
'''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 							snake_case				(		UpperCAmelCase   ,	UpperCAmelCase=0.999   ,	UpperCAmelCase="cosine"   ,	)->      str:







      """simple docstring"""
      if alpha_transform_type == "cosine":

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

      elif alpha_transform_type == "exp":

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

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

      __A													=      []
      for i in range(UpperCAmelCase						):
            __A													=      i / num_diffusion_timesteps
            __A													=      (i + 1) / num_diffusion_timesteps
            betas.append(min(1 - alpha_bar_fn(UpperCAmelCase						) / alpha_bar_fn(UpperCAmelCase						)   ,	UpperCAmelCase						)						)
      return torch.tensor(UpperCAmelCase   ,	dtype=torch.floataa						)


class 			UpperCamelCase__	(     SCREAMING_SNAKE_CASE   ,  SCREAMING_SNAKE_CASE):
 UpperCAmelCase__     :     Optional[Any]    					=       [e.name for e in KarrasDiffusionSchedulers]
 UpperCAmelCase__     :     Tuple    					=       2






 @register_to_config
 def __init__(      self	:Any						,					_A	:int = 1_000						,					_A	:float = 0.00_085						,					_A	:float = 0.012						,					_A	:str = "linear"						,					_A	:Optional[Union[np.ndarray, List[float]]] = None						,					_A	:str = "epsilon"						,					_A	:str = "linspace"						,					_A	:int = 0						,					)					->      List[Any]:







       '''simple docstring'''



       if trained_betas is not None:
             __A													=      torch.tensor(_A						,					dtype=torch.floataa      )
       elif beta_schedule == "linear":
             __A													=      torch.linspace(_A						,					_A						,					_A						,					dtype=torch.floataa      )
       elif beta_schedule == "scaled_linear":
             # this schedule is very specific to the latent diffusion model.
             __A													=      (
                 torch.linspace(beta_start**0.5						,					beta_end**0.5						,					_A						,					dtype=torch.floataa      ) ** 2
             )
       elif beta_schedule == "squaredcos_cap_v2":
             # Glide cosine schedule
             __A													=      betas_for_alpha_bar(_A      )
       else:
             raise NotImplementedError(F'{beta_schedule} does is not implemented for {self.__class__}'      )

       __A													=      1.0 - self.betas
       __A													=      torch.cumprod(self.alphas						,					dim=0      )

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






 def 						lowercase_    (      self	:Any						,					_A	:List[str]						,					_A	:Optional[Any]=None      )					->      Tuple:







       '''simple docstring'''



       if schedule_timesteps is None:
             __A													=      self.timesteps

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

       return indices[pos].item()






 @property
 def 						lowercase_    (      self	:List[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 						lowercase_    (      self	:Tuple						,					_A	:torch.FloatTensor						,					_A	:Union[float, torch.FloatTensor]						,					)					->      torch.FloatTensor:







       '''simple docstring'''



       __A													=      self.index_for_timestep(_A      )

       if self.state_in_first_order:
             __A													=      self.sigmas[step_index]
       else:
             __A													=      self.sigmas_interpol[step_index - 1]

       __A													=      sample / ((sigma**2 + 1) ** 0.5)
       return sample






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







       '''simple docstring'''



       __A													=      num_inference_steps

       __A													=      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													=      np.linspace(0						,					num_train_timesteps - 1						,					_A						,					dtype=_A      )[::-1].copy()
       elif self.config.timestep_spacing == "leading":
             __A													=      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													=      (np.arange(0						,					_A      ) * step_ratio).round()[::-1].copy().astype(_A      )
             timesteps += self.config.steps_offset
       elif self.config.timestep_spacing == "trailing":
             __A													=      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													=      (np.arange(_A						,					0						,					-step_ratio      )).round().copy().astype(_A      )
             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													=      np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5      )
       __A													=      torch.from_numpy(np.log(_A      )      ).to(_A      )

       __A													=      np.interp(_A						,					np.arange(0						,					len(_A      )      )						,					_A      )
       __A													=      np.concatenate([sigmas, [0.0]]      ).astype(np.floataa      )
       __A													=      torch.from_numpy(_A      ).to(device=_A      )

