





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 SCREAMING_SNAKE_CASE_ :





					"""simple docstring"""


					def __init__( self	,  _lowerCAmelCase = "cpu"	,  _lowerCAmelCase = "openai/clip-vit-large-patch14"	):
										lowerCamelCase__       =       device
										lowerCamelCase__       =       CLIPTokenizerFast.from_pretrained(_lowerCAmelCase	)
										lowerCamelCase__       =       [0.48_145_466, 0.4_578_275, 0.40_821_073]
										lowerCamelCase__       =       [0.26_862_954, 0.26_130_258, 0.27_577_711]
										lowerCamelCase__       =       torchvision.transforms.Normalize(self.image_mean	,  self.image_std	)
										lowerCamelCase__       =       torchvision.transforms.Resize(224	)
										lowerCamelCase__       =       torchvision.transforms.CenterCrop(224	)





					def 					__magic_name__ ( self	,  _lowerCAmelCase	):
										lowerCamelCase__       =       self.resize(_lowerCAmelCase	)
										lowerCamelCase__       =       self.center_crop(_lowerCAmelCase	)
										lowerCamelCase__       =       self.normalize(_lowerCAmelCase	)
										return images






					def __call__( self	,  _lowerCAmelCase=None	,  _lowerCAmelCase=None	,  **_lowerCAmelCase	):
										lowerCamelCase__       =       self.tokenizer(text=_lowerCAmelCase	,  **_lowerCAmelCase	)
										lowerCamelCase__       =       self.preprocess_img(_lowerCAmelCase	)
										lowerCamelCase__       =       {key: value.to(self.device	) for (key, value) in encoding.items()}
										return encoding

class SCREAMING_SNAKE_CASE_ (					nn.Module   ):





					"""simple docstring"""


					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	,  ):
										super().__init__()
										lowerCamelCase__       =       None
										lowerCamelCase__       =       device if device else get_device()
										if vqgan:
															lowerCamelCase__       =       vqgan
										else:
															lowerCamelCase__       =       load_vqgan(self.device	,  conf_path=_lowerCAmelCase	,  ckpt_path=_lowerCAmelCase	)
										self.vqgan.eval()
										if clip:
															lowerCamelCase__       =       clip
										else:
															lowerCamelCase__       =       CLIPModel.from_pretrained("openai/clip-vit-base-patch32"	)
										self.clip.to(self.device	)
										lowerCamelCase__       =       ProcessorGradientFlow(device=self.device	)

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





					def 					__magic_name__ ( self	,  _lowerCAmelCase=None	,  _lowerCAmelCase=None	,  _lowerCAmelCase=5	,  _lowerCAmelCase=True	):
										lowerCamelCase__       =       []
										if output_path is None:
															lowerCamelCase__       =       "./animation.gif"
										if input_path is None:
															lowerCamelCase__       =       self.save_path
										lowerCamelCase__       =       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?)"	)
										lowerCamelCase__       =       total_duration / len(_lowerCAmelCase	)
										lowerCamelCase__       =       [frame_duration] * len(_lowerCAmelCase	)
										if extend_frames:
															lowerCamelCase__       =       1.5
															lowerCamelCase__       =       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 					__magic_name__ ( self	,  _lowerCAmelCase=None	,  _lowerCAmelCase=None	):
										if not (path or img):
															raise ValueError("Input either path or tensor"	)
										if img is not None:
															raise NotImplementedError
										lowerCamelCase__       =       preprocess(Image.open(_lowerCAmelCase	)	,  target_image_size=256	).to(self.device	)
										lowerCamelCase__       =       preprocess_vqgan(_lowerCAmelCase	)
										lowerCamelCase__    ,		*lowerCamelCase__       =       self.vqgan.encode(_lowerCAmelCase	)
										return z





					def 					__magic_name__ ( self	,  _lowerCAmelCase	):
										lowerCamelCase__       =       self.latent.detach().requires_grad_()
										lowerCamelCase__       =       base_latent + transform_vector
										if self.quantize:
															lowerCamelCase__    ,		*lowerCamelCase__       =       self.vqgan.quantize(_lowerCAmelCase	)
										else:
															lowerCamelCase__       =       trans_latent
										return self.vqgan.decode(_lowerCAmelCase	)





