






"""simple docstring"""
import argparse
import json
import os
from pathlib import Path

import requests
import torch

from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging


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


_lowerCAmelCase         =							'''https://openaipublic.azureedge.net/jukebox/models/'''
_lowerCAmelCase         =							{
    '''jukebox-1b-lyrics''': [
        '''5b/vqvae.pth.tar''',
        '''5b/prior_level_0.pth.tar''',
        '''5b/prior_level_1.pth.tar''',
        '''1b_lyrics/prior_level_2.pth.tar''',
    ],
    '''jukebox-5b-lyrics''': [
        '''5b/vqvae.pth.tar''',
        '''5b/prior_level_0.pth.tar''',
        '''5b/prior_level_1.pth.tar''',
        '''5b_lyrics/prior_level_2.pth.tar''',
    ],
}






def 			__UpperCamelCase	(		snake_case__					):
	if key.endswith(""".model.1.bias"""					) and len(key.split("""."""					)					) > 10:
		A_		:						Dict	    =   key.replace(""".model.1.bias"""   ,   """.conv1d_1.bias"""					)
	elif key.endswith(""".model.1.weight"""					) and len(key.split("""."""					)					) > 10:
		A_		:						str	    =   key.replace(""".model.1.weight"""   ,   """.conv1d_1.weight"""					)
	elif key.endswith(""".model.3.bias"""					) and len(key.split("""."""					)					) > 10:
		A_		:						Tuple	    =   key.replace(""".model.3.bias"""   ,   """.conv1d_2.bias"""					)
	elif key.endswith(""".model.3.weight"""					) and len(key.split("""."""					)					) > 10:
		A_		:						List[str]	    =   key.replace(""".model.3.weight"""   ,   """.conv1d_2.weight"""					)

	if "conditioner_blocks.0." in key:
		A_		:						int	    =   key.replace("""conditioner_blocks.0"""   ,   """conditioner_blocks"""					)

	if "prime_prior" in key:
		A_		:						Tuple	    =   key.replace("""prime_prior"""   ,   """encoder"""					)

	if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
		A_		:						int	    =   key.replace(""".emb."""   ,   """."""					)

	if key.endswith("""k"""					):  # replace vqvae.X.k with vqvae.X.codebook
		return key.replace(""".k"""   ,   """.codebook"""					)
	if "y_emb." in key:
		return key.replace("""y_emb."""   ,   """metadata_embedding."""					)

	if "x_emb.emb." in key:
		A_		:						int	    =   key.replace("""0.x_emb.emb"""   ,   """embed_tokens"""					)

	if "prime_state_ln" in key:
		return key.replace("""prime_state_ln"""   ,   """encoder.final_layer_norm"""					)
	if ".ln" in key:
		return key.replace(""".ln"""   ,   """.layer_norm"""					)
	if "_ln" in key:
		return key.replace("""_ln"""   ,   """_layer_norm"""					)

	if "prime_state_proj" in key:
		return key.replace("""prime_state_proj"""   ,   """encoder.proj_in"""					)
	if "prime_x_out" in key:
		return key.replace("""prime_x_out"""   ,   """encoder.lm_head"""					)
	if "prior.x_out" in key:
		return key.replace("""x_out"""   ,   """fc_proj_out"""					)
	if "x_emb" in key:
		return key.replace("""x_emb"""   ,   """embed_tokens"""					)

	return key






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__   ,   snake_case__   ,   snake_case__					):
	A_		:						List[str]	    =   {}
	import re

	A_		:						Optional[int]	    =   re.compile(R"""encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)"""					)
	A_		:						Optional[int]	    =   re.compile(
	    R"""encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"""					)
	A_		:						Optional[Any]	    =   re.compile(R"""encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)"""					)

	A_		:						Tuple	    =   re.compile(R"""decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)"""					)
	A_		:						Optional[Any]	    =   re.compile(
	    R"""decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"""					)
	A_		:						Tuple	    =   re.compile(R"""decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)"""					)

