
'''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 	_snake_case						(      A_       :    int     ,		A_       :    Union[str, Any]=0.999     ,		A_       :    int="cosine"     ,		):
  """simple docstring"""







  if alpha_transform_type == "cosine":

    def alpha_bar_fn(A_       :    Optional[Any]   ):
      return math.cos((t + 0.008) / 1.008 * math.pi / 2   ) ** 2

  elif alpha_transform_type == "exp":

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

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

  a_      :	Any      =							[]
  for i in range(lowerCamelCase__   ):
    a_      :	Tuple      =							i / num_diffusion_timesteps
    a_      :	Dict      =							(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  _UpperCAmelCase ( lowerCAmelCase__	,lowerCAmelCase__    ):

     """simple docstring"""

     a_    	=					[e.name for e in KarrasDiffusionSchedulers]
     a_    	=					2


     @register_to_config
     def __init__(      self							,		lowerCAmelCase_ = 10_00							,		lowerCAmelCase_ = 0.00085							,		lowerCAmelCase_ = 0.012							,		lowerCAmelCase_ = "linear"							,		lowerCAmelCase_ = None							,		lowerCAmelCase_ = "epsilon"							,		lowerCAmelCase_ = "linspace"							,		lowerCAmelCase_ = 0							,		):

       '''simple docstring'''

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

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

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


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_=None ):

       '''simple docstring'''

       if schedule_timesteps is None:
         a_      :	int      =							self.timesteps

       a_      :	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:
         a_      :	Union[str, Any]      =							1 if len(UpperCamelCase_ ) > 1 else 0
       else:
         a_      :	Union[str, Any]      =							timestep.cpu().item() if torch.is_tensor(UpperCamelCase_ ) else timestep
         a_      :	Dict      =							self._index_counter[timestep_int]

       return indices[pos].item()


     @property
     def 							_lowerCAmelCase  (      self ):

       '''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_							,		):

       '''simple docstring'''

       a_      :	Tuple      =							self.index_for_timestep(UpperCamelCase_ )

       if self.state_in_first_order:
         a_      :	Tuple      =							self.sigmas[step_index]
       else:
         a_      :	Tuple      =							self.sigmas_interpol[step_index]

       a_      :	Any      =							sample / ((sigma**2 + 1) ** 0.5)
       return sample


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_ = None							,		lowerCAmelCase_ = None							,		):

       '''simple docstring'''

       a_      :	Union[str, Any]      =							num_inference_steps

       a_      :	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":
         a_      :	int      =							np.linspace(0							,		num_train_timesteps - 1							,		UpperCamelCase_							,		dtype=UpperCamelCase_ )[::-1].copy()
       elif self.config.timestep_spacing == "leading":
         a_      :	Union[str, 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
         a_      :	Tuple      =							(np.arange(0							,		UpperCamelCase_ ) * step_ratio).round()[::-1].copy().astype(UpperCamelCase_ )
         timesteps += self.config.steps_offset
       elif self.config.timestep_spacing == "trailing":
         a_      :	List[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
         a_      :	Union[str, Any]      =							(np.arange(UpperCamelCase_							,		0							,		-step_ratio )).round().copy().astype(UpperCamelCase_ )
         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_      :	Union[str, Any]      =							np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5 )
       a_      :	str      =							torch.from_numpy(np.log(UpperCamelCase_ ) ).to(UpperCamelCase_ )

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

       # interpolate sigmas
       a_      :	Tuple      =							sigmas.log().lerp(sigmas.roll(1 ).log()							,		0.5 ).exp()

       a_      :	Tuple      =							torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2 ), sigmas[-1:]] )
       a_      :	Union[str, Any]      =							torch.cat(
           [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2 ), sigmas_interpol[-1:]] )

       if str(UpperCamelCase_ ).startswith("""mps""" ):
         # mps does not support float64
         a_      :	Union[str, Any]      =							torch.from_numpy(UpperCamelCase_ ).to(UpperCamelCase_							,		dtype=torch.floataa )
       else:
         a_      :	Tuple      =							torch.from_numpy(UpperCamelCase_ ).to(UpperCamelCase_ )

       # interpolate timesteps
       a_      :	Tuple      =							self.sigma_to_t(UpperCamelCase_ ).to(UpperCamelCase_							,		dtype=timesteps.dtype )
       a_      :	int      =							torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None])							,		dim=-1 ).flatten()

       a_      :	List[str]      =							torch.cat([timesteps[:1], interleaved_timesteps] )

       a_      :	str      =							None

       # for exp beta schedules, such as the one for `pipeline_shap_e.py`
       # we need an index counter
       a_      :	List[str]      =							defaultdict(UpperCamelCase_ )


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_ ):

       '''simple docstring'''

       a_      :	Union[str, Any]      =							sigma.log()

