



'''simple docstring'''

import os
from glob import glob

import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn

from transformers import CLIPModel, CLIPTokenizerFast
from utils import get_device, get_timestamp, show_pil


class 	a__     :


	def __init__(self : List[Any],					__UpperCAmelCase : str = "cpu",					__UpperCAmelCase : str = "openai/clip-vit-large-patch14"      )     ->			None:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Tuple      					= device
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= CLIPTokenizerFast.from_pretrained(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= [0.4814_5466, 0.457_8275, 0.4082_1073]
					SCREAMING_SNAKE_CASE  :						Any      					= [0.2686_2954, 0.2613_0258, 0.2757_7711]
					SCREAMING_SNAKE_CASE  :						str      					= torchvision.transforms.Normalize(self.image_mean,					self.image_std      )
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= torchvision.transforms.Resize(224      )
					SCREAMING_SNAKE_CASE  :						Any      					= torchvision.transforms.CenterCrop(224      )


	def 				lowercase__  (self : Optional[int],					__UpperCAmelCase : Dict      )     ->			Optional[int]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= self.resize(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= self.center_crop(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						int      					= self.normalize(__UpperCAmelCase      )
					return images






	def __call__(self : Tuple,					__UpperCAmelCase : str=None,					__UpperCAmelCase : Any=None,					**__UpperCAmelCase : List[Any]      )     ->			List[str]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						List[Any]      					= self.tokenizer(text=__UpperCAmelCase,					**__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						int      					= self.preprocess_img(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						int      					= {key: value.to(self.device      ) for (key, value) in encoding.items()}
					return encoding



class 	a__     (				nn.Module	):


	def __init__(self : int,					__UpperCAmelCase : Dict=10,					__UpperCAmelCase : Union[str, Any]=0.01,					__UpperCAmelCase : List[str]=None,					__UpperCAmelCase : Tuple=None,					__UpperCAmelCase : Dict=None,					__UpperCAmelCase : Dict=None,					__UpperCAmelCase : Optional[Any]=None,					__UpperCAmelCase : Tuple=None,					__UpperCAmelCase : int=False,					__UpperCAmelCase : List[str]=True,					__UpperCAmelCase : str="image",					__UpperCAmelCase : Tuple=True,					__UpperCAmelCase : str=False,					__UpperCAmelCase : Dict=False,					__UpperCAmelCase : Any=False,					)     ->			None:

					"""simple docstring"""

					super().__init__()
					SCREAMING_SNAKE_CASE  :						List[str]      					= None
					SCREAMING_SNAKE_CASE  :						List[str]      					= device if device else get_device()
					if vqgan:
									SCREAMING_SNAKE_CASE  :						str      					= vqgan
					else:
									SCREAMING_SNAKE_CASE  :						Optional[int]      					= load_vqgan(self.device,					conf_path=__UpperCAmelCase,					ckpt_path=__UpperCAmelCase      )
					self.vqgan.eval()
					if clip:
									SCREAMING_SNAKE_CASE  :						Any      					= clip
					else:
									SCREAMING_SNAKE_CASE  :						Tuple      					= CLIPModel.from_pretrained('''openai/clip-vit-base-patch32'''      )
					self.clip.to(self.device      )
					SCREAMING_SNAKE_CASE  :						List[str]      					= ProcessorGradientFlow(device=self.device      )

					SCREAMING_SNAKE_CASE  :						Any      					= iterations
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= lr
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= log
					SCREAMING_SNAKE_CASE  :						List[str]      					= make_grid
					SCREAMING_SNAKE_CASE  :						Dict      					= return_val
					SCREAMING_SNAKE_CASE  :						List[str]      					= quantize
					SCREAMING_SNAKE_CASE  :						Dict      					= self.vqgan.decoder.z_shape


