'''simple docstring'''


import logging
import os
import threading
import time


try:
 import warnings
except ImportError:
 snake_case__					:      List[str]				      =  None

try:
 import msvcrt
except ImportError:
 snake_case__					:      Optional[Any]				      =  None

try:
 import fcntl
except ImportError:
 snake_case__					:      int				      =  None


# Backward compatibility
# ------------------------------------------------
try:
 TimeoutError
except NameError:
 snake_case__					:      Union[str, Any]				      =  OSError


# Data
# ------------------------------------------------
snake_case__					:      str				      =  [
    '''Timeout''',
    '''BaseFileLock''',
    '''WindowsFileLock''',
    '''UnixFileLock''',
    '''SoftFileLock''',
    '''FileLock''',
]

snake_case__					:      Any				      =  '''3.0.12'''


snake_case__					:      List[str]				      =  None


def  _lowerCamelCase   (				):



     """simple docstring"""





     global _logger
     UpperCAmelCase_							:       List[str]       =				_logger or logging.getLogger(__name__       )
     return _logger






class 					__SCREAMING_SNAKE_CASE						(	lowerCamelCase_	):







  '''simple docstring'''






  def __init__(			self				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       Dict       =				lock_file
       return None






  def __str__(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       Union[str, Any]       =				F'''The file lock \'{self.lock_file}\' could not be acquired.'''
       return temp







class 					__SCREAMING_SNAKE_CASE						:







  '''simple docstring'''






  def __init__(			self				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       int       =				lock
       return None


  def __enter__(			self						):




       '''simple docstring'''

       return self.lock






  def __exit__(			self				,		snake_case_				,		snake_case_				,		snake_case_						):




       '''simple docstring'''

       self.lock.release()
       return None







class 					__SCREAMING_SNAKE_CASE						:







  '''simple docstring'''






  def __init__(			self				,		snake_case_				,		snake_case_=-1				,		snake_case_=None						):




       '''simple docstring'''

       UpperCAmelCase_							:       Union[str, Any]       =				max_filename_length if max_filename_length is not None else 2_5_5
       # Hash the filename if it's too long
       UpperCAmelCase_							:       Any       =				self.hash_filename_if_too_long(snake_case_				,		snake_case_						)
       # The path to the lock file.
       UpperCAmelCase_							:       Dict       =				lock_file

       # The file descriptor for the *_lock_file* as it is returned by the
       # os.open() function.
       # This file lock is only NOT None, if the object currently holds the
       # lock.
       UpperCAmelCase_							:       Any       =				None

       # The default timeout value.
       UpperCAmelCase_							:       Optional[int]       =				timeout

       # We use this lock primarily for the lock counter.
       UpperCAmelCase_							:       Optional[Any]       =				threading.Lock()

       # The lock counter is used for implementing the nested locking
       # mechanism. Whenever the lock is acquired, the counter is increased and
       # the lock is only released, when this value is 0 again.
       UpperCAmelCase_							:       List[str]       =				0
       return None


  @property
  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       return self._lock_file


  @property
  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       return self._timeout


  @timeout.setter
  def _UpperCamelCase					(			self				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       Union[str, Any]       =				float(snake_case_						)
       return None


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       raise NotImplementedError()


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       raise NotImplementedError()


  @property
  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       return self._lock_file_fd is not None


  def _UpperCamelCase					(			self				,		snake_case_=None				,		snake_case_=0.05						):




       '''simple docstring'''

       if timeout is None:
            UpperCAmelCase_							:       Any       =				self.timeout

       # Increment the number right at the beginning.
       # We can still undo it, if something fails.
       with self._thread_lock:
            self._lock_counter += 1

