




"""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
class      lowerCAmelCase_						(a__  ):


				"""simple docstring"""



				__UpperCamelCase				:						torch.FloatTensor
				__UpperCamelCase				:						torch.FloatTensor






def lowercase_  (       _snake_case       ,_snake_case=0.999       ,_snake_case="cosine"       ,):
							if alpha_transform_type == "cosine":

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

							elif alpha_transform_type == "exp":

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

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

							SCREAMING_SNAKE_CASE__						:	Tuple									=       []
							for i in range(_snake_case						):
														SCREAMING_SNAKE_CASE__						:	List[str]									=       i / num_diffusion_timesteps
														SCREAMING_SNAKE_CASE__						:	int									=       (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      lowerCAmelCase_						(a__			, a__  ):


				"""simple docstring"""



				__UpperCamelCase				:						Optional[int] 	=       1



				@register_to_config
				def __init__(self       ,	SCREAMING_SNAKE_CASE__ = 10_00       ,	SCREAMING_SNAKE_CASE__ = 0.0001       ,	SCREAMING_SNAKE_CASE__ = 0.02       ,	SCREAMING_SNAKE_CASE__ = "linear"       ,	SCREAMING_SNAKE_CASE__ = 0.0       ,	SCREAMING_SNAKE_CASE__ = None       ,	SCREAMING_SNAKE_CASE__ = True       ,	) ->		Union[str, Any]:



											"""simple docstring"""




											if trained_betas is not None:
																		SCREAMING_SNAKE_CASE__						:	Any									=       torch.from_numpy(SCREAMING_SNAKE_CASE__   )
											elif beta_schedule == "linear":
																		SCREAMING_SNAKE_CASE__						:	Any									=       torch.linspace(SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__       ,	dtype=torch.floataa   )
											elif beta_schedule == "scaled_linear":
																		# this schedule is very specific to the latent diffusion model.
																		SCREAMING_SNAKE_CASE__						:	str									=       (
																		    torch.linspace(beta_start**0.5       ,	beta_end**0.5       ,	SCREAMING_SNAKE_CASE__       ,	dtype=torch.floataa   ) ** 2
																		)
											elif beta_schedule == "squaredcos_cap_v2":
																		# Glide cosine schedule
																		SCREAMING_SNAKE_CASE__						:	List[str]									=       betas_for_alpha_bar(SCREAMING_SNAKE_CASE__   )
											elif beta_schedule == "sigmoid":
																		# GeoDiff sigmoid schedule
																		SCREAMING_SNAKE_CASE__						:	int									=       torch.linspace(-6       ,	6       ,	SCREAMING_SNAKE_CASE__   )
																		SCREAMING_SNAKE_CASE__						:	List[Any]									=       torch.sigmoid(SCREAMING_SNAKE_CASE__   ) * (beta_end - beta_start) + beta_start
											else:
																		raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}'''   )

											SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       1.0 - self.betas
											SCREAMING_SNAKE_CASE__						:	Tuple									=       torch.cumprod(self.alphas       ,	dim=0   )
											SCREAMING_SNAKE_CASE__						:	List[Any]									=       torch.tensor(1.0   )

											SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       torch.tensor(1.0   )

											# standard deviation of the initial noise distribution
											SCREAMING_SNAKE_CASE__						:	Optional[int]									=       1.0

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

											SCREAMING_SNAKE_CASE__						:	List[str]									=       eta



				def      __magic_name__   (self       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__ = None   ) ->		torch.FloatTensor:



											"""simple docstring"""




											return sample



				def      __magic_name__   (self       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__ = 10       ,	SCREAMING_SNAKE_CASE__ = 10       ,	SCREAMING_SNAKE_CASE__ = None       ,	) ->		Optional[int]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:	Tuple									=       min(self.config.num_train_timesteps       ,	SCREAMING_SNAKE_CASE__   )
											SCREAMING_SNAKE_CASE__						:	List[Any]									=       num_inference_steps

											SCREAMING_SNAKE_CASE__						:	List[Any]									=       []

											SCREAMING_SNAKE_CASE__						:	List[str]									=       {}
											for j in range(0       ,	num_inference_steps - jump_length       ,	SCREAMING_SNAKE_CASE__   ):
																		SCREAMING_SNAKE_CASE__						:	int									=       jump_n_sample - 1

											SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       num_inference_steps
											while t >= 1:
																		SCREAMING_SNAKE_CASE__						:	Optional[int]									=       t - 1
																		timesteps.append(SCREAMING_SNAKE_CASE__   )

