

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


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






@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
class _UpperCamelCase							(							lowerCAmelCase__				):
   '''simple docstring'''







   lowerCamelCase__          =42
   lowerCamelCase__          =None

def 	lowerCamelCase__	(   _a							,     _a=0.999							,     _a="cosine"							,     ):
 if alpha_transform_type == "cosine":

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

 elif alpha_transform_type == "exp":

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

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

 SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    []
 for i in range(_a):
  SCREAMING_SNAKE_CASE				: int			       =    i / num_diffusion_timesteps
  SCREAMING_SNAKE_CASE				: Any			       =    (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							(							lowerCAmelCase__							,		lowerCAmelCase__				):
   '''simple docstring'''







   lowerCamelCase__          =[e.name for e in KarrasDiffusionSchedulers]
   lowerCamelCase__          =1
   @register_to_config
   def __init__(     self							:	str , a							:	int = 1000 , a							:	float = 0.0001 , a							:	float = 0.02 , a							:	str = "linear" , a							:	Optional[Union[np.ndarray, List[float]]] = None , a							:	str = "epsilon" , a							:	str = "linspace" , a							:	int = 0 , )							->    int:

    """simple docstring"""

    if trained_betas is not None:
     SCREAMING_SNAKE_CASE				: List[str]			       =    torch.tensor(_UpperCAmelCase , dtype=torch.floataa		)
    elif beta_schedule == "linear":
     SCREAMING_SNAKE_CASE				: List[str]			       =    torch.linspace(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , dtype=torch.floataa		)
    elif beta_schedule == "scaled_linear":
     # this schedule is very specific to the latent diffusion model.
     SCREAMING_SNAKE_CASE				: Optional[int]			       =    (
         torch.linspace(beta_start**0.5 , beta_end**0.5 , _UpperCAmelCase , dtype=torch.floataa		) ** 2
     )
    elif beta_schedule == "squaredcos_cap_v2":
     # Glide cosine schedule
     SCREAMING_SNAKE_CASE				: Optional[int]			       =    betas_for_alpha_bar(_UpperCAmelCase		)
    else:
     raise NotImplementedError(F"{beta_schedule} does is not implemented for {self.__class__}"		)

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

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

    # setable values
    SCREAMING_SNAKE_CASE				: Optional[Any]			       =    None
    SCREAMING_SNAKE_CASE				: Optional[int]			       =    np.linspace(0 , num_train_timesteps - 1 , _UpperCAmelCase , dtype=_UpperCAmelCase		)[::-1].copy()
    SCREAMING_SNAKE_CASE				: List[Any]			       =    torch.from_numpy(_UpperCAmelCase		)
    SCREAMING_SNAKE_CASE				: List[str]			       =    False
   @property
   def 							__UpperCamelCase	(     self							:	List[str]		)							->    Optional[int]:

    """simple docstring"""

    if self.config.timestep_spacing in ["linspace", "trailing"]:
     return self.sigmas.max()

    return (self.sigmas.max() ** 2 + 1) ** 0.5
   def 							__UpperCamelCase	(     self							:	int , a							:	torch.FloatTensor , a							:	Union[float, torch.FloatTensor]		)							->    torch.FloatTensor:

    """simple docstring"""

    if isinstance(_UpperCAmelCase , torch.Tensor		):
     SCREAMING_SNAKE_CASE				: List[Any]			       =    timestep.to(self.timesteps.device		)
    SCREAMING_SNAKE_CASE				: Tuple			       =    (self.timesteps == timestep).nonzero().item()
    SCREAMING_SNAKE_CASE				: Any			       =    self.sigmas[step_index]
    SCREAMING_SNAKE_CASE				: Any			       =    sample / ((sigma**2 + 1) ** 0.5)
    SCREAMING_SNAKE_CASE				: str			       =    True
    return sample
   def 							__UpperCamelCase	(     self							:	int , a							:	int , a							:	Union[str, torch.device] = None		)							->    Any:

    """simple docstring"""

    SCREAMING_SNAKE_CASE				: str			       =    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 , _UpperCAmelCase , dtype=_UpperCAmelCase		)[
         ::-1
     ].copy()
    elif self.config.timestep_spacing == "leading":
     SCREAMING_SNAKE_CASE				: List[str]			       =    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				: List[str]			       =    (np.arange(0 , _UpperCAmelCase		) * step_ratio).round()[::-1].copy().astype(_UpperCAmelCase		)
     timesteps += self.config.steps_offset
    elif self.config.timestep_spacing == "trailing":
     SCREAMING_SNAKE_CASE				: 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
     SCREAMING_SNAKE_CASE				: Any			       =    (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'."		)

