






import argparse

import requests
import torch

# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis_float32 (there's also the fix_lavis branch)
# also note: to convert Vicuna checkpoints, we had to include /home/niels/python_projects/checkpoints/FastChat/vicuna-7b in lavis/configs/models/blip2/blip2_instruct_vicuna7b.yaml
# same for Vicuna-13b
from lavis.models import load_model_and_preprocess
from PIL import Image

from transformers import (
    AutoTokenizer,
    BlipImageProcessor,
    InstructBlipConfig,
    InstructBlipForConditionalGeneration,
    InstructBlipProcessor,
    InstructBlipQFormerConfig,
    InstructBlipVisionConfig,
    LlamaConfig,
    LlamaTokenizerFast,
    TaConfig,
    TaTokenizerFast,
)
from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD

def      lowerCAmelCase__	(    ):
		snake_case_				:							str						 =       "https://raw.githubusercontent.com/salesforce/LAVIS/main/docs/_static/Confusing-Pictures.jpg"
		snake_case_				:							Tuple						 =       Image.open(requests.get(_a  ,      stream=_a    ).raw    ).convert("RGB"    )

		return image

def      lowerCAmelCase__	(    _a       :			Optional[int]    ):
		snake_case_				:							Optional[int]						 =       []
		# fmt: off

		# vision encoder
		rename_keys.append(("visual_encoder.cls_token", "vision_model.embeddings.class_embedding")    )
		rename_keys.append(("visual_encoder.pos_embed", "vision_model.embeddings.position_embedding")    )
		rename_keys.append(("visual_encoder.patch_embed.proj.weight", "vision_model.embeddings.patch_embedding.weight")    )
		rename_keys.append(("visual_encoder.patch_embed.proj.bias", "vision_model.embeddings.patch_embedding.bias")    )
		rename_keys.append(("ln_vision.weight", "vision_model.post_layernorm.weight")    )
		rename_keys.append(("ln_vision.bias", "vision_model.post_layernorm.bias")    )

		for i in range(config.vision_config.num_hidden_layers    ):
				rename_keys.append((F'''visual_encoder.blocks.{i}.norm1.weight''', F'''vision_model.encoder.layers.{i}.layer_norm1.weight''')    )
				rename_keys.append((F'''visual_encoder.blocks.{i}.norm1.bias''', F'''vision_model.encoder.layers.{i}.layer_norm1.bias''')    )
				rename_keys.append((F'''visual_encoder.blocks.{i}.norm2.weight''', F'''vision_model.encoder.layers.{i}.layer_norm2.weight''')    )
				rename_keys.append((F'''visual_encoder.blocks.{i}.norm2.bias''', F'''vision_model.encoder.layers.{i}.layer_norm2.bias''')    )
				rename_keys.append((F'''visual_encoder.blocks.{i}.attn.qkv.weight''', F'''vision_model.encoder.layers.{i}.self_attn.qkv.weight''')    )
				rename_keys.append((F'''visual_encoder.blocks.{i}.attn.proj.weight''', F'''vision_model.encoder.layers.{i}.self_attn.projection.weight''',)    )
				rename_keys.append((F'''visual_encoder.blocks.{i}.attn.proj.bias''', F'''vision_model.encoder.layers.{i}.self_attn.projection.bias''')    )
				rename_keys.append((F'''visual_encoder.blocks.{i}.mlp.fc1.weight''', F'''vision_model.encoder.layers.{i}.mlp.fc1.weight''')    )
				rename_keys.append((F'''visual_encoder.blocks.{i}.mlp.fc1.bias''', F'''vision_model.encoder.layers.{i}.mlp.fc1.bias''')    )
				rename_keys.append((F'''visual_encoder.blocks.{i}.mlp.fc2.weight''', F'''vision_model.encoder.layers.{i}.mlp.fc2.weight''')    )
				rename_keys.append((F'''visual_encoder.blocks.{i}.mlp.fc2.bias''', F'''vision_model.encoder.layers.{i}.mlp.fc2.bias''')    )

		# QFormer
		rename_keys.append(("Qformer.bert.embeddings.LayerNorm.weight", "qformer.embeddings.layernorm.weight")    )
		rename_keys.append(("Qformer.bert.embeddings.LayerNorm.bias", "qformer.embeddings.layernorm.bias")    )

