





import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image

from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
_A   :       int    					=    logging.get_logger(__name__)



def 			_a		(       UpperCAmelCase	)							->							List[str]:







   """simple docstring"""
   lowerCamelCase__  :		Tuple     =				SwinConfig(
       embed_dim=192     ,    depths=(2, 2, 18, 2)     ,    num_heads=(6, 12, 24, 48)     ,    window_size=12     ,    out_features=['''stage2''', '''stage3''', '''stage4''']     ,    )

   lowerCamelCase__  :		int     =				DetaConfig(
       backbone_config=_A     ,    num_queries=900     ,    encoder_ffn_dim=2048     ,    decoder_ffn_dim=2048     ,    num_feature_levels=5     ,    assign_first_stage=_A     ,    with_box_refine=_A     ,    two_stage=_A     ,    )

   # set labels
   lowerCamelCase__  :		Optional[int]     =				"""huggingface/label-files"""
   if "o365" in model_name:
      lowerCamelCase__  :		str     =				366
      lowerCamelCase__  :		str     =				"""object365-id2label.json"""
   else:
      lowerCamelCase__  :		List[str]     =				91
      lowerCamelCase__  :		int     =				"""coco-detection-id2label.json"""

   lowerCamelCase__  :		Dict     =				num_labels
   lowerCamelCase__  :		str     =				json.load(open(cached_download(hf_hub_url(_A     ,    _A     ,    repo_type='''dataset'''	)	)     ,    '''r'''	)	)
   lowerCamelCase__  :		List[str]     =				{int(_A	): v for k, v in idalabel.items()}
   lowerCamelCase__  :		Dict     =				idalabel
   lowerCamelCase__  :		int     =				{v: k for k, v in idalabel.items()}

   return config



def 			_a		(       UpperCAmelCase	)							->							int:







   """simple docstring"""
   lowerCamelCase__  :		Tuple     =				[]

   # stem
   # fmt: off
   rename_keys.append(('''backbone.0.body.patch_embed.proj.weight''', '''model.backbone.model.embeddings.patch_embeddings.projection.weight''')	)
   rename_keys.append(('''backbone.0.body.patch_embed.proj.bias''', '''model.backbone.model.embeddings.patch_embeddings.projection.bias''')	)
   rename_keys.append(('''backbone.0.body.patch_embed.norm.weight''', '''model.backbone.model.embeddings.norm.weight''')	)
   rename_keys.append(('''backbone.0.body.patch_embed.norm.bias''', '''model.backbone.model.embeddings.norm.bias''')	)
   # stages
   for i in range(len(config.backbone_config.depths	)	):
      for j in range(config.backbone_config.depths[i]	):
         rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.norm1.weight", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight")	)
         rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.norm1.bias", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias")	)
         rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_bias_table", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table")	)
         rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_index", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index")	)
         rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.attn.proj.weight", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight")	)
         rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.attn.proj.bias", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias")	)
         rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.norm2.weight", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight")	)
         rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.norm2.bias", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias")	)
         rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.weight", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight")	)
         rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.bias", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias")	)
         rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.weight", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.weight")	)
         rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.bias", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.bias")	)

      if i < 3:
         rename_keys.append((f"backbone.0.body.layers.{i}.downsample.reduction.weight", f"model.backbone.model.encoder.layers.{i}.downsample.reduction.weight")	)
         rename_keys.append((f"backbone.0.body.layers.{i}.downsample.norm.weight", f"model.backbone.model.encoder.layers.{i}.downsample.norm.weight")	)
         rename_keys.append((f"backbone.0.body.layers.{i}.downsample.norm.bias", f"model.backbone.model.encoder.layers.{i}.downsample.norm.bias")	)

   rename_keys.append(('''backbone.0.body.norm1.weight''', '''model.backbone.model.hidden_states_norms.stage2.weight''')	)
   rename_keys.append(('''backbone.0.body.norm1.bias''', '''model.backbone.model.hidden_states_norms.stage2.bias''')	)
   rename_keys.append(('''backbone.0.body.norm2.weight''', '''model.backbone.model.hidden_states_norms.stage3.weight''')	)
   rename_keys.append(('''backbone.0.body.norm2.bias''', '''model.backbone.model.hidden_states_norms.stage3.bias''')	)
   rename_keys.append(('''backbone.0.body.norm3.weight''', '''model.backbone.model.hidden_states_norms.stage4.weight''')	)
   rename_keys.append(('''backbone.0.body.norm3.bias''', '''model.backbone.model.hidden_states_norms.stage4.bias''')	)