	A_		:						Optional[int]	    =   re.compile(R"""conditioner_blocks.(\d*).cond.model.(\d*).(\d).(bias|weight)"""					)
	A_		:						Dict	    =   re.compile(
	    R"""conditioner_blocks.(\d*).cond.model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"""					)
	A_		:						List[str]	    =   re.compile(R"""conditioner_blocks.(\d*).cond.model.(\d*).(bias|weight)"""					)

	for original_key, value in state_dict.items():
		# rename vqvae.encoder keys
		if re_encoder_block_conv_in.fullmatch(lowerCAmelCase__					):
			A_		:						List[Any]	    =   re_encoder_block_conv_in.match(lowerCAmelCase__					)
			A_		:						Any	    =   regex_match.groups()
			A_		:						Tuple	    =   int(groups[2]					) * 2 + int(groups[3]					)
			A_		:						int	    =   F"""encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.{groups[-1]}"""
			A_		:						Optional[Any]	    =   re_encoder_block_conv_in.sub(lowerCAmelCase__   ,   lowerCAmelCase__					)

		elif re_encoder_block_resnet.fullmatch(lowerCAmelCase__					):
			A_		:						Dict	    =   re_encoder_block_resnet.match(lowerCAmelCase__					)
			A_		:						Dict	    =   regex_match.groups()
			A_		:						Tuple	    =   int(groups[2]					) * 2 + int(groups[3]					)
			A_		:						List[Any]	    =   {"""1""": 1, """3""": 2}[groups[-2]]
			A_		:						List[str]	    =   F"""encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}."""
			A_		:						Optional[Any]	    =   F"""resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"""
			A_		:						Dict	    =   prefix + resnet_block
			A_		:						List[str]	    =   re_encoder_block_resnet.sub(lowerCAmelCase__   ,   lowerCAmelCase__					)

		elif re_encoder_block_proj_out.fullmatch(lowerCAmelCase__					):
			A_		:						Optional[Any]	    =   re_encoder_block_proj_out.match(lowerCAmelCase__					)
			A_		:						List[Any]	    =   regex_match.groups()
			A_		:						Union[str, Any]	    =   F"""encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}"""
			A_		:						str	    =   re_encoder_block_proj_out.sub(lowerCAmelCase__   ,   lowerCAmelCase__					)

		# rename vqvae.decoder keys
		elif re_decoder_block_conv_out.fullmatch(lowerCAmelCase__					):
			A_		:						Tuple	    =   re_decoder_block_conv_out.match(lowerCAmelCase__					)
			A_		:						Tuple	    =   regex_match.groups()
			A_		:						Dict	    =   int(groups[2]					) * 2 + int(groups[3]					) - 2
			A_		:						Tuple	    =   F"""decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}.{groups[-1]}"""
			A_		:						Optional[int]	    =   re_decoder_block_conv_out.sub(lowerCAmelCase__   ,   lowerCAmelCase__					)

		elif re_decoder_block_resnet.fullmatch(lowerCAmelCase__					):
			A_		:						Optional[int]	    =   re_decoder_block_resnet.match(lowerCAmelCase__					)
			A_		:						List[Any]	    =   regex_match.groups()
			A_		:						Tuple	    =   int(groups[2]					) * 2 + int(groups[3]					) - 2
			A_		:						List[str]	    =   {"""1""": 1, """3""": 2}[groups[-2]]
			A_		:						Union[str, Any]	    =   F"""decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}."""
			A_		:						int	    =   F"""resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"""
			A_		:						Optional[Any]	    =   prefix + resnet_block
			A_		:						Union[str, Any]	    =   re_decoder_block_resnet.sub(lowerCAmelCase__   ,   lowerCAmelCase__					)

		elif re_decoder_block_proj_in.fullmatch(lowerCAmelCase__					):
			A_		:						List[Any]	    =   re_decoder_block_proj_in.match(lowerCAmelCase__					)
			A_		:						int	    =   regex_match.groups()
			A_		:						List[Any]	    =   F"""decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}"""
			A_		:						Any	    =   re_decoder_block_proj_in.sub(lowerCAmelCase__   ,   lowerCAmelCase__					)

