"""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    __snake_case       :



      def __init__(						self    ,			lowercase = "cpu"    ,			lowercase = "openai/clip-vit-large-patch14")	->			None:





        '''simple docstring'''





        a__:     List[str]										=	device
        a__:     str										=	CLIPTokenizerFast.from_pretrained(lowercase)
        a__:     int										=	[0.48145466, 0.4578275, 0.40821073]
        a__:     Union[str, Any]										=	[0.26862954, 0.26130258, 0.27577711]
        a__:     List[str]										=	torchvision.transforms.Normalize(self.image_mean    ,			self.image_std)
        a__:     Optional[int]										=	torchvision.transforms.Resize(2_24)
        a__:     Optional[Any]										=	torchvision.transforms.CenterCrop(2_24)



      def   lowerCamelCase_   (						self    ,			lowercase)	->			Any:





        '''simple docstring'''





        a__:     str										=	self.resize(lowercase)
        a__:     str										=	self.center_crop(lowercase)
        a__:     Dict										=	self.normalize(lowercase)
        return images



      def __call__(						self    ,			lowercase=None    ,			lowercase=None    ,			**lowercase)	->			Union[str, Any]:





        '''simple docstring'''





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







class    __snake_case       (     nn.Module				):



      def __init__(						self    ,			lowercase=10    ,			lowercase=0.01    ,			lowercase=None    ,			lowercase=None    ,			lowercase=None    ,			lowercase=None    ,			lowercase=None    ,			lowercase=None    ,			lowercase=False    ,			lowercase=True    ,			lowercase="image"    ,			lowercase=True    ,			lowercase=False    ,			lowercase=False    ,			lowercase=False    ,			)	->			None:





        '''simple docstring'''





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

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



      def   lowerCamelCase_   (						self    ,			lowercase=None    ,			lowercase=None    ,			lowercase=5    ,			lowercase=True)	->			Any:





        '''simple docstring'''





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



      def   lowerCamelCase_   (						self    ,			lowercase=None    ,			lowercase=None)	->			Any:





        '''simple docstring'''





        if not (path or img):
          raise ValueError('Input either path or tensor')
        if img is not None:
          raise NotImplementedError
        a__:     List[Any]										=	preprocess(Image.open(lowercase)    ,			target_image_size=2_56).to(self.device)
        a__:     Union[str, Any]										=	preprocess_vqgan(lowercase)
        a__							,    *a__:     List[Any]										=	self.vqgan.encode(lowercase)
        return z



      def   lowerCamelCase_   (						self    ,			lowercase)	->			str:





        '''simple docstring'''





        a__:     int										=	self.latent.detach().requires_grad_()
        a__:     str										=	base_latent + transform_vector
        if self.quantize:
          a__							,    *a__:     int										=	self.vqgan.quantize(lowercase)
        else:
          a__:     List[str]										=	trans_latent
        return self.vqgan.decode(lowercase)



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase    ,			lowercase=None)	->			str:





        '''simple docstring'''





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



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase    ,			lowercase)	->			str:





        '''simple docstring'''





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



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase    ,			lowercase)	->			Union[str, Any]:





        '''simple docstring'''





        a__:     Union[str, Any]										=	torch.randn_like(self.latent    ,			requires_grad=lowercase    ,			device=self.device)
        a__:     int										=	torch.optim.Adam([vector]    ,			lr=self.lr)

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



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase    ,			lowercase)	->			List[str]:





        '''simple docstring'''





        wandb.init(reinit=lowercase    ,			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__:     Any										=	Image.open(lowercase)
          a__:     Union[str, Any]										=	image.resize((2_56, 2_56))
          wandb.log('Original Image'    ,			wandb.Image(lowercase))



      def   lowerCamelCase_   (						self    ,			lowercase)	->			List[str]:





        '''simple docstring'''





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



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase=None    ,			lowercase=None    ,			lowercase=True    ,			lowercase=False    ,			lowercase=True    ,			lowercase=True    ,			lowercase=None    ,			)	->			str:





        '''simple docstring'''





        if image_path:
          a__:     Union[str, Any]										=	self._get_latent(lowercase)
        else:
          a__:     List[Any]										=	torch.randn(self.latent_dim    ,			device=self.device)
        if self.log:
          self._init_logging(lowercase    ,			lowercase    ,			lowercase)

