




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       a_       :





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




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						device
				SCREAMING_SNAKE_CASE_								=						CLIPTokenizerFast.from_pretrained(_lowerCamelCase      )
				SCREAMING_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]
				SCREAMING_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]
				SCREAMING_SNAKE_CASE_								=						torchvision.transforms.Normalize(self.image_mean      ,     self.image_std      )
				SCREAMING_SNAKE_CASE_								=						torchvision.transforms.Resize(224      )
				SCREAMING_SNAKE_CASE_								=						torchvision.transforms.CenterCrop(224      )





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      )  ->					Optional[Any]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						self.resize(_lowerCamelCase      )
				SCREAMING_SNAKE_CASE_								=						self.center_crop(_lowerCamelCase      )
				SCREAMING_SNAKE_CASE_								=						self.normalize(_lowerCamelCase      )
				return images





	def __call__(							self      ,     SCREAMING_SNAKE_CASE=None      ,     SCREAMING_SNAKE_CASE=None      ,     **SCREAMING_SNAKE_CASE      )  ->					Optional[int]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						self.tokenizer(text=_lowerCamelCase      ,     **_lowerCamelCase      )
				SCREAMING_SNAKE_CASE_								=						self.preprocess_img(_lowerCamelCase      )
				SCREAMING_SNAKE_CASE_								=						{key: value.to(self.device      ) for (key, value) in encoding.items()}
				return encoding




class       a_       (     nn.Module     ):





	def __init__(							self      ,     SCREAMING_SNAKE_CASE=10      ,     SCREAMING_SNAKE_CASE=0.0_1      ,     SCREAMING_SNAKE_CASE=None      ,     SCREAMING_SNAKE_CASE=None      ,     SCREAMING_SNAKE_CASE=None      ,     SCREAMING_SNAKE_CASE=None      ,     SCREAMING_SNAKE_CASE=None      ,     SCREAMING_SNAKE_CASE=None      ,     SCREAMING_SNAKE_CASE=False      ,     SCREAMING_SNAKE_CASE=True      ,     SCREAMING_SNAKE_CASE="image"      ,     SCREAMING_SNAKE_CASE=True      ,     SCREAMING_SNAKE_CASE=False      ,     SCREAMING_SNAKE_CASE=False      ,     SCREAMING_SNAKE_CASE=False      ,     )  ->					int:




				"""simple docstring"""



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

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





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE=None      ,     SCREAMING_SNAKE_CASE=None      ,     SCREAMING_SNAKE_CASE=5      ,     SCREAMING_SNAKE_CASE=True      )  ->					Any:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						[]
				if output_path is None:
							SCREAMING_SNAKE_CASE_								=						'./animation.gif'
				if input_path is None:
							SCREAMING_SNAKE_CASE_								=						self.save_path
				SCREAMING_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?)'      )
				SCREAMING_SNAKE_CASE_								=						total_duration / len(_lowerCamelCase      )
				SCREAMING_SNAKE_CASE_								=						[frame_duration] * len(_lowerCamelCase      )
				if extend_frames:
							SCREAMING_SNAKE_CASE_								=						1.5
							SCREAMING_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 						A_(							self      ,     SCREAMING_SNAKE_CASE=None      ,     SCREAMING_SNAKE_CASE=None      )  ->					Union[str, Any]:




				"""simple docstring"""



				if not (path or img):
							raise ValueError('Input either path or tensor'      )
				if img is not None:
							raise NotImplementedError
				SCREAMING_SNAKE_CASE_								=						preprocess(Image.open(_lowerCamelCase      )      ,     target_image_size=256      ).to(self.device      )
				SCREAMING_SNAKE_CASE_								=						preprocess_vqgan(_lowerCamelCase      )
				SCREAMING_SNAKE_CASE_								=						self.vqgan.encode(_lowerCamelCase      )
				return z





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      )  ->					List[Any]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						self.latent.detach().requires_grad_()
				SCREAMING_SNAKE_CASE_								=						base_latent + transform_vector
				if self.quantize:
							SCREAMING_SNAKE_CASE_								=						self.vqgan.quantize(_lowerCamelCase      )
				else:
							SCREAMING_SNAKE_CASE_								=						trans_latent
				return self.vqgan.decode(_lowerCamelCase      )





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE=None      )  ->					Tuple:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						self.clip_preprocessor(text=_lowerCamelCase      ,     images=_lowerCamelCase      ,     return_tensors='pt'      ,     padding=_lowerCamelCase      )
				SCREAMING_SNAKE_CASE_								=						self.clip(**_lowerCamelCase      )
				SCREAMING_SNAKE_CASE_								=						clip_outputs.logits_per_image
				if weights is not None:
							SCREAMING_SNAKE_CASE_								=						similarity_logits * weights
				return similarity_logits.sum()





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      )  ->					int:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						self._get_clip_similarity(pos_prompts['prompts']      ,     _lowerCamelCase      ,     weights=(1 / pos_prompts['weights'])      )
				if neg_prompts:
							SCREAMING_SNAKE_CASE_								=						self._get_clip_similarity(neg_prompts['prompts']      ,     _lowerCamelCase      ,     weights=neg_prompts['weights']      )
				else:
							SCREAMING_SNAKE_CASE_								=						torch.tensor([1]      ,     device=self.device      )
				SCREAMING_SNAKE_CASE_								=						-torch.log(_lowerCamelCase      ) + torch.log(_lowerCamelCase      )
				return loss