		# rename prior cond.model to upsampler.upsample_block and resnet
		elif re_prior_cond_conv_out.fullmatch(lowerCAmelCase__					):
			A_		:						str	    =   re_prior_cond_conv_out.match(lowerCAmelCase__					)
			A_		:						Tuple	    =   regex_match.groups()
			A_		:						Tuple	    =   int(groups[1]					) * 2 + int(groups[2]					) - 2
			A_		:						Optional[int]	    =   F"""conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}"""
			A_		:						Tuple	    =   re_prior_cond_conv_out.sub(lowerCAmelCase__   ,   lowerCAmelCase__					)

		elif re_prior_cond_resnet.fullmatch(lowerCAmelCase__					):
			A_		:						Optional[Any]	    =   re_prior_cond_resnet.match(lowerCAmelCase__					)
			A_		:						int	    =   regex_match.groups()
			A_		:						List[str]	    =   int(groups[1]					) * 2 + int(groups[2]					) - 2
			A_		:						List[Any]	    =   {"""1""": 1, """3""": 2}[groups[-2]]
			A_		:						str	    =   F"""conditioner_blocks.upsampler.upsample_block.{block_index}."""
			A_		:						List[str]	    =   F"""resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"""
			A_		:						Union[str, Any]	    =   prefix + resnet_block
			A_		:						Optional[Any]	    =   re_prior_cond_resnet.sub(lowerCAmelCase__   ,   lowerCAmelCase__					)

		elif re_prior_cond_proj_in.fullmatch(lowerCAmelCase__					):
			A_		:						Optional[Any]	    =   re_prior_cond_proj_in.match(lowerCAmelCase__					)
			A_		:						Optional[Any]	    =   regex_match.groups()
			A_		:						Dict	    =   F"""conditioner_blocks.upsampler.proj_in.{groups[-1]}"""
			A_		:						List[str]	    =   re_prior_cond_proj_in.sub(lowerCAmelCase__   ,   lowerCAmelCase__					)

		# keep original key
		else:
			A_		:						str	    =   original_key

		A_		:						str	    =   replace_key(lowerCAmelCase__					)

		if F"""{key_prefix}.{key}""" not in model_state_dict or key is None:
			print(F"""failed converting {original_key} to {key}, does not match"""					)

		# handle missmatched shape
		elif value.shape != model_state_dict[F"""{key_prefix}.{key}"""].shape:
			A_		:						Tuple	    =   model_state_dict[F"""{key_prefix}.{key}"""]
			print(F"""{original_key}-> {key} : \nshape {val.shape} and { value.shape}, do not match"""					)
			A_		:						Union[str, Any]	    =   original_key

		A_		:						Tuple	    =   original_key
		A_		:						Optional[int]	    =   value

	return new_dict






@torch.no_grad()
def 			__UpperCamelCase	(		snake_case__=None   ,   snake_case__=None					):
	for file in MODEL_MAPPING[model_name]:
		if not os.path.isfile(F"""{pytorch_dump_folder_path}/{file.split("/"					)[-1]}"""					):
			A_		:						Tuple	    =   requests.get(F"""{PREFIX}{file}"""   ,   allow_redirects=lowerCAmelCase__					)
			os.makedirs(F"""{pytorch_dump_folder_path}/"""   ,   exist_ok=lowerCAmelCase__					)
			open(F"""{pytorch_dump_folder_path}/{file.split("/"					)[-1]}"""   ,   """wb"""					).write(r.content					)

	A_		:						str	    =   MODEL_MAPPING[model_name.split("""/"""					)[-1]]

	A_		:						Optional[int]	    =   JukeboxConfig.from_pretrained(lowerCAmelCase__					)
	A_		:						Union[str, Any]	    =   JukeboxModel(lowerCAmelCase__					)

	A_		:						List[Any]	    =   []
	A_		:						Any	    =   {}
	for i, dict_name in enumerate(lowerCAmelCase__					):
		A_		:						Union[str, Any]	    =   torch.load(F"""{pytorch_dump_folder_path}/{dict_name.split("/"					)[-1]}"""					)["""model"""]

