
# 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 		lowercase_       :
	# setable values
	A__						:				Optional[jnp.ndarray]         =    None
	A__						:				Optional[jnp.ndarray]         =    None
	A__						:				Optional[jnp.ndarray]         =    None

	@classmethod
	def lowerCamelCase_				(      cls     ):




								"""simple docstring"""


								return cls()



@dataclass
class 		lowercase_       (       __SCREAMING_SNAKE_CASE				):
	A__						:				ScoreSdeVeSchedulerState
	A__						:				jnp.ndarray
	A__						:				Optional[jnp.ndarray]         =    None



class 		lowercase_       (       __SCREAMING_SNAKE_CASE							,	__SCREAMING_SNAKE_CASE				):

	@property
	def lowerCamelCase_				(      self     ):




								"""simple docstring"""


								return True

	@register_to_config
	def __init__(      self     ,	__UpperCamelCase = 2_0_0_0     ,	__UpperCamelCase = 0.15     ,	__UpperCamelCase = 0.01     ,	__UpperCamelCase = 1_3_4_8.0     ,	__UpperCamelCase = 1e-5     ,	__UpperCamelCase = 1     ,	):




								"""simple docstring"""


								pass

	def lowerCamelCase_				(      self     ):




								"""simple docstring"""


								UpperCamelCase_							       =       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 lowerCamelCase_				(      self     ,	__UpperCamelCase     ,	__UpperCamelCase     ,	__UpperCamelCase = ()     ,	__UpperCamelCase = None     ):




								"""simple docstring"""


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

								UpperCamelCase_							       =       jnp.linspace(1     ,	__UpperCamelCase     ,	__UpperCamelCase     )
								return state.replace(timesteps=__UpperCamelCase     )

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




								"""simple docstring"""


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

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

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

	def lowerCamelCase_				(      self     ,	__UpperCamelCase     ,	__UpperCamelCase     ,	__UpperCamelCase     ):




								"""simple docstring"""


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

	def lowerCamelCase_				(      self     ,	__UpperCamelCase     ,	__UpperCamelCase     ,	__UpperCamelCase     ,	__UpperCamelCase     ,	__UpperCamelCase     ,	__UpperCamelCase = True     ,	):




								"""simple docstring"""


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

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

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

								#  equation 6: sample noise for the diffusion term of
								UpperCamelCase_							       =       random.split(__UpperCamelCase     ,	num=1     )
								UpperCamelCase_							       =       random.normal(key=__UpperCamelCase     ,	shape=sample.shape     )
								UpperCamelCase_							       =       sample - drift  # subtract because `dt` is a small negative timestep
								# TODO is the variable diffusion the correct scaling term for the noise?
								UpperCamelCase_							       =       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 lowerCamelCase_				(      self     ,	__UpperCamelCase     ,	__UpperCamelCase     ,	__UpperCamelCase     ,	__UpperCamelCase     ,	__UpperCamelCase = True     ,	):




								"""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
								UpperCamelCase_							       =       random.split(__UpperCamelCase     ,	num=1     )
								UpperCamelCase_							       =       random.normal(key=__UpperCamelCase     ,	shape=sample.shape     )

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

								# compute corrected sample: model_output term and noise term
								UpperCamelCase_							       =       step_size.flatten()
								UpperCamelCase_							       =       broadcast_to_shape_from_left(__UpperCamelCase     ,	sample.shape     )
								UpperCamelCase_							       =       sample + step_size * model_output
								UpperCamelCase_							       =       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     ):




								"""simple docstring"""


								return self.config.num_train_timesteps





def 				lowerCamelCase__				(						a__  :  dict	,      a__  :  int     )		->	Optional[Any]:
       if not root or root not in binary_tree_mirror_dictionary:
              return
       UpperCamelCase_       ,				UpperCamelCase_							       =       binary_tree_mirror_dictionary[root][:2]
       UpperCamelCase_							       =       [right_child, left_child]
       binary_tree_mirror_dict(a__	,      a__     )
       binary_tree_mirror_dict(a__	,      a__     )




def 				lowerCamelCase__				(						a__  :  dict	,      a__  :  int = 1     )		->	dict:
       if not binary_tree:
              raise ValueError("""binary tree cannot be empty"""     )
       if root not in binary_tree:
              UpperCamelCase_							       =       f'''root {root} is not present in the binary_tree'''
              raise ValueError(a__     )
       UpperCamelCase_							       =       dict(a__     )
       binary_tree_mirror_dict(a__	,      a__     )
       return binary_tree_mirror_dictionary


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



