


'''simple docstring'''



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

import numpy as np
import torch

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



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


     """simple docstring"""






     SCREAMING_SNAKE_CASE :				torch.FloatTensor
     SCREAMING_SNAKE_CASE :				Optional[torch.FloatTensor]							     =     None




def 			_a			(		_lowercase      :			Optional[int]    ,      _lowercase      :			Dict=0.999    ,      _lowercase      :			int="cosine"    ,      ):





     '''simple docstring'''






     if alpha_transform_type == "cosine":

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

     elif alpha_transform_type == "exp":

          def alpha_bar_fn(_lowercase      :			Union[str, Any]						):
               return math.exp(t * -12.0						)

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

     __UpperCAmelCase			:				List[str]													=    []
     for i in range(_lowercase						):
          __UpperCAmelCase			:				Union[str, Any]													=    i / num_diffusion_timesteps
          __UpperCAmelCase			:				Union[str, Any]													=    (i + 1) / num_diffusion_timesteps
          betas.append(min(1 - alpha_bar_fn(_lowercase						) / alpha_bar_fn(_lowercase						)    ,      _lowercase						)						)
     return torch.tensor(_lowercase    ,      dtype=torch.floataa						)



class 			a       (	_a			,						_a ):


     """simple docstring"""






     @register_to_config
     def __init__(       self  :	Union[str, Any]							,				snake_case  :	int = 1000							,				snake_case  :	str = "fixed_small_log"							,				snake_case  :	bool = True							,				snake_case  :	Optional[float] = 1.0							,				snake_case  :	str = "epsilon"							,				snake_case  :	str = "squaredcos_cap_v2"							,				)		->							Optional[int]:
          if beta_schedule != "squaredcos_cap_v2":
               raise ValueError('''UnCLIPScheduler only supports `beta_schedule`: \'squaredcos_cap_v2\''''    )

          __UpperCAmelCase			:				List[Any]													=    betas_for_alpha_bar(snake_case    )

          __UpperCAmelCase			:				List[str]													=    1.0 - self.betas
          __UpperCAmelCase			:				Union[str, Any]													=    torch.cumprod(self.alphas							,				dim=0    )
          __UpperCAmelCase			:				Optional[int]													=    torch.tensor(1.0    )

          # standard deviation of the initial noise distribution
          __UpperCAmelCase			:				List[str]													=    1.0

          # setable values
          __UpperCAmelCase			:				Optional[int]													=    None
          __UpperCAmelCase			:				Optional[int]													=    torch.from_numpy(np.arange(0							,				snake_case    )[::-1].copy()    )

          __UpperCAmelCase			:				Union[str, Any]													=    variance_type

     def 				lowerCamelCase__				(       self  :	Optional[int]							,				snake_case  :	torch.FloatTensor							,				snake_case  :	Optional[int] = None    )		->							torch.FloatTensor:
          return sample

     def 				lowerCamelCase__				(       self  :	Dict							,				snake_case  :	int							,				snake_case  :	Union[str, torch.device] = None    )		->							Dict:
          __UpperCAmelCase			:				str													=    num_inference_steps
          __UpperCAmelCase			:				Any													=    (self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
          __UpperCAmelCase			:				Optional[int]													=    (np.arange(0							,				snake_case    ) * step_ratio).round()[::-1].copy().astype(np.intaa    )
          __UpperCAmelCase			:				Union[str, Any]													=    torch.from_numpy(snake_case    ).to(snake_case    )

     def 				lowerCamelCase__				(       self  :	Union[str, Any]							,				snake_case  :	List[str]							,				snake_case  :	str=None							,				snake_case  :	Dict=None							,				snake_case  :	List[str]=None    )		->							str:
          if prev_timestep is None:
               __UpperCAmelCase			:				Optional[int]													=    t - 1

          __UpperCAmelCase			:				List[Any]													=    self.alphas_cumprod[t]
          __UpperCAmelCase			:				List[str]													=    self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
          __UpperCAmelCase			:				Tuple													=    1 - alpha_prod_t
          __UpperCAmelCase			:				Tuple													=    1 - alpha_prod_t_prev

          if prev_timestep == t - 1:
               __UpperCAmelCase			:				int													=    self.betas[t]
          else:
               __UpperCAmelCase			:				Dict													=    1 - alpha_prod_t / alpha_prod_t_prev

          # For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
          # and sample from it to get previous sample
          # x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
          __UpperCAmelCase			:				List[Any]													=    beta_prod_t_prev / beta_prod_t * beta

          if variance_type is None:
               __UpperCAmelCase			:				int													=    self.config.variance_type

          # hacks - were probably added for training stability
          if variance_type == "fixed_small_log":
               __UpperCAmelCase			:				List[Any]													=    torch.log(torch.clamp(snake_case							,				min=1E-20    )    )
               __UpperCAmelCase			:				Any													=    torch.exp(0.5 * variance    )
          elif variance_type == "learned_range":
               # NOTE difference with DDPM scheduler
               __UpperCAmelCase			:				Union[str, Any]													=    variance.log()
               __UpperCAmelCase			:				Union[str, Any]													=    beta.log()

               __UpperCAmelCase			:				Optional[Any]													=    (predicted_variance + 1) / 2
               __UpperCAmelCase			:				List[str]													=    frac * max_log + (1 - frac) * min_log

          return variance

     def 				lowerCamelCase__				(       self  :	List[str]							,				snake_case  :	torch.FloatTensor							,				snake_case  :	int							,				snake_case  :	torch.FloatTensor							,				snake_case  :	Optional[int] = None							,				snake_case  :	Union[str, Any]=None							,				snake_case  :	bool = True							,				)		->							Union[UnCLIPSchedulerOutput, Tuple]:
          __UpperCAmelCase			:				Optional[Any]													=    timestep

          if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
               __UpperCAmelCase							,     __UpperCAmelCase			:				Optional[Any]													=    torch.split(snake_case							,				sample.shape[1]							,				dim=1    )
          else:
               __UpperCAmelCase			:				Union[str, Any]													=    None

