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

import numpy as np
import torch

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


__lowerCAmelCase   :     Tuple      =						logging.get_logger(__name__)  # pylint: disable=invalid-name


@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
class   __lowerCAmelCase		(		lowerCAmelCase_ ):



      """simple docstring"""







      A__		:     torch.FloatTensor
      A__		:     Optional[torch.FloatTensor]       				=      None






def       UpperCAmelCase_					(  __lowerCAmelCase		,   __lowerCAmelCase=0.999		,   __lowerCAmelCase="cosine"		,   )	->  str:
    if alpha_transform_type == "cosine":

        def alpha_bar_fn(__lowerCAmelCase		):
            return math.cos((t + 0.008) / 1.008 * math.pi / 2		) ** 2

    elif alpha_transform_type == "exp":

        def alpha_bar_fn(__lowerCAmelCase		):
            return math.exp(t * -12.0		)

    else:
        raise ValueError(F'Unsupported alpha_tranform_type: {alpha_transform_type}'		)

    __lowercase						:     Union[str, Any]		      =    []
    for i in range(__lowerCAmelCase		):
        __lowercase						:     int		      =    i / num_diffusion_timesteps
        __lowercase						:     Union[str, Any]		      =    (i + 1) / num_diffusion_timesteps
        betas.append(min(1 - alpha_bar_fn(__lowerCAmelCase		) / alpha_bar_fn(__lowerCAmelCase		)		,   __lowerCAmelCase		)		)
    return torch.tensor(__lowerCAmelCase		,   dtype=torch.floataa		)





class   __lowerCAmelCase		(		lowerCAmelCase_	,	lowerCAmelCase_ ):



      """simple docstring"""







      A__		:     Dict       				=      [e.name for e in KarrasDiffusionSchedulers]
      A__		:     Optional[int]       				=      1




      @register_to_config
      def __init__( self						:    Union[str, Any]			,    _snake_case						:    int = 1000			,    _snake_case						:    float = 0.00_01			,    _snake_case						:    float = 0.02			,    _snake_case						:    str = "linear"			,    _snake_case						:    Optional[Union[np.ndarray, List[float]]] = None			,    _snake_case						:    str = "epsilon"			,    _snake_case						:    str = "linspace"			,    _snake_case						:    int = 0			,    ):
          if trained_betas is not None:
              __lowercase						:     Optional[Any]		      =    torch.tensor(_snake_case			,    dtype=torch.floataa			)
          elif beta_schedule == "linear":
              __lowercase						:     Optional[Any]		      =    torch.linspace(_snake_case			,    _snake_case			,    _snake_case			,    dtype=torch.floataa			)
          elif beta_schedule == "scaled_linear":
              # this schedule is very specific to the latent diffusion model.
              __lowercase						:     int		      =    (
                  torch.linspace(beta_start**0.5			,    beta_end**0.5			,    _snake_case			,    dtype=torch.floataa			) ** 2
              )
          elif beta_schedule == "squaredcos_cap_v2":
              # Glide cosine schedule
              __lowercase						:     Optional[Any]		      =    betas_for_alpha_bar(_snake_case			)
          else:
              raise NotImplementedError(F'{beta_schedule} does is not implemented for {self.__class__}'			)

          __lowercase						:     List[str]		      =    1.0 - self.betas
          __lowercase						:     List[Any]		      =    torch.cumprod(self.alphas			,    dim=0			)

          __lowercase						:     Union[str, Any]		      =    np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5			)
          __lowercase						:     Dict		      =    np.concatenate([sigmas[::-1], [0.0]]			).astype(np.floataa			)
          __lowercase						:     Dict		      =    torch.from_numpy(_snake_case			)

          # setable values
          __lowercase						:     Union[str, Any]		      =    None
          __lowercase						:     Any		      =    np.linspace(0			,    num_train_timesteps - 1			,    _snake_case			,    dtype=_snake_case			)[::-1].copy()
          __lowercase						:     Any		      =    torch.from_numpy(_snake_case			)
          __lowercase						:     List[str]		      =    False




