




import argparse
import json
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    SegformerConfig,
    SegformerForImageClassification,
    SegformerForSemanticSegmentation,
    SegformerImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
UpperCamelCase								=					logging.get_logger(__name__)



def 							_A       (						lowerCAmelCase_					: List[Any]       ,    lowerCAmelCase_					: List[Any]=False      ):
     """simple docstring"""

     lowerCAmelCase__          =			OrderedDict()
     for key, value in state_dict.items():
          if encoder_only and not key.startswith("head"      ):
               lowerCAmelCase__          =			"segformer.encoder." + key
          if key.startswith("backbone"      ):
               lowerCAmelCase__          =			key.replace("backbone"       ,    "segformer.encoder"      )
          if "patch_embed" in key:
               # replace for example patch_embed1 by patch_embeddings.0
               lowerCAmelCase__          =			key[key.find("patch_embed"      ) + len("patch_embed"      )]
               lowerCAmelCase__          =			key.replace(F'patch_embed{idx}'       ,    F'patch_embeddings.{int(_lowerCAmelCase      )-1}'      )
          if "norm" in key:
               lowerCAmelCase__          =			key.replace("norm"       ,    "layer_norm"      )
          if "segformer.encoder.layer_norm" in key:
               # replace for example layer_norm1 by layer_norm.0
               lowerCAmelCase__          =			key[key.find("segformer.encoder.layer_norm"      ) + len("segformer.encoder.layer_norm"      )]
               lowerCAmelCase__          =			key.replace(F'layer_norm{idx}'       ,    F'layer_norm.{int(_lowerCAmelCase      )-1}'      )
          if "layer_norm1" in key:
               lowerCAmelCase__          =			key.replace("layer_norm1"       ,    "layer_norm_1"      )
          if "layer_norm2" in key:
               lowerCAmelCase__          =			key.replace("layer_norm2"       ,    "layer_norm_2"      )
          if "block" in key:
               # replace for example block1 by block.0
               lowerCAmelCase__          =			key[key.find("block"      ) + len("block"      )]
               lowerCAmelCase__          =			key.replace(F'block{idx}'       ,    F'block.{int(_lowerCAmelCase      )-1}'      )
          if "attn.q" in key:
               lowerCAmelCase__          =			key.replace("attn.q"       ,    "attention.self.query"      )
          if "attn.proj" in key:
               lowerCAmelCase__          =			key.replace("attn.proj"       ,    "attention.output.dense"      )
          if "attn" in key:
               lowerCAmelCase__          =			key.replace("attn"       ,    "attention.self"      )
          if "fc1" in key:
               lowerCAmelCase__          =			key.replace("fc1"       ,    "dense1"      )
          if "fc2" in key:
               lowerCAmelCase__          =			key.replace("fc2"       ,    "dense2"      )
          if "linear_pred" in key:
               lowerCAmelCase__          =			key.replace("linear_pred"       ,    "classifier"      )
          if "linear_fuse" in key:
               lowerCAmelCase__          =			key.replace("linear_fuse.conv"       ,    "linear_fuse"      )
               lowerCAmelCase__          =			key.replace("linear_fuse.bn"       ,    "batch_norm"      )
          if "linear_c" in key:
               # replace for example linear_c4 by linear_c.3
               lowerCAmelCase__          =			key[key.find("linear_c"      ) + len("linear_c"      )]
               lowerCAmelCase__          =			key.replace(F'linear_c{idx}'       ,    F'linear_c.{int(_lowerCAmelCase      )-1}'      )
          if key.startswith("head"      ):
               lowerCAmelCase__          =			key.replace("head"       ,    "classifier"      )
          lowerCAmelCase__          =			value

     return new_state_dict



def 							_A       (						lowerCAmelCase_					: List[Any]       ,    lowerCAmelCase_					: Any      ):
     """simple docstring"""

