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 SchedulerMixin


lowerCAmelCase__					=				logging.get_logger(__name__)  # pylint: disable=invalid-name
@dataclass
class        a_				(		lowercase__		):






 '''simple docstring'''






 UpperCAmelCase_							=      42






class        a_				(		lowercase__ ,  lowercase__		):






 '''simple docstring'''






 UpperCAmelCase_							=      1




 @register_to_config
 def __init__(    self      :						int    ,					lowercase__      :						int = 40    ,					lowercase__      :						float = 0.002    ,					lowercase__      :						float = 80.0    ,					lowercase__      :						float = 0.5    ,					lowercase__      :						float = 1.0    ,					lowercase__      :						float = 7.0    ,					lowercase__      :						bool = True    ,					):







        '''simple docstring'''



        lowerCAmelCase__  			=      sigma_max

        lowerCAmelCase__  			=      np.linspace(0    ,					1    ,					_A)
        lowerCAmelCase__  			=      self._convert_to_karras(_A)
        lowerCAmelCase__  			=      self.sigma_to_t(_A)

        # setable values
        lowerCAmelCase__  			=      None
        lowerCAmelCase__  			=      torch.from_numpy(_A)
        lowerCAmelCase__  			=      torch.from_numpy(_A)
        lowerCAmelCase__  			=      False
        lowerCAmelCase__  			=      False




 def 	__snake_case		(    self      :						Optional[Any]    ,					lowercase__      :						Any    ,					lowercase__      :						Tuple=None):







        '''simple docstring'''



        if schedule_timesteps is None:
               lowerCAmelCase__  			=      self.timesteps

        lowerCAmelCase__  			=      (schedule_timesteps == timestep).nonzero()
        return indices.item()




 def 	__snake_case		(    self      :						Tuple    ,					lowercase__      :						torch.FloatTensor    ,					lowercase__      :						Union[float, torch.FloatTensor]):







        '''simple docstring'''



        if isinstance(_A    ,					torch.Tensor):
               lowerCAmelCase__  			=      timestep.to(self.timesteps.device)
        lowerCAmelCase__  			=      self.index_for_timestep(_A)
        lowerCAmelCase__  			=      self.sigmas[step_idx]

        lowerCAmelCase__  			=      sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)

        lowerCAmelCase__  			=      True
        return sample




 def 	__snake_case		(    self      :						Union[str, Any]    ,					lowercase__      :						Union[float, np.ndarray]):







        '''simple docstring'''



        if not isinstance(_A    ,					np.ndarray):
               lowerCAmelCase__  			=      np.array(_A    ,					dtype=np.floataa)

        lowerCAmelCase__  			=      1_000 * 0.25 * np.log(sigmas + 1e-44)

        return timesteps




 def 	__snake_case		(    self      :						Dict    ,					lowercase__      :						Optional[int] = None    ,					lowercase__      :						Union[str, torch.device] = None    ,					lowercase__      :						Optional[List[int]] = None    ,					):







        '''simple docstring'''



        if num_inference_steps is None and timesteps is None:
               raise ValueError('Exactly one of `num_inference_steps` or `timesteps` must be supplied.')

        if num_inference_steps is not None and timesteps is not None:
               raise ValueError('Can only pass one of `num_inference_steps` or `timesteps`.')

        # Follow DDPMScheduler custom timesteps logic
        if timesteps is not None:
               for i in range(1    ,					len(_A)):
                      if timesteps[i] >= timesteps[i - 1]:
                             raise ValueError('`timesteps` must be in descending order.')

               if timesteps[0] >= self.config.num_train_timesteps:
                      raise ValueError(
                          F"""`timesteps` must start before `self.config.train_timesteps`:"""
                          F""" {self.config.num_train_timesteps}.""")

               lowerCAmelCase__  			=      np.array(_A    ,					dtype=np.intaa)
               lowerCAmelCase__  			=      True
        else:
               if num_inference_steps > self.config.num_train_timesteps:
                      raise ValueError(
                          F"""`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:"""
                          F""" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle"""
                          F""" maximal {self.config.num_train_timesteps} timesteps.""")

               lowerCAmelCase__  			=      num_inference_steps

               lowerCAmelCase__  			=      self.config.num_train_timesteps // self.num_inference_steps
               lowerCAmelCase__  			=      (np.arange(0    ,					_A) * step_ratio).round()[::-1].copy().astype(np.intaa)
               lowerCAmelCase__  			=      False

