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 , lowerCAmelCase = "cpu" , lowerCAmelCase = "openai/clip-vit-large-patch14"   )       ->					None:
     SCREAMING_SNAKE_CASE__:   str=      device
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      CLIPTokenizerFast.from_pretrained(lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   Any=      [0.48145466, 0.4578275, 0.40821073]
     SCREAMING_SNAKE_CASE__:   Any=      [0.26862954, 0.26130258, 0.27577711]
     SCREAMING_SNAKE_CASE__:   Optional[int]=      torchvision.transforms.Normalize(self.image_mean , self.image_std   )
     SCREAMING_SNAKE_CASE__:   str=      torchvision.transforms.Resize(224   )
     SCREAMING_SNAKE_CASE__:   Optional[int]=      torchvision.transforms.CenterCrop(224   )
    def 						UpperCamelCase_				(  self , lowerCAmelCase   )       ->					Any:
     SCREAMING_SNAKE_CASE__:   str=      self.resize(lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   List[str]=      self.center_crop(lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   List[str]=      self.normalize(lowerCAmelCase   )
     return images



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






class 		_lowerCamelCase		(   nn.Module    ):
    def __init__(  self , lowerCAmelCase=10 , lowerCAmelCase=0.01 , lowerCAmelCase=None , lowerCAmelCase=None , lowerCAmelCase=None , lowerCAmelCase=None , lowerCAmelCase=None , lowerCAmelCase=None , lowerCAmelCase=False , lowerCAmelCase=True , lowerCAmelCase="image" , lowerCAmelCase=True , lowerCAmelCase=False , lowerCAmelCase=False , lowerCAmelCase=False , )       ->					None:
     super().__init__()
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      None
     SCREAMING_SNAKE_CASE__:   Any=      device if device else get_device()
     if vqgan:
      SCREAMING_SNAKE_CASE__:   Tuple=      vqgan
     else:
      SCREAMING_SNAKE_CASE__:   Optional[Any]=      load_vqgan(self.device , conf_path=lowerCAmelCase , ckpt_path=lowerCAmelCase   )
     self.vqgan.eval()
     if clip:
      SCREAMING_SNAKE_CASE__:   List[str]=      clip
     else:
      SCREAMING_SNAKE_CASE__:   Optional[Any]=      CLIPModel.from_pretrained('''openai/clip-vit-base-patch32'''   )
     self.clip.to(self.device   )
     SCREAMING_SNAKE_CASE__:   Optional[int]=      ProcessorGradientFlow(device=self.device   )

