






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		:       int						,_SCREAMING_SNAKE_CASE		:       str = "cpu"						,_SCREAMING_SNAKE_CASE		:       str = "openai/clip-vit-large-patch14"		)    ->   None:


						'''simple docstring'''
						A	 =      device
						A	 =      CLIPTokenizerFast.from_pretrained(_SCREAMING_SNAKE_CASE		)
						A	 =      [0.48145466, 0.4578275, 0.40821073]
						A	 =      [0.26862954, 0.26130258, 0.27577711]
						A	 =      torchvision.transforms.Normalize(self.image_mean						,self.image_std		)
						A	 =      torchvision.transforms.Resize(2_2_4		)
						A	 =      torchvision.transforms.CenterCrop(2_2_4		)
		def       A(  self		:       Tuple						,_SCREAMING_SNAKE_CASE		:       Optional[Any]		)    ->   List[str]:


						'''simple docstring'''
						A	 =      self.resize(_SCREAMING_SNAKE_CASE		)
						A	 =      self.center_crop(_SCREAMING_SNAKE_CASE		)
						A	 =      self.normalize(_SCREAMING_SNAKE_CASE		)
						return images







		def __call__(  self		:       Any						,_SCREAMING_SNAKE_CASE		:       Dict=None						,_SCREAMING_SNAKE_CASE		:       Any=None						,**_SCREAMING_SNAKE_CASE		:       int		)    ->   Any:


						'''simple docstring'''
						A	 =      self.tokenizer(text=_SCREAMING_SNAKE_CASE						,**_SCREAMING_SNAKE_CASE		)
						A	 =      self.preprocess_img(_SCREAMING_SNAKE_CASE		)
						A	 =      {key: value.to(self.device		) for (key, value) in encoding.items()}
						return encoding


class      UpperCamelCase (				nn.Module ):







		"""simple docstring"""



		def __init__(  self		:       Any						,_SCREAMING_SNAKE_CASE		:       Optional[int]=1_0						,_SCREAMING_SNAKE_CASE		:       Tuple=0.01						,_SCREAMING_SNAKE_CASE		:       int=None						,_SCREAMING_SNAKE_CASE		:       Union[str, Any]=None						,_SCREAMING_SNAKE_CASE		:       Tuple=None						,_SCREAMING_SNAKE_CASE		:       int=None						,_SCREAMING_SNAKE_CASE		:       Optional[int]=None						,_SCREAMING_SNAKE_CASE		:       Tuple=None						,_SCREAMING_SNAKE_CASE		:       str=False						,_SCREAMING_SNAKE_CASE		:       Union[str, Any]=True						,_SCREAMING_SNAKE_CASE		:       Any="image"						,_SCREAMING_SNAKE_CASE		:       Optional[Any]=True						,_SCREAMING_SNAKE_CASE		:       Dict=False						,_SCREAMING_SNAKE_CASE		:       Dict=False						,_SCREAMING_SNAKE_CASE		:       List[str]=False						,)    ->   None:


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

						A	 =      iterations
						A	 =      lr
						A	 =      log
						A	 =      make_grid
						A	 =      return_val
						A	 =      quantize
						A	 =      self.vqgan.decoder.z_shape
		def       A(  self		:       str						,_SCREAMING_SNAKE_CASE		:       str=None						,_SCREAMING_SNAKE_CASE		:       Tuple=None						,_SCREAMING_SNAKE_CASE		:       Dict=5						,_SCREAMING_SNAKE_CASE		:       str=True		)    ->   Union[str, Any]:


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


						'''simple docstring'''
						if not (path or img):
										raise ValueError('Input either path or tensor'		)
						if img is not None:
										raise NotImplementedError
						A	 =      preprocess(Image.open(_SCREAMING_SNAKE_CASE		)						,target_image_size=2_5_6		).to(self.device		)
						A	 =      preprocess_vqgan(_SCREAMING_SNAKE_CASE		)
						A ,		*A	 =      self.vqgan.encode(_SCREAMING_SNAKE_CASE		)
						return z
		def       A(  self		:       List[str]						,_SCREAMING_SNAKE_CASE		:       List[str]		)    ->   int:


