






'''simple docstring'''





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				:				List[str]					=		logging.get_logger(__name__)  # pylint: disable=invalid-name
@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
class 					lowercase							(				_a   ):


    '''simple docstring'''

    lowerCAmelCase__   					=				42
    lowerCAmelCase__   					=				None






def  lowerCamelCase_  (     lowercase__					,  lowercase__=0.999					,  lowercase__="cosine"					,  ):





 if alpha_transform_type == "cosine":

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

 elif alpha_transform_type == "exp":

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

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

 lowerCamelCase__	=  []
 for i in range(SCREAMING_SNAKE_CASE_):
  lowerCamelCase__	=  i / num_diffusion_timesteps
  lowerCamelCase__	=  (i + 1) / num_diffusion_timesteps
  betas.append(min(1 - alpha_bar_fn(SCREAMING_SNAKE_CASE_) / alpha_bar_fn(SCREAMING_SNAKE_CASE_)					,  SCREAMING_SNAKE_CASE_))
 return torch.tensor(SCREAMING_SNAKE_CASE_					,  dtype=torch.floataa)
class 					lowercase							(				_a			,							_a   ):


    '''simple docstring'''

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

    @register_to_config
    def __init__(       self  :  Tuple					,  __lowerCamelCase  :  Any = 1000					,  __lowerCamelCase  :  Optional[int] = 0.0_0_0_1					,  __lowerCamelCase  :  Tuple = 0.0_2					,  __lowerCamelCase  :  int = "linear"					,  __lowerCamelCase  :  Tuple = None					,  __lowerCamelCase  :  Dict = "epsilon"					,  __lowerCamelCase  :  Dict = "linspace"					,  __lowerCamelCase  :  Any = 0					,  )				->				int:

     '''simple docstring'''




     if trained_betas is not None:
      lowerCamelCase__	=  torch.tensor(__SCREAMING_SNAKE_CASE					,  dtype=torch.floataa  )
     elif beta_schedule == "linear":
      lowerCamelCase__	=  torch.linspace(__SCREAMING_SNAKE_CASE					,  __SCREAMING_SNAKE_CASE					,  __SCREAMING_SNAKE_CASE					,  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					,  __SCREAMING_SNAKE_CASE					,  dtype=torch.floataa  ) ** 2
      )
     elif beta_schedule == "squaredcos_cap_v2":
      # Glide cosine schedule
      lowerCamelCase__	=  betas_for_alpha_bar(__SCREAMING_SNAKE_CASE  )
     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(__SCREAMING_SNAKE_CASE  )

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

    @property
    def  a__					(       self  :  str  )				->				List[str]:

     '''simple docstring'''




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

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

    def  a__					(       self  :  Dict					,  __lowerCamelCase  :  str					,  __lowerCamelCase  :  Optional[int]  )				->				Union[str, Any]:

     '''simple docstring'''




     if isinstance(__SCREAMING_SNAKE_CASE					,  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  a__					(       self  :  str					,  __lowerCamelCase  :  Dict					,  __lowerCamelCase  :  Dict = None  )				->				str:

     '''simple docstring'''




     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					,  __SCREAMING_SNAKE_CASE					,  dtype=__SCREAMING_SNAKE_CASE  )[
          ::-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					,  __SCREAMING_SNAKE_CASE  ) * step_ratio).round()[::-1].copy().astype(__SCREAMING_SNAKE_CASE  )
      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(__SCREAMING_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\'.'''  )

     lowerCamelCase__	=  np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5  )
     lowerCamelCase__	=  np.interp(__SCREAMING_SNAKE_CASE					,  np.arange(0					,  len(__SCREAMING_SNAKE_CASE  )  )					,  __SCREAMING_SNAKE_CASE  )
     lowerCamelCase__	=  np.concatenate([sigmas, [0.0]]  ).astype(np.floataa  )
     lowerCamelCase__	=  torch.from_numpy(__SCREAMING_SNAKE_CASE  ).to(device=__SCREAMING_SNAKE_CASE  )
     if str(__SCREAMING_SNAKE_CASE  ).startswith("mps"  ):
      # mps does not support float64
      lowerCamelCase__	=  torch.from_numpy(__SCREAMING_SNAKE_CASE  ).to(__SCREAMING_SNAKE_CASE					,  dtype=torch.floataa  )
     else:
      lowerCamelCase__	=  torch.from_numpy(__SCREAMING_SNAKE_CASE  ).to(device=__SCREAMING_SNAKE_CASE  )

