





'''simple docstring'''



import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest






class 							__lowercase       (      lowerCAmelCase__						):







	'''simple docstring'''

	a    :				Optional[Any]											=       (DDPMScheduler,)


	def 						_UpperCAmelCase     (self     ,**_lowerCamelCase )							->							Any:







				'''simple docstring'''





				__lowercase										=							{
				    '''num_train_timesteps''': 1000,
				    '''beta_start''': 0.0_0_0_1,
				    '''beta_end''': 0.0_2,
				    '''beta_schedule''': '''linear''',
				    '''variance_type''': '''fixed_small''',
				    '''clip_sample''': True,
				}

				config.update(**_lowerCamelCase )
				return config


	def 						_UpperCAmelCase     (self )							->							int:







				'''simple docstring'''





				for timesteps in [1, 5, 100, 1000]:
							self.check_over_configs(num_train_timesteps=_lowerCamelCase )


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







				'''simple docstring'''





				for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]     ,[0.0_0_2, 0.0_2, 0.2, 2] ):
							self.check_over_configs(beta_start=_lowerCamelCase     ,beta_end=_lowerCamelCase )


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







				'''simple docstring'''





				for schedule in ["linear", "squaredcos_cap_v2"]:
							self.check_over_configs(beta_schedule=_lowerCamelCase )


	def 						_UpperCAmelCase     (self )							->							Any:







				'''simple docstring'''





				for variance in ["fixed_small", "fixed_large", "other"]:
							self.check_over_configs(variance_type=_lowerCamelCase )


	def 						_UpperCAmelCase     (self )							->							Dict:







				'''simple docstring'''





				for clip_sample in [True, False]:
							self.check_over_configs(clip_sample=_lowerCamelCase )


	def 						_UpperCAmelCase     (self )							->							Any:







				'''simple docstring'''





				self.check_over_configs(thresholding=_lowerCamelCase )
				for threshold in [0.5, 1.0, 2.0]:
							for prediction_type in ["epsilon", "sample", "v_prediction"]:
										self.check_over_configs(
										    thresholding=_lowerCamelCase     ,prediction_type=_lowerCamelCase     ,sample_max_value=_lowerCamelCase     ,)


	def 						_UpperCAmelCase     (self )							->							Any:







				'''simple docstring'''





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


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







				'''simple docstring'''





				for t in [0, 500, 999]:
							self.check_over_forward(time_step=_lowerCamelCase )


	def 						_UpperCAmelCase     (self )							->							Dict:







				'''simple docstring'''





				__lowercase										=							self.scheduler_classes[0]
				__lowercase										=							self.get_scheduler_config()
				__lowercase										=							scheduler_class(**_lowerCamelCase )

				assert torch.sum(torch.abs(scheduler._get_variance(0 ) - 0.0 ) ) < 1E-5
				assert torch.sum(torch.abs(scheduler._get_variance(487 ) - 0.0_0_9_7_9 ) ) < 1E-5
				assert torch.sum(torch.abs(scheduler._get_variance(999 ) - 0.0_2 ) ) < 1E-5


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







				'''simple docstring'''





				__lowercase										=							self.scheduler_classes[0]
				__lowercase										=							self.get_scheduler_config()
				__lowercase										=							scheduler_class(**_lowerCamelCase )

				__lowercase										=							len(_lowerCamelCase )

				__lowercase										=							self.dummy_model()
				__lowercase										=							self.dummy_sample_deter
				__lowercase										=							torch.manual_seed(0 )

				for t in reversed(range(_lowerCamelCase ) ):
							# 1. predict noise residual
							__lowercase										=							model(_lowerCamelCase     ,_lowerCamelCase )

							# 2. predict previous mean of sample x_t-1
							__lowercase										=							scheduler.step(_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase     ,generator=_lowerCamelCase ).prev_sample