					def 					__magic_name__ ( self	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase=None	):
										lowerCamelCase__       =       self.clip_preprocessor(text=_lowerCAmelCase	,  images=_lowerCAmelCase	,  return_tensors="pt"	,  padding=_lowerCAmelCase	)
										lowerCamelCase__       =       self.clip(**_lowerCAmelCase	)
										lowerCamelCase__       =       clip_outputs.logits_per_image
										if weights is not None:
															lowerCamelCase__       =       similarity_logits * weights
										return similarity_logits.sum()





					def 					__magic_name__ ( self	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	):
										lowerCamelCase__       =       self._get_clip_similarity(pos_prompts["prompts"]	,  _lowerCAmelCase	,  weights=(1 / pos_prompts["weights"])	)
										if neg_prompts:
															lowerCamelCase__       =       self._get_clip_similarity(neg_prompts["prompts"]	,  _lowerCAmelCase	,  weights=neg_prompts["weights"]	)
										else:
															lowerCamelCase__       =       torch.tensor([1]	,  device=self.device	)
										lowerCamelCase__       =       -torch.log(_lowerCAmelCase	) + torch.log(_lowerCAmelCase	)
										return loss





					def 					__magic_name__ ( self	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	):
										lowerCamelCase__       =       torch.randn_like(self.latent	,  requires_grad=_lowerCAmelCase	,  device=self.device	)
										lowerCamelCase__       =       torch.optim.Adam([vector]	,  lr=self.lr	)

										for i in range(self.iterations	):
															optim.zero_grad()
															lowerCamelCase__       =       self._add_vector(_lowerCAmelCase	)
															lowerCamelCase__       =       loop_post_process(_lowerCAmelCase	)
															lowerCamelCase__       =       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 					__magic_name__ ( self	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	):
										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:
															lowerCamelCase__       =       Image.open(_lowerCAmelCase	)
															lowerCamelCase__       =       image.resize((256, 256)	)
															wandb.log("Original Image"	,  wandb.Image(_lowerCAmelCase	)	)





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






					def 					__magic_name__ ( self	,  _lowerCAmelCase	,  _lowerCAmelCase=None	,  _lowerCAmelCase=None	,  _lowerCAmelCase=True	,  _lowerCAmelCase=False	,  _lowerCAmelCase=True	,  _lowerCAmelCase=True	,  _lowerCAmelCase=None	,  ):
										if image_path:
															lowerCamelCase__       =       self._get_latent(_lowerCAmelCase	)
										else:
															lowerCamelCase__       =       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."
										lowerCamelCase__       =       self.process_prompts(_lowerCAmelCase	)
										lowerCamelCase__       =       self.process_prompts(_lowerCAmelCase	)
										if save_final and save_path is None:
															lowerCamelCase__       =       os.path.join("./outputs/"	,  "_".join(pos_prompts["prompts"]	)	)
										if not os.path.exists(_lowerCAmelCase	):
															os.makedirs(_lowerCAmelCase	)
										else:
															lowerCamelCase__       =       save_path + "_" + get_timestamp()
															os.makedirs(_lowerCAmelCase	)
										lowerCamelCase__       =       save_path

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

										lowerCamelCase__       =       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"	)	)








def    __UpperCamelCase      (    a,     a) ->Optional[int]:
					lowerCamelCase__       =       [1]
					for i in range(2,     a):
										factorials.append(factorials[-1] * i)
					assert 0 <= k < factorials[-1] * n, "k out of bounds"

					lowerCamelCase__       =       []
					lowerCamelCase__       =       list(range(a))

					# Find permutation
					while factorials:
										lowerCamelCase__       =       factorials.pop()
										lowerCamelCase__    ,		lowerCamelCase__       =       divmod(a,     a)
										permutation.append(elements[number])
										elements.remove(elements[number])
					permutation.append(elements[0])

					return permutation


if __name__ == "__main__":
						import doctest

						doctest.testmod()