      @property
      def 						snake_case_     ( self						:    Optional[int]			):
          # standard deviation of the initial noise distribution
          if self.config.timestep_spacing in ["linspace", "trailing"]:
              return self.sigmas.max()

          return (self.sigmas.max() ** 2 + 1) ** 0.5




      def 						snake_case_     ( self						:    int			,    _snake_case						:    torch.FloatTensor			,    _snake_case						:    Union[float, torch.FloatTensor]			):
          if isinstance(_snake_case			,    torch.Tensor			):
              __lowercase						:     int		      =    timestep.to(self.timesteps.device			)
          __lowercase						:     str		      =    (self.timesteps == timestep).nonzero().item()
          __lowercase						:     Any		      =    self.sigmas[step_index]
          __lowercase						:     Dict		      =    sample / ((sigma**2 + 1) ** 0.5)
          __lowercase						:     Union[str, Any]		      =    True
          return sample




      def 						snake_case_     ( self						:    int			,    _snake_case						:    int			,    _snake_case						:    Union[str, torch.device] = None			):
          __lowercase						:     Optional[Any]		      =    num_inference_steps

          # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
          if self.config.timestep_spacing == "linspace":
              __lowercase						:     Any		      =    np.linspace(0			,    self.config.num_train_timesteps - 1			,    _snake_case			,    dtype=_snake_case			)[
                  ::-1
              ].copy()
          elif self.config.timestep_spacing == "leading":
              __lowercase						:     int		      =    self.config.num_train_timesteps // self.num_inference_steps
              # creates integer timesteps by multiplying by ratio
              # casting to int to avoid issues when num_inference_step is power of 3
              __lowercase						:     List[str]		      =    (np.arange(0			,    _snake_case			) * step_ratio).round()[::-1].copy().astype(_snake_case			)
              timesteps += self.config.steps_offset
          elif self.config.timestep_spacing == "trailing":
              __lowercase						:     Tuple		      =    self.config.num_train_timesteps / self.num_inference_steps
              # creates integer timesteps by multiplying by ratio
              # casting to int to avoid issues when num_inference_step is power of 3
              __lowercase						:     Optional[Any]		      =    (np.arange(self.config.num_train_timesteps			,    0			,    -step_ratio			)).round().copy().astype(_snake_case			)
              timesteps -= 1
          else:
              raise ValueError(
                  F'{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'			)

          __lowercase						:     Optional[int]		      =    np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5			)
          __lowercase						:     Optional[int]		      =    np.interp(_snake_case			,    np.arange(0			,    len(_snake_case			)			)			,    _snake_case			)
          __lowercase						:     Any		      =    np.concatenate([sigmas, [0.0]]			).astype(np.floataa			)
          __lowercase						:     Dict		      =    torch.from_numpy(_snake_case			).to(device=_snake_case			)
          if str(_snake_case			).startswith('''mps'''			):
              # mps does not support float64
              __lowercase						:     int		      =    torch.from_numpy(_snake_case			).to(_snake_case			,    dtype=torch.floataa			)
          else:
              __lowercase						:     List[Any]		      =    torch.from_numpy(_snake_case			).to(device=_snake_case			)




      def 						snake_case_     ( self						:    List[Any]			,    _snake_case						:    torch.FloatTensor			,    _snake_case						:    Union[float, torch.FloatTensor]			,    _snake_case						:    torch.FloatTensor			,    _snake_case						:    Optional[torch.Generator] = None			,    _snake_case						:    bool = True			,    ):

          if (
              isinstance(_snake_case			,    _snake_case			)
              or isinstance(_snake_case			,    torch.IntTensor			)
              or isinstance(_snake_case			,    torch.LongTensor			)
          ):
              raise ValueError(
                  (
                      '''Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to'''
                      ''' `EulerDiscreteScheduler.step()` is not supported. Make sure to pass'''
                      ''' one of the `scheduler.timesteps` as a timestep.'''
                  )			,    )

          if not self.is_scale_input_called:
              logger.warning(
                  '''The `scale_model_input` function should be called before `step` to ensure correct denoising. '''
                  '''See `StableDiffusionPipeline` for a usage example.'''			)

          if isinstance(_snake_case			,    torch.Tensor			):
              __lowercase						:     Any		      =    timestep.to(self.timesteps.device			)

          __lowercase						:     Any		      =    (self.timesteps == timestep).nonzero().item()
          __lowercase						:     List[str]		      =    self.sigmas[step_index]

          # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
          if self.config.prediction_type == "epsilon":
              __lowercase						:     List[str]		      =    sample - sigma * model_output
          elif self.config.prediction_type == "v_prediction":
              # * c_out + input * c_skip
              __lowercase						:     Optional[int]		      =    model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
          elif self.config.prediction_type == "sample":
              raise NotImplementedError('''prediction_type not implemented yet: sample'''			)
          else:
              raise ValueError(
                  F'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'			)

