



"""simple docstring"""







from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, logging, randn_tensor
from .scheduling_utils import SchedulerMixin


__snake_case	:				Optional[int]  					=			logging.get_logger(__name__)  # pylint: disable=invalid-name

@dataclass
class       UpperCamelCase  (					a				):





     """simple docstring"""






     _lowerCamelCase					: torch.FloatTensor


class       UpperCamelCase  (					a      , a				):





     """simple docstring"""






     _lowerCamelCase					: int				   =1





     @register_to_config
     def __init__(					self :							int	,      _lowerCamelCase :							int = 4_0	,      _lowerCamelCase :							float = 0.002	,      _lowerCamelCase :							float = 80.0	,      _lowerCamelCase :							float = 0.5	,      _lowerCamelCase :							float = 1.0	,      _lowerCamelCase :							float = 7.0	,      _lowerCamelCase :							bool = True	,      ):
          # standard deviation of the initial noise distribution
          A__				=   sigma_max

          A__				=   np.linspace(0	,      1	,      _lowerCamelCase )
          A__				=   self._convert_to_karras(_lowerCamelCase )
          A__				=   self.sigma_to_t(_lowerCamelCase )

          # setable values
          A__				=   None
          A__				=   torch.from_numpy(_lowerCamelCase )
          A__				=   torch.from_numpy(_lowerCamelCase )
          A__				=   False
          A__				=   False





     def 						A__  (					self :							str	,      _lowerCamelCase :							Optional[Any]	,      _lowerCamelCase :							Optional[int]=None ):
          if schedule_timesteps is None:
               A__				=   self.timesteps

          A__				=   (schedule_timesteps == timestep).nonzero()
          return indices.item()





     def 						A__  (					self :							Optional[int]	,      _lowerCamelCase :							torch.FloatTensor	,      _lowerCamelCase :							Union[float, torch.FloatTensor] ):
          # Get sigma corresponding to timestep
          if isinstance(_lowerCamelCase	,      torch.Tensor ):
               A__				=   timestep.to(self.timesteps.device )
          A__				=   self.index_for_timestep(_lowerCamelCase )
          A__				=   self.sigmas[step_idx]

          A__				=   sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)

          A__				=   True
          return sample





     def 						A__  (					self :							Dict	,      _lowerCamelCase :							Union[float, np.ndarray] ):
          if not isinstance(_lowerCamelCase	,      np.ndarray ):
               A__				=   np.array(_lowerCamelCase	,      dtype=np.floataa )

          A__				=   1_0_0_0 * 0.25 * np.log(sigmas + 1E-44 )

          return timesteps





     def 						A__  (					self :							Any	,      _lowerCamelCase :							Optional[int] = None	,      _lowerCamelCase :							Union[str, torch.device] = None	,      _lowerCamelCase :							Optional[List[int]] = None	,      ):
          if num_inference_steps is None and timesteps is None:
               raise ValueError('''Exactly one of `num_inference_steps` or `timesteps` must be supplied.''' )

          if num_inference_steps is not None and timesteps is not None:
               raise ValueError('''Can only pass one of `num_inference_steps` or `timesteps`.''' )

          # Follow DDPMScheduler custom timesteps logic
          if timesteps is not None:
               for i in range(1	,      len(_lowerCamelCase ) ):
                    if timesteps[i] >= timesteps[i - 1]:
                         raise ValueError('''`timesteps` must be in descending order.''' )

               if timesteps[0] >= self.config.num_train_timesteps:
                    raise ValueError(
                        F'''`timesteps` must start before `self.config.train_timesteps`:'''
                        F''' {self.config.num_train_timesteps}.''' )

               A__				=   np.array(_lowerCamelCase	,      dtype=np.intaa )
               A__				=   True
          else:
               if num_inference_steps > self.config.num_train_timesteps:
                    raise ValueError(
                        F'''`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:'''
                        F''' {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle'''
                        F''' maximal {self.config.num_train_timesteps} timesteps.''' )

               A__				=   num_inference_steps

               A__				=   self.config.num_train_timesteps // self.num_inference_steps
               A__				=   (np.arange(0	,      _lowerCamelCase ) * step_ratio).round()[::-1].copy().astype(np.intaa )
               A__				=   False

          # Map timesteps to Karras sigmas directly for multistep sampling
          # See https://github.com/openai/consistency_models/blob/main/cm/karras_diffusion.py#L675
          A__				=   self.config.num_train_timesteps
          A__				=   timesteps[::-1].copy()
          A__				=   ramp / (num_train_timesteps - 1)
          A__				=   self._convert_to_karras(_lowerCamelCase )
          A__				=   self.sigma_to_t(_lowerCamelCase )

          A__				=   np.concatenate([sigmas, [self.sigma_min]] ).astype(np.floataa )
          A__				=   torch.from_numpy(_lowerCamelCase ).to(device=_lowerCamelCase )

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





     def 						A__  (					self :							Optional[Any]	,      _lowerCamelCase :							Any ):
          A__				=   self.config.sigma_min
          A__				=   self.config.sigma_max