     for i in range(config.num_encoder_blocks      ):
          for j in range(config.depths[i]      ):
               # read in weights + bias of keys and values (which is a single matrix in the original implementation)
               lowerCAmelCase__          =			state_dict.pop(F'segformer.encoder.block.{i}.{j}.attention.self.kv.weight'      )
               lowerCAmelCase__          =			state_dict.pop(F'segformer.encoder.block.{i}.{j}.attention.self.kv.bias'      )
               # next, add keys and values (in that order) to the state dict
               lowerCAmelCase__          =			kv_weight[
                   : config.hidden_sizes[i], :
               ]
               lowerCAmelCase__          =			kv_bias[: config.hidden_sizes[i]]
               lowerCAmelCase__          =			kv_weight[
                   config.hidden_sizes[i] :, :
               ]
               lowerCAmelCase__          =			kv_bias[
                   config.hidden_sizes[i] :
               ]



def 							_A       (						):
     """simple docstring"""

     lowerCAmelCase__          =			"http://images.cocodataset.org/val2017/000000039769.jpg"
     lowerCAmelCase__          =			Image.open(requests.get(_lowerCAmelCase       ,    stream=_lowerCAmelCase      ).raw      )

     return image



@torch.no_grad()
def 							_A       (						lowerCAmelCase_					: List[str]       ,    lowerCAmelCase_					: int       ,    lowerCAmelCase_					: str      ):
     """simple docstring"""

     lowerCAmelCase__          =			SegformerConfig()
     lowerCAmelCase__          =			False

     # set attributes based on model_name
     lowerCAmelCase__          =			"huggingface/label-files"
     if "segformer" in model_name:
          lowerCAmelCase__          =			model_name[len("segformer."      ) : len("segformer."      ) + 2]
          if "ade" in model_name:
               lowerCAmelCase__          =			150
               lowerCAmelCase__          =			"ade20k-id2label.json"
               lowerCAmelCase__          =			(1, 150, 128, 128)
          elif "city" in model_name:
               lowerCAmelCase__          =			19
               lowerCAmelCase__          =			"cityscapes-id2label.json"
               lowerCAmelCase__          =			(1, 19, 128, 128)
          else:
               raise ValueError(F'Model {model_name} not supported'      )
     elif "mit" in model_name:
          lowerCAmelCase__          =			True
          lowerCAmelCase__          =			model_name[4:6]
          lowerCAmelCase__          =			1000
          lowerCAmelCase__          =			"imagenet-1k-id2label.json"
          lowerCAmelCase__          =			(1, 1000)
     else:
          raise ValueError(F'Model {model_name} not supported'      )

     # set config attributes
     lowerCAmelCase__          =			json.load(open(hf_hub_download(_lowerCAmelCase       ,    _lowerCAmelCase       ,    repo_type="dataset"      )       ,    "r"      )      )
     lowerCAmelCase__          =			{int(_lowerCAmelCase      ): v for k, v in idalabel.items()}
     lowerCAmelCase__          =			idalabel
     lowerCAmelCase__          =			{v: k for k, v in idalabel.items()}
     if size == "b0":
          pass
     elif size == "b1":
          lowerCAmelCase__          =			[64, 128, 320, 512]
          lowerCAmelCase__          =			256
     elif size == "b2":
          lowerCAmelCase__          =			[64, 128, 320, 512]
          lowerCAmelCase__          =			768
          lowerCAmelCase__          =			[3, 4, 6, 3]
     elif size == "b3":
          lowerCAmelCase__          =			[64, 128, 320, 512]
          lowerCAmelCase__          =			768
          lowerCAmelCase__          =			[3, 4, 18, 3]
     elif size == "b4":
          lowerCAmelCase__          =			[64, 128, 320, 512]
          lowerCAmelCase__          =			768
          lowerCAmelCase__          =			[3, 8, 27, 3]
     elif size == "b5":
          lowerCAmelCase__          =			[64, 128, 320, 512]
          lowerCAmelCase__          =			768
          lowerCAmelCase__          =			[3, 6, 40, 3]
     else:
          raise ValueError(F'Size {size} not supported'      )

     # load image processor (only resize + normalize)
     lowerCAmelCase__          =			SegformerImageProcessor(
         image_scale=(512, 512)       ,    keep_ratio=_lowerCAmelCase       ,    align=_lowerCAmelCase       ,    do_random_crop=_lowerCAmelCase      )

     # prepare image
     lowerCAmelCase__          =			prepare_img()
     lowerCAmelCase__          =			image_processor(images=_lowerCAmelCase       ,    return_tensors="pt"      ).pixel_values

     logger.info(F'Converting model {model_name}...'      )

