




# 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_							:



	"""simple docstring"""


	__magic_name__      :					Optional[jnp.ndarray]			  =		None
	__magic_name__      :					Optional[jnp.ndarray]			  =		None
	__magic_name__      :					Optional[jnp.ndarray]			  =		None






	@classmethod
	def 				lowerCamelCase__    (				cls		:	Optional[Any]  )  ->					List[str]:







						"""simple docstring"""







						return cls()






@dataclass
class       SCREAMING_SNAKE_CASE_							(					_UpperCamelCase      ):



	"""simple docstring"""


	__magic_name__      :					ScoreSdeVeSchedulerState
	__magic_name__      :					jnp.ndarray
	__magic_name__      :					Optional[jnp.ndarray]			  =		None






class       SCREAMING_SNAKE_CASE_							(					_UpperCamelCase  ,     _UpperCamelCase      ):



	"""simple docstring"""


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







						"""simple docstring"""







						return True






	@register_to_config
	def __init__(				self		:	int				,    lowerCAmelCase		:	int = 2000				,    lowerCAmelCase		:	float = 0.15				,    lowerCAmelCase		:	float = 0.01				,    lowerCAmelCase		:	float = 13_48.0				,    lowerCAmelCase		:	float = 1E-5				,    lowerCAmelCase		:	int = 1				,    )  ->					int:







						"""simple docstring"""







						pass






	def 				lowerCamelCase__    (				self		:	List[Any]  )  ->					int:







						"""simple docstring"""







						__UpperCamelCase				:  int								=			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 				lowerCamelCase__    (				self		:	Any				,    lowerCAmelCase		:	ScoreSdeVeSchedulerState				,    lowerCAmelCase		:	int				,    lowerCAmelCase		:	Tuple = ()				,    lowerCAmelCase		:	float = None  )  ->					ScoreSdeVeSchedulerState:







						"""simple docstring"""







						__UpperCamelCase				:  Optional[Any]								=			sampling_eps if sampling_eps is not None else self.config.sampling_eps

						__UpperCamelCase				:  int								=			jnp.linspace(1				,    lowerCAmelCase				,    lowerCAmelCase  )
						return state.replace(timesteps=lowerCAmelCase  )






	def 				lowerCamelCase__    (				self		:	List[Any]				,    lowerCAmelCase		:	ScoreSdeVeSchedulerState				,    lowerCAmelCase		:	int				,    lowerCAmelCase		:	float = None				,    lowerCAmelCase		:	float = None				,    lowerCAmelCase		:	float = None				,    )  ->					ScoreSdeVeSchedulerState:







						"""simple docstring"""







						__UpperCamelCase				:  Dict								=			sigma_min if sigma_min is not None else self.config.sigma_min
						__UpperCamelCase				:  Optional[int]								=			sigma_max if sigma_max is not None else self.config.sigma_max
						__UpperCamelCase				:  Optional[int]								=			sampling_eps if sampling_eps is not None else self.config.sampling_eps
						if state.timesteps is None:
											__UpperCamelCase				:  List[str]								=			self.set_timesteps(lowerCAmelCase				,    lowerCAmelCase				,    lowerCAmelCase  )

						__UpperCamelCase				:  Any								=			jnp.exp(jnp.linspace(jnp.log(lowerCAmelCase  )				,    jnp.log(lowerCAmelCase  )				,    lowerCAmelCase  )  )
						__UpperCamelCase				:  Union[str, Any]								=			jnp.array([sigma_min * (sigma_max / sigma_min) ** t for t in state.timesteps]  )

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






	def 				lowerCamelCase__    (				self		:	Dict				,    lowerCAmelCase		:	Optional[int]				,    lowerCAmelCase		:	Any				,    lowerCAmelCase		:	Any  )  ->					List[str]:







						"""simple docstring"""







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






	def 				lowerCamelCase__    (				self		:	Tuple				,    lowerCAmelCase		:	ScoreSdeVeSchedulerState				,    lowerCAmelCase		:	jnp.ndarray				,    lowerCAmelCase		:	int				,    lowerCAmelCase		:	jnp.ndarray				,    lowerCAmelCase		:	random.KeyArray				,    lowerCAmelCase		:	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"""  )

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

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

						#  equation 6: sample noise for the diffusion term of
						__UpperCamelCase				:  List[Any]								=			random.split(lowerCAmelCase				,    num=1  )
						__UpperCamelCase				:  int								=			random.normal(key=lowerCAmelCase				,    shape=sample.shape  )
						__UpperCamelCase				:  int								=			sample - drift  # subtract because `dt` is a small negative timestep
						# TODO is the variable diffusion the correct scaling term for the noise?
						__UpperCamelCase				:  List[Any]								=			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 				lowerCamelCase__    (				self		:	List[str]				,    lowerCAmelCase		:	ScoreSdeVeSchedulerState				,    lowerCAmelCase		:	jnp.ndarray				,    lowerCAmelCase		:	jnp.ndarray				,    lowerCAmelCase		:	random.KeyArray				,    lowerCAmelCase		:	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
						__UpperCamelCase				:  str								=			random.split(lowerCAmelCase				,    num=1  )
						__UpperCamelCase				:  Dict								=			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				:  Optional[int]								=			jnp.linalg.norm(lowerCAmelCase  )
						__UpperCamelCase				:  Dict								=			(self.config.snr * noise_norm / grad_norm) ** 2 * 2
						__UpperCamelCase				:  int								=			step_size * jnp.ones(sample.shape[0]  )

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







						"""simple docstring"""







						return self.config.num_train_timesteps












import os





def 							A__   ()    ->	int:
					__UpperCamelCase				:  int								=			os.path.dirname(os.path.realpath(snake_case							)							)
					__UpperCamelCase				:  List[str]								=			os.path.join(snake_case		,  """triangle.txt"""							)

					with open(snake_case							) as in_file:
										__UpperCamelCase				:  Optional[Any]								=			[[int(snake_case							) for i in line.split()] for line in in_file]

					while len(snake_case							) != 1:
										__UpperCamelCase				:  Union[str, Any]								=			triangle.pop()
										__UpperCamelCase				:  List[str]								=			triangle[-1]
										for j in range(len(snake_case							) - 1							):
															curr_row[j] += max(last_row[j]		,  last_row[j + 1]							)
					return triangle[0][0]


if __name__ == "__main__":
	print(solution())






