

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 _UpperCamelCase							:
			'''simple docstring'''







			def __init__(			self							: Dict      ,      a							: str = "cpu"      ,      a							: str = "openai/clip-vit-large-patch14"	)							->       List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Tuple				     =       device
				SCREAMING_SNAKE_CASE   :      str				     =       CLIPTokenizerFast.from_pretrained(__UpperCamelCase	)
				SCREAMING_SNAKE_CASE   :      Dict				     =       [0.4814_5466, 0.457_8275, 0.4082_1073]
				SCREAMING_SNAKE_CASE   :      int				     =       [0.2686_2954, 0.2613_0258, 0.2757_7711]
				SCREAMING_SNAKE_CASE   :      Dict				     =       torchvision.transforms.Normalize(self.image_mean      ,      self.image_std	)
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       torchvision.transforms.Resize(224	)
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       torchvision.transforms.CenterCrop(224	)
			def 							__UpperCamelCase (			self							: str      ,      a							: Dict	)							->       List[Any]:

				"""simple docstring"""

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






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

				"""simple docstring"""

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



class _UpperCamelCase							(							nn.Module   ):
			'''simple docstring'''







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

				"""simple docstring"""

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

				SCREAMING_SNAKE_CASE   :      List[str]				     =       iterations
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       lr
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       log
				SCREAMING_SNAKE_CASE   :      Any				     =       make_grid
				SCREAMING_SNAKE_CASE   :      str				     =       return_val
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       quantize
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       self.vqgan.decoder.z_shape
			def 							__UpperCamelCase (			self							: Optional[int]      ,      a							: List[str]=None      ,      a							: Optional[Any]=None      ,      a							: str=5      ,      a							: int=True	)							->       Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      List[Any]				     =       []
				if output_path is None:
					SCREAMING_SNAKE_CASE   :      Dict				     =       "./animation.gif"
				if input_path is None:
					SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       self.save_path
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       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   :      List[Any]				     =       total_duration / len(__UpperCamelCase	)
				SCREAMING_SNAKE_CASE   :      Any				     =       [frame_duration] * len(__UpperCamelCase	)
				if extend_frames:
					SCREAMING_SNAKE_CASE   :      Tuple				     =       1.5
					SCREAMING_SNAKE_CASE   :      Tuple				     =       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 							__UpperCamelCase (			self							: List[Any]      ,      a							: Union[str, Any]=None      ,      a							: Dict=None	)							->       Optional[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[str]				     =       preprocess(Image.open(__UpperCamelCase	)      ,      target_image_size=256	).to(self.device	)
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       preprocess_vqgan(__UpperCamelCase	)
				SCREAMING_SNAKE_CASE     ,*SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       self.vqgan.encode(__UpperCamelCase	)
				return z
			def 							__UpperCamelCase (			self							: int      ,      a							: Dict	)							->       Union[str, Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Any				     =       self.latent.detach().requires_grad_()
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       base_latent + transform_vector
				if self.quantize:
					SCREAMING_SNAKE_CASE     ,*SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       self.vqgan.quantize(__UpperCamelCase	)
				else:
					SCREAMING_SNAKE_CASE   :      Tuple				     =       trans_latent
				return self.vqgan.decode(__UpperCamelCase	)
			def 							__UpperCamelCase (			self							: Tuple      ,      a							: int      ,      a							: Tuple      ,      a							: Dict=None	)							->       Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       self.clip_preprocessor(text=__UpperCamelCase      ,      images=__UpperCamelCase      ,      return_tensors="pt"      ,      padding=__UpperCamelCase	)
				SCREAMING_SNAKE_CASE   :      str				     =       self.clip(**__UpperCamelCase	)
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       clip_outputs.logits_per_image
				if weights is not None:
					SCREAMING_SNAKE_CASE   :      Dict				     =       similarity_logits * weights
				return similarity_logits.sum()
			def 							__UpperCamelCase (			self							: Optional[Any]      ,      a							: List[Any]      ,      a							: str      ,      a							: Union[str, Any]	)							->       Tuple:

				"""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   :      List[str]				     =       self._get_clip_similarity(neg_prompts["prompts"]      ,      __UpperCamelCase      ,      weights=neg_prompts["weights"]	)
				else:
					SCREAMING_SNAKE_CASE   :      Dict				     =       torch.tensor([1]      ,      device=self.device	)
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       -torch.log(__UpperCamelCase	) + torch.log(__UpperCamelCase	)
				return loss
			def 							__UpperCamelCase (			self							: Optional[Any]      ,      a							: Any      ,      a							: str      ,      a							: Optional[int]	)							->       List[Any]:

				"""simple docstring"""

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

				for i in range(self.iterations	):
					optim.zero_grad()
					SCREAMING_SNAKE_CASE   :      int				     =       self._add_vector(__UpperCamelCase	)
					SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       loop_post_process(__UpperCamelCase	)
					SCREAMING_SNAKE_CASE   :      List[str]				     =       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 							__UpperCamelCase (			self							: List[Any]      ,      a							: str      ,      a							: Dict      ,      a							: Any	)							->       Any:

