





'''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_				(   __lowercase				: int     ,	__lowercase				: int=0.999     ,	__lowercase				: Dict="cosine"     ,	)						->			Tuple:


     '''simple docstring'''



     if alpha_transform_type == "cosine":

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

     elif alpha_transform_type == "exp":

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

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

     _UpperCAmelCase					=  []
     for i in range(_lowerCAmelCase			):
          _UpperCAmelCase					=  i / num_diffusion_timesteps
          _UpperCAmelCase					=  (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_      (    _a				,		_a     ):
 _lowerCamelCase    :					Optional[Any]       			=				[e.name for e in KarrasDiffusionSchedulers]
 _lowerCamelCase    :					Union[str, Any]       			=				2




 @register_to_config
 def __init__(   self			:       Union[str, Any]      ,     snake_case_			:       int = 1_0_0_0      ,     snake_case_			:       float = 0.0_0_0_8_5      ,     snake_case_			:       float = 0.0_1_2      ,     snake_case_			:       str = "linear"      ,     snake_case_			:       Optional[Union[np.ndarray, List[float]]] = None      ,     snake_case_			:       str = "epsilon"      ,     snake_case_			:       Optional[bool] = False      ,     snake_case_			:       Optional[bool] = False      ,     snake_case_			:       float = 1.0      ,     snake_case_			:       str = "linspace"      ,     snake_case_			:       int = 0      ,     ):

      if trained_betas is not None:
           _UpperCAmelCase					=  torch.tensor(snake_case_      ,     dtype=torch.floataa       )
      elif beta_schedule == "linear":
           _UpperCAmelCase					=  torch.linspace(snake_case_      ,     snake_case_      ,     snake_case_      ,     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      ,     snake_case_      ,     dtype=torch.floataa       ) ** 2
           )
      elif beta_schedule == "squaredcos_cap_v2":
           # Glide cosine schedule
           _UpperCAmelCase					=  betas_for_alpha_bar(snake_case_      ,     alpha_transform_type="cosine"       )
      elif beta_schedule == "exp":
           _UpperCAmelCase					=  betas_for_alpha_bar(snake_case_      ,     alpha_transform_type="exp"       )
      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       )

      #  set all values
      self.set_timesteps(snake_case_      ,     snake_case_      ,     snake_case_       )
      _UpperCAmelCase					=  use_karras_sigmas




 def 	lowercase      (   self			:       Any      ,     snake_case_			:       Dict      ,     snake_case_			:       str=None       ):

      if schedule_timesteps is None:
           _UpperCAmelCase					=  self.timesteps

      _UpperCAmelCase					=  (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					=  1 if len(snake_case_       ) > 1 else 0
      else:
           _UpperCAmelCase					=  timestep.cpu().item() if torch.is_tensor(snake_case_       ) else timestep
           _UpperCAmelCase					=  self._index_counter[timestep_int]

      return indices[pos].item()




 @property
 def 	lowercase      (   self			:       Optional[Any]       ):

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

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




 def 	lowercase      (   self			:       Tuple      ,     snake_case_			:       torch.FloatTensor      ,     snake_case_			:       Union[float, torch.FloatTensor]      ,     ):
      _UpperCAmelCase					=  self.index_for_timestep(snake_case_       )

      _UpperCAmelCase					=  self.sigmas[step_index]
      _UpperCAmelCase					=  sample / ((sigma**2 + 1) ** 0.5)
      return sample




 def 	lowercase      (   self			:       int      ,     snake_case_			:       int      ,     snake_case_			:       Union[str, torch.device] = None      ,     snake_case_			:       Optional[int] = None      ,     ):
      _UpperCAmelCase					=  num_inference_steps

      _UpperCAmelCase					=  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					=  np.linspace(0      ,     num_train_timesteps - 1      ,     snake_case_      ,     dtype=snake_case_       )[::-1].copy()
      elif self.config.timestep_spacing == "leading":
           _UpperCAmelCase					=  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      ,     snake_case_       ) * step_ratio).round()[::-1].copy().astype(snake_case_       )
           timesteps += self.config.steps_offset
      elif self.config.timestep_spacing == "trailing":
           _UpperCAmelCase					=  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(snake_case_      ,     0      ,     -step_ratio       )).round().copy().astype(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\'.'       )

      _UpperCAmelCase					=  np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5       )
      _UpperCAmelCase					=  np.log(snake_case_       )
      _UpperCAmelCase					=  np.interp(snake_case_      ,     np.arange(0      ,     len(snake_case_       )       )      ,     snake_case_       )

      if self.config.use_karras_sigmas:
           _UpperCAmelCase					=  self._convert_to_karras(in_sigmas=snake_case_      ,     num_inference_steps=self.num_inference_steps       )
           _UpperCAmelCase					=  np.array([self._sigma_to_t(snake_case_      ,     snake_case_       ) for sigma in sigmas]       )