     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      iterations
     SCREAMING_SNAKE_CASE__:   str=      lr
     SCREAMING_SNAKE_CASE__:   List[Any]=      log
     SCREAMING_SNAKE_CASE__:   List[str]=      make_grid
     SCREAMING_SNAKE_CASE__:   List[Any]=      return_val
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      quantize
     SCREAMING_SNAKE_CASE__:   List[Any]=      self.vqgan.decoder.z_shape
    def 						UpperCamelCase_				(  self , lowerCAmelCase=None , lowerCAmelCase=None , lowerCAmelCase=5 , lowerCAmelCase=True   )       ->					Union[str, Any]:
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      []
     if output_path is None:
      SCREAMING_SNAKE_CASE__:   Dict=      "./animation.gif"
     if input_path is None:
      SCREAMING_SNAKE_CASE__:   Optional[Any]=      self.save_path
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      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?)'''   )
     SCREAMING_SNAKE_CASE__:   List[str]=      total_duration / len(lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   str=      [frame_duration] * len(lowerCAmelCase   )
     if extend_frames:
      SCREAMING_SNAKE_CASE__:   Dict=      1.5
      SCREAMING_SNAKE_CASE__:   List[str]=      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 						UpperCamelCase_				(  self , lowerCAmelCase=None , lowerCAmelCase=None   )       ->					str:
     if not (path or img):
      raise ValueError('''Input either path or tensor'''   )
     if img is not None:
      raise NotImplementedError
     SCREAMING_SNAKE_CASE__:   Dict=      preprocess(Image.open(lowerCAmelCase   ) , target_image_size=256   ).to(self.device   )
     SCREAMING_SNAKE_CASE__:   Tuple=      preprocess_vqgan(lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   int=      self.vqgan.encode(lowerCAmelCase   )
     return z
    def 						UpperCamelCase_				(  self , lowerCAmelCase   )       ->					List[Any]:
     SCREAMING_SNAKE_CASE__:   Tuple=      self.latent.detach().requires_grad_()
     SCREAMING_SNAKE_CASE__:   Dict=      base_latent + transform_vector
     if self.quantize:
      SCREAMING_SNAKE_CASE__:   str=      self.vqgan.quantize(lowerCAmelCase   )
     else:
      SCREAMING_SNAKE_CASE__:   int=      trans_latent
     return self.vqgan.decode(lowerCAmelCase   )
    def 						UpperCamelCase_				(  self , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase=None   )       ->					Union[str, Any]:
     SCREAMING_SNAKE_CASE__:   Tuple=      self.clip_preprocessor(text=lowerCAmelCase , images=lowerCAmelCase , return_tensors='''pt''' , padding=lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   Tuple=      self.clip(**lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      clip_outputs.logits_per_image
     if weights is not None:
      SCREAMING_SNAKE_CASE__:   str=      similarity_logits * weights
     return similarity_logits.sum()
    def 						UpperCamelCase_				(  self , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase   )       ->					int:
     SCREAMING_SNAKE_CASE__:   List[str]=      self._get_clip_similarity(pos_prompts['''prompts'''] , lowerCAmelCase , weights=(1 / pos_prompts['''weights'''])   )
     if neg_prompts:
      SCREAMING_SNAKE_CASE__:   Tuple=      self._get_clip_similarity(neg_prompts['''prompts'''] , lowerCAmelCase , weights=neg_prompts['''weights''']   )
     else:
      SCREAMING_SNAKE_CASE__:   str=      torch.tensor([1] , device=self.device   )
     SCREAMING_SNAKE_CASE__:   Optional[int]=      -torch.log(lowerCAmelCase   ) + torch.log(lowerCAmelCase   )
     return loss
    def 						UpperCamelCase_				(  self , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase   )       ->					Any:
     SCREAMING_SNAKE_CASE__:   int=      torch.randn_like(self.latent , requires_grad=lowerCAmelCase , device=self.device   )
     SCREAMING_SNAKE_CASE__:   Any=      torch.optim.Adam([vector] , lr=self.lr   )

     for i in range(self.iterations   ):
      optim.zero_grad()
      SCREAMING_SNAKE_CASE__:   Tuple=      self._add_vector(lowerCAmelCase   )
      SCREAMING_SNAKE_CASE__:   Union[str, Any]=      loop_post_process(lowerCAmelCase   )
      SCREAMING_SNAKE_CASE__:   Optional[Any]=      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 						UpperCamelCase_				(  self , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase   )       ->					List[str]:
     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:
      SCREAMING_SNAKE_CASE__:   List[str]=      Image.open(lowerCAmelCase   )
      SCREAMING_SNAKE_CASE__:   int=      image.resize((256, 256)   )
      wandb.log('''Original Image''' , wandb.Image(lowerCAmelCase   )   )
    def 						UpperCamelCase_				(  self , lowerCAmelCase   )       ->					int:
     if not prompts:
      return []
     SCREAMING_SNAKE_CASE__:   int=      []
     SCREAMING_SNAKE_CASE__:   Any=      []
     if isinstance(lowerCAmelCase , lowerCAmelCase   ):
      SCREAMING_SNAKE_CASE__:   int=      [prompt.strip() for prompt in prompts.split('''|'''   )]
     for prompt in prompts:
      if isinstance(lowerCAmelCase , (tuple, list)   ):
       SCREAMING_SNAKE_CASE__:   List[Any]=      prompt[0]
       SCREAMING_SNAKE_CASE__:   List[Any]=      float(prompt[1]   )
      elif ":" in prompt:
       SCREAMING_SNAKE_CASE__:   str=      prompt.split(''':'''   )
       SCREAMING_SNAKE_CASE__:   int=      float(lowerCAmelCase   )
      else:
       SCREAMING_SNAKE_CASE__:   int=      prompt
       SCREAMING_SNAKE_CASE__:   Tuple=      1.0
      processed_prompts.append(lowerCAmelCase   )
      weights.append(lowerCAmelCase   )
     return {
         "prompts": processed_prompts,
         "weights": torch.tensor(lowerCAmelCase , device=self.device   ),
     }