	def 				lowercase__  (self : int,					__UpperCAmelCase : Optional[int]=None,					__UpperCAmelCase : Any=None,					__UpperCAmelCase : List[str]=5,					__UpperCAmelCase : Tuple=True      )     ->			Any:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Optional[int]      					= []
					if output_path is None:
									SCREAMING_SNAKE_CASE  :						Dict      					= '''./animation.gif'''
					if input_path is None:
									SCREAMING_SNAKE_CASE  :						str      					= self.save_path
					SCREAMING_SNAKE_CASE  :						Tuple      					= sorted(glob(input_path + '''/*'''      )      )
					if not len(__UpperCAmelCase      ):
									raise ValueError(
									    '''No images found in save path, aborting (did you pass save_intermediate=True to the generate'''
									    ''' function?)'''      )
					if len(__UpperCAmelCase      ) == 1:
									print('''Only one image found in save path, (did you pass save_intermediate=True to the generate function?)'''      )
					SCREAMING_SNAKE_CASE  :						int      					= total_duration / len(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Dict      					= [frame_duration] * len(__UpperCAmelCase      )
					if extend_frames:
									SCREAMING_SNAKE_CASE  :						Dict      					= 1.5
									SCREAMING_SNAKE_CASE  :						Any      					= 3
					for file_name in paths:
									if file_name.endswith('''.png'''      ):
													images.append(imageio.imread(__UpperCAmelCase      )      )
					imageio.mimsave(__UpperCAmelCase,					__UpperCAmelCase,					duration=__UpperCAmelCase      )
					print(F'''gif saved to {output_path}'''      )


	def 				lowercase__  (self : int,					__UpperCAmelCase : List[str]=None,					__UpperCAmelCase : Tuple=None      )     ->			Any:

					"""simple docstring"""

					if not (path or img):
									raise ValueError('''Input either path or tensor'''      )
					if img is not None:
									raise NotImplementedError
					SCREAMING_SNAKE_CASE  :						List[Any]      					= preprocess(Image.open(__UpperCAmelCase      ),					target_image_size=256      ).to(self.device      )
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= preprocess_vqgan(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						List[Any]      					= self.vqgan.encode(__UpperCAmelCase      )
					return z


	def 				lowercase__  (self : int,					__UpperCAmelCase : Any      )     ->			Union[str, Any]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						List[str]      					= self.latent.detach().requires_grad_()
					SCREAMING_SNAKE_CASE  :						Tuple      					= base_latent + transform_vector
					if self.quantize:
									SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= self.vqgan.quantize(__UpperCAmelCase      )
					else:
									SCREAMING_SNAKE_CASE  :						int      					= trans_latent
					return self.vqgan.decode(__UpperCAmelCase      )


	def 				lowercase__  (self : Optional[int],					__UpperCAmelCase : Tuple,					__UpperCAmelCase : Optional[int],					__UpperCAmelCase : str=None      )     ->			int:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Optional[int]      					= self.clip_preprocessor(text=__UpperCAmelCase,					images=__UpperCAmelCase,					return_tensors='''pt''',					padding=__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Dict      					= self.clip(**__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						List[Any]      					= clip_outputs.logits_per_image
					if weights is not None:
									SCREAMING_SNAKE_CASE  :						Optional[Any]      					= similarity_logits * weights
					return similarity_logits.sum()


	def 				lowercase__  (self : int,					__UpperCAmelCase : List[Any],					__UpperCAmelCase : List[Any],					__UpperCAmelCase : Any      )     ->			Optional[Any]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						int      					= self._get_clip_similarity(pos_prompts['''prompts'''],					__UpperCAmelCase,					weights=(1 / pos_prompts['''weights'''])      )
					if neg_prompts:
									SCREAMING_SNAKE_CASE  :						str      					= self._get_clip_similarity(neg_prompts['''prompts'''],					__UpperCAmelCase,					weights=neg_prompts['''weights''']      )
					else:
									SCREAMING_SNAKE_CASE  :						Optional[Any]      					= torch.tensor([1],					device=self.device      )
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= -torch.log(__UpperCAmelCase      ) + torch.log(__UpperCAmelCase      )
					return loss


