

'''simple docstring'''




import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class        lowercase_	(		a__ ):

  '''simple docstring'''





  __lowerCAmelCase					: List[Any]      =   (LMSDiscreteScheduler,)
  __lowerCAmelCase					: str      =   10
  def 						snake_case_			(     self   , **a_     )							->				Any:


    """simple docstring"""
    UpperCAmelCase													=	{
        "num_train_timesteps": 1_1_0_0,
        "beta_start": 0.0001,
        "beta_end": 0.02,
        "beta_schedule": "linear",
    }

    config.update(**__A     )
    return config
  def 						snake_case_			(     self     )							->				Tuple:


    """simple docstring"""







    for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
      self.check_over_configs(num_train_timesteps=__A     )
  def 						snake_case_			(     self     )							->				Tuple:


    """simple docstring"""







    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=__A   , beta_end=__A     )
  def 						snake_case_			(     self     )							->				str:


    """simple docstring"""







    for schedule in ["linear", "scaled_linear"]:
      self.check_over_configs(beta_schedule=__A     )
  def 						snake_case_			(     self     )							->				List[Any]:


    """simple docstring"""







    for prediction_type in ["epsilon", "v_prediction"]:
      self.check_over_configs(prediction_type=__A     )
  def 						snake_case_			(     self     )							->				List[Any]:


    """simple docstring"""







    for t in [0, 5_0_0, 8_0_0]:
      self.check_over_forward(time_step=__A     )
  def 						snake_case_			(     self     )							->				Any:


    """simple docstring"""
    UpperCAmelCase													=	self.scheduler_classes[0]
    UpperCAmelCase													=	self.get_scheduler_config()
    UpperCAmelCase													=	scheduler_class(**__A     )

    scheduler.set_timesteps(self.num_inference_steps     )

    UpperCAmelCase													=	self.dummy_model()
    UpperCAmelCase													=	self.dummy_sample_deter * scheduler.init_noise_sigma

    for i, t in enumerate(scheduler.timesteps     ):
      UpperCAmelCase													=	scheduler.scale_model_input(__A   , __A     )

      UpperCAmelCase													=	model(__A   , __A     )

      UpperCAmelCase													=	scheduler.step(__A   , __A   , __A     )
      UpperCAmelCase													=	output.prev_sample

    UpperCAmelCase													=	torch.sum(torch.abs(__A     )     )
    UpperCAmelCase													=	torch.mean(torch.abs(__A     )     )

    assert abs(result_sum.item() - 1006.388     ) < 1E-2
    assert abs(result_mean.item() - 1.31     ) < 1E-3
  def 						snake_case_			(     self     )							->				Any:


    """simple docstring"""
    UpperCAmelCase													=	self.scheduler_classes[0]
    UpperCAmelCase													=	self.get_scheduler_config(prediction_type='v_prediction'     )
    UpperCAmelCase													=	scheduler_class(**__A     )

    scheduler.set_timesteps(self.num_inference_steps     )

    UpperCAmelCase													=	self.dummy_model()
    UpperCAmelCase													=	self.dummy_sample_deter * scheduler.init_noise_sigma

    for i, t in enumerate(scheduler.timesteps     ):
      UpperCAmelCase													=	scheduler.scale_model_input(__A   , __A     )

      UpperCAmelCase													=	model(__A   , __A     )

      UpperCAmelCase													=	scheduler.step(__A   , __A   , __A     )
      UpperCAmelCase													=	output.prev_sample

    UpperCAmelCase													=	torch.sum(torch.abs(__A     )     )
    UpperCAmelCase													=	torch.mean(torch.abs(__A     )     )

    assert abs(result_sum.item() - 0.0017     ) < 1E-2
    assert abs(result_mean.item() - 2.2_676E-06     ) < 1E-3
  def 						snake_case_			(     self     )							->				int:


    """simple docstring"""
    UpperCAmelCase													=	self.scheduler_classes[0]
    UpperCAmelCase													=	self.get_scheduler_config()
    UpperCAmelCase													=	scheduler_class(**__A     )

    scheduler.set_timesteps(self.num_inference_steps   , device=__A     )

    UpperCAmelCase													=	self.dummy_model()
    UpperCAmelCase													=	self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
    UpperCAmelCase													=	sample.to(__A     )

    for i, t in enumerate(scheduler.timesteps     ):
      UpperCAmelCase													=	scheduler.scale_model_input(__A   , __A     )

      UpperCAmelCase													=	model(__A   , __A     )

      UpperCAmelCase													=	scheduler.step(__A   , __A   , __A     )
      UpperCAmelCase													=	output.prev_sample

