
"""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_						:
 """simple docstring"""




 def __init__(			self							:			Tuple  , a_							:			str = "cpu"  , a_							:			str = "openai/clip-vit-large-patch14"			)->       None:



      """simple docstring"""

      UpperCAmelCase_    :     str            =	device
      UpperCAmelCase_    :     Tuple            =	CLIPTokenizerFast.from_pretrained(a_			)
      UpperCAmelCase_    :     Optional[Any]            =	[0.48145466, 0.4578275, 0.40821073]
      UpperCAmelCase_    :     Tuple            =	[0.26862954, 0.26130258, 0.27577711]
      UpperCAmelCase_    :     Optional[int]            =	torchvision.transforms.Normalize(self.image_mean  , self.image_std			)
      UpperCAmelCase_    :     int            =	torchvision.transforms.Resize(2_24			)
      UpperCAmelCase_    :     Tuple            =	torchvision.transforms.CenterCrop(2_24			)




 def 	a   (			self							:			int  , a_							:			Dict			)->       Optional[Any]:



      """simple docstring"""

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






 def __call__(			self							:			Any  , a_							:			Any=None  , a_							:			Optional[int]=None  , **a_							:			Union[str, Any]			)->       Dict:



      """simple docstring"""

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



class 		UpperCAmelCase_						(nn.Module    ):
 """simple docstring"""




 def __init__(			self							:			Any  , a_							:			Union[str, Any]=10  , a_							:			Tuple=0.01  , a_							:			List[Any]=None  , a_							:			List[Any]=None  , a_							:			Any=None  , a_							:			List[str]=None  , a_							:			int=None  , a_							:			Union[str, Any]=None  , a_							:			List[Any]=False  , a_							:			str=True  , a_							:			Optional[int]="image"  , a_							:			int=True  , a_							:			str=False  , a_							:			Any=False  , a_							:			List[Any]=False  , )->       None:



      """simple docstring"""

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

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




 def 	a   (			self							:			int  , a_							:			Union[str, Any]=None  , a_							:			Any=None  , a_							:			Dict=5  , a_							:			str=True			)->       Any:



      """simple docstring"""

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




 def 	a   (			self							:			Optional[Any]  , a_							:			Union[str, Any]=None  , a_							:			Optional[Any]=None			)->       Optional[int]:



      """simple docstring"""

      if not (path or img):
           raise ValueError("""Input either path or tensor"""			)
      if img is not None:
           raise NotImplementedError
      UpperCAmelCase_    :     Optional[int]            =	preprocess(Image.open(a_			)  , target_image_size=2_56			).to(self.device			)
      UpperCAmelCase_    :     List[Any]            =	preprocess_vqgan(a_			)
      UpperCAmelCase_       ,*UpperCAmelCase_    :     Tuple            =	self.vqgan.encode(a_			)
      return z




 def 	a   (			self							:			Optional[int]  , a_							:			Optional[Any]			)->       List[Any]:



      """simple docstring"""

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




 def 	a   (			self							:			Optional[int]  , a_							:			Any  , a_							:			Optional[int]  , a_							:			Any=None			)->       int:



      """simple docstring"""

      UpperCAmelCase_    :     Optional[int]            =	self.clip_preprocessor(text=a_  , images=a_  , return_tensors="""pt"""  , padding=a_			)
      UpperCAmelCase_    :     Optional[Any]            =	self.clip(**a_			)
      UpperCAmelCase_    :     Optional[int]            =	clip_outputs.logits_per_image
      if weights is not None:
           UpperCAmelCase_    :     Optional[Any]            =	similarity_logits * weights
      return similarity_logits.sum()




 def 	a   (			self							:			int  , a_							:			Tuple  , a_							:			Dict  , a_							:			Dict			)->       List[Any]:



      """simple docstring"""

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




 def 	a   (			self							:			Union[str, Any]  , a_							:			List[str]  , a_							:			List[str]  , a_							:			Tuple			)->       Dict:



      """simple docstring"""

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

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




 def 	a   (			self							:			Union[str, Any]  , a_							:			List[Any]  , a_							:			List[str]  , a_							:			Tuple			)->       Tuple:



      """simple docstring"""

      wandb.init(reinit=a_  , 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_    :     List[str]            =	Image.open(a_			)
           UpperCAmelCase_    :     Optional[Any]            =	image.resize((2_56, 2_56)			)
           wandb.log("""Original Image"""  , wandb.Image(a_			)			)




 def 	a   (			self							:			Union[str, Any]  , a_							:			List[str]			)->       Dict:



      """simple docstring"""

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






 def 	a   (			self							:			Optional[Any]  , a_							:			Optional[Any]  , a_							:			List[Any]=None  , a_							:			Dict=None  , a_							:			Any=True  , a_							:			List[str]=False  , a_							:			Any=True  , a_							:			Any=True  , a_							:			Union[str, Any]=None  , )->       List[str]:



      """simple docstring"""

      if image_path:
           UpperCAmelCase_    :     int            =	self._get_latent(a_			)
      else:
           UpperCAmelCase_    :     Any            =	torch.randn(self.latent_dim  , device=self.device			)
      if self.log:
           self._init_logging(a_  , a_  , a_			)

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

      UpperCAmelCase_    :     Optional[int]            =	self.vqgan.decode(self.latent			)[0]
      if show_intermediate:
           print("""Original Image"""			)
           show_pil(custom_to_pil(a_			)			)

      UpperCAmelCase_    :     Dict            =	loop_post_process(a_			)
      for iter, transformed_img in enumerate(self._optimize_CLIP(a_  , a_  , a_			)			):
           if show_intermediate:
                show_pil(a_			)
           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(a_			)}			)
      if show_final:
           show_pil(a_			)
      if save_final:
           transformed_img.save(os.path.join(self.save_path  , f'''iter_{iter:03d}_final.png'''			)			)



"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase_         =       {
    "configuration_timesformer": ["TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "TimesformerConfig"],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowercase_         =       [
           "TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
           "TimesformerModel",
           "TimesformerForVideoClassification",
           "TimesformerPreTrainedModel",
       ]

if TYPE_CHECKING:
       from .configuration_timesformer import TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, TimesformerConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_timesformer import (
                  TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TimesformerForVideoClassification,
                  TimesformerModel,
                  TimesformerPreTrainedModel,
              )

else:
       import sys

       lowercase_         =       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

