


'''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 						_lowerCamelCase					:




 '''simple docstring'''


 def __init__(      self      ,      __lowercase = "cpu"      ,      __lowercase = "openai/clip-vit-large-patch14"						):

  """simple docstring"""







  __A  :		Any       =			device
  __A  :		Tuple       =			CLIPTokenizerFast.from_pretrained(_lowercase						)
  __A  :		List[str]       =			[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]
  __A  :		Optional[Any]       =			[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]
  __A  :		Optional[int]       =			torchvision.transforms.Normalize(self.image_mean      ,      self.image_std						)
  __A  :		Tuple       =			torchvision.transforms.Resize(224						)
  __A  :		str       =			torchvision.transforms.CenterCrop(224						)


 def  snake_case__   (      self      ,      __lowercase						):

  """simple docstring"""







  __A  :		int       =			self.resize(_lowercase						)
  __A  :		List[str]       =			self.center_crop(_lowercase						)
  __A  :		Union[str, Any]       =			self.normalize(_lowercase						)
  return images







 def __call__(      self      ,      __lowercase=None      ,      __lowercase=None      ,      **__lowercase						):

  """simple docstring"""







  __A  :		Any       =			self.tokenizer(text=_lowercase      ,      **_lowercase						)
  __A  :		Union[str, Any]       =			self.preprocess_img(_lowercase						)
  __A  :		Any       =			{key: value.to(self.device						) for (key, value) in encoding.items()}
  return encoding


class 						_lowerCamelCase					(    nn.Module				):




 '''simple docstring'''


 def __init__(      self      ,      __lowercase=10      ,      __lowercase=0.0_1      ,      __lowercase=None      ,      __lowercase=None      ,      __lowercase=None      ,      __lowercase=None      ,      __lowercase=None      ,      __lowercase=None      ,      __lowercase=False      ,      __lowercase=True      ,      __lowercase="image"      ,      __lowercase=True      ,      __lowercase=False      ,      __lowercase=False      ,      __lowercase=False      ,      ):

  """simple docstring"""







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

  __A  :		Any       =			iterations
  __A  :		Optional[Any]       =			lr
  __A  :		Optional[Any]       =			log
  __A  :		Dict       =			make_grid
  __A  :		Dict       =			return_val
  __A  :		Tuple       =			quantize
  __A  :		Optional[Any]       =			self.vqgan.decoder.z_shape


 def  snake_case__   (      self      ,      __lowercase=None      ,      __lowercase=None      ,      __lowercase=5      ,      __lowercase=True						):

  """simple docstring"""







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


 def  snake_case__   (      self      ,      __lowercase=None      ,      __lowercase=None						):

  """simple docstring"""







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


 def  snake_case__   (      self      ,      __lowercase						):

  """simple docstring"""







  __A  :		List[str]       =			self.latent.detach().requires_grad_()
  __A  :		Optional[Any]       =			base_latent + transform_vector
  if self.quantize:
   __A  :		str       =			self.vqgan.quantize(_lowercase						)
  else:
   __A  :		str       =			trans_latent
  return self.vqgan.decode(_lowercase						)


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase      ,      __lowercase=None						):

  """simple docstring"""







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


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase      ,      __lowercase						):

  """simple docstring"""







  __A  :		Any       =			self._get_clip_similarity(pos_prompts['prompts']      ,      _lowercase      ,      weights=(1 / pos_prompts['weights'])						)
  if neg_prompts:
   __A  :		Tuple       =			self._get_clip_similarity(neg_prompts['prompts']      ,      _lowercase      ,      weights=neg_prompts['weights']						)
  else:
   __A  :		Any       =			torch.tensor([1]      ,      device=self.device						)
  __A  :		Dict       =			-torch.log(_lowercase						) + torch.log(_lowercase						)
  return loss


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase      ,      __lowercase						):

  """simple docstring"""







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

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


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase      ,      __lowercase						):

  """simple docstring"""







  wandb.init(reinit=_lowercase      ,      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:
   __A  :		Optional[int]       =			Image.open(_lowercase						)
   __A  :		Union[str, Any]       =			image.resize((256, 256)						)
   wandb.log('Original Image'      ,      wandb.Image(_lowercase						)						)


 def  snake_case__   (      self      ,      __lowercase						):

  """simple docstring"""







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







 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase=None      ,      __lowercase=None      ,      __lowercase=True      ,      __lowercase=False      ,      __lowercase=True      ,      __lowercase=True      ,      __lowercase=None      ,      ):

  """simple docstring"""







  if image_path:
   __A  :		int       =			self._get_latent(_lowercase						)
  else:
   __A  :		Optional[Any]       =			torch.randn(self.latent_dim      ,      device=self.device						)
  if self.log:
   self._init_logging(_lowercase      ,      _lowercase      ,      _lowercase						)

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

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

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










'''simple docstring'''
import math

class 						_lowerCamelCase					:




 '''simple docstring'''


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase						):

  """simple docstring"""







  __A  :		List[str]       =			0.0
  __A  :		Optional[int]       =			0.0
  for i in range(len(__lowercase						)						):
   da += math.pow((sample[i] - weights[0][i])      ,      2						)
   da += math.pow((sample[i] - weights[1][i])      ,      2						)
   return 0 if da > da else 1
  return 0







 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase						):

  """simple docstring"""







  for i in range(len(__lowercase						)						):
   weights[j][i] += alpha * (sample[i] - weights[j][i])
  return weights


def 			_lowercase       (				):
 # Training Examples ( m, n )
 __A  :		Union[str, Any]       =			[[1, 1, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 1, 1]]

 # weight initialization ( n, C )
 __A  :		Tuple       =			[[0.2, 0.6, 0.5, 0.9], [0.8, 0.4, 0.7, 0.3]]

 # training
 __A  :		Optional[int]       =			SelfOrganizingMap()
 __A  :		int       =			3
 __A  :		Optional[Any]       =			0.5

 for _ in range(UpperCamelCase__			):
  for j in range(len(UpperCamelCase__			)			):
   # training sample
   __A  :		Dict       =			training_samples[j]

   # Compute the winning vector
   __A  :		List[Any]       =			self_organizing_map.get_winner(UpperCamelCase__,				UpperCamelCase__			)

   # Update the winning vector
   __A  :		Union[str, Any]       =			self_organizing_map.update(UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__			)

    # classify test sample
 __A  :		int       =			[0, 0, 0, 1]
 __A  :		List[Any]       =			self_organizing_map.get_winner(UpperCamelCase__,				UpperCamelCase__			)

 # results
 print(f"""Clusters that the test sample belongs to : {winner}"""			)
 print(f"""Weights that have been trained : {weights}"""			)


# running the main() function
if __name__ == "__main__":
 main()