       UpperCAmelCase_							:       List[str]       =				id(self						)
       UpperCAmelCase_							:       Optional[Any]       =				self._lock_file
       UpperCAmelCase_							:       Optional[Any]       =				time.time()
       try:
            while True:
                 with self._thread_lock:
                      if not self.is_locked:
                           logger().debug(F'''Attempting to acquire lock {lock_id} on {lock_filename}'''						)
                           self._acquire()

                 if self.is_locked:
                      logger().debug(F'''Lock {lock_id} acquired on {lock_filename}'''						)
                      break
                 elif timeout >= 0 and time.time() - start_time > timeout:
                      logger().debug(F'''Timeout on acquiring lock {lock_id} on {lock_filename}'''						)
                      raise Timeout(self._lock_file						)
                 else:
                      logger().debug(
                          F'''Lock {lock_id} not acquired on {lock_filename}, waiting {poll_intervall} seconds ...'''						)
                      time.sleep(snake_case_						)
       except:  # noqa
            # Something did go wrong, so decrement the counter.
            with self._thread_lock:
                 UpperCAmelCase_							:       str       =				max(0				,		self._lock_counter - 1						)

            raise
       return _Acquire_ReturnProxy(lock=self						)


  def _UpperCamelCase					(			self				,		snake_case_=False						):




       '''simple docstring'''

       with self._thread_lock:
            if self.is_locked:
                 self._lock_counter -= 1

                 if self._lock_counter == 0 or force:
                      UpperCAmelCase_							:       Optional[int]       =				id(self						)
                      UpperCAmelCase_							:       Any       =				self._lock_file

                      logger().debug(F'''Attempting to release lock {lock_id} on {lock_filename}'''						)
                      self._release()
                      UpperCAmelCase_							:       Tuple       =				0
                      logger().debug(F'''Lock {lock_id} released on {lock_filename}'''						)

       return None


  def __enter__(			self						):




       '''simple docstring'''

       self.acquire()
       return self


  def __exit__(			self				,		snake_case_				,		snake_case_				,		snake_case_						):




       '''simple docstring'''

       self.release()
       return None


  def __del__(			self						):




       '''simple docstring'''

       self.release(force=snake_case_						)
       return None






  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       Tuple       =				os.path.basename(snake_case_						)
       if len(snake_case_						) > max_length and max_length > 0:
            UpperCAmelCase_							:       Dict       =				os.path.dirname(snake_case_						)
            UpperCAmelCase_							:       Any       =				str(hash(snake_case_						)						)
            UpperCAmelCase_							:       Dict       =				filename[: max_length - len(snake_case_						) - 8] + '...' + hashed_filename + '.lock'
            return os.path.join(snake_case_				,		snake_case_						)
       else:
            return path







class 					__SCREAMING_SNAKE_CASE						(	lowerCamelCase_	):







  '''simple docstring'''






  def __init__(			self				,		snake_case_				,		snake_case_=-1				,		snake_case_=None						):




       '''simple docstring'''

       from .file_utils import relative_to_absolute_path

       super().__init__(snake_case_				,		timeout=snake_case_				,		max_filename_length=snake_case_						)
       UpperCAmelCase_							:       List[Any]       =				'\\\\?\\' + relative_to_absolute_path(self.lock_file						)


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       List[Any]       =				os.O_RDWR | os.O_CREAT | os.O_TRUNC

       try:
            UpperCAmelCase_							:       Dict       =				os.open(self._lock_file				,		snake_case_						)
       except OSError:
            pass
       else:
            try:
                 msvcrt.locking(snake_case_				,		msvcrt.LK_NBLCK				,		1						)
            except OSError:
                 os.close(snake_case_						)
            else:
                 UpperCAmelCase_							:       List[Any]       =				fd
       return None






  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       int       =				self._lock_file_fd
       UpperCAmelCase_							:       Union[str, Any]       =				None
       msvcrt.locking(snake_case_				,		msvcrt.LK_UNLCK				,		1						)
       os.close(snake_case_						)

       try:
            os.remove(self._lock_file						)
       # Probably another instance of the application
       # that acquired the file lock.
       except OSError:
            pass
       return None







class 					__SCREAMING_SNAKE_CASE						(	lowerCamelCase_	):







  '''simple docstring'''






  def __init__(			self				,		snake_case_				,		snake_case_=-1				,		snake_case_=None						):




       '''simple docstring'''

