


'''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->EulerDiscrete
class    lowercase_	(       a__  ):
    __UpperCAmelCase       =      42
    __UpperCAmelCase       =      None







def _UpperCamelCase			(	__A ,     __A=0.999 ,     __A="cosine" ,     )				->						Tuple:


      '''simple docstring'''



      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}'''       )

      UpperCamelCase__      =							[]
      for i in range(__A       ):
            UpperCamelCase__      =							i / num_diffusion_timesteps
            UpperCamelCase__      =							(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    lowercase_	(       a__    ,		a__  ):
    __UpperCAmelCase       =      [e.name for e in KarrasDiffusionSchedulers]
    __UpperCAmelCase       =      1


    @register_to_config
    def __init__(	self    ,   a = 10_00    ,   a = 0.0001    ,   a = 0.02    ,   a = "linear"    ,   a = None    ,   a = "epsilon"    ,   a = "linear"    ,   a = False    ,   a = "linspace"    ,   a = 0    ,   ):
          if trained_betas is not None:
                UpperCamelCase__      =							torch.tensor(a    ,   dtype=torch.floataa       )
          elif beta_schedule == "linear":
                UpperCamelCase__      =							torch.linspace(a    ,   a    ,   a    ,   dtype=torch.floataa       )
          elif beta_schedule == "scaled_linear":
                # this schedule is very specific to the latent diffusion model.
                UpperCamelCase__      =							(
                    torch.linspace(beta_start**0.5    ,   beta_end**0.5    ,   a    ,   dtype=torch.floataa       ) ** 2
                )
          elif beta_schedule == "squaredcos_cap_v2":
                # Glide cosine schedule
                UpperCamelCase__      =							betas_for_alpha_bar(a       )
          else:
                raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}'''       )

          UpperCamelCase__      =							1.0 - self.betas
          UpperCamelCase__      =							torch.cumprod(self.alphas    ,   dim=0       )

          UpperCamelCase__      =							np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5       )
          UpperCamelCase__      =							np.concatenate([sigmas[::-1], [0.0]]       ).astype(np.floataa       )
          UpperCamelCase__      =							torch.from_numpy(a       )

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


    @property
    def   __a				(	self       ):
          # 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    ,   a    ,   a       ):
          if isinstance(a    ,   torch.Tensor       ):
                UpperCamelCase__      =							timestep.to(self.timesteps.device       )
          UpperCamelCase__      =							(self.timesteps == timestep).nonzero().item()
          UpperCamelCase__      =							self.sigmas[step_index]

          UpperCamelCase__      =							sample / ((sigma**2 + 1) ** 0.5)

          UpperCamelCase__      =							True
          return sample


    def   __a				(	self    ,   a    ,   a = None       ):
          UpperCamelCase__      =							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":
                UpperCamelCase__      =							np.linspace(0    ,   self.config.num_train_timesteps - 1    ,   a    ,   dtype=a       )[
                    ::-1
                ].copy()
          elif self.config.timestep_spacing == "leading":
                UpperCamelCase__      =							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
                UpperCamelCase__      =							(np.arange(0    ,   a       ) * step_ratio).round()[::-1].copy().astype(a       )
                timesteps += self.config.steps_offset
          elif self.config.timestep_spacing == "trailing":
                UpperCamelCase__      =							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
                UpperCamelCase__      =							(np.arange(self.config.num_train_timesteps    ,   0    ,   -step_ratio       )).round().copy().astype(a       )
                timesteps -= 1
          else:
                raise ValueError(
                    f'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'''       )

          UpperCamelCase__      =							np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5       )
          UpperCamelCase__      =							np.log(a       )

          if self.config.interpolation_type == "linear":
                UpperCamelCase__      =							np.interp(a    ,   np.arange(0    ,   len(a       )       )    ,   a       )
          elif self.config.interpolation_type == "log_linear":
                UpperCamelCase__      =							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:
                UpperCamelCase__      =							self._convert_to_karras(in_sigmas=a    ,   num_inference_steps=self.num_inference_steps       )
                UpperCamelCase__      =							np.array([self._sigma_to_t(a    ,   a       ) for sigma in sigmas]       )

          UpperCamelCase__      =							np.concatenate([sigmas, [0.0]]       ).astype(np.floataa       )
          UpperCamelCase__      =							torch.from_numpy(a       ).to(device=a       )
          if str(a       ).startswith("mps"       ):
                # mps does not support float64
                UpperCamelCase__      =							torch.from_numpy(a       ).to(a    ,   dtype=torch.floataa       )
          else:
                UpperCamelCase__      =							torch.from_numpy(a       ).to(device=a       )


    def   __a				(	self    ,   a    ,   a       ):
          # get log sigma
          UpperCamelCase__      =							np.log(a       )