   # transformer encoder
   for i in range(config.encoder_layers	):
      rename_keys.append((f"transformer.encoder.layers.{i}.self_attn.sampling_offsets.weight", f"model.encoder.layers.{i}.self_attn.sampling_offsets.weight")	)
      rename_keys.append((f"transformer.encoder.layers.{i}.self_attn.sampling_offsets.bias", f"model.encoder.layers.{i}.self_attn.sampling_offsets.bias")	)
      rename_keys.append((f"transformer.encoder.layers.{i}.self_attn.attention_weights.weight", f"model.encoder.layers.{i}.self_attn.attention_weights.weight")	)
      rename_keys.append((f"transformer.encoder.layers.{i}.self_attn.attention_weights.bias", f"model.encoder.layers.{i}.self_attn.attention_weights.bias")	)
      rename_keys.append((f"transformer.encoder.layers.{i}.self_attn.value_proj.weight", f"model.encoder.layers.{i}.self_attn.value_proj.weight")	)
      rename_keys.append((f"transformer.encoder.layers.{i}.self_attn.value_proj.bias", f"model.encoder.layers.{i}.self_attn.value_proj.bias")	)
      rename_keys.append((f"transformer.encoder.layers.{i}.self_attn.output_proj.weight", f"model.encoder.layers.{i}.self_attn.output_proj.weight")	)
      rename_keys.append((f"transformer.encoder.layers.{i}.self_attn.output_proj.bias", f"model.encoder.layers.{i}.self_attn.output_proj.bias")	)
      rename_keys.append((f"transformer.encoder.layers.{i}.norm1.weight", f"model.encoder.layers.{i}.self_attn_layer_norm.weight")	)
      rename_keys.append((f"transformer.encoder.layers.{i}.norm1.bias", f"model.encoder.layers.{i}.self_attn_layer_norm.bias")	)
      rename_keys.append((f"transformer.encoder.layers.{i}.linear1.weight", f"model.encoder.layers.{i}.fc1.weight")	)
      rename_keys.append((f"transformer.encoder.layers.{i}.linear1.bias", f"model.encoder.layers.{i}.fc1.bias")	)
      rename_keys.append((f"transformer.encoder.layers.{i}.linear2.weight", f"model.encoder.layers.{i}.fc2.weight")	)
      rename_keys.append((f"transformer.encoder.layers.{i}.linear2.bias", f"model.encoder.layers.{i}.fc2.bias")	)
      rename_keys.append((f"transformer.encoder.layers.{i}.norm2.weight", f"model.encoder.layers.{i}.final_layer_norm.weight")	)
      rename_keys.append((f"transformer.encoder.layers.{i}.norm2.bias", f"model.encoder.layers.{i}.final_layer_norm.bias")	)

   # transformer decoder
   for i in range(config.decoder_layers	):
      rename_keys.append((f"transformer.decoder.layers.{i}.cross_attn.sampling_offsets.weight", f"model.decoder.layers.{i}.encoder_attn.sampling_offsets.weight")	)
      rename_keys.append((f"transformer.decoder.layers.{i}.cross_attn.sampling_offsets.bias", f"model.decoder.layers.{i}.encoder_attn.sampling_offsets.bias")	)
      rename_keys.append((f"transformer.decoder.layers.{i}.cross_attn.attention_weights.weight", f"model.decoder.layers.{i}.encoder_attn.attention_weights.weight")	)
      rename_keys.append((f"transformer.decoder.layers.{i}.cross_attn.attention_weights.bias", f"model.decoder.layers.{i}.encoder_attn.attention_weights.bias")	)
      rename_keys.append((f"transformer.decoder.layers.{i}.cross_attn.value_proj.weight", f"model.decoder.layers.{i}.encoder_attn.value_proj.weight")	)
      rename_keys.append((f"transformer.decoder.layers.{i}.cross_attn.value_proj.bias", f"model.decoder.layers.{i}.encoder_attn.value_proj.bias")	)
      rename_keys.append((f"transformer.decoder.layers.{i}.cross_attn.output_proj.weight", f"model.decoder.layers.{i}.encoder_attn.output_proj.weight")	)
      rename_keys.append((f"transformer.decoder.layers.{i}.cross_attn.output_proj.bias", f"model.decoder.layers.{i}.encoder_attn.output_proj.bias")	)
      rename_keys.append((f"transformer.decoder.layers.{i}.norm1.weight", f"model.decoder.layers.{i}.encoder_attn_layer_norm.weight")	)
      rename_keys.append((f"transformer.decoder.layers.{i}.norm1.bias", f"model.decoder.layers.{i}.encoder_attn_layer_norm.bias")	)
      rename_keys.append((f"transformer.decoder.layers.{i}.self_attn.out_proj.weight", f"model.decoder.layers.{i}.self_attn.out_proj.weight")	)
      rename_keys.append((f"transformer.decoder.layers.{i}.self_attn.out_proj.bias", f"model.decoder.layers.{i}.self_attn.out_proj.bias")	)
      rename_keys.append((f"transformer.decoder.layers.{i}.norm2.weight", f"model.decoder.layers.{i}.self_attn_layer_norm.weight")	)
      rename_keys.append((f"transformer.decoder.layers.{i}.norm2.bias", f"model.decoder.layers.{i}.self_attn_layer_norm.bias")	)
      rename_keys.append((f"transformer.decoder.layers.{i}.linear1.weight", f"model.decoder.layers.{i}.fc1.weight")	)
      rename_keys.append((f"transformer.decoder.layers.{i}.linear1.bias", f"model.decoder.layers.{i}.fc1.bias")	)
      rename_keys.append((f"transformer.decoder.layers.{i}.linear2.weight", f"model.decoder.layers.{i}.fc2.weight")	)
      rename_keys.append((f"transformer.decoder.layers.{i}.linear2.bias", f"model.decoder.layers.{i}.fc2.bias")	)
      rename_keys.append((f"transformer.decoder.layers.{i}.norm3.weight", f"model.decoder.layers.{i}.final_layer_norm.weight")	)
      rename_keys.append((f"transformer.decoder.layers.{i}.norm3.bias", f"model.decoder.layers.{i}.final_layer_norm.bias")	)