	def 				lowercase__  (self : Any,					__UpperCAmelCase : Union[str, Any],					__UpperCAmelCase : str,					__UpperCAmelCase : int      )     ->			Union[str, Any]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						str      					= torch.randn_like(self.latent,					requires_grad=__UpperCAmelCase,					device=self.device      )
					SCREAMING_SNAKE_CASE  :						List[str]      					= torch.optim.Adam([vector],					lr=self.lr      )

					for i in range(self.iterations      ):
									optim.zero_grad()
									SCREAMING_SNAKE_CASE  :						Optional[int]      					= self._add_vector(__UpperCAmelCase      )
									SCREAMING_SNAKE_CASE  :						List[str]      					= loop_post_process(__UpperCAmelCase      )
									SCREAMING_SNAKE_CASE  :						Optional[int]      					= self._get_CLIP_loss(__UpperCAmelCase,					__UpperCAmelCase,					__UpperCAmelCase      )
									print('''CLIP loss''',					__UpperCAmelCase      )
									if self.log:
													wandb.log({'''CLIP Loss''': clip_loss}      )
									clip_loss.backward(retain_graph=__UpperCAmelCase      )
									optim.step()
									if self.return_val == "image":
													yield custom_to_pil(transformed_img[0]      )
									else:
													yield vector


	def 				lowercase__  (self : str,					__UpperCAmelCase : Union[str, Any],					__UpperCAmelCase : Tuple,					__UpperCAmelCase : Dict      )     ->			Optional[int]:

					"""simple docstring"""

					wandb.init(reinit=__UpperCAmelCase,					project='''face-editor'''      )
					wandb.config.update({'''Positive Prompts''': positive_prompts}      )
					wandb.config.update({'''Negative Prompts''': negative_prompts}      )
					wandb.config.update({'''lr''': self.lr, '''iterations''': self.iterations}      )
					if image_path:
									SCREAMING_SNAKE_CASE  :						List[str]      					= Image.open(__UpperCAmelCase      )
									SCREAMING_SNAKE_CASE  :						List[str]      					= image.resize((256, 256)      )
									wandb.log('''Original Image''',					wandb.Image(__UpperCAmelCase      )      )


	def 				lowercase__  (self : Dict,					__UpperCAmelCase : int      )     ->			Union[str, Any]:

					"""simple docstring"""

					if not prompts:
									return []
					SCREAMING_SNAKE_CASE  :						List[Any]      					= []
					SCREAMING_SNAKE_CASE  :						int      					= []
					if isinstance(__UpperCAmelCase,					__UpperCAmelCase      ):
									SCREAMING_SNAKE_CASE  :						List[str]      					= [prompt.strip() for prompt in prompts.split('''|'''      )]
					for prompt in prompts:
									if isinstance(__UpperCAmelCase,					(tuple, list)      ):
													SCREAMING_SNAKE_CASE  :						Tuple      					= prompt[0]
													SCREAMING_SNAKE_CASE  :						Tuple      					= float(prompt[1]      )
									elif ":" in prompt:
													SCREAMING_SNAKE_CASE  :						Optional[Any]      					= prompt.split(''':'''      )
													SCREAMING_SNAKE_CASE  :						List[Any]      					= float(__UpperCAmelCase      )
									else:
													SCREAMING_SNAKE_CASE  :						Optional[int]      					= prompt
													SCREAMING_SNAKE_CASE  :						Tuple      					= 1.0
									processed_prompts.append(__UpperCAmelCase      )
									weights.append(__UpperCAmelCase      )
					return {
					    "prompts": processed_prompts,
					    "weights": torch.tensor(__UpperCAmelCase,					device=self.device      ),
					}






	def 				lowercase__  (self : List[str],					__UpperCAmelCase : Optional[int],					__UpperCAmelCase : Tuple=None,					__UpperCAmelCase : Optional[int]=None,					__UpperCAmelCase : Optional[int]=True,					__UpperCAmelCase : List[str]=False,					__UpperCAmelCase : Union[str, Any]=True,					__UpperCAmelCase : Dict=True,					__UpperCAmelCase : int=None,					)     ->			Any:

