
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

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

import flax
import jax.numpy as jnp
from jax import random

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import FlaxSchedulerMixin, FlaxSchedulerOutput, broadcast_to_shape_from_left

@flax.struct.dataclass
class 				_UpperCamelCase :






			'''simple docstring'''

			__UpperCamelCase					:  Optional[jnp.ndarray]    					=       None
			__UpperCamelCase					:  Optional[jnp.ndarray]    					=       None
			__UpperCamelCase					:  Optional[jnp.ndarray]    					=       None

			@classmethod
			def 					lowerCAmelCase__		(				cls       :				Optional[int]					):
							return cls()




@dataclass
class 				_UpperCamelCase (    _A     ):






			'''simple docstring'''

			__UpperCamelCase					:  ScoreSdeVeSchedulerState
			__UpperCamelCase					:  jnp.ndarray
			__UpperCamelCase					:  Optional[jnp.ndarray]    					=       None




class 				_UpperCamelCase (    _A   ,				_A     ):






			'''simple docstring'''

			@property
			def 					lowerCAmelCase__		(				self       :				Union[str, Any]					):
							return True

			@register_to_config
			def __init__(				self       :				Dict  ,		snake_case_       :				int = 2000  ,		snake_case_       :				float = 0.15  ,		snake_case_       :				float = 0.01  ,		snake_case_       :				float = 1348.0  ,		snake_case_       :				float = 1e-5  ,		snake_case_       :				int = 1  ,		):
							pass

			def 					lowerCAmelCase__		(				self       :				Any					):
							UpperCamelCase_:							Optional[Any]		=  ScoreSdeVeSchedulerState.create()
							return self.set_sigmas(
							    snake_case_  ,		self.config.num_train_timesteps  ,		self.config.sigma_min  ,		self.config.sigma_max  ,		self.config.sampling_eps  ,		)

			def 					lowerCAmelCase__		(				self       :				Optional[Any]  ,		snake_case_       :				ScoreSdeVeSchedulerState  ,		snake_case_       :				int  ,		snake_case_       :				Tuple = ()  ,		snake_case_       :				float = None					):
							UpperCamelCase_:							str		=  sampling_eps if sampling_eps is not None else self.config.sampling_eps

							UpperCamelCase_:							Tuple		=  jnp.linspace(1  ,		snake_case_  ,		snake_case_					)
							return state.replace(timesteps=snake_case_					)

			def 					lowerCAmelCase__		(				self       :				Tuple  ,		snake_case_       :				ScoreSdeVeSchedulerState  ,		snake_case_       :				int  ,		snake_case_       :				float = None  ,		snake_case_       :				float = None  ,		snake_case_       :				float = None  ,		):
							UpperCamelCase_:							Any		=  sigma_min if sigma_min is not None else self.config.sigma_min
							UpperCamelCase_:							str		=  sigma_max if sigma_max is not None else self.config.sigma_max
							UpperCamelCase_:							int		=  sampling_eps if sampling_eps is not None else self.config.sampling_eps
							if state.timesteps is None:
											UpperCamelCase_:							Any		=  self.set_timesteps(snake_case_  ,		snake_case_  ,		snake_case_					)

							UpperCamelCase_:							str		=  jnp.exp(jnp.linspace(jnp.log(snake_case_					)  ,		jnp.log(snake_case_					)  ,		snake_case_					)					)
							UpperCamelCase_:							List[Any]		=  jnp.array([sigma_min * (sigma_max / sigma_min) ** t for t in state.timesteps]					)

							return state.replace(discrete_sigmas=snake_case_  ,		sigmas=snake_case_					)

			def 					lowerCAmelCase__		(				self       :				str  ,		snake_case_       :				Union[str, Any]  ,		snake_case_       :				List[Any]  ,		snake_case_       :				Optional[Any]					):
							return jnp.where(timesteps == 0  ,		jnp.zeros_like(snake_case_					)  ,		state.discrete_sigmas[timesteps - 1]					)

			def 					lowerCAmelCase__		(				self       :				str  ,		snake_case_       :				ScoreSdeVeSchedulerState  ,		snake_case_       :				jnp.ndarray  ,		snake_case_       :				int  ,		snake_case_       :				jnp.ndarray  ,		snake_case_       :				random.KeyArray  ,		snake_case_       :				bool = True  ,		):
							if state.timesteps is None:
											raise ValueError(
											    """`state.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"""					)

							UpperCamelCase_:							Any		=  timestep * jnp.ones(
							    sample.shape[0]  ,		)
							UpperCamelCase_:							List[Any]		=  (timestep * (len(state.timesteps					) - 1)).long()

							UpperCamelCase_:							Union[str, Any]		=  state.discrete_sigmas[timesteps]
							UpperCamelCase_:							Optional[Any]		=  self.get_adjacent_sigma(snake_case_  ,		snake_case_  ,		snake_case_					)
							UpperCamelCase_:							Union[str, Any]		=  jnp.zeros_like(snake_case_					)
							UpperCamelCase_:							Optional[int]		=  (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
							UpperCamelCase_:							Dict		=  diffusion.flatten()
							UpperCamelCase_:							List[Any]		=  broadcast_to_shape_from_left(snake_case_  ,		sample.shape					)
							UpperCamelCase_:							int		=  drift - diffusion**2 * model_output

							#  equation 6: sample noise for the diffusion term of
							UpperCamelCase_:							Dict		=  random.split(snake_case_  ,		num=1					)
							UpperCamelCase_:							List[Any]		=  random.normal(key=snake_case_  ,		shape=sample.shape					)
							UpperCamelCase_:							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?
							UpperCamelCase_:							List[str]		=  prev_sample_mean + diffusion * noise  # add impact of diffusion field g

							if not return_dict:
											return (prev_sample, prev_sample_mean, state)

							return FlaxSdeVeOutput(prev_sample=snake_case_  ,		prev_sample_mean=snake_case_  ,		state=snake_case_					)

			def 					lowerCAmelCase__		(				self       :				Tuple  ,		snake_case_       :				ScoreSdeVeSchedulerState  ,		snake_case_       :				jnp.ndarray  ,		snake_case_       :				jnp.ndarray  ,		snake_case_       :				random.KeyArray  ,		snake_case_       :				bool = True  ,		):
							if state.timesteps is None:
											raise ValueError(
											    """`state.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
							UpperCamelCase_:							List[Any]		=  random.split(snake_case_  ,		num=1					)
							UpperCamelCase_:							Union[str, Any]		=  random.normal(key=snake_case_  ,		shape=sample.shape					)

							# compute step size from the model_output, the noise, and the snr
							UpperCamelCase_:							Tuple		=  jnp.linalg.norm(snake_case_					)
							UpperCamelCase_:							Dict		=  jnp.linalg.norm(snake_case_					)
							UpperCamelCase_:							Optional[int]		=  (self.config.snr * noise_norm / grad_norm) ** 2 * 2
							UpperCamelCase_:							Dict		=  step_size * jnp.ones(sample.shape[0]					)

							# compute corrected sample: model_output term and noise term
							UpperCamelCase_:							str		=  step_size.flatten()
							UpperCamelCase_:							List[str]		=  broadcast_to_shape_from_left(snake_case_  ,		sample.shape					)
							UpperCamelCase_:							Optional[Any]		=  sample + step_size * model_output
							UpperCamelCase_:							Optional[int]		=  prev_sample_mean + ((step_size * 2) ** 0.5) * noise

							if not return_dict:
											return (prev_sample, state)

							return FlaxSdeVeOutput(prev_sample=snake_case_  ,		state=snake_case_					)




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






