




'''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 					SCREAMING_SNAKE_CASE(   __lowercase		,				__lowercase=0.9_9_9		,				__lowercase="cosine"		,				)			-> str:
     if alpha_transform_type == "cosine":

          def alpha_bar_fn(__lowercase	):
               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(__lowercase	):
               return math.exp(t * -1_2.0	)

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

     A:		List[str]          =		[]
     for i in range(__lowercase	):
          A:		Union[str, Any]          =		i / num_diffusion_timesteps
          A:		List[Any]          =		(i + 1) / num_diffusion_timesteps
          betas.append(min(1 - alpha_bar_fn(__lowercase	) / alpha_bar_fn(__lowercase	)		,				__lowercase	)	)
     return torch.tensor(__lowercase		,				dtype=torch.floataa	)






class 						lowerCAmelCase_	(       UpperCAmelCase_       ,					UpperCAmelCase_							):







  '''simple docstring'''

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



  @register_to_config
  def __init__( self				:							Any					,  SCREAMING_SNAKE_CASE_				:							int = 10_00					,  SCREAMING_SNAKE_CASE_				:							float = 0.0_0085					,  SCREAMING_SNAKE_CASE_				:							float = 0.012					,  SCREAMING_SNAKE_CASE_				:							str = "linear"					,  SCREAMING_SNAKE_CASE_				:							Optional[Union[np.ndarray, List[float]]] = None					,  SCREAMING_SNAKE_CASE_				:							str = "epsilon"					,  SCREAMING_SNAKE_CASE_				:							str = "linspace"					,  SCREAMING_SNAKE_CASE_				:							int = 0					,  )			->		List[Any]:







       '''simple docstring'''



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

       A:		Dict          =		1.0 - self.betas
       A:		Optional[Any]          =		torch.cumprod(self.alphas					,  dim=0	)

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



  def 					_snake_case     ( self				:							Optional[Any]					,  SCREAMING_SNAKE_CASE_				:							Any					,  SCREAMING_SNAKE_CASE_				:							Optional[int]=None	)			->		Union[str, Any]:







       '''simple docstring'''



       if schedule_timesteps is None:
            A:		List[str]          =		self.timesteps

       A:		Dict          =		(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:
            A:		Any          =		1 if len(SCREAMING_SNAKE_CASE_	) > 1 else 0
       else:
            A:		List[Any]          =		timestep.cpu().item() if torch.is_tensor(SCREAMING_SNAKE_CASE_	) else timestep
            A:		int          =		self._index_counter[timestep_int]

       return indices[pos].item()



  @property
  def 					_snake_case     ( self				:							Optional[Any]	)			->		str:







       '''simple docstring'''



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

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



  def 					_snake_case     ( self				:							Optional[Any]					,  SCREAMING_SNAKE_CASE_				:							torch.FloatTensor					,  SCREAMING_SNAKE_CASE_				:							Union[float, torch.FloatTensor]					,  )			->		torch.FloatTensor:







       '''simple docstring'''



       A:		Dict          =		self.index_for_timestep(SCREAMING_SNAKE_CASE_	)

       if self.state_in_first_order:
            A:		str          =		self.sigmas[step_index]
       else:
            A:		int          =		self.sigmas_interpol[step_index - 1]

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



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







       '''simple docstring'''



       A:		Tuple          =		num_inference_steps

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

       A:		Tuple          =		np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5	)
       A:		List[str]          =		torch.from_numpy(np.log(SCREAMING_SNAKE_CASE_	)	).to(SCREAMING_SNAKE_CASE_	)

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

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

       # compute interpolated sigmas
       A:		List[str]          =		sigmas.log().lerp(sigmas_down.log()					,  0.5	).exp()
       A:		List[str]          =		0.0

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

       if str(SCREAMING_SNAKE_CASE_	).startswith('''mps'''	):
            # mps does not support float64
            A:		Optional[Any]          =		torch.from_numpy(SCREAMING_SNAKE_CASE_	).to(SCREAMING_SNAKE_CASE_					,  dtype=torch.floataa	)
       else:
            A:		Optional[int]          =		torch.from_numpy(SCREAMING_SNAKE_CASE_	).to(SCREAMING_SNAKE_CASE_	)

       A:		List[str]          =		self.sigma_to_t(SCREAMING_SNAKE_CASE_	).to(SCREAMING_SNAKE_CASE_					,  dtype=timesteps.dtype	)
       A:		Optional[Any]          =		torch.stack((timesteps_interpol[:-2, None], timesteps[1:, None])					,  dim=-1	).flatten()