    UpperCAmelCase													=	torch.sum(torch.abs(__A     )     )
    UpperCAmelCase													=	torch.mean(torch.abs(__A     )     )

    assert abs(result_sum.item() - 1006.388     ) < 1E-2
    assert abs(result_mean.item() - 1.31     ) < 1E-3





  def 						snake_case_			(     self     )							->				Tuple:


    """simple docstring"""
    UpperCAmelCase													=	self.scheduler_classes[0]
    UpperCAmelCase													=	self.get_scheduler_config()
    UpperCAmelCase													=	scheduler_class(**__A   , use_karras_sigmas=__A     )

    scheduler.set_timesteps(self.num_inference_steps   , device=__A     )

    UpperCAmelCase													=	self.dummy_model()
    UpperCAmelCase													=	self.dummy_sample_deter.to(__A     ) * scheduler.init_noise_sigma
    UpperCAmelCase													=	sample.to(__A     )

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

      UpperCAmelCase													=	model(__A   , __A     )

      UpperCAmelCase													=	scheduler.step(__A   , __A   , __A     )
      UpperCAmelCase													=	output.prev_sample

    UpperCAmelCase													=	torch.sum(torch.abs(__A     )     )
    UpperCAmelCase													=	torch.mean(torch.abs(__A     )     )

    assert abs(result_sum.item() - 3812.9927     ) < 2E-2
    assert abs(result_mean.item() - 4.9648     ) < 1E-3





'''simple docstring'''




from collections import OrderedDict
from dataclasses import fields
from typing import Any, Tuple

import numpy as np

from .import_utils import is_torch_available


def       lowerCamelCase__		(						SCREAMING_SNAKE_CASE      :   Optional[Any]						):
  if is_torch_available():
    import torch

    if isinstance(SCREAMING_SNAKE_CASE       ,   torch.Tensor						):
      return True

  return isinstance(SCREAMING_SNAKE_CASE       ,   np.ndarray						)



class        lowercase_	(		a ):

  '''simple docstring'''





  def 						snake_case_			(     self     )							->				List[str]:


    """simple docstring"""
    UpperCAmelCase													=	fields(self     )

    # Safety and consistency checks
    if not len(a_     ):
      raise ValueError(F'''{self.__class__.__name__} has no fields.'''     )

    UpperCAmelCase													=	getattr(self   , class_fields[0].name     )
    UpperCAmelCase													=	all(getattr(self   , field.name     ) is None for field in class_fields[1:]     )

    if other_fields_are_none and isinstance(a_   , a_     ):
      for key, value in first_field.items():
        UpperCAmelCase													=	value
    else:
      for field in class_fields:
        UpperCAmelCase													=	getattr(self   , field.name     )
        if v is not None:
          UpperCAmelCase													=	v
  def __delitem__(     self   , *a_   , **a_     )							->				int:


    """simple docstring"""
    raise Exception(F'''You cannot use ``__delitem__`` on a {self.__class__.__name__} instance.'''     )
  def 						snake_case_			(     self   , *a_   , **a_     )							->				Tuple:


    """simple docstring"""
    raise Exception(F'''You cannot use ``setdefault`` on a {self.__class__.__name__} instance.'''     )
  def 						snake_case_			(     self   , *a_   , **a_     )							->				int:


    """simple docstring"""
    raise Exception(F'''You cannot use ``pop`` on a {self.__class__.__name__} instance.'''     )
  def 						snake_case_			(     self   , *a_   , **a_     )							->				Dict:


    """simple docstring"""
    raise Exception(F'''You cannot use ``update`` on a {self.__class__.__name__} instance.'''     )
  def __getitem__(     self   , a_     )							->				Tuple:


    """simple docstring"""
    if isinstance(a_   , a_     ):
      UpperCAmelCase													=	dict(self.items()     )
      return inner_dict[k]
    else:
      return self.to_tuple()[k]
  def __setattr__(     self   , a_   , a_     )							->				Dict:


    """simple docstring"""
    if name in self.keys() and value is not None:
      # Don't call self.__setitem__ to avoid recursion errors
      super().__setitem__(a_   , a_     )
    super().__setattr__(a_   , a_     )
  def __setitem__(     self   , a_   , a_     )							->				str:


    """simple docstring"""
    # Will raise a KeyException if needed
    super().__setitem__(a_   , a_     )
    # Don't call self.__setattr__ to avoid recursion errors
    super().__setattr__(a_   , a_     )





  def 						snake_case_			(     self     )							->				Tuple[Any]:


    """simple docstring"""
    return tuple(self[k] for k in self.keys()     )