       # compute up and down sigmas
       __A													=      sigmas.roll(-1      )
       __A													=      0.0
       __A													=      (sigmas_next**2 * (sigmas**2 - sigmas_next**2) / sigmas**2) ** 0.5
       __A													=      (sigmas_next**2 - sigmas_up**2) ** 0.5
       __A													=      0.0

       # compute interpolated sigmas
       __A													=      sigmas.log().lerp(sigmas_down.log()						,					0.5      ).exp()
       __A													=      0.0

       # set sigmas
       __A													=      torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2      ), sigmas[-1:]]      )
       __A													=      torch.cat(
           [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2      ), sigmas_interpol[-1:]]      )
       __A													=      torch.cat([sigmas_up[:1], sigmas_up[1:].repeat_interleave(2      ), sigmas_up[-1:]]      )
       __A													=      torch.cat([sigmas_down[:1], sigmas_down[1:].repeat_interleave(2      ), sigmas_down[-1:]]      )

       if str(_A      ).startswith('mps'      ):
             # mps does not support float64
             __A													=      torch.from_numpy(_A      ).to(_A						,					dtype=torch.floataa      )
       else:
             __A													=      torch.from_numpy(_A      ).to(_A      )

       __A													=      self.sigma_to_t(_A      ).to(_A						,					dtype=timesteps.dtype      )
       __A													=      torch.stack((timesteps_interpol[:-2, None], timesteps[1:, None])						,					dim=-1      ).flatten()

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

       __A													=      None

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






 def 						lowercase_    (      self	:str						,					_A	:Any      )					->      Tuple:







       '''simple docstring'''



       __A													=      sigma.log()

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

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

       __A													=      self.log_sigmas[low_idx]
       __A													=      self.log_sigmas[high_idx]

       # interpolate sigmas
       __A													=      (low - log_sigma) / (low - high)
       __A													=      w.clamp(0						,					1      )

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






 @property
 def 						lowercase_    (      self	:List[Any]      )					->      Optional[int]:







       '''simple docstring'''



       return self.sample is None






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







       '''simple docstring'''



       __A													=      self.index_for_timestep(_A      )

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

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

       __A													=      model_output.device
       __A													=      randn_tensor(model_output.shape						,					dtype=model_output.dtype						,					device=_A						,					generator=_A      )

       # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
       if self.config.prediction_type == "epsilon":
             __A													=      sigma_hat if self.state_in_first_order else sigma_interpol
             __A													=      sample - sigma_input * model_output
       elif self.config.prediction_type == "v_prediction":
             __A													=      sigma_hat if self.state_in_first_order else sigma_interpol
             __A													=      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													=      (sample - pred_original_sample) / sigma_hat
             # 3. delta timestep
             __A													=      sigma_interpol - sigma_hat

             # store for 2nd order step
             __A													=      sample
             __A													=      dt
             __A													=      sample + derivative * dt
       else:
             # DPM-Solver-2
             # 2. Convert to an ODE derivative for 2nd order
             __A													=      (sample - pred_original_sample) / sigma_interpol
             # 3. delta timestep
             __A													=      sigma_down - sigma_hat

             __A													=      self.sample
             __A													=      None

             __A													=      sample + derivative * dt
             __A													=      prev_sample + noise * sigma_up

       if not return_dict:
             return (prev_sample,)

       return SchedulerOutput(prev_sample=_A      )






 def 						lowercase_    (      self	:List[str]						,					_A	:torch.FloatTensor						,					_A	:torch.FloatTensor						,					_A	:torch.FloatTensor						,					)					->      torch.FloatTensor:







       '''simple docstring'''



       __A													=      self.sigmas.to(device=original_samples.device						,					dtype=original_samples.dtype      )
       if original_samples.device.type == "mps" and torch.is_floating_point(_A      ):
             # mps does not support float64
             __A													=      self.timesteps.to(original_samples.device						,					dtype=torch.floataa      )
             __A													=      timesteps.to(original_samples.device						,					dtype=torch.floataa      )
       else:
             __A													=      self.timesteps.to(original_samples.device      )
             __A													=      timesteps.to(original_samples.device      )

