



"""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     UpperCAmelCase    :



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



     """simple docstring"""
     _snake_case   	=      device
     _snake_case   	=      CLIPTokenizerFast.from_pretrained(__lowerCamelCase				)
     _snake_case   	=      [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]
     _snake_case   	=      [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]
     _snake_case   	=      torchvision.transforms.Normalize(self.image_mean							,       self.image_std				)
     _snake_case   	=      torchvision.transforms.Resize(2_2_4				)
     _snake_case   	=      torchvision.transforms.CenterCrop(2_2_4				)



   def    __UpperCAmelCase    (       self    :	int							,       __lowerCamelCase    :	Optional[Any]				):



     """simple docstring"""
     _snake_case   	=      self.resize(__lowerCamelCase				)
     _snake_case   	=      self.center_crop(__lowerCamelCase				)
     _snake_case   	=      self.normalize(__lowerCamelCase				)
     return images





   def __call__(       self    :	Optional[int]							,       __lowerCamelCase    :	List[Any]=None							,       __lowerCamelCase    :	int=None							,       **__lowerCamelCase    :	List[Any]				):



     """simple docstring"""
     _snake_case   	=      self.tokenizer(text=__lowerCamelCase							,       **__lowerCamelCase				)
     _snake_case   	=      self.preprocess_img(__lowerCamelCase				)
     _snake_case   	=      {key: value.to(self.device				) for (key, value) in encoding.items()}
     return encoding



class     UpperCAmelCase    (	nn.Module  ):



   def __init__(       self    :	Union[str, Any]							,       __lowerCamelCase    :	Union[str, Any]=1_0							,       __lowerCamelCase    :	Optional[Any]=0.0_1							,       __lowerCamelCase    :	Dict=None							,       __lowerCamelCase    :	int=None							,       __lowerCamelCase    :	Optional[Any]=None							,       __lowerCamelCase    :	Optional[Any]=None							,       __lowerCamelCase    :	int=None							,       __lowerCamelCase    :	Tuple=None							,       __lowerCamelCase    :	List[Any]=False							,       __lowerCamelCase    :	int=True							,       __lowerCamelCase    :	int="image"							,       __lowerCamelCase    :	Union[str, Any]=True							,       __lowerCamelCase    :	Optional[Any]=False							,       __lowerCamelCase    :	Tuple=False							,       __lowerCamelCase    :	Optional[int]=False							,       ):



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

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



   def    __UpperCAmelCase    (       self    :	int							,       __lowerCamelCase    :	Optional[int]=None							,       __lowerCamelCase    :	Any=None							,       __lowerCamelCase    :	Optional[Any]=5							,       __lowerCamelCase    :	Tuple=True				):



     """simple docstring"""
     _snake_case   	=      []
     if output_path is None:
       _snake_case   	=      '''./animation.gif'''
     if input_path is None:
       _snake_case   	=      self.save_path
     _snake_case   	=      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?)'''				)
     _snake_case   	=      total_duration / len(__lowerCamelCase				)
     _snake_case   	=      [frame_duration] * len(__lowerCamelCase				)
     if extend_frames:
       _snake_case   	=      1.5
       _snake_case   	=      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    :	Optional[int]							,       __lowerCamelCase    :	List[str]=None							,       __lowerCamelCase    :	Any=None				):



     """simple docstring"""
     if not (path or img):
       raise ValueError('''Input either path or tensor'''				)
     if img is not None:
       raise NotImplementedError
     _snake_case   	=      preprocess(Image.open(__lowerCamelCase				)							,       target_image_size=2_5_6				).to(self.device				)
     _snake_case   	=      preprocess_vqgan(__lowerCamelCase				)
     _snake_case	,     *_snake_case   	=      self.vqgan.encode(__lowerCamelCase				)
     return z



   def    __UpperCAmelCase    (       self    :	List[Any]							,       __lowerCamelCase    :	Dict				):



     """simple docstring"""
     _snake_case   	=      self.latent.detach().requires_grad_()
     _snake_case   	=      base_latent + transform_vector
     if self.quantize:
       _snake_case	,     *_snake_case   	=      self.vqgan.quantize(__lowerCamelCase				)
     else:
       _snake_case   	=      trans_latent
     return self.vqgan.decode(__lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	Optional[Any]							,       __lowerCamelCase    :	Tuple							,       __lowerCamelCase    :	List[Any]							,       __lowerCamelCase    :	Any=None				):



     """simple docstring"""
     _snake_case   	=      self.clip_preprocessor(text=__lowerCamelCase							,       images=__lowerCamelCase							,       return_tensors='''pt'''							,       padding=__lowerCamelCase				)
     _snake_case   	=      self.clip(**__lowerCamelCase				)
     _snake_case   	=      clip_outputs.logits_per_image
     if weights is not None:
       _snake_case   	=      similarity_logits * weights
     return similarity_logits.sum()



   def    __UpperCAmelCase    (       self    :	Optional[int]							,       __lowerCamelCase    :	str							,       __lowerCamelCase    :	List[Any]							,       __lowerCamelCase    :	Tuple				):