   # fmt: on

   return rename_keys



def 			_a		(       UpperCAmelCase     ,    UpperCAmelCase     ,    UpperCAmelCase	)							->							Any:







   """simple docstring"""
   lowerCamelCase__  :		Any     =				dct.pop(_A	)
   lowerCamelCase__  :		Optional[int]     =				val



def 			_a		(       UpperCAmelCase     ,    UpperCAmelCase	)							->							List[Any]:







   """simple docstring"""
   lowerCamelCase__  :		List[str]     =				[int(backbone_config.embed_dim * 2**i	) for i in range(len(backbone_config.depths	)	)]
   for i in range(len(backbone_config.depths	)	):
      lowerCamelCase__  :		Tuple     =				num_features[i]
      for j in range(backbone_config.depths[i]	):
         # fmt: off
         # read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
         lowerCamelCase__  :		Dict     =				state_dict.pop(f"backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.weight"	)
         lowerCamelCase__  :		List[Any]     =				state_dict.pop(f"backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.bias"	)
         # next, add query, keys and values (in that order) to the state dict
         lowerCamelCase__  :		str     =				in_proj_weight[:dim, :]
         lowerCamelCase__  :		List[Any]     =				in_proj_bias[: dim]
         lowerCamelCase__  :		Tuple     =				in_proj_weight[
             dim : dim * 2, :
         ]
         lowerCamelCase__  :		Tuple     =				in_proj_bias[
             dim : dim * 2
         ]
         lowerCamelCase__  :		Union[str, Any]     =				in_proj_weight[
             -dim :, :
         ]
         lowerCamelCase__  :		Tuple     =				in_proj_bias[-dim :]
         # fmt: on



def 			_a		(       UpperCAmelCase     ,    UpperCAmelCase	)							->							Optional[Any]:







   """simple docstring"""
   # transformer decoder self-attention layers
   lowerCamelCase__  :		Optional[int]     =				config.d_model
   for i in range(config.decoder_layers	):
      # read in weights + bias of input projection layer of self-attention
      lowerCamelCase__  :		str     =				state_dict.pop(f"transformer.decoder.layers.{i}.self_attn.in_proj_weight"	)
      lowerCamelCase__  :		str     =				state_dict.pop(f"transformer.decoder.layers.{i}.self_attn.in_proj_bias"	)
      # next, add query, keys and values (in that order) to the state dict
      lowerCamelCase__  :		Dict     =				in_proj_weight[:hidden_size, :]
      lowerCamelCase__  :		List[Any]     =				in_proj_bias[:hidden_size]
      lowerCamelCase__  :		Any     =				in_proj_weight[
          hidden_size : hidden_size * 2, :
      ]
      lowerCamelCase__  :		List[Any]     =				in_proj_bias[hidden_size : hidden_size * 2]
      lowerCamelCase__  :		Dict     =				in_proj_weight[-hidden_size:, :]
      lowerCamelCase__  :		Union[str, Any]     =				in_proj_bias[-hidden_size:]



def 			_a		(       )							->							Tuple:







