
'''simple docstring'''



import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest





class       A				(							__UpperCAmelCase					):
			lowerCamelCase				:				Tuple									=	(CMStochasticIterativeScheduler,)
			lowerCamelCase				:				Optional[int]									=	10



			def A__			(       self							,  **lowerCamelCase__							)      ->		List[Any]:




									'''simple docstring'''



									lowercase__							=							{
									    'num_train_timesteps': 201,
									    'sigma_min': 0.0_02,
									    'sigma_max': 80.0,
									}

									config.update(**lowerCamelCase__							)
									return config



			def A__			(       self							)      ->		List[str]:




									'''simple docstring'''



									lowercase__							=							10

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

									scheduler.set_timesteps(lowerCamelCase__							)

									lowercase__							=							scheduler.timesteps[0]
									lowercase__							=							scheduler.timesteps[1]

									lowercase__							=							self.dummy_sample
									lowercase__							=							0.1 * sample

									lowercase__							=							scheduler.step(lowerCamelCase__							,  lowerCamelCase__							,  lowerCamelCase__							).prev_sample
									lowercase__							=							scheduler.step(lowerCamelCase__							,  lowerCamelCase__							,  lowerCamelCase__							).prev_sample

									self.assertEqual(output_a.shape							,  sample.shape							)
									self.assertEqual(output_a.shape							,  output_a.shape							)



			def A__			(       self							)      ->		int:




									'''simple docstring'''



									for timesteps in [10, 50, 100, 1_000]:
															self.check_over_configs(num_train_timesteps=lowerCamelCase__							)



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




									'''simple docstring'''



									for clip_denoised in [True, False]:
															self.check_over_configs(clip_denoised=lowerCamelCase__							)



			def A__			(       self							)      ->		str:




									'''simple docstring'''



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

									lowercase__							=							1
									scheduler.set_timesteps(lowerCamelCase__							)
									lowercase__							=							scheduler.timesteps

									lowercase__							=							torch.manual_seed(0							)

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

									for i, t in enumerate(lowerCamelCase__							):
															# 1. scale model input
															lowercase__							=							scheduler.scale_model_input(lowerCamelCase__							,  lowerCamelCase__							)

															# 2. predict noise residual
															lowercase__							=							model(lowerCamelCase__							,  lowerCamelCase__							)

															# 3. predict previous sample x_t-1
															lowercase__							=							scheduler.step(lowerCamelCase__							,  lowerCamelCase__							,  lowerCamelCase__							,  generator=lowerCamelCase__							).prev_sample

															lowercase__							=							pred_prev_sample

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

									assert abs(result_sum.item() - 192.7_614							) < 1e-2
									assert abs(result_mean.item() - 0.25_10							) < 1e-3



			def A__			(       self							)      ->		Optional[Any]:




									'''simple docstring'''



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

									lowercase__							=							[106, 0]
									scheduler.set_timesteps(timesteps=lowerCamelCase__							)
									lowercase__							=							scheduler.timesteps

									lowercase__							=							torch.manual_seed(0							)

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

									for t in timesteps:
															# 1. scale model input
															lowercase__							=							scheduler.scale_model_input(lowerCamelCase__							,  lowerCamelCase__							)

															# 2. predict noise residual
															lowercase__							=							model(lowerCamelCase__							,  lowerCamelCase__							)

															# 3. predict previous sample x_t-1
															lowercase__							=							scheduler.step(lowerCamelCase__							,  lowerCamelCase__							,  lowerCamelCase__							,  generator=lowerCamelCase__							).prev_sample

															lowercase__							=							pred_prev_sample

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

									assert abs(result_sum.item() - 347.6_357							) < 1e-2
									assert abs(result_mean.item() - 0.45_27							) < 1e-3



			def A__			(       self							)      ->		Tuple:




									'''simple docstring'''



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

									lowercase__							=							[39, 30, 12, 15, 0]

									with self.assertRaises(lowerCamelCase__							,  msg="""`timesteps` must be in descending order."""							):
															scheduler.set_timesteps(timesteps=lowerCamelCase__							)



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




									'''simple docstring'''



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

									lowercase__							=							[39, 30, 12, 1, 0]
									lowercase__							=							len(lowerCamelCase__							)

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



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




									'''simple docstring'''



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

									lowercase__							=							[scheduler.config.num_train_timesteps]

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











'''simple docstring'''
from typing import TYPE_CHECKING

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


snake_case_					:      List[Any]    			=    {'configuration_van': ['VAN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'VanConfig']}


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_					:      str    			=    [
					    'VAN_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'VanForImageClassification',
					    'VanModel',
					    'VanPreTrainedModel',
					]

if TYPE_CHECKING:
					from .configuration_van import VAN_PRETRAINED_CONFIG_ARCHIVE_MAP, VanConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_van import (
										    VAN_PRETRAINED_MODEL_ARCHIVE_LIST,
										    VanForImageClassification,
										    VanModel,
										    VanPreTrainedModel,
										)

else:
					import sys

					snake_case_					:      List[Any]    			=    _LazyModule(__name__, globals()['__file__'], _import_structure)


