


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       lowerCamelCase__   :





     def __init__(self		,     UpperCAmelCase = "cpu"		,     UpperCAmelCase = "openai/clip-vit-large-patch14"       )     ->   Tuple:
      _lowercase							      =device
      _lowercase							      =CLIPTokenizerFast.from_pretrained(__lowerCamelCase       )
      _lowercase							      =[0.4814_5466, 0.457_8275, 0.4082_1073]
      _lowercase							      =[0.2686_2954, 0.2613_0258, 0.2757_7711]
      _lowercase							      =torchvision.transforms.Normalize(self.image_mean		,     self.image_std       )
      _lowercase							      =torchvision.transforms.Resize(2_2_4       )
      _lowercase							      =torchvision.transforms.CenterCrop(2_2_4       )





     def      __A					(self		,     UpperCAmelCase       )     ->   Optional[int]:
      _lowercase							      =self.resize(__lowerCamelCase       )
      _lowercase							      =self.center_crop(__lowerCamelCase       )
      _lowercase							      =self.normalize(__lowerCamelCase       )
      return images





     def __call__(self		,     UpperCAmelCase=None		,     UpperCAmelCase=None		,     **UpperCAmelCase       )     ->   Union[str, Any]:
      _lowercase							      =self.tokenizer(text=__lowerCamelCase		,     **__lowerCamelCase       )
      _lowercase							      =self.preprocess_img(__lowerCamelCase       )
      _lowercase							      ={key: value.to(self.device       ) for (key, value) in encoding.items()}
      return encoding







class       lowerCamelCase__   (       nn.Module):





     def __init__(self		,     UpperCAmelCase=1_0		,     UpperCAmelCase=0.01		,     UpperCAmelCase=None		,     UpperCAmelCase=None		,     UpperCAmelCase=None		,     UpperCAmelCase=None		,     UpperCAmelCase=None		,     UpperCAmelCase=None		,     UpperCAmelCase=False		,     UpperCAmelCase=True		,     UpperCAmelCase="image"		,     UpperCAmelCase=True		,     UpperCAmelCase=False		,     UpperCAmelCase=False		,     UpperCAmelCase=False		,     )     ->   str:
      super().__init__()
      _lowercase							      =None
      _lowercase							      =device if device else get_device()
      if vqgan:
       _lowercase							      =vqgan
      else:
       _lowercase							      =load_vqgan(self.device		,     conf_path=__lowerCamelCase		,     ckpt_path=__lowerCamelCase       )
      self.vqgan.eval()
      if clip:
       _lowercase							      =clip
      else:
       _lowercase							      =CLIPModel.from_pretrained('''openai/clip-vit-base-patch32'''       )
      self.clip.to(self.device       )
      _lowercase							      =ProcessorGradientFlow(device=self.device       )

      _lowercase							      =iterations
      _lowercase							      =lr
      _lowercase							      =log
      _lowercase							      =make_grid
      _lowercase							      =return_val
      _lowercase							      =quantize
      _lowercase							      =self.vqgan.decoder.z_shape





     def      __A					(self		,     UpperCAmelCase=None		,     UpperCAmelCase=None		,     UpperCAmelCase=5		,     UpperCAmelCase=True       )     ->   int:
      _lowercase							      =[]
      if output_path is None:
       _lowercase							      ="./animation.gif"
      if input_path is None:
       _lowercase							      =self.save_path
      _lowercase							      =sorted(glob(input_path + '''/*'''       )       )
      if not len(__lowerCamelCase       ):
       raise ValueError(
           '''No images found in save path, aborting (did you pass save_intermediate=True to the generate'''
           ''' function?)'''       )
      if len(__lowerCamelCase       ) == 1:
       print('''Only one image found in save path, (did you pass save_intermediate=True to the generate function?)'''       )
      _lowercase							      =total_duration / len(__lowerCamelCase       )
      _lowercase							      =[frame_duration] * len(__lowerCamelCase       )
      if extend_frames:
       _lowercase							      =1.5
       _lowercase							      =3
      for file_name in paths:
       if file_name.endswith('''.png'''       ):
        images.append(imageio.imread(__lowerCamelCase       )       )
      imageio.mimsave(__lowerCamelCase		,     __lowerCamelCase		,     duration=__lowerCamelCase       )
      print(f"gif saved to {output_path}"       )





     def      __A					(self		,     UpperCAmelCase=None		,     UpperCAmelCase=None       )     ->   int:



      if not (path or img):
       raise ValueError('''Input either path or tensor'''       )
      if img is not None:
       raise NotImplementedError
      _lowercase							      =preprocess(Image.open(__lowerCamelCase       )		,     target_image_size=2_5_6       ).to(self.device       )
      _lowercase							      =preprocess_vqgan(__lowerCamelCase       )
      _lowercase							      =self.vqgan.encode(__lowerCamelCase       )
      return z





     def      __A					(self		,     UpperCAmelCase       )     ->   List[str]:
      _lowercase							      =self.latent.detach().requires_grad_()
      _lowercase							      =base_latent + transform_vector
      if self.quantize:
       _lowercase							      =self.vqgan.quantize(__lowerCamelCase       )
      else:
       _lowercase							      =trans_latent
      return self.vqgan.decode(__lowerCamelCase       )





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase=None       )     ->   List[Any]:
      _lowercase							      =self.clip_preprocessor(text=__lowerCamelCase		,     images=__lowerCamelCase		,     return_tensors='''pt'''		,     padding=__lowerCamelCase       )
      _lowercase							      =self.clip(**__lowerCamelCase       )
      _lowercase							      =clip_outputs.logits_per_image
      if weights is not None:
       _lowercase							      =similarity_logits * weights
      return similarity_logits.sum()





