



"""simple docstring"""

import math
from typing import Union

import torch

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







class    __lowercase     (					__SCREAMING_SNAKE_CASE					,    __SCREAMING_SNAKE_CASE):







						"""simple docstring"""







						_A		:							str							       =		1







						@register_to_config
						def __init__(self   ,		lowercase__=20_00   ,		lowercase__=0.1   ,		lowercase__=20   ,		lowercase__=1e-3       ):
											snake_case_					:		List[str] 					=					None
											snake_case_					:		Tuple 					=					None
											snake_case_					:		Any 					=					None







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__ = None       ):
											snake_case_					:		int 					=					torch.linspace(1   ,		self.config.sampling_eps   ,		_a   ,		device=_a       )







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__   ,		lowercase__   ,		lowercase__=None       ):




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

											# TODO(Patrick) better comments + non-PyTorch
											# postprocess model score
											snake_case_					:		str 					=					(
											    -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
											)
											snake_case_					:		Union[str, Any] 					=					torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff       )       )
											snake_case_					:		Any 					=					std.flatten()
											while len(std.shape       ) < len(score.shape       ):
																snake_case_					:		List[str] 					=					std.unsqueeze(-1       )
											snake_case_					:		Dict 					=					-score / std

											# compute
											snake_case_					:		Dict 					=					-1.0 / len(self.timesteps       )

											snake_case_					:		Optional[int] 					=					self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
											snake_case_					:		Optional[int] 					=					beta_t.flatten()
											while len(beta_t.shape       ) < len(x.shape       ):
																snake_case_					:		str 					=					beta_t.unsqueeze(-1       )
											snake_case_					:		int 					=					-0.5 * beta_t * x

											snake_case_					:		List[str] 					=					torch.sqrt(_a       )
											snake_case_					:		Any 					=					drift - diffusion**2 * score
											snake_case_					:		Optional[int] 					=					x + drift * dt

											# add noise
											snake_case_					:		Optional[Any] 					=					randn_tensor(x.shape   ,		layout=x.layout   ,		generator=_a   ,		device=x.device   ,		dtype=x.dtype       )
											snake_case_					:		Dict 					=					x_mean + diffusion * math.sqrt(-dt       ) * noise

											return x, x_mean







						def __len__(self       ):
											return self.config.num_train_timesteps









"""simple docstring"""

def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  list			):



					"""simple docstring"""





					snake_case_					:		Optional[int] 					=					len(SCREAMING_SNAKE_CASE__			)
					for i in range(1				,    SCREAMING_SNAKE_CASE__			):
										snake_case_					:		Tuple 					=					collection[i]
										snake_case_					:		Tuple 					=					0
										snake_case_					:		str 					=					i - 1

										while low <= high:
															snake_case_					:		Optional[int] 					=					(low + high) // 2
															if val < collection[mid]:
																				snake_case_					:		List[str] 					=					mid - 1
															else:
																				snake_case_					:		str 					=					mid + 1
										for j in range(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__				,    -1			):
															snake_case_					:		List[str] 					=					collection[j - 1]
										snake_case_					:		Any 					=					val
					return collection


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