		# fmt: on
		return rename_keys

def      lowerCAmelCase__	(    _a       :			List[Any]  ,      _a       :			str  ,      _a       :			List[Any]    ):
		snake_case_				:							Optional[int]						 =       dct.pop(_a    )
		snake_case_				:							Tuple						 =       val

def      lowerCAmelCase__	(    _a       :			Union[str, Any]  ,      _a       :			Optional[int]    ):
		for i in range(config.vision_config.num_hidden_layers    ):
				# read in original q and v biases
				snake_case_				:							List[Any]						 =       state_dict.pop(F'''visual_encoder.blocks.{i}.attn.q_bias'''    )
				snake_case_				:							Union[str, Any]						 =       state_dict.pop(F'''visual_encoder.blocks.{i}.attn.v_bias'''    )

				# next, set bias in the state dict
				snake_case_				:							List[Any]						 =       torch.cat((q_bias, torch.zeros_like(_a  ,      requires_grad=_a    ), v_bias)    )
				snake_case_				:							List[Any]						 =       qkv_bias

def      lowerCAmelCase__	(    _a       :			Any    ):
		snake_case_				:							Optional[int]						 =       3_64 if "coco" in model_name else 2_24
		snake_case_				:							Any						 =       InstructBlipVisionConfig(image_size=_a    ).to_dict()

		# make sure the models have proper bos_token_id and eos_token_id set (important for generation)
		# seems like flan-T5 models don't have bos_token_id properly set?
		if "t5-xl" in model_name:
				snake_case_				:							List[str]						 =       TaConfig.from_pretrained("google/flan-t5-xl"  ,      dense_act_fn="gelu"  ,      bos_token_id=1    ).to_dict()
		elif "t5-xxl" in model_name:
				snake_case_				:							Optional[int]						 =       TaConfig.from_pretrained("google/flan-t5-xxl"  ,      dense_act_fn="gelu"  ,      bos_token_id=1    ).to_dict()
		elif "vicuna-7b" in model_name:
				snake_case_				:							Optional[Any]						 =       LlamaConfig.from_pretrained("decapoda-research/llama-7b-hf"  ,      vocab_size=3_20_01    ).to_dict()
		elif "vicuna-13b" in model_name:
				snake_case_				:							Optional[Any]						 =       LlamaConfig.from_pretrained("decapoda-research/llama-13b-hf"  ,      vocab_size=3_20_01    ).to_dict()
		else:
				raise ValueError("Model name not supported"    )

		# the authors add one special "[DEC]" token to the vocab of Q-Former, hence vocab size = 30522 + 1
		snake_case_				:							int						 =       InstructBlipQFormerConfig(vocab_size=3_05_23    ).to_dict()
		snake_case_				:							Optional[int]						 =       InstructBlipConfig(vision_config=_a  ,      text_config=_a  ,      qformer_config=_a    )

		return config, image_size

@torch.no_grad()
def      lowerCAmelCase__	(    _a       :			Union[str, Any]  ,      _a       :			List[str]=None  ,      _a       :			List[Any]=False    ):
		snake_case_				:							List[Any]						 =       AutoTokenizer.from_pretrained("bert-base-uncased"  ,      truncation_side="left"    )
		qformer_tokenizer.add_special_tokens({"bos_token": "[DEC]"}    )

		if "t5" in model_name:
				snake_case_				:							Any						 =       TaTokenizerFast.from_pretrained("google/flan-t5-xl"  ,      truncation_side="left"    )
		elif "vicuna" in model_name:
				# the following was used in the original implementation:
				# tokenizer = LlamaTokenizer.from_pretrained("huggyllama/llama-7b", use_fast=False, truncation_side="left")
				# tokenizer.add_special_tokens({"pad_token": "[PAD]"})
				# tokenizer.add_special_tokens({"bos_token": "</s>"})
				# tokenizer.add_special_tokens({"eos_token": "</s>"})
				# tokenizer.add_special_tokens({"unk_token": "</s>"})
				snake_case_				:							str						 =       LlamaTokenizerFast.from_pretrained(
				    "huggyllama/llama-7b"  ,      truncation_side="left"  ,      bos_token="</s>"  ,      unk_token="</s>"    )
				tokenizer.add_special_tokens({"pad_token": "[PAD]"}    )

		snake_case_ ,       snake_case_				:							List[str]						 =       get_blipa_config(_a    )
		snake_case_				:							str						 =       InstructBlipForConditionalGeneration(_a    ).eval()

		snake_case_				:							List[Any]						 =       {
		    "instructblip-vicuna-7b": ("blip2_vicuna_instruct", "vicuna7b"),
		    "instructblip-vicuna-13b": ("blip2_vicuna_instruct", "vicuna13b"),
		    "instructblip-flan-t5-xl": ("blip2_t5_instruct", "flant5xl"),
		    "instructblip-flan-t5-xxl": ("blip2_t5_instruct", "flant5xxl"),
		}

		snake_case_ ,       snake_case_				:							List[Any]						 =       model_name_to_original[model_name]

