'''simple docstring'''

import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput







def __UpperCamelCase(   _A					:				Optional[Any]  ,			_A					:				Optional[Any]=0.9_9_9  ,			_A					:				Optional[Any]="cosine"  ,			):
   '''simple docstring'''
   if alpha_transform_type == "cosine":

      def alpha_bar_fn(_A					:				str    ):
         return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2    ) ** 2

   elif alpha_transform_type == "exp":

      def alpha_bar_fn(_A					:				List[str]    ):
         return math.exp(t * -1_2.0    )

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

   UpperCAmelCase__							:				Any   				=  []
   for i in range(_A    ):
      UpperCAmelCase__							:				Dict   				=  i / num_diffusion_timesteps
      UpperCAmelCase__							:				Tuple   				=  (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	(      lowerCAmelCase		,lowerCAmelCase   ):
      '''simple docstring'''


      UpperCAmelCase_      :      List[Any]      							=   [e.name for e in KarrasDiffusionSchedulers]
      UpperCAmelCase_      :      Optional[Any]      							=   2




      @register_to_config
      def __init__(				self					,lowerCamelCase_ = 1000					,lowerCamelCase_ = 0.0_0085					,lowerCamelCase_ = 0.012					,lowerCamelCase_ = "linear"					,lowerCamelCase_ = None					,lowerCamelCase_ = "epsilon"					,lowerCamelCase_ = False					,lowerCamelCase_ = False					,lowerCamelCase_ = 1.0					,lowerCamelCase_ = "linspace"					,lowerCamelCase_ = 0					,)			->			Optional[Any]:



         '''simple docstring'''



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

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

         #  set all values
         self.set_timesteps(lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_							)
         UpperCAmelCase__							:				int   				=  use_karras_sigmas




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_=None							)			->			Optional[Any]:



         '''simple docstring'''



         if schedule_timesteps is None:
            UpperCAmelCase__							:				Tuple   				=  self.timesteps

         UpperCAmelCase__							:				Any   				=  (schedule_timesteps == timestep).nonzero()

         # The sigma index that is taken for the **very** first `step`
         # is always the second index (or the last index if there is only 1)
         # This way we can ensure we don't accidentally skip a sigma in
         # case we start in the middle of the denoising schedule (e.g. for image-to-image)
         if len(self._index_counter							) == 0:
            UpperCAmelCase__							:				Optional[int]   				=  1 if len(lowerCamelCase_							) > 1 else 0
         else:
            UpperCAmelCase__							:				Optional[int]   				=  timestep.cpu().item() if torch.is_tensor(lowerCamelCase_							) else timestep
            UpperCAmelCase__							:				Optional[int]   				=  self._index_counter[timestep_int]

         return indices[pos].item()




      @property
      def 		lowerCAmelCase__					(				self							)			->			Union[str, Any]:



         '''simple docstring'''



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

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




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_					,)			->			torch.FloatTensor:



         '''simple docstring'''



         UpperCAmelCase__							:				Tuple   				=  self.index_for_timestep(lowerCamelCase_							)

         UpperCAmelCase__							:				Optional[int]   				=  self.sigmas[step_index]
         UpperCAmelCase__							:				Any   				=  sample / ((sigma**2 + 1) ** 0.5)
         return sample




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_ = None					,lowerCamelCase_ = None					,)			->			Union[str, Any]:



         '''simple docstring'''



         UpperCAmelCase__							:				Union[str, Any]   				=  num_inference_steps

         UpperCAmelCase__							:				Tuple   				=  num_train_timesteps or self.config.num_train_timesteps

         # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
         if self.config.timestep_spacing == "linspace":
            UpperCAmelCase__							:				Dict   				=  np.linspace(0					,num_train_timesteps - 1					,lowerCamelCase_					,dtype=lowerCamelCase_							)[::-1].copy()
         elif self.config.timestep_spacing == "leading":
            UpperCAmelCase__							:				Tuple   				=  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__							:				List[Any]   				=  (np.arange(0					,lowerCamelCase_							) * step_ratio).round()[::-1].copy().astype(lowerCamelCase_							)
            timesteps += self.config.steps_offset
         elif self.config.timestep_spacing == "trailing":
            UpperCAmelCase__							:				int   				=  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__							:				Union[str, Any]   				=  (np.arange(lowerCamelCase_					,0					,-step_ratio							)).round().copy().astype(lowerCamelCase_							)
            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__							:				List[Any]   				=  np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5							)
         UpperCAmelCase__							:				Optional[int]   				=  np.log(lowerCamelCase_							)
         UpperCAmelCase__							:				List[str]   				=  np.interp(lowerCamelCase_					,np.arange(0					,len(lowerCamelCase_							)							)					,lowerCamelCase_							)

