






import math
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput
class        a       (					lowercase__				,			lowercase__  ):



      """simple docstring"""


      a    :					Dict							=      1
      @register_to_config
      def __init__( self   :     int						, __lowercase   :     int = 1000						, __lowercase   :     Optional[Union[np.ndarray, List[float]]] = None   )				-> Union[str, Any]:
        # set `betas`, `alphas`, `timesteps`
        self.set_timesteps(__lowercase   )

        # standard deviation of the initial noise distribution
        __UpperCAmelCase				:      List[Any]	 =    1.0

        # For now we only support F-PNDM, i.e. the runge-kutta method
        # For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
        # mainly at formula (9), (12), (13) and the Algorithm 2.
        __UpperCAmelCase				:      List[Any]	 =    4

        # running values
        __UpperCAmelCase				:      str	 =    []
      def       UpperCAmelCase ( self   :     Union[str, Any]						, __lowercase   :     int						, __lowercase   :     Union[str, torch.device] = None   )				-> int:
        __UpperCAmelCase				:      int	 =    num_inference_steps
        __UpperCAmelCase				:      Union[str, Any]	 =    torch.linspace(1						, 0						, num_inference_steps + 1   )[:-1]
        __UpperCAmelCase				:      Union[str, Any]	 =    torch.cat([steps, torch.tensor([0.0]   )]   )

        if self.config.trained_betas is not None:
          __UpperCAmelCase				:      Dict	 =    torch.tensor(self.config.trained_betas						, dtype=torch.floataa   )
        else:
          __UpperCAmelCase				:      Dict	 =    torch.sin(steps * math.pi / 2   ) ** 2

        __UpperCAmelCase				:      List[Any]	 =    (1.0 - self.betas**2) ** 0.5

        __UpperCAmelCase				:      Tuple	 =    (torch.atana(self.betas						, self.alphas   ) / math.pi * 2)[:-1]
        __UpperCAmelCase				:      Dict	 =    timesteps.to(__lowercase   )

        __UpperCAmelCase				:      Optional[Any]	 =    []
      def       UpperCAmelCase ( self   :     Optional[int]						, __lowercase   :     torch.FloatTensor						, __lowercase   :     int						, __lowercase   :     torch.FloatTensor						, __lowercase   :     bool = True						, )				-> Union[SchedulerOutput, Tuple]:
        if self.num_inference_steps is None:
          raise ValueError(
              """Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"""   )

        __UpperCAmelCase				:      List[str]	 =    (self.timesteps == timestep).nonzero().item()
        __UpperCAmelCase				:      Optional[Any]	 =    timestep_index + 1

        __UpperCAmelCase				:      List[str]	 =    sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
        self.ets.append(__lowercase   )

        if len(self.ets   ) == 1:
          __UpperCAmelCase				:      Tuple	 =    self.ets[-1]
        elif len(self.ets   ) == 2:
          __UpperCAmelCase				:      Union[str, Any]	 =    (3 * self.ets[-1] - self.ets[-2]) / 2
        elif len(self.ets   ) == 3:
          __UpperCAmelCase				:      Union[str, Any]	 =    (23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12
        else:
          __UpperCAmelCase				:      List[Any]	 =    (1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4])

        __UpperCAmelCase				:      Union[str, Any]	 =    self._get_prev_sample(__lowercase						, __lowercase						, __lowercase						, __lowercase   )

        if not return_dict:
          return (prev_sample,)

        return SchedulerOutput(prev_sample=__lowercase   )
      def       UpperCAmelCase ( self   :     Optional[Any]						, __lowercase   :     torch.FloatTensor						, *__lowercase   :     Optional[Any]						, **__lowercase   :     Any   )				-> torch.FloatTensor:
        return sample
      def       UpperCAmelCase ( self   :     Tuple						, __lowercase   :     Tuple						, __lowercase   :     List[str]						, __lowercase   :     Optional[int]						, __lowercase   :     Dict   )				-> str:
        __UpperCAmelCase				:      int	 =    self.alphas[timestep_index]
        __UpperCAmelCase				:      Tuple	 =    self.betas[timestep_index]

        __UpperCAmelCase				:      Any	 =    self.alphas[prev_timestep_index]
        __UpperCAmelCase				:      List[str]	 =    self.betas[prev_timestep_index]

        __UpperCAmelCase				:      List[str]	 =    (sample - sigma * ets) / max(__lowercase						, 1e-8   )
        __UpperCAmelCase				:      List[Any]	 =    next_alpha * pred + ets * next_sigma

        return prev_sample




      def __len__( self   :     Tuple   )				-> str:
        return self.config.num_train_timesteps








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

from packaging import version





def 						__A						(      *__lowerCamelCase				,     __lowerCamelCase = None				,     __lowerCamelCase=True				,     __lowerCamelCase=2       ) ->			Dict:
	from .. import __version__

	a									=				take_from
	a									=				()
	if not isinstance(args[0]				,     __lowerCamelCase       ):
		a									=				(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}'       )

		a									=				None
		if isinstance(__lowerCamelCase				,     __lowerCamelCase       ) and attribute in deprecated_kwargs:
			values += (deprecated_kwargs.pop(__lowerCamelCase       ),)
			a									=				f'The `{attribute}` argument is deprecated and will be removed in version {version_name}.'
		elif hasattr(__lowerCamelCase				,     __lowerCamelCase       ):
			values += (getattr(__lowerCamelCase				,     __lowerCamelCase       ),)
			a									=				f'The `{attribute}` attribute is deprecated and will be removed in version {version_name}.'
		elif deprecated_kwargs is None:
			a									=				f'`{attribute}` is deprecated and will be removed in version {version_name}.'

		if warning is not None:
			a									=				warning + """ """ if standard_warn else """"""
			warnings.warn(warning + message				,     __lowerCamelCase				,     stacklevel=__lowerCamelCase       )

	if isinstance(__lowerCamelCase				,     __lowerCamelCase       ) and len(__lowerCamelCase       ) > 0:
		a									=				inspect.getouterframes(inspect.currentframe()       )[1]
		a									=				call_frame.filename
		a									=				call_frame.lineno
		a									=				call_frame.function
		a							,					a									=				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

