






"""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 ..utils import randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput






def 			lowerCamelCase_    (		_lowerCamelCase     ,  _lowerCamelCase=0.999     ,  _lowerCamelCase="cosine"     ,  ):
 if alpha_transform_type == "cosine":

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

 elif alpha_transform_type == "exp":

  def alpha_bar_fn(_lowerCamelCase							):
   return math.exp(t * -12.0							)

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

 lowerCamelCase__	:     Optional[int]								=		[]
 for i in range(_lowerCamelCase							):
  lowerCamelCase__	:     Optional[int]								=		i / num_diffusion_timesteps
  lowerCamelCase__	:     int								=		(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 						a_ (      snake_case_    ,		snake_case_					):







      '''simple docstring'''

      lowerCamelCase__			:							int					=      [e.name for e in KarrasDiffusionSchedulers]
      lowerCamelCase__			:							Optional[Any]					=      2




      @register_to_config
      def __init__(self,	lowerCamelCase_ = 1_0_0_0,	lowerCamelCase_ = 0.00_085,	lowerCamelCase_ = 0.012,	lowerCamelCase_ = "linear",	lowerCamelCase_ = None,	lowerCamelCase_ = "epsilon",	lowerCamelCase_ = "linspace",	lowerCamelCase_ = 0,	):

       '''simple docstring'''



       if trained_betas is not None:
        lowerCamelCase__	:     Union[str, Any]								=		torch.tensor(lowerCamelCase_,	dtype=torch.floataa   )
       elif beta_schedule == "linear":
        lowerCamelCase__	:     List[str]								=		torch.linspace(lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	dtype=torch.floataa   )
       elif beta_schedule == "scaled_linear":
        # this schedule is very specific to the latent diffusion model.
        lowerCamelCase__	:     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
        lowerCamelCase__	:     Union[str, Any]								=		betas_for_alpha_bar(lowerCamelCase_   )
       else:
        raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}'''   )

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

       #  set all values
       self.set_timesteps(lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_   )




      def      a__				(self,	lowerCamelCase_,	lowerCamelCase_=None   ):

       '''simple docstring'''



       if schedule_timesteps is None:
        lowerCamelCase__	:     Any								=		self.timesteps

       lowerCamelCase__	:     List[str]								=		(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:
        lowerCamelCase__	:     Any								=		1 if len(lowerCamelCase_   ) > 1 else 0
       else:
        lowerCamelCase__	:     Tuple								=		timestep.cpu().item() if torch.is_tensor(lowerCamelCase_   ) else timestep
        lowerCamelCase__	:     Dict								=		self._index_counter[timestep_int]

       return indices[pos].item()




      @property
      def      a__				(self   ):

       '''simple docstring'''



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

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




      def      a__				(self,	lowerCamelCase_,	lowerCamelCase_,	):

       '''simple docstring'''



       lowerCamelCase__	:     Optional[Any]								=		self.index_for_timestep(lowerCamelCase_   )

       if self.state_in_first_order:
        lowerCamelCase__	:     List[Any]								=		self.sigmas[step_index]
       else:
        lowerCamelCase__	:     int								=		self.sigmas_interpol[step_index - 1]

       lowerCamelCase__	:     List[str]								=		sample / ((sigma**2 + 1) ** 0.5)
       return sample




      def      a__				(self,	lowerCamelCase_,	lowerCamelCase_ = None,	lowerCamelCase_ = None,	):

       '''simple docstring'''



       lowerCamelCase__	:     int								=		num_inference_steps

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

       lowerCamelCase__	:     Dict								=		np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5   )
       lowerCamelCase__	:     Optional[int]								=		torch.from_numpy(np.log(lowerCamelCase_   )   ).to(lowerCamelCase_   )

       lowerCamelCase__	:     Optional[Any]								=		np.interp(lowerCamelCase_,	np.arange(0,	len(lowerCamelCase_   )   ),	lowerCamelCase_   )
       lowerCamelCase__	:     Optional[int]								=		np.concatenate([sigmas, [0.0]]   ).astype(np.floataa   )
       lowerCamelCase__	:     Tuple								=		torch.from_numpy(lowerCamelCase_   ).to(device=lowerCamelCase_   )