		# load original model
		print("Loading original model..."    )
		snake_case_				:							Optional[Any]						 =       "cuda:1" if torch.cuda.is_available() else "cpu"
		snake_case_				:							Union[str, Any]						 =       "cuda:2" if torch.cuda.is_available() else "cpu"
		snake_case_ ,       snake_case_ ,       snake_case_				:							Any						 =       load_model_and_preprocess(
		    name=_a  ,      model_type=_a  ,      is_eval=_a  ,      device=_a    )
		original_model.eval()
		print("Done!"    )

		# update state dict keys
		snake_case_				:							Any						 =       original_model.state_dict()
		snake_case_				:							Union[str, Any]						 =       create_rename_keys(_a    )
		for src, dest in rename_keys:
				rename_key(_a  ,      _a  ,      _a    )

		# some keys can be renamed efficiently
		for key, val in state_dict.copy().items():
				snake_case_				:							int						 =       state_dict.pop(_a    )
				if key.startswith("Qformer.bert"    ):
						snake_case_				:							Dict						 =       key.replace("Qformer.bert"  ,      "qformer"    )
				if "attention.self" in key:
						snake_case_				:							List[Any]						 =       key.replace("self"  ,      "attention"    )
				if "llm_proj" in key:
						snake_case_				:							List[str]						 =       key.replace("llm_proj"  ,      "language_projection"    )
				if "t5_proj" in key:
						snake_case_				:							Any						 =       key.replace("t5_proj"  ,      "language_projection"    )
				if key.startswith("llm_model"    ):
						snake_case_				:							Optional[int]						 =       key.replace("llm_model"  ,      "language_model"    )
				if key.startswith("t5"    ):
						snake_case_				:							Tuple						 =       key.replace("t5"  ,      "language"    )
				snake_case_				:							List[Any]						 =       val

		# read in qv biases
		read_in_q_v_bias(_a  ,      _a    )

		# note: weights get loaded in torch.float32 by default
		hf_model.load_state_dict(_a  ,      strict=_a    )

		snake_case_				:							Optional[int]						 =       load_demo_image()
		snake_case_				:							Optional[Any]						 =       "What is unusual about this image?"

		# create processor
		snake_case_				:							str						 =       BlipImageProcessor(
		    size={"height": image_size, "width": image_size}  ,      image_mean=_a  ,      image_std=_a    )
		snake_case_				:							Dict						 =       InstructBlipProcessor(
		    image_processor=_a  ,      tokenizer=_a  ,      qformer_tokenizer=_a  ,      )
		snake_case_				:							Optional[int]						 =       processor(images=_a  ,      text=_a  ,      return_tensors="pt"    ).to(_a    )

		# make sure processor creates exact same pixel values
		snake_case_				:							Optional[int]						 =       vis_processors["eval"](_a    ).unsqueeze(0    ).to(_a    )
		snake_case_				:							Dict						 =       inputs.pixel_values
		assert torch.allclose(original_pixel_values.to(pixel_values.device    )  ,      _a    )

		original_model.to(_a    )
		hf_model.to(_a    )
		with torch.no_grad():
				if "vicuna" in model_name:
						snake_case_				:							Optional[Any]						 =       original_model({"image": original_pixel_values, "text_input": [prompt]}    ).logits
						snake_case_				:							Tuple						 =       hf_model(**_a    ).logits
				else:
						snake_case_				:							Any						 =       original_model(
						    {"image": original_pixel_values, "text_input": [prompt], "text_output": ["\n"]}    ).logits
						snake_case_				:							Union[str, Any]						 =       tokenizer("\n"  ,      return_tensors="pt"    ).input_ids.to(_a    )
						snake_case_				:							Union[str, Any]						 =       label_input_ids.masked_fill(label_input_ids == tokenizer.pad_token_id  ,      -1_00    )
						snake_case_				:							Optional[int]						 =       hf_model(**_a  ,      labels=_a    ).logits

		print("First values of original logits:"  ,      original_logits[0, :3, :3]    )
		print("First values of HF logits:"  ,      logits[0, :3, :3]    )

