






# 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
	snake_case__				:	Optional[jnp.ndarray]	      =    None
	snake_case__				:	Optional[jnp.ndarray]	      =    None
	snake_case__				:	Optional[jnp.ndarray]	      =    None





	@classmethod
	def 		_A					(						cls							:   Optional[Any]      ):
		return cls()

@dataclass
class        _SCREAMING_SNAKE_CASE			(     _a ):
	snake_case__				:	ScoreSdeVeSchedulerState
	snake_case__				:	jnp.ndarray
	snake_case__				:	Optional[jnp.ndarray]	      =    None

class        _SCREAMING_SNAKE_CASE			(     _a						,  _a ):





	@property
	def 		_A					(						self							:   Any      ):
		return True





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





	def 		_A					(						self							:   Dict      ):
		UpperCamelCase	:Tuple		=      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							:   Any					,    __lowerCamelCase							:   ScoreSdeVeSchedulerState					,    __lowerCamelCase							:   int					,    __lowerCamelCase							:   Tuple = ()					,    __lowerCamelCase							:   float = None      ):
		UpperCamelCase	:Dict		=      sampling_eps if sampling_eps is not None else self.config.sampling_eps

		UpperCamelCase	:Tuple		=      jnp.linspace(1					,    __lowerCamelCase					,    __lowerCamelCase      )
		return state.replace(timesteps=__lowerCamelCase      )





	def 		_A					(						self							:   List[str]					,    __lowerCamelCase							:   ScoreSdeVeSchedulerState					,    __lowerCamelCase							:   int					,    __lowerCamelCase							:   float = None					,    __lowerCamelCase							:   float = None					,    __lowerCamelCase							:   float = None					,    ):
		UpperCamelCase	:List[Any]		=      sigma_min if sigma_min is not None else self.config.sigma_min
		UpperCamelCase	:Any		=      sigma_max if sigma_max is not None else self.config.sigma_max
		UpperCamelCase	:Dict		=      sampling_eps if sampling_eps is not None else self.config.sampling_eps
		if state.timesteps is None:
			UpperCamelCase	:int		=      self.set_timesteps(__lowerCamelCase					,    __lowerCamelCase					,    __lowerCamelCase      )

		UpperCamelCase	:int		=      jnp.exp(jnp.linspace(jnp.log(__lowerCamelCase      )					,    jnp.log(__lowerCamelCase      )					,    __lowerCamelCase      )      )
		UpperCamelCase	:List[Any]		=      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							:   Dict					,    __lowerCamelCase							:   int					,    __lowerCamelCase							:   Any					,    __lowerCamelCase							:   Optional[int]      ):
		return jnp.where(timesteps == 0					,    jnp.zeros_like(__lowerCamelCase      )					,    state.discrete_sigmas[timesteps - 1]      )





	def 		_A					(						self							:   Tuple					,    __lowerCamelCase							:   ScoreSdeVeSchedulerState					,    __lowerCamelCase							:   jnp.ndarray					,    __lowerCamelCase							:   int					,    __lowerCamelCase							:   jnp.ndarray					,    __lowerCamelCase							:   random.KeyArray					,    __lowerCamelCase							:   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	:List[Any]		=      timestep * jnp.ones(
		    sample.shape[0]					,    )
		UpperCamelCase	:Union[str, Any]		=      (timestep * (len(state.timesteps      ) - 1)).long()

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

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





	def 		_A					(						self							:   Dict					,    __lowerCamelCase							:   ScoreSdeVeSchedulerState					,    __lowerCamelCase							:   jnp.ndarray					,    __lowerCamelCase							:   jnp.ndarray					,    __lowerCamelCase							:   random.KeyArray					,    __lowerCamelCase							:   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	:Union[str, Any]		=      random.split(__lowerCamelCase					,    num=1      )
		UpperCamelCase	:str		=      random.normal(key=__lowerCamelCase					,    shape=sample.shape      )

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

		# compute corrected sample: model_output term and noise term
		UpperCamelCase	:Optional[int]		=      step_size.flatten()
		UpperCamelCase	:Tuple		=      broadcast_to_shape_from_left(__lowerCamelCase					,    sample.shape      )
		UpperCamelCase	:str		=      sample + step_size * model_output
		UpperCamelCase	:Dict		=      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							:   Optional[int]      ):
		return self.config.num_train_timesteps








class        _SCREAMING_SNAKE_CASE			:





	def __init__(						self							:   Tuple      ):
		UpperCamelCase	:List[str]		=      [0]
		UpperCamelCase	:Optional[int]		=      0





	def 		_A					(						self							:   Dict					,    __lowerCamelCase							:   int      ):
		UpperCamelCase	:List[str]		=      self.__heap[i]
		while i // 2 > 0:
			if self.__heap[i] > self.__heap[i // 2]:
				UpperCamelCase	:Any		=      self.__heap[i // 2]
				UpperCamelCase	:Optional[Any]		=      temporary
			i //= 2





	def 		_A					(						self							:   Optional[int]					,    __lowerCamelCase							:   int      ):
		self.__heap.append(__lowerCamelCase      )
		self.__size += 1
		self.__swap_up(self.__size      )





	def 		_A					(						self							:   Optional[Any]					,    __lowerCamelCase							:   int      ):
		while self.__size >= 2 * i:
			if 2 * i + 1 > self.__size:
				UpperCamelCase	:List[Any]		=      2 * i
			else:
				if self.__heap[2 * i] > self.__heap[2 * i + 1]:
					UpperCamelCase	:Union[str, Any]		=      2 * i
				else:
					UpperCamelCase	:Dict		=      2 * i + 1
			UpperCamelCase	:Optional[Any]		=      self.__heap[i]
			if self.__heap[i] < self.__heap[bigger_child]:
				UpperCamelCase	:List[str]		=      self.__heap[bigger_child]
				UpperCamelCase	:List[str]		=      temporary
			UpperCamelCase	:Optional[int]		=      bigger_child





	def 		_A					(						self							:   Optional[Any]      ):
		UpperCamelCase	:str		=      self.__heap[1]
		UpperCamelCase	:Tuple		=      self.__heap[self.__size]
		self.__size -= 1
		self.__heap.pop()
		self.__swap_down(1      )
		return max_value





	@property
	def 		_A					(						self							:   int      ):
		return self.__heap[1:]





	def __len__(						self							:   str      ):
		return self.__size


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	# create an instance of BinaryHeap
	UpperCAmelCase_					:		Optional[int]           = BinaryHeap()
	binary_heap.insert(6)
	binary_heap.insert(10)
	binary_heap.insert(15)
	binary_heap.insert(12)
	# pop root(max-values because it is max heap)
	print(binary_heap.pop())  # 15
	print(binary_heap.pop())  # 12
	# get the list and size after operations
	print(binary_heap.get_list)
	print(len(binary_heap))
