





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


__lowercase							:Optional[int]							=	logging.get_logger(__name__)  # pylint: disable=invalid-name



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






							"""simple docstring"""

							snake_case_							=    42
							snake_case_							=    None







def    UpperCAmelCase				(  _lowerCamelCase   :	List[Any]      , _lowerCamelCase   :	str=0.9_9_9      , _lowerCamelCase   :	List[Any]="cosine"      , ):





						'''simple docstring'''


						if alpha_transform_type == "cosine":

												def alpha_bar_fn(_lowerCamelCase   :	Union[str, 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(_lowerCamelCase   :	List[Any]  ):
																		return math.exp(t * -1_2.0  )

						else:
												raise ValueError(f"""Unsupported alpha_tranform_type: {alpha_transform_type}"""  )

						SCREAMING_SNAKE_CASE__  :					List[Any] =       []
						for i in range(_lowerCamelCase  ):
												SCREAMING_SNAKE_CASE__  :					int =       i / num_diffusion_timesteps
												SCREAMING_SNAKE_CASE__  :					int =       (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  _a  (					lowercase__       ,				lowercase__				):






							"""simple docstring"""

							snake_case_							=    [e.name for e in KarrasDiffusionSchedulers]
							snake_case_							=    1

							@register_to_config
							def __init__(   self					:  Optional[Any]				,						a					:  int = 10_00				,						a					:  float = 0.0001				,						a					:  float = 0.02				,						a					:  str = "linear"				,						a					:  Optional[Union[np.ndarray, List[float]]] = None				,						a					:  str = "epsilon"				,						a					:  str = "linear"				,						a					:  Optional[bool] = False				,						a					:  str = "linspace"				,						a					:  int = 0				,						)						->List[Any]:
													if trained_betas is not None:
																			SCREAMING_SNAKE_CASE__  :					Any =       torch.tensor(_lowerCAmelCase				,						dtype=torch.floataa     )
													elif beta_schedule == "linear":
																			SCREAMING_SNAKE_CASE__  :					Tuple =       torch.linspace(_lowerCAmelCase				,						_lowerCAmelCase				,						_lowerCAmelCase				,						dtype=torch.floataa     )
													elif beta_schedule == "scaled_linear":
																			# this schedule is very specific to the latent diffusion model.
																			SCREAMING_SNAKE_CASE__  :					Tuple =       (
																			    torch.linspace(beta_start**0.5				,						beta_end**0.5				,						_lowerCAmelCase				,						dtype=torch.floataa     ) ** 2
																			)
													elif beta_schedule == "squaredcos_cap_v2":
																			# Glide cosine schedule
																			SCREAMING_SNAKE_CASE__  :					str =       betas_for_alpha_bar(_lowerCAmelCase     )
													else:
																			raise NotImplementedError(f"""{beta_schedule} does is not implemented for {self.__class__}"""     )

													SCREAMING_SNAKE_CASE__  :					Optional[Any] =       1.0 - self.betas
													SCREAMING_SNAKE_CASE__  :					Tuple =       torch.cumprod(self.alphas				,						dim=0     )

													SCREAMING_SNAKE_CASE__  :					Optional[Any] =       np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5     )
													SCREAMING_SNAKE_CASE__  :					List[Any] =       np.concatenate([sigmas[::-1], [0.0]]     ).astype(np.floataa     )
													SCREAMING_SNAKE_CASE__  :					Any =       torch.from_numpy(_lowerCAmelCase     )

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

							@property
							def   A_	(   self					:  Tuple     )						->Optional[Any]:
													# 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   A_	(   self					:  List[Any]				,						a					:  torch.FloatTensor				,						a					:  Union[float, torch.FloatTensor]     )						->List[Any]:
													if isinstance(_lowerCAmelCase				,						torch.Tensor     ):
																			SCREAMING_SNAKE_CASE__  :					Any =       timestep.to(self.timesteps.device     )
													SCREAMING_SNAKE_CASE__  :					Tuple =       (self.timesteps == timestep).nonzero().item()
													SCREAMING_SNAKE_CASE__  :					List[Any] =       self.sigmas[step_index]

													SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       sample / ((sigma**2 + 1) ** 0.5)