       UpperCAmelCase_							:       List[str]       =				os.statvfs(os.path.dirname(snake_case_						)						).f_namemax
       super().__init__(snake_case_				,		timeout=snake_case_				,		max_filename_length=snake_case_						)


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       int       =				os.O_RDWR | os.O_CREAT | os.O_TRUNC
       UpperCAmelCase_							:       List[str]       =				os.open(self._lock_file				,		snake_case_						)

       try:
            fcntl.flock(snake_case_				,		fcntl.LOCK_EX | fcntl.LOCK_NB						)
       except OSError:
            os.close(snake_case_						)
       else:
            UpperCAmelCase_							:       List[str]       =				fd
       return None






  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       Union[str, Any]       =				self._lock_file_fd
       UpperCAmelCase_							:       str       =				None
       fcntl.flock(snake_case_				,		fcntl.LOCK_UN						)
       os.close(snake_case_						)
       return None







class 					__SCREAMING_SNAKE_CASE						(	lowerCamelCase_	):







  '''simple docstring'''






  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       Union[str, Any]       =				os.O_WRONLY | os.O_CREAT | os.O_EXCL | os.O_TRUNC
       try:
            UpperCAmelCase_							:       List[str]       =				os.open(self._lock_file				,		snake_case_						)
       except OSError:
            pass
       else:
            UpperCAmelCase_							:       Dict       =				fd
       return None






  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       os.close(self._lock_file_fd						)
       UpperCAmelCase_							:       Optional[Any]       =				None

       try:
            os.remove(self._lock_file						)
       # The file is already deleted and that's what we want.
       except OSError:
            pass
       return None







snake_case__					:      List[str]				      =  None

if msvcrt:
 snake_case__					:      Any				      =  WindowsFileLock
elif fcntl:
 snake_case__					:      List[str]				      =  UnixFileLock
else:
 snake_case__					:      Optional[Any]				      =  SoftFileLock

 if warnings is not None:
  warnings.warn('''only soft file lock is available''')







'''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 					__SCREAMING_SNAKE_CASE						:







  '''simple docstring'''






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




       '''simple docstring'''

       UpperCAmelCase_							:       Any       =				device
       UpperCAmelCase_							:       Tuple       =				CLIPTokenizerFast.from_pretrained(snake_case_						)
       UpperCAmelCase_							:       Optional[Any]       =				[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]
       UpperCAmelCase_							:       Union[str, Any]       =				[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]
       UpperCAmelCase_							:       Tuple       =				torchvision.transforms.Normalize(self.image_mean				,		self.image_std						)
       UpperCAmelCase_							:       Optional[Any]       =				torchvision.transforms.Resize(2_2_4						)
       UpperCAmelCase_							:       Any       =				torchvision.transforms.CenterCrop(2_2_4						)


  def _UpperCamelCase					(			self				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       Optional[int]       =				self.resize(snake_case_						)
       UpperCAmelCase_							:       Tuple       =				self.center_crop(snake_case_						)
       UpperCAmelCase_							:       Optional[Any]       =				self.normalize(snake_case_						)
       return images






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




       '''simple docstring'''

       UpperCAmelCase_							:       str       =				self.tokenizer(text=snake_case_				,		**snake_case_						)
       UpperCAmelCase_							:       Optional[Any]       =				self.preprocess_img(snake_case_						)
       UpperCAmelCase_							:       Optional[int]       =				{key: value.to(self.device						) for (key, value) in encoding.items()}
       return encoding







class 					__SCREAMING_SNAKE_CASE						(	nn.Module	):







  '''simple docstring'''






  def __init__(			self				,		snake_case_=1_0				,		snake_case_=0.01				,		snake_case_=None				,		snake_case_=None				,		snake_case_=None				,		snake_case_=None				,		snake_case_=None				,		snake_case_=None				,		snake_case_=False				,		snake_case_=True				,		snake_case_="image"				,		snake_case_=True				,		snake_case_=False				,		snake_case_=False				,		snake_case_=False				,		):




       '''simple docstring'''

