



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      ,   __lowerCamelCase							:    str = "cpu"      ,   __lowerCamelCase							:    str = "openai/clip-vit-large-patch14" ):


             '''simple docstring'''



             lowerCamelCase__	:       Union[str, Any]					=						device
             lowerCamelCase__	:       Any					=						CLIPTokenizerFast.from_pretrained(__lowerCamelCase )
             lowerCamelCase__	:       Tuple					=						[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]
             lowerCamelCase__	:       Optional[Any]					=						[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]
             lowerCamelCase__	:       List[str]					=						torchvision.transforms.Normalize(self.image_mean      ,   self.image_std )
             lowerCamelCase__	:       str					=						torchvision.transforms.Resize(224 )
             lowerCamelCase__	:       Union[str, Any]					=						torchvision.transforms.CenterCrop(224 )





       def 		lowerCAmelCase  (     self							:    int      ,   __lowerCamelCase							:    Union[str, Any] ):


             '''simple docstring'''



             lowerCamelCase__	:       int					=						self.resize(__lowerCamelCase )
             lowerCamelCase__	:       Tuple					=						self.center_crop(__lowerCamelCase )
             lowerCamelCase__	:       Any					=						self.normalize(__lowerCamelCase )
             return images





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


             '''simple docstring'''



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







class     _lowercase				(							nn.Module):


       """simple docstring"""




       def __init__(     self							:    Union[str, Any]      ,   __lowerCamelCase							:    List[Any]=10      ,   __lowerCamelCase							:    Optional[int]=0.0_1      ,   __lowerCamelCase							:    List[Any]=None      ,   __lowerCamelCase							:    Optional[int]=None      ,   __lowerCamelCase							:    str=None      ,   __lowerCamelCase							:    Optional[Any]=None      ,   __lowerCamelCase							:    Union[str, Any]=None      ,   __lowerCamelCase							:    List[Any]=None      ,   __lowerCamelCase							:    Dict=False      ,   __lowerCamelCase							:    Optional[int]=True      ,   __lowerCamelCase							:    Optional[int]="image"      ,   __lowerCamelCase							:    Tuple=True      ,   __lowerCamelCase							:    int=False      ,   __lowerCamelCase							:    Optional[int]=False      ,   __lowerCamelCase							:    int=False      ,   ):


             '''simple docstring'''



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

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





       def 		lowerCAmelCase  (     self							:    str      ,   __lowerCamelCase							:    List[Any]=None      ,   __lowerCamelCase							:    List[Any]=None      ,   __lowerCamelCase							:    int=5      ,   __lowerCamelCase							:    Dict=True ):


             '''simple docstring'''



             lowerCamelCase__	:       Any					=						[]
             if output_path is None:
                   lowerCamelCase__	:       Optional[int]					=						'./animation.gif'
             if input_path is None:
                   lowerCamelCase__	:       List[str]					=						self.save_path
             lowerCamelCase__	:       Tuple					=						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__	:       List[Any]					=						total_duration / len(__lowerCamelCase )
             lowerCamelCase__	:       Union[str, Any]					=						[frame_duration] * len(__lowerCamelCase )
             if extend_frames:
                   lowerCamelCase__	:       Union[str, Any]					=						1.5
                   lowerCamelCase__	:       Union[str, Any]					=						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 		lowerCAmelCase  (     self							:    str      ,   __lowerCamelCase							:    Dict=None      ,   __lowerCamelCase							:    List[str]=None ):


             '''simple docstring'''



             if not (path or img):
                   raise ValueError("Input either path or tensor" )
             if img is not None:
                   raise NotImplementedError
             lowerCamelCase__	:       Any					=						preprocess(Image.open(__lowerCamelCase )      ,   target_image_size=256 ).to(self.device )
             lowerCamelCase__	:       str					=						preprocess_vqgan(__lowerCamelCase )
             lowerCamelCase__	:       List[Any]					=						self.vqgan.encode(__lowerCamelCase )
             return z





       def 		lowerCAmelCase  (     self							:    str      ,   __lowerCamelCase							:    List[str] ):