     """simple docstring"""
     _snake_case   	=      self._get_clip_similarity(pos_prompts['''prompts''']							,       __lowerCamelCase							,       weights=(1 / pos_prompts['''weights'''])				)
     if neg_prompts:
       _snake_case   	=      self._get_clip_similarity(neg_prompts['''prompts''']							,       __lowerCamelCase							,       weights=neg_prompts['''weights''']				)
     else:
       _snake_case   	=      torch.tensor([1]							,       device=self.device				)
     _snake_case   	=      -torch.log(__lowerCamelCase				) + torch.log(__lowerCamelCase				)
     return loss



   def    __UpperCAmelCase    (       self    :	Optional[int]							,       __lowerCamelCase    :	Optional[int]							,       __lowerCamelCase    :	Optional[int]							,       __lowerCamelCase    :	Dict				):



     """simple docstring"""
     _snake_case   	=      torch.randn_like(self.latent							,       requires_grad=__lowerCamelCase							,       device=self.device				)
     _snake_case   	=      torch.optim.Adam([vector]							,       lr=self.lr				)

     for i in range(self.iterations				):
       optim.zero_grad()
       _snake_case   	=      self._add_vector(__lowerCamelCase				)
       _snake_case   	=      loop_post_process(__lowerCamelCase				)
       _snake_case   	=      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    :	Optional[Any]							,       __lowerCamelCase    :	Optional[int]							,       __lowerCamelCase    :	Tuple							,       __lowerCamelCase    :	List[str]				):



     """simple docstring"""
     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:
       _snake_case   	=      Image.open(__lowerCamelCase				)
       _snake_case   	=      image.resize((2_5_6, 2_5_6)				)
       wandb.log('''Original Image'''							,       wandb.Image(__lowerCamelCase				)				)



   def    __UpperCAmelCase    (       self    :	Dict							,       __lowerCamelCase    :	Any				):



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





   def    __UpperCAmelCase    (       self    :	int							,       __lowerCamelCase    :	Any							,       __lowerCamelCase    :	Tuple=None							,       __lowerCamelCase    :	List[Any]=None							,       __lowerCamelCase    :	Dict=True							,       __lowerCamelCase    :	int=False							,       __lowerCamelCase    :	Optional[int]=True							,       __lowerCamelCase    :	Union[str, Any]=True							,       __lowerCamelCase    :	Optional[int]=None							,       ):



     """simple docstring"""
     if image_path:
       _snake_case   	=      self._get_latent(__lowerCamelCase				)
     else:
       _snake_case   	=      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."
     _snake_case   	=      self.process_prompts(__lowerCamelCase				)
     _snake_case   	=      self.process_prompts(__lowerCamelCase				)
     if save_final and save_path is None:
       _snake_case   	=      os.path.join('''./outputs/'''							,       '''_'''.join(pos_prompts['''prompts''']				)				)
     if not os.path.exists(__lowerCamelCase				):
       os.makedirs(__lowerCamelCase				)
     else:
       _snake_case   	=      save_path + '''_''' + get_timestamp()
       os.makedirs(__lowerCamelCase				)
     _snake_case   	=      save_path

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

     _snake_case   	=      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"""				)				)












from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
    import PIL


_UpperCAmelCase							:			Any        =							logging.get_logger(__name__)

class   lowerCAmelCase				(   __UpperCamelCase     ):
 UpperCAmelCase__						=						["""pixel_values"""]
 def __init__( self    : Any     ,	UpperCAmelCase    : bool = True     ,	UpperCAmelCase    : Dict[str, int] = None     ,	UpperCAmelCase    : int = 0.9     ,	UpperCAmelCase    : PILImageResampling = PILImageResampling.BICUBIC     ,	UpperCAmelCase    : bool = True     ,	UpperCAmelCase    : Dict[str, int] = None     ,	UpperCAmelCase    : Union[int, float] = 1 / 255     ,	UpperCAmelCase    : bool = True     ,	UpperCAmelCase    : bool = True     ,	UpperCAmelCase    : Optional[Union[float, List[float]]] = None     ,	UpperCAmelCase    : Optional[Union[float, List[float]]] = None     ,	**UpperCAmelCase    : str     ,	)     ->      None:
     super().__init__(**UpperCAmelCase      )
     lowerCamelCase__       : int   					=						size if size is not None else {'shortest_edge': 224}
     lowerCamelCase__       : Tuple   					=						get_size_dict(UpperCAmelCase     ,	default_to_square=UpperCAmelCase      )
     lowerCamelCase__       : Any   					=						crop_size if crop_size is not None else {'height': 224, 'width': 224}
     lowerCamelCase__       : str   					=						get_size_dict(UpperCAmelCase     ,	param_name='crop_size'      )

