






"""simple docstring"""


import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest






class __a   (   snake_case_				):
  __lowercase    :    str						=					(EulerAncestralDiscreteScheduler,)
  __lowercase    :    int						=					10



  def 						A_				(self  :		Tuple				,			**lowercase_  :		Tuple   )		->							Optional[int]:
   __A	:       str       =	{
       'num_train_timesteps': 1_100,
       'beta_start': 0.0001,
       'beta_end': 0.02,
       'beta_schedule': 'linear',
   }

   config.update(**lowercase_   )
   return config



  def 						A_				(self  :		Optional[int]   )		->							List[Any]:
   for timesteps in [10, 50, 100, 1_000]:
    self.check_over_configs(num_train_timesteps=lowercase_   )



  def 						A_				(self  :		str   )		->							str:
   for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]				,			[0.0002, 0.002, 0.02]   ):
    self.check_over_configs(beta_start=lowercase_				,			beta_end=lowercase_   )



  def 						A_				(self  :		Optional[int]   )		->							Dict:
   for schedule in ["linear", "scaled_linear"]:
    self.check_over_configs(beta_schedule=lowercase_   )



  def 						A_				(self  :		Optional[int]   )		->							List[Any]:
   for prediction_type in ["epsilon", "v_prediction"]:
    self.check_over_configs(prediction_type=lowercase_   )



  def 						A_				(self  :		int   )		->							int:
   __A	:       List[str]       =	self.scheduler_classes[0]
   __A	:       Dict       =	self.get_scheduler_config()
   __A	:       List[str]       =	scheduler_class(**lowercase_   )

   scheduler.set_timesteps(self.num_inference_steps   )

   __A	:       List[Any]       =	torch.manual_seed(0   )

   __A	:       Tuple       =	self.dummy_model()
   __A	:       str       =	self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
   __A	:       List[Any]       =	sample.to(lowercase_   )

   for i, t in enumerate(scheduler.timesteps   ):
    __A	:       List[str]       =	scheduler.scale_model_input(lowercase_				,			lowercase_   )

    __A	:       List[Any]       =	model(lowercase_				,			lowercase_   )

    __A	:       Dict       =	scheduler.step(lowercase_				,			lowercase_				,			lowercase_				,			generator=lowercase_   )
    __A	:       Optional[Any]       =	output.prev_sample

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

   assert abs(result_sum.item() - 152.3192   ) < 1E-2
   assert abs(result_mean.item() - 0.1983   ) < 1E-3



  def 						A_				(self  :		List[Any]   )		->							Union[str, Any]:
   __A	:       Any       =	self.scheduler_classes[0]
   __A	:       List[Any]       =	self.get_scheduler_config(prediction_type='v_prediction'   )
   __A	:       Optional[Any]       =	scheduler_class(**lowercase_   )

   scheduler.set_timesteps(self.num_inference_steps   )

   __A	:       Dict       =	torch.manual_seed(0   )

   __A	:       str       =	self.dummy_model()
   __A	:       List[Any]       =	self.dummy_sample_deter * scheduler.init_noise_sigma
   __A	:       Tuple       =	sample.to(lowercase_   )

   for i, t in enumerate(scheduler.timesteps   ):
    __A	:       Optional[int]       =	scheduler.scale_model_input(lowercase_				,			lowercase_   )

    __A	:       Dict       =	model(lowercase_				,			lowercase_   )

    __A	:       List[Any]       =	scheduler.step(lowercase_				,			lowercase_				,			lowercase_				,			generator=lowercase_   )
    __A	:       str       =	output.prev_sample

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

   assert abs(result_sum.item() - 108.4439   ) < 1E-2
   assert abs(result_mean.item() - 0.1412   ) < 1E-3



  def 						A_				(self  :		Optional[Any]   )		->							Optional[Any]:
   __A	:       List[Any]       =	self.scheduler_classes[0]
   __A	:       List[str]       =	self.get_scheduler_config()
   __A	:       Dict       =	scheduler_class(**lowercase_   )

   scheduler.set_timesteps(self.num_inference_steps				,			device=lowercase_   )
   __A	:       Optional[Any]       =	torch.manual_seed(0   )

   __A	:       Tuple       =	self.dummy_model()
   __A	:       Any       =	self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
   __A	:       List[str]       =	sample.to(lowercase_   )

   for t in scheduler.timesteps:
    __A	:       Any       =	scheduler.scale_model_input(lowercase_				,			lowercase_   )

    __A	:       List[Any]       =	model(lowercase_				,			lowercase_   )

    __A	:       Any       =	scheduler.step(lowercase_				,			lowercase_				,			lowercase_				,			generator=lowercase_   )
    __A	:       Optional[int]       =	output.prev_sample

   __A	:       Any       =	torch.sum(torch.abs(lowercase_   )   )
   __A	:       Union[str, Any]       =	torch.mean(torch.abs(lowercase_   )   )

   assert abs(result_sum.item() - 152.3192   ) < 1E-2
   assert abs(result_mean.item() - 0.1983   ) < 1E-3













"""simple docstring"""


from __future__ import annotations

from typing import Any






class __a   :



  def __init__(self  :		Optional[Any]				,			lowercase_  :		Any				,			lowercase_  :		Any   )		->							None:  # noqa: A002
   __A	:       Optional[Any]       =	item
   __A	:       Union[str, Any]       =	next






class __a   :



  def __init__(self  :		Tuple   )		->							None:
   __A	:       Node | None       =	None
   __A	:       Dict       =	0



  def 						A_				(self  :		str				,			lowercase_  :		Any   )		->							None:
   __A	:       Dict       =	Node(lowercase_				,			self.head   )
   self.size += 1



  def 						A_				(self  :		Optional[int]   )		->							Any:
   # Switched 'self.is_empty()' to 'self.head is None'
   # because mypy was considering the possibility that 'self.head'
   # can be None in below else part and giving error
   if self.head is None:
    return None
   else:
    __A	:       Dict       =	self.head.item
    __A	:       List[str]       =	self.head.next
    self.size -= 1
    return item



  def 						A_				(self  :		List[str]   )		->							bool:
   return self.head is None



  def __str__(self  :		Any   )		->							str:
   if self.is_empty():
    return ""
   else:
    __A	:       List[str]       =	self.head
    __A	:       Union[str, Any]       =	''
    __A	:       list[str]       =	[]
    while iterate:
     item_list.append(str(iterate.item   )   )
     __A	:       List[str]       =	iterate.next

    __A	:       str       =	' --> '.join(lowercase_   )

    return item_str



  def __len__(self  :		Union[str, Any]   )		->							int:
   return self.size





