

from heapq import heappop, heappush

import numpy as np



def       __lowerCAmelCase      (						A       ,     A       ,     A       ,     A       ,     ):
	UpperCAmelCase_				,   UpperCAmelCase_       				=			grid.shape
	UpperCAmelCase_       				=			[-1, 1, 0, 0]
	UpperCAmelCase_       				=			[0, 0, -1, 1]
	if allow_diagonal:
		dx += [-1, -1, 1, 1]
		dy += [-1, 1, -1, 1]

	UpperCAmelCase_				,   UpperCAmelCase_       				=			[(0, source)], set()
	UpperCAmelCase_       				=			np.full((rows, cols)       ,     np.inf    )
	UpperCAmelCase_       				=			0
	UpperCAmelCase_       				=			np.empty((rows, cols)       ,     dtype=A    )
	UpperCAmelCase_       				=			None

	while queue:
		((UpperCAmelCase_)				,   (UpperCAmelCase_))       				=			heappop(A    )
		if (x, y) in visited:
			continue
		visited.add((x, y)    )

		if (x, y) == destination:
			UpperCAmelCase_       				=			[]
			while (x, y) != source:
				path.append((x, y)    )
				UpperCAmelCase_				,   UpperCAmelCase_       				=			predecessors[x, y]
			path.append(A    )  # add the source manually
			path.reverse()
			return matrix[destination], path

		for i in range(len(A    )    ):
			UpperCAmelCase_				,   UpperCAmelCase_       				=			x + dx[i], y + dy[i]
			if 0 <= nx < rows and 0 <= ny < cols:
				UpperCAmelCase_       				=			grid[nx][ny]
				if next_node == 1 and matrix[nx, ny] > dist + 1:
					heappush(A       ,     (dist + 1, (nx, ny))    )
					UpperCAmelCase_       				=			dist + 1
					UpperCAmelCase_       				=			(x, y)

	return np.inf, []


if __name__ == "__main__":
				import doctest

				doctest.testmod()









import importlib

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



def       __lowerCAmelCase      (						A       ,     A=False    ):
	UpperCAmelCase_       				=			OmegaConf.load(A    )
	if display:
		print(yaml.dump(OmegaConf.to_container(A    )    )    )
	return config



def       __lowerCAmelCase      (						A       ,     A=None       ,     A=None    ):
	if conf_path is None:
		UpperCAmelCase_       				=			"./model_checkpoints/vqgan_only.yaml"
	UpperCAmelCase_       				=			load_config(A       ,     display=A    )
	UpperCAmelCase_       				=			VQModel(**config.model.params    )
	if ckpt_path is None:
		UpperCAmelCase_       				=			"./model_checkpoints/vqgan_only.pt"
	UpperCAmelCase_       				=			torch.load(A       ,     map_location=A    )
	if ".ckpt" in ckpt_path:
		UpperCAmelCase_       				=			sd["state_dict"]
	model.load_state_dict(A       ,     strict=A    )
	model.to(A    )
	del sd
	return model



def       __lowerCAmelCase      (						A       ,     A    ):
	UpperCAmelCase_				,   UpperCAmelCase_				,   UpperCAmelCase_       				=			model.encode(A    )
	print(F"VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}"    )
	UpperCAmelCase_       				=			model.decode(A    )
	return xrec



def       __lowerCAmelCase      (						A       ,     A=False    ):
	UpperCAmelCase_				,   UpperCAmelCase_       				=			string.rsplit("."       ,     1    )
	if reload:
		UpperCAmelCase_       				=			importlib.import_module(A    )
		importlib.reload(A    )
	return getattr(importlib.import_module(A       ,     package=A    )       ,     cls    )



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



def       __lowerCAmelCase      (						A       ,     A       ,     A=True       ,     A=True    ):
	UpperCAmelCase_       				=			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       __lowerCAmelCase      (						A       ,     A       ,     A       ,     A    ):
	# load the specified checkpoint
	if ckpt:
		UpperCAmelCase_       				=			torch.load(A       ,     map_location="cpu"    )
		UpperCAmelCase_       				=			pl_sd["global_step"]
		print(F"loaded model from global step {global_step}."    )
	else:
		UpperCAmelCase_       				=			{"state_dict": None}
		UpperCAmelCase_       				=			None
	UpperCAmelCase_       				=			load_model_from_config(config.model       ,     pl_sd["state_dict"]       ,     gpu=A       ,     eval_mode=A    )["model"]
	return model, global_step