

"""simple docstring"""
import importlib

import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel

def        snake_case   (     A__			,A__=False		):
			UpperCAmelCase_  :				List[str]      =      OmegaConf.load(A__		)
			if display:
						print(yaml.dump(OmegaConf.to_container(A__		)		)		)
			return config

def        snake_case   (     A__			,A__=None			,A__=None		):
			if conf_path is None:
						UpperCAmelCase_  :				Union[str, Any]      =      "./model_checkpoints/vqgan_only.yaml"
			UpperCAmelCase_  :				int      =      load_config(A__			,display=A__		)
			UpperCAmelCase_  :				Tuple      =      VQModel(**config.model.params		)
			if ckpt_path is None:
						UpperCAmelCase_  :				Tuple      =      "./model_checkpoints/vqgan_only.pt"
			UpperCAmelCase_  :				Optional[int]      =      torch.load(A__			,map_location=A__		)
			if ".ckpt" in ckpt_path:
						UpperCAmelCase_  :				List[str]      =      sd["state_dict"]
			model.load_state_dict(A__			,strict=A__		)
			model.to(A__		)
			del sd
			return model

def        snake_case   (     A__			,A__		):
			UpperCAmelCase_  :				List[Any]      =      model.encode(A__		)
			print(F"""VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}"""		)
			UpperCAmelCase_  :				Union[str, Any]      =      model.decode(A__		)
			return xrec

def        snake_case   (     A__			,A__=False		):
			UpperCAmelCase_  :				Tuple      =      string.rsplit("."			,1		)
			if reload:
						UpperCAmelCase_  :				Optional[int]      =      importlib.import_module(A__		)
						importlib.reload(A__		)
			return getattr(importlib.import_module(A__			,package=A__		)			,cls		)

def        snake_case   (     A__		):
			if "target" not in config:
						raise KeyError("Expected key `target` to instantiate."		)
			return get_obj_from_str(config["target"]		)(**config.get("params"			,{}		)		)

def        snake_case   (     A__			,A__			,A__=True			,A__=True		):
			UpperCAmelCase_  :				Optional[int]      =      instantiate_from_config(A__		)
			if sd is not None:
						model.load_state_dict(A__		)
			if gpu:
						model.cuda()
			if eval_mode:
						model.eval()
			return {"model": model}




def        snake_case   (     A__			,A__			,A__			,A__		):
			# load the specified checkpoint
			if ckpt:
						UpperCAmelCase_  :				Union[str, Any]      =      torch.load(A__			,map_location="cpu"		)
						UpperCAmelCase_  :				List[str]      =      pl_sd["global_step"]
						print(F"""loaded model from global step {global_step}."""		)
			else:
						UpperCAmelCase_  :				Dict      =      {"state_dict": None}
						UpperCAmelCase_  :				int      =      None
			UpperCAmelCase_  :				Dict      =      load_model_from_config(config.model			,pl_sd["state_dict"]			,gpu=A__			,eval_mode=A__		)["model"]
			return model, global_step











"""simple docstring"""
from __future__ import annotations

import time

lowerCamelCase_       			=   list[tuple[int, int]]

lowerCamelCase_       			=   [
    [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],
]

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







class     UpperCamelCase_ :





	def __init__(   self      :				int			,			lowerCAmelCase_      :				int			,			lowerCAmelCase_      :				int			,			lowerCAmelCase_      :				int			,			lowerCAmelCase_      :				int			,			lowerCAmelCase_      :				Node | None		)		->    List[str]:
				UpperCAmelCase_  :				List[Any]      =      pos_x
				UpperCAmelCase_  :				Dict      =      pos_y
				UpperCAmelCase_  :				List[Any]      =      (pos_y, pos_x)
				UpperCAmelCase_  :				int      =      goal_x
				UpperCAmelCase_  :				int      =      goal_y
				UpperCAmelCase_  :				Union[str, Any]      =      parent







class     UpperCamelCase_ :





	def __init__(   self      :				Dict			,			lowerCAmelCase_      :				tuple[int, int]			,			lowerCAmelCase_      :				tuple[int, int]		)		->    int:
				UpperCAmelCase_  :				Dict      =      Node(start[1]			,			start[0]			,			goal[1]			,			goal[0]			,			lowerCAmelCase_		)
				UpperCAmelCase_  :				List[str]      =      Node(goal[1]			,			goal[0]			,			goal[1]			,			goal[0]			,			lowerCAmelCase_		)