       # get distribution
       a_      :	Dict      =							log_sigma - self.log_sigmas[:, None]

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

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

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

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


     @property
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       return self.sample is None


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_ = True							,		):

       '''simple docstring'''

       a_      :	Optional[int]      =							self.index_for_timestep(UpperCamelCase_ )

       # advance index counter by 1
       a_      :	List[str]      =							timestep.cpu().item() if torch.is_tensor(UpperCamelCase_ ) else timestep
       self._index_counter[timestep_int] += 1

       if self.state_in_first_order:
         a_      :	int      =							self.sigmas[step_index]
         a_      :	str      =							self.sigmas_interpol[step_index + 1]
         a_      :	Optional[int]      =							self.sigmas[step_index + 1]
       else:
         # 2nd order / KDPM2's method
         a_      :	List[str]      =							self.sigmas[step_index - 1]
         a_      :	int      =							self.sigmas_interpol[step_index]
         a_      :	int      =							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
       a_      :	Tuple      =							0
       a_      :	Optional[int]      =							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":
         a_      :	Optional[int]      =							sigma_hat if self.state_in_first_order else sigma_interpol
         a_      :	Union[str, Any]      =							sample - sigma_input * model_output
       elif self.config.prediction_type == "v_prediction":
         a_      :	Tuple      =							sigma_hat if self.state_in_first_order else sigma_interpol
         a_      :	int      =							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[Any]      =							sigma_interpol - sigma_hat

         # store for 2nd order step
         a_      :	int      =							sample
       else:
         # DPM-Solver-2
         # 2. Convert to an ODE derivative for 2nd order
         a_      :	int      =							(sample - pred_original_sample) / sigma_interpol

         # 3. delta timestep
         a_      :	Any      =							sigma_next - sigma_hat

         a_      :	Dict      =							self.sample
         a_      :	str      =							None

       a_      :	List[Any]      =							sample + derivative * dt

       if not return_dict:
         return (prev_sample,)

       return SchedulerOutput(prev_sample=UpperCamelCase_ )


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		):

       '''simple docstring'''

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

       a_      :	Tuple      =							[self.index_for_timestep(UpperCamelCase_							,		UpperCamelCase_ ) for t in timesteps]

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

       a_      :	Union[str, Any]      =							original_samples + noise * sigma
       return noisy_samples






     def __len__(      self ):

       '''simple docstring'''

       return self.config.num_train_timesteps




'''simple docstring'''

def 	_snake_case						(      A_       :    Optional[int]   ):
  """simple docstring"""







  if not head:
    return True
  # split the list to two parts
  a_      , a_      :	Union[str, Any]      =							head.next, head
  while fast and fast.next:
    a_      :	Dict      =							fast.next.next
    a_      :	List[str]      =							slow.next
  a_      :	str      =							slow.next
  a_      :	Optional[int]      =							None  # Don't forget here! But forget still works!
  # reverse the second part
  a_      :	Dict      =							None
  while second:
    a_      :	Any      =							second.next
    a_      :	Any      =							node
    a_      :	Optional[Any]      =							second
    a_      :	Optional[Any]      =							nxt
  # compare two parts
  # second part has the same or one less node
  while node:
    if node.val != head.val:
      return False
    a_      :	List[str]      =							node.next
    a_      :	Dict      =							head.next
  return True






def 	_snake_case						(      A_       :    List[str]   ):
  """simple docstring"""







  if not head or not head.next:
    return True

  # 1. Get the midpoint (slow)
  a_      :	Optional[Any]      =							head
  while fast and fast.next:
    a_      , a_      :	Optional[int]      =							fast.next.next, slow.next

  # 2. Push the second half into the stack
  a_      :	List[str]      =							[slow.val]
  while slow.next:
    a_      :	List[str]      =							slow.next
    stack.append(slow.val   )

  # 3. Comparison
  while stack:
    if stack.pop() != cur.val:
      return False
    a_      :	Union[str, Any]      =							cur.next

  return True






def 	_snake_case						(      A_       :    Optional[int]   ):
  """simple docstring"""







  if not head or not head.next:
    return True
  a_      :	List[Any]      =							{}
  a_      :	int      =							0
  while head:
    if head.val in d:
      d[head.val].append(A_   )
    else:
      a_      :	int      =							[pos]
    a_      :	List[str]      =							head.next
    pos += 1
  a_      :	str      =							pos - 1
  a_      :	List[str]      =							0
  for v in d.values():
    if len(A_   ) % 2 != 0:
      middle += 1
    else:
      a_      :	Optional[int]      =							0
      for i in range(0     ,		len(A_   )   ):
        if v[i] + v[len(A_   ) - 1 - step] != checksum:
          return False
        step += 1
    if middle > 1:
      return False
  return True


