





'''simple docstring'''




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

import torch

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




@dataclass
class 			UpperCamelCase__ ( a_       ):




					"""simple docstring"""
					SCREAMING_SNAKE_CASE__            =     42
					SCREAMING_SNAKE_CASE__            =     42







class 			UpperCamelCase__ ( a_		, a_       ):




					"""simple docstring"""
					SCREAMING_SNAKE_CASE__            =     1


					@register_to_config
					def __init__(		self							:       List[Any]	,		lowerCamelCase_							:       Any = 20_00	,		lowerCamelCase_							:       List[Any] = 0.15	,		lowerCamelCase_							:       List[str] = 0.01	,		lowerCamelCase_							:       Optional[Any] = 13_48.0	,		lowerCamelCase_							:       Optional[int] = 1e-5	,		lowerCamelCase_							:       List[str] = 1	,		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE      :							Dict						=       sigma_max

							# setable values
							SCREAMING_SNAKE_CASE      :							List[str]						=       None

							self.set_sigmas(_a	,		_a	,		_a	,		_a		)


					def lowerCamelCase_       (		self							:       Tuple	,		lowerCamelCase_							:       Optional[int]	,		lowerCamelCase_							:       Dict = None		):

							'''simple docstring'''



							return sample


					def lowerCamelCase_       (		self							:       Any	,		lowerCamelCase_							:       Optional[Any]	,		lowerCamelCase_							:       str = None	,		lowerCamelCase_							:       int = None		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE      :							List[Any]						=       sampling_eps if sampling_eps is not None else self.config.sampling_eps

							SCREAMING_SNAKE_CASE      :							Tuple						=       torch.linspace(1	,		_a	,		_a	,		device=_a		)


					def lowerCamelCase_       (		self							:       Optional[int]	,		lowerCamelCase_							:       Dict	,		lowerCamelCase_							:       Dict = None	,		lowerCamelCase_							:       Optional[int] = None	,		lowerCamelCase_							:       Any = None		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE      :							List[Any]						=       sigma_min if sigma_min is not None else self.config.sigma_min
							SCREAMING_SNAKE_CASE      :							Tuple						=       sigma_max if sigma_max is not None else self.config.sigma_max
							SCREAMING_SNAKE_CASE      :							List[str]						=       sampling_eps if sampling_eps is not None else self.config.sampling_eps
							if self.timesteps is None:
									self.set_timesteps(_a	,		_a		)

							SCREAMING_SNAKE_CASE      :							Optional[Any]						=       sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
							SCREAMING_SNAKE_CASE      :							Union[str, Any]						=       torch.exp(torch.linspace(math.log(_a		)	,		math.log(_a		)	,		_a		)		)
							SCREAMING_SNAKE_CASE      :							int						=       torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]		)


					def lowerCamelCase_       (		self							:       int	,		lowerCamelCase_							:       List[Any]	,		lowerCamelCase_							:       List[str]		):

							'''simple docstring'''



							return torch.where(
							    timesteps == 0	,		torch.zeros_like(t.to(timesteps.device		)		)	,		self.discrete_sigmas[timesteps - 1].to(timesteps.device		)	,		)


					def lowerCamelCase_       (		self							:       Tuple	,		lowerCamelCase_							:       Any	,		lowerCamelCase_							:       int	,		lowerCamelCase_							:       Tuple	,		lowerCamelCase_							:       List[str] = None	,		lowerCamelCase_							:       Optional[Any] = True	,		):

							'''simple docstring'''



							if self.timesteps is None:
									raise ValueError(
									    """`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"""		)

							SCREAMING_SNAKE_CASE      :							Tuple						=       timestep * torch.ones(
							    sample.shape[0]	,		device=sample.device		)  # torch.repeat_interleave(timestep, sample.shape[0])
							SCREAMING_SNAKE_CASE      :							int						=       (timestep * (len(self.timesteps		) - 1)).long()

							# mps requires indices to be in the same device, so we use cpu as is the default with cuda
							SCREAMING_SNAKE_CASE      :							str						=       timesteps.to(self.discrete_sigmas.device		)

							SCREAMING_SNAKE_CASE      :							int						=       self.discrete_sigmas[timesteps].to(sample.device		)
							SCREAMING_SNAKE_CASE      :							Dict						=       self.get_adjacent_sigma(_a	,		_a		).to(sample.device		)
							SCREAMING_SNAKE_CASE      :							Tuple						=       torch.zeros_like(_a		)
							SCREAMING_SNAKE_CASE      :							List[str]						=       (sigma**2 - adjacent_sigma**2) ** 0.5

							# equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
							# also equation 47 shows the analog from SDE models to ancestral sampling methods
							SCREAMING_SNAKE_CASE      :							List[Any]						=       diffusion.flatten()
							while len(diffusion.shape		) < len(sample.shape		):
									SCREAMING_SNAKE_CASE      :							List[Any]						=       diffusion.unsqueeze(-1		)
							SCREAMING_SNAKE_CASE      :							List[str]						=       drift - diffusion**2 * model_output