          # 1. compute alphas, betas
          if prev_timestep is None:
               __UpperCAmelCase			:				List[str]													=    t - 1

          __UpperCAmelCase			:				str													=    self.alphas_cumprod[t]
          __UpperCAmelCase			:				List[str]													=    self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
          __UpperCAmelCase			:				Optional[Any]													=    1 - alpha_prod_t
          __UpperCAmelCase			:				Union[str, Any]													=    1 - alpha_prod_t_prev

          if prev_timestep == t - 1:
               __UpperCAmelCase			:				Optional[int]													=    self.betas[t]
               __UpperCAmelCase			:				Union[str, Any]													=    self.alphas[t]
          else:
               __UpperCAmelCase			:				Optional[int]													=    1 - alpha_prod_t / alpha_prod_t_prev
               __UpperCAmelCase			:				Optional[Any]													=    1 - beta

          # 2. compute predicted original sample from predicted noise also called
          # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
          if self.config.prediction_type == "epsilon":
               __UpperCAmelCase			:				Union[str, Any]													=    (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
          elif self.config.prediction_type == "sample":
               __UpperCAmelCase			:				List[str]													=    model_output
          else:
               raise ValueError(
                   f'prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`'
                   ''' for the UnCLIPScheduler.'''    )

          # 3. Clip "predicted x_0"
          if self.config.clip_sample:
               __UpperCAmelCase			:				Optional[Any]													=    torch.clamp(
                   snake_case							,				-self.config.clip_sample_range							,				self.config.clip_sample_range    )

          # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
          # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
          __UpperCAmelCase			:				Optional[Any]													=    (alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
          __UpperCAmelCase			:				int													=    alpha ** 0.5 * beta_prod_t_prev / beta_prod_t

          # 5. Compute predicted previous sample µ_t
          # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
          __UpperCAmelCase			:				Dict													=    pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

          # 6. Add noise
          __UpperCAmelCase			:				Optional[int]													=    0
          if t > 0:
               __UpperCAmelCase			:				List[Any]													=    randn_tensor(
                   model_output.shape							,				dtype=model_output.dtype							,				generator=snake_case							,				device=model_output.device    )

               __UpperCAmelCase			:				Tuple													=    self._get_variance(
                   snake_case							,				predicted_variance=snake_case							,				prev_timestep=snake_case							,				)

               if self.variance_type == "fixed_small_log":
                    __UpperCAmelCase			:				Union[str, Any]													=    variance
               elif self.variance_type == "learned_range":
                    __UpperCAmelCase			:				List[Any]													=    (0.5 * variance).exp()
               else:
                    raise ValueError(
                        f'variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`'
                        ''' for the UnCLIPScheduler.'''    )

               __UpperCAmelCase			:				Tuple													=    variance * variance_noise

          __UpperCAmelCase			:				str													=    pred_prev_sample + variance

          if not return_dict:
               return (pred_prev_sample,)

          return UnCLIPSchedulerOutput(prev_sample=snake_case							,				pred_original_sample=snake_case    )






     def 				lowerCamelCase__				(       self  :	Tuple							,				snake_case  :	torch.FloatTensor							,				snake_case  :	torch.FloatTensor							,				snake_case  :	torch.IntTensor							,				)		->							torch.FloatTensor:
          # Make sure alphas_cumprod and timestep have same device and dtype as original_samples
          __UpperCAmelCase			:				Tuple													=    self.alphas_cumprod.to(device=original_samples.device							,				dtype=original_samples.dtype    )
          __UpperCAmelCase			:				Dict													=    timesteps.to(original_samples.device    )

          __UpperCAmelCase			:				Optional[Any]													=    alphas_cumprod[timesteps] ** 0.5
          __UpperCAmelCase			:				List[str]													=    sqrt_alpha_prod.flatten()
          while len(sqrt_alpha_prod.shape    ) < len(original_samples.shape    ):
               __UpperCAmelCase			:				List[Any]													=    sqrt_alpha_prod.unsqueeze(-1    )

          __UpperCAmelCase			:				str													=    (1 - alphas_cumprod[timesteps]) ** 0.5
          __UpperCAmelCase			:				Dict													=    sqrt_one_minus_alpha_prod.flatten()
          while len(sqrt_one_minus_alpha_prod.shape    ) < len(original_samples.shape    ):
               __UpperCAmelCase			:				str													=    sqrt_one_minus_alpha_prod.unsqueeze(-1    )

          __UpperCAmelCase			:				List[str]													=    sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
          return noisy_samples



'''simple docstring'''



def 			_a			(		_lowercase      :			List[str]						):





     '''simple docstring'''






     __UpperCAmelCase			:				int													=    len(_lowercase						)
     while cur > 1:
          # Find the maximum number in arr
          __UpperCAmelCase			:				Union[str, Any]													=    arr.index(max(arr[0:cur]						)						)
          # Reverse from 0 to mi
          __UpperCAmelCase			:				Any													=    arr[mi::-1] + arr[mi + 1 : len(_lowercase						)]
          # Reverse whole list
          __UpperCAmelCase			:				List[str]													=    arr[cur - 1 :: -1] + arr[cur : len(_lowercase						)]
          cur -= 1
     return arr


if __name__ == "__main__":
      __UpperCAmelCase    :Any				       =     input("Enter numbers separated by a comma:\n").strip()
      __UpperCAmelCase    :Optional[int]				       =     [int(item) for item in user_input.split(",")]
      print(pancake_sort(unsorted))