


'''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						,    __a = "cpu"						,    __a = "openai/clip-vit-large-patch14"							):

						'''simple docstring'''





						__a      :	List[str]    =  device
						__a      :	Tuple    =  CLIPTokenizerFast.from_pretrained(__a							)
						__a      :	int    =  [0.48145466, 0.4578275, 0.40821073]
						__a      :	str    =  [0.26862954, 0.26130258, 0.27577711]
						__a      :	int    =  torchvision.transforms.Normalize(self.image_mean						,    self.image_std							)
						__a      :	int    =  torchvision.transforms.Resize(224							)
						__a      :	Union[str, Any]    =  torchvision.transforms.CenterCrop(224							)




	def 				__UpperCAmelCase							(       self						,    __a							):

						'''simple docstring'''





						__a      :	Union[str, Any]    =  self.resize(__a							)
						__a      :	List[Any]    =  self.center_crop(__a							)
						__a      :	Optional[Any]    =  self.normalize(__a							)
						return images





	def __call__(       self						,    __a=None						,    __a=None						,    **__a							):

						'''simple docstring'''





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





class 							__UpperCamelCase    (		nn.Module ):




	def __init__(       self						,    __a=10						,    __a=0.01						,    __a=None						,    __a=None						,    __a=None						,    __a=None						,    __a=None						,    __a=None						,    __a=False						,    __a=True						,    __a="image"						,    __a=True						,    __a=False						,    __a=False						,    __a=False						,    ):

						'''simple docstring'''





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

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




	def 				__UpperCAmelCase							(       self						,    __a=None						,    __a=None						,    __a=5						,    __a=True							):

						'''simple docstring'''





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

						'''simple docstring'''





						if not (path or img):
											raise ValueError('Input either path or tensor'							)
						if img is not None:
											raise NotImplementedError
						__a      :	Optional[int]    =  preprocess(Image.open(__a							)						,    target_image_size=256							).to(self.device							)
						__a      :	Optional[Any]    =  preprocess_vqgan(__a							)
						__a   ,    *__a      :	Optional[int]    =  self.vqgan.encode(__a							)
						return z




	def 				__UpperCAmelCase							(       self						,    __a							):

						'''simple docstring'''





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




	def 				__UpperCAmelCase							(       self						,    __a						,    __a						,    __a=None							):

						'''simple docstring'''





						__a      :	Union[str, Any]    =  self.clip_preprocessor(text=__a						,    images=__a						,    return_tensors='pt'						,    padding=__a							)
						__a      :	Dict    =  self.clip(**__a							)
						__a      :	List[str]    =  clip_outputs.logits_per_image
						if weights is not None:
											__a      :	Tuple    =  similarity_logits * weights
						return similarity_logits.sum()




	def 				__UpperCAmelCase							(       self						,    __a						,    __a						,    __a							):

						'''simple docstring'''





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




	def 				__UpperCAmelCase							(       self						,    __a						,    __a						,    __a							):

						'''simple docstring'''





						__a      :	Dict    =  torch.randn_like(self.latent						,    requires_grad=__a						,    device=self.device							)
						__a      :	List[Any]    =  torch.optim.Adam([vector]						,    lr=self.lr							)

						for i in range(self.iterations							):
											optim.zero_grad()
											__a      :	str    =  self._add_vector(__a							)
											__a      :	int    =  loop_post_process(__a							)
											__a      :	Optional[Any]    =  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 				__UpperCAmelCase							(       self						,    __a						,    __a						,    __a							):

						'''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:
											__a      :	List[Any]    =  Image.open(__a							)
											__a      :	int    =  image.resize((256, 256)							)
											wandb.log('Original Image'						,    wandb.Image(__a							)							)




	def 				__UpperCAmelCase							(       self						,    __a							):

						'''simple docstring'''





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





	def 				__UpperCAmelCase							(       self						,    __a						,    __a=None						,    __a=None						,    __a=True						,    __a=False						,    __a=True						,    __a=True						,    __a=None						,    ):

						'''simple docstring'''





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

						__a      :	Union[str, Any]    =  self.vqgan.decode(self.latent							)[0]
						if show_intermediate:
											print('Original Image'							)
											show_pil(custom_to_pil(__a							)							)

						__a      :	List[Any]    =  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 dataclasses import dataclass
from typing import Optional, Tuple

import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput





@dataclass
class 							__UpperCamelCase    (		lowerCAmelCase_ ):
	A_           =   None
	A_           =   None
	A_           =   None
	A_           =   None





class 							__UpperCamelCase    (		lowerCAmelCase_ ):




	def __init__(       self						,    __a=1						,    __a=0						,    __a=2						,    __a=512						,    __a="cls"						,    __a=False						,    __a=True						,    **__a						,    ):

						'''simple docstring'''





						super().__init__(pad_token_id=__a						,    bos_token_id=__a						,    eos_token_id=__a						,    **__a							)
						__a      :	Any    =  project_dim
						__a      :	Optional[Any]    =  pooler_fn
						__a      :	int    =  learn_encoder
						__a      :	str    =  use_attention_mask





class 							__UpperCamelCase    (		lowerCAmelCase_ ):
	A_           =   [r"pooler", r"logit_scale"]
	A_           =   [r"position_ids", r"predictions.decoder.bias"]
	A_           =   "roberta"
	A_           =   RobertaSeriesConfig




	def __init__(       self						,    __a							):

						'''simple docstring'''





						super().__init__(__a							)
						__a      :	Optional[Any]    =  XLMRobertaModel(__a							)
						__a      :	str    =  nn.Linear(config.hidden_size						,    config.project_dim							)
						__a      :	Optional[int]    =  getattr(__a						,    'has_pre_transformation'						,    __a							)
						if self.has_pre_transformation:
											__a      :	int    =  nn.Linear(config.hidden_size						,    config.project_dim							)
											__a      :	List[str]    =  nn.LayerNorm(config.hidden_size						,    eps=config.layer_norm_eps							)
						self.post_init()





	def 				__UpperCAmelCase							(       self						,    __a = None						,    __a = None						,    __a = None						,    __a = None						,    __a = None						,    __a = None						,    __a = None						,    __a = None						,    __a = None						,    __a = None						,    __a = None						,    ):

						'''simple docstring'''





						__a      :	Optional[Any]    =  return_dict if return_dict is not None else self.config.use_return_dict

						__a      :	Tuple    =  self.base_model(
						    input_ids=__a						,    attention_mask=__a						,    token_type_ids=__a						,    position_ids=__a						,    head_mask=__a						,    inputs_embeds=__a						,    encoder_hidden_states=__a						,    encoder_attention_mask=__a						,    output_attentions=__a						,    output_hidden_states=True if self.has_pre_transformation else output_hidden_states						,    return_dict=__a						,    )

						if self.has_pre_transformation:
											__a      :	Optional[Any]    =  outputs['hidden_states'][-2]
											__a      :	Optional[int]    =  self.pre_LN(__a							)
											__a      :	Union[str, Any]    =  self.transformation_pre(__a							)

											return TransformationModelOutput(
											    projection_state=__a						,    last_hidden_state=outputs.last_hidden_state						,    hidden_states=outputs.hidden_states						,    attentions=outputs.attentions						,    )
						else:
											__a      :	Optional[Any]    =  self.transformation(outputs.last_hidden_state							)
											return TransformationModelOutput(
											    projection_state=__a						,    last_hidden_state=outputs.last_hidden_state						,    hidden_states=outputs.hidden_states						,    attentions=outputs.attentions						,    )