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

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

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



"""simple docstring"""
import logging
import random

import ray

from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex


lowercase__					       =	logging.getLogger(__name__)




class    __snake_case       :



      def __init__(						self)	->			Optional[int]:





        '''simple docstring'''





        a__:     Optional[Any]										=	False



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase)	->			str:





        '''simple docstring'''





        if not self.initialized:
          a__:     Optional[int]										=	RagRetriever(
              lowercase    ,			question_encoder_tokenizer=lowercase    ,			generator_tokenizer=lowercase    ,			index=lowercase    ,			init_retrieval=lowercase    ,			)
          a__:     Optional[int]										=	True



      def   lowerCamelCase_   (						self)	->			int:





        '''simple docstring'''





        self.retriever.index.init_index()



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase)	->			Union[str, Any]:





        '''simple docstring'''





        a__							,    a__:     str										=	self.retriever._main_retrieve(lowercase    ,			lowercase)
        return doc_ids, retrieved_doc_embeds







class    __snake_case       (     __lowerCAmelCase				):



      def __init__(						self    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase=None)	->			int:





        '''simple docstring'''





        if index is not None and index.is_initialized() and len(lowercase) > 0:
          raise ValueError(
              'When using Ray for distributed fine-tuning, '
              'you\'ll need to provide the paths instead, '
              'as the dataset and the index are loaded '
              'separately. More info in examples/rag/use_own_knowledge_dataset.py ')
        super().__init__(
            lowercase    ,			question_encoder_tokenizer=lowercase    ,			generator_tokenizer=lowercase    ,			index=lowercase    ,			init_retrieval=lowercase    ,			)
        a__:     Any										=	retrieval_workers
        if len(self.retrieval_workers) > 0:
          ray.get(
              [
                  worker.create_rag_retriever.remote(lowercase    ,			lowercase    ,			lowercase    ,			lowercase)
                  for worker in self.retrieval_workers
              ])



      def   lowerCamelCase_   (						self)	->			Any:





        '''simple docstring'''





        logger.info('initializing retrieval')

        if len(self.retrieval_workers) > 0:
          ray.get([worker.init_retrieval.remote() for worker in self.retrieval_workers])
        else:
          # Non-distributed training. Load index into this same process.
          self.index.init_index()



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase)	->			Union[str, Any]:





        '''simple docstring'''





        if len(self.retrieval_workers) > 0:
          # Select a random retrieval actor.
          a__:     int										=	self.retrieval_workers[random.randint(0    ,			len(self.retrieval_workers) - 1)]
          a__							,    a__:     List[Any]										=	ray.get(random_worker.retrieve.remote(lowercase    ,			lowercase))
        else:
          a__							,    a__:     Dict										=	self._main_retrieve(lowercase    ,			lowercase)
        return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(lowercase)



      @classmethod
      def   lowerCamelCase_   (						cls    ,			lowercase    ,			lowercase=None    ,			**lowercase)	->			Tuple:





        '''simple docstring'''





        return super(lowercase    ,			cls).get_tokenizers(lowercase    ,			lowercase    ,			**lowercase)



      @classmethod
      def   lowerCamelCase_   (						cls    ,			lowercase    ,			lowercase    ,			lowercase=None    ,			**lowercase)	->			Union[str, Any]:





        '''simple docstring'''





        a__:     Optional[int]										=	kwargs.pop('config'    ,			lowercase) or RagConfig.from_pretrained(lowercase    ,			**lowercase)
        a__:     Union[str, Any]										=	RagTokenizer.from_pretrained(lowercase    ,			config=lowercase)
        a__:     int										=	rag_tokenizer.question_encoder
        a__:     Any										=	rag_tokenizer.generator
        if indexed_dataset is not None:
          a__:     List[Any]										=	'custom'
          a__:     Optional[Any]										=	CustomHFIndex(config.retrieval_vector_size    ,			lowercase)
        else:
          a__:     Dict										=	cls._build_index(lowercase)
        return cls(
            lowercase    ,			question_encoder_tokenizer=lowercase    ,			generator_tokenizer=lowercase    ,			retrieval_workers=lowercase    ,			index=lowercase    ,			)


