"""simple docstring"""
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin, SchedulerOutput



@dataclass
class 					lowerCAmelCase_      (     lowerCAmelCase   ):



    """simple docstring"""
    _lowerCAmelCase       :      torch.FloatTensor
    _lowerCAmelCase       :      torch.FloatTensor





class 					lowerCAmelCase_      (     lowerCAmelCase       ,      lowerCAmelCase   ):



    """simple docstring"""
    _lowerCAmelCase       :      str     					=  1

    @register_to_config
    def __init__(			self						,      lowerCAmelCase = 20_00						,      lowerCAmelCase = 0.15						,      lowerCAmelCase = 0.01						,      lowerCAmelCase = 13_48.0						,      lowerCAmelCase = 1E-5						,      lowerCAmelCase = 1						,      ):






          """simple docstring"""

          snake_case        =     sigma_max

          # setable values
          snake_case        =     None

          self.set_sigmas(lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase      )

    def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase = None      ):






          """simple docstring"""

          return sample

    def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase = None						,      lowerCAmelCase = None      ):






          """simple docstring"""

          snake_case        =     sampling_eps if sampling_eps is not None else self.config.sampling_eps

          snake_case        =     torch.linspace(1						,      lowerCAmelCase						,      lowerCAmelCase						,      device=lowerCAmelCase      )

    def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase = None						,      lowerCAmelCase = None						,      lowerCAmelCase = None      ):






          """simple docstring"""

          snake_case        =     sigma_min if sigma_min is not None else self.config.sigma_min
          snake_case        =     sigma_max if sigma_max is not None else self.config.sigma_max
          snake_case        =     sampling_eps if sampling_eps is not None else self.config.sampling_eps
          if self.timesteps is None:
                self.set_timesteps(lowerCAmelCase						,      lowerCAmelCase      )

          snake_case        =     sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
          snake_case        =     torch.exp(torch.linspace(math.log(lowerCAmelCase      )						,      math.log(lowerCAmelCase      )						,      lowerCAmelCase      )      )
          snake_case        =     torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]      )

    def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase      ):






          """simple docstring"""

          return torch.where(
              timesteps == 0						,      torch.zeros_like(t.to(timesteps.device      )      )						,      self.discrete_sigmas[timesteps - 1].to(timesteps.device      )						,      )

    def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase = None						,      lowerCAmelCase = True						,      ):






          """simple docstring"""

          if self.timesteps is None:
                raise ValueError(
                    '`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'      )

          snake_case        =     timestep * torch.ones(
              sample.shape[0]						,      device=sample.device      )  # torch.repeat_interleave(timestep, sample.shape[0])
          snake_case        =     (timestep * (len(self.timesteps      ) - 1)).long()

          # mps requires indices to be in the same device, so we use cpu as is the default with cuda
          snake_case        =     timesteps.to(self.discrete_sigmas.device      )

          snake_case        =     self.discrete_sigmas[timesteps].to(sample.device      )
          snake_case        =     self.get_adjacent_sigma(lowerCAmelCase						,      lowerCAmelCase      ).to(sample.device      )
          snake_case        =     torch.zeros_like(lowerCAmelCase      )
          snake_case        =     (sigma**2 - adjacent_sigma**2) ** 0.5

          # equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
          # also equation 47 shows the analog from SDE models to ancestral sampling methods
          snake_case        =     diffusion.flatten()
          while len(diffusion.shape      ) < len(sample.shape      ):
                snake_case        =     diffusion.unsqueeze(-1      )
          snake_case        =     drift - diffusion**2 * model_output

          #  equation 6: sample noise for the diffusion term of
          snake_case        =     randn_tensor(
              sample.shape						,      layout=sample.layout						,      generator=lowerCAmelCase						,      device=sample.device						,      dtype=sample.dtype      )
          snake_case        =     sample - drift  # subtract because `dt` is a small negative timestep
          # TODO is the variable diffusion the correct scaling term for the noise?
          snake_case        =     prev_sample_mean + diffusion * noise  # add impact of diffusion field g

          if not return_dict:
                return (prev_sample, prev_sample_mean)

          return SdeVeOutput(prev_sample=lowerCAmelCase						,      prev_sample_mean=lowerCAmelCase      )

    def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase = None						,      lowerCAmelCase = True						,      ):






          """simple docstring"""

          if self.timesteps is None:
                raise ValueError(
                    '`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'      )

          # For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
          # sample noise for correction
          snake_case        =     randn_tensor(sample.shape						,      layout=sample.layout						,      generator=lowerCAmelCase      ).to(sample.device      )

          # compute step size from the model_output, the noise, and the snr
          snake_case        =     torch.norm(model_output.reshape(model_output.shape[0]						,      -1      )						,      dim=-1      ).mean()
          snake_case        =     torch.norm(noise.reshape(noise.shape[0]						,      -1      )						,      dim=-1      ).mean()
          snake_case        =     (self.config.snr * noise_norm / grad_norm) ** 2 * 2
          snake_case        =     step_size * torch.ones(sample.shape[0]      ).to(sample.device      )
          # self.repeat_scalar(step_size, sample.shape[0])

          # compute corrected sample: model_output term and noise term
          snake_case        =     step_size.flatten()
          while len(step_size.shape      ) < len(sample.shape      ):
                snake_case        =     step_size.unsqueeze(-1      )
          snake_case        =     sample + step_size * model_output
          snake_case        =     prev_sample_mean + ((step_size * 2) ** 0.5) * noise

          if not return_dict:
                return (prev_sample,)

          return SchedulerOutput(prev_sample=lowerCAmelCase      )

    def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      ):






          """simple docstring"""

          snake_case        =     timesteps.to(original_samples.device      )
          snake_case        =     self.discrete_sigmas.to(original_samples.device      )[timesteps]
          snake_case        =     (
              noise * sigmas[:, None, None, None]
              if noise is not None
              else torch.randn_like(lowerCAmelCase      ) * sigmas[:, None, None, None]
          )
          snake_case        =     noise + original_samples
          return noisy_samples

    def __len__(			self      ):






          """simple docstring"""

          return self.config.num_train_timesteps



"""simple docstring"""
import cmath
import math





def 				lowerCAmelCase__      (     _UpperCamelCase    :			float	,     _UpperCamelCase    :			float	,     _UpperCamelCase    :			float	,     _UpperCamelCase    :			float   )   ->			complex:





						"""simple docstring"""






						snake_case        =     math.radians(_UpperCamelCase   )
						snake_case        =     math.radians(_UpperCamelCase   )

						# Convert voltage and current to rectangular form
						snake_case        =     cmath.rect(_UpperCamelCase	,     _UpperCamelCase   )
						snake_case        =     cmath.rect(_UpperCamelCase	,     _UpperCamelCase   )

						# Calculate apparent power
						return voltage_rect * current_rect


if __name__ == "__main__":
					import doctest

					doctest.testmod()


