





import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest

class    __A  (    a     ):
				__A  =					(LMSDiscreteScheduler,)
				__A  =					10


				def _snake_case							(		self ,			**UpperCAmelCase_				):
								lowerCamelCase									={
								    """num_train_timesteps""": 1100,
								    """beta_start""": 0.0_0_0_1,
								    """beta_end""": 0.0_2,
								    """beta_schedule""": """linear""",
								}

								config.update(**UpperCAmelCase_				)
								return config


				def _snake_case							(		self				):
								for timesteps in [10, 50, 100, 1000]:
												self.check_over_configs(num_train_timesteps=UpperCAmelCase_				)


				def _snake_case							(		self				):
								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=UpperCAmelCase_ ,			beta_end=UpperCAmelCase_				)


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


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


				def _snake_case							(		self				):
								for t in [0, 500, 800]:
												self.check_over_forward(time_step=UpperCAmelCase_				)


				def _snake_case							(		self				):
								lowerCamelCase									=self.scheduler_classes[0]
								lowerCamelCase									=self.get_scheduler_config()
								lowerCamelCase									=scheduler_class(**UpperCAmelCase_				)

								scheduler.set_timesteps(self.num_inference_steps				)

								lowerCamelCase									=self.dummy_model()
								lowerCamelCase									=self.dummy_sample_deter * scheduler.init_noise_sigma

								for i, t in enumerate(scheduler.timesteps				):
												lowerCamelCase									=scheduler.scale_model_input(UpperCAmelCase_ ,			UpperCAmelCase_				)

												lowerCamelCase									=model(UpperCAmelCase_ ,			UpperCAmelCase_				)

												lowerCamelCase									=scheduler.step(UpperCAmelCase_ ,			UpperCAmelCase_ ,			UpperCAmelCase_				)
												lowerCamelCase									=output.prev_sample

								lowerCamelCase									=torch.sum(torch.abs(UpperCAmelCase_				)				)
								lowerCamelCase									=torch.mean(torch.abs(UpperCAmelCase_				)				)

								assert abs(result_sum.item() - 1_0_0_6.3_8_8				) < 1E-2
								assert abs(result_mean.item() - 1.3_1				) < 1E-3


				def _snake_case							(		self				):
								lowerCamelCase									=self.scheduler_classes[0]
								lowerCamelCase									=self.get_scheduler_config(prediction_type="""v_prediction"""				)
								lowerCamelCase									=scheduler_class(**UpperCAmelCase_				)

								scheduler.set_timesteps(self.num_inference_steps				)

								lowerCamelCase									=self.dummy_model()
								lowerCamelCase									=self.dummy_sample_deter * scheduler.init_noise_sigma

								for i, t in enumerate(scheduler.timesteps				):
												lowerCamelCase									=scheduler.scale_model_input(UpperCAmelCase_ ,			UpperCAmelCase_				)

												lowerCamelCase									=model(UpperCAmelCase_ ,			UpperCAmelCase_				)

												lowerCamelCase									=scheduler.step(UpperCAmelCase_ ,			UpperCAmelCase_ ,			UpperCAmelCase_				)
												lowerCamelCase									=output.prev_sample

								lowerCamelCase									=torch.sum(torch.abs(UpperCAmelCase_				)				)
								lowerCamelCase									=torch.mean(torch.abs(UpperCAmelCase_				)				)

								assert abs(result_sum.item() - 0.0_0_1_7				) < 1E-2
								assert abs(result_mean.item() - 2.2676E-06				) < 1E-3


				def _snake_case							(		self				):
								lowerCamelCase									=self.scheduler_classes[0]
								lowerCamelCase									=self.get_scheduler_config()
								lowerCamelCase									=scheduler_class(**UpperCAmelCase_				)

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

								lowerCamelCase									=self.dummy_model()
								lowerCamelCase									=self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
								lowerCamelCase									=sample.to(UpperCAmelCase_				)

								for i, t in enumerate(scheduler.timesteps				):
												lowerCamelCase									=scheduler.scale_model_input(UpperCAmelCase_ ,			UpperCAmelCase_				)

												lowerCamelCase									=model(UpperCAmelCase_ ,			UpperCAmelCase_				)

												lowerCamelCase									=scheduler.step(UpperCAmelCase_ ,			UpperCAmelCase_ ,			UpperCAmelCase_				)
												lowerCamelCase									=output.prev_sample

								lowerCamelCase									=torch.sum(torch.abs(UpperCAmelCase_				)				)
								lowerCamelCase									=torch.mean(torch.abs(UpperCAmelCase_				)				)

								assert abs(result_sum.item() - 1_0_0_6.3_8_8				) < 1E-2
								assert abs(result_mean.item() - 1.3_1				) < 1E-3


				def _snake_case							(		self				):
								lowerCamelCase									=self.scheduler_classes[0]
								lowerCamelCase									=self.get_scheduler_config()
								lowerCamelCase									=scheduler_class(**UpperCAmelCase_ ,			use_karras_sigmas=UpperCAmelCase_				)

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

								lowerCamelCase									=self.dummy_model()
								lowerCamelCase									=self.dummy_sample_deter.to(UpperCAmelCase_				) * scheduler.init_noise_sigma
								lowerCamelCase									=sample.to(UpperCAmelCase_				)

								for t in scheduler.timesteps:
												lowerCamelCase									=scheduler.scale_model_input(UpperCAmelCase_ ,			UpperCAmelCase_				)

												lowerCamelCase									=model(UpperCAmelCase_ ,			UpperCAmelCase_				)

												lowerCamelCase									=scheduler.step(UpperCAmelCase_ ,			UpperCAmelCase_ ,			UpperCAmelCase_				)
												lowerCamelCase									=output.prev_sample

								lowerCamelCase									=torch.sum(torch.abs(UpperCAmelCase_				)				)
								lowerCamelCase									=torch.mean(torch.abs(UpperCAmelCase_				)				)

								assert abs(result_sum.item() - 3_8_1_2.9_9_2_7				) < 2E-2
								assert abs(result_mean.item() - 4.9_6_4_8				) < 1E-3












from typing import TYPE_CHECKING

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


UpperCAmelCase__    :      Optional[Any]       					={
    '''configuration_donut_swin''': ['''DONUT_SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DonutSwinConfig'''],
    '''processing_donut''': ['''DonutProcessor'''],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__    :      Any       					=[
							    '''DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''DonutSwinModel''',
							    '''DonutSwinPreTrainedModel''',
							]

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__    :      Tuple       					=['''DonutFeatureExtractor''']
							UpperCAmelCase__    :      List[str]       					=['''DonutImageProcessor''']


if TYPE_CHECKING:
							from .configuration_donut_swin import DONUT_SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, DonutSwinConfig
							from .processing_donut import DonutProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_donut_swin import (
														    DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
														    DonutSwinModel,
														    DonutSwinPreTrainedModel,
														)

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_donut import DonutFeatureExtractor
														from .image_processing_donut import DonutImageProcessor

else:
							import sys

							UpperCAmelCase__    :      int       					=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