      _UpperCAmelCase					=  np.concatenate([sigmas, [0.0]]       ).astype(np.floataa       )
      _UpperCAmelCase					=  torch.from_numpy(snake_case_       ).to(device=snake_case_       )
      _UpperCAmelCase					=  torch.cat([sigmas[:1], sigmas[1:-1].repeat_interleave(2       ), sigmas[-1:]]       )

      _UpperCAmelCase					=  torch.from_numpy(snake_case_       )
      _UpperCAmelCase					=  torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2       )]       )

      if str(snake_case_       ).startswith("mps"       ):
           # mps does not support float64
           _UpperCAmelCase					=  timesteps.to(snake_case_      ,     dtype=torch.floataa       )
      else:
           _UpperCAmelCase					=  timesteps.to(device=snake_case_       )

      # empty dt and derivative
      _UpperCAmelCase					=  None
      _UpperCAmelCase					=  None

      # for exp beta schedules, such as the one for `pipeline_shap_e.py`
      # we need an index counter
      _UpperCAmelCase					=  defaultdict(snake_case_       )




 def 	lowercase      (   self			:       Any      ,     snake_case_			:       Any      ,     snake_case_			:       List[str]       ):
      _UpperCAmelCase					=  np.log(snake_case_       )

      # 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(snake_case_      ,     0      ,     1       )

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




 def 	lowercase      (   self			:       List[Any]      ,     snake_case_			:       torch.FloatTensor      ,     snake_case_			:       str       ):
      _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      ,     snake_case_       )
      _UpperCAmelCase					=  sigma_min ** (1 / rho)
      _UpperCAmelCase					=  sigma_max ** (1 / rho)
      _UpperCAmelCase					=  (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
      return sigmas




 @property
 def 	lowercase      (   self			:       List[Any]       ):
      return self.dt is None




 def 	lowercase      (   self			:       int      ,     snake_case_			:       Union[torch.FloatTensor, np.ndarray]      ,     snake_case_			:       Union[float, torch.FloatTensor]      ,     snake_case_			:       Union[torch.FloatTensor, np.ndarray]      ,     snake_case_			:       bool = True      ,     ):
      _UpperCAmelCase					=  self.index_for_timestep(snake_case_       )

      # advance index counter by 1
      _UpperCAmelCase					=  timestep.cpu().item() if torch.is_tensor(snake_case_       ) else timestep
      self._index_counter[timestep_int] += 1

      if self.state_in_first_order:
           _UpperCAmelCase					=  self.sigmas[step_index]
           _UpperCAmelCase					=  self.sigmas[step_index + 1]
      else:
           # 2nd order / Heun's method
           _UpperCAmelCase					=  self.sigmas[step_index - 1]
           _UpperCAmelCase					=  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					=  0
      _UpperCAmelCase					=  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					=  sigma_hat if self.state_in_first_order else sigma_next
           _UpperCAmelCase					=  sample - sigma_input * model_output
      elif self.config.prediction_type == "v_prediction":
           _UpperCAmelCase					=  sigma_hat if self.state_in_first_order else sigma_next
           _UpperCAmelCase					=  model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
               sample / (sigma_input**2 + 1)
           )
      elif self.config.prediction_type == "sample":
           _UpperCAmelCase					=  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					=  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					=  (sample - pred_original_sample) / sigma_hat
           # 3. delta timestep
           _UpperCAmelCase					=  sigma_next - sigma_hat

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

           # 3. take prev timestep & sample
           _UpperCAmelCase					=  self.dt
           _UpperCAmelCase					=  self.sample

           # free dt and derivative
           # Note, this puts the scheduler in "first order mode"
           _UpperCAmelCase					=  None
           _UpperCAmelCase					=  None
           _UpperCAmelCase					=  None

      _UpperCAmelCase					=  sample + derivative * dt

      if not return_dict:
           return (prev_sample,)

      return SchedulerOutput(prev_sample=snake_case_       )




 def 	lowercase      (   self			:       Union[str, Any]      ,     snake_case_			:       torch.FloatTensor      ,     snake_case_			:       torch.FloatTensor      ,     snake_case_			:       torch.FloatTensor      ,     ):
      _UpperCAmelCase					=  self.sigmas.to(device=original_samples.device      ,     dtype=original_samples.dtype       )
      if original_samples.device.type == "mps" and torch.is_floating_point(snake_case_       ):
           # 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					=  [self.index_for_timestep(snake_case_      ,     snake_case_       ) 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			:       Any       ):
      return self.config.num_train_timesteps








from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


A				:       Optional[Any]      =  {'configuration_van': ['VAN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'VanConfig']}


try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A				:       Optional[Any]      =  [
	    'VAN_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'VanForImageClassification',
	    'VanModel',
	    'VanPreTrainedModel',
	]

if TYPE_CHECKING:
	from .configuration_van import VAN_PRETRAINED_CONFIG_ARCHIVE_MAP, VanConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_van import (
		    VAN_PRETRAINED_MODEL_ARCHIVE_LIST,
		    VanForImageClassification,
		    VanModel,
		    VanPreTrainedModel,
		)

else:
	import sys

	A				:       Union[str, Any]      =  _LazyModule(__name__, globals()['__file__'], _import_structure)