					"""simple docstring"""

					if image_path:
									SCREAMING_SNAKE_CASE  :						str      					= self._get_latent(__UpperCAmelCase      )
					else:
									SCREAMING_SNAKE_CASE  :						Any      					= torch.randn(self.latent_dim,					device=self.device      )
					if self.log:
									self._init_logging(__UpperCAmelCase,					__UpperCAmelCase,					__UpperCAmelCase      )

					assert pos_prompts, "You must provide at least one positive prompt."
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= self.process_prompts(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Any      					= self.process_prompts(__UpperCAmelCase      )
					if save_final and save_path is None:
									SCREAMING_SNAKE_CASE  :						List[str]      					= os.path.join('''./outputs/''',					'''_'''.join(pos_prompts['''prompts''']      )      )
					if not os.path.exists(__UpperCAmelCase      ):
									os.makedirs(__UpperCAmelCase      )
					else:
									SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= save_path + '''_''' + get_timestamp()
									os.makedirs(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						List[str]      					= save_path

					SCREAMING_SNAKE_CASE  :						List[str]      					= self.vqgan.decode(self.latent      )[0]
					if show_intermediate:
									print('''Original Image'''      )
									show_pil(custom_to_pil(__UpperCAmelCase      )      )

					SCREAMING_SNAKE_CASE  :						Tuple      					= loop_post_process(__UpperCAmelCase      )
					for iter, transformed_img in enumerate(self._optimize_CLIP(__UpperCAmelCase,					__UpperCAmelCase,					__UpperCAmelCase      )      ):
									if show_intermediate:
													show_pil(__UpperCAmelCase      )
									if save_intermediate:
													transformed_img.save(os.path.join(self.save_path,					F'''iter_{iter:03d}.png'''      )      )
									if self.log:
													wandb.log({'''Image''': wandb.Image(__UpperCAmelCase      )}      )
					if show_final:
									show_pil(__UpperCAmelCase      )
					if save_final:
									transformed_img.save(os.path.join(self.save_path,					F'''iter_{iter:03d}_final.png'''      )      )









'''simple docstring'''

import heapq
import sys

import numpy as np

snake_case_			=     tuple[int, int]


class 	a__     :


	def __init__(self : int      )     ->			int:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						str      					= []
					SCREAMING_SNAKE_CASE  :						Tuple      					= set()


	def 				lowercase__  (self : Any      )     ->			Dict:

					"""simple docstring"""

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


	def 				lowercase__  (self : Dict      )     ->			Union[str, Any]:

					"""simple docstring"""

					return len(self.elements      ) == 0


	def 				lowercase__  (self : Dict,					__UpperCAmelCase : int,					__UpperCAmelCase : List[Any]      )     ->			Tuple:

					"""simple docstring"""

					if item not in self.set:
									heapq.heappush(self.elements,					(priority, item)      )
									self.set.add(__UpperCAmelCase      )
					else:
									# update
									# print("update", item)
									SCREAMING_SNAKE_CASE  :						List[Any]      					= []
									((SCREAMING_SNAKE_CASE)					,					(SCREAMING_SNAKE_CASE))  :						Any      					= heapq.heappop(self.elements      )
									while x != item:
													temp.append((pri, x)      )
													((SCREAMING_SNAKE_CASE)					,					(SCREAMING_SNAKE_CASE))  :						Any      					= heapq.heappop(self.elements      )
									temp.append((priority, item)      )
									for pro, xxx in temp:
													heapq.heappush(self.elements,					(pro, xxx)      )


	def 				lowercase__  (self : Union[str, Any],					__UpperCAmelCase : Optional[Any]      )     ->			Optional[int]:

