



import math
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 KarrasDiffusionSchedulers, SchedulerMixin


__snake_case	:				Tuple  					=			logging.get_logger(__name__)  # pylint: disable=invalid-name

@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
class       UpperCamelCase  (					_a				):





     """simple docstring"""






     _lowerCamelCase					: str				   =42
     _lowerCamelCase					: Tuple				   =None



def   a_	(			__a		,							__a=0.9_99		,							__a="cosine"		,							):
     if alpha_transform_type == "cosine":

          def alpha_bar_fn(__a      ):
               return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2      ) ** 2

     elif alpha_transform_type == "exp":

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

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

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

class       UpperCamelCase  (					_a      , _a				):





     """simple docstring"""






     _lowerCamelCase					: Union[str, Any]				   =[e.name for e in KarrasDiffusionSchedulers]
     _lowerCamelCase					: int				   =1





     @register_to_config
     def __init__(					self :							int	,      _lowerCamelCase :							int = 1_0_0_0	,      _lowerCamelCase :							Any = 0.0_001	,      _lowerCamelCase :							int = 0.02	,      _lowerCamelCase :							List[Any] = "linear"	,      _lowerCamelCase :							Any = None	,      _lowerCamelCase :							str = "epsilon"	,      _lowerCamelCase :							Dict = "linspace"	,      _lowerCamelCase :							List[str] = 0	,      ):
          if trained_betas is not None:
               A__				=   torch.tensor(lowerCamelCase__	,      dtype=torch.floataa )
          elif beta_schedule == "linear":
               A__				=   torch.linspace(lowerCamelCase__	,      lowerCamelCase__	,      lowerCamelCase__	,      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	,      lowerCamelCase__	,      dtype=torch.floataa ) ** 2
               )
          elif beta_schedule == "squaredcos_cap_v2":
               # Glide cosine schedule
               A__				=   betas_for_alpha_bar(lowerCamelCase__ )
          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 )

          A__				=   np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5 )
          A__				=   np.concatenate([sigmas[::-1], [0.0]] ).astype(np.floataa )
          A__				=   torch.from_numpy(lowerCamelCase__ )

          # setable values
          A__				=   None
          A__				=   np.linspace(0	,      num_train_timesteps - 1	,      lowerCamelCase__	,      dtype=lowerCamelCase__ )[::-1].copy()
          A__				=   torch.from_numpy(lowerCamelCase__ )
          A__				=   False





     @property
     def 						A__  (					self :							Tuple ):
          # standard deviation of the initial noise distribution
          if self.config.timestep_spacing in ["linspace", "trailing"]:
               return self.sigmas.max()

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





     def 						A__  (					self :							int	,      _lowerCamelCase :							str	,      _lowerCamelCase :							List[str] ):
          if isinstance(lowerCamelCase__	,      torch.Tensor ):
               A__				=   timestep.to(self.timesteps.device )
          A__				=   (self.timesteps == timestep).nonzero().item()
          A__				=   self.sigmas[step_index]
          A__				=   sample / ((sigma**2 + 1) ** 0.5)
          A__				=   True
          return sample





     def 						A__  (					self :							Optional[Any]	,      _lowerCamelCase :							Any	,      _lowerCamelCase :							str = None ):
          A__				=   num_inference_steps

          # "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	,      self.config.num_train_timesteps - 1	,      lowerCamelCase__	,      dtype=lowerCamelCase__ )[
                   ::-1
               ].copy()
          elif self.config.timestep_spacing == "leading":
               A__				=   self.config.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	,      lowerCamelCase__ ) * step_ratio).round()[::-1].copy().astype(lowerCamelCase__ )
               timesteps += self.config.steps_offset
          elif self.config.timestep_spacing == "trailing":
               A__				=   self.config.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(self.config.num_train_timesteps	,      0	,      -step_ratio )).round().copy().astype(lowerCamelCase__ )
               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__				=   np.interp(lowerCamelCase__	,      np.arange(0	,      len(lowerCamelCase__ ) )	,      lowerCamelCase__ )
          A__				=   np.concatenate([sigmas, [0.0]] ).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 :							List[Any]	,      _lowerCamelCase :							Tuple	,      _lowerCamelCase :							int	,      _lowerCamelCase :							Dict	,      _lowerCamelCase :							Union[str, Any] = None	,      _lowerCamelCase :							Any = 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'''
                       ''' `EulerDiscreteScheduler.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.timesteps == timestep).nonzero().item()
          A__				=   self.sigmas[step_index]

          # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
          if self.config.prediction_type == "epsilon":
               A__				=   sample - sigma * model_output
          elif self.config.prediction_type == "v_prediction":
               # * c_out + input * c_skip
               A__				=   model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**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`''' )

          A__				=   self.sigmas[step_index]
          A__				=   self.sigmas[step_index + 1]
          A__				=   (sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
          A__				=   (sigma_to**2 - sigma_up**2) ** 0.5

          # 2. Convert to an ODE derivative
          A__				=   (sample - pred_original_sample) / sigma

          A__				=   sigma_down - sigma

          A__				=   sample + derivative * dt

          A__				=   model_output.device
          A__				=   randn_tensor(model_output.shape	,      dtype=model_output.dtype	,      device=lowerCamelCase__	,      generator=lowerCamelCase__ )

          A__				=   prev_sample + noise * sigma_up

          if not return_dict:
               return (prev_sample,)

          return EulerAncestralDiscreteSchedulerOutput(
              prev_sample=lowerCamelCase__	,      pred_original_sample=lowerCamelCase__ )





     def 						A__  (					self :							List[Any]	,      _lowerCamelCase :							Any	,      _lowerCamelCase :							Any	,      _lowerCamelCase :							Any	,      ):
          # 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 :							int ):
          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)))



