										assert abs(result_mean.item() - 0.2_3_0_0_3_8_7_2_7_3_0_9_8_1_8_1_1		) < 1E-2
						elif torch_device in ["cuda"]:
										assert abs(result_sum.item() - 1_77.63_65_35_64_45_31_25		) < 1E-2
										assert abs(result_mean.item() - 0.2_3_0_0_3_8_7_2_7_3_0_9_8_1_8_1_1		) < 1E-2
						else:
										assert abs(result_sum.item() - 1_70.3_13_52_23_38_86_72		) < 1E-2
										assert abs(result_mean.item() - 0.2_3_0_0_3_8_7_2_7_3_0_9_8_1_8_1_1		) < 1E-2



"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a_       							=    {
    "configuration_upernet": ["UperNetConfig"],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a_       							=    [
		    "UperNetForSemanticSegmentation",
		    "UperNetPreTrainedModel",
		]


if TYPE_CHECKING:
		from .configuration_upernet import UperNetConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_upernet import UperNetForSemanticSegmentation, UperNetPreTrainedModel


else:
		import sys

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