






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 					_snake_case						:






	def __init__(		self					,UpperCamelCase = "cpu"					,UpperCamelCase = "openai/clip-vit-large-patch14"       )						->    None:
					snake_case__      :str         =   device
					snake_case__      :Union[str, Any]         =   CLIPTokenizerFast.from_pretrained(UpperCamelCase       )
					snake_case__      :Any         =   [0.48145466, 0.4578275, 0.40821073]
					snake_case__      :Any         =   [0.26862954, 0.26130258, 0.27577711]
					snake_case__      :Optional[int]         =   torchvision.transforms.Normalize(self.image_mean					,self.image_std       )
					snake_case__      :str         =   torchvision.transforms.Resize(224       )
					snake_case__      :Optional[int]         =   torchvision.transforms.CenterCrop(224       )






	def    lowerCAmelCase_							(		self					,UpperCamelCase       )						->    Any:
					snake_case__      :str         =   self.resize(UpperCamelCase       )
					snake_case__      :List[str]         =   self.center_crop(UpperCamelCase       )
					snake_case__      :List[str]         =   self.normalize(UpperCamelCase       )
					return images






	def __call__(		self					,UpperCamelCase=None					,UpperCamelCase=None					,**UpperCamelCase       )						->    Union[str, Any]:
					snake_case__      :Optional[Any]         =   self.tokenizer(text=UpperCamelCase					,**UpperCamelCase       )
					snake_case__      :Tuple         =   self.preprocess_img(UpperCamelCase       )
					snake_case__      :Optional[Any]         =   {key: value.to(self.device       ) for (key, value) in encoding.items()}
					return encoding




class 					_snake_case						(			nn.Module ):






	def __init__(		self					,UpperCamelCase=10					,UpperCamelCase=0.01					,UpperCamelCase=None					,UpperCamelCase=None					,UpperCamelCase=None					,UpperCamelCase=None					,UpperCamelCase=None					,UpperCamelCase=None					,UpperCamelCase=False					,UpperCamelCase=True					,UpperCamelCase="image"					,UpperCamelCase=True					,UpperCamelCase=False					,UpperCamelCase=False					,UpperCamelCase=False					,)						->    None:
					super().__init__()
					snake_case__      :Optional[Any]         =   None
					snake_case__      :Any         =   device if device else get_device()
					if vqgan:
									snake_case__      :Tuple         =   vqgan
					else:
									snake_case__      :Optional[Any]         =   load_vqgan(self.device					,conf_path=UpperCamelCase					,ckpt_path=UpperCamelCase       )
					self.vqgan.eval()
					if clip:
									snake_case__      :List[str]         =   clip
					else:
									snake_case__      :Optional[Any]         =   CLIPModel.from_pretrained("openai/clip-vit-base-patch32"       )
					self.clip.to(self.device       )
					snake_case__      :Optional[int]         =   ProcessorGradientFlow(device=self.device       )

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






	def    lowerCAmelCase_							(		self					,UpperCamelCase=None					,UpperCamelCase=None					,UpperCamelCase=5					,UpperCamelCase=True       )						->    Union[str, Any]:
					snake_case__      :Union[str, Any]         =   []
					if output_path is None:
									snake_case__      :Dict         =   "./animation.gif"
					if input_path is None:
									snake_case__      :Optional[Any]         =   self.save_path
					snake_case__      :Union[str, Any]         =   sorted(glob(input_path + "/*"       )       )
					if not len(UpperCamelCase       ):
									raise ValueError(
									    "No images found in save path, aborting (did you pass save_intermediate=True to the generate"
									    " function?)"       )
					if len(UpperCamelCase       ) == 1:
									print("Only one image found in save path, (did you pass save_intermediate=True to the generate function?)"       )
					snake_case__      :List[str]         =   total_duration / len(UpperCamelCase       )
					snake_case__      :str         =   [frame_duration] * len(UpperCamelCase       )
					if extend_frames:
									snake_case__      :Dict         =   1.5
									snake_case__      :List[str]         =   3
					for file_name in paths:
									if file_name.endswith(".png"       ):
													images.append(imageio.imread(UpperCamelCase       )       )
					imageio.mimsave(UpperCamelCase					,UpperCamelCase					,duration=UpperCamelCase       )
					print(f'gif saved to {output_path}'       )






