



'''simple docstring'''



import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union

import flax
import jax.numpy as jnp

from ..utils import BaseOutput


_SCREAMING_SNAKE_CASE					=    '''scheduler_config.json'''






class 							__lowercase       (      lowerCAmelCase__						):







	'''simple docstring'''

	a    :				Any											=       1
	a    :				Dict											=       2
	a    :				Optional[Any]											=       3
	a    :				List[str]											=       4
	a    :				Any											=       5






@dataclass
class 							__lowercase       (      lowerCAmelCase__						):







	'''simple docstring'''

	a    :				jnp.ndarray






class 							__lowercase       :







	'''simple docstring'''

	a    :				str											=       SCHEDULER_CONFIG_NAME
	a    :				Union[str, Any]											=       ["dtype"]
	a    :				str											=       []
	a    :				List[Any]											=       True


	@classmethod
	def 						_UpperCAmelCase     (cls     ,_lowerCamelCase = None     ,_lowerCamelCase = None     ,_lowerCamelCase=False     ,**_lowerCamelCase     ,)							->							Tuple:







				'''simple docstring'''





				__lowercase						,		__lowercase										=							cls.load_config(
				    pretrained_model_name_or_path=_lowerCamelCase     ,subfolder=_lowerCamelCase     ,return_unused_kwargs=_lowerCamelCase     ,**_lowerCamelCase     ,)
				__lowercase						,		__lowercase										=							cls.from_config(_lowerCamelCase     ,return_unused_kwargs=_lowerCamelCase     ,**_lowerCamelCase )

				if hasattr(_lowerCamelCase     ,'''create_state''' ) and getattr(_lowerCamelCase     ,'''has_state'''     ,_lowerCamelCase ):
							__lowercase										=							scheduler.create_state()

				if return_unused_kwargs:
							return scheduler, state, unused_kwargs

				return scheduler, state


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase     ,_lowerCamelCase = False     ,**_lowerCamelCase )							->							str:







				'''simple docstring'''





				self.save_config(save_directory=_lowerCamelCase     ,push_to_hub=_lowerCamelCase     ,**_lowerCamelCase )


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







				'''simple docstring'''





				return self._get_compatibles()


	@classmethod
	def 						_UpperCAmelCase     (cls )							->							int:







				'''simple docstring'''





				__lowercase										=							list(set([cls.__name__] + cls._compatibles ) )
				__lowercase										=							importlib.import_module(__name__.split('''.''' )[0] )
				__lowercase										=							[
				    getattr(_lowerCamelCase     ,_lowerCamelCase ) for c in compatible_classes_str if hasattr(_lowerCamelCase     ,_lowerCamelCase )
				]
				return compatible_classes





def    _lowerCAmelCase						(				lowerCamelCase_			:  jnp.ndarray		,       lowerCamelCase_			:  Tuple[int]				):
			assert len(lowerCamelCase_				) >= x.ndim
			return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(lowerCamelCase_				) - x.ndim)				)		,       lowerCamelCase_				)




def    _lowerCAmelCase						(				lowerCamelCase_			:  int		,       lowerCamelCase_			:  Union[str, Any]=0.9_99		,       lowerCamelCase_			:  Union[str, Any]=jnp.floataa				):

			def alpha_bar(lowerCamelCase_			:  Any				):
						return math.cos((time_step + 0.0_08) / 1.0_08 * math.pi / 2				) ** 2

			__lowercase										=							[]
			for i in range(lowerCamelCase_				):
						__lowercase										=							i / num_diffusion_timesteps
						__lowercase										=							(i + 1) / num_diffusion_timesteps
						betas.append(min(1 - alpha_bar(lowerCamelCase_				) / alpha_bar(lowerCamelCase_				)		,       lowerCamelCase_				)				)
			return jnp.array(lowerCamelCase_		,       dtype=lowerCamelCase_				)






@flax.struct.dataclass
class 							__lowercase       :







	'''simple docstring'''

	a    :				jnp.ndarray
	a    :				jnp.ndarray
	a    :				jnp.ndarray


	@classmethod
	def 						_UpperCAmelCase     (cls     ,_lowerCamelCase )							->							Optional[Any]:







				'''simple docstring'''





