'''simple docstring'''






import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest







class lowercase_       (		_UpperCamelCase			):







   """simple docstring"""

   __lowerCAmelCase         =   (DDPMParallelScheduler,)
   def 	__UpperCAmelCase   (      self      :			str, **UpperCamelCase__      :			List[str]     )  ->			Tuple:
        _A    		=  {
            'num_train_timesteps': 10_00,
            'beta_start': 0.0_001,
            'beta_end': 0.02,
            'beta_schedule': 'linear',
            'variance_type': 'fixed_small',
            'clip_sample': True,
        }

        config.update(**UpperCamelCase__     )
        return config
   def 	__UpperCAmelCase   (      self      :			List[Any]     )  ->			Any:
        for timesteps in [1, 5, 1_00, 10_00]:
             self.check_over_configs(num_train_timesteps=UpperCamelCase__     )
   def 	__UpperCAmelCase   (      self      :			Optional[Any]     )  ->			Optional[int]:
        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      :			List[Any]     )  ->			List[str]:
        for schedule in ["linear", "squaredcos_cap_v2"]:
             self.check_over_configs(beta_schedule=UpperCamelCase__     )
   def 	__UpperCAmelCase   (      self      :			Tuple     )  ->			str:
        for variance in ["fixed_small", "fixed_large", "other"]:
             self.check_over_configs(variance_type=UpperCamelCase__     )
   def 	__UpperCAmelCase   (      self      :			List[Any]     )  ->			Union[str, Any]:
        for clip_sample in [True, False]:
             self.check_over_configs(clip_sample=UpperCamelCase__     )
   def 	__UpperCAmelCase   (      self      :			Tuple     )  ->			List[str]:
        self.check_over_configs(thresholding=UpperCamelCase__     )
        for threshold in [0.5, 1.0, 2.0]:
             for prediction_type in ["epsilon", "sample", "v_prediction"]:
                  self.check_over_configs(
                      thresholding=UpperCamelCase__, prediction_type=UpperCamelCase__, sample_max_value=UpperCamelCase__, )
   def 	__UpperCAmelCase   (      self      :			Optional[Any]     )  ->			Dict:
        for prediction_type in ["epsilon", "sample", "v_prediction"]:
             self.check_over_configs(prediction_type=UpperCamelCase__     )
   def 	__UpperCAmelCase   (      self      :			Tuple     )  ->			Tuple:
        for t in [0, 5_00, 9_99]:
             self.check_over_forward(time_step=UpperCamelCase__     )
   def 	__UpperCAmelCase   (      self      :			Tuple     )  ->			Optional[int]:
        _A    		=  self.scheduler_classes[0]
        _A    		=  self.get_scheduler_config()
        _A    		=  scheduler_class(**UpperCamelCase__     )

        assert torch.sum(torch.abs(scheduler._get_variance(0     ) - 0.0     )     ) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(4_87     ) - 0.00_979     )     ) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(9_99     ) - 0.02     )     ) < 1e-5
   def 	__UpperCAmelCase   (      self      :			Union[str, Any]     )  ->			Optional[Any]:
        _A    		=  self.scheduler_classes[0]
        _A    		=  self.get_scheduler_config()
        _A    		=  scheduler_class(**UpperCamelCase__     )

        _A    		=  len(UpperCamelCase__     )

        _A    		=  self.dummy_model()
        _A    		=  self.dummy_sample_deter
        _A    		=  self.dummy_sample_deter + 0.1
        _A    		=  self.dummy_sample_deter - 0.1

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

        _A    		=  model(samples.flatten(0, 1     ), timesteps.flatten(0, 1     )     )
        _A    		=  scheduler.batch_step_no_noise(UpperCamelCase__, timesteps.flatten(0, 1     ), samples.flatten(0, 1     )     )

        _A    		=  torch.sum(torch.abs(UpperCamelCase__     )     )
        _A    		=  torch.mean(torch.abs(UpperCamelCase__     )     )

        assert abs(result_sum.item() - 1_153.1_833     ) < 1e-2
        assert abs(result_mean.item() - 0.5_005     ) < 1e-3
   def 	__UpperCAmelCase   (      self      :			List[str]     )  ->			Optional[int]:
        _A    		=  self.scheduler_classes[0]
        _A    		=  self.get_scheduler_config()
        _A    		=  scheduler_class(**UpperCamelCase__     )

