
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


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


@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerDiscrete
class 			UpperCamelCase_				(  snake_case__     ):




      '''simple docstring'''







      a		:torch.FloatTensor
      a		:Optional[torch.FloatTensor]						 =      None
def lowerCamelCase_       (			A			:      Dict		,					A			:      List[str]=0.9_9_9		,					A			:      Tuple="cosine"		,					):




      """simple docstring"""






      if alpha_transform_type == "cosine":

            def alpha_bar_fn(A			:      Any						):
                  return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2						) ** 2

      elif alpha_transform_type == "exp":

            def alpha_bar_fn(A			:      Optional[int]						):
                  return math.exp(t * -12.0						)

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

      lowerCAmelCase_						=      []
      for i in range(A						):
            lowerCAmelCase_						=      i / num_diffusion_timesteps
            lowerCAmelCase_						=      (i + 1) / num_diffusion_timesteps
            betas.append(min(1 - alpha_bar_fn(A						) / alpha_bar_fn(A						)		,					A						)						)
      return torch.tensor(A		,					dtype=torch.floataa						)




class 			UpperCamelCase_				(  snake_case__					,       snake_case__     ):




      '''simple docstring'''







      a		:List[Any]						 =      [e.name for e in KarrasDiffusionSchedulers]
      a		:Tuple						 =      1







      @register_to_config
      def __init__( self					,					_UpperCAmelCase = 1_000					,					_UpperCAmelCase = 0.0001					,					_UpperCAmelCase = 0.02					,					_UpperCAmelCase = "linear"					,					_UpperCAmelCase = None					,					_UpperCAmelCase = "epsilon"					,					_UpperCAmelCase = "linear"					,					_UpperCAmelCase = False					,					_UpperCAmelCase = "linspace"					,					_UpperCAmelCase = 0					,					):
            if trained_betas is not None:
                  lowerCAmelCase_						=      torch.tensor(_UpperCAmelCase					,					dtype=torch.floataa)
            elif beta_schedule == "linear":
                  lowerCAmelCase_						=      torch.linspace(_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase					,					dtype=torch.floataa)
            elif beta_schedule == "scaled_linear":
                  # this schedule is very specific to the latent diffusion model.
                  lowerCAmelCase_						=      (
                      torch.linspace(beta_start**0.5					,					beta_end**0.5					,					_UpperCAmelCase					,					dtype=torch.floataa) ** 2
                  )
            elif beta_schedule == "squaredcos_cap_v2":
                  # Glide cosine schedule
                  lowerCAmelCase_						=      betas_for_alpha_bar(_UpperCAmelCase)
            else:
                  raise NotImplementedError(f'{beta_schedule} does is not implemented for {self.__class__}')

            lowerCAmelCase_						=      1.0 - self.betas
            lowerCAmelCase_						=      torch.cumprod(self.alphas					,					dim=0)

            lowerCAmelCase_						=      np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
            lowerCAmelCase_						=      np.concatenate([sigmas[::-1], [0.0]]).astype(np.floataa)
            lowerCAmelCase_						=      torch.from_numpy(_UpperCAmelCase)

            # setable values
            lowerCAmelCase_						=      None
            lowerCAmelCase_						=      np.linspace(0					,					num_train_timesteps - 1					,					_UpperCAmelCase					,					dtype=_UpperCAmelCase)[::-1].copy()
            lowerCAmelCase_						=      torch.from_numpy(_UpperCAmelCase)
            lowerCAmelCase_						=      False
            lowerCAmelCase_						=      use_karras_sigmas







      @property
      def lowercase__		( self):
            # 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 lowercase__		( self					,					_UpperCAmelCase					,					_UpperCAmelCase):
            if isinstance(_UpperCAmelCase					,					torch.Tensor):
                  lowerCAmelCase_						=      timestep.to(self.timesteps.device)
            lowerCAmelCase_						=      (self.timesteps == timestep).nonzero().item()
            lowerCAmelCase_						=      self.sigmas[step_index]

            lowerCAmelCase_						=      sample / ((sigma**2 + 1) ** 0.5)