       __A													=      [self.index_for_timestep(_A						,					_A      ) for t in timesteps]

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

       __A													=      original_samples + noise * sigma
       return noisy_samples






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







       '''simple docstring'''



       return self.config.num_train_timesteps














"""simple docstring"""


import math




def 					a			(  UpperCamelCase_				:					int		)	->		str:
 if number == 0:
  __A						:							Dict      =						'Zero'
  return words
 else:
  __A						:							List[str]      =						math.logaa(UpperCamelCase_		)
  __A						:							Union[str, Any]      =						digits + 1
  __A						:							List[str]      =						{}
  __A						:							int      =						''
  __A						:							Any      =						'One'
  __A						:							Tuple      =						'Two'
  __A						:							Any      =						'Three'
  __A						:							Any      =						'Four'
  __A						:							Optional[Any]      =						'Five'
  __A						:							List[Any]      =						'Six'
  __A						:							List[Any]      =						'Seven'
  __A						:							Dict      =						'Eight'
  __A						:							Union[str, Any]      =						'Nine'

  __A						:							List[Any]      =						{}
  __A						:							Tuple      =						''
  __A						:							List[Any]      =						'Twenty'
  __A						:							Optional[Any]      =						'Thirty'
  __A						:							Any      =						'Forty'
  __A						:							Optional[int]      =						'Fifty'
  __A						:							Dict      =						'Sixty'
  __A						:							Dict      =						'Seventy'
  __A						:							Optional[Any]      =						'Eighty'
  __A						:							Any      =						'Ninety'

  __A						:							int      =						{}
  __A						:							List[str]      =						'Ten'
  __A						:							Optional[Any]      =						'Eleven'
  __A						:							Dict      =						'Twelve'
  __A						:							Union[str, Any]      =						'Thirteen'
  __A						:							Union[str, Any]      =						'Fourteen'
  __A						:							Dict      =						'Fifteen'
  __A						:							str      =						'Sixteen'
  __A						:							str      =						'Seventeen'
  __A						:							Union[str, Any]      =						'Eighteen'
  __A						:							Union[str, Any]      =						'Nineteen'

  __A						:							Optional[Any]      =						{}
  __A						:							Optional[int]      =						'Hundred,'
  __A						:							Union[str, Any]      =						'Thousand,'
  __A						:							Union[str, Any]      =						'Lakh,'
  __A						:							Union[str, Any]      =						'Crore,'

  __A						:							Optional[Any]      =						number
  __A						:							Optional[Any]      =						''
  __A						:							List[str]      =						0
  __A						:							List[str]      =						int(UpperCamelCase_		)
  while counter < digits:
   __A						:							Any      =						temp_num % 1_0
   if counter % 2 == 0:
    __A						:							Optional[Any]      =						''
    if counter in placevalue and current != 0:
     __A						:							Union[str, Any]      =						placevalue[counter]
    if counter == 2:
     __A						:							Dict      =						singles[current] + addition + words
    elif counter == 0:
     if ((temp_num % 1_0_0) // 1_0) == 1:
      __A						:							Any      =						teens[current] + addition + words
      __A						:							int      =						temp_num // 1_0
      counter += 1
     else:
      __A						:							Optional[Any]      =						singles[current] + addition + words

    else:
     __A						:							str      =						doubles[current] + addition + words

   else:
    if counter == 1:
     if current == 1:
      __A						:							List[Any]      =						teens[number % 1_0] + words
     else:
      __A						:							Union[str, Any]      =						''
      if counter in placevalue:
       __A						:							int      =						placevalue[counter]
      __A						:							Optional[Any]      =						doubles[current] + addition + words
    else:
     __A						:							Any      =						''
     if counter in placevalue:
      if current == 0 and ((temp_num % 1_0_0) // 1_0) == 0:
       __A						:							Optional[int]      =						''
      else:
       __A						:							Optional[int]      =						placevalue[counter]
     if ((temp_num % 1_0_0) // 1_0) == 1:
      __A						:							Dict      =						teens[current] + addition + words
      __A						:							Any      =						temp_num // 1_0
      counter += 1
     else:
      __A						:							List[str]      =						singles[current] + addition + words
   counter += 1
   __A						:							Dict      =						temp_num // 1_0
 return words


if __name__ == "__main__":
   import doctest

   doctest.testmod()