    def  a__					(       self  :  Optional[Any]					,  __lowerCamelCase  :  str					,  __lowerCamelCase  :  Optional[Any]					,  __lowerCamelCase  :  Optional[int]					,  __lowerCamelCase  :  List[str] = None					,  __lowerCamelCase  :  Optional[Any] = True					,  )				->				Tuple:

     '''simple docstring'''




     if (
         isinstance(__SCREAMING_SNAKE_CASE					,  __SCREAMING_SNAKE_CASE  )
         or isinstance(__SCREAMING_SNAKE_CASE					,  torch.IntTensor  )
         or isinstance(__SCREAMING_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(__SCREAMING_SNAKE_CASE					,  torch.Tensor  ):
      lowerCamelCase__	=  timestep.to(self.timesteps.device  )

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

     # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
     if self.config.prediction_type == "epsilon":
      lowerCamelCase__	=  sample - sigma * 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))
     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`'''  )

     lowerCamelCase__	=  self.sigmas[step_index]
     lowerCamelCase__	=  self.sigmas[step_index + 1]
     lowerCamelCase__	=  (sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
     lowerCamelCase__	=  (sigma_to**2 - sigma_up**2) ** 0.5

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

     lowerCamelCase__	=  sigma_down - sigma

     lowerCamelCase__	=  sample + derivative * dt

     lowerCamelCase__	=  model_output.device
     lowerCamelCase__	=  randn_tensor(model_output.shape					,  dtype=model_output.dtype					,  device=__SCREAMING_SNAKE_CASE					,  generator=__SCREAMING_SNAKE_CASE  )

     lowerCamelCase__	=  prev_sample + noise * sigma_up

     if not return_dict:
      return (prev_sample,)

     return EulerAncestralDiscreteSchedulerOutput(
         prev_sample=__SCREAMING_SNAKE_CASE					,  pred_original_sample=__SCREAMING_SNAKE_CASE  )

    def  a__					(       self  :  Dict					,  __lowerCamelCase  :  Optional[Any]					,  __lowerCamelCase  :  str					,  __lowerCamelCase  :  Tuple					,  )				->				List[str]:

     '''simple docstring'''




     lowerCamelCase__	=  self.sigmas.to(device=original_samples.device					,  dtype=original_samples.dtype  )
     if original_samples.device.type == "mps" and torch.is_floating_point(__SCREAMING_SNAKE_CASE  ):
      # 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  :  str  )				->				str:

     '''simple docstring'''




     return self.config.num_train_timesteps





import sys


def   a						(				SCREAMING_SNAKE_CASE_   :		int				,      SCREAMING_SNAKE_CASE_   :		list[int]							):






       """simple docstring"""





       if not numbers:
              return 0
       if target == 0 and target in numbers:
              return 0
       if not isinstance(SCREAMING_SNAKE_CASE_				,      (list, tuple)							) or not all(
           isinstance(SCREAMING_SNAKE_CASE_				,      SCREAMING_SNAKE_CASE_							) for number in numbers							):
              raise ValueError('''numbers must be an iterable of integers'''							)

       UpperCamelCase     :						Dict 						=		0
       UpperCamelCase     :						Tuple 						=		sys.maxsize

       while right < len(SCREAMING_SNAKE_CASE_							):
              curr_sum += numbers[right]
              while curr_sum >= target and left <= right:
                     UpperCamelCase     :						int 						=		min(SCREAMING_SNAKE_CASE_				,      right - left + 1							)
                     curr_sum -= numbers[left]
                     left += 1
              right += 1

       return 0 if min_len == sys.maxsize else min_len