            lowerCAmelCase_						=      True
            return sample







      def lowercase__		( self					,					_UpperCAmelCase					,					_UpperCAmelCase = None):
            lowerCAmelCase_						=      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":
                  lowerCAmelCase_						=      np.linspace(0					,					self.config.num_train_timesteps - 1					,					_UpperCAmelCase					,					dtype=_UpperCAmelCase)[
                      ::-1
                  ].copy()
            elif self.config.timestep_spacing == "leading":
                  lowerCAmelCase_						=      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
                  lowerCAmelCase_						=      (np.arange(0					,					_UpperCAmelCase) * step_ratio).round()[::-1].copy().astype(_UpperCAmelCase)
                  timesteps += self.config.steps_offset
            elif self.config.timestep_spacing == "trailing":
                  lowerCAmelCase_						=      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
                  lowerCAmelCase_						=      (np.arange(self.config.num_train_timesteps					,					0					,					-step_ratio)).round().copy().astype(_UpperCAmelCase)
                  timesteps -= 1
            else:
                  raise ValueError(
                      f'{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.')

            lowerCAmelCase_						=      np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
            lowerCAmelCase_						=      np.log(_UpperCAmelCase)

            if self.config.interpolation_type == "linear":
                  lowerCAmelCase_						=      np.interp(_UpperCAmelCase					,					np.arange(0					,					len(_UpperCAmelCase))					,					_UpperCAmelCase)
            elif self.config.interpolation_type == "log_linear":
                  lowerCAmelCase_						=      torch.linspace(np.log(sigmas[-1])					,					np.log(sigmas[0])					,					num_inference_steps + 1).exp()
            else:
                  raise ValueError(
                      f'{self.config.interpolation_type} is not implemented. Please specify interpolation_type to either'
                      ''' \'linear\' or \'log_linear\'''')

            if self.use_karras_sigmas:
                  lowerCAmelCase_						=      self._convert_to_karras(in_sigmas=_UpperCAmelCase					,					num_inference_steps=self.num_inference_steps)
                  lowerCAmelCase_						=      np.array([self._sigma_to_t(_UpperCAmelCase					,					_UpperCAmelCase) for sigma in sigmas])

            lowerCAmelCase_						=      np.concatenate([sigmas, [0.0]]).astype(np.floataa)
            lowerCAmelCase_						=      torch.from_numpy(_UpperCAmelCase).to(device=_UpperCAmelCase)
            if str(_UpperCAmelCase).startswith('''mps'''):
                  # mps does not support float64
                  lowerCAmelCase_						=      torch.from_numpy(_UpperCAmelCase).to(_UpperCAmelCase					,					dtype=torch.floataa)
            else:
                  lowerCAmelCase_						=      torch.from_numpy(_UpperCAmelCase).to(device=_UpperCAmelCase)







      def lowercase__		( self					,					_UpperCAmelCase					,					_UpperCAmelCase):
            # get log sigma
            lowerCAmelCase_						=      np.log(_UpperCAmelCase)

            # get distribution
            lowerCAmelCase_						=      log_sigma - log_sigmas[:, np.newaxis]

            # get sigmas range
            lowerCAmelCase_						=      np.cumsum((dists >= 0)					,					axis=0).argmax(axis=0).clip(max=log_sigmas.shape[0] - 2)
            lowerCAmelCase_						=      low_idx + 1

            lowerCAmelCase_						=      log_sigmas[low_idx]
            lowerCAmelCase_						=      log_sigmas[high_idx]

            # interpolate sigmas
            lowerCAmelCase_						=      (low - log_sigma) / (low - high)
            lowerCAmelCase_						=      np.clip(_UpperCAmelCase					,					0					,					1)