    def 						UpperCamelCase_				(  self , lowerCAmelCase , lowerCAmelCase=None , lowerCAmelCase=None , lowerCAmelCase=True , lowerCAmelCase=False , lowerCAmelCase=True , lowerCAmelCase=True , lowerCAmelCase=None , )       ->					Union[str, Any]:
     if image_path:
      SCREAMING_SNAKE_CASE__:   Tuple=      self._get_latent(lowerCAmelCase   )
     else:
      SCREAMING_SNAKE_CASE__:   str=      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."
     SCREAMING_SNAKE_CASE__:   Dict=      self.process_prompts(lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      self.process_prompts(lowerCAmelCase   )
     if save_final and save_path is None:
      SCREAMING_SNAKE_CASE__:   Any=      os.path.join('''./outputs/''' , '''_'''.join(pos_prompts['''prompts''']   )   )
     if not os.path.exists(lowerCAmelCase   ):
      os.makedirs(lowerCAmelCase   )
     else:
      SCREAMING_SNAKE_CASE__:   str=      save_path + "_" + get_timestamp()
      os.makedirs(lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   Any=      save_path

     SCREAMING_SNAKE_CASE__:   List[Any]=      self.vqgan.decode(self.latent   )[0]
     if show_intermediate:
      print('''Original Image'''   )
      show_pil(custom_to_pil(lowerCAmelCase   )   )

     SCREAMING_SNAKE_CASE__:   int=      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'   )   )


import os







def 			A__      (	snake_case_ :		str = "matrix.txt"      ):
 with open(os.path.join(os.path.dirname(snake_case_      )       ,				snake_case_      )      ) as in_file:
  SCREAMING_SNAKE_CASE__:   Dict=      in_file.read()

 SCREAMING_SNAKE_CASE__:   Optional[int]=      [[int(snake_case_      ) for cell in row.split(''','''      )] for row in data.strip().splitlines()]
 SCREAMING_SNAKE_CASE__:   int=      [[0 for cell in row] for row in grid]
 SCREAMING_SNAKE_CASE__:   Dict=      len(grid[0]      )

 SCREAMING_SNAKE_CASE__:   Dict=      [[0 for i in range(snake_case_      )] for j in range(snake_case_      )]
 SCREAMING_SNAKE_CASE__:   Optional[int]=      grid[0][0]
 for i in range(1       ,				snake_case_      ):
  SCREAMING_SNAKE_CASE__:   Optional[int]=      grid[0][i] + dp[0][i - 1]
 for i in range(1       ,				snake_case_      ):
  SCREAMING_SNAKE_CASE__:   List[Any]=      grid[i][0] + dp[i - 1][0]

 for i in range(1       ,				snake_case_      ):
  for j in range(1       ,				snake_case_      ):
   SCREAMING_SNAKE_CASE__:   Dict=      grid[i][j] + min(dp[i - 1][j]       ,				dp[i][j - 1]      )

 return dp[-1][-1]


if __name__ == "__main__":
      print(f'''{solution() = }''')