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase       )     ->   str:
      _lowercase							      =self._get_clip_similarity(pos_prompts['''prompts''']		,     __lowerCamelCase		,     weights=(1 / pos_prompts['''weights'''])       )
      if neg_prompts:
       _lowercase							      =self._get_clip_similarity(neg_prompts['''prompts''']		,     __lowerCamelCase		,     weights=neg_prompts['''weights''']       )
      else:
       _lowercase							      =torch.tensor([1]		,     device=self.device       )
      _lowercase							      =-torch.log(__lowerCamelCase       ) + torch.log(__lowerCamelCase       )
      return loss





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase       )     ->   int:
      _lowercase							      =torch.randn_like(self.latent		,     requires_grad=__lowerCamelCase		,     device=self.device       )
      _lowercase							      =torch.optim.Adam([vector]		,     lr=self.lr       )

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





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase       )     ->   Optional[Any]:
      wandb.init(reinit=__lowerCamelCase		,     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:
       _lowercase							      =Image.open(__lowerCamelCase       )
       _lowercase							      =image.resize((2_5_6, 2_5_6)       )
       wandb.log('''Original Image'''		,     wandb.Image(__lowerCamelCase       )       )





     def      __A					(self		,     UpperCAmelCase       )     ->   Optional[int]:



      if not prompts:
       return []
      _lowercase							      =[]
      _lowercase							      =[]
      if isinstance(__lowerCamelCase		,     __lowerCamelCase       ):
       _lowercase							      =[prompt.strip() for prompt in prompts.split('''|'''       )]
      for prompt in prompts:
       if isinstance(__lowerCamelCase		,     (tuple, list)       ):
        _lowercase							      =prompt[0]
        _lowercase							      =float(prompt[1]       )
       elif ":" in prompt:
        _lowercase							      =prompt.split(''':'''       )
        _lowercase							      =float(__lowerCamelCase       )
       else:
        _lowercase							      =prompt
        _lowercase							      =1.0
       processed_prompts.append(__lowerCamelCase       )
       weights.append(__lowerCamelCase       )
      return {
          "prompts": processed_prompts,
          "weights": torch.tensor(__lowerCamelCase		,     device=self.device       ),
      }





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase=None		,     UpperCAmelCase=None		,     UpperCAmelCase=True		,     UpperCAmelCase=False		,     UpperCAmelCase=True		,     UpperCAmelCase=True		,     UpperCAmelCase=None		,     )     ->   List[str]:



      if image_path:
       _lowercase							      =self._get_latent(__lowerCamelCase       )
      else:
       _lowercase							      =torch.randn(self.latent_dim		,     device=self.device       )
      if self.log:
       self._init_logging(__lowerCamelCase		,     __lowerCamelCase		,     __lowerCamelCase       )

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

      _lowercase							      =self.vqgan.decode(self.latent       )[0]
      if show_intermediate:
       print('''Original Image'''       )
       show_pil(custom_to_pil(__lowerCamelCase       )       )

      _lowercase							      =loop_post_process(__lowerCamelCase       )
      for iter, transformed_img in enumerate(self._optimize_CLIP(__lowerCamelCase		,     __lowerCamelCase		,     __lowerCamelCase       )       ):
       if show_intermediate:
        show_pil(__lowerCamelCase       )
       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(__lowerCamelCase       )}       )
      if show_final:
       show_pil(__lowerCamelCase       )
      if save_final:
       transformed_img.save(os.path.join(self.save_path		,     f"iter_{iter:03d}_final.png"       )       )





from __future__ import annotations

A      :    Union[str, Any]		=					{
    "A": ["B", "C", "E"],
    "B": ["A", "D", "E"],
    "C": ["A", "F", "G"],
    "D": ["B"],
    "E": ["A", "B", "D"],
    "F": ["C"],
    "G": ["C"],
}







class 			_lowercase :


       """simple docstring"""




       def __init__(   self							:			Tuple					,      __lowerCamelCase							:			dict[str, list[str]]					,      __lowerCamelCase							:			str       ):


             '''simple docstring'''



             lowerCamelCase__		:				Union[str, Any]    =  graph
             # mapping node to its parent in resulting breadth first tree
             lowerCamelCase__		:				dict[str, str | None]    =  {}
             lowerCamelCase__		:				Dict    =  source_vertex





       def 						lowerCAmelCase     (   self							:			Any       ):


             '''simple docstring'''



             lowerCamelCase__		:				int    =  {self.source_vertex}
             lowerCamelCase__		:				Optional[int]    =  None
             lowerCamelCase__		:				Dict    =  [self.source_vertex]  # first in first out queue

             while queue:
                   lowerCamelCase__		:				Optional[int]    =  queue.pop(0       )
                   for adjacent_vertex in self.graph[vertex]:
                         if adjacent_vertex not in visited:
                               visited.add(__lowerCamelCase       )
                               lowerCamelCase__		:				List[str]    =  vertex
                               queue.append(__lowerCamelCase       )





       def 						lowerCAmelCase     (   self							:			Optional[Any]					,      __lowerCamelCase							:			str       ):


             '''simple docstring'''



             if target_vertex == self.source_vertex:
                   return self.source_vertex

             lowerCamelCase__		:				Tuple    =  self.parent.get(__lowerCamelCase       )
             if target_vertex_parent is None:
                   lowerCamelCase__		:				Tuple    =  (
                       f"No path from vertex: {self.source_vertex} to vertex: {target_vertex}"
                   )
                   raise ValueError(__lowerCamelCase       )

             return self.shortest_path(__lowerCamelCase       ) + f"->{target_vertex}"


if __name__ == "__main__":
    A      :    List[str]		=					Graph(graph, "G")
    g.breath_first_search()
    print(g.shortest_path("D"))
    print(g.shortest_path("G"))
    print(g.shortest_path("Foo"))


