


'''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_		:





						"""simple docstring"""




						UpperCAmelCase__	:       Optional[jnp.ndarray]         =     None
						UpperCAmelCase__	:       Optional[jnp.ndarray]         =     None
						UpperCAmelCase__	:       Optional[jnp.ndarray]         =     None
						@classmethod
						def 					__A					( cls				:					List[Any]  )							->					Any:
										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 					__A					( self				:					Optional[Any]  )							->					Optional[Any]:
										return True
						@register_to_config
						def __init__( self				:					Optional[Any]	,				_lowerCamelCase				:					int = 20_00	,				_lowerCamelCase				:					float = 0.15	,				_lowerCamelCase				:					float = 0.01	,				_lowerCamelCase				:					float = 1_348.0	,				_lowerCamelCase				:					float = 1e-5	,				_lowerCamelCase				:					int = 1	,				)							->					Union[str, Any]:
										pass
						def 					__A					( self				:					Any  )							->					str:
										__magic_name__	  = ScoreSdeVeSchedulerState.create()
										return self.set_sigmas(
										    _lowerCamelCase	,				self.config.num_train_timesteps	,				self.config.sigma_min	,				self.config.sigma_max	,				self.config.sampling_eps	,				)
						def 					__A					( self				:					List[str]	,				_lowerCamelCase				:					ScoreSdeVeSchedulerState	,				_lowerCamelCase				:					int	,				_lowerCamelCase				:					Tuple = ()	,				_lowerCamelCase				:					float = None  )							->					ScoreSdeVeSchedulerState:
										__magic_name__	  = sampling_eps if sampling_eps is not None else self.config.sampling_eps

										__magic_name__	  = jnp.linspace(1	,				_lowerCamelCase	,				_lowerCamelCase  )
										return state.replace(timesteps=_lowerCamelCase  )
						def 					__A					( self				:					Optional[Any]	,				_lowerCamelCase				:					ScoreSdeVeSchedulerState	,				_lowerCamelCase				:					int	,				_lowerCamelCase				:					float = None	,				_lowerCamelCase				:					float = None	,				_lowerCamelCase				:					float = None	,				)							->					ScoreSdeVeSchedulerState:
										__magic_name__	  = sigma_min if sigma_min is not None else self.config.sigma_min
										__magic_name__	  = sigma_max if sigma_max is not None else self.config.sigma_max
										__magic_name__	  = sampling_eps if sampling_eps is not None else self.config.sampling_eps
										if state.timesteps is None:
														__magic_name__	  = self.set_timesteps(_lowerCamelCase	,				_lowerCamelCase	,				_lowerCamelCase  )

										__magic_name__	  = jnp.exp(jnp.linspace(jnp.log(_lowerCamelCase  )	,				jnp.log(_lowerCamelCase  )	,				_lowerCamelCase  )  )
										__magic_name__	  = jnp.array([sigma_min * (sigma_max / sigma_min) ** t for t in state.timesteps]  )

										return state.replace(discrete_sigmas=_lowerCamelCase	,				sigmas=_lowerCamelCase  )
						def 					__A					( self				:					Optional[int]	,				_lowerCamelCase				:					Dict	,				_lowerCamelCase				:					Union[str, Any]	,				_lowerCamelCase				:					Optional[Any]  )							->					Optional[int]:
										return jnp.where(timesteps == 0	,				jnp.zeros_like(_lowerCamelCase  )	,				state.discrete_sigmas[timesteps - 1]  )
						def 					__A					( self				:					str	,				_lowerCamelCase				:					ScoreSdeVeSchedulerState	,				_lowerCamelCase				:					jnp.ndarray	,				_lowerCamelCase				:					int	,				_lowerCamelCase				:					jnp.ndarray	,				_lowerCamelCase				:					random.KeyArray	,				_lowerCamelCase				:					bool = True	,				)							->					Union[FlaxSdeVeOutput, Tuple]:
										if state.timesteps is None:
														raise ValueError(
														    "`state.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"  )

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

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

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

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

										# compute corrected sample: model_output term and noise term
										__magic_name__	  = step_size.flatten()
										__magic_name__	  = broadcast_to_shape_from_left(_lowerCamelCase	,				sample.shape  )
										__magic_name__	  = sample + step_size * model_output
										__magic_name__	  = prev_sample_mean + ((step_size * 2) ** 0.5) * noise

										if not return_dict:
														return (prev_sample, state)

										return FlaxSdeVeOutput(prev_sample=_lowerCamelCase	,				state=_lowerCamelCase  )

						def __len__( self				:					int  )							->					str:
										return self.config.num_train_timesteps







'''simple docstring'''
from __future__ import annotations




def      __snake_case       (       lowerCamelCase_ :				list[list[int]]	,  lowerCamelCase_ :				list[list[int]]						):
				'''simple docstring'''




				assert len(lowerCamelCase_						) == len(lowerCamelCase_						)

				__magic_name__	  = len(lowerCamelCase_						)
				__magic_name__	  = list(range(lowerCamelCase_						)						)
				__magic_name__	  = [-1] * n  # who the donor has donated to
				__magic_name__	  = [-1] * n  # who the recipient has received from
				__magic_name__	  = [0] * n

				while unmatched_donors:
								__magic_name__	  = unmatched_donors[0]
								__magic_name__	  = donor_pref[donor]
								__magic_name__	  = donor_preference[num_donations[donor]]
								num_donations[donor] += 1
								__magic_name__	  = recipient_pref[recipient]
								__magic_name__	  = rec_record[recipient]

								if prev_donor != -1:
												if rec_preference.index(lowerCamelCase_						) > rec_preference.index(lowerCamelCase_						):
																__magic_name__	  = donor
																__magic_name__	  = recipient
																unmatched_donors.append(lowerCamelCase_						)
																unmatched_donors.remove(lowerCamelCase_						)
								else:
												__magic_name__	  = donor
												__magic_name__	  = recipient
												unmatched_donors.remove(lowerCamelCase_						)
				return donor_record