     # load original state dict
     if encoder_only:
          lowerCAmelCase__          =			torch.load(_lowerCAmelCase       ,    map_location=torch.device("cpu"      )      )
     else:
          lowerCAmelCase__          =			torch.load(_lowerCAmelCase       ,    map_location=torch.device("cpu"      )      )["state_dict"]

     # rename keys
     lowerCAmelCase__          =			rename_keys(_lowerCAmelCase       ,    encoder_only=_lowerCAmelCase      )
     if not encoder_only:
          del state_dict["decode_head.conv_seg.weight"]
          del state_dict["decode_head.conv_seg.bias"]

     # key and value matrices need special treatment
     read_in_k_v(_lowerCAmelCase       ,    _lowerCAmelCase      )

     # create HuggingFace model and load state dict
     if encoder_only:
          lowerCAmelCase__          =			False
          lowerCAmelCase__          =			SegformerForImageClassification(_lowerCAmelCase      )
     else:
          lowerCAmelCase__          =			SegformerForSemanticSegmentation(_lowerCAmelCase      )
     model.load_state_dict(_lowerCAmelCase      )
     model.eval()

     # forward pass
     lowerCAmelCase__          =			model(_lowerCAmelCase      )
     lowerCAmelCase__          =			outputs.logits

     # set expected_slice based on model name
     # ADE20k checkpoints
     if model_name == "segformer.b0.512x512.ade.160k":
          lowerCAmelCase__          =			torch.tensor(
              [
                  [[-4.6310, -5.5232, -6.2356], [-5.1921, -6.1444, -6.5996], [-5.4424, -6.2790, -6.7574]],
                  [[-12.1391, -13.3122, -13.9554], [-12.8732, -13.9352, -14.3563], [-12.9438, -13.8226, -14.2513]],
                  [[-12.5134, -13.4686, -14.4915], [-12.8669, -14.4343, -14.7758], [-13.2523, -14.5819, -15.0694]],
              ]      )
     elif model_name == "segformer.b1.512x512.ade.160k":
          lowerCAmelCase__          =			torch.tensor(
              [
                  [[-7.5820, -8.7231, -8.3215], [-8.0600, -10.3529, -10.0304], [-7.5208, -9.4103, -9.6239]],
                  [[-12.6918, -13.8994, -13.7137], [-13.3196, -15.7523, -15.4789], [-12.9343, -14.8757, -14.9689]],
                  [[-11.1911, -11.9421, -11.3243], [-11.3342, -13.6839, -13.3581], [-10.3909, -12.1832, -12.4858]],
              ]      )
     elif model_name == "segformer.b2.512x512.ade.160k":
          lowerCAmelCase__          =			torch.tensor(
              [
                  [[-11.8173, -14.3850, -16.3128], [-14.5648, -16.5804, -18.6568], [-14.7223, -15.7387, -18.4218]],
                  [[-15.7290, -17.9171, -19.4423], [-18.3105, -19.9448, -21.4661], [-17.9296, -18.6497, -20.7910]],
                  [[-15.0783, -17.0336, -18.2789], [-16.8771, -18.6870, -20.1612], [-16.2454, -17.1426, -19.5055]],
              ]      )
     elif model_name == "segformer.b3.512x512.ade.160k":
          lowerCAmelCase__          =			torch.tensor(
              [
                  [[-9.0878, -10.2081, -10.1891], [-9.3144, -10.7941, -10.9843], [-9.2294, -10.3855, -10.5704]],
                  [[-12.2316, -13.9068, -13.6102], [-12.9161, -14.3702, -14.3235], [-12.5233, -13.7174, -13.7932]],
