



'''simple docstring'''





import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest






class    lowerCamelCase_       (							SCREAMING_SNAKE_CASE_	):
  """simple docstring"""




  a_				   =(DDPMParallelScheduler,)




  def 		_lowercase	(					self :      Optional[int]     ,      **_a :      Tuple  )   ->   Optional[Any]:
    __lowerCamelCase			:			List[Any]       			=					{
        'num_train_timesteps': 1000,
        'beta_start': 0.0001,
        'beta_end': 0.02,
        'beta_schedule': 'linear',
        'variance_type': 'fixed_small',
        'clip_sample': True,
    }

    config.update(**_a  )
    return config




  def 		_lowercase	(					self :      Optional[int]  )   ->   int:
    for timesteps in [1, 5, 100, 1000]:
      self.check_over_configs(num_train_timesteps=_a  )




  def 		_lowercase	(					self :      str  )   ->   Union[str, Any]:
    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=_a     ,      beta_end=_a  )




  def 		_lowercase	(					self :      int  )   ->   Tuple:
    for schedule in ["linear", "squaredcos_cap_v2"]:
      self.check_over_configs(beta_schedule=_a  )




  def 		_lowercase	(					self :      Tuple  )   ->   List[Any]:
    for variance in ["fixed_small", "fixed_large", "other"]:
      self.check_over_configs(variance_type=_a  )




  def 		_lowercase	(					self :      List[Any]  )   ->   Any:
    for clip_sample in [True, False]:
      self.check_over_configs(clip_sample=_a  )




  def 		_lowercase	(					self :      List[str]  )   ->   Tuple:
    self.check_over_configs(thresholding=_a  )
    for threshold in [0.5, 1.0, 2.0]:
      for prediction_type in ["epsilon", "sample", "v_prediction"]:
        self.check_over_configs(
            thresholding=_a     ,      prediction_type=_a     ,      sample_max_value=_a     ,      )




  def 		_lowercase	(					self :      Any  )   ->   Optional[int]:
    for prediction_type in ["epsilon", "sample", "v_prediction"]:
      self.check_over_configs(prediction_type=_a  )




  def 		_lowercase	(					self :      Optional[int]  )   ->   List[str]:
    for t in [0, 500, 999]:
      self.check_over_forward(time_step=_a  )




  def 		_lowercase	(					self :      List[Any]  )   ->   str:
    __lowerCamelCase			:			Union[str, Any]       			=					self.scheduler_classes[0]
    __lowerCamelCase			:			str       			=					self.get_scheduler_config()
    __lowerCamelCase			:			Optional[Any]       			=					scheduler_class(**_a  )

    assert torch.sum(torch.abs(scheduler._get_variance(0  ) - 0.0  )  ) < 1e-5
    assert torch.sum(torch.abs(scheduler._get_variance(487  ) - 0.00979  )  ) < 1e-5
    assert torch.sum(torch.abs(scheduler._get_variance(999  ) - 0.02  )  ) < 1e-5




  def 		_lowercase	(					self :      List[Any]  )   ->   Dict:
    __lowerCamelCase			:			Optional[Any]       			=					self.scheduler_classes[0]
    __lowerCamelCase			:			Tuple       			=					self.get_scheduler_config()
    __lowerCamelCase			:			Optional[Any]       			=					scheduler_class(**_a  )

    __lowerCamelCase			:			int       			=					len(_a  )

    __lowerCamelCase			:			List[str]       			=					self.dummy_model()
    __lowerCamelCase			:			int       			=					self.dummy_sample_deter
    __lowerCamelCase			:			int       			=					self.dummy_sample_deter + 0.1
    __lowerCamelCase			:			int       			=					self.dummy_sample_deter - 0.1

    __lowerCamelCase			:			Optional[int]       			=					samplea.shape[0]
    __lowerCamelCase			:			int       			=					torch.stack([samplea, samplea, samplea]     ,      dim=0  )
    __lowerCamelCase			:			Optional[int]       			=					torch.arange(_a  )[0:3, None].repeat(1     ,      _a  )

    __lowerCamelCase			:			List[str]       			=					model(samples.flatten(0     ,      1  )     ,      timesteps.flatten(0     ,      1  )  )
    __lowerCamelCase			:			int       			=					scheduler.batch_step_no_noise(_a     ,      timesteps.flatten(0     ,      1  )     ,      samples.flatten(0     ,      1  )  )

    __lowerCamelCase			:			Tuple       			=					torch.sum(torch.abs(_a  )  )
    __lowerCamelCase			:			Dict       			=					torch.mean(torch.abs(_a  )  )

    assert abs(result_sum.item() - 1153.1833  ) < 1e-2
    assert abs(result_mean.item() - 0.5005  ) < 1e-3




  def 		_lowercase	(					self :      Dict  )   ->   Optional[int]:
    __lowerCamelCase			:			Optional[Any]       			=					self.scheduler_classes[0]
    __lowerCamelCase			:			Tuple       			=					self.get_scheduler_config()
    __lowerCamelCase			:			Optional[Any]       			=					scheduler_class(**_a  )

    __lowerCamelCase			:			List[str]       			=					len(_a  )

    __lowerCamelCase			:			Any       			=					self.dummy_model()
    __lowerCamelCase			:			Optional[int]       			=					self.dummy_sample_deter
    __lowerCamelCase			:			int       			=					torch.manual_seed(0  )

    for t in reversed(range(_a  )  ):
      # 1. predict noise residual
      __lowerCamelCase			:			str       			=					model(_a     ,      _a  )