        # Map timesteps to Karras sigmas directly for multistep sampling
        # See https://github.com/openai/consistency_models/blob/main/cm/karras_diffusion.py#L675
        lowerCAmelCase__  			=      self.config.num_train_timesteps
        lowerCAmelCase__  			=      timesteps[::-1].copy()
        lowerCAmelCase__  			=      ramp / (num_train_timesteps - 1)
        lowerCAmelCase__  			=      self._convert_to_karras(_A)
        lowerCAmelCase__  			=      self.sigma_to_t(_A)

        lowerCAmelCase__  			=      np.concatenate([sigmas, [self.sigma_min]]).astype(np.floataa)
        lowerCAmelCase__  			=      torch.from_numpy(_A).to(device=_A)

        if str(_A).startswith('mps'):
               # mps does not support float64
               lowerCAmelCase__  			=      torch.from_numpy(_A).to(_A    ,					dtype=torch.floataa)
        else:
               lowerCAmelCase__  			=      torch.from_numpy(_A).to(device=_A)




 def 	__snake_case		(    self      :						str    ,					lowercase__      :						Optional[int]):







        '''simple docstring'''



        lowerCAmelCase__  			=      self.config.sigma_min
        lowerCAmelCase__  			=      self.config.sigma_max

        lowerCAmelCase__  			=      self.config.rho
        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 	__snake_case		(    self      :						Union[str, Any]    ,					lowercase__      :						Dict):







        '''simple docstring'''



        lowerCAmelCase__  			=      self.config.sigma_data

        lowerCAmelCase__  			=      sigma_data**2 / (sigma**2 + sigma_data**2)
        lowerCAmelCase__  			=      sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
        return c_skip, c_out




 def 	__snake_case		(    self      :						List[str]    ,					lowercase__      :						str):







        '''simple docstring'''



        lowerCAmelCase__  			=      self.config.sigma_min
        lowerCAmelCase__  			=      self.config.sigma_data

        lowerCAmelCase__  			=      sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
        lowerCAmelCase__  			=      (sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
        return c_skip, c_out




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







        '''simple docstring'''



        if (
            isinstance(_A    ,					_A)
            or isinstance(_A    ,					torch.IntTensor)
            or isinstance(_A    ,					torch.LongTensor)
        ):
               raise ValueError(
                   (
                       'Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to'
                       F""" `{self.__class__}.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(_A    ,					torch.Tensor):
               lowerCAmelCase__  			=      timestep.to(self.timesteps.device)

        lowerCAmelCase__  			=      self.config.sigma_min
        lowerCAmelCase__  			=      self.config.sigma_max

        lowerCAmelCase__  			=      self.index_for_timestep(_A)

        # sigma_next corresponds to next_t in original implementation
        lowerCAmelCase__  			=      self.sigmas[step_index]
        if step_index + 1 < self.config.num_train_timesteps:
               lowerCAmelCase__  			=      self.sigmas[step_index + 1]
        else:
               # Set sigma_next to sigma_min
               lowerCAmelCase__  			=      self.sigmas[-1]

        # Get scalings for boundary conditions
        lowerCAmelCase__  			=      self.get_scalings_for_boundary_condition(_A)

        # 1. Denoise model output using boundary conditions
        lowerCAmelCase__  			=      c_out * model_output + c_skip * sample
        if self.config.clip_denoised:
               lowerCAmelCase__  			=      denoised.clamp(-1    ,					1)

        # 2. Sample z ~ N(0, s_noise^2 * I)
        # Noise is not used for onestep sampling.
        if len(self.timesteps) > 1:
               lowerCAmelCase__  			=      randn_tensor(
                   model_output.shape    ,					dtype=model_output.dtype    ,					device=model_output.device    ,					generator=_A)
        else:
               lowerCAmelCase__  			=      torch.zeros_like(_A)
        lowerCAmelCase__  			=      noise * self.config.s_noise

        lowerCAmelCase__  			=      sigma_next.clamp(min=_A    ,					max=_A)

        # 3. Return noisy sample
        # tau = sigma_hat, eps = sigma_min
        lowerCAmelCase__  			=      denoised + z * (sigma_hat**2 - sigma_min**2) ** 0.5

        if not return_dict:
               return (prev_sample,)

        return CMStochasticIterativeSchedulerOutput(prev_sample=_A)




 def 	__snake_case		(    self      :						Dict    ,					lowercase__      :						torch.FloatTensor    ,					lowercase__      :						torch.FloatTensor    ,					lowercase__      :						torch.FloatTensor    ,					):







        '''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(_A):
               # 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):







        '''simple docstring'''



        return self.config.num_train_timesteps

def    __lowerCamelCase	(		lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			):
       if target == 0:
              answer.append(path.copy()			)
       else:
              for index in range(lowerCAmelCase__			,						len(lowerCAmelCase__			)			):
                     if target >= candidates[index]:
                            path.append(candidates[index]			)
                            backtrack(lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						target - candidates[index]			,						lowerCAmelCase__			)
                            path.pop(len(lowerCAmelCase__			) - 1			)






def    __lowerCamelCase	(		lowerCAmelCase__			,						lowerCAmelCase__			):
       lowerCAmelCase__  			=      []  # type: list[int]
       lowerCAmelCase__  			=      []  # type: list[int]
       backtrack(lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						0			)
       return answer






def    __lowerCamelCase	(		):
       print(combination_sum([-8, 2.3, 0]			,						1			)			)


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    main()