          # get distribution
          UpperCamelCase__      =							log_sigma - log_sigmas[:, np.newaxis]

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

          UpperCamelCase__      =							log_sigmas[low_idx]
          UpperCamelCase__      =							log_sigmas[high_idx]

          # interpolate sigmas
          UpperCamelCase__      =							(low - log_sigma) / (low - high)
          UpperCamelCase__      =							np.clip(a    ,   0    ,   1       )

          # transform interpolation to time range
          UpperCamelCase__      =							(1 - w) * low_idx + w * high_idx
          UpperCamelCase__      =							t.reshape(sigma.shape       )
          return t


    def   __a				(	self    ,   a    ,   a       ):
          UpperCamelCase__      =							in_sigmas[-1].item()
          UpperCamelCase__      =							in_sigmas[0].item()

          UpperCamelCase__      =							7.0  # 7.0 is the value used in the paper
          UpperCamelCase__      =							np.linspace(0    ,   1    ,   a       )
          UpperCamelCase__      =							sigma_min ** (1 / rho)
          UpperCamelCase__      =							sigma_max ** (1 / rho)
          UpperCamelCase__      =							(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
          return sigmas


    def   __a				(	self    ,   a    ,   a    ,   a    ,   a = 0.0    ,   a = 0.0    ,   a = float("inf"       )    ,   a = 1.0    ,   a = None    ,   a = True    ,   ):

          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"
                        " `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(a    ,   torch.Tensor       ):
                UpperCamelCase__      =							timestep.to(self.timesteps.device       )

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

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

          UpperCamelCase__      =							randn_tensor(
              model_output.shape    ,   dtype=model_output.dtype    ,   device=model_output.device    ,   generator=a       )

          UpperCamelCase__      =							noise * s_noise
          UpperCamelCase__      =							sigma * (gamma + 1)

          if gamma > 0:
                UpperCamelCase__      =							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":
                UpperCamelCase__      =							model_output
          elif self.config.prediction_type == "epsilon":
                UpperCamelCase__      =							sample - sigma_hat * model_output
          elif self.config.prediction_type == "v_prediction":
                # * c_out + input * c_skip
                UpperCamelCase__      =							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
          UpperCamelCase__      =							(sample - pred_original_sample) / sigma_hat

          UpperCamelCase__      =							self.sigmas[step_index + 1] - sigma_hat

          UpperCamelCase__      =							sample + derivative * dt

          if not return_dict:
                return (prev_sample,)

          return EulerDiscreteSchedulerOutput(prev_sample=a    ,   pred_original_sample=a       )


    def   __a				(	self    ,   a    ,   a    ,   a    ,   ):
          # Make sure sigmas and timesteps have the same device and dtype as original_samples
          UpperCamelCase__      =							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
                UpperCamelCase__      =							self.timesteps.to(original_samples.device    ,   dtype=torch.floataa       )
                UpperCamelCase__      =							timesteps.to(original_samples.device    ,   dtype=torch.floataa       )
          else:
                UpperCamelCase__      =							self.timesteps.to(original_samples.device       )
                UpperCamelCase__      =							timesteps.to(original_samples.device       )

          UpperCamelCase__      =							[(schedule_timesteps == t).nonzero().item() for t in timesteps]

          UpperCamelCase__      =							sigmas[step_indices].flatten()
          while len(sigma.shape       ) < len(original_samples.shape       ):
                UpperCamelCase__      =							sigma.unsqueeze(-1       )

          UpperCamelCase__      =							original_samples + noise * sigma
          return noisy_samples


    def __len__(	self       ):
          return self.config.num_train_timesteps









'''simple docstring'''







def _UpperCamelCase			(	__A ,     __A       )				->						int:


      '''simple docstring'''



      return (input_a, input_a).count(0       ) % 2







def _UpperCamelCase			(	)				->						None:


      '''simple docstring'''



      assert xor_gate(0 ,     0       ) == 0
      assert xor_gate(0 ,     1       ) == 1
      assert xor_gate(1 ,     0       ) == 1
      assert xor_gate(1 ,     1       ) == 0


if __name__ == "__main__":
  print(xor_gate(0, 0))
  print(xor_gate(0, 1))