				UpperCAmelCase_  :				List[str]      =      [self.start]
				UpperCAmelCase_  :				List[Any]      =      False





	def   _SCREAMING_SNAKE_CASE   (   self      :				int		)		->    Path | None:
				while self.node_queue:
							UpperCAmelCase_  :				Optional[int]      =      self.node_queue.pop(0		)

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

							UpperCAmelCase_  :				Union[str, Any]      =      self.get_successors(lowerCAmelCase_		)

							for node in successors:
										self.node_queue.append(lowerCAmelCase_		)

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





	def   _SCREAMING_SNAKE_CASE   (   self      :				Tuple			,			lowerCAmelCase_      :				Node		)		->    list[Node]:
				UpperCAmelCase_  :				Union[str, Any]      =      []
				for action in delta:
							UpperCAmelCase_  :				Optional[Any]      =      parent.pos_x + action[1]
							UpperCAmelCase_  :				Tuple      =      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			,			lowerCAmelCase_		)		)
				return successors





	def   _SCREAMING_SNAKE_CASE   (   self      :				Union[str, Any]			,			lowerCAmelCase_      :				Node | None		)		->    Path:
				UpperCAmelCase_  :				Any      =      node
				UpperCAmelCase_  :				Any      =      []
				while current_node is not None:
							path.append((current_node.pos_y, current_node.pos_x)		)
							UpperCAmelCase_  :				int      =      current_node.parent
				path.reverse()
				return path







class     UpperCamelCase_ :





	def __init__(   self      :				str			,			lowerCAmelCase_      :				int			,			lowerCAmelCase_      :				Tuple		)		->    Tuple:
				UpperCAmelCase_  :				Union[str, Any]      =      BreadthFirstSearch(lowerCAmelCase_			,			lowerCAmelCase_		)
				UpperCAmelCase_  :				Any      =      BreadthFirstSearch(lowerCAmelCase_			,			lowerCAmelCase_		)
				UpperCAmelCase_  :				Any      =      False





	def   _SCREAMING_SNAKE_CASE   (   self      :				List[Any]		)		->    Path | None:
				while self.fwd_bfs.node_queue or self.bwd_bfs.node_queue:
							UpperCAmelCase_  :				List[Any]      =      self.fwd_bfs.node_queue.pop(0		)
							UpperCAmelCase_  :				Tuple      =      self.bwd_bfs.node_queue.pop(0		)

							if current_bwd_node.pos == current_fwd_node.pos:
										UpperCAmelCase_  :				Optional[int]      =      True
										return self.retrace_bidirectional_path(
										    lowerCAmelCase_			,			lowerCAmelCase_		)

							UpperCAmelCase_  :				Any      =      current_bwd_node
							UpperCAmelCase_  :				Optional[int]      =      current_fwd_node

							UpperCAmelCase_  :				Any      =      {
							    self.fwd_bfs: self.fwd_bfs.get_successors(lowerCAmelCase_		),
							    self.bwd_bfs: self.bwd_bfs.get_successors(lowerCAmelCase_		),
							}

							for bfs in [self.fwd_bfs, self.bwd_bfs]:
										for node in successors[bfs]:
													bfs.node_queue.append(lowerCAmelCase_		)

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





	def   _SCREAMING_SNAKE_CASE   (   self      :				int			,			lowerCAmelCase_      :				Node			,			lowerCAmelCase_      :				Node		)		->    Path:
				UpperCAmelCase_  :				Tuple      =      self.fwd_bfs.retrace_path(lowerCAmelCase_		)
				UpperCAmelCase_  :				str      =      self.bwd_bfs.retrace_path(lowerCAmelCase_		)
				bwd_path.pop()
				bwd_path.reverse()
				UpperCAmelCase_  :				int      =      fwd_path + bwd_path
				return path


if __name__ == "__main__":
			# all coordinates are given in format [y,x]
			import doctest

			doctest.testmod()
			lowerCamelCase_       			=   (0, 0)
			lowerCamelCase_       			=   (len(grid) - 1, len(grid[0]) - 1)
			for elem in grid:
						print(elem)

			lowerCamelCase_       			=   time.time()
			lowerCamelCase_       			=   BreadthFirstSearch(init, goal)
			lowerCamelCase_       			=   bfs.search()
			lowerCamelCase_       			=   time.time() - start_bfs_time

			print('''Unidirectional BFS computation time : ''', bfs_time)

			lowerCamelCase_       			=   time.time()
			lowerCamelCase_       			=   BidirectionalBreadthFirstSearch(init, goal)
			lowerCamelCase_       			=   bd_bfs.search()
			lowerCamelCase_       			=   time.time() - start_bd_bfs_time

			print('''Bidirectional BFS computation time : ''', bd_bfs_time)



