

'''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     lowercase_			:




  """simple docstring"""




  def __init__(   self     :  int      ,lowercase__     :  str = "cpu"      ,lowercase__     :  str = "openai/clip-vit-large-patch14"    ):
   __lowercase													=				device
   __lowercase													=				CLIPTokenizerFast.from_pretrained(lowercase__    )
   __lowercase													=				[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]
   __lowercase													=				[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]
   __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       SCREAMING_SNAKE_CASE   (   self     :  List[Any]      ,lowercase__     :  int    ):
   __lowercase													=				self.resize(lowercase__    )
   __lowercase													=				self.center_crop(lowercase__    )
   __lowercase													=				self.normalize(lowercase__    )
   return images






  def __call__(   self     :  Dict      ,lowercase__     :  str=None      ,lowercase__     :  Optional[int]=None      ,**lowercase__     :  List[str]    ):
   __lowercase													=				self.tokenizer(text=lowercase__      ,**lowercase__    )
   __lowercase													=				self.preprocess_img(lowercase__    )
   __lowercase													=				{key: value.to(self.device    ) for (key, value) in encoding.items()}
   return encoding






class     lowercase_			(nn.Module   ):




  """simple docstring"""




  def __init__(   self     :  Optional[Any]      ,lowercase__     :  str=1_0      ,lowercase__     :  str=0.0_1      ,lowercase__     :  Optional[int]=None      ,lowercase__     :  Optional[Any]=None      ,lowercase__     :  List[str]=None      ,lowercase__     :  Any=None      ,lowercase__     :  Optional[int]=None      ,lowercase__     :  str=None      ,lowercase__     :  List[str]=False      ,lowercase__     :  Union[str, Any]=True      ,lowercase__     :  Optional[int]="image"      ,lowercase__     :  Tuple=True      ,lowercase__     :  int=False      ,lowercase__     :  Tuple=False      ,lowercase__     :  Optional[Any]=False      ,):
   super().__init__()
   __lowercase													=				None
   __lowercase													=				device if device else get_device()
   if vqgan:
    __lowercase													=				vqgan
   else:
    __lowercase													=				load_vqgan(self.device      ,conf_path=lowercase__      ,ckpt_path=lowercase__    )
   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       SCREAMING_SNAKE_CASE   (   self     :  Optional[int]      ,lowercase__     :  Optional[int]=None      ,lowercase__     :  str=None      ,lowercase__     :  Dict=5      ,lowercase__     :  Tuple=True    ):
   __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(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?)'''    )
   __lowercase													=				total_duration / len(lowercase__    )
   __lowercase													=				[frame_duration] * len(lowercase__    )
   if extend_frames:
    __lowercase													=				1.5
    __lowercase													=				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       SCREAMING_SNAKE_CASE   (   self     :  Union[str, Any]      ,lowercase__     :  Any=None      ,lowercase__     :  Tuple=None    ):
   if not (path or img):
    raise ValueError('''Input either path or tensor'''    )
   if img is not None:
    raise NotImplementedError
   __lowercase													=				preprocess(Image.open(lowercase__    )      ,target_image_size=2_5_6    ).to(self.device    )
   __lowercase													=				preprocess_vqgan(lowercase__    )
   __lowercase						,			*__lowercase													=				self.vqgan.encode(lowercase__    )
   return z






  def       SCREAMING_SNAKE_CASE   (   self     :  Dict      ,lowercase__     :  Union[str, Any]    ):
   __lowercase													=				self.latent.detach().requires_grad_()
   __lowercase													=				base_latent + transform_vector
   if self.quantize:
    __lowercase						,			*__lowercase													=				self.vqgan.quantize(lowercase__    )
   else:
    __lowercase													=				trans_latent
   return self.vqgan.decode(lowercase__    )






  def       SCREAMING_SNAKE_CASE   (   self     :  int      ,lowercase__     :  Dict      ,lowercase__     :  Tuple      ,lowercase__     :  Dict=None    ):
   __lowercase													=				self.clip_preprocessor(text=lowercase__      ,images=lowercase__      ,return_tensors='''pt'''      ,padding=lowercase__    )
   __lowercase													=				self.clip(**lowercase__    )
   __lowercase													=				clip_outputs.logits_per_image
   if weights is not None:
    __lowercase													=				similarity_logits * weights
   return similarity_logits.sum()