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

       A:		Optional[int]          =		None

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



  def 					_snake_case     ( self				:							Union[str, Any]					,  SCREAMING_SNAKE_CASE_				:							Dict	)			->		Dict:







       '''simple docstring'''



       A:		Any          =		sigma.log()

       # get distribution
       A:		List[str]          =		log_sigma - self.log_sigmas[:, None]

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

       A:		List[Any]          =		self.log_sigmas[low_idx]
       A:		str          =		self.log_sigmas[high_idx]

       # interpolate sigmas
       A:		Tuple          =		(low - log_sigma) / (low - high)
       A:		Any          =		w.clamp(0					,  1	)

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



  @property
  def 					_snake_case     ( self				:							Dict	)			->		Optional[int]:







       '''simple docstring'''



       return self.sample is None



  def 					_snake_case     ( self				:							str					,  SCREAMING_SNAKE_CASE_				:							Union[torch.FloatTensor, np.ndarray]					,  SCREAMING_SNAKE_CASE_				:							Union[float, torch.FloatTensor]					,  SCREAMING_SNAKE_CASE_				:							Union[torch.FloatTensor, np.ndarray]					,  SCREAMING_SNAKE_CASE_				:							Optional[torch.Generator] = None					,  SCREAMING_SNAKE_CASE_				:							bool = True					,  )			->		Union[SchedulerOutput, Tuple]:







       '''simple docstring'''



       A:		int          =		self.index_for_timestep(SCREAMING_SNAKE_CASE_	)

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

       if self.state_in_first_order:
            A:		Any          =		self.sigmas[step_index]
            A:		Tuple          =		self.sigmas_interpol[step_index]
            A:		Optional[int]          =		self.sigmas_up[step_index]
            A:		Optional[Any]          =		self.sigmas_down[step_index - 1]
       else:
            # 2nd order / KPDM2's method
            A:		Dict          =		self.sigmas[step_index - 1]
            A:		Dict          =		self.sigmas_interpol[step_index - 1]
            A:		Dict          =		self.sigmas_up[step_index - 1]
            A:		Optional[int]          =		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
       A:		int          =		0
       A:		List[str]          =		sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

       A:		Optional[Any]          =		model_output.device
       A:		List[Any]          =		randn_tensor(model_output.shape					,  dtype=model_output.dtype					,  device=SCREAMING_SNAKE_CASE_					,  generator=SCREAMING_SNAKE_CASE_	)

       # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
       if self.config.prediction_type == "epsilon":
            A:		Any          =		sigma_hat if self.state_in_first_order else sigma_interpol
            A:		str          =		sample - sigma_input * model_output
       elif self.config.prediction_type == "v_prediction":
            A:		Union[str, Any]          =		sigma_hat if self.state_in_first_order else sigma_interpol
            A:		Tuple          =		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
            A:		Optional[int]          =		(sample - pred_original_sample) / sigma_hat
            # 3. delta timestep
            A:		List[str]          =		sigma_interpol - sigma_hat

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

            A:		Any          =		self.sample
            A:		List[Any]          =		None

            A:		Any          =		sample + derivative * dt
            A:		List[Any]          =		prev_sample + noise * sigma_up

       if not return_dict:
            return (prev_sample,)

       return SchedulerOutput(prev_sample=SCREAMING_SNAKE_CASE_	)



  def 					_snake_case     ( self				:							Optional[int]					,  SCREAMING_SNAKE_CASE_				:							torch.FloatTensor					,  SCREAMING_SNAKE_CASE_				:							torch.FloatTensor					,  SCREAMING_SNAKE_CASE_				:							torch.FloatTensor					,  )			->		torch.FloatTensor:







       '''simple docstring'''



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

       A:		Union[str, Any]          =		[self.index_for_timestep(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_	) for t in timesteps]

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

       A:		int          =		original_samples + noise * sigma
       return noisy_samples




  def __len__( self				:							int	)			->		Tuple:







       '''simple docstring'''



       return self.config.num_train_timesteps












'''simple docstring'''


def 					SCREAMING_SNAKE_CASE(   __lowercase	)			-> int:
     if not isinstance(__lowercase		,				__lowercase	):
          raise ValueError('''double_factorial() only accepts integral values'''	)
     if n < 0:
          raise ValueError('''double_factorial() not defined for negative values'''	)
     return 1 if n <= 1 else n * double_factorial(n - 2	)


if __name__ == "__main__":
     import doctest

     doctest.testmod()






