"""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        __SCREAMING_SNAKE_CASE     :
				# setable values
				UpperCAmelCase	=      None
				UpperCAmelCase	=      None
				UpperCAmelCase	=      None







				@classmethod
				def   lowercase_		(    cls    :List[str]  )      ->					Union[str, Any]:




									"""simple docstring"""



									return cls()





@dataclass
class        __SCREAMING_SNAKE_CASE     (   _lowerCAmelCase							):
				UpperCAmelCase	=      42
				UpperCAmelCase	=      42
				UpperCAmelCase	=      None





class        __SCREAMING_SNAKE_CASE     (   _lowerCAmelCase						,       _lowerCAmelCase							):







				@property
				def   lowercase_		(    self    :List[str]  )      ->					Optional[Any]:




									"""simple docstring"""



									return True







				@register_to_config
				def __init__(    self    :Any      ,__UpperCAmelCase    :int = 20_00      ,__UpperCAmelCase    :float = 0.15      ,__UpperCAmelCase    :float = 0.01      ,__UpperCAmelCase    :float = 1_348.0      ,__UpperCAmelCase    :float = 1E-5      ,__UpperCAmelCase    :int = 1      ,)      ->					str:




									"""simple docstring"""



									pass







				def   lowercase_		(    self    :Dict  )      ->					str:




									"""simple docstring"""



									lowerCamelCase__		:   List[Any]  				=					ScoreSdeVeSchedulerState.create()
									return self.set_sigmas(
									    __UpperCAmelCase      ,self.config.num_train_timesteps      ,self.config.sigma_min      ,self.config.sigma_max      ,self.config.sampling_eps      ,)







				def   lowercase_		(    self    :Optional[int]      ,__UpperCAmelCase    :ScoreSdeVeSchedulerState      ,__UpperCAmelCase    :int      ,__UpperCAmelCase    :Tuple = ()      ,__UpperCAmelCase    :float = None  )      ->					ScoreSdeVeSchedulerState:




									"""simple docstring"""



									lowerCamelCase__		:   str  				=					sampling_eps if sampling_eps is not None else self.config.sampling_eps

									lowerCamelCase__		:   str  				=					jnp.linspace(1      ,__UpperCAmelCase      ,__UpperCAmelCase  )
									return state.replace(timesteps=__UpperCAmelCase  )







				def   lowercase_		(    self    :Dict      ,__UpperCAmelCase    :ScoreSdeVeSchedulerState      ,__UpperCAmelCase    :int      ,__UpperCAmelCase    :float = None      ,__UpperCAmelCase    :float = None      ,__UpperCAmelCase    :float = None      ,)      ->					ScoreSdeVeSchedulerState:




									"""simple docstring"""



									lowerCamelCase__		:   Optional[int]  				=					sigma_min if sigma_min is not None else self.config.sigma_min
									lowerCamelCase__		:   int  				=					sigma_max if sigma_max is not None else self.config.sigma_max
									lowerCamelCase__		:   Tuple  				=					sampling_eps if sampling_eps is not None else self.config.sampling_eps
									if state.timesteps is None:
														lowerCamelCase__		:   Optional[int]  				=					self.set_timesteps(__UpperCAmelCase      ,__UpperCAmelCase      ,__UpperCAmelCase  )

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

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







				def   lowercase_		(    self    :List[str]      ,__UpperCAmelCase    :Union[str, Any]      ,__UpperCAmelCase    :List[str]      ,__UpperCAmelCase    :Optional[Any]  )      ->					Any:




									"""simple docstring"""



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







				def   lowercase_		(    self    :str      ,__UpperCAmelCase    :ScoreSdeVeSchedulerState      ,__UpperCAmelCase    :jnp.ndarray      ,__UpperCAmelCase    :int      ,__UpperCAmelCase    :jnp.ndarray      ,__UpperCAmelCase    :random.KeyArray      ,__UpperCAmelCase    :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'''  )

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

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

									#  equation 6: sample noise for the diffusion term of
									lowerCamelCase__		:   List[Any]  				=					random.split(__UpperCAmelCase      ,num=1  )
									lowerCamelCase__		:   List[Any]  				=					random.normal(key=__UpperCAmelCase      ,shape=sample.shape  )
									lowerCamelCase__		:   Optional[Any]  				=					sample - drift  # subtract because `dt` is a small negative timestep
									# TODO is the variable diffusion the correct scaling term for the noise?
									lowerCamelCase__		:   Optional[int]  				=					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=__UpperCAmelCase      ,prev_sample_mean=__UpperCAmelCase      ,state=__UpperCAmelCase  )







				def   lowercase_		(    self    :Union[str, Any]      ,__UpperCAmelCase    :ScoreSdeVeSchedulerState      ,__UpperCAmelCase    :jnp.ndarray      ,__UpperCAmelCase    :jnp.ndarray      ,__UpperCAmelCase    :random.KeyArray      ,__UpperCAmelCase    :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
									lowerCamelCase__		:   List[str]  				=					random.split(__UpperCAmelCase      ,num=1  )
									lowerCamelCase__		:   Optional[Any]  				=					random.normal(key=__UpperCAmelCase      ,shape=sample.shape  )

									# compute step size from the model_output, the noise, and the snr
									lowerCamelCase__		:   Optional[Any]  				=					jnp.linalg.norm(__UpperCAmelCase  )
									lowerCamelCase__		:   Union[str, Any]  				=					jnp.linalg.norm(__UpperCAmelCase  )
									lowerCamelCase__		:   Optional[Any]  				=					(self.config.snr * noise_norm / grad_norm) ** 2 * 2
									lowerCamelCase__		:   str  				=					step_size * jnp.ones(sample.shape[0]  )

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

									if not return_dict:
														return (prev_sample, state)

									return FlaxSdeVeOutput(prev_sample=__UpperCAmelCase      ,state=__UpperCAmelCase  )







				def __len__(    self    :int  )      ->					List[str]:




									"""simple docstring"""



									return self.config.num_train_timesteps







"""simple docstring"""




def 				__a       (			_lowercase							, _lowercase						):







					"""simple docstring"""





					if not root or root not in binary_tree_mirror_dictionary:
										return
					lowerCamelCase__				,						lowerCamelCase__		:   Tuple  				=					binary_tree_mirror_dictionary[root][:2]
					lowerCamelCase__		:   Optional[Any]  				=					[right_child, left_child]
					binary_tree_mirror_dict(_lowercase							, _lowercase						)
					binary_tree_mirror_dict(_lowercase							, _lowercase						)



def 				__a       (			_lowercase							, _lowercase = 1						):







					"""simple docstring"""





					if not binary_tree:
										raise ValueError('''binary tree cannot be empty'''						)
					if root not in binary_tree:
										lowerCamelCase__		:   Dict  				=					f"""root {root} is not present in the binary_tree"""
										raise ValueError(_lowercase						)
					lowerCamelCase__		:   Optional[Any]  				=					dict(_lowercase						)
					binary_tree_mirror_dict(_lowercase							, _lowercase						)
					return binary_tree_mirror_dictionary


if __name__ == "__main__":
					UpperCAmelCase       :    Any									=			{1: [2, 3], 2: [4, 5], 3: [6, 7], 7: [8, 9]}
					print(f'''Binary tree: {binary_tree}''')
					UpperCAmelCase       :    List[Any]									=			binary_tree_mirror(binary_tree, 5)
					print(f'''Binary tree mirror: {binary_tree_mirror_dictionary}''')






