import logging
import random

import ray

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


lowercase_     :   str 				=				logging.getLogger(__name__)






class 		_lowerCamelCase		:
    def __init__(  self   )       ->					Optional[int]:
     SCREAMING_SNAKE_CASE__:   int=      False
    def 						UpperCamelCase_				(  self , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase   )       ->					Optional[Any]:
     if not self.initialized:
      SCREAMING_SNAKE_CASE__:   List[Any]=      RagRetriever(
          lowerCAmelCase , question_encoder_tokenizer=lowerCAmelCase , generator_tokenizer=lowerCAmelCase , index=lowerCAmelCase , init_retrieval=lowerCAmelCase , )
      SCREAMING_SNAKE_CASE__:   Optional[Any]=      True
    def 						UpperCamelCase_				(  self   )       ->					Any:
     self.retriever.index.init_index()



    def 						UpperCamelCase_				(  self , lowerCAmelCase , lowerCAmelCase   )       ->					Dict:
     SCREAMING_SNAKE_CASE__,	SCREAMING_SNAKE_CASE__:   List[str]=      self.retriever._main_retrieve(lowerCAmelCase , lowerCAmelCase   )
     return doc_ids, retrieved_doc_embeds






class 		_lowerCamelCase		(   UpperCamelCase_    ):
    def __init__(  self , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase=None   )       ->					List[Any]:
     if index is not None and index.is_initialized() and len(lowerCAmelCase   ) > 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__(
         lowerCAmelCase , question_encoder_tokenizer=lowerCAmelCase , generator_tokenizer=lowerCAmelCase , index=lowerCAmelCase , init_retrieval=lowerCAmelCase , )
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      retrieval_workers
     if len(self.retrieval_workers   ) > 0:
      ray.get(
          [
              worker.create_rag_retriever.remote(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase   )
              for worker in self.retrieval_workers
          ]   )
    def 						UpperCamelCase_				(  self   )       ->					Dict:
     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 						UpperCamelCase_				(  self , lowerCAmelCase , lowerCAmelCase   )       ->					List[Any]:
     if len(self.retrieval_workers   ) > 0:
      # Select a random retrieval actor.
      SCREAMING_SNAKE_CASE__:   Union[str, Any]=      self.retrieval_workers[random.randint(0 , len(self.retrieval_workers   ) - 1   )]
      SCREAMING_SNAKE_CASE__,	SCREAMING_SNAKE_CASE__:   Optional[Any]=      ray.get(random_worker.retrieve.remote(lowerCAmelCase , lowerCAmelCase   )   )
     else:
      SCREAMING_SNAKE_CASE__,	SCREAMING_SNAKE_CASE__:   Optional[Any]=      self._main_retrieve(lowerCAmelCase , lowerCAmelCase   )
     return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(lowerCAmelCase   )
    @classmethod
    def 						UpperCamelCase_				(  cls , lowerCAmelCase , lowerCAmelCase=None , **lowerCAmelCase   )       ->					Union[str, Any]:
     return super(lowerCAmelCase , cls   ).get_tokenizers(lowerCAmelCase , lowerCAmelCase , **lowerCAmelCase   )



    @classmethod
    def 						UpperCamelCase_				(  cls , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase=None , **lowerCAmelCase   )       ->					Any:
     SCREAMING_SNAKE_CASE__:   Dict=      kwargs.pop('''config''' , lowerCAmelCase   ) or RagConfig.from_pretrained(lowerCAmelCase , **lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      RagTokenizer.from_pretrained(lowerCAmelCase , config=lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   Tuple=      rag_tokenizer.question_encoder
     SCREAMING_SNAKE_CASE__:   Tuple=      rag_tokenizer.generator
     if indexed_dataset is not None:
      SCREAMING_SNAKE_CASE__:   Tuple=      '''custom'''
      SCREAMING_SNAKE_CASE__:   Dict=      CustomHFIndex(config.retrieval_vector_size , lowerCAmelCase   )
     else:
      SCREAMING_SNAKE_CASE__:   Dict=      cls._build_index(lowerCAmelCase   )
     return cls(
         lowerCAmelCase , question_encoder_tokenizer=lowerCAmelCase , generator_tokenizer=lowerCAmelCase , retrieval_workers=lowerCAmelCase , index=lowerCAmelCase , )


import json
import os
from collections import Counter

import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset


lowercase_     :   List[str] 				=				{1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (3, 2), 7: (7, 1), 8: (4, 2), 9: (3, 3)}






class 		_lowerCamelCase		(   nn.Module    ):
    def __init__(  self , lowerCAmelCase   )       ->					str:
     super().__init__()
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      torchvision.models.resnetaaa(pretrained=lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      list(model.children()   )[:-2]
     SCREAMING_SNAKE_CASE__:   Tuple=      nn.Sequential(*lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]   )



