


'''simple docstring'''

from __future__ import annotations

_UpperCAmelCase :       Tuple										= list[tuple[int, int]]

_UpperCAmelCase :       List[Any]										= [
    [0, 0, 0, 0, 0, 0, 0],
    [0, 1, 0, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0],
    [1, 0, 1, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 1, 0, 0],
]

_UpperCAmelCase :       Union[str, Any]										= ([-1, 0], [0, -1], [1, 0], [0, 1])  # up, left, down, right


class 					a__ :







				"""simple docstring"""



				def __init__(self				,	__lowercase				,	__lowercase				,	__lowercase				,	__lowercase				,	__lowercase				,	__lowercase				,	):
										__lowerCAmelCase											= pos_x
										__lowerCAmelCase											= pos_y
										__lowerCAmelCase											= (pos_y, pos_x)
										__lowerCAmelCase											= goal_x
										__lowerCAmelCase											= goal_y
										__lowerCAmelCase											= g_cost
										__lowerCAmelCase											= parent
										__lowerCAmelCase											= self.calculate_heuristic()




				def     _snake_case     (self						):
										__lowerCAmelCase											= abs(self.pos_x - self.goal_x						)
										__lowerCAmelCase											= abs(self.pos_y - self.goal_y						)
										return dx + dy




				def __lt__(self				,	__lowercase						):
										return self.f_cost < other.f_cost


class 					a__ :







				"""simple docstring"""



				def __init__(self				,	__lowercase				,	__lowercase						):
										__lowerCAmelCase											= Node(start[1]				,	start[0]				,	goal[1]				,	goal[0]				,	0				,	lowerCAmelCase_						)
										__lowerCAmelCase											= Node(goal[1]				,	goal[0]				,	goal[1]				,	goal[0]				,	9_99_99				,	lowerCAmelCase_						)

										__lowerCAmelCase											= [self.start]
										__lowerCAmelCase											= []

										__lowerCAmelCase											= False




				def     _snake_case     (self						):
										while self.open_nodes:
																# Open Nodes are sorted using __lt__
																self.open_nodes.sort()
																__lowerCAmelCase											= self.open_nodes.pop(0						)

																if current_node.pos == self.target.pos:
																						__lowerCAmelCase											= True
																						return self.retrace_path(lowerCAmelCase_						)

																self.closed_nodes.append(lowerCAmelCase_						)
																__lowerCAmelCase											= self.get_successors(lowerCAmelCase_						)

																for child_node in successors:
																						if child_node in self.closed_nodes:
																												continue

																						if child_node not in self.open_nodes:
																												self.open_nodes.append(lowerCAmelCase_						)
																						else:
																												# retrieve the best current path
																												__lowerCAmelCase											= self.open_nodes.pop(self.open_nodes.index(lowerCAmelCase_						)						)

																												if child_node.g_cost < better_node.g_cost:
																																		self.open_nodes.append(lowerCAmelCase_						)
																												else:
																																		self.open_nodes.append(lowerCAmelCase_						)

										if not self.reached:
																return [self.start.pos]
										return None




				def     _snake_case     (self				,	__lowercase						):
										__lowerCAmelCase											= []
										for action in delta:
																__lowerCAmelCase											= parent.pos_x + action[1]
																__lowerCAmelCase											= parent.pos_y + action[0]

																if not (0 <= pos_x <= len(grid[0]						) - 1 and 0 <= pos_y <= len(lowerCAmelCase_						) - 1):
																						continue

																if grid[pos_y][pos_x] != 0:
																						continue

																successors.append(
																    Node(
																        lowerCAmelCase_				,	lowerCAmelCase_				,	self.target.pos_y				,	self.target.pos_x				,	parent.g_cost + 1				,	lowerCAmelCase_				,	)						)
										return successors




				def     _snake_case     (self				,	__lowercase						):
										__lowerCAmelCase											= node
										__lowerCAmelCase											= []
										while current_node is not None:
																path.append((current_node.pos_y, current_node.pos_x)						)
																__lowerCAmelCase											= current_node.parent
										path.reverse()
										return path


if __name__ == "__main__":
	_UpperCAmelCase :       List[Any]										= (0, 0)
	_UpperCAmelCase :       Optional[Any]										= (len(grid) - 1, len(grid[0]) - 1)
	for elem in grid:
		print(elem)

	print("""------""")

	_UpperCAmelCase :       Union[str, Any]										= GreedyBestFirst(init, goal)
	_UpperCAmelCase :       Optional[int]										= greedy_bf.search()
	if path:
		for pos_x, pos_y in path:
			_UpperCAmelCase :       str										= 2

		for elem in grid:
			print(elem)









"""simple docstring"""
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 ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin







@flax.struct.dataclass
class 			UpperCamelCase_       :
	# setable values
	__magic_name__	      =				None
	__magic_name__	      =				None
	__magic_name__	      =				None  # sigma(t_i)





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







@dataclass
class 			UpperCamelCase_       (__A     ):
	__magic_name__	      =				42
	__magic_name__	      =				42
	__magic_name__	      =				42







class 			UpperCamelCase_       (__A				,						__A     ):