		A_		:						List[str]	    =   {}
		for k in old_dic.keys():
			if k.endswith(""".b"""					):
				A_		:						Union[str, Any]	    =   old_dic[k]
			elif k.endswith(""".w"""					):
				A_		:						Optional[int]	    =   old_dic[k]
			elif "level_2" not in dict_name and "cond.model." in k:
				A_		:						Optional[Any]	    =   old_dic[k]
			else:
				A_		:						Union[str, Any]	    =   old_dic[k]

		A_		:						List[Any]	    =   """vqvae""" if i == 0 else F"""priors.{3 - i}"""
		A_		:						List[Any]	    =   fix_jukebox_keys(lowerCAmelCase__   ,   model.state_dict()   ,   lowerCAmelCase__   ,   lowerCAmelCase__					)
		weight_dict.append(lowerCAmelCase__					)

	A_		:						str	    =   weight_dict.pop(0					)
	model.vqvae.load_state_dict(lowerCAmelCase__					)
	for i in range(len(lowerCAmelCase__					)					):
		model.priors[i].load_state_dict(weight_dict[2 - i]					)

	Path(lowerCAmelCase__					).mkdir(exist_ok=lowerCAmelCase__					)
	with open(F"""{pytorch_dump_folder_path}/mapping.json"""   ,   """w"""					) as txtfile:
		json.dump(lowerCAmelCase__   ,   lowerCAmelCase__					)

	print(F"""Saving model {model_name} to {pytorch_dump_folder_path}"""					)
	model.save_pretrained(lowerCAmelCase__					)

	return weight_dict


if __name__ == "__main__":
							_lowerCAmelCase         =							argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    "--model_name",
							    default="jukebox-5b-lyrics",
							    type=str,
							    help="Name of the model you\'d like to convert.",
							)
							parser.add_argument(
							    "--pytorch_dump_folder_path",
							    default="jukebox-5b-lyrics-converted",
							    type=str,
							    help="Path to the output PyTorch model directory.",
							)
							_lowerCAmelCase         =							parser.parse_args()
							convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)









"""simple docstring"""
import heapq
import sys

import numpy as np

_lowerCAmelCase         =							tuple[int, int]



class      SCREAMING_SNAKE_CASE  :





	"""simple docstring"""




	def __init__(self							):
		A_		:						Any	    =   []
		A_		:						str	    =   set()





	def    lowerCamelCase(self							):
		if not self.empty():
			return self.elements[0][0]
		else:
			return float("""inf"""							)





	def    lowerCamelCase(self							):
		return len(self.elements							) == 0





	def    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_							):
		if item not in self.set:
			heapq.heappush(self.elements     ,     (priority, item)							)
			self.set.add(lowerCAmelCase_							)
		else:
			# update
			# print("update", item)
			A_		:						Union[str, Any]	    =   []
			((A_)    ,     (A_))		:						Union[str, Any]	    =   heapq.heappop(self.elements							)
			while x != item:
				temp.append((pri, x)							)
				((A_)    ,     (A_))		:						Any	    =   heapq.heappop(self.elements							)
			temp.append((priority, item)							)
			for pro, xxx in temp:
				heapq.heappush(self.elements     ,     (pro, xxx)							)





	def    lowerCamelCase(self     ,     lowerCAmelCase_							):
		if item in self.set:
			self.set.remove(lowerCAmelCase_							)
			A_		:						int	    =   []
			((A_)    ,     (A_))		:						Union[str, Any]	    =   heapq.heappop(self.elements							)
			while x != item:
				temp.append((pro, x)							)
				((A_)    ,     (A_))		:						List[Any]	    =   heapq.heappop(self.elements							)
			for prito, yyy in temp:
				heapq.heappush(self.elements     ,     (prito, yyy)							)





	def    lowerCamelCase(self							):
		return self.elements[0][1]





	def    lowerCamelCase(self							):
		((A_)    ,     (A_))		:						Optional[Any]	    =   heapq.heappop(self.elements							)
		self.set.remove(lowerCAmelCase_							)
		return (priority, item)






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__					):
	# euclidean distance
	A_		:						Optional[Any]	    =   np.array(snake_case__					)
	A_		:						List[str]	    =   np.array(snake_case__					)
	return np.linalg.norm(a - b					)