         if self.config.use_karras_sigmas:
            UpperCAmelCase__							:				Tuple   				=  self._convert_to_karras(in_sigmas=lowerCamelCase_					,num_inference_steps=self.num_inference_steps							)
            UpperCAmelCase__							:				int   				=  np.array([self._sigma_to_t(lowerCamelCase_					,lowerCamelCase_							) for sigma in sigmas]							)

         UpperCAmelCase__							:				Dict   				=  np.concatenate([sigmas, [0.0]]							).astype(np.floataa							)
         UpperCAmelCase__							:				Any   				=  torch.from_numpy(lowerCamelCase_							).to(device=lowerCamelCase_							)
         UpperCAmelCase__							:				str   				=  torch.cat([sigmas[:1], sigmas[1:-1].repeat_interleave(2							), sigmas[-1:]]							)

         UpperCAmelCase__							:				Optional[int]   				=  torch.from_numpy(lowerCamelCase_							)
         UpperCAmelCase__							:				Tuple   				=  torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2							)]							)

         if str(lowerCamelCase_							).startswith('''mps'''							):
            # mps does not support float64
            UpperCAmelCase__							:				Any   				=  timesteps.to(lowerCamelCase_					,dtype=torch.floataa							)
         else:
            UpperCAmelCase__							:				int   				=  timesteps.to(device=lowerCamelCase_							)

         # empty dt and derivative
         UpperCAmelCase__							:				Dict   				=  None
         UpperCAmelCase__							:				List[Any]   				=  None

         # for exp beta schedules, such as the one for `pipeline_shap_e.py`
         # we need an index counter
         UpperCAmelCase__							:				Tuple   				=  defaultdict(lowerCamelCase_							)




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



         '''simple docstring'''



         UpperCAmelCase__							:				Dict   				=  np.log(lowerCamelCase_							)

         # get distribution
         UpperCAmelCase__							:				List[str]   				=  log_sigma - log_sigmas[:, np.newaxis]

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

         UpperCAmelCase__							:				Optional[Any]   				=  log_sigmas[low_idx]
         UpperCAmelCase__							:				str   				=  log_sigmas[high_idx]

         # interpolate sigmas
         UpperCAmelCase__							:				Optional[int]   				=  (low - log_sigma) / (low - high)
         UpperCAmelCase__							:				Optional[int]   				=  np.clip(lowerCamelCase_					,0					,1							)

         # transform interpolation to time range
         UpperCAmelCase__							:				Optional[Any]   				=  (1 - w) * low_idx + w * high_idx
         UpperCAmelCase__							:				Tuple   				=  t.reshape(sigma.shape							)
         return t




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_							)			->			torch.FloatTensor:



         '''simple docstring'''



         UpperCAmelCase__							:				float   				=  in_sigmas[-1].item()
         UpperCAmelCase__							:				float   				=  in_sigmas[0].item()

         UpperCAmelCase__							:				Tuple   				=  7.0  # 7.0 is the value used in the paper
         UpperCAmelCase__							:				Optional[int]   				=  np.linspace(0					,1					,lowerCamelCase_							)
         UpperCAmelCase__							:				int   				=  sigma_min ** (1 / rho)
         UpperCAmelCase__							:				Any   				=  sigma_max ** (1 / rho)
         UpperCAmelCase__							:				Any   				=  (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
         return sigmas




      @property
      def 		lowerCAmelCase__					(				self							)			->			Any:



         '''simple docstring'''



         return self.dt is None




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_ = True					,)			->			Union[SchedulerOutput, Tuple]:



         '''simple docstring'''



         UpperCAmelCase__							:				Optional[int]   				=  self.index_for_timestep(lowerCamelCase_							)

         # advance index counter by 1
         UpperCAmelCase__							:				Union[str, Any]   				=  timestep.cpu().item() if torch.is_tensor(lowerCamelCase_							) else timestep
         self._index_counter[timestep_int] += 1

         if self.state_in_first_order:
            UpperCAmelCase__							:				Any   				=  self.sigmas[step_index]
            UpperCAmelCase__							:				str   				=  self.sigmas[step_index + 1]
         else:
            # 2nd order / Heun's method
            UpperCAmelCase__							:				Optional[int]   				=  self.sigmas[step_index - 1]
            UpperCAmelCase__							:				Tuple   				=  self.sigmas[step_index]