		# assert values
		assert original_logits.shape == logits.shape
		snake_case_				:							Optional[Any]						 =       1E-4 if "vicuna" in model_name else 1E-5
		assert torch.allclose(original_logits.to(logits.device    )  ,      _a  ,      atol=_a    )
		print("Looks ok!"    )

		print("Generating with original model..."    )
		snake_case_				:							Dict						 =       original_model.generate({"image": original_pixel_values, "prompt": prompt}  ,      num_beams=5    )

		# important: we need to cast the weights of the HF model to the appropriate type
		print("Generating with HF model..."    )
		snake_case_				:							List[Any]						 =       hf_model.generate(
		    **_a  ,      do_sample=_a  ,      num_beams=5  ,      max_length=2_56  ,      min_length=1  ,      top_p=0.9  ,      repetition_penalty=1.5  ,      length_penalty=1.0  ,      temperature=1  ,      )
		if "vicuna" in model_name:
				# convert output id 0 to 2 (eos_token_id)
				# TODO add this in the generate method?
				snake_case_				:							Any						 =       2
		print("Original generation:"  ,      _a    )
		snake_case_				:							Dict						 =       processor.batch_decode(_a  ,      skip_special_tokens=_a    )
		snake_case_				:							Any						 =       [text.strip() for text in output_text]
		print("HF generation:"  ,      _a    )

		if pytorch_dump_folder_path is not None:
				processor.save_pretrained(_a    )
				hf_model.save_pretrained(_a    )

		if push_to_hub:
				processor.push_to_hub(F'''Salesforce/{model_name}'''    )
				hf_model.push_to_hub(F'''Salesforce/{model_name}'''    )


if __name__ == "__main__":
							lowercase  :							List[Any]			  =    argparse.ArgumentParser()
							lowercase  :							Any			  =    [
							    '''instructblip-vicuna-7b''',
							    '''instructblip-vicuna-13b''',
							    '''instructblip-flan-t5-xl''',
							    '''instructblip-flan-t5-xxl''',
							]
							parser.add_argument(
							    '''--model_name''',
							    default='''instructblip-flan-t5-xl''',
							    choices=choices,
							    type=str,
							    help='''Path to hf config.json of model to convert''',
							)
							parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
							parser.add_argument(
							    '''--push_to_hub''',
							    action='''store_true''',
							    help='''Whether to push the model and processor to the hub after converting''',
							)

							lowercase  :							Optional[Any]			  =    parser.parse_args()

							convert_blipa_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)








import heapq
import sys

import numpy as np

lowercase  :							str			  =    tuple[int, int]




class 		UpperCAmelCase_						:


						'''simple docstring'''




						def __init__(	self				) ->				Optional[int]:
								snake_case_				:							int						 =       []
								snake_case_				:							int						 =       set()






						def       _lowerCAmelCase       (	self				) ->				List[Any]:
								if not self.empty():
										return self.elements[0][0]
								else:
										return float("inf"				)






						def       _lowerCAmelCase       (	self				) ->				Optional[int]:
								return len(self.elements				) == 0






						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				) ->				str:
								if item not in self.set:
										heapq.heappush(self.elements		,				(priority, item)				)
										self.set.add(_SCREAMING_SNAKE_CASE				)
								else:
										# update
										# print("update", item)
										snake_case_				:							Any						 =       []
										((snake_case_) ,       (snake_case_))				:							Optional[Any]						 =       heapq.heappop(self.elements				)
										while x != item:
												temp.append((pri, x)				)
												((snake_case_) ,       (snake_case_))				:							Optional[int]						 =       heapq.heappop(self.elements				)
										temp.append((priority, item)				)
										for pro, xxx in temp:
												heapq.heappush(self.elements		,				(pro, xxx)				)






						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE				) ->				Any:
								if item in self.set:
										self.set.remove(_SCREAMING_SNAKE_CASE				)
										snake_case_				:							Optional[Any]						 =       []
										((snake_case_) ,       (snake_case_))				:							Tuple						 =       heapq.heappop(self.elements				)
										while x != item:
												temp.append((pro, x)				)
												((snake_case_) ,       (snake_case_))				:							Dict						 =       heapq.heappop(self.elements				)
										for prito, yyy in temp:
												heapq.heappush(self.elements		,				(prito, yyy)				)






						def       _lowerCAmelCase       (	self				) ->				Optional[Any]:
								return self.elements[0][1]






						def       _lowerCAmelCase       (	self				) ->				Optional[int]:
								((snake_case_) ,       (snake_case_))				:							Any						 =       heapq.heappop(self.elements				)
								self.set.remove(_SCREAMING_SNAKE_CASE				)
								return (priority, item)