					"""simple docstring"""

					if item in self.set:
									self.set.remove(__UpperCAmelCase      )
									SCREAMING_SNAKE_CASE  :						Optional[int]      					= []
									((SCREAMING_SNAKE_CASE)					,					(SCREAMING_SNAKE_CASE))  :						Any      					= heapq.heappop(self.elements      )
									while x != item:
													temp.append((pro, x)      )
													((SCREAMING_SNAKE_CASE)					,					(SCREAMING_SNAKE_CASE))  :						Optional[Any]      					= heapq.heappop(self.elements      )
									for prito, yyy in temp:
													heapq.heappush(self.elements,					(prito, yyy)      )


	def 				lowercase__  (self : Tuple      )     ->			Dict:

					"""simple docstring"""

					return self.elements[0][1]






	def 				lowercase__  (self : Optional[Any]      )     ->			Tuple:

					"""simple docstring"""

					((SCREAMING_SNAKE_CASE)					,					(SCREAMING_SNAKE_CASE))  :						Optional[int]      					= heapq.heappop(self.elements      )
					self.set.remove(__UpperCAmelCase      )
					return (priority, item)






def       __lowercase  (_SCREAMING_SNAKE_CASE      :TPos    ,	_SCREAMING_SNAKE_CASE      :TPos       ):
				# euclidean distance
				SCREAMING_SNAKE_CASE  :						str      					= np.array(_SCREAMING_SNAKE_CASE       )
				SCREAMING_SNAKE_CASE  :						List[Any]      					= np.array(_SCREAMING_SNAKE_CASE       )
				return np.linalg.norm(a - b       )






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






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






def       __lowercase  (_SCREAMING_SNAKE_CASE      :TPos    ,	_SCREAMING_SNAKE_CASE      :int    ,	_SCREAMING_SNAKE_CASE      :TPos    ,	_SCREAMING_SNAKE_CASE      :dict[TPos, float]       ):
				SCREAMING_SNAKE_CASE  :						List[str]      					= g_function[start] + Wa * heuristics[i](_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE       )
				return ans






def       __lowercase  (_SCREAMING_SNAKE_CASE      :Optional[Any]    ,	_SCREAMING_SNAKE_CASE      :List[str]    ,	_SCREAMING_SNAKE_CASE      :Tuple       ):
				SCREAMING_SNAKE_CASE  :						Optional[int]      					= np.chararray((n, n)       )
				for i in range(_SCREAMING_SNAKE_CASE       ):
								for j in range(_SCREAMING_SNAKE_CASE       ):
												SCREAMING_SNAKE_CASE  :						Tuple      					= '''*'''

				for i in range(_SCREAMING_SNAKE_CASE       ):
								for j in range(_SCREAMING_SNAKE_CASE       ):
												if (j, (n - 1) - i) in blocks:
																SCREAMING_SNAKE_CASE  :						Tuple      					= '''#'''

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

				for i in range(_SCREAMING_SNAKE_CASE       ):
								for j in range(_SCREAMING_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:-'''       )
				SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= back_pointer[goal]
				while x != start:
								print(_SCREAMING_SNAKE_CASE    ,	end=''' '''       )
								SCREAMING_SNAKE_CASE  :						Optional[int]      					= back_pointer[x]
				print(_SCREAMING_SNAKE_CASE       )
				sys.exit()






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

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

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






def       __lowercase  ():
				SCREAMING_SNAKE_CASE  :						Optional[int]      					= []
				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


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

snake_case_			=     [
    (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),
]
snake_case_			=     make_common_ground()


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

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

snake_case_			=     1






def       __lowercase  (_SCREAMING_SNAKE_CASE      :TPos    ,	_SCREAMING_SNAKE_CASE      :TPos    ,	_SCREAMING_SNAKE_CASE      :int       ):
				SCREAMING_SNAKE_CASE  :						Any      					= {start: 0, goal: float('''inf'''       )}
				SCREAMING_SNAKE_CASE  :						Tuple      					= {start: -1, goal: -1}
				SCREAMING_SNAKE_CASE  :						Tuple      					= []
				SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= set()

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

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