  def       SCREAMING_SNAKE_CASE   (   self     :  List[str]      ,lowercase__     :  Tuple      ,lowercase__     :  str      ,lowercase__     :  List[str]    ):
   __lowercase													=				self._get_clip_similarity(pos_prompts['''prompts''']      ,lowercase__      ,weights=(1 / pos_prompts['''weights'''])    )
   if neg_prompts:
    __lowercase													=				self._get_clip_similarity(neg_prompts['''prompts''']      ,lowercase__      ,weights=neg_prompts['''weights''']    )
   else:
    __lowercase													=				torch.tensor([1]      ,device=self.device    )
   __lowercase													=				-torch.log(lowercase__    ) + torch.log(lowercase__    )
   return loss






  def       SCREAMING_SNAKE_CASE   (   self     :  Union[str, Any]      ,lowercase__     :  List[str]      ,lowercase__     :  Optional[Any]      ,lowercase__     :  List[str]    ):
   __lowercase													=				torch.randn_like(self.latent      ,requires_grad=lowercase__      ,device=self.device    )
   __lowercase													=				torch.optim.Adam([vector]      ,lr=self.lr    )

   for i in range(self.iterations    ):
    optim.zero_grad()
    __lowercase													=				self._add_vector(lowercase__    )
    __lowercase													=				loop_post_process(lowercase__    )
    __lowercase													=				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       SCREAMING_SNAKE_CASE   (   self     :  List[str]      ,lowercase__     :  Optional[int]      ,lowercase__     :  int      ,lowercase__     :  List[str]    ):
   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:
    __lowercase													=				Image.open(lowercase__    )
    __lowercase													=				image.resize((2_5_6, 2_5_6)    )
    wandb.log('''Original Image'''      ,wandb.Image(lowercase__    )    )






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






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

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

   __lowercase													=				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'''






from __future__ import annotations

import matplotlib.pyplot as plt  # type: ignore
import numpy

# initial triangle of Koch snowflake
lowerCAmelCase__      		=			numpy.array([0, 0])
lowerCAmelCase__      		=			numpy.array([0.5, 0.8_660_254])
lowerCAmelCase__      		=			numpy.array([1, 0])
lowerCAmelCase__      		=			[VECTOR_1, VECTOR_2, VECTOR_3, VECTOR_1]


def 	_A  (					A__  ,       A__   ):






 """simple docstring"""



 __lowercase													=				initial_vectors
 for _ in range(A__   ):
  __lowercase													=				iteration_step(A__   )
 return vectors


def 	_A  (					A__   ):






 """simple docstring"""



 __lowercase													=				[]
 for i, start_vector in enumerate(vectors[:-1]   ):
  __lowercase													=				vectors[i + 1]
  new_vectors.append(A__   )
  __lowercase													=				end_vector - start_vector
  new_vectors.append(start_vector + difference_vector / 3   )
  new_vectors.append(
      start_vector + difference_vector / 3 + rotate(difference_vector / 3  ,       60   )   )
  new_vectors.append(start_vector + difference_vector * 2 / 3   )
 new_vectors.append(vectors[-1]   )
 return new_vectors


def 	_A  (					A__  ,       A__   ):






 """simple docstring"""



 __lowercase													=				numpy.radians(A__   )
 __lowercase						,			__lowercase													=				numpy.cos(A__   ), numpy.sin(A__   )
 __lowercase													=				numpy.array(((c, -s), (s, c))   )
 return numpy.dot(A__  ,       A__   )


def 	_A  (					A__   ):






 """simple docstring"""



 __lowercase													=				plt.gca()
 axes.set_aspect('''equal'''   )

 # matplotlib.pyplot.plot takes a list of all x-coordinates and a list of all
 # y-coordinates as inputs, which are constructed from the vector-list using
 # zip()
 __lowercase						,			__lowercase													=				zip(*A__   )
 plt.plot(A__  ,       A__   )
 plt.show()


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 lowerCAmelCase__      		=			iterate(INITIAL_VECTORS, 5)
 plot(processed_vectors)

