
"""simple docstring"""






import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest

class 					__a     ( __a       ):



							'''simple docstring'''







							_lowerCamelCase					:			Dict          =   (DDPMParallelScheduler,)
							def   SCREAMING_SNAKE_CASE						(     self  ,    **_lowerCamelCase       )			->							int:




										'''simple docstring'''
										__lowercase       =       {
										    "num_train_timesteps": 1_000,
										    "beta_start": 0.0001,
										    "beta_end": 0.02,
										    "beta_schedule": "linear",
										    "variance_type": "fixed_small",
										    "clip_sample": True,
										}

										config.update(**_lowerCamelCase       )
										return config
							def   SCREAMING_SNAKE_CASE						(     self       )			->							List[str]:




										'''simple docstring'''
										for timesteps in [1, 5, 100, 1_000]:
													self.check_over_configs(num_train_timesteps=_lowerCamelCase       )
							def   SCREAMING_SNAKE_CASE						(     self       )			->							int:




										'''simple docstring'''
										for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]  ,    [0.002, 0.02, 0.2, 2]       ):
													self.check_over_configs(beta_start=_lowerCamelCase  ,    beta_end=_lowerCamelCase       )
							def   SCREAMING_SNAKE_CASE						(     self       )			->							Optional[Any]:




										'''simple docstring'''
										for schedule in ["linear", "squaredcos_cap_v2"]:
													self.check_over_configs(beta_schedule=_lowerCamelCase       )
							def   SCREAMING_SNAKE_CASE						(     self       )			->							Optional[Any]:




										'''simple docstring'''
										for variance in ["fixed_small", "fixed_large", "other"]:
													self.check_over_configs(variance_type=_lowerCamelCase       )
							def   SCREAMING_SNAKE_CASE						(     self       )			->							int:




										'''simple docstring'''
										for clip_sample in [True, False]:
													self.check_over_configs(clip_sample=_lowerCamelCase       )
							def   SCREAMING_SNAKE_CASE						(     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   SCREAMING_SNAKE_CASE						(     self       )			->							str:




										'''simple docstring'''
										for prediction_type in ["epsilon", "sample", "v_prediction"]:
													self.check_over_configs(prediction_type=_lowerCamelCase       )
							def   SCREAMING_SNAKE_CASE						(     self       )			->							Any:




										'''simple docstring'''
										for t in [0, 500, 999]:
													self.check_over_forward(time_step=_lowerCamelCase       )
							def   SCREAMING_SNAKE_CASE						(     self       )			->							List[Any]:




										'''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_0979       )       ) < 1e-5
										assert torch.sum(torch.abs(scheduler._get_variance(999       ) - 0.02       )       ) < 1e-5
							def   SCREAMING_SNAKE_CASE						(     self       )			->							str:




										'''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       =       self.dummy_sample_deter + 0.1
										__lowercase       =       self.dummy_sample_deter - 0.1

										__lowercase       =       samplea.shape[0]
										__lowercase       =       torch.stack([samplea, samplea, samplea]  ,    dim=0       )
										__lowercase       =       torch.arange(_lowerCamelCase       )[0:3, None].repeat(1  ,    _lowerCamelCase       )

										__lowercase       =       model(samples.flatten(0  ,    1       )  ,    timesteps.flatten(0  ,    1       )       )
										__lowercase       =       scheduler.batch_step_no_noise(_lowerCamelCase  ,    timesteps.flatten(0  ,    1       )  ,    samples.flatten(0  ,    1       )       )

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

										assert abs(result_sum.item() - 1153.1833       ) < 1e-2
										assert abs(result_mean.item() - 0.5005       ) < 1e-3
							def   SCREAMING_SNAKE_CASE						(     self       )			->							Dict:




										'''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

													__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() - 0.3372       ) < 1e-3
							def   SCREAMING_SNAKE_CASE						(     self       )			->							Dict:




										'''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

													__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() - 0.2631       ) < 1e-3
							def   SCREAMING_SNAKE_CASE						(     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]

										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   SCREAMING_SNAKE_CASE						(     self       )			->							Any:




										'''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   SCREAMING_SNAKE_CASE						(     self       )			->							str:




										'''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   SCREAMING_SNAKE_CASE						(     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_flax_available,
    is_tf_available,
    is_torch_available,
)


_lowercase							=				{'''configuration_vit_mae''': ['''VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTMAEConfig''']}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_lowercase							=				[
					    '''VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''ViTMAEForPreTraining''',
					    '''ViTMAELayer''',
					    '''ViTMAEModel''',
					    '''ViTMAEPreTrainedModel''',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_lowercase							=				[
					    '''TFViTMAEForPreTraining''',
					    '''TFViTMAEModel''',
					    '''TFViTMAEPreTrainedModel''',
					]

if TYPE_CHECKING:
					from .configuration_vit_mae import VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMAEConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_vit_mae import (
										    VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST,
										    ViTMAEForPreTraining,
										    ViTMAELayer,
										    ViTMAEModel,
										    ViTMAEPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_vit_mae import TFViTMAEForPreTraining, TFViTMAEModel, TFViTMAEPreTrainedModel


else:
					import sys

					_lowercase							=				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





