






"""simple docstring"""



import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest







class    lowercase__			(							UpperCAmelCase__   ):
			_UpperCAmelCase		:Dict   			= (DDIMParallelScheduler,)
			_UpperCAmelCase		:Tuple   			= (("eta", 0.0), ("num_inference_steps", 50))


			def 	UpperCAmelCase__						(      self     :  str   ,       **snake_case__     :  str					):
										lowerCamelCase_       :							Any									={
										    "num_train_timesteps": 1000,
										    "beta_start": 0.0_001,
										    "beta_end": 0.02,
										    "beta_schedule": "linear",
										    "clip_sample": True,
										}

										config.update(**UpperCAmelCase__					)
										return config


			def 	UpperCAmelCase__						(      self     :  int   ,       **snake_case__     :  List[Any]					):
										lowerCamelCase_       :							Tuple									=self.scheduler_classes[0]
										lowerCamelCase_       :							Optional[int]									=self.get_scheduler_config(**UpperCAmelCase__					)
										lowerCamelCase_       :							int									=scheduler_class(**UpperCAmelCase__					)

										lowerCamelCase_							,						lowerCamelCase_       :							Optional[Any]									=10, 0.0

										lowerCamelCase_       :							List[str]									=self.dummy_model()
										lowerCamelCase_       :							Dict									=self.dummy_sample_deter

										scheduler.set_timesteps(UpperCAmelCase__					)

										for t in scheduler.timesteps:
																	lowerCamelCase_       :							str									=model(UpperCAmelCase__   ,       UpperCAmelCase__					)
																	lowerCamelCase_       :							Optional[int]									=scheduler.step(UpperCAmelCase__   ,       UpperCAmelCase__   ,       UpperCAmelCase__   ,       UpperCAmelCase__					).prev_sample

										return sample


			def 	UpperCAmelCase__						(      self     :  List[str]					):

										for timesteps in [100, 500, 1000]:
																	self.check_over_configs(num_train_timesteps=UpperCAmelCase__					)


			def 	UpperCAmelCase__						(      self     :  Optional[int]					):

										for steps_offset in [0, 1]:
																	self.check_over_configs(steps_offset=UpperCAmelCase__					)

										lowerCamelCase_       :							int									=self.scheduler_classes[0]
										lowerCamelCase_       :							List[Any]									=self.get_scheduler_config(steps_offset=1					)
										lowerCamelCase_       :							Union[str, Any]									=scheduler_class(**UpperCAmelCase__					)
										scheduler.set_timesteps(5					)
										assert torch.equal(scheduler.timesteps   ,       torch.LongTensor([801, 601, 401, 201, 1]					)					)


			def 	UpperCAmelCase__						(      self     :  str					):

										for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1]   ,       [0.002, 0.02, 0.2, 2]					):
																	self.check_over_configs(beta_start=UpperCAmelCase__   ,       beta_end=UpperCAmelCase__					)


			def 	UpperCAmelCase__						(      self     :  Tuple					):

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


			def 	UpperCAmelCase__						(      self     :  int					):

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


			def 	UpperCAmelCase__						(      self     :  List[Any]					):

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


			def 	UpperCAmelCase__						(      self     :  Optional[int]					):

										for timestep_spacing in ["trailing", "leading"]:
																	self.check_over_configs(timestep_spacing=UpperCAmelCase__					)


			def 	UpperCAmelCase__						(      self     :  Optional[Any]					):

										for rescale_betas_zero_snr in [True, False]:
																	self.check_over_configs(rescale_betas_zero_snr=UpperCAmelCase__					)


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


			def 	UpperCAmelCase__						(      self     :  Tuple					):

										for t in [1, 10, 49]:
																	self.check_over_forward(time_step=UpperCAmelCase__					)


			def 	UpperCAmelCase__						(      self     :  Tuple					):

										for t, num_inference_steps in zip([1, 10, 50]   ,       [10, 50, 500]					):
																	self.check_over_forward(time_step=UpperCAmelCase__   ,       num_inference_steps=UpperCAmelCase__					)


			def 	UpperCAmelCase__						(      self     :  Optional[Any]					):

										for t, eta in zip([1, 10, 49]   ,       [0.0, 0.5, 1.0]					):
																	self.check_over_forward(time_step=UpperCAmelCase__   ,       eta=UpperCAmelCase__					)


			def 	UpperCAmelCase__						(      self     :  List[Any]					):
										lowerCamelCase_       :							Optional[Any]									=self.scheduler_classes[0]
										lowerCamelCase_       :							Union[str, Any]									=self.get_scheduler_config()
										lowerCamelCase_       :							Optional[Any]									=scheduler_class(**UpperCAmelCase__					)

										assert torch.sum(torch.abs(scheduler._get_variance(0   ,       0					) - 0.0					)					) < 1E-5
										assert torch.sum(torch.abs(scheduler._get_variance(420   ,       400					) - 0.14_771					)					) < 1E-5
										assert torch.sum(torch.abs(scheduler._get_variance(980   ,       960					) - 0.32_460					)					) < 1E-5
										assert torch.sum(torch.abs(scheduler._get_variance(0   ,       0					) - 0.0					)					) < 1E-5
										assert torch.sum(torch.abs(scheduler._get_variance(487   ,       486					) - 0.00_979					)					) < 1E-5
										assert torch.sum(torch.abs(scheduler._get_variance(999   ,       998					) - 0.02					)					) < 1E-5