													SCREAMING_SNAKE_CASE__  :					int =       True
													return sample

							def   A_	(   self					:  Union[str, Any]				,						a					:  int				,						a					:  Union[str, torch.device] = None     )						->Optional[int]:
													SCREAMING_SNAKE_CASE__  :					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":
																			SCREAMING_SNAKE_CASE__  :					Tuple =       np.linspace(0				,						self.config.num_train_timesteps - 1				,						_lowerCAmelCase				,						dtype=_lowerCAmelCase     )[
																			    ::-1
																			].copy()
													elif self.config.timestep_spacing == "leading":
																			SCREAMING_SNAKE_CASE__  :					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
																			SCREAMING_SNAKE_CASE__  :					Tuple =       (np.arange(0				,						_lowerCAmelCase     ) * step_ratio).round()[::-1].copy().astype(_lowerCAmelCase     )
																			timesteps += self.config.steps_offset
													elif self.config.timestep_spacing == "trailing":
																			SCREAMING_SNAKE_CASE__  :					Any =       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
																			SCREAMING_SNAKE_CASE__  :					Dict =       (np.arange(self.config.num_train_timesteps				,						0				,						-step_ratio     )).round().copy().astype(_lowerCAmelCase     )
																			timesteps -= 1
													else:
																			raise ValueError(
																			    f"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."""     )

													SCREAMING_SNAKE_CASE__  :					List[Any] =       np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5     )
													SCREAMING_SNAKE_CASE__  :					Dict =       np.log(_lowerCAmelCase     )

													if self.config.interpolation_type == "linear":
																			SCREAMING_SNAKE_CASE__  :					Any =       np.interp(_lowerCAmelCase				,						np.arange(0				,						len(_lowerCAmelCase     )     )				,						_lowerCAmelCase     )
													elif self.config.interpolation_type == "log_linear":
																			SCREAMING_SNAKE_CASE__  :					List[Any] =       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:
																			SCREAMING_SNAKE_CASE__  :					List[str] =       self._convert_to_karras(in_sigmas=_lowerCAmelCase				,						num_inference_steps=self.num_inference_steps     )
																			SCREAMING_SNAKE_CASE__  :					int =       np.array([self._sigma_to_t(_lowerCAmelCase				,						_lowerCAmelCase     ) for sigma in sigmas]     )

													SCREAMING_SNAKE_CASE__  :					Optional[Any] =       np.concatenate([sigmas, [0.0]]     ).astype(np.floataa     )
													SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       torch.from_numpy(_lowerCAmelCase     ).to(device=_lowerCAmelCase     )
													if str(_lowerCAmelCase     ).startswith("mps"     ):
																			# mps does not support float64
																			SCREAMING_SNAKE_CASE__  :					Optional[Any] =       torch.from_numpy(_lowerCAmelCase     ).to(_lowerCAmelCase				,						dtype=torch.floataa     )
													else:
																			SCREAMING_SNAKE_CASE__  :					Dict =       torch.from_numpy(_lowerCAmelCase     ).to(device=_lowerCAmelCase     )

							def   A_	(   self					:  Any				,						a					:  str				,						a					:  Optional[Any]     )						->Any:
													# get log sigma
													SCREAMING_SNAKE_CASE__  :					Dict =       np.log(_lowerCAmelCase     )

													# get distribution
													SCREAMING_SNAKE_CASE__  :					Optional[Any] =       log_sigma - log_sigmas[:, np.newaxis]

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

													SCREAMING_SNAKE_CASE__  :					Dict =       log_sigmas[low_idx]
													SCREAMING_SNAKE_CASE__  :					int =       log_sigmas[high_idx]

													# interpolate sigmas
													SCREAMING_SNAKE_CASE__  :					Optional[Any] =       (low - log_sigma) / (low - high)
													SCREAMING_SNAKE_CASE__  :					Tuple =       np.clip(_lowerCAmelCase				,						0				,						1     )