       # compute up and down sigmas
       lowerCamelCase__	:     Union[str, Any]								=		sigmas.roll(-1   )
       lowerCamelCase__	:     List[str]								=		0.0
       lowerCamelCase__	:     List[Any]								=		(sigmas_next**2 * (sigmas**2 - sigmas_next**2) / sigmas**2) ** 0.5
       lowerCamelCase__	:     Dict								=		(sigmas_next**2 - sigmas_up**2) ** 0.5
       lowerCamelCase__	:     Union[str, Any]								=		0.0

       # compute interpolated sigmas
       lowerCamelCase__	:     Any								=		sigmas.log().lerp(sigmas_down.log(),	0.5   ).exp()
       lowerCamelCase__	:     Union[str, Any]								=		0.0

       # set sigmas
       lowerCamelCase__	:     Tuple								=		torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2   ), sigmas[-1:]]   )
       lowerCamelCase__	:     List[str]								=		torch.cat(
           [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2   ), sigmas_interpol[-1:]]   )
       lowerCamelCase__	:     List[Any]								=		torch.cat([sigmas_up[:1], sigmas_up[1:].repeat_interleave(2   ), sigmas_up[-1:]]   )
       lowerCamelCase__	:     Optional[int]								=		torch.cat([sigmas_down[:1], sigmas_down[1:].repeat_interleave(2   ), sigmas_down[-1:]]   )

       if str(lowerCamelCase_   ).startswith('mps'   ):
        # mps does not support float64
        lowerCamelCase__	:     Union[str, Any]								=		torch.from_numpy(lowerCamelCase_   ).to(lowerCamelCase_,	dtype=torch.floataa   )
       else:
        lowerCamelCase__	:     List[Any]								=		torch.from_numpy(lowerCamelCase_   ).to(lowerCamelCase_   )

       lowerCamelCase__	:     int								=		self.sigma_to_t(lowerCamelCase_   ).to(lowerCamelCase_,	dtype=timesteps.dtype   )
       lowerCamelCase__	:     Optional[Any]								=		torch.stack((timesteps_interpol[:-2, None], timesteps[1:, None]),	dim=-1   ).flatten()

       lowerCamelCase__	:     Optional[int]								=		torch.cat([timesteps[:1], interleaved_timesteps]   )

       lowerCamelCase__	:     Dict								=		None

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




      def      a__				(self,	lowerCamelCase_   ):

       '''simple docstring'''



       lowerCamelCase__	:     List[Any]								=		sigma.log()

       # get distribution
       lowerCamelCase__	:     Tuple								=		log_sigma - self.log_sigmas[:, None]

       # get sigmas range
       lowerCamelCase__	:     int								=		dists.ge(0   ).cumsum(dim=0   ).argmax(dim=0   ).clamp(max=self.log_sigmas.shape[0] - 2   )
       lowerCamelCase__	:     int								=		low_idx + 1

       lowerCamelCase__	:     Union[str, Any]								=		self.log_sigmas[low_idx]
       lowerCamelCase__	:     List[Any]								=		self.log_sigmas[high_idx]

       # interpolate sigmas
       lowerCamelCase__	:     Dict								=		(low - log_sigma) / (low - high)
       lowerCamelCase__	:     Optional[int]								=		w.clamp(0,	1   )

       # transform interpolation to time range
       lowerCamelCase__	:     Optional[Any]								=		(1 - w) * low_idx + w * high_idx
       lowerCamelCase__	:     Union[str, Any]								=		t.view(sigma.shape   )
       return t




      @property
      def      a__				(self   ):

       '''simple docstring'''



       return self.sample is None




      def      a__				(self,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_ = None,	lowerCamelCase_ = True,	):

       '''simple docstring'''



       lowerCamelCase__	:     Any								=		self.index_for_timestep(lowerCamelCase_   )