				__lowercase										=							scheduler.config

				if config.trained_betas is not None:
							__lowercase										=							jnp.asarray(config.trained_betas     ,dtype=scheduler.dtype )
				elif config.beta_schedule == "linear":
							__lowercase										=							jnp.linspace(config.beta_start     ,config.beta_end     ,config.num_train_timesteps     ,dtype=scheduler.dtype )
				elif config.beta_schedule == "scaled_linear":
							# this schedule is very specific to the latent diffusion model.
							__lowercase										=							(
							    jnp.linspace(
							        config.beta_start**0.5     ,config.beta_end**0.5     ,config.num_train_timesteps     ,dtype=scheduler.dtype )
							    ** 2
							)
				elif config.beta_schedule == "squaredcos_cap_v2":
							# Glide cosine schedule
							__lowercase										=							betas_for_alpha_bar(config.num_train_timesteps     ,dtype=scheduler.dtype )
				else:
							raise NotImplementedError(
							    f"beta_schedule {config.beta_schedule} is not implemented for scheduler {scheduler.__class__.__name__}" )

				__lowercase										=							1.0 - betas

				__lowercase										=							jnp.cumprod(_lowerCamelCase     ,axis=0 )

				return cls(
				    alphas=_lowerCamelCase     ,betas=_lowerCamelCase     ,alphas_cumprod=_lowerCamelCase     ,)





def    _lowerCAmelCase						(				lowerCamelCase_			:  CommonSchedulerState		,       lowerCamelCase_			:  jnp.ndarray		,       lowerCamelCase_			:  jnp.ndarray		,       lowerCamelCase_			:  jnp.ndarray				):
			__lowercase										=							state.alphas_cumprod

			__lowercase										=							alphas_cumprod[timesteps] ** 0.5
			__lowercase										=							sqrt_alpha_prod.flatten()
			__lowercase										=							broadcast_to_shape_from_left(lowerCamelCase_		,       original_samples.shape				)

			__lowercase										=							(1 - alphas_cumprod[timesteps]) ** 0.5
			__lowercase										=							sqrt_one_minus_alpha_prod.flatten()
			__lowercase										=							broadcast_to_shape_from_left(lowerCamelCase_		,       original_samples.shape				)

			return sqrt_alpha_prod, sqrt_one_minus_alpha_prod




def    _lowerCAmelCase						(				lowerCamelCase_			:  CommonSchedulerState		,       lowerCamelCase_			:  jnp.ndarray		,       lowerCamelCase_			:  jnp.ndarray		,       lowerCamelCase_			:  jnp.ndarray				):
			__lowercase						,		__lowercase										=							get_sqrt_alpha_prod(lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_				)
			__lowercase										=							sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
			return noisy_samples




def    _lowerCAmelCase						(				lowerCamelCase_			:  CommonSchedulerState		,       lowerCamelCase_			:  jnp.ndarray		,       lowerCamelCase_			:  jnp.ndarray		,       lowerCamelCase_			:  jnp.ndarray				):
			__lowercase						,		__lowercase										=							get_sqrt_alpha_prod(lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_				)
			__lowercase										=							sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
			return velocity












'''simple docstring'''



from __future__ import annotations

import time
from math import sqrt

# 1 for manhattan, 0 for euclidean
_SCREAMING_SNAKE_CASE					=    0

_SCREAMING_SNAKE_CASE					=    [
    [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],
]

_SCREAMING_SNAKE_CASE					=    [[-1, 0], [0, -1], [1, 0], [0, 1]]  # up, left, down, right

_SCREAMING_SNAKE_CASE					=    tuple[int, int]






class 							__lowercase       :







	'''simple docstring'''

	def __init__(self     ,_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase     ,)							->							None:







				'''simple docstring'''





				__lowercase										=							pos_x
				__lowercase										=							pos_y
				__lowercase										=							(pos_y, pos_x)
				__lowercase										=							goal_x
				__lowercase										=							goal_y
				__lowercase										=							g_cost
				__lowercase										=							parent
				__lowercase										=							self.calculate_heuristic()
				__lowercase										=							self.g_cost + self.h_cost


	def 						_UpperCAmelCase     (self )							->							float:







				'''simple docstring'''





				__lowercase										=							self.pos_x - self.goal_x
				__lowercase										=							self.pos_y - self.goal_y
				if HEURISTIC == 1:
							return abs(_lowerCamelCase ) + abs(_lowerCamelCase )
				else:
							return sqrt(dy**2 + dx**2 )


	def __lt__(self     ,_lowerCamelCase )							->							bool:







				'''simple docstring'''





				return self.f_cost < other.f_cost






class 							__lowercase       :







	'''simple docstring'''

	def __init__(self     ,_lowerCamelCase     ,_lowerCamelCase )							->							List[Any]:







				'''simple docstring'''