            # transform interpolation to time range
            lowerCAmelCase_						=      (1 - w) * low_idx + w * high_idx
            lowerCAmelCase_						=      t.reshape(sigma.shape)
            return t







      def lowercase__		( self					,					_UpperCAmelCase					,					_UpperCAmelCase):
            lowerCAmelCase_						=      in_sigmas[-1].item()
            lowerCAmelCase_						=      in_sigmas[0].item()

            lowerCAmelCase_						=      7.0  # 7.0 is the value used in the paper
            lowerCAmelCase_						=      np.linspace(0					,					1					,					_UpperCAmelCase)
            lowerCAmelCase_						=      sigma_min ** (1 / rho)
            lowerCAmelCase_						=      sigma_max ** (1 / rho)
            lowerCAmelCase_						=      (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
            return sigmas







      def lowercase__		( self					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase = 0.0					,					_UpperCAmelCase = 0.0					,					_UpperCAmelCase = float('''inf''')					,					_UpperCAmelCase = 1.0					,					_UpperCAmelCase = None					,					_UpperCAmelCase = True					,					):

            if (
                isinstance(_UpperCAmelCase					,					_UpperCAmelCase)
                or isinstance(_UpperCAmelCase					,					torch.IntTensor)
                or isinstance(_UpperCAmelCase					,					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(_UpperCAmelCase					,					torch.Tensor):
                  lowerCAmelCase_						=      timestep.to(self.timesteps.device)

            lowerCAmelCase_						=      (self.timesteps == timestep).nonzero().item()
            lowerCAmelCase_						=      self.sigmas[step_index]

            lowerCAmelCase_						=      min(s_churn / (len(self.sigmas) - 1)					,					2**0.5 - 1) if s_tmin <= sigma <= s_tmax else 0.0

            lowerCAmelCase_						=      randn_tensor(
                model_output.shape					,					dtype=model_output.dtype					,					device=model_output.device					,					generator=_UpperCAmelCase)

            lowerCAmelCase_						=      noise * s_noise
            lowerCAmelCase_						=      sigma * (gamma + 1)

            if gamma > 0:
                  lowerCAmelCase_						=      sample + eps * (sigma_hat**2 - sigma**2) ** 0.5

            # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
            # NOTE: "original_sample" should not be an expected prediction_type but is left in for
            # backwards compatibility
            if self.config.prediction_type == "original_sample" or self.config.prediction_type == "sample":
                  lowerCAmelCase_						=      model_output
            elif self.config.prediction_type == "epsilon":
                  lowerCAmelCase_						=      sample - sigma_hat * model_output
            elif self.config.prediction_type == "v_prediction":
                  # * c_out + input * c_skip
                  lowerCAmelCase_						=      model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
            else:
                  raise ValueError(
                      f'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`')

            # 2. Convert to an ODE derivative
            lowerCAmelCase_						=      (sample - pred_original_sample) / sigma_hat

            lowerCAmelCase_						=      self.sigmas[step_index + 1] - sigma_hat

            lowerCAmelCase_						=      sample + derivative * dt

            if not return_dict:
                  return (prev_sample,)

            return EulerDiscreteSchedulerOutput(prev_sample=_UpperCAmelCase					,					pred_original_sample=_UpperCAmelCase)







      def lowercase__		( self					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase					,					):
            # Make sure sigmas and timesteps have the same device and dtype as original_samples
            lowerCAmelCase_						=      self.sigmas.to(device=original_samples.device					,					dtype=original_samples.dtype)
            if original_samples.device.type == "mps" and torch.is_floating_point(_UpperCAmelCase):
                  # mps does not support float64
                  lowerCAmelCase_						=      self.timesteps.to(original_samples.device					,					dtype=torch.floataa)
                  lowerCAmelCase_						=      timesteps.to(original_samples.device					,					dtype=torch.floataa)
            else:
                  lowerCAmelCase_						=      self.timesteps.to(original_samples.device)
                  lowerCAmelCase_						=      timesteps.to(original_samples.device)

            lowerCAmelCase_						=      [(schedule_timesteps == t).nonzero().item() for t in timesteps]

            lowerCAmelCase_						=      sigmas[step_indices].flatten()
            while len(sigma.shape) < len(original_samples.shape):
                  lowerCAmelCase_						=      sigma.unsqueeze(-1)

            lowerCAmelCase_						=      original_samples + noise * sigma
            return noisy_samples







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






def _a					(			SCREAMING_SNAKE_CASE						:     str						)       ->       str:

 """simple docstring"""






 return "".join(chr(ord(SCREAMING_SNAKE_CASE						) + 32						) if 'A' <= char <= 'Z' else char for char in word						)


if __name__ == "__main__":
  from doctest import testmod

  testmod()