				"""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   :      Optional[int]				     =       Image.open(__UpperCamelCase	)
					SCREAMING_SNAKE_CASE   :      str				     =       image.resize((256, 256)	)
					wandb.log("Original Image"      ,      wandb.Image(__UpperCamelCase	)	)
			def 							__UpperCamelCase (			self							: Dict      ,      a							: Optional[Any]	)							->       Tuple:

				"""simple docstring"""

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






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

				"""simple docstring"""

				if image_path:
					SCREAMING_SNAKE_CASE   :      Optional[int]				     =       self._get_latent(__UpperCamelCase	)
				else:
					SCREAMING_SNAKE_CASE   :      Union[str, 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   :      List[Any]				     =       self.process_prompts(__UpperCamelCase	)
				SCREAMING_SNAKE_CASE   :      Any				     =       self.process_prompts(__UpperCamelCase	)
				if save_final and save_path is None:
					SCREAMING_SNAKE_CASE   :      Dict				     =       os.path.join("./outputs/"      ,      "_".join(pos_prompts["prompts"]	)	)
				if not os.path.exists(__UpperCamelCase	):
					os.makedirs(__UpperCamelCase	)
				else:
					SCREAMING_SNAKE_CASE   :      Optional[int]				     =       save_path + "_" + get_timestamp()
					os.makedirs(__UpperCamelCase	)
				SCREAMING_SNAKE_CASE   :      int				     =       save_path

				SCREAMING_SNAKE_CASE   :      Any				     =       self.vqgan.decode(self.latent	)[0]
				if show_intermediate:
					print("Original Image"	)
					show_pil(custom_to_pil(__UpperCamelCase	)	)

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       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"	)	)


import numpy as np






class _UpperCamelCase							:
			'''simple docstring'''







			def __init__(			self							: Dict	)							->       str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      List[str]				     =       (0, 0)
				SCREAMING_SNAKE_CASE   :      Any				     =       None
				SCREAMING_SNAKE_CASE   :      Any				     =       0
				SCREAMING_SNAKE_CASE   :      List[str]				     =       0
				SCREAMING_SNAKE_CASE   :      Any				     =       0
			def __eq__(			self							: List[str]      ,      a							: Any	)							->       Tuple:

				"""simple docstring"""

				return self.position == cell.position






			def 							__UpperCamelCase (			self							: Optional[Any]	)							->       str:

				"""simple docstring"""

				print(self.position	)






class _UpperCamelCase							:
			'''simple docstring'''







			def __init__(			self							: List[Any]      ,      a							: List[Any]=(5, 5)	)							->       Optional[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       np.zeros(a	)
				SCREAMING_SNAKE_CASE   :      str				     =       world_size[0]
				SCREAMING_SNAKE_CASE   :      int				     =       world_size[1]
			def 							__UpperCamelCase (			self							: Union[str, Any]	)							->       Optional[int]:

				"""simple docstring"""

				print(self.w	)






			def 							__UpperCamelCase (			self							: Dict      ,      a							: Dict	)							->       Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       [
				    (-1, -1),
				    (-1, 0),
				    (-1, 1),
				    (0, -1),
				    (0, 1),
				    (1, -1),
				    (1, 0),
				    (1, 1),
				]
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       cell.position[0]
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       cell.position[1]
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       []
				for n in neughbour_cord:
					SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       current_x + n[0]
					SCREAMING_SNAKE_CASE   :      int				     =       current_y + n[1]
					if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
						SCREAMING_SNAKE_CASE   :      int				     =       Cell()
						SCREAMING_SNAKE_CASE   :      str				     =       (x, y)
						SCREAMING_SNAKE_CASE   :      Any				     =       cell
						neighbours.append(a	)
				return neighbours

def  lowerCamelCase__ (					_a							,			_a							,			_a):
	SCREAMING_SNAKE_CASE   :      Tuple				     =       []
	SCREAMING_SNAKE_CASE   :      Tuple				     =       []
	_open.append(_a)

	while _open:
		SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       np.argmin([n.f for n in _open])
		SCREAMING_SNAKE_CASE   :      List[str]				     =       _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
			SCREAMING_SNAKE_CASE   :      Tuple				     =       current.g + 1
			SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      List[str]				     =       n.position
			SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      str				     =       goal.position
			SCREAMING_SNAKE_CASE   :      Any				     =       (ya - ya) ** 2 + (xa - xa) ** 2
			SCREAMING_SNAKE_CASE   :      List[str]				     =       n.h + n.g

			for c in _open:
				if c == n and c.f < n.f:
					continue
			_open.append(_a)
	SCREAMING_SNAKE_CASE   :      Tuple				     =       []
	while current.parent is not None:
		path.append(current.position)
		SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       current.parent
	path.append(current.position)
	return path[::-1]


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