         # currently only gamma=0 is supported. This usually works best anyways.
         # We can support gamma in the future but then need to scale the timestep before
         # passing it to the model which requires a change in API
         UpperCAmelCase__							:				List[str]   				=  0
         UpperCAmelCase__							:				List[Any]   				=  sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

         # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
         if self.config.prediction_type == "epsilon":
            UpperCAmelCase__							:				int   				=  sigma_hat if self.state_in_first_order else sigma_next
            UpperCAmelCase__							:				Tuple   				=  sample - sigma_input * model_output
         elif self.config.prediction_type == "v_prediction":
            UpperCAmelCase__							:				Optional[int]   				=  sigma_hat if self.state_in_first_order else sigma_next
            UpperCAmelCase__							:				Dict   				=  model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
                sample / (sigma_input**2 + 1)
            )
         elif self.config.prediction_type == "sample":
            UpperCAmelCase__							:				Any   				=  model_output
         else:
            raise ValueError(
                f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''							)

         if self.config.clip_sample:
            UpperCAmelCase__							:				Tuple   				=  pred_original_sample.clamp(
                -self.config.clip_sample_range					,self.config.clip_sample_range							)

         if self.state_in_first_order:
            # 2. Convert to an ODE derivative for 1st order
            UpperCAmelCase__							:				Any   				=  (sample - pred_original_sample) / sigma_hat
            # 3. delta timestep
            UpperCAmelCase__							:				Optional[Any]   				=  sigma_next - sigma_hat

            # store for 2nd order step
            UpperCAmelCase__							:				Tuple   				=  derivative
            UpperCAmelCase__							:				Tuple   				=  dt
            UpperCAmelCase__							:				Dict   				=  sample
         else:
            # 2. 2nd order / Heun's method
            UpperCAmelCase__							:				str   				=  (sample - pred_original_sample) / sigma_next
            UpperCAmelCase__							:				Dict   				=  (self.prev_derivative + derivative) / 2

            # 3. take prev timestep & sample
            UpperCAmelCase__							:				List[Any]   				=  self.dt
            UpperCAmelCase__							:				List[Any]   				=  self.sample

            # free dt and derivative
            # Note, this puts the scheduler in "first order mode"
            UpperCAmelCase__							:				Optional[Any]   				=  None
            UpperCAmelCase__							:				List[str]   				=  None
            UpperCAmelCase__							:				Dict   				=  None

         UpperCAmelCase__							:				Union[str, Any]   				=  sample + derivative * dt

         if not return_dict:
            return (prev_sample,)

         return SchedulerOutput(prev_sample=lowerCamelCase_							)




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_					,)			->			torch.FloatTensor:



         '''simple docstring'''



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

         UpperCAmelCase__							:				str   				=  [self.index_for_timestep(lowerCamelCase_					,lowerCamelCase_							) for t in timesteps]

         UpperCAmelCase__							:				int   				=  sigmas[step_indices].flatten()
         while len(sigma.shape							) < len(original_samples.shape							):
            UpperCAmelCase__							:				Optional[Any]   				=  sigma.unsqueeze(-1							)

         UpperCAmelCase__							:				Dict   				=  original_samples + noise * sigma
         return noisy_samples




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



         '''simple docstring'''



         return self.config.num_train_timesteps





'''simple docstring'''

from __future__ import annotations

from collections import namedtuple







def __UpperCamelCase(   _A					:				float  ,			_A					:				float  ,			_A					:				float    ):
   '''simple docstring'''
   UpperCAmelCase__							:				int   				=  namedtuple('''result'''  ,			'''name value'''    )
   if (voltage, current, power).count(0    ) != 1:
      raise ValueError('''Only one argument must be 0'''    )
   elif power < 0:
      raise ValueError(
          '''Power cannot be negative in any electrical/electronics system'''    )
   elif voltage == 0:
      return result('''voltage'''  ,			power / current    )
   elif current == 0:
      return result('''current'''  ,			power / voltage    )
   elif power == 0:
      return result('''power'''  ,			float(round(abs(voltage * current    )  ,			2    )    )    )
   else:
      raise ValueError('''Exactly one argument must be 0'''    )


if __name__ == "__main__":
  import doctest

  doctest.testmod()




