




"""simple docstring"""






import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest






class lowerCAmelCase__	(     lowercase						):






			'''simple docstring'''



			lowerCamelCase__      =			(DDPMScheduler,)
			def 					A_    (  self  ,      **lowercase  ):
							_lowerCamelCase     :			Dict    =    {
							    'num_train_timesteps': 1000,
							    'beta_start': 0.00_01,
							    'beta_end': 0.02,
							    'beta_schedule': 'linear',
							    'variance_type': 'fixed_small',
							    'clip_sample': True,
							}

							config.update(**lowercase  )
							return config
			def 					A_    (  self  ):
							for timesteps in [1, 5, 100, 1000]:
											self.check_over_configs(num_train_timesteps=lowercase  )
			def 					A_    (  self  ):
							for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]  ,      [0.0_02, 0.02, 0.2, 2]  ):
											self.check_over_configs(beta_start=lowercase  ,      beta_end=lowercase  )
			def 					A_    (  self  ):
							for schedule in ["linear", "squaredcos_cap_v2"]:
											self.check_over_configs(beta_schedule=lowercase  )
			def 					A_    (  self  ):
							for variance in ["fixed_small", "fixed_large", "other"]:
											self.check_over_configs(variance_type=lowercase  )
			def 					A_    (  self  ):
							for clip_sample in [True, False]:
											self.check_over_configs(clip_sample=lowercase  )
			def 					A_    (  self  ):
							self.check_over_configs(thresholding=lowercase  )
							for threshold in [0.5, 1.0, 2.0]:
											for prediction_type in ["epsilon", "sample", "v_prediction"]:
															self.check_over_configs(
															    thresholding=lowercase  ,      prediction_type=lowercase  ,      sample_max_value=lowercase  ,      )
			def 					A_    (  self  ):
							for prediction_type in ["epsilon", "sample", "v_prediction"]:
											self.check_over_configs(prediction_type=lowercase  )
			def 					A_    (  self  ):
							for t in [0, 500, 999]:
											self.check_over_forward(time_step=lowercase  )
			def 					A_    (  self  ):
							_lowerCamelCase     :			Union[str, Any]    =    self.scheduler_classes[0]
							_lowerCamelCase     :			int    =    self.get_scheduler_config()
							_lowerCamelCase     :			List[Any]    =    scheduler_class(**lowercase  )

							assert torch.sum(torch.abs(scheduler._get_variance(0  ) - 0.0  )  ) < 1E-5
							assert torch.sum(torch.abs(scheduler._get_variance(487  ) - 0.0_09_79  )  ) < 1E-5
							assert torch.sum(torch.abs(scheduler._get_variance(999  ) - 0.02  )  ) < 1E-5
			def 					A_    (  self  ):
							_lowerCamelCase     :			Optional[int]    =    self.scheduler_classes[0]
							_lowerCamelCase     :			int    =    self.get_scheduler_config()
							_lowerCamelCase     :			Optional[int]    =    scheduler_class(**lowercase  )

							_lowerCamelCase     :			Tuple    =    len(lowercase  )

							_lowerCamelCase     :			int    =    self.dummy_model()
							_lowerCamelCase     :			Tuple    =    self.dummy_sample_deter
							_lowerCamelCase     :			Tuple    =    torch.manual_seed(0  )

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

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

											# if t > 0:
											#     noise = self.dummy_sample_deter
											#     variance = scheduler.get_variance(t) ** (0.5) * noise
											#
											# sample = pred_prev_sample + variance
											_lowerCamelCase     :			List[str]    =    pred_prev_sample

							_lowerCamelCase     :			List[Any]    =    torch.sum(torch.abs(lowercase  )  )
							_lowerCamelCase     :			Dict    =    torch.mean(torch.abs(lowercase  )  )

							assert abs(result_sum.item() - 2_58.96_06  ) < 1E-2
							assert abs(result_mean.item() - 0.33_72  ) < 1E-3
			def 					A_    (  self  ):
							_lowerCamelCase     :			str    =    self.scheduler_classes[0]
							_lowerCamelCase     :			int    =    self.get_scheduler_config(prediction_type='v_prediction'  )
							_lowerCamelCase     :			List[Any]    =    scheduler_class(**lowercase  )

							_lowerCamelCase     :			List[Any]    =    len(lowercase  )

							_lowerCamelCase     :			int    =    self.dummy_model()
							_lowerCamelCase     :			Any    =    self.dummy_sample_deter
							_lowerCamelCase     :			Any    =    torch.manual_seed(0  )

							for t in reversed(range(lowercase  )  ):
											# 1. predict noise residual
											_lowerCamelCase     :			Union[str, Any]    =    model(lowercase  ,      lowercase  )

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

											# if t > 0:
											#     noise = self.dummy_sample_deter
											#     variance = scheduler.get_variance(t) ** (0.5) * noise
											#
											# sample = pred_prev_sample + variance
											_lowerCamelCase     :			Optional[int]    =    pred_prev_sample

							_lowerCamelCase     :			List[str]    =    torch.sum(torch.abs(lowercase  )  )
							_lowerCamelCase     :			Union[str, Any]    =    torch.mean(torch.abs(lowercase  )  )

							assert abs(result_sum.item() - 2_02.02_96  ) < 1E-2
							assert abs(result_mean.item() - 0.26_31  ) < 1E-3
			def 					A_    (  self  ):
							_lowerCamelCase     :			List[Any]    =    self.scheduler_classes[0]
							_lowerCamelCase     :			List[str]    =    self.get_scheduler_config()
							_lowerCamelCase     :			Any    =    scheduler_class(**lowercase  )

							_lowerCamelCase     :			Dict    =    [100, 87, 50, 1, 0]

							scheduler.set_timesteps(timesteps=lowercase  )

							_lowerCamelCase     :			Any    =    scheduler.timesteps

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

											_lowerCamelCase     :			Optional[int]    =    scheduler.previous_timestep(lowercase  )
											_lowerCamelCase     :			Tuple    =    prev_t.item()

											self.assertEqual(lowercase  ,      lowercase  )
			def 					A_    (  self  ):
							_lowerCamelCase     :			Union[str, Any]    =    self.scheduler_classes[0]
							_lowerCamelCase     :			str    =    self.get_scheduler_config()
							_lowerCamelCase     :			Tuple    =    scheduler_class(**lowercase  )

							_lowerCamelCase     :			List[Any]    =    [100, 87, 50, 51, 0]

							with self.assertRaises(lowercase  ,      msg='`custom_timesteps` must be in descending order.'  ):
											scheduler.set_timesteps(timesteps=lowercase  )
			def 					A_    (  self  ):
							_lowerCamelCase     :			str    =    self.scheduler_classes[0]
							_lowerCamelCase     :			List[Any]    =    self.get_scheduler_config()
							_lowerCamelCase     :			int    =    scheduler_class(**lowercase  )

							_lowerCamelCase     :			int    =    [100, 87, 50, 1, 0]
							_lowerCamelCase     :			Optional[int]    =    len(lowercase  )

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






			def 					A_    (  self  ):
							_lowerCamelCase     :			Optional[int]    =    self.scheduler_classes[0]
							_lowerCamelCase     :			int    =    self.get_scheduler_config()
							_lowerCamelCase     :			List[Any]    =    scheduler_class(**lowercase  )

							_lowerCamelCase     :			int    =    [scheduler.config.num_train_timesteps]

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





"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase__     						=						{"""configuration_vit_msn""": ["""VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ViTMSNConfig"""]}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowercase__     						=						[
			    """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

			lowercase__     						=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)