	def    lowerCAmelCase_							(		self					,UpperCamelCase=None					,UpperCamelCase=None       )						->    str:
					if not (path or img):
									raise ValueError("Input either path or tensor"       )
					if img is not None:
									raise NotImplementedError
					snake_case__      :Dict         =   preprocess(Image.open(UpperCamelCase       )					,target_image_size=256       ).to(self.device       )
					snake_case__      :Tuple         =   preprocess_vqgan(UpperCamelCase       )
					snake_case__    ,						*snake_case__      :int         =   self.vqgan.encode(UpperCamelCase       )
					return z






	def    lowerCAmelCase_							(		self					,UpperCamelCase       )						->    List[Any]:
					snake_case__      :Tuple         =   self.latent.detach().requires_grad_()
					snake_case__      :Dict         =   base_latent + transform_vector
					if self.quantize:
									snake_case__    ,						*snake_case__      :str         =   self.vqgan.quantize(UpperCamelCase       )
					else:
									snake_case__      :int         =   trans_latent
					return self.vqgan.decode(UpperCamelCase       )






	def    lowerCAmelCase_							(		self					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase=None       )						->    Union[str, Any]:
					snake_case__      :Tuple         =   self.clip_preprocessor(text=UpperCamelCase					,images=UpperCamelCase					,return_tensors="pt"					,padding=UpperCamelCase       )
					snake_case__      :Tuple         =   self.clip(**UpperCamelCase       )
					snake_case__      :Optional[Any]         =   clip_outputs.logits_per_image
					if weights is not None:
									snake_case__      :str         =   similarity_logits * weights
					return similarity_logits.sum()






	def    lowerCAmelCase_							(		self					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase       )						->    int:
					snake_case__      :List[str]         =   self._get_clip_similarity(pos_prompts["prompts"]					,UpperCamelCase					,weights=(1 / pos_prompts["weights"])       )
					if neg_prompts:
									snake_case__      :Tuple         =   self._get_clip_similarity(neg_prompts["prompts"]					,UpperCamelCase					,weights=neg_prompts["weights"]       )
					else:
									snake_case__      :str         =   torch.tensor([1]					,device=self.device       )
					snake_case__      :Optional[int]         =   -torch.log(UpperCamelCase       ) + torch.log(UpperCamelCase       )
					return loss






	def    lowerCAmelCase_							(		self					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase       )						->    Any:
					snake_case__      :int         =   torch.randn_like(self.latent					,requires_grad=UpperCamelCase					,device=self.device       )
					snake_case__      :Any         =   torch.optim.Adam([vector]					,lr=self.lr       )

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






	def    lowerCAmelCase_							(		self					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase       )						->    List[str]:
					wandb.init(reinit=UpperCamelCase					,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__      :List[str]         =   Image.open(UpperCamelCase       )
									snake_case__      :int         =   image.resize((256, 256)       )
									wandb.log("Original Image"					,wandb.Image(UpperCamelCase       )       )






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






	def    lowerCAmelCase_							(		self					,UpperCamelCase					,UpperCamelCase=None					,UpperCamelCase=None					,UpperCamelCase=True					,UpperCamelCase=False					,UpperCamelCase=True					,UpperCamelCase=True					,UpperCamelCase=None					,)						->    Union[str, Any]:
					if image_path:
									snake_case__      :Tuple         =   self._get_latent(UpperCamelCase       )
					else:
									snake_case__      :str         =   torch.randn(self.latent_dim					,device=self.device       )
					if self.log:
									self._init_logging(UpperCamelCase					,UpperCamelCase					,UpperCamelCase       )

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

					snake_case__      :List[Any]         =   self.vqgan.decode(self.latent       )[0]
					if show_intermediate:
									print("Original Image"       )
									show_pil(custom_to_pil(UpperCamelCase       )       )

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







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


__UpperCAmelCase							:   Tuple							=  {}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__UpperCAmelCase							:   List[Any]							=  ["BartphoTokenizer"]

if TYPE_CHECKING:
	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_bartpho import BartphoTokenizer

else:
	import sys

	__UpperCAmelCase							:   Tuple							=  _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)