													# transform interpolation to time range
													SCREAMING_SNAKE_CASE__  :					List[Any] =       (1 - w) * low_idx + w * high_idx
													SCREAMING_SNAKE_CASE__  :					Dict =       t.reshape(sigma.shape     )
													return t

							def   A_	(   self					:  List[Any]				,						a					:  torch.FloatTensor				,						a					:  Optional[Any]     )						->Dict:
													SCREAMING_SNAKE_CASE__  :					float =       in_sigmas[-1].item()
													SCREAMING_SNAKE_CASE__  :					float =       in_sigmas[0].item()

													SCREAMING_SNAKE_CASE__  :					int =       7.0  # 7.0 is the value used in the paper
													SCREAMING_SNAKE_CASE__  :					Tuple =       np.linspace(0				,						1				,						_lowerCAmelCase     )
													SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       sigma_min ** (1 / rho)
													SCREAMING_SNAKE_CASE__  :					List[Any] =       sigma_max ** (1 / rho)
													SCREAMING_SNAKE_CASE__  :					int =       (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
													return sigmas

							def   A_	(   self					:  List[Any]				,						a					:  torch.FloatTensor				,						a					:  Union[float, torch.FloatTensor]				,						a					:  torch.FloatTensor				,						a					:  float = 0.0				,						a					:  float = 0.0				,						a					:  float = float("inf"     )				,						a					:  float = 1.0				,						a					:  Optional[torch.Generator] = None				,						a					:  bool = True				,						)						->Dict:

													if (
													    isinstance(_lowerCAmelCase				,						_lowerCAmelCase     )
													    or isinstance(_lowerCAmelCase				,						torch.IntTensor     )
													    or isinstance(_lowerCAmelCase				,						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(_lowerCAmelCase				,						torch.Tensor     ):
																			SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       timestep.to(self.timesteps.device     )

													SCREAMING_SNAKE_CASE__  :					Optional[Any] =       (self.timesteps == timestep).nonzero().item()
													SCREAMING_SNAKE_CASE__  :					Optional[Any] =       self.sigmas[step_index]

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

													SCREAMING_SNAKE_CASE__  :					List[str] =       randn_tensor(
													    model_output.shape				,						dtype=model_output.dtype				,						device=model_output.device				,						generator=_lowerCAmelCase     )

													SCREAMING_SNAKE_CASE__  :					Any =       noise * s_noise
													SCREAMING_SNAKE_CASE__  :					List[str] =       sigma * (gamma + 1)

													if gamma > 0:
																			SCREAMING_SNAKE_CASE__  :					int =       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":
																			SCREAMING_SNAKE_CASE__  :					str =       model_output
													elif self.config.prediction_type == "epsilon":
																			SCREAMING_SNAKE_CASE__  :					Optional[int] =       sample - sigma_hat * model_output
													elif self.config.prediction_type == "v_prediction":
																			# * c_out + input * c_skip
																			SCREAMING_SNAKE_CASE__  :					List[Any] =       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
													SCREAMING_SNAKE_CASE__  :					Dict =       (sample - pred_original_sample) / sigma_hat

													SCREAMING_SNAKE_CASE__  :					List[str] =       self.sigmas[step_index + 1] - sigma_hat

													SCREAMING_SNAKE_CASE__  :					Any =       sample + derivative * dt

													if not return_dict:
																			return (prev_sample,)

													return EulerDiscreteSchedulerOutput(prev_sample=_lowerCAmelCase				,						pred_original_sample=_lowerCAmelCase     )

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

													SCREAMING_SNAKE_CASE__  :					int =       [(schedule_timesteps == t).nonzero().item() for t in timesteps]

													SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       sigmas[step_indices].flatten()
													while len(sigma.shape     ) < len(original_samples.shape     ):
																			SCREAMING_SNAKE_CASE__  :					List[str] =       sigma.unsqueeze(-1     )

													SCREAMING_SNAKE_CASE__  :					List[Any] =       original_samples + noise * sigma
													return noisy_samples






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





def    UpperCAmelCase__	(  lowerCamelCase ):
  lowercase     :Optional[int]   						=    len(lowerCamelCase )
  if n in (0, 1):
    return 0
  return sum(query * (n - i - 1) for i, query in enumerate(sorted(lowerCamelCase ) ) )


if __name__ == "__main__":
     import doctest

     doctest.testmod()