			def 	UpperCAmelCase__						(      self     :  Union[str, Any]					):
										lowerCamelCase_       :							str									=self.scheduler_classes[0]
										lowerCamelCase_       :							Optional[Any]									=self.get_scheduler_config()
										lowerCamelCase_       :							List[str]									=scheduler_class(**UpperCAmelCase__					)

										lowerCamelCase_							,						lowerCamelCase_       :							Tuple									=10, 0.0
										scheduler.set_timesteps(UpperCAmelCase__					)

										lowerCamelCase_       :							List[str]									=self.dummy_model()
										lowerCamelCase_       :							List[str]									=self.dummy_sample_deter
										lowerCamelCase_       :							List[Any]									=self.dummy_sample_deter + 0.1
										lowerCamelCase_       :							str									=self.dummy_sample_deter - 0.1

										lowerCamelCase_       :							int									=samplea.shape[0]
										lowerCamelCase_       :							Union[str, Any]									=torch.stack([samplea, samplea, samplea]   ,       dim=0					)
										lowerCamelCase_       :							List[str]									=torch.arange(UpperCAmelCase__					)[0:3, None].repeat(1   ,       UpperCAmelCase__					)

										lowerCamelCase_       :							Dict									=model(samples.flatten(0   ,       1					)   ,       timesteps.flatten(0   ,       1					)					)
										lowerCamelCase_       :							Any									=scheduler.batch_step_no_noise(UpperCAmelCase__   ,       timesteps.flatten(0   ,       1					)   ,       samples.flatten(0   ,       1					)   ,       UpperCAmelCase__					)

										lowerCamelCase_       :							Any									=torch.sum(torch.abs(UpperCAmelCase__					)					)
										lowerCamelCase_       :							Optional[Any]									=torch.mean(torch.abs(UpperCAmelCase__					)					)

										assert abs(result_sum.item() - 1_147.7_904					) < 1E-2
										assert abs(result_mean.item() - 0.4_982					) < 1E-3


			def 	UpperCAmelCase__						(      self     :  List[Any]					):
										lowerCamelCase_       :							List[str]									=self.full_loop()

										lowerCamelCase_       :							str									=torch.sum(torch.abs(UpperCAmelCase__					)					)
										lowerCamelCase_       :							Optional[int]									=torch.mean(torch.abs(UpperCAmelCase__					)					)

										assert abs(result_sum.item() - 172.0_067					) < 1E-2
										assert abs(result_mean.item() - 0.223_967					) < 1E-3


			def 	UpperCAmelCase__						(      self     :  Any					):
										lowerCamelCase_       :							Any									=self.full_loop(prediction_type="v_prediction"					)

										lowerCamelCase_       :							Any									=torch.sum(torch.abs(UpperCAmelCase__					)					)
										lowerCamelCase_       :							Tuple									=torch.mean(torch.abs(UpperCAmelCase__					)					)

										assert abs(result_sum.item() - 52.5_302					) < 1E-2
										assert abs(result_mean.item() - 0.0_684					) < 1E-3


			def 	UpperCAmelCase__						(      self     :  Optional[Any]					):
										lowerCamelCase_       :							List[str]									=self.full_loop(set_alpha_to_one=UpperCAmelCase__   ,       beta_start=0.01					)
										lowerCamelCase_       :							Any									=torch.sum(torch.abs(UpperCAmelCase__					)					)
										lowerCamelCase_       :							int									=torch.mean(torch.abs(UpperCAmelCase__					)					)

										assert abs(result_sum.item() - 149.8_295					) < 1E-2
										assert abs(result_mean.item() - 0.1_951					) < 1E-3


			def 	UpperCAmelCase__						(      self     :  List[Any]					):
										lowerCamelCase_       :							List[str]									=self.full_loop(set_alpha_to_one=UpperCAmelCase__   ,       beta_start=0.01					)
										lowerCamelCase_       :							Optional[Any]									=torch.sum(torch.abs(UpperCAmelCase__					)					)
										lowerCamelCase_       :							List[Any]									=torch.mean(torch.abs(UpperCAmelCase__					)					)

										assert abs(result_sum.item() - 149.0_784					) < 1E-2
										assert abs(result_mean.item() - 0.1_941					) < 1E-3









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowerCamelCase	:		List[str]							       =   {"""configuration_vit_msn""": ["""VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ViTMSNConfig"""]}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCamelCase	:		List[Any]							       =   [
      """VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """ViTMSNModel""",
      """ViTMSNForImageClassification""",
      """ViTMSNPreTrainedModel""",
  ]

if TYPE_CHECKING:
  from .configuration_vit_msn import VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMSNConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_vit_msn import (
        VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST,
        ViTMSNForImageClassification,
        ViTMSNModel,
        ViTMSNPreTrainedModel,
    )

else:
  import sys

  _lowerCamelCase	:		Tuple							       =   _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