    def 						UpperCamelCase_				(  self , lowerCAmelCase   )       ->					List[str]:
     # Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
     SCREAMING_SNAKE_CASE__:   str=      self.pool(self.model(lowerCAmelCase   )   )
     SCREAMING_SNAKE_CASE__:   str=      torch.flatten(lowerCAmelCase , start_dim=2   )
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      out.transpose(1 , 2   ).contiguous()
     return out  # BxNx2048






class 		_lowerCamelCase		(   UpperCamelCase_    ):
    def __init__(  self , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase   )       ->					Any:
     SCREAMING_SNAKE_CASE__:   Optional[int]=      [json.loads(lowerCAmelCase   ) for l in open(lowerCAmelCase   )]
     SCREAMING_SNAKE_CASE__:   List[str]=      os.path.dirname(lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   int=      tokenizer
     SCREAMING_SNAKE_CASE__:   Any=      labels
     SCREAMING_SNAKE_CASE__:   List[Any]=      len(lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   Any=      max_seq_length

     SCREAMING_SNAKE_CASE__:   Optional[Any]=      transforms
    def __len__(  self   )       ->					Optional[Any]:
     return len(self.data   )
    def __getitem__(  self , lowerCAmelCase   )       ->					Tuple:
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      torch.LongTensor(self.tokenizer.encode(self.data[index]['''text'''] , add_special_tokens=lowerCAmelCase   )   )
     SCREAMING_SNAKE_CASE__,	SCREAMING_SNAKE_CASE__,	SCREAMING_SNAKE_CASE__:   Tuple=      sentence[0], sentence[1:-1], sentence[-1]
     SCREAMING_SNAKE_CASE__:   Tuple=      sentence[: self.max_seq_length]

     SCREAMING_SNAKE_CASE__:   Optional[Any]=      torch.zeros(self.n_classes   )
     SCREAMING_SNAKE_CASE__:   Dict=      1

     SCREAMING_SNAKE_CASE__:   Optional[int]=      Image.open(os.path.join(self.data_dir , self.data[index]['''img''']   )   ).convert('''RGB'''   )
     SCREAMING_SNAKE_CASE__:   int=      self.transforms(lowerCAmelCase   )

     return {
         "image_start_token": start_token,
         "image_end_token": end_token,
         "sentence": sentence,
         "image": image,
         "label": label,
     }



    def 						UpperCamelCase_				(  self   )       ->					Optional[int]:
     SCREAMING_SNAKE_CASE__:   List[Any]=      Counter()
     for row in self.data:
      label_freqs.update(row['''label''']   )
     return label_freqs







def 			A__      (	snake_case_ :		int      ):
 SCREAMING_SNAKE_CASE__:   Any=      [len(row['''sentence''']      ) for row in batch]
 SCREAMING_SNAKE_CASE__,	SCREAMING_SNAKE_CASE__:   Optional[int]=      len(snake_case_      ), max(snake_case_      )

 SCREAMING_SNAKE_CASE__:   Union[str, Any]=      torch.zeros(snake_case_       ,				snake_case_       ,				dtype=torch.long      )
 SCREAMING_SNAKE_CASE__:   List[Any]=      torch.zeros(snake_case_       ,				snake_case_       ,				dtype=torch.long      )

 for i_batch, (input_row, length) in enumerate(zip(snake_case_       ,				snake_case_      )      ):
  SCREAMING_SNAKE_CASE__:   Optional[int]=      input_row['''sentence''']
  SCREAMING_SNAKE_CASE__:   Dict=      1

 SCREAMING_SNAKE_CASE__:   Optional[Any]=      torch.stack([row['''image'''] for row in batch]      )
 SCREAMING_SNAKE_CASE__:   List[str]=      torch.stack([row['''label'''] for row in batch]      )
 SCREAMING_SNAKE_CASE__:   Any=      torch.stack([row['''image_start_token'''] for row in batch]      )
 SCREAMING_SNAKE_CASE__:   Optional[int]=      torch.stack([row['''image_end_token'''] for row in batch]      )

 return text_tensor, mask_tensor, img_tensor, img_start_token, img_end_token, tgt_tensor







def 			A__      (	):
 return [
     "Crime",
     "Drama",
     "Thriller",
     "Action",
     "Comedy",
     "Romance",
     "Documentary",
     "Short",
     "Mystery",
     "History",
     "Family",
     "Adventure",
     "Fantasy",
     "Sci-Fi",
     "Western",
     "Horror",
     "Sport",
     "War",
     "Music",
     "Musical",
     "Animation",
     "Biography",
     "Film-Noir",
 ]







def 			A__      (	):
 return transforms.Compose(
     [
         transforms.Resize(256      ),
         transforms.CenterCrop(224      ),
         transforms.ToTensor(),
         transforms.Normalize(
             mean=[0.46_77_70_44, 0.44_53_14_29, 0.40_66_10_17]       ,				std=[0.12_22_19_94, 0.12_14_58_35, 0.14_38_04_69]       ,				),
     ]      )