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

       UpperCAmelCase_							:       Dict       =				iterations
       UpperCAmelCase_							:       Dict       =				lr
       UpperCAmelCase_							:       str       =				log
       UpperCAmelCase_							:       Tuple       =				make_grid
       UpperCAmelCase_							:       Union[str, Any]       =				return_val
       UpperCAmelCase_							:       List[Any]       =				quantize
       UpperCAmelCase_							:       int       =				self.vqgan.decoder.z_shape


  def _UpperCamelCase					(			self				,		snake_case_=None				,		snake_case_=None				,		snake_case_=5				,		snake_case_=True						):




       '''simple docstring'''

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


  def _UpperCamelCase					(			self				,		snake_case_=None				,		snake_case_=None						):




       '''simple docstring'''

       if not (path or img):
            raise ValueError('Input either path or tensor'						)
       if img is not None:
            raise NotImplementedError
       UpperCAmelCase_							:       Optional[Any]       =				preprocess(Image.open(snake_case_						)				,		target_image_size=2_5_6						).to(self.device						)
       UpperCAmelCase_							:       Dict       =				preprocess_vqgan(snake_case_						)
       UpperCAmelCase_       ,      *UpperCAmelCase_							:       Tuple       =				self.vqgan.encode(snake_case_						)
       return z


  def _UpperCamelCase					(			self				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       Optional[Any]       =				self.latent.detach().requires_grad_()
       UpperCAmelCase_							:       List[Any]       =				base_latent + transform_vector
       if self.quantize:
            UpperCAmelCase_       ,      *UpperCAmelCase_							:       Tuple       =				self.vqgan.quantize(snake_case_						)
       else:
            UpperCAmelCase_							:       Optional[int]       =				trans_latent
       return self.vqgan.decode(snake_case_						)


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_				,		snake_case_=None						):




       '''simple docstring'''

       UpperCAmelCase_							:       int       =				self.clip_preprocessor(text=snake_case_				,		images=snake_case_				,		return_tensors='pt'				,		padding=snake_case_						)
       UpperCAmelCase_							:       Any       =				self.clip(**snake_case_						)
       UpperCAmelCase_							:       Dict       =				clip_outputs.logits_per_image
       if weights is not None:
            UpperCAmelCase_							:       Union[str, Any]       =				similarity_logits * weights
       return similarity_logits.sum()


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       Optional[Any]       =				self._get_clip_similarity(pos_prompts['prompts']				,		snake_case_				,		weights=(1 / pos_prompts['weights'])						)
       if neg_prompts:
            UpperCAmelCase_							:       List[Any]       =				self._get_clip_similarity(neg_prompts['prompts']				,		snake_case_				,		weights=neg_prompts['weights']						)
       else:
            UpperCAmelCase_							:       Union[str, Any]       =				torch.tensor([1]				,		device=self.device						)
       UpperCAmelCase_							:       Dict       =				-torch.log(snake_case_						) + torch.log(snake_case_						)
       return loss


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_				,		snake_case_						):




       '''simple docstring'''

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

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


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_				,		snake_case_						):




       '''simple docstring'''

       wandb.init(reinit=snake_case_				,		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:
            UpperCAmelCase_							:       str       =				Image.open(snake_case_						)
            UpperCAmelCase_							:       str       =				image.resize((2_5_6, 2_5_6)						)
            wandb.log('Original Image'				,		wandb.Image(snake_case_						)						)


  def _UpperCamelCase					(			self				,		snake_case_						):




       '''simple docstring'''

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






  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_=None				,		snake_case_=None				,		snake_case_=True				,		snake_case_=False				,		snake_case_=True				,		snake_case_=True				,		snake_case_=None				,		):




       '''simple docstring'''

       if image_path:
            UpperCAmelCase_							:       List[Any]       =				self._get_latent(snake_case_						)
       else:
            UpperCAmelCase_							:       Any       =				torch.randn(self.latent_dim				,		device=self.device						)
       if self.log:
            self._init_logging(snake_case_				,		snake_case_				,		snake_case_						)

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

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

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