     lowerCamelCase__       : Tuple   					=						do_resize
     lowerCamelCase__       : str   					=						size
     lowerCamelCase__       : List[str]   					=						crop_pct
     lowerCamelCase__       : Any   					=						resample
     lowerCamelCase__       : Tuple   					=						do_center_crop
     lowerCamelCase__       : Any   					=						crop_size
     lowerCamelCase__       : Optional[int]   					=						do_rescale
     lowerCamelCase__       : Optional[Any]   					=						rescale_factor
     lowerCamelCase__       : Union[str, Any]   					=						do_normalize
     lowerCamelCase__       : Any   					=						image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
     lowerCamelCase__       : Optional[Any]   					=						image_std if image_std is not None else IMAGENET_DEFAULT_STD
 def 	A_      ( self    : Optional[Any]     ,	UpperCAmelCase    : np.ndarray     ,	UpperCAmelCase    : Dict[str, int]     ,	UpperCAmelCase    : Optional[float] = None     ,	UpperCAmelCase    : PILImageResampling = PILImageResampling.BICUBIC     ,	UpperCAmelCase    : Optional[Union[str, ChannelDimension]] = None     ,	**UpperCAmelCase    : int     ,	)     ->      np.ndarray:
     lowerCamelCase__       : Any   					=						get_size_dict(UpperCAmelCase     ,	default_to_square=UpperCAmelCase      )
     if "shortest_edge" not in size and ("height" not in size or "width" not in size):
         raise ValueError(F"""size must contain 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"""      )
     if crop_pct is not None:
         if "shortest_edge" in size:
             lowerCamelCase__       : List[Any]   					=						int(size['shortest_edge'] / crop_pct      )
         elif "height" in size and "width" in size:
             if size["height"] == size["width"]:
                 lowerCamelCase__       : int   					=						int(size['height'] / crop_pct      )
             else:
                 lowerCamelCase__       : Any   					=						(int(size['height'] / crop_pct      ), int(size['width'] / crop_pct      ))
         else:
             raise ValueError('Invalid size for resize: {}'.format(UpperCAmelCase      )      )

         lowerCamelCase__       : Union[str, Any]   					=						get_resize_output_image_size(UpperCAmelCase     ,	size=UpperCAmelCase     ,	default_to_square=UpperCAmelCase      )
     else:
         if "shortest_edge" in size:
             lowerCamelCase__       : int   					=						get_resize_output_image_size(UpperCAmelCase     ,	size=size['shortest_edge']     ,	default_to_square=UpperCAmelCase      )
         elif "height" in size and "width" in size:
             lowerCamelCase__       : List[Any]   					=						(size['height'], size['width'])
         else:
             raise ValueError('Invalid size for resize: {}'.format(UpperCAmelCase      )      )

     return resize(UpperCAmelCase     ,	size=UpperCAmelCase     ,	resample=UpperCAmelCase     ,	data_format=UpperCAmelCase     ,	**UpperCAmelCase      )
 def 	A_      ( self    : Optional[Any]     ,	UpperCAmelCase    : np.ndarray     ,	UpperCAmelCase    : Dict[str, int]     ,	UpperCAmelCase    : Optional[Union[str, ChannelDimension]] = None     ,	**UpperCAmelCase    : str     ,	)     ->      np.ndarray:
     lowerCamelCase__       : Union[str, Any]   					=						get_size_dict(UpperCAmelCase      )
     if "height" not in size or "width" not in size:
         raise ValueError(F"""size must contain 'height' and 'width' as keys. Got {size.keys()}"""      )
     return center_crop(UpperCAmelCase     ,	size=(size['height'], size['width'])     ,	data_format=UpperCAmelCase     ,	**UpperCAmelCase      )
 def 	A_      ( self    : Union[str, Any]     ,	UpperCAmelCase    : np.ndarray     ,	UpperCAmelCase    : Union[int, float]     ,	UpperCAmelCase    : Optional[Union[str, ChannelDimension]] = None     ,	**UpperCAmelCase    : List[Any]     ,	)     ->      int:
     return rescale(UpperCAmelCase     ,	scale=UpperCAmelCase     ,	data_format=UpperCAmelCase     ,	**UpperCAmelCase      )
 def 	A_      ( self    : Dict     ,	UpperCAmelCase    : np.ndarray     ,	UpperCAmelCase    : Union[float, List[float]]     ,	UpperCAmelCase    : Union[float, List[float]]     ,	UpperCAmelCase    : Optional[Union[str, ChannelDimension]] = None     ,	**UpperCAmelCase    : Dict     ,	)     ->      np.ndarray:
     return normalize(UpperCAmelCase     ,	mean=UpperCAmelCase     ,	std=UpperCAmelCase     ,	data_format=UpperCAmelCase     ,	**UpperCAmelCase      )