    SCREAMING_SNAKE_CASE				: Tuple			       =    np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5		)
    SCREAMING_SNAKE_CASE				: Optional[int]			       =    np.interp(_UpperCAmelCase , np.arange(0 , len(_UpperCAmelCase		)		) , _UpperCAmelCase		)
    SCREAMING_SNAKE_CASE				: Tuple			       =    np.concatenate([sigmas, [0.0]]		).astype(np.floataa		)
    SCREAMING_SNAKE_CASE				: str			       =    torch.from_numpy(_UpperCAmelCase		).to(device=_UpperCAmelCase		)
    if str(_UpperCAmelCase		).startswith("mps"		):
     # mps does not support float64
     SCREAMING_SNAKE_CASE				: Dict			       =    torch.from_numpy(_UpperCAmelCase		).to(_UpperCAmelCase , dtype=torch.floataa		)
    else:
     SCREAMING_SNAKE_CASE				: str			       =    torch.from_numpy(_UpperCAmelCase		).to(device=_UpperCAmelCase		)
   def 							__UpperCamelCase	(     self							:	List[Any] , a							:	torch.FloatTensor , a							:	Union[float, torch.FloatTensor] , a							:	torch.FloatTensor , a							:	Optional[torch.Generator] = None , a							:	bool = True , )							->    Union[EulerAncestralDiscreteSchedulerOutput, Tuple]:

    """simple docstring"""

    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		):
     SCREAMING_SNAKE_CASE				: Any			       =    timestep.to(self.timesteps.device		)

    SCREAMING_SNAKE_CASE				: Optional[int]			       =    (self.timesteps == timestep).nonzero().item()
    SCREAMING_SNAKE_CASE				: int			       =    self.sigmas[step_index]

    # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
    if self.config.prediction_type == "epsilon":
     SCREAMING_SNAKE_CASE				: Any			       =    sample - sigma * model_output
    elif self.config.prediction_type == "v_prediction":
     # * c_out + input * c_skip
     SCREAMING_SNAKE_CASE				: Tuple			       =    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`"		)

    SCREAMING_SNAKE_CASE				: Optional[Any]			       =    self.sigmas[step_index]
    SCREAMING_SNAKE_CASE				: Tuple			       =    self.sigmas[step_index + 1]
    SCREAMING_SNAKE_CASE				: int			       =    (sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
    SCREAMING_SNAKE_CASE				: Optional[Any]			       =    (sigma_to**2 - sigma_up**2) ** 0.5

    # 2. Convert to an ODE derivative
    SCREAMING_SNAKE_CASE				: List[Any]			       =    (sample - pred_original_sample) / sigma

    SCREAMING_SNAKE_CASE				: Any			       =    sigma_down - sigma

    SCREAMING_SNAKE_CASE				: Tuple			       =    sample + derivative * dt

    SCREAMING_SNAKE_CASE				: Any			       =    model_output.device
    SCREAMING_SNAKE_CASE				: Optional[Any]			       =    randn_tensor(model_output.shape , dtype=model_output.dtype , device=_UpperCAmelCase , generator=_UpperCAmelCase		)

    SCREAMING_SNAKE_CASE				: Optional[Any]			       =    prev_sample + noise * sigma_up

    if not return_dict:
     return (prev_sample,)

    return EulerAncestralDiscreteSchedulerOutput(
        prev_sample=_UpperCAmelCase , pred_original_sample=_UpperCAmelCase		)
   def 							__UpperCamelCase	(     self							:	List[str] , a							:	torch.FloatTensor , a							:	torch.FloatTensor , a							:	torch.FloatTensor , )							->    torch.FloatTensor:

    """simple docstring"""

    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(_UpperCAmelCase		):
     # mps does not support float64
     SCREAMING_SNAKE_CASE				: Optional[Any]			       =    self.timesteps.to(original_samples.device , dtype=torch.floataa		)
     SCREAMING_SNAKE_CASE				: Dict			       =    timesteps.to(original_samples.device , dtype=torch.floataa		)
    else:
     SCREAMING_SNAKE_CASE				: List[str]			       =    self.timesteps.to(original_samples.device		)
     SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    timesteps.to(original_samples.device		)

    SCREAMING_SNAKE_CASE				: str			       =    [(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				: Dict			       =    sigma.unsqueeze(-1		)

    SCREAMING_SNAKE_CASE				: Dict			       =    original_samples + noise * sigma
    return noisy_samples






   def __len__(     self							:	str		)							->    str:

    """simple docstring"""

    return self.config.num_train_timesteps


from __future__ import annotations

def 	lowerCamelCase__	(   _a							,     _a							,     _a):
	if len(_a) == 0:
		raise ValueError("find_min() arg is an empty sequence")
	if (
	    left >= len(_a)
	    or left < -len(_a)
	    or right >= len(_a)
	    or right < -len(_a)
	):
		raise IndexError("list index out of range")
	if left == right:
		return nums[left]
	SCREAMING_SNAKE_CASE				: List[str]			       =    (left + right) >> 1  # the middle
	SCREAMING_SNAKE_CASE				: Tuple			       =    find_min(_a							,     _a							,     _a)  # find min in range[left, mid]
	SCREAMING_SNAKE_CASE				: Optional[Any]			       =    find_min(_a							,     mid + 1							,     _a)  # find min in range[mid + 1, right]

	return left_min if left_min <= right_min else right_min


if __name__ == "__main__":
			import doctest

			doctest.testmod(verbose=True)