





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


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

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




 """simple docstring"""



 SCREAMING_SNAKE_CASE_     : Optional[Any]      		=42
 SCREAMING_SNAKE_CASE_     : Dict      		=None






def     lowercase__       (       __lowercase						:  Tuple    ,  __lowercase						:  Union[str, Any]=0.9_9_9    ,  __lowercase						:  int="cosine"    ,  )			->				Tuple:



       """simple docstring"""






       if alpha_transform_type == "cosine":

              def alpha_bar_fn(__lowercase						:  int					):
                     return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2					) ** 2

       elif alpha_transform_type == "exp":

              def alpha_bar_fn(__lowercase						:  Any					):
                     return math.exp(t * -1_2.0					)

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

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


class 					snake_case					(   _a      ,	_a	):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_     : Tuple      		=[e.name for e in KarrasDiffusionSchedulers]
 SCREAMING_SNAKE_CASE_     : str      		=1






 @register_to_config
 def __init__(	self	:							Tuple							,     __A	:							int = 1_0_0_0							,     __A	:							float = 0.0001							,     __A	:							float = 0.02							,     __A	:							str = "linear"							,     __A	:							Optional[Union[np.ndarray, List[float]]] = None							,     __A	:							str = "epsilon"							,     __A	:							str = "linspace"							,     __A	:							int = 0							,     ):
        if trained_betas is not None:
               __UpperCamelCase	=  torch.tensor(__A							,     dtype=torch.floataa     )
        elif beta_schedule == "linear":
               __UpperCamelCase	=  torch.linspace(__A							,     __A							,     __A							,     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							,     __A							,     dtype=torch.floataa     ) ** 2
               )
        elif beta_schedule == "squaredcos_cap_v2":
               # Glide cosine schedule
               __UpperCamelCase	=  betas_for_alpha_bar(__A     )
        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     )

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

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






 @property
 def 	_lowerCamelCase (	self	:							Any     ):
        # 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 	_lowerCamelCase (	self	:							Optional[Any]							,     __A	:							torch.FloatTensor							,     __A	:							Union[float, torch.FloatTensor]     ):
        if isinstance(__A							,     torch.Tensor     ):
               __UpperCamelCase	=  timestep.to(self.timesteps.device     )
        __UpperCamelCase	=  (self.timesteps == timestep).nonzero().item()
        __UpperCamelCase	=  self.sigmas[step_index]
        __UpperCamelCase	=  sample / ((sigma**2 + 1) ** 0.5)
        __UpperCamelCase	=  True
        return sample






 def 	_lowerCamelCase (	self	:							List[Any]							,     __A	:							int							,     __A	:							Union[str, torch.device] = None     ):
        __UpperCamelCase	=  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":
               __UpperCamelCase	=  np.linspace(0							,     self.config.num_train_timesteps - 1							,     __A							,     dtype=__A     )[
                   ::-1
               ].copy()
        elif self.config.timestep_spacing == "leading":
               __UpperCamelCase	=  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
               __UpperCamelCase	=  (np.arange(0							,     __A     ) * step_ratio).round()[::-1].copy().astype(__A     )
               timesteps += self.config.steps_offset
        elif self.config.timestep_spacing == "trailing":
               __UpperCamelCase	=  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
               __UpperCamelCase	=  (np.arange(self.config.num_train_timesteps							,     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\'.'''     )

        __UpperCamelCase	=  np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5     )
        __UpperCamelCase	=  np.interp(__A							,     np.arange(0							,     len(__A     )     )							,     __A     )
        __UpperCamelCase	=  np.concatenate([sigmas, [0.0]]     ).astype(np.floataa     )
        __UpperCamelCase	=  torch.from_numpy(__A     ).to(device=__A     )
        if str(__A     ).startswith('mps'     ):
               # mps does not support float64
               __UpperCamelCase	=  torch.from_numpy(__A     ).to(__A							,     dtype=torch.floataa     )
        else:
               __UpperCamelCase	=  torch.from_numpy(__A     ).to(device=__A     )






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

        if (
            isinstance(__A							,     __A     )
            or isinstance(__A							,     torch.IntTensor     )
            or isinstance(__A							,     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(__A							,     torch.Tensor     ):
               __UpperCamelCase	=  timestep.to(self.timesteps.device     )

        __UpperCamelCase	=  (self.timesteps == timestep).nonzero().item()
        __UpperCamelCase	=  self.sigmas[step_index]

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

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

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

        __UpperCamelCase	=  sigma_down - sigma

        __UpperCamelCase	=  sample + derivative * dt

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

        __UpperCamelCase	=  prev_sample + noise * sigma_up

        if not return_dict:
               return (prev_sample,)

        return EulerAncestralDiscreteSchedulerOutput(
            prev_sample=__A							,     pred_original_sample=__A     )






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







'''simple docstring'''
def     lowercase__       (       __lowercase						:  str    ,  __lowercase						:  Tuple					)			->				str:



       """simple docstring"""






       sequence.append(__lowercase					)

       __UpperCamelCase	=  0
       while sequence[index] != target:
              index += 1

       sequence.pop()

       if index == len(__lowercase					):
              return None

       return index


if __name__ == "__main__":
    a__							:							Any			=input('''Enter numbers separated by comma:\n''').strip()
    a__							:							Any			=[int(item) for item in user_input.split(''',''')]

    a__							:							Tuple			=input('''Enter a single number to be found in the list:\n''')
    a__							:							List[Any]			=int(target_input)
    a__							:							List[Any]			=sentinel_linear_search(sequence, target)
    if result is not None:
        print(f'{target} found at positions: {result}')
    else:
        print('''Not found''')
