'''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 			__lowercase		:







     """simple docstring"""






     def __init__(		self     ,			_lowerCamelCase = "cpu"     ,			_lowerCamelCase = "openai/clip-vit-large-patch14"    ):
         __UpperCamelCase	:	Optional[int]		    =  device
         __UpperCamelCase	:	Any		    =  CLIPTokenizerFast.from_pretrained(_lowerCamelCase    )
         __UpperCamelCase	:	Dict		    =  [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]
         __UpperCamelCase	:	Dict		    =  [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]
         __UpperCamelCase	:	Tuple		    =  torchvision.transforms.Normalize(self.image_mean     ,			self.image_std    )
         __UpperCamelCase	:	Optional[int]		    =  torchvision.transforms.Resize(2_2_4    )
         __UpperCamelCase	:	str		    =  torchvision.transforms.CenterCrop(2_2_4    )







     def lowerCAmelCase     (		self     ,			_lowerCamelCase    ):
         __UpperCamelCase	:	Dict		    =  self.resize(_lowerCamelCase    )
         __UpperCamelCase	:	Union[str, Any]		    =  self.center_crop(_lowerCamelCase    )
         __UpperCamelCase	:	Union[str, Any]		    =  self.normalize(_lowerCamelCase    )
         return images







     def __call__(		self     ,			_lowerCamelCase=None     ,			_lowerCamelCase=None     ,			**_lowerCamelCase    ):
         __UpperCamelCase	:	Optional[Any]		    =  self.tokenizer(text=_lowerCamelCase     ,			**_lowerCamelCase    )
         __UpperCamelCase	:	List[str]		    =  self.preprocess_img(_lowerCamelCase    )
         __UpperCamelCase	:	str		    =  {key: value.to(self.device    ) for (key, value) in encoding.items()}
         return encoding






class 			__lowercase		(						nn.Module						):







     """simple docstring"""






     def __init__(		self     ,			_lowerCamelCase=1_0     ,			_lowerCamelCase=0.0_1     ,			_lowerCamelCase=None     ,			_lowerCamelCase=None     ,			_lowerCamelCase=None     ,			_lowerCamelCase=None     ,			_lowerCamelCase=None     ,			_lowerCamelCase=None     ,			_lowerCamelCase=False     ,			_lowerCamelCase=True     ,			_lowerCamelCase="image"     ,			_lowerCamelCase=True     ,			_lowerCamelCase=False     ,			_lowerCamelCase=False     ,			_lowerCamelCase=False     ,			):
         super().__init__()
         __UpperCamelCase	:	str		    =  None
         __UpperCamelCase	:	int		    =  device if device else get_device()
         if vqgan:
             __UpperCamelCase	:	Optional[int]		    =  vqgan
         else:
             __UpperCamelCase	:	Dict		    =  load_vqgan(self.device     ,			conf_path=_lowerCamelCase     ,			ckpt_path=_lowerCamelCase    )
         self.vqgan.eval()
         if clip:
             __UpperCamelCase	:	int		    =  clip
         else:
             __UpperCamelCase	:	List[Any]		    =  CLIPModel.from_pretrained('openai/clip-vit-base-patch32'    )
         self.clip.to(self.device    )
         __UpperCamelCase	:	List[str]		    =  ProcessorGradientFlow(device=self.device    )

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







     def lowerCAmelCase     (		self     ,			_lowerCamelCase=None     ,			_lowerCamelCase=None     ,			_lowerCamelCase=5     ,			_lowerCamelCase=True    ):
         __UpperCamelCase	:	int		    =  []
         if output_path is None:
             __UpperCamelCase	:	Dict		    =  './animation.gif'
         if input_path is None:
             __UpperCamelCase	:	List[str]		    =  self.save_path
         __UpperCamelCase	:	List[str]		    =  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?)'    )
         __UpperCamelCase	:	List[str]		    =  total_duration / len(_lowerCamelCase    )
         __UpperCamelCase	:	List[str]		    =  [frame_duration] * len(_lowerCamelCase    )
         if extend_frames:
             __UpperCamelCase	:	Union[str, Any]		    =  1.5
             __UpperCamelCase	:	Optional[int]		    =  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     ,			_lowerCamelCase=None     ,			_lowerCamelCase=None    ):
         if not (path or img):
             raise ValueError('Input either path or tensor'    )
         if img is not None:
             raise NotImplementedError
         __UpperCamelCase	:	Union[str, Any]		    =  preprocess(Image.open(_lowerCamelCase    )     ,			target_image_size=2_5_6    ).to(self.device    )
         __UpperCamelCase	:	Any		    =  preprocess_vqgan(_lowerCamelCase    )
         __UpperCamelCase    ,					*__UpperCamelCase	:	List[str]		    =  self.vqgan.encode(_lowerCamelCase    )
         return z







     def lowerCAmelCase     (		self     ,			_lowerCamelCase    ):
         __UpperCamelCase	:	Optional[int]		    =  self.latent.detach().requires_grad_()
         __UpperCamelCase	:	List[str]		    =  base_latent + transform_vector
         if self.quantize:
             __UpperCamelCase    ,					*__UpperCamelCase	:	Dict		    =  self.vqgan.quantize(_lowerCamelCase    )
         else:
             __UpperCamelCase	:	Union[str, Any]		    =  trans_latent
         return self.vqgan.decode(_lowerCamelCase    )