                  [[-14.6275, -15.2490, -14.9727], [-14.3400, -15.9687, -16.2827], [-14.1484, -15.4033, -15.8937]],
              ]      )
     elif model_name == "segformer.b4.512x512.ade.160k":
          lowerCAmelCase__          =			torch.tensor(
              [
                  [[-12.3144, -13.2447, -14.0802], [-13.3614, -14.5816, -15.6117], [-13.3340, -14.4433, -16.2219]],
                  [[-19.2781, -20.4128, -20.7506], [-20.6153, -21.6566, -22.0998], [-19.9800, -21.0430, -22.1494]],
                  [[-18.8739, -19.7804, -21.1834], [-20.1233, -21.6765, -23.2944], [-20.0315, -21.2641, -23.6944]],
              ]      )
     elif model_name == "segformer.b5.640x640.ade.160k":
          lowerCAmelCase__          =			torch.tensor(
              [
                  [[-9.5524, -12.0835, -11.7348], [-10.5229, -13.6446, -14.5662], [-9.5842, -12.8851, -13.9414]],
                  [[-15.3432, -17.5323, -17.0818], [-16.3330, -18.9255, -19.2101], [-15.1340, -17.7848, -18.3971]],
                  [[-12.6072, -14.9486, -14.6631], [-13.7629, -17.0907, -17.7745], [-12.7899, -16.1695, -17.1671]],
              ]      )
     # Cityscapes checkpoints
     elif model_name == "segformer.b0.1024x1024.city.160k":
          lowerCAmelCase__          =			torch.tensor(
              [
                  [[-11.9295, -13.4057, -14.8106], [-13.3431, -14.8179, -15.3781], [-14.2836, -15.5942, -16.1588]],
                  [[-11.4906, -12.8067, -13.6564], [-13.1189, -14.0500, -14.1543], [-13.8748, -14.5136, -14.8789]],
                  [[0.5374, 0.1067, -0.4742], [0.1141, -0.2255, -0.7099], [-0.3000, -0.5924, -1.3105]],
              ]      )
     elif model_name == "segformer.b0.512x1024.city.160k":
          lowerCAmelCase__          =			torch.tensor(
              [
                  [[-7.8217, -9.8767, -10.1717], [-9.4438, -10.9058, -11.4047], [-9.7939, -12.3495, -12.1079]],
                  [[-7.1514, -9.5336, -10.0860], [-9.7776, -11.6822, -11.8439], [-10.1411, -12.7655, -12.8972]],
                  [[0.3021, 0.0805, -0.2310], [-0.0328, -0.1605, -0.2714], [-0.1408, -0.5477, -0.6976]],
              ]      )
     elif model_name == "segformer.b0.640x1280.city.160k":
          lowerCAmelCase__          =			torch.tensor(
              [
                  [
                      [-1.1_3_7_2E0_1, -1.2_7_8_7E0_1, -1.3_4_7_7E0_1],
                      [-1.2_5_3_6E0_1, -1.4_1_9_4E0_1, -1.4_4_0_9E0_1],
                      [-1.3_2_1_7E0_1, -1.4_8_8_8E0_1, -1.5_3_2_7E0_1],
                  ],
                  [
                      [-1.4_7_9_1E0_1, -1.7_1_2_2E0_1, -1.8_2_7_7E0_1],
                      [-1.7_1_6_3E0_1, -1.9_1_9_2E0_1, -1.9_5_3_3E0_1],
                      [-1.7_8_9_7E0_1, -1.9_9_9_1E0_1, -2.0_3_1_5E0_1],
                  ],
                  [
                      [7.6_7_2_3E-0_1, 4.1_9_2_1E-0_1, -7.7_8_7_8E-0_2],
                      [4.7_7_7_2E-0_1, 9.5_5_5_7E-0_3, -2.8_0_8_2E-0_1],
                      [3.6_0_3_2E-0_1, -2.4_8_2_6E-0_1, -5.1_1_6_8E-0_1],
                  ],
              ]      )