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      )  ->					List[str]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						torch.randn_like(self.latent      ,     requires_grad=_lowerCamelCase      ,     device=self.device      )
				SCREAMING_SNAKE_CASE_								=						torch.optim.Adam([vector]      ,     lr=self.lr      )

				for i in range(self.iterations      ):
							optim.zero_grad()
							SCREAMING_SNAKE_CASE_								=						self._add_vector(_lowerCamelCase      )
							SCREAMING_SNAKE_CASE_								=						loop_post_process(_lowerCamelCase      )
							SCREAMING_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 						A_(							self      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      )  ->					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:
							SCREAMING_SNAKE_CASE_								=						Image.open(_lowerCamelCase      )
							SCREAMING_SNAKE_CASE_								=						image.resize((256, 256)      )
							wandb.log('Original Image'      ,     wandb.Image(_lowerCamelCase      )      )





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      )  ->					List[str]:




				"""simple docstring"""



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





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE=None      ,     SCREAMING_SNAKE_CASE=None      ,     SCREAMING_SNAKE_CASE=True      ,     SCREAMING_SNAKE_CASE=False      ,     SCREAMING_SNAKE_CASE=True      ,     SCREAMING_SNAKE_CASE=True      ,     SCREAMING_SNAKE_CASE=None      ,     )  ->					Tuple:




				"""simple docstring"""



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

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

				SCREAMING_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'      )      )


"""simple docstring"""






from pathlib import PurePosixPath
from typing import Optional

import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo

from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url





class 				a__					(						A__       ):
     UpperCAmelCase__         =      ''''''
     UpperCAmelCase__         =      '''hf-legacy'''  # "hf://"" is reserved for hffs



     def __init__(							self :Dict      ,  _lowerCamelCase :Optional[DatasetInfo] = None      ,  _lowerCamelCase :Optional[str] = None      ,  **_lowerCamelCase :Tuple      ,  ):







      '''simple docstring'''



      super().__init__(self      ,  **_lowerCamelCase   )
      UpperCamelCase_     :					List[str]   	=repo_info
      UpperCamelCase_     :					Any   	=token
      UpperCamelCase_     :					Tuple   	=None



     def  lowerCamelCase_						(							self :Dict   ):







      '''simple docstring'''



      if self.dir_cache is None:
       UpperCamelCase_     :					Any   	={}
       for hf_file in self.repo_info.siblings:
        # TODO(QL): add sizes
        UpperCamelCase_     :					Optional[Any]   	={
            'name': hf_file.rfilename,
            'size': None,
            'type': 'file',
        }
        self.dir_cache.update(
            {
                str(_lowerCamelCase   ): {'name': str(_lowerCamelCase   ), 'size': None, 'type': 'directory'}
                for d in list(PurePosixPath(hf_file.rfilename   ).parents   )[:-1]
            }   )



     def  lowerCamelCase_						(							self :Union[str, Any]      ,  _lowerCamelCase :str      ,  _lowerCamelCase :str = "rb"      ,  **_lowerCamelCase :str      ,  ):







      '''simple docstring'''



      if not isinstance(self.repo_info      ,  _lowerCamelCase   ):
       raise NotImplementedError(f'''Open is only implemented for dataset repositories, but got {self.repo_info}'''   )
      UpperCamelCase_     :					List[Any]   	=hf_hub_url(self.repo_info.id      ,  _lowerCamelCase      ,  revision=self.repo_info.sha   )
      return fsspec.open(
          _lowerCamelCase      ,  mode=_lowerCamelCase      ,  headers=get_authentication_headers_for_url(_lowerCamelCase      ,  use_auth_token=self.token   )      ,  client_kwargs={'trust_env': True}      ,  ).open()



     def  lowerCamelCase_						(							self :Optional[Any]      ,  _lowerCamelCase :Tuple      ,  **_lowerCamelCase :Any   ):







      '''simple docstring'''



      self._get_dirs()
      UpperCamelCase_     :					Tuple   	=self._strip_protocol(_lowerCamelCase   )
      if path in self.dir_cache:
       return self.dir_cache[path]
      else:
       raise FileNotFoundError(_lowerCamelCase   )




     def  lowerCamelCase_						(							self :Optional[Any]      ,  _lowerCamelCase :List[str]      ,  _lowerCamelCase :List[Any]=False      ,  **_lowerCamelCase :Any   ):







      '''simple docstring'''



      self._get_dirs()
      UpperCamelCase_     :					str   	=PurePosixPath(path.strip('/'   )   )
      UpperCamelCase_     :					List[str]   	={}
      for p, f in self.dir_cache.items():
       UpperCamelCase_     :					List[Any]   	=PurePosixPath(p.strip('/'   )   )
       UpperCamelCase_     :					Tuple   	=p.parent
       if root == path:
        UpperCamelCase_     :					int   	=f
      UpperCamelCase_     :					Optional[int]   	=list(paths.values()   )
      if detail:
       return out
      else:
       return sorted(f['name'] for f in out   )