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

       if self.state_in_first_order:
        lowerCamelCase__	:     Any								=		self.sigmas[step_index]
        lowerCamelCase__	:     str								=		self.sigmas_interpol[step_index]
        lowerCamelCase__	:     str								=		self.sigmas_up[step_index]
        lowerCamelCase__	:     Optional[Any]								=		self.sigmas_down[step_index - 1]
       else:
        # 2nd order / KPDM2's method
        lowerCamelCase__	:     List[Any]								=		self.sigmas[step_index - 1]
        lowerCamelCase__	:     int								=		self.sigmas_interpol[step_index - 1]
        lowerCamelCase__	:     Optional[Any]								=		self.sigmas_up[step_index - 1]
        lowerCamelCase__	:     Dict								=		self.sigmas_down[step_index - 1]

       # 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
       lowerCamelCase__	:     List[Any]								=		0
       lowerCamelCase__	:     List[Any]								=		sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

       lowerCamelCase__	:     Optional[int]								=		model_output.device
       lowerCamelCase__	:     Union[str, Any]								=		randn_tensor(model_output.shape,	dtype=model_output.dtype,	device=lowerCamelCase_,	generator=lowerCamelCase_   )

       # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
       if self.config.prediction_type == "epsilon":
        lowerCamelCase__	:     str								=		sigma_hat if self.state_in_first_order else sigma_interpol
        lowerCamelCase__	:     int								=		sample - sigma_input * model_output
       elif self.config.prediction_type == "v_prediction":
        lowerCamelCase__	:     List[Any]								=		sigma_hat if self.state_in_first_order else sigma_interpol
        lowerCamelCase__	:     List[Any]								=		model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
            sample / (sigma_input**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`'''   )

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

        # store for 2nd order step
        lowerCamelCase__	:     Union[str, Any]								=		sample
        lowerCamelCase__	:     List[str]								=		dt
        lowerCamelCase__	:     Optional[int]								=		sample + derivative * dt
       else:
        # DPM-Solver-2
        # 2. Convert to an ODE derivative for 2nd order
        lowerCamelCase__	:     Optional[int]								=		(sample - pred_original_sample) / sigma_interpol
        # 3. delta timestep
        lowerCamelCase__	:     int								=		sigma_down - sigma_hat

        lowerCamelCase__	:     Optional[int]								=		self.sample
        lowerCamelCase__	:     Any								=		None

        lowerCamelCase__	:     Optional[int]								=		sample + derivative * dt
        lowerCamelCase__	:     Tuple								=		prev_sample + noise * sigma_up

       if not return_dict:
        return (prev_sample,)

       return SchedulerOutput(prev_sample=lowerCamelCase_   )




      def      a__				(self,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	):

       '''simple docstring'''



       lowerCamelCase__	:     int								=		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
        lowerCamelCase__	:     Union[str, Any]								=		self.timesteps.to(original_samples.device,	dtype=torch.floataa   )
        lowerCamelCase__	:     Dict								=		timesteps.to(original_samples.device,	dtype=torch.floataa   )
       else:
        lowerCamelCase__	:     Tuple								=		self.timesteps.to(original_samples.device   )
        lowerCamelCase__	:     Optional[Any]								=		timesteps.to(original_samples.device   )

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

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

       lowerCamelCase__	:     Tuple								=		original_samples + noise * sigma
       return noisy_samples





      def __len__(self   ):

       '''simple docstring'''



       return self.config.num_train_timesteps









"""simple docstring"""
from __future__ import annotations






def 			lowerCamelCase_    (		_lowerCamelCase							):
 lowerCamelCase__	:     int
 lowerCamelCase__	:     list[str]								=		list(_lowerCamelCase							)
 lowerCamelCase__							,	lowerCamelCase__	:     Union[str, Any]								=		_vector[:-1], int(_vector[-1]							)
 lowerCamelCase__	:     list[int]								=		[int(_lowerCamelCase							) for digit in __vector]

 vector.reverse()
 for i, digit in enumerate(_lowerCamelCase							):
  if i & 1 == 0:
   lowerCamelCase__	:     int								=		digit * 2
   if doubled > 9:
    doubled -= 9
   check_digit += doubled
  else:
   check_digit += digit

 return check_digit % 10 == 0


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      assert is_luhn("79927398713")
      assert not is_luhn("79927398714")