						'''simple docstring'''
						A	 =      self.latent.detach().requires_grad_()
						A	 =      base_latent + transform_vector
						if self.quantize:
										A ,		*A	 =      self.vqgan.quantize(_SCREAMING_SNAKE_CASE		)
						else:
										A	 =      trans_latent
						return self.vqgan.decode(_SCREAMING_SNAKE_CASE		)
		def       A(  self		:       Any						,_SCREAMING_SNAKE_CASE		:       int						,_SCREAMING_SNAKE_CASE		:       int						,_SCREAMING_SNAKE_CASE		:       int=None		)    ->   int:


						'''simple docstring'''
						A	 =      self.clip_preprocessor(text=_SCREAMING_SNAKE_CASE						,images=_SCREAMING_SNAKE_CASE						,return_tensors='pt'						,padding=_SCREAMING_SNAKE_CASE		)
						A	 =      self.clip(**_SCREAMING_SNAKE_CASE		)
						A	 =      clip_outputs.logits_per_image
						if weights is not None:
										A	 =      similarity_logits * weights
						return similarity_logits.sum()
		def       A(  self		:       Tuple						,_SCREAMING_SNAKE_CASE		:       Tuple						,_SCREAMING_SNAKE_CASE		:       Optional[Any]						,_SCREAMING_SNAKE_CASE		:       Union[str, Any]		)    ->   Optional[Any]:


						'''simple docstring'''
						A	 =      self._get_clip_similarity(pos_prompts['prompts']						,_SCREAMING_SNAKE_CASE						,weights=(1 / pos_prompts['weights'])		)
						if neg_prompts:
										A	 =      self._get_clip_similarity(neg_prompts['prompts']						,_SCREAMING_SNAKE_CASE						,weights=neg_prompts['weights']		)
						else:
										A	 =      torch.tensor([1]						,device=self.device		)
						A	 =      -torch.log(_SCREAMING_SNAKE_CASE		) + torch.log(_SCREAMING_SNAKE_CASE		)
						return loss
		def       A(  self		:       Dict						,_SCREAMING_SNAKE_CASE		:       List[str]						,_SCREAMING_SNAKE_CASE		:       Union[str, Any]						,_SCREAMING_SNAKE_CASE		:       Tuple		)    ->   Dict:


						'''simple docstring'''
						A	 =      torch.randn_like(self.latent						,requires_grad=_SCREAMING_SNAKE_CASE						,device=self.device		)
						A	 =      torch.optim.Adam([vector]						,lr=self.lr		)

						for i in range(self.iterations		):
										optim.zero_grad()
										A	 =      self._add_vector(_SCREAMING_SNAKE_CASE		)
										A	 =      loop_post_process(_SCREAMING_SNAKE_CASE		)
										A	 =      self._get_CLIP_loss(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		)
										print('CLIP loss'						,_SCREAMING_SNAKE_CASE		)
										if self.log:
														wandb.log({'CLIP Loss': clip_loss}		)
										clip_loss.backward(retain_graph=_SCREAMING_SNAKE_CASE		)
										optim.step()
										if self.return_val == "image":
														yield custom_to_pil(transformed_img[0]		)
										else:
														yield vector
		def       A(  self		:       str						,_SCREAMING_SNAKE_CASE		:       str						,_SCREAMING_SNAKE_CASE		:       Optional[int]						,_SCREAMING_SNAKE_CASE		:       Optional[int]		)    ->   List[str]:


						'''simple docstring'''
						wandb.init(reinit=_SCREAMING_SNAKE_CASE						,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	 =      Image.open(_SCREAMING_SNAKE_CASE		)
										A	 =      image.resize((2_5_6, 2_5_6)		)
										wandb.log('Original Image'						,wandb.Image(_SCREAMING_SNAKE_CASE		)		)
		def       A(  self		:       List[str]						,_SCREAMING_SNAKE_CASE		:       Tuple		)    ->   Union[str, Any]:


