



'''simple docstring'''





from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin






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




  a_												=42
  a_												=42
  a_												=None






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




  a_												=2




  @register_to_config
  def __init__( self      :	Tuple			,	_a      :	float = 0.02			,	_a      :	float = 100			,	_a      :	float = 1.007			,	_a      :	float = 80			,	_a      :	float = 0.05			,	_a      :	float = 50			,	)					->					Union[str, Any]:
    # standard deviation of the initial noise distribution
    __lowerCamelCase       :       Tuple           = sigma_max

    # setable values
    __lowerCamelCase       :       int           = None
    __lowerCamelCase       :       np.IntTensor           = None
    __lowerCamelCase       :       torch.FloatTensor           = None  # sigma(t_i)




  def 						_lowercase     ( self      :	Tuple			,	_a      :	torch.FloatTensor			,	_a      :	Optional[int] = None   )					->					torch.FloatTensor:
    return sample




  def 						_lowercase     ( self      :	Optional[Any]			,	_a      :	int			,	_a      :	Union[str, torch.device] = None   )					->					Dict:
    __lowerCamelCase       :       Optional[Any]           = num_inference_steps
    __lowerCamelCase       :       Union[str, Any]           = np.arange(0			,	self.num_inference_steps   )[::-1].copy()
    __lowerCamelCase       :       Any           = torch.from_numpy(_a   ).to(_a   )
    __lowerCamelCase       :       List[Any]           = [
        (
            self.config.sigma_max**2
            * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
        )
        for i in self.timesteps
    ]
    __lowerCamelCase       :       Union[str, Any]           = torch.tensor(_a			,	dtype=torch.floataa			,	device=_a   )




  def 						_lowercase     ( self      :	Optional[Any]			,	_a      :	torch.FloatTensor			,	_a      :	float			,	_a      :	Optional[torch.Generator] = None   )					->					Tuple[torch.FloatTensor, float]:
    if self.config.s_min <= sigma <= self.config.s_max:
      __lowerCamelCase       :       Tuple           = min(self.config.s_churn / self.num_inference_steps			,	2**0.5 - 1   )
    else:
      __lowerCamelCase       :       int           = 0

    # sample eps ~ N(0, S_noise^2 * I)
    __lowerCamelCase       :       Optional[Any]           = self.config.s_noise * randn_tensor(sample.shape			,	generator=_a   ).to(sample.device   )
    __lowerCamelCase       :       int           = sigma + gamma * sigma
    __lowerCamelCase       :       int           = sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

    return sample_hat, sigma_hat




  def 						_lowercase     ( self      :	str			,	_a      :	torch.FloatTensor			,	_a      :	float			,	_a      :	float			,	_a      :	torch.FloatTensor			,	_a      :	bool = True			,	)					->					Union[KarrasVeOutput, Tuple]:
    __lowerCamelCase       :       List[Any]           = sample_hat + sigma_hat * model_output
    __lowerCamelCase       :       Any           = (sample_hat - pred_original_sample) / sigma_hat
    __lowerCamelCase       :       Union[str, Any]           = sample_hat + (sigma_prev - sigma_hat) * derivative

    if not return_dict:
      return (sample_prev, derivative)

    return KarrasVeOutput(
        prev_sample=_a			,	derivative=_a			,	pred_original_sample=_a   )




  def 						_lowercase     ( self      :	List[Any]			,	_a      :	torch.FloatTensor			,	_a      :	float			,	_a      :	float			,	_a      :	torch.FloatTensor			,	_a      :	torch.FloatTensor			,	_a      :	torch.FloatTensor			,	_a      :	bool = True			,	)					->					Union[KarrasVeOutput, Tuple]:
    __lowerCamelCase       :       Any           = sample_prev + sigma_prev * model_output
    __lowerCamelCase       :       Tuple           = (sample_prev - pred_original_sample) / sigma_prev
    __lowerCamelCase       :       Dict           = sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

    if not return_dict:
      return (sample_prev, derivative)

    return KarrasVeOutput(
        prev_sample=_a			,	derivative=_a			,	pred_original_sample=_a   )




  def 						_lowercase     ( self      :	List[Any]			,	_a      :	Union[str, Any]			,	_a      :	List[Any]			,	_a      :	str   )					->					Any:
    raise NotImplementedError()











'''simple docstring'''





import inspect
import warnings
from typing import Any, Dict, Optional, Union

from packaging import version

def        a_     (							*_lowerCAmelCase       ,_lowerCAmelCase = None       ,_lowerCAmelCase=True       ,_lowerCAmelCase=2       )      ->      List[str]:
  from .. import __version__

  __lowerCamelCase       :       Any           = take_from
  __lowerCamelCase       :       Optional[int]           = ()
  if not isinstance(args[0]       ,_lowerCAmelCase       ):
    __lowerCamelCase       :       Optional[Any]           = (args,)

  for attribute, version_name, message in args:
    if version.parse(version.parse(_lowerCAmelCase       ).base_version       ) >= version.parse(_lowerCAmelCase       ):
      raise ValueError(
          F'The deprecation tuple {(attribute, version_name, message)} should be removed since diffusers\''
          F' version {__version__} is >= {version_name}'       )

    __lowerCamelCase       :       Union[str, Any]           = None
    if isinstance(_lowerCAmelCase       ,_lowerCAmelCase       ) and attribute in deprecated_kwargs:
      values += (deprecated_kwargs.pop(_lowerCAmelCase       ),)
      __lowerCamelCase       :       Optional[Any]           = F'The `{attribute}` argument is deprecated and will be removed in version {version_name}.'
    elif hasattr(_lowerCAmelCase       ,_lowerCAmelCase       ):
      values += (getattr(_lowerCAmelCase       ,_lowerCAmelCase       ),)
      __lowerCamelCase       :       List[str]           = F'The `{attribute}` attribute is deprecated and will be removed in version {version_name}.'
    elif deprecated_kwargs is None:
      __lowerCamelCase       :       Optional[Any]           = F'`{attribute}` is deprecated and will be removed in version {version_name}.'

    if warning is not None:
      __lowerCamelCase       :       Optional[int]           = warning + ' ' if standard_warn else ''
      warnings.warn(warning + message       ,_lowerCAmelCase       ,stacklevel=_lowerCAmelCase       )

  if isinstance(_lowerCAmelCase       ,_lowerCAmelCase       ) and len(_lowerCAmelCase       ) > 0:
    __lowerCamelCase       :       Optional[Any]           = inspect.getouterframes(inspect.currentframe()       )[1]
    __lowerCamelCase       :       List[str]           = call_frame.filename
    __lowerCamelCase       :       int           = call_frame.lineno
    __lowerCamelCase       :       Union[str, Any]           = call_frame.function
    __lowerCamelCase       ,__lowerCamelCase       :       Union[str, Any]           = next(iter(deprecated_kwargs.items()       )       )
    raise TypeError(F'{function} in {filename} line {line_number-1} got an unexpected keyword argument `{key}`'       )

  if len(_lowerCAmelCase       ) == 0:
    return
  elif len(_lowerCAmelCase       ) == 1:
    return values[0]
  return values