     elif model_name == "segformer.b0.768x768.city.160k":
          lowerCAmelCase__          =			torch.tensor(
              [
                  [[-9.4959, -11.3087, -11.7479], [-11.0025, -12.6540, -12.3319], [-11.4064, -13.0487, -12.9905]],
                  [[-9.8905, -11.3084, -12.0854], [-11.1726, -12.7698, -12.9583], [-11.5985, -13.3278, -14.1774]],
                  [[0.2213, 0.0192, -0.2466], [-0.1731, -0.4213, -0.4874], [-0.3126, -0.6541, -1.1389]],
              ]      )
     elif model_name == "segformer.b1.1024x1024.city.160k":
          lowerCAmelCase__          =			torch.tensor(
              [
                  [[-13.5748, -13.9111, -12.6500], [-14.3500, -15.3683, -14.2328], [-14.7532, -16.0424, -15.6087]],
                  [[-17.1651, -15.8725, -12.9653], [-17.2580, -17.3718, -14.8223], [-16.6058, -16.8783, -16.7452]],
                  [[-3.6456, -3.0209, -1.4203], [-3.0797, -3.1959, -2.0000], [-1.8757, -1.9217, -1.6997]],
              ]      )
     elif model_name == "segformer.b2.1024x1024.city.160k":
          lowerCAmelCase__          =			torch.tensor(
              [
                  [[-16.0976, -16.4856, -17.3962], [-16.6234, -19.0342, -19.7685], [-16.0900, -18.0661, -19.1180]],
                  [[-18.4750, -18.8488, -19.5074], [-19.4030, -22.1570, -22.5977], [-19.1191, -20.8486, -22.3783]],
                  [[-4.5178, -5.5037, -6.5109], [-5.0884, -7.2174, -8.0334], [-4.4156, -5.8117, -7.2970]],
              ]      )
     elif model_name == "segformer.b3.1024x1024.city.160k":
          lowerCAmelCase__          =			torch.tensor(
              [
                  [[-14.2081, -14.4732, -14.1977], [-14.5867, -16.4423, -16.6356], [-13.4441, -14.9685, -16.8696]],
                  [[-14.4576, -14.7073, -15.0451], [-15.0816, -17.6237, -17.9873], [-14.4213, -16.0199, -18.5992]],
                  [[-4.7349, -4.9588, -5.0966], [-4.3210, -6.9325, -7.2591], [-3.4312, -4.7484, -7.1917]],
              ]      )
     elif model_name == "segformer.b4.1024x1024.city.160k":
          lowerCAmelCase__          =			torch.tensor(
              [
                  [[-11.7737, -11.9526, -11.3273], [-13.6692, -14.4574, -13.8878], [-13.8937, -14.6924, -15.9345]],
                  [[-14.6706, -14.5330, -14.1306], [-16.1502, -16.8180, -16.4269], [-16.8338, -17.8939, -20.1746]],
                  [[1.0491, 0.8289, 1.0310], [1.1044, 0.5219, 0.8055], [1.0899, 0.6926, 0.5590]],
              ]      )
     elif model_name == "segformer.b5.1024x1024.city.160k":
          lowerCAmelCase__          =			torch.tensor(
              [
                  [[-12.5641, -13.4777, -13.0684], [-13.9587, -15.8983, -16.6557], [-13.3109, -15.7350, -16.3141]],
                  [[-14.7074, -15.4352, -14.5944], [-16.6353, -18.1663, -18.6120], [-15.1702, -18.0329, -18.1547]],
                  [[-1.7990, -2.0951, -1.7784], [-2.6397, -3.8245, -3.9686], [-1.5264, -2.8126, -2.9316]],
              ]      )
     else:
          lowerCAmelCase__          =			logits.argmax(-1      ).item()
          print("Predicted class:"       ,    model.config.idalabel[predicted_class_idx]      )

