
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
class 				_UpperCamelCase (    _A     ):






			'''simple docstring'''

			__UpperCamelCase					:  torch.FloatTensor
			__UpperCamelCase					:  torch.FloatTensor







def 				A__    (  lowerCamelCase   ,			lowerCamelCase=0.999   ,			lowerCamelCase="cosine"   ,			)     ->		List[str]:
				if alpha_transform_type == "cosine":

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

				elif alpha_transform_type == "exp":

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

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

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





class 				_UpperCamelCase (    _A   ,				_A     ):






			'''simple docstring'''

			__UpperCamelCase					:  List[str]    					=       1

			@register_to_config
			def __init__(				self       :				Optional[Any]  ,		snake_case_       :				int = 1000  ,		snake_case_       :				float = 0.0001  ,		snake_case_       :				float = 0.02  ,		snake_case_       :				str = "linear"  ,		snake_case_       :				float = 0.0  ,		snake_case_       :				Optional[np.ndarray] = None  ,		snake_case_       :				bool = True  ,		):
							if trained_betas is not None:
											UpperCamelCase_:							Optional[Any]		=  torch.from_numpy(snake_case_					)
							elif beta_schedule == "linear":
											UpperCamelCase_:							List[Any]		=  torch.linspace(snake_case_  ,		snake_case_  ,		snake_case_  ,		dtype=torch.floataa					)
							elif beta_schedule == "scaled_linear":
											# this schedule is very specific to the latent diffusion model.
											UpperCamelCase_:							int		=  (
											    torch.linspace(beta_start**0.5  ,		beta_end**0.5  ,		snake_case_  ,		dtype=torch.floataa					) ** 2
											)
							elif beta_schedule == "squaredcos_cap_v2":
											# Glide cosine schedule
											UpperCamelCase_:							Any		=  betas_for_alpha_bar(snake_case_					)
							elif beta_schedule == "sigmoid":
											# GeoDiff sigmoid schedule
											UpperCamelCase_:							Tuple		=  torch.linspace(-6  ,		6  ,		snake_case_					)
											UpperCamelCase_:							int		=  torch.sigmoid(snake_case_					) * (beta_end - beta_start) + beta_start
							else:
											raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}'''					)

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

							UpperCamelCase_:							List[Any]		=  torch.tensor(1.0					)

							# standard deviation of the initial noise distribution
							UpperCamelCase_:							Union[str, Any]		=  1.0

							# setable values
							UpperCamelCase_:							List[Any]		=  None
							UpperCamelCase_:							Union[str, Any]		=  torch.from_numpy(np.arange(0  ,		snake_case_					)[::-1].copy()					)

							UpperCamelCase_:							Optional[Any]		=  eta

			def 					lowerCAmelCase__		(				self       :				Tuple  ,		snake_case_       :				torch.FloatTensor  ,		snake_case_       :				Optional[int] = None					):
							return sample

			def 					lowerCAmelCase__		(				self       :				Dict  ,		snake_case_       :				int  ,		snake_case_       :				int = 10  ,		snake_case_       :				int = 10  ,		snake_case_       :				Union[str, torch.device] = None  ,		):
							UpperCamelCase_:							Any		=  min(self.config.num_train_timesteps  ,		snake_case_					)
							UpperCamelCase_:							Optional[Any]		=  num_inference_steps

							UpperCamelCase_:							int		=  []

							UpperCamelCase_:							Optional[Any]		=  {}
							for j in range(0  ,		num_inference_steps - jump_length  ,		snake_case_					):
											UpperCamelCase_:							Optional[int]		=  jump_n_sample - 1

							UpperCamelCase_:							str		=  num_inference_steps
							while t >= 1:
											UpperCamelCase_:							List[Any]		=  t - 1
											timesteps.append(snake_case_					)

											if jumps.get(snake_case_  ,		0					) > 0:
															UpperCamelCase_:							Any		=  jumps[t] - 1
															for _ in range(snake_case_					):
																			UpperCamelCase_:							List[Any]		=  t + 1
																			timesteps.append(snake_case_					)

							UpperCamelCase_:							List[str]		=  np.array(snake_case_					) * (self.config.num_train_timesteps // self.num_inference_steps)
							UpperCamelCase_:							Dict		=  torch.from_numpy(snake_case_					).to(snake_case_					)

			def 					lowerCAmelCase__		(				self       :				Any  ,		snake_case_       :				str					):
							UpperCamelCase_:							Optional[int]		=  t - self.config.num_train_timesteps // self.num_inference_steps

							UpperCamelCase_:							int		=  self.alphas_cumprod[t]
							UpperCamelCase_:							Tuple		=  self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
							UpperCamelCase_:							Optional[int]		=  1 - alpha_prod_t
							UpperCamelCase_:							Any		=  1 - 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
							# Is equivalent to formula (16) in https://arxiv.org/pdf/2010.02502.pdf
							# without eta.
							# variance = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * self.betas[t]
							UpperCamelCase_:							str		=  (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

							return variance

			def 					lowerCAmelCase__		(				self       :				Tuple  ,		snake_case_       :				torch.FloatTensor  ,		snake_case_       :				int  ,		snake_case_       :				torch.FloatTensor  ,		snake_case_       :				torch.FloatTensor  ,		snake_case_       :				torch.FloatTensor  ,		snake_case_       :				Optional[torch.Generator] = None  ,		snake_case_       :				bool = True  ,		):
							UpperCamelCase_:							int		=  timestep
							UpperCamelCase_:							List[Any]		=  timestep - self.config.num_train_timesteps // self.num_inference_steps