				__lowercase										=							Node(start[1]     ,start[0]     ,goal[1]     ,goal[0]     ,0     ,_lowerCamelCase )
				__lowercase										=							Node(goal[1]     ,goal[0]     ,goal[1]     ,goal[0]     ,99999     ,_lowerCamelCase )

				__lowercase										=							[self.start]
				__lowercase										=							[]

				__lowercase										=							False


	def 						_UpperCAmelCase     (self )							->							list[TPosition]:







				'''simple docstring'''





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

							if current_node.pos == self.target.pos:
										return self.retrace_path(_lowerCamelCase )

							self.closed_nodes.append(_lowerCamelCase )
							__lowercase										=							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
													__lowercase										=							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 )

				return [self.start.pos]


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase )							->							list[Node]:







				'''simple docstring'''





				__lowercase										=							[]
				for action in delta:
							__lowercase										=							parent.pos_x + action[1]
							__lowercase										=							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 						_UpperCAmelCase     (self     ,_lowerCamelCase )							->							list[TPosition]:







				'''simple docstring'''





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






class 							__lowercase       :







	'''simple docstring'''

	def __init__(self     ,_lowerCamelCase     ,_lowerCamelCase )							->							None:







				'''simple docstring'''





				__lowercase										=							AStar(_lowerCamelCase     ,_lowerCamelCase )
				__lowercase										=							AStar(_lowerCamelCase     ,_lowerCamelCase )
				__lowercase										=							False


	def 						_UpperCAmelCase     (self )							->							list[TPosition]:







				'''simple docstring'''





				while self.fwd_astar.open_nodes or self.bwd_astar.open_nodes:
							self.fwd_astar.open_nodes.sort()
							self.bwd_astar.open_nodes.sort()
							__lowercase										=							self.fwd_astar.open_nodes.pop(0 )
							__lowercase										=							self.bwd_astar.open_nodes.pop(0 )

							if current_bwd_node.pos == current_fwd_node.pos:
										return self.retrace_bidirectional_path(
										    _lowerCamelCase     ,_lowerCamelCase )

							self.fwd_astar.closed_nodes.append(_lowerCamelCase )
							self.bwd_astar.closed_nodes.append(_lowerCamelCase )

							__lowercase										=							current_bwd_node
							__lowercase										=							current_fwd_node

							__lowercase										=							{
							    self.fwd_astar: self.fwd_astar.get_successors(_lowerCamelCase ),
							    self.bwd_astar: self.bwd_astar.get_successors(_lowerCamelCase ),
							}

							for astar in [self.fwd_astar, self.bwd_astar]:
										for child_node in successors[astar]:
													if child_node in astar.closed_nodes:
																continue

													if child_node not in astar.open_nodes:
																astar.open_nodes.append(_lowerCamelCase )
													else:
																# retrieve the best current path
																__lowercase										=							astar.open_nodes.pop(
																    astar.open_nodes.index(_lowerCamelCase ) )

																if child_node.g_cost < better_node.g_cost:
																			astar.open_nodes.append(_lowerCamelCase )
																else:
																			astar.open_nodes.append(_lowerCamelCase )

				return [self.fwd_astar.start.pos]


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase     ,_lowerCamelCase )							->							list[TPosition]:







				'''simple docstring'''





				__lowercase										=							self.fwd_astar.retrace_path(_lowerCamelCase )
				__lowercase										=							self.bwd_astar.retrace_path(_lowerCamelCase )
				bwd_path.pop()
				bwd_path.reverse()
				__lowercase										=							fwd_path + bwd_path
				return path


if __name__ == "__main__":
	# all coordinates are given in format [y,x]
	_SCREAMING_SNAKE_CASE					=    (0, 0)
	_SCREAMING_SNAKE_CASE					=    (len(grid) - 1, len(grid[0]) - 1)
	for elem in grid:
		print(elem)

	_SCREAMING_SNAKE_CASE					=    time.time()
	_SCREAMING_SNAKE_CASE					=    AStar(init, goal)
	_SCREAMING_SNAKE_CASE					=    a_star.search()
	_SCREAMING_SNAKE_CASE					=    time.time() - start_time
	print(f'''AStar execution time = {end_time:f} seconds''')

	_SCREAMING_SNAKE_CASE					=    time.time()
	_SCREAMING_SNAKE_CASE					=    BidirectionalAStar(init, goal)
	_SCREAMING_SNAKE_CASE					=    time.time() - bd_start_time
	print(f'''BidirectionalAStar execution time = {bd_end_time:f} seconds''')