							# if t > 0:
							#     noise = self.dummy_sample_deter
							#     variance = scheduler.get_variance(t) ** (0.5) * noise
							#
							# sample = pred_prev_sample + variance
							__lowercase										=							pred_prev_sample

				__lowercase										=							torch.sum(torch.abs(_lowerCamelCase ) )
				__lowercase										=							torch.mean(torch.abs(_lowerCamelCase ) )

				assert abs(result_sum.item() - 258.9606 ) < 1E-2
				assert abs(result_mean.item() - 0.3_3_7_2 ) < 1E-3


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







				'''simple docstring'''





				__lowercase										=							self.scheduler_classes[0]
				__lowercase										=							self.get_scheduler_config(prediction_type='''v_prediction''' )
				__lowercase										=							scheduler_class(**_lowerCamelCase )

				__lowercase										=							len(_lowerCamelCase )

				__lowercase										=							self.dummy_model()
				__lowercase										=							self.dummy_sample_deter
				__lowercase										=							torch.manual_seed(0 )

				for t in reversed(range(_lowerCamelCase ) ):
							# 1. predict noise residual
							__lowercase										=							model(_lowerCamelCase     ,_lowerCamelCase )

							# 2. predict previous mean of sample x_t-1
							__lowercase										=							scheduler.step(_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase     ,generator=_lowerCamelCase ).prev_sample

							# if t > 0:
							#     noise = self.dummy_sample_deter
							#     variance = scheduler.get_variance(t) ** (0.5) * noise
							#
							# sample = pred_prev_sample + variance
							__lowercase										=							pred_prev_sample

				__lowercase										=							torch.sum(torch.abs(_lowerCamelCase ) )
				__lowercase										=							torch.mean(torch.abs(_lowerCamelCase ) )

				assert abs(result_sum.item() - 202.0296 ) < 1E-2
				assert abs(result_mean.item() - 0.2_6_3_1 ) < 1E-3


	def 						_UpperCAmelCase     (self )							->							str:







				'''simple docstring'''





				__lowercase										=							self.scheduler_classes[0]
				__lowercase										=							self.get_scheduler_config()
				__lowercase										=							scheduler_class(**_lowerCamelCase )

				__lowercase										=							[100, 87, 50, 1, 0]

				scheduler.set_timesteps(timesteps=_lowerCamelCase )

				__lowercase										=							scheduler.timesteps

				for i, timestep in enumerate(_lowerCamelCase ):
							if i == len(_lowerCamelCase ) - 1:
										__lowercase										=							-1
							else:
										__lowercase										=							timesteps[i + 1]

							__lowercase										=							scheduler.previous_timestep(_lowerCamelCase )
							__lowercase										=							prev_t.item()

							self.assertEqual(_lowerCamelCase     ,_lowerCamelCase )


	def 						_UpperCAmelCase     (self )							->							str:







				'''simple docstring'''





				__lowercase										=							self.scheduler_classes[0]
				__lowercase										=							self.get_scheduler_config()
				__lowercase										=							scheduler_class(**_lowerCamelCase )

				__lowercase										=							[100, 87, 50, 51, 0]

				with self.assertRaises(_lowerCamelCase     ,msg='''`custom_timesteps` must be in descending order.''' ):
							scheduler.set_timesteps(timesteps=_lowerCamelCase )


	def 						_UpperCAmelCase     (self )							->							List[Any]:







				'''simple docstring'''





				__lowercase										=							self.scheduler_classes[0]
				__lowercase										=							self.get_scheduler_config()
				__lowercase										=							scheduler_class(**_lowerCamelCase )

				__lowercase										=							[100, 87, 50, 1, 0]
				__lowercase										=							len(_lowerCamelCase )

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


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







				'''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


_SCREAMING_SNAKE_CASE					=    {'''configuration_van''': ['''VAN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''VanConfig''']}


try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_SCREAMING_SNAKE_CASE					=    [
	    '''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

	_SCREAMING_SNAKE_CASE					=    _LazyModule(__name__, globals()['''__file__'''], _import_structure)