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







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


						'''simple docstring'''
						if image_path:
										A	 =      self._get_latent(_SCREAMING_SNAKE_CASE		)
						else:
										A	 =      torch.randn(self.latent_dim						,device=self.device		)
						if self.log:
										self._init_logging(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		)

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

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

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













import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class      UpperCamelCase (				snake_case__ ):







		"""simple docstring"""



		snake_case    =   ["image_processor", "tokenizer"]
		snake_case    =   "LayoutLMv2ImageProcessor"
		snake_case    =   ("LayoutXLMTokenizer", "LayoutXLMTokenizerFast")
		def __init__(  self		:       List[str]						,_SCREAMING_SNAKE_CASE		:       List[str]=None						,_SCREAMING_SNAKE_CASE		:       Optional[int]=None						,**_SCREAMING_SNAKE_CASE		:       Tuple		)    ->   List[str]:


						'''simple docstring'''
						if "feature_extractor" in kwargs:
										warnings.warn(
										    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
										    ' instead.'						,_SCREAMING_SNAKE_CASE						,)
										A	 =      kwargs.pop('feature_extractor'		)

						A	 =      image_processor if image_processor is not None else feature_extractor
						if image_processor is None:
										raise ValueError('You need to specify an `image_processor`.'		)
						if tokenizer is None:
										raise ValueError('You need to specify a `tokenizer`.'		)

						super().__init__(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		)
		def __call__(  self		:       Union[str, Any]						,_SCREAMING_SNAKE_CASE		:       Optional[int]						,_SCREAMING_SNAKE_CASE		:       Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None						,_SCREAMING_SNAKE_CASE		:       Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None						,_SCREAMING_SNAKE_CASE		:       Union[List[List[int]], List[List[List[int]]]] = None						,_SCREAMING_SNAKE_CASE		:       Optional[Union[List[int], List[List[int]]]] = None						,_SCREAMING_SNAKE_CASE		:       bool = True						,_SCREAMING_SNAKE_CASE		:       Union[bool, str, PaddingStrategy] = False						,_SCREAMING_SNAKE_CASE		:       Union[bool, str, TruncationStrategy] = None						,_SCREAMING_SNAKE_CASE		:       Optional[int] = None						,_SCREAMING_SNAKE_CASE		:       int = 0						,_SCREAMING_SNAKE_CASE		:       Optional[int] = None						,_SCREAMING_SNAKE_CASE		:       Optional[bool] = None						,_SCREAMING_SNAKE_CASE		:       Optional[bool] = None						,_SCREAMING_SNAKE_CASE		:       bool = False						,_SCREAMING_SNAKE_CASE		:       bool = False						,_SCREAMING_SNAKE_CASE		:       bool = False						,_SCREAMING_SNAKE_CASE		:       bool = False						,_SCREAMING_SNAKE_CASE		:       bool = True						,_SCREAMING_SNAKE_CASE		:       Optional[Union[str, TensorType]] = None						,**_SCREAMING_SNAKE_CASE		:       Tuple						,)    ->   BatchEncoding:


						'''simple docstring'''
						# verify input
						if self.image_processor.apply_ocr and (boxes is not None):
										raise ValueError(
										    'You cannot provide bounding boxes '
										    'if you initialized the image processor with apply_ocr set to True.'		)

						if self.image_processor.apply_ocr and (word_labels is not None):
										raise ValueError(
										    'You cannot provide word labels if you initialized the image processor with apply_ocr set to True.'		)

						if return_overflowing_tokens is True and return_offsets_mapping is False:
										raise ValueError('You cannot return overflowing tokens without returning the offsets mapping.'		)

