
'''simple docstring'''







import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class 		__UpperCAmelCase   (      __lowerCAmelCase		):
							__lowercase   	= (EulerDiscreteScheduler,)
							__lowercase   	= 10


							def 				lowerCamelCase       ( self					,     **lowerCAmelCase_						):





								"""simple docstring"""






								_snake_case     		=	{
								    '''num_train_timesteps''': 11_00,
								    '''beta_start''': 0.0001,
								    '''beta_end''': 0.02,
								    '''beta_schedule''': '''linear''',
								}

								config.update(**lowerCamelCase__						)
								return config


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






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


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								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=lowerCamelCase__					,     beta_end=lowerCamelCase__						)


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






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


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






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


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	self.scheduler_classes[0]
								_snake_case     		=	self.get_scheduler_config()
								_snake_case     		=	scheduler_class(**lowerCamelCase__						)

								scheduler.set_timesteps(self.num_inference_steps						)

								_snake_case     		=	torch.manual_seed(0						)

								_snake_case     		=	self.dummy_model()
								_snake_case     		=	self.dummy_sample_deter * scheduler.init_noise_sigma
								_snake_case     		=	sample.to(lowerCamelCase__						)

								for i, t in enumerate(scheduler.timesteps						):
									_snake_case     		=	scheduler.scale_model_input(lowerCamelCase__					,     lowerCamelCase__						)

									_snake_case     		=	model(lowerCamelCase__					,     lowerCamelCase__						)

									_snake_case     		=	scheduler.step(lowerCamelCase__					,     lowerCamelCase__					,     lowerCamelCase__					,     generator=lowerCamelCase__						)
									_snake_case     		=	output.prev_sample

								_snake_case     		=	torch.sum(torch.abs(lowerCamelCase__						)						)
								_snake_case     		=	torch.mean(torch.abs(lowerCamelCase__						)						)

								assert abs(result_sum.item() - 10.0807						) < 1E-2
								assert abs(result_mean.item() - 0.0131						) < 1E-3


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	self.scheduler_classes[0]
								_snake_case     		=	self.get_scheduler_config(prediction_type='v_prediction'						)
								_snake_case     		=	scheduler_class(**lowerCamelCase__						)

								scheduler.set_timesteps(self.num_inference_steps						)

								_snake_case     		=	torch.manual_seed(0						)

								_snake_case     		=	self.dummy_model()
								_snake_case     		=	self.dummy_sample_deter * scheduler.init_noise_sigma
								_snake_case     		=	sample.to(lowerCamelCase__						)

								for i, t in enumerate(scheduler.timesteps						):
									_snake_case     		=	scheduler.scale_model_input(lowerCamelCase__					,     lowerCamelCase__						)

									_snake_case     		=	model(lowerCamelCase__					,     lowerCamelCase__						)

									_snake_case     		=	scheduler.step(lowerCamelCase__					,     lowerCamelCase__					,     lowerCamelCase__					,     generator=lowerCamelCase__						)
									_snake_case     		=	output.prev_sample

								_snake_case     		=	torch.sum(torch.abs(lowerCamelCase__						)						)
								_snake_case     		=	torch.mean(torch.abs(lowerCamelCase__						)						)

								assert abs(result_sum.item() - 0.0002						) < 1E-2
								assert abs(result_mean.item() - 2.2_676E-06						) < 1E-3


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	self.scheduler_classes[0]
								_snake_case     		=	self.get_scheduler_config()
								_snake_case     		=	scheduler_class(**lowerCamelCase__						)

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

								_snake_case     		=	torch.manual_seed(0						)

								_snake_case     		=	self.dummy_model()
								_snake_case     		=	self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
								_snake_case     		=	sample.to(lowerCamelCase__						)

								for t in scheduler.timesteps:
									_snake_case     		=	scheduler.scale_model_input(lowerCamelCase__					,     lowerCamelCase__						)

									_snake_case     		=	model(lowerCamelCase__					,     lowerCamelCase__						)

									_snake_case     		=	scheduler.step(lowerCamelCase__					,     lowerCamelCase__					,     lowerCamelCase__					,     generator=lowerCamelCase__						)
									_snake_case     		=	output.prev_sample

								_snake_case     		=	torch.sum(torch.abs(lowerCamelCase__						)						)
								_snake_case     		=	torch.mean(torch.abs(lowerCamelCase__						)						)

								assert abs(result_sum.item() - 10.0807						) < 1E-2
								assert abs(result_mean.item() - 0.0131						) < 1E-3




							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	self.scheduler_classes[0]
								_snake_case     		=	self.get_scheduler_config()
								_snake_case     		=	scheduler_class(**lowerCamelCase__					,     use_karras_sigmas=lowerCamelCase__						)

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

								_snake_case     		=	torch.manual_seed(0						)

								_snake_case     		=	self.dummy_model()
								_snake_case     		=	self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
								_snake_case     		=	sample.to(lowerCamelCase__						)

								for t in scheduler.timesteps:
									_snake_case     		=	scheduler.scale_model_input(lowerCamelCase__					,     lowerCamelCase__						)

									_snake_case     		=	model(lowerCamelCase__					,     lowerCamelCase__						)

									_snake_case     		=	scheduler.step(lowerCamelCase__					,     lowerCamelCase__					,     lowerCamelCase__					,     generator=lowerCamelCase__						)
									_snake_case     		=	output.prev_sample

								_snake_case     		=	torch.sum(torch.abs(lowerCamelCase__						)						)
								_snake_case     		=	torch.mean(torch.abs(lowerCamelCase__						)						)

								assert abs(result_sum.item() - 124.52299499511719						) < 1E-2
								assert abs(result_mean.item() - 0.16213932633399963						) < 1E-3



'''simple docstring'''







from typing import TYPE_CHECKING

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


lowercase					:							int   =      {
    "configuration_mask2former": [
        "MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "Mask2FormerConfig",
    ],
}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase					:							Optional[int]   =      ["Mask2FormerImageProcessor"]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase					:							Optional[Any]   =      [
					    "MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "Mask2FormerForUniversalSegmentation",
					    "Mask2FormerModel",
					    "Mask2FormerPreTrainedModel",
					]

if TYPE_CHECKING:
					from .configuration_maskaformer import MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskaFormerConfig

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .image_processing_maskaformer import MaskaFormerImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_maskaformer import (
										    MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    MaskaFormerForUniversalSegmentation,
										    MaskaFormerModel,
										    MaskaFormerPreTrainedModel,
										)


else:
					import sys

					lowercase					:							int   =      _LazyModule(__name__, globals()["__file__"], _import_structure)