def      lowerCAmelCase__	(    _a       :			TPos  ,      _a       :			TPos    ):
		# euclidean distance
		snake_case_				:							Optional[Any]						 =       np.array(_a    )
		snake_case_				:							Dict						 =       np.array(_a    )
		return np.linalg.norm(a - b    )

def      lowerCAmelCase__	(    _a       :			TPos  ,      _a       :			TPos    ):
		# integer division by time variable
		return consistent_heuristic(_a  ,      _a    ) // t

def      lowerCAmelCase__	(    _a       :			TPos  ,      _a       :			TPos    ):
		# manhattan distance
		return abs(p[0] - goal[0]    ) + abs(p[1] - goal[1]    )

def      lowerCAmelCase__	(    _a       :			TPos  ,      _a       :			int  ,      _a       :			TPos  ,      _a       :			dict[TPos, float]    ):
		snake_case_				:							List[Any]						 =       g_function[start] + Wa * heuristics[i](_a  ,      _a    )
		return ans

def      lowerCAmelCase__	(    _a       :			Tuple  ,      _a       :			Union[str, Any]  ,      _a       :			List[Any]    ):
		snake_case_				:							Dict						 =       np.chararray((n, n)    )
		for i in range(_a    ):
				for j in range(_a    ):
						snake_case_				:							List[str]						 =       "*"

		for i in range(_a    ):
				for j in range(_a    ):
						if (j, (n - 1) - i) in blocks:
								snake_case_				:							Optional[Any]						 =       "#"

		snake_case_				:							Optional[int]						 =       "-"
		snake_case_				:							Tuple						 =       back_pointer[goal]
		while x != start:
				((snake_case_) ,       (snake_case_))				:							Any						 =       x
				# print(x)
				snake_case_				:							List[str]						 =       "-"
				snake_case_				:							Dict						 =       back_pointer[x]
		snake_case_				:							str						 =       "-"

		for i in range(_a    ):
				for j in range(_a    ):
						if (i, j) == (0, n - 1):
								print(grid[i][j]  ,      end=" "    )
								print("<-- End position"  ,      end=" "    )
						else:
								print(grid[i][j]  ,      end=" "    )
				print()
		print("^"    )
		print("Start position"    )
		print()
		print("# is an obstacle"    )
		print("- is the path taken by algorithm"    )
		print("PATH TAKEN BY THE ALGORITHM IS:-"    )
		snake_case_				:							List[Any]						 =       back_pointer[goal]
		while x != start:
				print(_a  ,      end=" "    )
				snake_case_				:							int						 =       back_pointer[x]
		print(_a    )
		sys.exit()

def      lowerCAmelCase__	(    _a       :			TPos    ):
		if p[0] < 0 or p[0] > n - 1:
				return False
		if p[1] < 0 or p[1] > n - 1:
				return False
		return True

def      lowerCAmelCase__	(    _a       :			int  ,      _a       :			str  ,      _a       :			Optional[int]  ,      _a       :			List[str]  ,      _a       :			Optional[Any]  ,      _a       :			Dict  ,      _a       :			Tuple  ,      _a       :			Any  ,      ):
		for itera in range(_a    ):
				open_list[itera].remove_element(_a    )
		# print("s", s)
		# print("j", j)
		((snake_case_) ,       (snake_case_))				:							Tuple						 =       s
		snake_case_				:							Dict						 =       (x - 1, y)
		snake_case_				:							Union[str, Any]						 =       (x + 1, y)
		snake_case_				:							List[str]						 =       (x, y + 1)
		snake_case_				:							int						 =       (x, y - 1)

		for neighbours in [left, right, up, down]:
				if neighbours not in blocks:
						if valid(_a    ) and neighbours not in visited:
								# print("neighbour", neighbours)
								visited.add(_a    )
								snake_case_				:							Optional[int]						 =       -1
								snake_case_				:							int						 =       float("inf"    )

						if valid(_a    ) and g_function[neighbours] > g_function[s] + 1:
								snake_case_				:							int						 =       g_function[s] + 1
								snake_case_				:							Any						 =       s
								if neighbours not in close_list_anchor:
										open_list[0].put(_a  ,      key(_a  ,      0  ,      _a  ,      _a    )    )
										if neighbours not in close_list_inad:
												for var in range(1  ,      _a    ):
														if key(_a  ,      _a  ,      _a  ,      _a    ) <= Wa * key(
														    _a  ,      0  ,      _a  ,      _a    ):
																open_list[j].put(
																    _a  ,      key(_a  ,      _a  ,      _a  ,      _a    )    )

def      lowerCAmelCase__	(    ):
		snake_case_				:							Union[str, Any]						 =       []
		for x in range(1  ,      5    ):
				for y in range(1  ,      6    ):
						some_list.append((x, y)    )

		for x in range(15  ,      20    ):
				some_list.append((x, 17)    )

		for x in range(10  ,      19    ):
				for y in range(1  ,      15    ):
						some_list.append((x, y)    )