							# 1. compute alphas, betas
							UpperCamelCase_:							Dict		=  self.alphas_cumprod[t]
							UpperCamelCase_:							Any		=  self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
							UpperCamelCase_:							Tuple		=  1 - alpha_prod_t

							# 2. compute predicted original sample from predicted noise also called
							# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
							UpperCamelCase_:							Tuple		=  (sample - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5

							# 3. Clip "predicted x_0"
							if self.config.clip_sample:
											UpperCamelCase_:							List[Any]		=  torch.clamp(snake_case_  ,		-1  ,		1					)

							# We choose to follow RePaint Algorithm 1 to get x_{t-1}, however we
							# substitute formula (7) in the algorithm coming from DDPM paper
							# (formula (4) Algorithm 2 - Sampling) with formula (12) from DDIM paper.
							# DDIM schedule gives the same results as DDPM with eta = 1.0
							# Noise is being reused in 7. and 8., but no impact on quality has
							# been observed.

							# 5. Add noise
							UpperCamelCase_:							Tuple		=  model_output.device
							UpperCamelCase_:							int		=  randn_tensor(model_output.shape  ,		generator=snake_case_  ,		device=snake_case_  ,		dtype=model_output.dtype					)
							UpperCamelCase_:							List[str]		=  self.eta * self._get_variance(snake_case_					) ** 0.5

							UpperCamelCase_:							List[str]		=  0
							if t > 0 and self.eta > 0:
											UpperCamelCase_:							str		=  std_dev_t * noise

							# 6. compute "direction pointing to x_t" of formula (12)
							# from https://arxiv.org/pdf/2010.02502.pdf
							UpperCamelCase_:							Optional[Any]		=  (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

							# 7. compute x_{t-1} of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
							UpperCamelCase_:							Dict		=  alpha_prod_t_prev**0.5 * pred_original_sample + pred_sample_direction + variance

							# 8. Algorithm 1 Line 5 https://arxiv.org/pdf/2201.09865.pdf
							UpperCamelCase_:							Tuple		=  (alpha_prod_t_prev**0.5) * original_image + ((1 - alpha_prod_t_prev) ** 0.5) * noise

							# 9. Algorithm 1 Line 8 https://arxiv.org/pdf/2201.09865.pdf
							UpperCamelCase_:							str		=  mask * prev_known_part + (1.0 - mask) * prev_unknown_part

							if not return_dict:
											return (
											    pred_prev_sample,
											    pred_original_sample,
											)

							return RePaintSchedulerOutput(prev_sample=snake_case_  ,		pred_original_sample=snake_case_					)

			def 					lowerCAmelCase__		(				self       :				Optional[int]  ,		snake_case_       :				Union[str, Any]  ,		snake_case_       :				Any  ,		snake_case_       :				Tuple=None					):
							UpperCamelCase_:							str		=  self.config.num_train_timesteps // self.num_inference_steps

							for i in range(snake_case_					):
											UpperCamelCase_:							Optional[Any]		=  self.betas[timestep + i]
											if sample.device.type == "mps":
															# randn does not work reproducibly on mps
															UpperCamelCase_:							Tuple		=  randn_tensor(sample.shape  ,		dtype=sample.dtype  ,		generator=snake_case_					)
															UpperCamelCase_:							Any		=  noise.to(sample.device					)
											else:
															UpperCamelCase_:							List[Any]		=  randn_tensor(sample.shape  ,		generator=snake_case_  ,		device=sample.device  ,		dtype=sample.dtype					)

											# 10. Algorithm 1 Line 10 https://arxiv.org/pdf/2201.09865.pdf
											UpperCamelCase_:							int		=  (1 - beta) ** 0.5 * sample + beta**0.5 * noise

							return sample

			def 					lowerCAmelCase__		(				self       :				Optional[int]  ,		snake_case_       :				torch.FloatTensor  ,		snake_case_       :				torch.FloatTensor  ,		snake_case_       :				torch.IntTensor  ,		):
							raise NotImplementedError("""Use `DDPMScheduler.add_noise()` to train for sampling with RePaint."""					)




			def __len__(				self       :				Union[str, Any]					):
							return self.config.num_train_timesteps








def 				A__    (  lowerCamelCase  )     ->		Optional[Any]:
				UpperCamelCase_:							Optional[int]		=  len(lowerCamelCase  )
				for _ in range(
				    0   ,			int(((no_of_elements - 1) / 2) + 1  )  ):  # we don't need to traverse to end of list as
								for j in range(0   ,			no_of_elements - 1  ):
												if (
												    lst[j + 1] < lst[j]
												):  # applying bubble sort algorithm from left to right (or forwards)
																UpperCamelCase_:							str		=  lst[j + 1]
																UpperCamelCase_:							Tuple		=  lst[j]
																UpperCamelCase_:							str		=  temp
												if (
												    lst[no_of_elements - 1 - j] < lst[no_of_elements - 2 - j]
												):  # applying bubble sort algorithm from right to left (or backwards)
																UpperCamelCase_:							Union[str, Any]		=  lst[no_of_elements - 1 - j]
																UpperCamelCase_:							Dict		=  lst[no_of_elements - 2 - j]
																UpperCamelCase_:							List[Any]		=  temp
				return lst


if __name__ == "__main__":
							print("""enter the list to be sorted""")
							lowerCamelCase_  :       Union[str, Any]					      =     [int(x) for x in input().split()]  # inputing elements of the list in one line
							lowerCamelCase_  :       Union[str, Any]					      =     double_sort(lst)
							print("""the sorted list is""")
							print(sorted_lst)






