
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


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






@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
class 						UpperCAmelCase	(		A_				):
   A__      :	torch.FloatTensor
   A__      :	Optional[torch.FloatTensor]		  =  None



def     UpperCamelCase      (					__lowerCamelCase  :				Optional[Any]       ,      __lowerCamelCase  :				Any=0.999       ,      __lowerCamelCase  :				str="cosine"       ,      ):
       if alpha_transform_type == "cosine":

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

       elif alpha_transform_type == "exp":

              def alpha_bar_fn(__lowerCamelCase  :				List[Any]					):
                     return math.exp(t * -12.0					)

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

       snake_case :     Dict           =   []
       for i in range(__lowerCamelCase					):
              snake_case :     Optional[int]           =   i / num_diffusion_timesteps
              snake_case :     List[str]           =   (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 						UpperCAmelCase	(		A_ ,A_				):
   A__      :	Optional[Any]		  =  [e.name for e in KarrasDiffusionSchedulers]
   A__      :	List[str]		  =  1



   @register_to_config
   def __init__(self	:      int					,		snake_case__	:      int = 10_00					,		snake_case__	:      float = 0.0001					,		snake_case__	:      float = 0.02					,		snake_case__	:      str = "linear"					,		snake_case__	:      Optional[Union[np.ndarray, List[float]]] = None					,		snake_case__	:      str = "epsilon"					,		snake_case__	:      str = "linspace"					,		snake_case__	:      int = 0					,		)	->			Dict:


          '''simple docstring'''


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

          snake_case :     Optional[int]           =   1.0 - self.betas
          snake_case :     Union[str, Any]           =   torch.cumprod(self.alphas					,		dim=0				)

          snake_case :     Optional[Any]           =   np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5				)
          snake_case :     Union[str, Any]           =   np.concatenate([sigmas[::-1], [0.0]]				).astype(np.floataa				)
          snake_case :     int           =   torch.from_numpy(snake_case__				)

          # setable values
          snake_case :     Optional[int]           =   None
          snake_case :     Tuple           =   np.linspace(0					,		num_train_timesteps - 1					,		snake_case__					,		dtype=snake_case__				)[::-1].copy()
          snake_case :     Dict           =   torch.from_numpy(snake_case__				)
          snake_case :     List[str]           =   False



   @property
   def        _SCREAMING_SNAKE_CASE     (self	:      Optional[Any]				)	->			int:


          '''simple docstring'''


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

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



   def        _SCREAMING_SNAKE_CASE     (self	:      int					,		snake_case__	:      torch.FloatTensor					,		snake_case__	:      Union[float, torch.FloatTensor]				)	->			torch.FloatTensor:


          '''simple docstring'''


          if isinstance(snake_case__					,		torch.Tensor				):
                 snake_case :     str           =   timestep.to(self.timesteps.device				)
          snake_case :     Optional[int]           =   (self.timesteps == timestep).nonzero().item()
          snake_case :     Dict           =   self.sigmas[step_index]
          snake_case :     Optional[int]           =   sample / ((sigma**2 + 1) ** 0.5)
          snake_case :     List[str]           =   True
          return sample



   def        _SCREAMING_SNAKE_CASE     (self	:      Any					,		snake_case__	:      int					,		snake_case__	:      Union[str, torch.device] = None				)	->			Optional[int]:


          '''simple docstring'''


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

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



   def        _SCREAMING_SNAKE_CASE     (self	:      Optional[int]					,		snake_case__	:      torch.FloatTensor					,		snake_case__	:      Union[float, torch.FloatTensor]					,		snake_case__	:      torch.FloatTensor					,		snake_case__	:      Optional[torch.Generator] = None					,		snake_case__	:      bool = True					,		)	->			Union[EulerAncestralDiscreteSchedulerOutput, Tuple]:


          '''simple docstring'''


          if (
              isinstance(snake_case__					,		snake_case__				)
              or isinstance(snake_case__					,		torch.IntTensor				)
              or isinstance(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(snake_case__					,		torch.Tensor				):
                 snake_case :     List[Any]           =   timestep.to(self.timesteps.device				)

          snake_case :     Dict           =   (self.timesteps == timestep).nonzero().item()
          snake_case :     Any           =   self.sigmas[step_index]

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

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

          # 2. Convert to an ODE derivative
          snake_case :     Union[str, Any]           =   (sample - pred_original_sample) / sigma

          snake_case :     Union[str, Any]           =   sigma_down - sigma

          snake_case :     int           =   sample + derivative * dt

          snake_case :     List[Any]           =   model_output.device
          snake_case :     Dict           =   randn_tensor(model_output.shape					,		dtype=model_output.dtype					,		device=snake_case__					,		generator=snake_case__				)

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

          if not return_dict:
                 return (prev_sample,)

          return EulerAncestralDiscreteSchedulerOutput(
              prev_sample=snake_case__					,		pred_original_sample=snake_case__				)



   def        _SCREAMING_SNAKE_CASE     (self	:      List[Any]					,		snake_case__	:      torch.FloatTensor					,		snake_case__	:      torch.FloatTensor					,		snake_case__	:      torch.FloatTensor					,		)	->			torch.FloatTensor:


          '''simple docstring'''


          snake_case :     int           =   self.sigmas.to(device=original_samples.device					,		dtype=original_samples.dtype				)
          if original_samples.device.type == "mps" and torch.is_floating_point(snake_case__				):
                 # mps does not support float64
                 snake_case :     Any           =   self.timesteps.to(original_samples.device					,		dtype=torch.floataa				)
                 snake_case :     Union[str, Any]           =   timesteps.to(original_samples.device					,		dtype=torch.floataa				)
          else:
                 snake_case :     Union[str, Any]           =   self.timesteps.to(original_samples.device				)
                 snake_case :     Union[str, Any]           =   timesteps.to(original_samples.device				)

          snake_case :     Tuple           =   [(schedule_timesteps == t).nonzero().item() for t in timesteps]

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

          snake_case :     str           =   original_samples + noise * sigma
          return noisy_samples



   def __len__(self	:      Any				)	->			Union[str, Any]:


          '''simple docstring'''


          return self.config.num_train_timesteps







def     UpperCamelCase      (					__lowerCamelCase  :				list       ,      __lowerCamelCase  :				list       ,      __lowerCamelCase  :				list       ,      __lowerCamelCase  :				int       ,      __lowerCamelCase  :				int					):
       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     UpperCamelCase      (					__lowerCamelCase  :				list       ,      __lowerCamelCase  :				int					):
       snake_case :     Optional[Any]           =   []  # type: list[int]
       snake_case :     Tuple           =   []  # type: list[int]
       backtrack(__lowerCamelCase       ,      __lowerCamelCase       ,      __lowerCamelCase       ,      __lowerCamelCase       ,      0					)
       return answer


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


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   main()