          __lowercase						:     List[str]		      =    self.sigmas[step_index]
          __lowercase						:     Optional[Any]		      =    self.sigmas[step_index + 1]
          __lowercase						:     Union[str, Any]		      =    (sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
          __lowercase						:     Any		      =    (sigma_to**2 - sigma_up**2) ** 0.5

          # 2. Convert to an ODE derivative
          __lowercase						:     Dict		      =    (sample - pred_original_sample) / sigma

          __lowercase						:     Tuple		      =    sigma_down - sigma

          __lowercase						:     Any		      =    sample + derivative * dt

          __lowercase						:     int		      =    model_output.device
          __lowercase						:     Optional[Any]		      =    randn_tensor(model_output.shape			,    dtype=model_output.dtype			,    device=_snake_case			,    generator=_snake_case			)

          __lowercase						:     Tuple		      =    prev_sample + noise * sigma_up

          if not return_dict:
              return (prev_sample,)

          return EulerAncestralDiscreteSchedulerOutput(
              prev_sample=_snake_case			,    pred_original_sample=_snake_case			)




      def 						snake_case_     ( self						:    Tuple			,    _snake_case						:    torch.FloatTensor			,    _snake_case						:    torch.FloatTensor			,    _snake_case						:    torch.FloatTensor			,    ):
          # Make sure sigmas and timesteps have the same device and dtype as original_samples
          __lowercase						:     Dict		      =    self.sigmas.to(device=original_samples.device			,    dtype=original_samples.dtype			)
          if original_samples.device.type == "mps" and torch.is_floating_point(_snake_case			):
              # mps does not support float64
              __lowercase						:     str		      =    self.timesteps.to(original_samples.device			,    dtype=torch.floataa			)
              __lowercase						:     Optional[Any]		      =    timesteps.to(original_samples.device			,    dtype=torch.floataa			)
          else:
              __lowercase						:     Optional[Any]		      =    self.timesteps.to(original_samples.device			)
              __lowercase						:     List[Any]		      =    timesteps.to(original_samples.device			)

          __lowercase						:     List[Any]		      =    [(schedule_timesteps == t).nonzero().item() for t in timesteps]

          __lowercase						:     Optional[Any]		      =    sigmas[step_indices].flatten()
          while len(sigma.shape			) < len(original_samples.shape			):
              __lowercase						:     List[Any]		      =    sigma.unsqueeze(-1			)

          __lowercase						:     Optional[Any]		      =    original_samples + noise * sigma
          return noisy_samples




      def __len__( self						:    str			):
          return self.config.num_train_timesteps




from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__lowerCAmelCase   :     Dict      =						logging.get_logger(__name__)


class   __lowerCAmelCase		(		lowerCAmelCase_ ):



      """simple docstring"""







      A__		:     Union[str, Any]       				=      ['''pixel_values''']




      def __init__( self						:    int			,    _snake_case						:    bool = True			,    _snake_case						:    Optional[Dict[str, int]] = None			,    _snake_case						:    PILImageResampling = PILImageResampling.BILINEAR			,    _snake_case						:    bool = True			,    _snake_case						:    Union[int, float] = 1 / 255			,    _snake_case						:    bool = True			,    _snake_case						:    Optional[Union[float, List[float]]] = None			,    _snake_case						:    Optional[Union[float, List[float]]] = None			,    **_snake_case						:    List[str]			,    ):
          super().__init__(**_snake_case			)
          __lowercase						:     Tuple		      =    size if size is not None else {'''height''': 224, '''width''': 224}
          __lowercase						:     List[str]		      =    get_size_dict(_snake_case			)
          __lowercase						:     Union[str, Any]		      =    do_resize
          __lowercase						:     int		      =    do_rescale
          __lowercase						:     List[Any]		      =    do_normalize
          __lowercase						:     Dict		      =    size
          __lowercase						:     Any		      =    resample
          __lowercase						:     int		      =    rescale_factor
          __lowercase						:     Optional[Any]		      =    image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
          __lowercase						:     Optional[Any]		      =    image_std if image_std is not None else IMAGENET_STANDARD_STD