 def 	A_      ( self    : str     ,	UpperCAmelCase    : ImageInput     ,	UpperCAmelCase    : bool = None     ,	UpperCAmelCase    : Dict[str, int] = None     ,	UpperCAmelCase    : int = None     ,	UpperCAmelCase    : PILImageResampling = None     ,	UpperCAmelCase    : bool = None     ,	UpperCAmelCase    : Dict[str, int] = None     ,	UpperCAmelCase    : bool = None     ,	UpperCAmelCase    : float = None     ,	UpperCAmelCase    : bool = None     ,	UpperCAmelCase    : Optional[Union[float, List[float]]] = None     ,	UpperCAmelCase    : Optional[Union[float, List[float]]] = None     ,	UpperCAmelCase    : Optional[Union[str, TensorType]] = None     ,	UpperCAmelCase    : ChannelDimension = ChannelDimension.FIRST     ,	**UpperCAmelCase    : Any     ,	)     ->      PIL.Image.Image:
     lowerCamelCase__       : Optional[Any]   					=						do_resize if do_resize is not None else self.do_resize
     lowerCamelCase__       : Any   					=						crop_pct if crop_pct is not None else self.crop_pct
     lowerCamelCase__       : Any   					=						resample if resample is not None else self.resample
     lowerCamelCase__       : Any   					=						do_center_crop if do_center_crop is not None else self.do_center_crop
     lowerCamelCase__       : Optional[int]   					=						do_rescale if do_rescale is not None else self.do_rescale
     lowerCamelCase__       : List[Any]   					=						rescale_factor if rescale_factor is not None else self.rescale_factor
     lowerCamelCase__       : Union[str, Any]   					=						do_normalize if do_normalize is not None else self.do_normalize
     lowerCamelCase__       : Any   					=						image_mean if image_mean is not None else self.image_mean
     lowerCamelCase__       : str   					=						image_std if image_std is not None else self.image_std

     lowerCamelCase__       : Optional[Any]   					=						size if size is not None else self.size
     lowerCamelCase__       : Optional[Any]   					=						get_size_dict(UpperCAmelCase     ,	default_to_square=UpperCAmelCase      )
     lowerCamelCase__       : List[str]   					=						crop_size if crop_size is not None else self.crop_size
     lowerCamelCase__       : int   					=						get_size_dict(UpperCAmelCase     ,	param_name='crop_size'      )

     lowerCamelCase__       : Any   					=						make_list_of_images(UpperCAmelCase      )

     if not valid_images(UpperCAmelCase      ):
         raise ValueError(
             'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
             'torch.Tensor, tf.Tensor or jax.ndarray.'      )

     if do_resize and size is None or resample is None:
         raise ValueError('Size and resample must be specified if do_resize is True.'      )

     if do_center_crop and crop_pct is None:
         raise ValueError('Crop_pct must be specified if do_center_crop is True.'      )

     if do_rescale and rescale_factor is None:
         raise ValueError('Rescale factor must be specified if do_rescale is True.'      )

     if do_normalize and (image_mean is None or image_std is None):
         raise ValueError('Image mean and std must be specified if do_normalize is True.'      )

     # All transformations expect numpy arrays.
     lowerCamelCase__       : Dict   					=						[to_numpy_array(UpperCAmelCase      ) for image in images]

     if do_resize:
         lowerCamelCase__       : Any   					=						[self.resize(image=UpperCAmelCase     ,	size=UpperCAmelCase     ,	crop_pct=UpperCAmelCase     ,	resample=UpperCAmelCase      ) for image in images]

     if do_center_crop:
         lowerCamelCase__       : Optional[Any]   					=						[self.center_crop(image=UpperCAmelCase     ,	size=UpperCAmelCase      ) for image in images]

     if do_rescale:
         lowerCamelCase__       : int   					=						[self.rescale(image=UpperCAmelCase     ,	scale=UpperCAmelCase      ) for image in images]

     if do_normalize:
         lowerCamelCase__       : List[Any]   					=						[self.normalize(image=UpperCAmelCase     ,	mean=UpperCAmelCase     ,	std=UpperCAmelCase      ) for image in images]

     lowerCamelCase__       : Dict   					=						[to_channel_dimension_format(UpperCAmelCase     ,	UpperCAmelCase      ) for image in images]

     lowerCamelCase__       : Dict   					=						{'pixel_values': images}
     return BatchFeature(data=UpperCAmelCase     ,	tensor_type=UpperCAmelCase      )