        _A    		=  len(UpperCamelCase__     )

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

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

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

             _A    		=  pred_prev_sample

        _A    		=  torch.sum(torch.abs(UpperCamelCase__     )     )
        _A    		=  torch.mean(torch.abs(UpperCamelCase__     )     )

        assert abs(result_sum.item() - 258.9_606     ) < 1e-2
        assert abs(result_mean.item() - 0.3_372     ) < 1e-3
   def 	__UpperCAmelCase   (      self      :			Dict     )  ->			Any:
        _A    		=  self.scheduler_classes[0]
        _A    		=  self.get_scheduler_config(prediction_type='v_prediction'     )
        _A    		=  scheduler_class(**UpperCamelCase__     )

        _A    		=  len(UpperCamelCase__     )

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

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

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

             _A    		=  pred_prev_sample

        _A    		=  torch.sum(torch.abs(UpperCamelCase__     )     )
        _A    		=  torch.mean(torch.abs(UpperCamelCase__     )     )

        assert abs(result_sum.item() - 202.0_296     ) < 1e-2
        assert abs(result_mean.item() - 0.2_631     ) < 1e-3
   def 	__UpperCAmelCase   (      self      :			Optional[int]     )  ->			Tuple:
        _A    		=  self.scheduler_classes[0]
        _A    		=  self.get_scheduler_config()
        _A    		=  scheduler_class(**UpperCamelCase__     )

        _A    		=  [1_00, 87, 50, 1, 0]

        scheduler.set_timesteps(timesteps=UpperCamelCase__     )

        _A    		=  scheduler.timesteps

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

             _A    		=  scheduler.previous_timestep(UpperCamelCase__     )
             _A    		=  prev_t.item()

             self.assertEqual(UpperCamelCase__, UpperCamelCase__     )
   def 	__UpperCAmelCase   (      self      :			Any     )  ->			Optional[int]:
        _A    		=  self.scheduler_classes[0]
        _A    		=  self.get_scheduler_config()
        _A    		=  scheduler_class(**UpperCamelCase__     )

        _A    		=  [1_00, 87, 50, 51, 0]

        with self.assertRaises(UpperCamelCase__, msg='`custom_timesteps` must be in descending order.'     ):
             scheduler.set_timesteps(timesteps=UpperCamelCase__     )
   def 	__UpperCAmelCase   (      self      :			Any     )  ->			Dict:
        _A    		=  self.scheduler_classes[0]
        _A    		=  self.get_scheduler_config()
        _A    		=  scheduler_class(**UpperCamelCase__     )

        _A    		=  [1_00, 87, 50, 1, 0]
        _A    		=  len(UpperCamelCase__     )

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

   def 	__UpperCAmelCase   (      self      :			Dict     )  ->			Dict:
        _A    		=  self.scheduler_classes[0]
        _A    		=  self.get_scheduler_config()
        _A    		=  scheduler_class(**UpperCamelCase__     )

        _A    		=  [scheduler.config.num_train_timesteps]

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





from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase_						:     Optional[int]	       =							logging.get_logger(__name__)




class   __lowercase						(						__snake_case       ):
	_A       =   "timm_backbone"






	def __init__(self				: Any      ,	snake_case				: List[Any]=None      ,	snake_case				: int=3      ,	snake_case				: Dict=True      ,	snake_case				: Union[str, Any]=True      ,	snake_case				: Any=None      ,	**snake_case				: int      ,	) ->			Optional[int]:
							super().__init__(**snake_case    )
							_lowercase       :	Dict           =   backbone
							_lowercase       :	Optional[Any]           =   num_channels
							_lowercase       :	Union[str, Any]           =   features_only
							_lowercase       :	Tuple           =   use_pretrained_backbone
							_lowercase       :	List[str]           =   True
							_lowercase       :	Tuple           =   out_indices if out_indices is not None else (-1,)