     def lowerCAmelCase     (		self     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase=None    ):
         __UpperCamelCase	:	List[Any]		    =  self.clip_preprocessor(text=_lowerCamelCase     ,			images=_lowerCamelCase     ,			return_tensors='pt'     ,			padding=_lowerCamelCase    )
         __UpperCamelCase	:	Optional[int]		    =  self.clip(**_lowerCamelCase    )
         __UpperCamelCase	:	Optional[Any]		    =  clip_outputs.logits_per_image
         if weights is not None:
             __UpperCamelCase	:	int		    =  similarity_logits * weights
         return similarity_logits.sum()







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







     def lowerCAmelCase     (		self     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase    ):
         __UpperCamelCase	:	Union[str, Any]		    =  torch.randn_like(self.latent     ,			requires_grad=_lowerCamelCase     ,			device=self.device    )
         __UpperCamelCase	:	Union[str, Any]		    =  torch.optim.Adam([vector]     ,			lr=self.lr    )

         for i in range(self.iterations    ):
             optim.zero_grad()
             __UpperCamelCase	:	Tuple		    =  self._add_vector(_lowerCamelCase    )
             __UpperCamelCase	:	Dict		    =  loop_post_process(_lowerCamelCase    )
             __UpperCamelCase	:	Optional[int]		    =  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     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase    ):
         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:
             __UpperCamelCase	:	List[Any]		    =  Image.open(_lowerCamelCase    )
             __UpperCamelCase	:	List[str]		    =  image.resize((2_5_6, 2_5_6)    )
             wandb.log('Original Image'     ,			wandb.Image(_lowerCamelCase    )    )







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







     def lowerCAmelCase     (		self     ,			_lowerCamelCase     ,			_lowerCamelCase=None     ,			_lowerCamelCase=None     ,			_lowerCamelCase=True     ,			_lowerCamelCase=False     ,			_lowerCamelCase=True     ,			_lowerCamelCase=True     ,			_lowerCamelCase=None     ,			):
         if image_path:
             __UpperCamelCase	:	Dict		    =  self._get_latent(_lowerCamelCase    )
         else:
             __UpperCamelCase	:	Union[str, Any]		    =  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."
         __UpperCamelCase	:	Optional[Any]		    =  self.process_prompts(_lowerCamelCase    )
         __UpperCamelCase	:	Optional[Any]		    =  self.process_prompts(_lowerCamelCase    )
         if save_final and save_path is None:
             __UpperCamelCase	:	List[str]		    =  os.path.join('./outputs/'     ,			'_'.join(pos_prompts['prompts']    )    )
         if not os.path.exists(_lowerCamelCase    ):
             os.makedirs(_lowerCamelCase    )
         else:
             __UpperCamelCase	:	Union[str, Any]		    =  save_path + '_' + get_timestamp()
             os.makedirs(_lowerCamelCase    )
         __UpperCamelCase	:	List[str]		    =  save_path

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

         __UpperCamelCase	:	Optional[int]		    =  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'''
import os
import unittest

from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class 			__lowercase		(						_lowerCamelCase			,							unittest.TestCase						):







     """simple docstring"""






     SCREAMING_SNAKE_CASE__		   =						LayoutLMTokenizer
     SCREAMING_SNAKE_CASE__		   =						LayoutLMTokenizerFast
     SCREAMING_SNAKE_CASE__		   =						True
     SCREAMING_SNAKE_CASE__		   =						True







     def lowerCAmelCase     (		self    ):
         super().setUp()

         __UpperCamelCase	:	List[str]		    =  [
             '[UNK]',
             '[CLS]',
             '[SEP]',
             'want',
             '##want',
             '##ed',
             'wa',
             'un',
             'runn',
             '##ing',
             ',',
             'low',
             'lowest',
         ]
         __UpperCamelCase	:	Union[str, Any]		    =  os.path.join(self.tmpdirname     ,			VOCAB_FILES_NAMES['vocab_file']    )
         with open(self.vocab_file     ,			'w'     ,			encoding='utf-8'    ) as vocab_writer:
             vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]    )    )







     def lowerCAmelCase     (		self     ,			**_lowerCamelCase    ):
         return LayoutLMTokenizer.from_pretrained(self.tmpdirname     ,			**_lowerCamelCase    )







     def lowerCAmelCase     (		self     ,			_lowerCamelCase    ):
         __UpperCamelCase	:	Dict		    =  'UNwant\u00E9d,running'
         __UpperCamelCase	:	Union[str, Any]		    =  'unwanted, running'
         return input_text, output_text







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Dict		    =  self.tokenizer_class(self.vocab_file    )

         __UpperCamelCase	:	Optional[Any]		    =  tokenizer.tokenize('UNwant\u00E9d,running'    )
         self.assertListEqual(_lowerCamelCase     ,			['un', '##want', '##ed', ',', 'runn', '##ing']    )
         self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCamelCase    )     ,			[7, 4, 5, 1_0, 8, 9]    )







     def lowerCAmelCase     (		self    ):
         pass