   """simple docstring"""
   lowerCamelCase__  :		Union[str, Any]     =				"""http://images.cocodataset.org/val2017/000000039769.jpg"""
   lowerCamelCase__  :		Optional[Any]     =				Image.open(requests.get(_A     ,    stream=_A	).raw	)

   return im



@torch.no_grad()
def 			_a		(       UpperCAmelCase     ,    UpperCAmelCase     ,    UpperCAmelCase	)							->							Optional[Any]:







   """simple docstring"""
   lowerCamelCase__  :		Any     =				get_deta_config(_A	)

   # load original state dict
   if model_name == "deta-swin-large":
      lowerCamelCase__  :		List[Any]     =				hf_hub_download(repo_id='''nielsr/deta-checkpoints'''     ,    filename='''adet_swin_ft.pth'''	)
   elif model_name == "deta-swin-large-o365":
      lowerCamelCase__  :		str     =				hf_hub_download(repo_id='''jozhang97/deta-swin-l-o365'''     ,    filename='''deta_swin_pt_o365.pth'''	)
   else:
      raise ValueError(f"Model name {model_name} not supported"	)

   lowerCamelCase__  :		Optional[int]     =				torch.load(_A     ,    map_location='''cpu'''	)["""model"""]

   # original state dict
   for name, param in state_dict.items():
      print(_A     ,    param.shape	)

   # rename keys
   lowerCamelCase__  :		Any     =				create_rename_keys(_A	)
   for src, dest in rename_keys:
      rename_key(_A     ,    _A     ,    _A	)
   read_in_swin_q_k_v(_A     ,    config.backbone_config	)
   read_in_decoder_q_k_v(_A     ,    _A	)

   # fix some prefixes
   for key in state_dict.copy().keys():
      if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
         lowerCamelCase__  :		List[str]     =				state_dict.pop(_A	)
         lowerCamelCase__  :		Optional[Any]     =				val
      if "input_proj" in key:
         lowerCamelCase__  :		Tuple     =				state_dict.pop(_A	)
         lowerCamelCase__  :		Dict     =				val
      if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
         lowerCamelCase__  :		Optional[Any]     =				state_dict.pop(_A	)
         lowerCamelCase__  :		Tuple     =				val

    # finally, create HuggingFace model and load state dict
   lowerCamelCase__  :		str     =				DetaForObjectDetection(_A	)
   model.load_state_dict(_A	)
   model.eval()

   lowerCamelCase__  :		int     =				"""cuda""" if torch.cuda.is_available() else """cpu"""
   model.to(_A	)

   # load image processor
   lowerCamelCase__  :		Dict     =				DetaImageProcessor(format='''coco_detection'''	)

   # verify our conversion on image
   lowerCamelCase__  :		List[Any]     =				prepare_img()
   lowerCamelCase__  :		Optional[int]     =				processor(images=_A     ,    return_tensors='''pt'''	)
   lowerCamelCase__  :		Any     =				encoding["""pixel_values"""]
   lowerCamelCase__  :		Dict     =				model(pixel_values.to(_A	)	)

   # verify logits
   print('''Logits:'''     ,    outputs.logits[0, :3, :3]	)
   print('''Boxes:'''     ,    outputs.pred_boxes[0, :3, :3]	)
   if model_name == "deta-swin-large":
      lowerCamelCase__  :		List[Any]     =				torch.tensor(
          [[-7.63_08, -2.84_85, -5.37_37], [-7.20_37, -4.55_05, -4.80_27], [-7.29_43, -4.26_11, -4.66_17]]	)
      lowerCamelCase__  :		Dict     =				torch.tensor([[0.49_87, 0.49_69, 0.99_99], [0.25_49, 0.54_98, 0.48_05], [0.54_98, 0.27_57, 0.05_69]]	)
   elif model_name == "deta-swin-large-o365":
      lowerCamelCase__  :		Dict     =				torch.tensor(
          [[-8.01_22, -3.57_20, -4.97_17], [-8.15_47, -3.68_86, -4.63_89], [-7.66_10, -3.61_94, -5.01_34]]	)
      lowerCamelCase__  :		Tuple     =				torch.tensor([[0.25_23, 0.55_49, 0.48_81], [0.77_15, 0.41_49, 0.46_01], [0.55_03, 0.27_53, 0.05_75]]	)
   assert torch.allclose(outputs.logits[0, :3, :3]     ,    expected_logits.to(_A	)     ,    atol=1E-4	)
   assert torch.allclose(outputs.pred_boxes[0, :3, :3]     ,    expected_boxes.to(_A	)     ,    atol=1E-4	)
   print('''Everything ok!'''	)

   if pytorch_dump_folder_path:
      # Save model and processor
      logger.info(f"Saving PyTorch model and processor to {pytorch_dump_folder_path}..."	)
      Path(_A	).mkdir(exist_ok=_A	)
      model.save_pretrained(_A	)
      processor.save_pretrained(_A	)