							#  equation 6: sample noise for the diffusion term of
							SCREAMING_SNAKE_CASE      :							Tuple						=       randn_tensor(
							    sample.shape	,		layout=sample.layout	,		generator=_a	,		device=sample.device	,		dtype=sample.dtype		)
							SCREAMING_SNAKE_CASE      :							Union[str, Any]						=       sample - drift  # subtract because `dt` is a small negative timestep
							# TODO is the variable diffusion the correct scaling term for the noise?
							SCREAMING_SNAKE_CASE      :							int						=       prev_sample_mean + diffusion * noise  # add impact of diffusion field g

							if not return_dict:
									return (prev_sample, prev_sample_mean)

							return SdeVeOutput(prev_sample=_a	,		prev_sample_mean=_a		)


					def lowerCamelCase_       (		self							:       Tuple	,		lowerCamelCase_							:       Optional[Any]	,		lowerCamelCase_							:       int	,		lowerCamelCase_							:       Optional[Any] = None	,		lowerCamelCase_							:       Tuple = True	,		):

							'''simple docstring'''



							if self.timesteps is None:
									raise ValueError(
									    """`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"""		)

							# For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
							# sample noise for correction
							SCREAMING_SNAKE_CASE      :							List[Any]						=       randn_tensor(sample.shape	,		layout=sample.layout	,		generator=_a		).to(sample.device		)

							# compute step size from the model_output, the noise, and the snr
							SCREAMING_SNAKE_CASE      :							Any						=       torch.norm(model_output.reshape(model_output.shape[0]	,		-1		)	,		dim=-1		).mean()
							SCREAMING_SNAKE_CASE      :							List[Any]						=       torch.norm(noise.reshape(noise.shape[0]	,		-1		)	,		dim=-1		).mean()
							SCREAMING_SNAKE_CASE      :							List[str]						=       (self.config.snr * noise_norm / grad_norm) ** 2 * 2
							SCREAMING_SNAKE_CASE      :							Dict						=       step_size * torch.ones(sample.shape[0]		).to(sample.device		)
							# self.repeat_scalar(step_size, sample.shape[0])

							# compute corrected sample: model_output term and noise term
							SCREAMING_SNAKE_CASE      :							Any						=       step_size.flatten()
							while len(step_size.shape		) < len(sample.shape		):
									SCREAMING_SNAKE_CASE      :							List[str]						=       step_size.unsqueeze(-1		)
							SCREAMING_SNAKE_CASE      :							List[str]						=       sample + step_size * model_output
							SCREAMING_SNAKE_CASE      :							Optional[Any]						=       prev_sample_mean + ((step_size * 2) ** 0.5) * noise

							if not return_dict:
									return (prev_sample,)

							return SchedulerOutput(prev_sample=_a		)


					def lowerCamelCase_       (		self							:       str	,		lowerCamelCase_							:       Optional[int]	,		lowerCamelCase_							:       Optional[Any]	,		lowerCamelCase_							:       List[str]	,		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE      :							Union[str, Any]						=       timesteps.to(original_samples.device		)
							SCREAMING_SNAKE_CASE      :							int						=       self.discrete_sigmas.to(original_samples.device		)[timesteps]
							SCREAMING_SNAKE_CASE      :							Tuple						=       (
							    noise * sigmas[:, None, None, None]
							    if noise is not None
							    else torch.randn_like(_a		) * sigmas[:, None, None, None]
							)
							SCREAMING_SNAKE_CASE      :							str						=       noise + original_samples
							return noisy_samples



					def __len__(		self							:       int		):

							'''simple docstring'''



							return self.config.num_train_timesteps





"""simple docstring"""




def   __magic_name__	(      __snake_case				:       list  ) ->				list:

       if len(__snake_case  ) < 2:
              return collection

       def circle_sort_util(__snake_case				:       list     , __snake_case				:       int     , __snake_case				:       int  ) -> bool:
              lowercase    :				List[Any]       		=							False

              if low == high:
                     return swapped

              lowercase    :				Union[str, Any]       		=							low
              lowercase    :				str       		=							high

              while left < right:
                     if collection[left] > collection[right]:
                            lowercase		,    lowercase    :				Optional[Any]       		=							(
                                collection[right],
                                collection[left],
                            )
                            lowercase    :				Tuple       		=							True

                     left += 1
                     right -= 1

              if left == right and collection[left] > collection[right + 1]:
                     lowercase		,    lowercase    :				str       		=							(
                         collection[right + 1],
                         collection[left],
                     )

                     lowercase    :				Union[str, Any]       		=							True

              lowercase    :				Any       		=							low + int((high - low) / 2  )
              lowercase    :				Tuple       		=							circle_sort_util(__snake_case     , __snake_case     , __snake_case  )
              lowercase    :				List[Any]       		=							circle_sort_util(__snake_case     , mid + 1     , __snake_case  )

              return swapped or left_swap or right_swap

       lowercase    :				int       		=							True

       while is_not_sorted is True:
              lowercase    :				int       		=							circle_sort_util(__snake_case     , 0     , len(__snake_case  ) - 1  )

       return collection


if __name__ == "__main__":
   _A			:       str   		=       input("""Enter numbers separated by a comma:\n""").strip()
   _A			:       Dict   		=       [int(item) for item in user_input.split(""",""")]
   print(circle_sort(unsorted))