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__					):
	# integer division by time variable
	return consistent_heuristic(snake_case__   ,   snake_case__					) // t






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__					):
	# manhattan distance
	return abs(p[0] - goal[0]					) + abs(p[1] - goal[1]					)






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__   ,   snake_case__   ,   snake_case__					):
	A_		:						Optional[int]	    =   g_function[start] + Wa * heuristics[i](snake_case__   ,   snake_case__					)
	return ans






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__   ,   snake_case__					):
	A_		:						Tuple	    =   np.chararray((n, n)					)
	for i in range(snake_case__					):
		for j in range(snake_case__					):
			A_		:						List[Any]	    =   """*"""

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

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

	for i in range(snake_case__					):
		for j in range(snake_case__					):
			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:-"""					)
	A_		:						List[str]	    =   back_pointer[goal]
	while x != start:
		print(snake_case__   ,   end=""" """					)
		A_		:						Optional[int]	    =   back_pointer[x]
	print(snake_case__					)
	sys.exit()






def 			__UpperCamelCase	(		snake_case__					):
	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 			__UpperCamelCase	(		snake_case__   ,   snake_case__   ,   snake_case__   ,   snake_case__   ,   snake_case__   ,   snake_case__   ,   snake_case__   ,   snake_case__   ,   ):
	for itera in range(snake_case__					):
		open_list[itera].remove_element(snake_case__					)
	# print("s", s)
	# print("j", j)
	((A_)    ,     (A_))		:						Any	    =   s
	A_		:						int	    =   (x - 1, y)
	A_		:						List[str]	    =   (x + 1, y)
	A_		:						Union[str, Any]	    =   (x, y + 1)
	A_		:						Any	    =   (x, y - 1)

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

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






def 			__UpperCamelCase	(		):
	A_		:						str	    =   []
	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


_lowerCAmelCase         =							{0: consistent_heuristic, 1: heuristic_a, 2: heuristic_a}

_lowerCAmelCase         =							[
    (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),
]
_lowerCAmelCase         =							make_common_ground()


_lowerCAmelCase         =							blocks_blk
# hyper parameters
_lowerCAmelCase         =							1
_lowerCAmelCase         =							1
_lowerCAmelCase         =							20
_lowerCAmelCase         =							3  # one consistent and two other inconsistent

# start and end destination
_lowerCAmelCase         =							(0, 0)
_lowerCAmelCase         =							(n - 1, n - 1)

_lowerCAmelCase         =							1






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__   ,   snake_case__					):
	A_		:						Tuple	    =   {start: 0, goal: float("""inf"""					)}
	A_		:						List[str]	    =   {start: -1, goal: -1}
	A_		:						str	    =   []
	A_		:						int	    =   set()

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

	A_		:						list[int]	    =   []
	A_		:						list[int]	    =   []
	while open_list[0].minkey() < float("""inf"""					):
		for i in range(1   ,   snake_case__					):
			# 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(snake_case__   ,   snake_case__   ,   snake_case__					)
				else:
					A_    ,     A_		:						Optional[Any]	    =   open_list[i].top_show()
					visited.add(snake_case__					)
					expand_state(
					    snake_case__   ,   snake_case__   ,   snake_case__   ,   snake_case__   ,   snake_case__   ,   snake_case__   ,   snake_case__   ,   snake_case__   ,   )
					close_list_inad.append(snake_case__					)
			else:
				if g_function[goal] <= open_list[0].minkey():
					if g_function[goal] < float("""inf"""					):
						do_something(snake_case__   ,   snake_case__   ,   snake_case__					)
				else:
					A_		:						int	    =   open_list[0].top_show()
					visited.add(snake_case__					)
					expand_state(
					    snake_case__   ,   0   ,   snake_case__   ,   snake_case__   ,   snake_case__   ,   snake_case__   ,   snake_case__   ,   snake_case__   ,   )
					close_list_anchor.append(snake_case__					)
	print("""No path found to goal"""					)
	print()
	for i in range(n - 1   ,   -1   ,   -1					):
		for j in range(snake_case__					):
			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)