   # Push to hub
   if push_to_hub:
      print('''Pushing model and processor to hub...'''	)
      model.push_to_hub(f"jozhang97/{model_name}"	)
      processor.push_to_hub(f"jozhang97/{model_name}"	)


if __name__ == "__main__":
       _A   :       Union[str, Any]    					=    argparse.ArgumentParser()

       parser.add_argument(
           '--model_name',
           type=str,
           default='deta-swin-large',
           choices=['deta-swin-large', 'deta-swin-large-o365'],
           help='Name of the model you\'d like to convert.',
       )
       parser.add_argument(
           '--pytorch_dump_folder_path',
           default=None,
           type=str,
           help='Path to the folder to output PyTorch model.',
       )
       parser.add_argument(
           '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
       )
       _A   :       Any    					=    parser.parse_args()
       convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)











"""simple docstring"""







import numpy as np


class      UpperCamelCase		:






						def __init__(		self  :List[Any]				)      ->Optional[Any]:
										lowercase    :      Dict       = (0, 0)
										lowercase    :      str       = None
										lowercase    :      Any       = 0
										lowercase    :      List[Any]       = 0
										lowercase    :      Union[str, Any]       = 0






						def __eq__(		self  :List[Any]			, __magic_name__  :Optional[Any]				)      ->Tuple:
										return self.position == cell.position






						def      __snake_case      (		self  :Any				)      ->List[Any]:
										print(self.position				)


class      UpperCamelCase		:






						def __init__(		self  :Any			, __magic_name__  :int=(5, 5)				)      ->Dict:
										lowercase    :      Tuple       = np.zeros(__magic_name__				)
										lowercase    :      List[str]       = world_size[0]
										lowercase    :      str       = world_size[1]






						def      __snake_case      (		self  :str				)      ->int:
										print(self.w				)






						def      __snake_case      (		self  :Any			, __magic_name__  :Any				)      ->Optional[int]:
										lowercase    :      Optional[Any]       = [
										    (-1, -1),
										    (-1, 0),
										    (-1, 1),
										    (0, -1),
										    (0, 1),
										    (1, -1),
										    (1, 0),
										    (1, 1),
										]
										lowercase    :      List[str]       = cell.position[0]
										lowercase    :      List[str]       = cell.position[1]
										lowercase    :      Optional[int]       = []
										for n in neughbour_cord:
														lowercase    :      List[Any]       = current_x + n[0]
														lowercase    :      List[Any]       = current_y + n[1]
														if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
																		lowercase    :      Tuple       = Cell()
																		lowercase    :      Union[str, Any]       = (x, y)
																		lowercase    :      Dict       = cell
																		neighbours.append(__magic_name__				)
										return neighbours


def      UpperCamelCase       ( _A  ,				_A  ,				_A )     ->       Tuple:
				lowercase    :      Dict       = []
				lowercase    :      List[str]       = []
				_open.append(_A )

				while _open:
								lowercase    :      List[str]       = np.argmin([n.f for n in _open] )
								lowercase    :      int       = _open[min_f]
								_closed.append(_open.pop(_A ) )
								if current == goal:
												break
								for n in world.get_neigbours(_A ):
												for c in _closed:
																if c == n:
																				continue
												lowercase    :      List[Any]       = current.g + 1
												lowercase      ,      lowercase    :      Any       = n.position
												lowercase      ,      lowercase    :      List[str]       = goal.position
												lowercase    :      Any       = (ya - ya) ** 2 + (xa - xa) ** 2
												lowercase    :      Union[str, Any]       = n.h + n.g

												for c in _open:
																if c == n and c.f < n.f:
																				continue
												_open.append(_A )
				lowercase    :      List[str]       = []
				while current.parent is not None:
								path.append(current.position )
								lowercase    :      int       = current.parent
				path.append(current.position )
				return path[::-1]


if __name__ == "__main__":
				_lowerCAmelCase            =	Gridworld()
				# Start position and goal
				_lowerCAmelCase            =	Cell()
				_lowerCAmelCase            =	(0, 0)
				_lowerCAmelCase            =	Cell()
				_lowerCAmelCase            =	(4, 4)
				print(F'path from {start.position} to {goal.position}')
				_lowerCAmelCase            =	astar(world, start, goal)
				#   Just for visual reasons.
				for i in s:
								_lowerCAmelCase            =	1
				print(world.w)
