



"""simple docstring"""







# 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__		:
		# setable values
		lowerCAmelCase_							:						Optional[jnp.ndarray]							=  None
		lowerCAmelCase_							:						Optional[jnp.ndarray]							=  None
		lowerCAmelCase_							:						Optional[jnp.ndarray]							=  None



		@classmethod
		def        A_		(			cls	:     int					)      ->    Dict:






									'''simple docstring'''

									return cls()






@dataclass
class   UpperCAmelCase__		(   UpperCamelCase  ):
		lowerCAmelCase_							:						ScoreSdeVeSchedulerState
		lowerCAmelCase_							:						jnp.ndarray
		lowerCAmelCase_							:						Optional[jnp.ndarray]							=  None






class   UpperCAmelCase__		(   UpperCamelCase ,UpperCamelCase  ):



		@property
		def        A_		(			self	:     List[Any]					)      ->    Tuple:






									'''simple docstring'''

									return True



		@register_to_config
		def __init__(			self	:     str   ,				snake_case	:     int = 2_000   ,				snake_case	:     float = 0.15   ,				snake_case	:     float = 0.01   ,				snake_case	:     float = 1348.0   ,				snake_case	:     float = 1E-5   ,				snake_case	:     int = 1   ,				)      ->    Dict:






									'''simple docstring'''

									pass



		def        A_		(			self	:     Any					)      ->    Tuple:






									'''simple docstring'''

									A    =       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        A_		(			self	:     Dict   ,				snake_case	:     ScoreSdeVeSchedulerState   ,				snake_case	:     int   ,				snake_case	:     Tuple = ()   ,				snake_case	:     float = None					)      ->    ScoreSdeVeSchedulerState:






									'''simple docstring'''

									A    =       sampling_eps if sampling_eps is not None else self.config.sampling_eps

									A    =       jnp.linspace(1   ,				snake_case   ,				snake_case					)
									return state.replace(timesteps=snake_case					)



		def        A_		(			self	:     Optional[Any]   ,				snake_case	:     ScoreSdeVeSchedulerState   ,				snake_case	:     int   ,				snake_case	:     float = None   ,				snake_case	:     float = None   ,				snake_case	:     float = None   ,				)      ->    ScoreSdeVeSchedulerState:






									'''simple docstring'''

									A    =       sigma_min if sigma_min is not None else self.config.sigma_min
									A    =       sigma_max if sigma_max is not None else self.config.sigma_max
									A    =       sampling_eps if sampling_eps is not None else self.config.sampling_eps
									if state.timesteps is None:
																A    =       self.set_timesteps(snake_case   ,				snake_case   ,				snake_case					)

									A    =       jnp.exp(jnp.linspace(jnp.log(snake_case					)   ,				jnp.log(snake_case					)   ,				snake_case					)					)
									A    =       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        A_		(			self	:     Optional[int]   ,				snake_case	:     Any   ,				snake_case	:     str   ,				snake_case	:     Union[str, Any]					)      ->    str:






									'''simple docstring'''

									return jnp.where(timesteps == 0   ,				jnp.zeros_like(snake_case					)   ,				state.discrete_sigmas[timesteps - 1]					)



		def        A_		(			self	:     Union[str, Any]   ,				snake_case	:     ScoreSdeVeSchedulerState   ,				snake_case	:     jnp.ndarray   ,				snake_case	:     int   ,				snake_case	:     jnp.ndarray   ,				snake_case	:     random.KeyArray   ,				snake_case	:     bool = True   ,				)      ->    Union[FlaxSdeVeOutput, Tuple]:






									'''simple docstring'''

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

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

									A    =       state.discrete_sigmas[timesteps]
									A    =       self.get_adjacent_sigma(snake_case   ,				snake_case   ,				snake_case					)
									A    =       jnp.zeros_like(snake_case					)
									A    =       (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
									A    =       diffusion.flatten()
									A    =       broadcast_to_shape_from_left(snake_case   ,				sample.shape					)
									A    =       drift - diffusion**2 * model_output

									#  equation 6: sample noise for the diffusion term of
									A    =       random.split(snake_case   ,				num=1					)
									A    =       random.normal(key=snake_case   ,				shape=sample.shape					)
									A    =       sample - drift  # subtract because `dt` is a small negative timestep
									# TODO is the variable diffusion the correct scaling term for the noise?
									A    =       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        A_		(			self	:     Optional[Any]   ,				snake_case	:     ScoreSdeVeSchedulerState   ,				snake_case	:     jnp.ndarray   ,				snake_case	:     jnp.ndarray   ,				snake_case	:     random.KeyArray   ,				snake_case	:     bool = True   ,				)      ->    Union[FlaxSdeVeOutput, Tuple]:






									'''simple docstring'''

									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
									A    =       random.split(snake_case   ,				num=1					)
									A    =       random.normal(key=snake_case   ,				shape=sample.shape					)

									# compute step size from the model_output, the noise, and the snr
									A    =       jnp.linalg.norm(snake_case					)
									A    =       jnp.linalg.norm(snake_case					)
									A    =       (self.config.snr * noise_norm / grad_norm) ** 2 * 2
									A    =       step_size * jnp.ones(sample.shape[0]					)

									# compute corrected sample: model_output term and noise term
									A    =       step_size.flatten()
									A    =       broadcast_to_shape_from_left(snake_case   ,				sample.shape					)
									A    =       sample + step_size * model_output
									A    =       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	:     str					)      ->    List[Any]:






									'''simple docstring'''

									return self.config.num_train_timesteps








"""simple docstring"""







def 	lowerCAmelCase__      (  lowerCamelCase__      )			->				List[str]:
							A    =       [701, 301, 132, 57, 23, 10, 4, 1]
							for gap in gaps:
														for i in range(lowerCamelCase__					,   len(lowerCamelCase__      )      ):
																					A    =       collection[i]
																					A    =       i
																					while j >= gap and collection[j - gap] > insert_value:
																												A    =       collection[j - gap]
																												j -= gap
																					if j != i:
																												A    =       insert_value
							return collection


if __name__ == "__main__":
							from doctest import testmod

							testmod()
							A     =      input('Enter numbers separated by a comma:\n').strip()
							A     =      [int(item) for item in user_input.split(',')]
							print(shell_sort(unsorted))