	@property
	def      _SCREAMING_SNAKE_CASE      (      self					: Tuple						)						->			str:
				return True





	@register_to_config
	def __init__(      self					: List[str]	,	lowerCAmelCase_					: float = 0.0_2	,	lowerCAmelCase_					: float = 100	,	lowerCAmelCase_					: float = 1.0_0_7	,	lowerCAmelCase_					: float = 80	,	lowerCAmelCase_					: float = 0.0_5	,	lowerCAmelCase_					: float = 50	,	)						->			Union[str, Any]:
				pass





	def      _SCREAMING_SNAKE_CASE      (      self					: Union[str, Any]						)						->			Tuple:
				return KarrasVeSchedulerState.create()





	def      _SCREAMING_SNAKE_CASE      (      self					: Union[str, Any]	,	lowerCAmelCase_					: KarrasVeSchedulerState	,	lowerCAmelCase_					: int	,	lowerCAmelCase_					: Tuple = ()						)						->			KarrasVeSchedulerState:
				UpperCAmelCase_     : Dict			     =					jnp.arange(0	,	lowerCAmelCase_						)[::-1].copy()
				UpperCAmelCase_     : Dict			     =					[
				    (
				        self.config.sigma_max**2
				        * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
				    )
				    for i in timesteps
				]

				return state.replace(
				    num_inference_steps=lowerCAmelCase_	,	schedule=jnp.array(lowerCAmelCase_	,	dtype=jnp.floataa						)	,	timesteps=lowerCAmelCase_	,	)





	def      _SCREAMING_SNAKE_CASE      (      self					: List[str]	,	lowerCAmelCase_					: KarrasVeSchedulerState	,	lowerCAmelCase_					: jnp.ndarray	,	lowerCAmelCase_					: float	,	lowerCAmelCase_					: random.KeyArray	,	)						->			Tuple[jnp.ndarray, float]:
				if self.config.s_min <= sigma <= self.config.s_max:
							UpperCAmelCase_     : Any			     =					min(self.config.s_churn / state.num_inference_steps	,	2**0.5 - 1						)
				else:
							UpperCAmelCase_     : Optional[int]			     =					0

				# sample eps ~ N(0, S_noise^2 * I)
				UpperCAmelCase_     : List[Any]			     =					random.split(lowerCAmelCase_	,	num=1						)
				UpperCAmelCase_     : List[str]			     =					self.config.s_noise * random.normal(key=lowerCAmelCase_	,	shape=sample.shape						)
				UpperCAmelCase_     : Optional[Any]			     =					sigma + gamma * sigma
				UpperCAmelCase_     : Any			     =					sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

				return sample_hat, sigma_hat





	def      _SCREAMING_SNAKE_CASE      (      self					: Union[str, Any]	,	lowerCAmelCase_					: KarrasVeSchedulerState	,	lowerCAmelCase_					: jnp.ndarray	,	lowerCAmelCase_					: float	,	lowerCAmelCase_					: float	,	lowerCAmelCase_					: jnp.ndarray	,	lowerCAmelCase_					: bool = True	,	)						->			Union[FlaxKarrasVeOutput, Tuple]:
				UpperCAmelCase_     : Union[str, Any]			     =					sample_hat + sigma_hat * model_output
				UpperCAmelCase_     : List[Any]			     =					(sample_hat - pred_original_sample) / sigma_hat
				UpperCAmelCase_     : Union[str, Any]			     =					sample_hat + (sigma_prev - sigma_hat) * derivative

				if not return_dict:
							return (sample_prev, derivative, state)

				return FlaxKarrasVeOutput(prev_sample=lowerCAmelCase_	,	derivative=lowerCAmelCase_	,	state=lowerCAmelCase_						)





	def      _SCREAMING_SNAKE_CASE      (      self					: List[str]	,	lowerCAmelCase_					: KarrasVeSchedulerState	,	lowerCAmelCase_					: jnp.ndarray	,	lowerCAmelCase_					: float	,	lowerCAmelCase_					: float	,	lowerCAmelCase_					: jnp.ndarray	,	lowerCAmelCase_					: jnp.ndarray	,	lowerCAmelCase_					: jnp.ndarray	,	lowerCAmelCase_					: bool = True	,	)						->			Union[FlaxKarrasVeOutput, Tuple]:
				UpperCAmelCase_     : str			     =					sample_prev + sigma_prev * model_output
				UpperCAmelCase_     : Any			     =					(sample_prev - pred_original_sample) / sigma_prev
				UpperCAmelCase_     : int			     =					sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

				if not return_dict:
							return (sample_prev, derivative, state)

				return FlaxKarrasVeOutput(prev_sample=lowerCAmelCase_	,	derivative=lowerCAmelCase_	,	state=lowerCAmelCase_						)





	def      _SCREAMING_SNAKE_CASE      (      self					: Union[str, Any]	,	lowerCAmelCase_					: KarrasVeSchedulerState	,	lowerCAmelCase_					: List[Any]	,	lowerCAmelCase_					: List[str]	,	lowerCAmelCase_					: Optional[Any]						)						->			Dict:
				raise NotImplementedError()