          A__				=   self.config.rho
          A__				=   sigma_min ** (1 / rho)
          A__				=   sigma_max ** (1 / rho)
          A__				=   (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
          return sigmas





     def 						A__  (					self :							Dict	,      _lowerCamelCase :							Optional[int] ):
          A__				=   self.config.sigma_data

          A__				=   sigma_data**2 / (sigma**2 + sigma_data**2)
          A__				=   sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
          return c_skip, c_out





     def 						A__  (					self :							Tuple	,      _lowerCamelCase :							Dict ):
          A__				=   self.config.sigma_min
          A__				=   self.config.sigma_data

          A__				=   sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
          A__				=   (sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
          return c_skip, c_out





     def 						A__  (					self :							List[Any]	,      _lowerCamelCase :							torch.FloatTensor	,      _lowerCamelCase :							Union[float, torch.FloatTensor]	,      _lowerCamelCase :							torch.FloatTensor	,      _lowerCamelCase :							Optional[torch.Generator] = None	,      _lowerCamelCase :							bool = True	,      ):

          if (
              isinstance(_lowerCamelCase	,      _lowerCamelCase )
              or isinstance(_lowerCamelCase	,      torch.IntTensor )
              or isinstance(_lowerCamelCase	,      torch.LongTensor )
          ):
               raise ValueError(
                   (
                       '''Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to'''
                       F''' `{self.__class__}.step()` is not supported. Make sure to pass'''
                       ''' one of the `scheduler.timesteps` as a timestep.'''
                   )	,      )

          if not self.is_scale_input_called:
               logger.warning(
                   '''The `scale_model_input` function should be called before `step` to ensure correct denoising. '''
                   '''See `StableDiffusionPipeline` for a usage example.''' )

          if isinstance(_lowerCamelCase	,      torch.Tensor ):
               A__				=   timestep.to(self.timesteps.device )

          A__				=   self.config.sigma_min
          A__				=   self.config.sigma_max

          A__				=   self.index_for_timestep(_lowerCamelCase )

          # sigma_next corresponds to next_t in original implementation
          A__				=   self.sigmas[step_index]
          if step_index + 1 < self.config.num_train_timesteps:
               A__				=   self.sigmas[step_index + 1]
          else:
               # Set sigma_next to sigma_min
               A__				=   self.sigmas[-1]

          # Get scalings for boundary conditions
          A__	,     A__				=   self.get_scalings_for_boundary_condition(_lowerCamelCase )

          # 1. Denoise model output using boundary conditions
          A__				=   c_out * model_output + c_skip * sample
          if self.config.clip_denoised:
               A__				=   denoised.clamp(-1	,      1 )

          # 2. Sample z ~ N(0, s_noise^2 * I)
          # Noise is not used for onestep sampling.
          if len(self.timesteps ) > 1:
               A__				=   randn_tensor(
                   model_output.shape	,      dtype=model_output.dtype	,      device=model_output.device	,      generator=_lowerCamelCase )
          else:
               A__				=   torch.zeros_like(_lowerCamelCase )
          A__				=   noise * self.config.s_noise

          A__				=   sigma_next.clamp(min=_lowerCamelCase	,      max=_lowerCamelCase )

          # 3. Return noisy sample
          # tau = sigma_hat, eps = sigma_min
          A__				=   denoised + z * (sigma_hat**2 - sigma_min**2) ** 0.5

          if not return_dict:
               return (prev_sample,)

          return CMStochasticIterativeSchedulerOutput(prev_sample=_lowerCamelCase )





     def 						A__  (					self :							Optional[Any]	,      _lowerCamelCase :							torch.FloatTensor	,      _lowerCamelCase :							torch.FloatTensor	,      _lowerCamelCase :							torch.FloatTensor	,      ):
          # Make sure sigmas and timesteps have the same device and dtype as original_samples
          A__				=   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
               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__				=   [(schedule_timesteps == t).nonzero().item() 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 :							List[Any] ):
          return self.config.num_train_timesteps








"""simple docstring"""







def   a_	(			__a      ):
     if not isinstance(__a		,							__a      ):
          raise TypeError('''recursive_lucas_number accepts only integer arguments.'''      )
     if n_th_number == 0:
          return 2
     if n_th_number == 1:
          return 1

     return recursive_lucas_number(n_th_number - 1      ) + recursive_lucas_number(
         n_th_number - 2      )



def   a_	(			__a      ):
     if not isinstance(__a		,							__a      ):
          raise TypeError('''dynamic_lucas_number accepts only integer arguments.'''      )
     A__	,     A__				=   2, 1
     for _ in range(__a      ):
          A__	,     A__				=   b, a + b
     return a


if __name__ == "__main__":
 from doctest import testmod

 testmod()
 __snake_case	:				Dict  					=			int(input('Enter the number of terms in lucas series:\n').strip())
 print('Using recursive function to calculate lucas series:')
 print(' '.join(str(recursive_lucas_number(i)) for i in range(n)))
 print('\nUsing dynamic function to calculate lucas series:')
 print(' '.join(str(dynamic_lucas_number(i)) for i in range(n)))