						# first, apply the image processor
						A	 =      self.image_processor(images=_SCREAMING_SNAKE_CASE						,return_tensors=_SCREAMING_SNAKE_CASE		)

						# second, apply the tokenizer
						if text is not None and self.image_processor.apply_ocr and text_pair is None:
										if isinstance(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		):
														A	 =      [text]  # add batch dimension (as the image processor always adds a batch dimension)
										A	 =      features['words']

						A	 =      self.tokenizer(
						    text=text if text is not None else features['words']						,text_pair=text_pair if text_pair is not None else None						,boxes=boxes if boxes is not None else features['boxes']						,word_labels=_SCREAMING_SNAKE_CASE						,add_special_tokens=_SCREAMING_SNAKE_CASE						,padding=_SCREAMING_SNAKE_CASE						,truncation=_SCREAMING_SNAKE_CASE						,max_length=_SCREAMING_SNAKE_CASE						,stride=_SCREAMING_SNAKE_CASE						,pad_to_multiple_of=_SCREAMING_SNAKE_CASE						,return_token_type_ids=_SCREAMING_SNAKE_CASE						,return_attention_mask=_SCREAMING_SNAKE_CASE						,return_overflowing_tokens=_SCREAMING_SNAKE_CASE						,return_special_tokens_mask=_SCREAMING_SNAKE_CASE						,return_offsets_mapping=_SCREAMING_SNAKE_CASE						,return_length=_SCREAMING_SNAKE_CASE						,verbose=_SCREAMING_SNAKE_CASE						,return_tensors=_SCREAMING_SNAKE_CASE						,**_SCREAMING_SNAKE_CASE						,)

						# add pixel values
						A	 =      features.pop('pixel_values'		)
						if return_overflowing_tokens is True:
										A	 =      self.get_overflowing_images(_SCREAMING_SNAKE_CASE						,encoded_inputs['overflow_to_sample_mapping']		)
						A	 =      images

						return encoded_inputs
		def       A(  self		:       Dict						,_SCREAMING_SNAKE_CASE		:       Optional[Any]						,_SCREAMING_SNAKE_CASE		:       Optional[Any]		)    ->   Dict:


						'''simple docstring'''
						# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
						A	 =      []
						for sample_idx in overflow_to_sample_mapping:
										images_with_overflow.append(images[sample_idx]		)

						if len(_SCREAMING_SNAKE_CASE		) != len(_SCREAMING_SNAKE_CASE		):
										raise ValueError(
										    'Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'
										    f' {len(_SCREAMING_SNAKE_CASE		)} and {len(_SCREAMING_SNAKE_CASE		)}'		)

						return images_with_overflow
		def       A(  self		:       List[str]						,*_SCREAMING_SNAKE_CASE		:       str						,**_SCREAMING_SNAKE_CASE		:       Union[str, Any]		)    ->   Tuple:


						'''simple docstring'''
						return self.tokenizer.batch_decode(*_SCREAMING_SNAKE_CASE						,**_SCREAMING_SNAKE_CASE		)
		def       A(  self		:       Dict						,*_SCREAMING_SNAKE_CASE		:       Tuple						,**_SCREAMING_SNAKE_CASE		:       int		)    ->   int:


						'''simple docstring'''
						return self.tokenizer.decode(*_SCREAMING_SNAKE_CASE						,**_SCREAMING_SNAKE_CASE		)
		@property
		def       A(  self		:       Any		)    ->   Union[str, Any]:


						'''simple docstring'''
						return ["input_ids", "bbox", "attention_mask", "image"]
		@property
		def       A(  self		:       Dict		)    ->   int:


						'''simple docstring'''
						warnings.warn(
						    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'						,_SCREAMING_SNAKE_CASE						,)
						return self.image_processor_class







		@property
		def       A(  self		:       Optional[int]		)    ->   Union[str, Any]:


						'''simple docstring'''
						warnings.warn(
						    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'						,_SCREAMING_SNAKE_CASE						,)
						return self.image_processor





