





import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class 				_lowerCAmelCase			(	UpperCAmelCase_							):


  '''simple docstring'''
  a_							:				Optional[int]					    =(EulerAncestralDiscreteScheduler,)
  a_							:				int					    =10







  def UpperCamelCase_       (					self					:    str					, **UpperCamelCase					:    Optional[Any]							):


    '''simple docstring'''





    _snake_case      : Union[str, Any]														=		{
        'num_train_timesteps': 11_00,
        'beta_start': 0.00_01,
        'beta_end': 0.02,
        'beta_schedule': 'linear',
    }

    config.update(**UpperCamelCase							)
    return config







  def UpperCamelCase_       (					self					:    List[str]							):


    '''simple docstring'''





    for timesteps in [10, 50, 1_00, 10_00]:
      self.check_over_configs(num_train_timesteps=UpperCamelCase							)







  def UpperCamelCase_       (					self					:    Tuple							):


    '''simple docstring'''





    for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01]					, [0.00_02, 0.0_02, 0.02]							):
      self.check_over_configs(beta_start=UpperCamelCase					, beta_end=UpperCamelCase							)







  def UpperCamelCase_       (					self					:    str							):


    '''simple docstring'''





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







  def UpperCamelCase_       (					self					:    Tuple							):


    '''simple docstring'''





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







  def UpperCamelCase_       (					self					:    Union[str, Any]							):


    '''simple docstring'''





    _snake_case      : List[str]														=		self.scheduler_classes[0]
    _snake_case      : Optional[int]														=		self.get_scheduler_config()
    _snake_case      : List[Any]														=		scheduler_class(**UpperCamelCase							)

    scheduler.set_timesteps(self.num_inference_steps							)

    _snake_case      : Optional[int]														=		torch.manual_seed(0							)

    _snake_case      : Any														=		self.dummy_model()
    _snake_case      : Union[str, Any]														=		self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
    _snake_case      : Dict														=		sample.to(UpperCamelCase							)

    for i, t in enumerate(scheduler.timesteps							):
      _snake_case      : Optional[Any]														=		scheduler.scale_model_input(UpperCamelCase					, UpperCamelCase							)

      _snake_case      : Optional[int]														=		model(UpperCamelCase					, UpperCamelCase							)

      _snake_case      : Any														=		scheduler.step(UpperCamelCase					, UpperCamelCase					, UpperCamelCase					, generator=UpperCamelCase							)
      _snake_case      : Dict														=		output.prev_sample

    _snake_case      : Dict														=		torch.sum(torch.abs(UpperCamelCase							)							)
    _snake_case      : List[str]														=		torch.mean(torch.abs(UpperCamelCase							)							)

    assert abs(result_sum.item() - 1_52.31_92							) < 1e-2
    assert abs(result_mean.item() - 0.19_83							) < 1e-3







  def UpperCamelCase_       (					self					:    Union[str, Any]							):


    '''simple docstring'''





    _snake_case      : List[Any]														=		self.scheduler_classes[0]
    _snake_case      : List[Any]														=		self.get_scheduler_config(prediction_type='v_prediction'							)
    _snake_case      : Tuple														=		scheduler_class(**UpperCamelCase							)

    scheduler.set_timesteps(self.num_inference_steps							)

    _snake_case      : Optional[Any]														=		torch.manual_seed(0							)

    _snake_case      : Dict														=		self.dummy_model()
    _snake_case      : Tuple														=		self.dummy_sample_deter * scheduler.init_noise_sigma
    _snake_case      : str														=		sample.to(UpperCamelCase							)

    for i, t in enumerate(scheduler.timesteps							):
      _snake_case      : Any														=		scheduler.scale_model_input(UpperCamelCase					, UpperCamelCase							)

      _snake_case      : List[str]														=		model(UpperCamelCase					, UpperCamelCase							)

      _snake_case      : Union[str, Any]														=		scheduler.step(UpperCamelCase					, UpperCamelCase					, UpperCamelCase					, generator=UpperCamelCase							)
      _snake_case      : Tuple														=		output.prev_sample

    _snake_case      : List[Any]														=		torch.sum(torch.abs(UpperCamelCase							)							)
    _snake_case      : Dict														=		torch.mean(torch.abs(UpperCamelCase							)							)

    assert abs(result_sum.item() - 1_08.44_39							) < 1e-2
    assert abs(result_mean.item() - 0.14_12							) < 1e-3







  def UpperCamelCase_       (					self					:    Union[str, Any]							):


    '''simple docstring'''





    _snake_case      : Tuple														=		self.scheduler_classes[0]
    _snake_case      : List[str]														=		self.get_scheduler_config()
    _snake_case      : str														=		scheduler_class(**UpperCamelCase							)

    scheduler.set_timesteps(self.num_inference_steps					, device=UpperCamelCase							)
    _snake_case      : Tuple														=		torch.manual_seed(0							)

    _snake_case      : Dict														=		self.dummy_model()
    _snake_case      : Optional[Any]														=		self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
    _snake_case      : Optional[int]														=		sample.to(UpperCamelCase							)

    for t in scheduler.timesteps:
      _snake_case      : Tuple														=		scheduler.scale_model_input(UpperCamelCase					, UpperCamelCase							)

      _snake_case      : List[str]														=		model(UpperCamelCase					, UpperCamelCase							)

      _snake_case      : str														=		scheduler.step(UpperCamelCase					, UpperCamelCase					, UpperCamelCase					, generator=UpperCamelCase							)
      _snake_case      : str														=		output.prev_sample

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

    assert abs(result_sum.item() - 1_52.31_92							) < 1e-2
    assert abs(result_mean.item() - 0.19_83							) < 1e-3












from __future__ import annotations

from collections.abc import Generator




def    __A    (   _lowercase       ):

					'''simple docstring'''







					if not isinstance(_lowercase    ,	_lowercase       ) or n < 1:
										raise Exception('''Sequence only defined for positive integers'''       )

					yield n
					while n != 1:
										if n % 2 == 0:
															n //= 2
										else:
															_A									=       3 * n + 1
										yield n




def    __A    (   ):

					'''simple docstring'''







					_A									=       43
					_A									=       tuple(collatz_sequence(_lowercase       )       )
					print(_lowercase       )
					print(f"""Collatz sequence from {n} took {len(_lowercase       )} steps."""       )


if __name__ == "__main__":
				main()