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

											SCREAMING_SNAKE_CASE__						:	Optional[int]									=       np.array(SCREAMING_SNAKE_CASE__   ) * (self.config.num_train_timesteps // self.num_inference_steps)
											SCREAMING_SNAKE_CASE__						:	List[str]									=       torch.from_numpy(SCREAMING_SNAKE_CASE__   ).to(SCREAMING_SNAKE_CASE__   )



				def      __magic_name__   (self       ,	SCREAMING_SNAKE_CASE__   ) ->		int:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:	List[Any]									=       t - self.config.num_train_timesteps // self.num_inference_steps

											SCREAMING_SNAKE_CASE__						:	Tuple									=       self.alphas_cumprod[t]
											SCREAMING_SNAKE_CASE__						:	int									=       self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
											SCREAMING_SNAKE_CASE__						:	Dict									=       1 - alpha_prod_t
											SCREAMING_SNAKE_CASE__						:	str									=       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]
											SCREAMING_SNAKE_CASE__						:	Tuple									=       (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

											return variance



				def      __magic_name__   (self       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__ = None       ,	SCREAMING_SNAKE_CASE__ = True       ,	) ->		Union[RePaintSchedulerOutput, Tuple]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:	Optional[int]									=       timestep
											SCREAMING_SNAKE_CASE__						:	Optional[int]									=       timestep - self.config.num_train_timesteps // self.num_inference_steps

											# 1. compute alphas, betas
											SCREAMING_SNAKE_CASE__						:	Optional[int]									=       self.alphas_cumprod[t]
											SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
											SCREAMING_SNAKE_CASE__						:	List[Any]									=       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
											SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       (sample - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5

											# 3. Clip "predicted x_0"
											if self.config.clip_sample:
																		SCREAMING_SNAKE_CASE__						:	Optional[int]									=       torch.clamp(SCREAMING_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
											SCREAMING_SNAKE_CASE__						:	Optional[int]									=       model_output.device
											SCREAMING_SNAKE_CASE__						:	Dict									=       randn_tensor(model_output.shape       ,	generator=SCREAMING_SNAKE_CASE__       ,	device=SCREAMING_SNAKE_CASE__       ,	dtype=model_output.dtype   )
											SCREAMING_SNAKE_CASE__						:	Any									=       self.eta * self._get_variance(SCREAMING_SNAKE_CASE__   ) ** 0.5

											SCREAMING_SNAKE_CASE__						:	Any									=       0
											if t > 0 and self.eta > 0:
																		SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       std_dev_t * noise

											# 6. compute "direction pointing to x_t" of formula (12)
											# from https://arxiv.org/pdf/2010.02502.pdf
											SCREAMING_SNAKE_CASE__						:	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
											SCREAMING_SNAKE_CASE__						:	Tuple									=       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
											SCREAMING_SNAKE_CASE__						:	Dict									=       (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
											SCREAMING_SNAKE_CASE__						:	List[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=SCREAMING_SNAKE_CASE__       ,	pred_original_sample=SCREAMING_SNAKE_CASE__   )



				def      __magic_name__   (self       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__=None   ) ->		Optional[Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:	Any									=       self.config.num_train_timesteps // self.num_inference_steps

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

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

											return sample



				def      __magic_name__   (self       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__       ,	) ->		torch.FloatTensor:



											"""simple docstring"""




											raise NotImplementedError("""Use `DDPMScheduler.add_noise()` to train for sampling with RePaint."""   )



				def __len__(self   ) ->		Optional[int]:



											"""simple docstring"""




											return self.config.num_train_timesteps






"""simple docstring"""




def lowercase_  (       _snake_case       ,_snake_case       ,_snake_case						):
							SCREAMING_SNAKE_CASE__						:	Any									=       index
							SCREAMING_SNAKE_CASE__						:	List[Any]									=       2 * index + 1
							SCREAMING_SNAKE_CASE__						:	Any									=       2 * index + 2
							if left_index < heap_size and unsorted[left_index] > unsorted[largest]:
														SCREAMING_SNAKE_CASE__						:	str									=       left_index

							if right_index < heap_size and unsorted[right_index] > unsorted[largest]:
														SCREAMING_SNAKE_CASE__						:	Any									=       right_index

							if largest != index:
														SCREAMING_SNAKE_CASE__				,				SCREAMING_SNAKE_CASE__						:	Tuple									=       unsorted[index], unsorted[largest]
														heapify(_snake_case       ,_snake_case       ,_snake_case						)

def lowercase_  (       _snake_case						):
							SCREAMING_SNAKE_CASE__						:	List[Any]									=       len(_snake_case						)
							for i in range(n // 2 - 1       ,-1       ,-1						):
														heapify(_snake_case       ,_snake_case       ,_snake_case						)
							for i in range(n - 1       ,0       ,-1						):
														SCREAMING_SNAKE_CASE__				,				SCREAMING_SNAKE_CASE__						:	Any									=       unsorted[i], unsorted[0]
														heapify(_snake_case       ,0       ,_snake_case						)
							return unsorted


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