     # verify logits
     if not encoder_only:
          assert logits.shape == expected_shape
          assert torch.allclose(logits[0, :3, :3, :3]       ,    _lowerCAmelCase       ,    atol=1E-2      )

     # finally, save model and image processor
     logger.info(F'Saving PyTorch model and image processor to {pytorch_dump_folder_path}...'      )
     Path(_lowerCAmelCase      ).mkdir(exist_ok=_lowerCAmelCase      )
     model.save_pretrained(_lowerCAmelCase      )
     image_processor.save_pretrained(_lowerCAmelCase      )


if __name__ == "__main__":
     UpperCamelCase								=					argparse.ArgumentParser()

     parser.add_argument(
         '--model_name',
         default='segformer.b0.512x512.ade.160k',
         type=str,
         help='Name of the model you\'d like to convert.',
     )
     parser.add_argument(
         '--checkpoint_path', default=None, type=str, help='Path to the original PyTorch checkpoint (.pth file).'
     )
     parser.add_argument(
         '--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
     )
     UpperCamelCase								=					parser.parse_args()
     convert_segformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)






from __future__ import annotations

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



def 							_A       (						lowerCAmelCase_					: list[list[int]]       ,    lowerCAmelCase_					: list[int]       ,    lowerCAmelCase_					: list[int]       ,    lowerCAmelCase_					: int       ,    lowerCAmelCase_					: list[list[int]]       ,    ):
     """simple docstring"""

     lowerCAmelCase__          =			[
         [0 for col in range(len(grid[0]      )      )] for row in range(len(lowerCAmelCase_      )      )
     ]  # the reference grid
     lowerCAmelCase__          =			1
     lowerCAmelCase__          =			[
         [0 for col in range(len(grid[0]      )      )] for row in range(len(lowerCAmelCase_      )      )
     ]  # the action grid

     lowerCAmelCase__          =			init[0]
     lowerCAmelCase__          =			init[1]
     lowerCAmelCase__          =			0
     lowerCAmelCase__          =			g + heuristic[x][y]  # cost from starting cell to destination cell
     lowerCAmelCase__          =			[[f, g, x, y]]

     lowerCAmelCase__          =			False  # flag that is set when search is complete
     lowerCAmelCase__          =			False  # flag set if we can't find expand

     while not found and not resign:
          if len(lowerCAmelCase_      ) == 0:
               raise ValueError("Algorithm is unable to find solution"      )
          else:  # to choose the least costliest action so as to move closer to the goal
               cell.sort()
               cell.reverse()
               lowerCAmelCase__          =			cell.pop()
               lowerCAmelCase__          =			next_cell[2]
               lowerCAmelCase__          =			next_cell[3]
               lowerCAmelCase__          =			next_cell[1]

               if x == goal[0] and y == goal[1]:
                    lowerCAmelCase__          =			True
               else:
                    for i in range(len(lowerCAmelCase_      )      ):  # to try out different valid actions
                         lowerCAmelCase__          =			x + DIRECTIONS[i][0]
                         lowerCAmelCase__          =			y + DIRECTIONS[i][1]
                         if xa >= 0 and xa < len(lowerCAmelCase_      ) and ya >= 0 and ya < len(grid[0]      ):
                              if closed[xa][ya] == 0 and grid[xa][ya] == 0:
                                   lowerCAmelCase__          =			g + cost
                                   lowerCAmelCase__          =			ga + heuristic[xa][ya]
                                   cell.append([fa, ga, xa, ya]      )
                                   lowerCAmelCase__          =			1
                                   lowerCAmelCase__          =			i
     lowerCAmelCase__          =			[]
     lowerCAmelCase__          =			goal[0]
     lowerCAmelCase__          =			goal[1]
     invpath.append([x, y]      )  # we get the reverse path from here
     while x != init[0] or y != init[1]:
          lowerCAmelCase__          =			x - DIRECTIONS[action[x][y]][0]
          lowerCAmelCase__          =			y - DIRECTIONS[action[x][y]][1]
          lowerCAmelCase__          =			xa
          lowerCAmelCase__          =			ya
          invpath.append([x, y]      )

     lowerCAmelCase__          =			[]
     for i in range(len(lowerCAmelCase_      )      ):
          path.append(invpath[len(lowerCAmelCase_      ) - 1 - i]      )
     return path, action


if __name__ == "__main__":
     UpperCamelCase								=					[
         [0, 1, 0, 0, 0, 0],
         [0, 1, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
         [0, 1, 0, 0, 0, 0],
         [0, 1, 0, 0, 1, 0],
         [0, 0, 0, 0, 1, 0],
     ]

     UpperCamelCase								=					[0, 0]
     # all coordinates are given in format [y,x]
     UpperCamelCase								=					[len(grid) - 1, len(grid[0]) - 1]
     UpperCamelCase								=					1

     # the cost map which pushes the path closer to the goal
     UpperCamelCase								=					[[0 for row in range(len(grid[0]))] for col in range(len(grid))]
     for i in range(len(grid)):
          for j in range(len(grid[0])):
               UpperCamelCase								=					abs(i - goal[0]) + abs(j - goal[1])
               if grid[i][j] == 1:
                    # added extra penalty in the heuristic map
                    UpperCamelCase								=					99

     UpperCamelCase						,  UpperCamelCase								=					search(grid, init, goal, cost, heuristic)

     print('ACTION MAP')
     for i in range(len(action)):
          print(action[i])

     for i in range(len(path)):
          print(path[i])