      # 2. predict previous mean of sample x_t-1
      __lowerCamelCase			:			Optional[int]       			=					scheduler.step(_a     ,      _a     ,      _a     ,      generator=_a  ).prev_sample

      __lowerCamelCase			:			Optional[Any]       			=					pred_prev_sample

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

    assert abs(result_sum.item() - 258.9606  ) < 1e-2
    assert abs(result_mean.item() - 0.3372  ) < 1e-3




  def 		_lowercase	(					self :      Any  )   ->   Optional[int]:
    __lowerCamelCase			:			int       			=					self.scheduler_classes[0]
    __lowerCamelCase			:			Tuple       			=					self.get_scheduler_config(prediction_type='v_prediction'  )
    __lowerCamelCase			:			Optional[int]       			=					scheduler_class(**_a  )

    __lowerCamelCase			:			List[str]       			=					len(_a  )

    __lowerCamelCase			:			int       			=					self.dummy_model()
    __lowerCamelCase			:			int       			=					self.dummy_sample_deter
    __lowerCamelCase			:			List[str]       			=					torch.manual_seed(0  )

    for t in reversed(range(_a  )  ):
      # 1. predict noise residual
      __lowerCamelCase			:			int       			=					model(_a     ,      _a  )

      # 2. predict previous mean of sample x_t-1
      __lowerCamelCase			:			int       			=					scheduler.step(_a     ,      _a     ,      _a     ,      generator=_a  ).prev_sample

      __lowerCamelCase			:			Tuple       			=					pred_prev_sample

    __lowerCamelCase			:			Dict       			=					torch.sum(torch.abs(_a  )  )
    __lowerCamelCase			:			Union[str, Any]       			=					torch.mean(torch.abs(_a  )  )

    assert abs(result_sum.item() - 202.0296  ) < 1e-2
    assert abs(result_mean.item() - 0.2631  ) < 1e-3




  def 		_lowercase	(					self :      str  )   ->   List[Any]:
    __lowerCamelCase			:			str       			=					self.scheduler_classes[0]
    __lowerCamelCase			:			str       			=					self.get_scheduler_config()
    __lowerCamelCase			:			Optional[Any]       			=					scheduler_class(**_a  )

    __lowerCamelCase			:			Union[str, Any]       			=					[100, 87, 50, 1, 0]

    scheduler.set_timesteps(timesteps=_a  )

    __lowerCamelCase			:			List[str]       			=					scheduler.timesteps

    for i, timestep in enumerate(_a  ):
      if i == len(_a  ) - 1:
        __lowerCamelCase			:			List[Any]       			=					-1
      else:
        __lowerCamelCase			:			List[str]       			=					timesteps[i + 1]

      __lowerCamelCase			:			str       			=					scheduler.previous_timestep(_a  )
      __lowerCamelCase			:			Optional[int]       			=					prev_t.item()

      self.assertEqual(_a     ,      _a  )




  def 		_lowercase	(					self :      Any  )   ->   Optional[Any]:
    __lowerCamelCase			:			Optional[int]       			=					self.scheduler_classes[0]
    __lowerCamelCase			:			str       			=					self.get_scheduler_config()
    __lowerCamelCase			:			Optional[Any]       			=					scheduler_class(**_a  )

    __lowerCamelCase			:			Tuple       			=					[100, 87, 50, 51, 0]

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




  def 		_lowercase	(					self :      int  )   ->   List[Any]:
    __lowerCamelCase			:			Tuple       			=					self.scheduler_classes[0]
    __lowerCamelCase			:			List[str]       			=					self.get_scheduler_config()
    __lowerCamelCase			:			Optional[int]       			=					scheduler_class(**_a  )

    __lowerCamelCase			:			List[Any]       			=					[100, 87, 50, 1, 0]
    __lowerCamelCase			:			List[Any]       			=					len(_a  )

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




  def 		_lowercase	(					self :      Tuple  )   ->   int:
    __lowerCamelCase			:			List[str]       			=					self.scheduler_classes[0]
    __lowerCamelCase			:			List[Any]       			=					self.get_scheduler_config()
    __lowerCamelCase			:			str       			=					scheduler_class(**_a  )

    __lowerCamelCase			:			int       			=					[scheduler.config.num_train_timesteps]

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











'''simple docstring'''





import random
from typing import Any

def 			a_	(				_lowerCAmelCase			) -> list[Any]:
  for _ in range(len(_lowerCAmelCase			)			):
    __lowerCamelCase			:			Optional[Any]       			=					random.randint(0			,len(_lowerCAmelCase			) - 1			)
    __lowerCamelCase			:			str       			=					random.randint(0			,len(_lowerCAmelCase			) - 1			)
    __lowerCamelCase			,__lowerCamelCase			:			List[Any]       			=					data[b], data[a]
  return data


if __name__ == "__main__":
   _UpperCamelCase											=		[0, 1, 2, 3, 4, 5, 6, 7]
   _UpperCamelCase											=		['python', 'says', 'hello', '!']
   print('Fisher-Yates Shuffle:')
   print('List', integers, strings)
   print('FY Shuffle', fisher_yates_shuffle(integers), fisher_yates_shuffle(strings))