    # L block
		for x in range(1  ,      4    ):
				for y in range(12  ,      19    ):
						some_list.append((x, y)    )
		for x in range(3  ,      13    ):
				for y in range(16  ,      19    ):
						some_list.append((x, y)    )
		return some_list


lowercase  :							Union[str, Any]			  =    {0: consistent_heuristic, 1: heuristic_a, 2: heuristic_a}

lowercase  :							Union[str, Any]			  =    [
    (0, 1),
    (1, 1),
    (2, 1),
    (3, 1),
    (4, 1),
    (5, 1),
    (6, 1),
    (7, 1),
    (8, 1),
    (9, 1),
    (10, 1),
    (11, 1),
    (12, 1),
    (13, 1),
    (14, 1),
    (15, 1),
    (16, 1),
    (17, 1),
    (18, 1),
    (19, 1),
]
lowercase  :							int			  =    make_common_ground()


lowercase  :							Optional[Any]			  =    blocks_blk
# hyper parameters
lowercase  :							Optional[Any]			  =    1
lowercase  :							str			  =    1
lowercase  :							Any			  =    20
lowercase  :							str			  =    3  # one consistent and two other inconsistent

# start and end destination
lowercase  :							Any			  =    (0, 0)
lowercase  :							int			  =    (n - 1, n - 1)

lowercase  :							str			  =    1

def      lowerCAmelCase__	(    _a       :			TPos  ,      _a       :			TPos  ,      _a       :			int    ):
		snake_case_				:							List[str]						 =       {start: 0, goal: float("inf"    )}
		snake_case_				:							List[Any]						 =       {start: -1, goal: -1}
		snake_case_				:							Optional[Any]						 =       []
		snake_case_				:							Dict						 =       set()

		for i in range(_a    ):
				open_list.append(PriorityQueue()    )
				open_list[i].put(_a  ,      key(_a  ,      _a  ,      _a  ,      _a    )    )

		snake_case_				:							list[int]						 =       []
		snake_case_				:							list[int]						 =       []
		while open_list[0].minkey() < float("inf"    ):
				for i in range(1  ,      _a    ):
						# print(open_list[0].minkey(), open_list[i].minkey())
						if open_list[i].minkey() <= Wa * open_list[0].minkey():
								global t
								t += 1
								if g_function[goal] <= open_list[i].minkey():
										if g_function[goal] < float("inf"    ):
												do_something(_a  ,      _a  ,      _a    )
								else:
										snake_case_ ,       snake_case_				:							Optional[Any]						 =       open_list[i].top_show()
										visited.add(_a    )
										expand_state(
										    _a  ,      _a  ,      _a  ,      _a  ,      _a  ,      _a  ,      _a  ,      _a  ,      )
										close_list_inad.append(_a    )
						else:
								if g_function[goal] <= open_list[0].minkey():
										if g_function[goal] < float("inf"    ):
												do_something(_a  ,      _a  ,      _a    )
								else:
										snake_case_				:							Tuple						 =       open_list[0].top_show()
										visited.add(_a    )
										expand_state(
										    _a  ,      0  ,      _a  ,      _a  ,      _a  ,      _a  ,      _a  ,      _a  ,      )
										close_list_anchor.append(_a    )
		print("No path found to goal"    )
		print()
		for i in range(n - 1  ,      -1  ,      -1    ):
				for j in range(_a    ):
						if (j, i) in blocks:
								print("#"  ,      end=" "    )
						elif (j, i) in back_pointer:
								if (j, i) == (n - 1, n - 1):
										print("*"  ,      end=" "    )
								else:
										print("-"  ,      end=" "    )
						else:
								print("*"  ,      end=" "    )
						if (j, i) == (n - 1, n - 1):
								print("<-- End position"  ,      end=" "    )
				print()
		print("^"    )
		print("Start position"    )
		print()
		print("# is an obstacle"    )
		print("- is the path taken by algorithm"    )


if __name__ == "__main__":
							multi_a_star(start, goal, n_heuristic)