      def 						snake_case_     ( self						:    int			,    _snake_case						:    np.ndarray			,    _snake_case						:    Dict[str, int]			,    _snake_case						:    PILImageResampling = PILImageResampling.BILINEAR			,    _snake_case						:    Optional[Union[str, ChannelDimension]] = None			,    **_snake_case						:    Optional[int]			,    ):
          __lowercase						:     List[Any]		      =    get_size_dict(_snake_case			)
          if "height" not in size or "width" not in size:
              raise ValueError(F'The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}'			)
          return resize(
              _snake_case			,    size=(size['''height'''], size['''width'''])			,    resample=_snake_case			,    data_format=_snake_case			,    **_snake_case			)




      def 						snake_case_     ( self						:    Union[str, Any]			,    _snake_case						:    np.ndarray			,    _snake_case						:    float			,    _snake_case						:    Optional[Union[str, ChannelDimension]] = None			,    **_snake_case						:    Dict			):
          return rescale(_snake_case			,    scale=_snake_case			,    data_format=_snake_case			,    **_snake_case			)




      def 						snake_case_     ( self						:    Any			,    _snake_case						:    np.ndarray			,    _snake_case						:    Union[float, List[float]]			,    _snake_case						:    Union[float, List[float]]			,    _snake_case						:    Optional[Union[str, ChannelDimension]] = None			,    **_snake_case						:    Any			,    ):
          return normalize(_snake_case			,    mean=_snake_case			,    std=_snake_case			,    data_format=_snake_case			,    **_snake_case			)




      def 						snake_case_     ( self						:    Dict			,    _snake_case						:    ImageInput			,    _snake_case						:    Optional[bool] = None			,    _snake_case						:    Dict[str, int] = None			,    _snake_case						:    PILImageResampling = None			,    _snake_case						:    Optional[bool] = None			,    _snake_case						:    Optional[float] = None			,    _snake_case						:    Optional[bool] = None			,    _snake_case						:    Optional[Union[float, List[float]]] = None			,    _snake_case						:    Optional[Union[float, List[float]]] = None			,    _snake_case						:    Optional[Union[str, TensorType]] = None			,    _snake_case						:    Union[str, ChannelDimension] = ChannelDimension.FIRST			,    **_snake_case						:    str			,    ):
          __lowercase						:     List[Any]		      =    do_resize if do_resize is not None else self.do_resize
          __lowercase						:     Any		      =    do_rescale if do_rescale is not None else self.do_rescale
          __lowercase						:     Optional[Any]		      =    do_normalize if do_normalize is not None else self.do_normalize
          __lowercase						:     Optional[Any]		      =    resample if resample is not None else self.resample
          __lowercase						:     Tuple		      =    rescale_factor if rescale_factor is not None else self.rescale_factor
          __lowercase						:     Dict		      =    image_mean if image_mean is not None else self.image_mean
          __lowercase						:     Any		      =    image_std if image_std is not None else self.image_std

          __lowercase						:     Optional[int]		      =    size if size is not None else self.size
          __lowercase						:     Tuple		      =    get_size_dict(_snake_case			)

          __lowercase						:     Optional[int]		      =    make_list_of_images(_snake_case			)

          if not valid_images(_snake_case			):
              raise ValueError(
                  '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
                  '''torch.Tensor, tf.Tensor or jax.ndarray.'''			)

          if do_resize and size is None:
              raise ValueError('''Size must be specified if do_resize is True.'''			)

          if do_rescale and rescale_factor is None:
              raise ValueError('''Rescale factor must be specified if do_rescale is True.'''			)

          # All transformations expect numpy arrays.
          __lowercase						:     Optional[Any]		      =    [to_numpy_array(_snake_case			) for image in images]

          if do_resize:
              __lowercase						:     List[Any]		      =    [self.resize(image=_snake_case			,    size=_snake_case			,    resample=_snake_case			) for image in images]

          if do_rescale:
              __lowercase						:     Tuple		      =    [self.rescale(image=_snake_case			,    scale=_snake_case			) for image in images]

          if do_normalize:
              __lowercase						:     Union[str, Any]		      =    [self.normalize(image=_snake_case			,    mean=_snake_case			,    std=_snake_case			) for image in images]

          __lowercase						:     Tuple		      =    [to_channel_dimension_format(_snake_case			,    _snake_case			) for image in images]

          __lowercase						:     List[str]		      =    {'''pixel_values''': images}
          return BatchFeature(data=_snake_case			,    tensor_type=_snake_case			)