             '''simple docstring'''



             lowerCamelCase__	:       List[Any]					=						self.latent.detach().requires_grad_()
             lowerCamelCase__	:       Optional[int]					=						base_latent + transform_vector
             if self.quantize:
                   lowerCamelCase__	:       Dict					=						self.vqgan.quantize(__lowerCamelCase )
             else:
                   lowerCamelCase__	:       int					=						trans_latent
             return self.vqgan.decode(__lowerCamelCase )





       def 		lowerCAmelCase  (     self							:    Dict      ,   __lowerCamelCase							:    Any      ,   __lowerCamelCase							:    Any      ,   __lowerCamelCase							:    List[Any]=None ):


             '''simple docstring'''



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





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


             '''simple docstring'''



             lowerCamelCase__	:       Optional[int]					=						self._get_clip_similarity(pos_prompts["prompts"]      ,   __lowerCamelCase      ,   weights=(1 / pos_prompts["weights"]) )
             if neg_prompts:
                   lowerCamelCase__	:       Optional[Any]					=						self._get_clip_similarity(neg_prompts["prompts"]      ,   __lowerCamelCase      ,   weights=neg_prompts["weights"] )
             else:
                   lowerCamelCase__	:       str					=						torch.tensor([1]      ,   device=self.device )
             lowerCamelCase__	:       Dict					=						-torch.log(__lowerCamelCase ) + torch.log(__lowerCamelCase )
             return loss





       def 		lowerCAmelCase  (     self							:    Union[str, Any]      ,   __lowerCamelCase							:    str      ,   __lowerCamelCase							:    Dict      ,   __lowerCamelCase							:    Union[str, Any] ):


             '''simple docstring'''



             lowerCamelCase__	:       Any					=						torch.randn_like(self.latent      ,   requires_grad=__lowerCamelCase      ,   device=self.device )
             lowerCamelCase__	:       Tuple					=						torch.optim.Adam([vector]      ,   lr=self.lr )

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


             '''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:
                   lowerCamelCase__	:       Optional[Any]					=						Image.open(__lowerCamelCase )
                   lowerCamelCase__	:       Dict					=						image.resize((256, 256) )
                   wandb.log("Original Image"      ,   wandb.Image(__lowerCamelCase ) )





       def 		lowerCAmelCase  (     self							:    Union[str, Any]      ,   __lowerCamelCase							:    List[Any] ):


             '''simple docstring'''



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





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


             '''simple docstring'''



             if image_path:
                   lowerCamelCase__	:       Dict					=						self._get_latent(__lowerCamelCase )
             else:
                   lowerCamelCase__	:       Tuple					=						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__	:       Union[str, Any]					=						self.process_prompts(__lowerCamelCase )
             lowerCamelCase__	:       Optional[Any]					=						self.process_prompts(__lowerCamelCase )
             if save_final and save_path is None:
                   lowerCamelCase__	:       Optional[int]					=						os.path.join("./outputs/"      ,   "_".join(pos_prompts["prompts"] ) )
             if not os.path.exists(__lowerCamelCase ):
                   os.makedirs(__lowerCamelCase )
             else:
                   lowerCamelCase__	:       Optional[int]					=						save_path + '_' + get_timestamp()
                   os.makedirs(__lowerCamelCase )
             lowerCamelCase__	:       Tuple					=						save_path

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

             lowerCamelCase__	:       Tuple					=						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" ) )






import os
from pathlib import Path

def    lowercase_	(	):

      """simple docstring"""




      from torch.utils.cpp_extension import load

      lowerCamelCase__	:       Any					=						Path(_A		).resolve().parent.parent.parent / "kernels" / "deformable_detr"
      lowerCamelCase__	:       Optional[int]					=						[
          root / filename
          for filename in [
              "vision.cpp",
              os.path.join("cpu"					,      "ms_deform_attn_cpu.cpp"		),
              os.path.join("cuda"					,      "ms_deform_attn_cuda.cu"		),
          ]
      ]

      load(
          "MultiScaleDeformableAttention"					,      _A					,      with_cuda=_A					,      extra_include_paths=[str(_A		)]					,      extra_cflags=["-DWITH_CUDA=1"]					,      extra_cuda_cflags=[
              "-DCUDA_HAS_FP16=1",
              "-D__CUDA_NO_HALF_OPERATORS__",
              "-D__CUDA_NO_HALF_CONVERSIONS__",
              "-D__CUDA_NO_HALF2_OPERATORS__",
          ]					,      )

      import MultiScaleDeformableAttention as MSDA

      return MSDA


