from __future__ import annotations

from sys import maxsize
from typing import Generic, TypeVar

lowercase_     :   Optional[Any] 				=				TypeVar('T')







def 			A__      (	snake_case_ :		int      ):
 return (position - 1) // 2







def 			A__      (	snake_case_ :		int      ):
 return (2 * position) + 1







def 			A__      (	snake_case_ :		int      ):
 return (2 * position) + 2






class 		_lowerCamelCase		(   Generic[T]    ):
      def __init__(  self   )       ->					str:
       SCREAMING_SNAKE_CASE__:   list[tuple[T, int]]=      []
       SCREAMING_SNAKE_CASE__:   dict[T, int]=      {}
       SCREAMING_SNAKE_CASE__:   int=      0
      def __len__(  self   )       ->					List[str]:
       return self.elements
      def __repr__(  self   )       ->					int:
       return str(self.heap   )
      def 						UpperCamelCase_				(  self   )       ->					Tuple:
       return self.elements == 0
      def 						UpperCamelCase_				(  self , lowerCAmelCase , lowerCAmelCase   )       ->					Dict:
       self.heap.append((elem, weight)   )
       SCREAMING_SNAKE_CASE__:   Optional[Any]=      self.elements
       self.elements += 1
       self._bubble_up(lowercase__   )
      def 						UpperCamelCase_				(  self   )       ->					int:






       if self.elements > 1:
        self._swap_nodes(0 , self.elements - 1   )
       SCREAMING_SNAKE_CASE__:   int=      self.heap.pop()
       del self.position_map[elem]
       self.elements -= 1
       if self.elements > 0:
        SCREAMING_SNAKE_CASE__:   List[Any]=      self.heap[0]
        self._bubble_down(lowercase__   )
       return elem
      def 						UpperCamelCase_				(  self , lowerCAmelCase , lowerCAmelCase   )       ->					Any:
       SCREAMING_SNAKE_CASE__:   Union[str, Any]=      self.position_map[elem]
       SCREAMING_SNAKE_CASE__:   Tuple=      (elem, weight)
       if position > 0:
        SCREAMING_SNAKE_CASE__:   Tuple=      get_parent_position(lowercase__   )
        SCREAMING_SNAKE_CASE__:   List[Any]=      self.heap[parent_position]
        if parent_weight > weight:
         self._bubble_up(lowercase__   )
        else:
         self._bubble_down(lowercase__   )
       else:
        self._bubble_down(lowercase__   )
      def 						UpperCamelCase_				(  self , lowerCAmelCase   )       ->					List[Any]:
       SCREAMING_SNAKE_CASE__:   int=      self.position_map[elem]
       if curr_pos == 0:
        return None
       SCREAMING_SNAKE_CASE__:   Optional[int]=      get_parent_position(lowercase__   )
       SCREAMING_SNAKE_CASE__:   List[Any]=      self.heap[curr_pos]
       SCREAMING_SNAKE_CASE__:   int=      self.heap[parent_position]
       if parent_weight > weight:
        self._swap_nodes(lowercase__ , lowercase__   )
        return self._bubble_up(lowercase__   )
       return None
      def 						UpperCamelCase_				(  self , lowerCAmelCase   )       ->					Dict:
       SCREAMING_SNAKE_CASE__:   int=      self.position_map[elem]
       SCREAMING_SNAKE_CASE__:   Optional[Any]=      self.heap[curr_pos]
       SCREAMING_SNAKE_CASE__:   int=      get_child_left_position(lowercase__   )
       SCREAMING_SNAKE_CASE__:   Union[str, Any]=      get_child_right_position(lowercase__   )
       if child_left_position < self.elements and child_right_position < self.elements:
        SCREAMING_SNAKE_CASE__:   str=      self.heap[child_left_position]
        SCREAMING_SNAKE_CASE__:   Optional[Any]=      self.heap[child_right_position]
        if child_right_weight < child_left_weight and child_right_weight < weight:
         self._swap_nodes(lowercase__ , lowercase__   )
         return self._bubble_down(lowercase__   )
       if child_left_position < self.elements:
        SCREAMING_SNAKE_CASE__:   Optional[int]=      self.heap[child_left_position]
        if child_left_weight < weight:
         self._swap_nodes(lowercase__ , lowercase__   )
         return self._bubble_down(lowercase__   )
       else:
        return None
       if child_right_position < self.elements:
        SCREAMING_SNAKE_CASE__:   List[Any]=      self.heap[child_right_position]
        if child_right_weight < weight:
         self._swap_nodes(lowercase__ , lowercase__   )
         return self._bubble_down(lowercase__   )
       return None



      def 						UpperCamelCase_				(  self , lowerCAmelCase , lowerCAmelCase   )       ->					Optional[int]:
       SCREAMING_SNAKE_CASE__:   List[str]=      self.heap[nodea_pos][0]
       SCREAMING_SNAKE_CASE__:   List[Any]=      self.heap[nodea_pos][0]
       SCREAMING_SNAKE_CASE__:   Optional[Any]=      (
           self.heap[nodea_pos],
           self.heap[nodea_pos],
       )
       SCREAMING_SNAKE_CASE__:   Dict=      nodea_pos
       SCREAMING_SNAKE_CASE__:   List[str]=      nodea_pos






class 		_lowerCamelCase		(   Generic[T]    ):
      def __init__(  self   )       ->					List[str]:
       SCREAMING_SNAKE_CASE__:   dict[T, dict[T, int]]=      {}
       SCREAMING_SNAKE_CASE__:   int=      0
      def __repr__(  self   )       ->					Tuple:
       return str(self.connections   )
      def __len__(  self   )       ->					Tuple:
       return self.nodes
      def 						UpperCamelCase_				(  self , lowerCAmelCase   )       ->					Tuple:






       if node not in self.connections:
        SCREAMING_SNAKE_CASE__:   List[str]=      {}
        self.nodes += 1



      def 						UpperCamelCase_				(  self , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase   )       ->					Any:
       self.add_node(lowercase__   )
       self.add_node(lowercase__   )
       SCREAMING_SNAKE_CASE__:   int=      weight
       SCREAMING_SNAKE_CASE__:   Any=      weight







def 			A__      (	snake_case_ :		GraphUndirectedWeighted[T]       ,				):
 SCREAMING_SNAKE_CASE__:   dict[T, int]=      {node: maxsize for node in graph.connections}
 SCREAMING_SNAKE_CASE__:   dict[T, T | None]=      {node: None for node in graph.connections}

 SCREAMING_SNAKE_CASE__:   MinPriorityQueue[T]=      MinPriorityQueue()
 for node, weight in dist.items():
  priority_queue.push(SCREAMING_SNAKE_CASE_       ,				SCREAMING_SNAKE_CASE_      )

 if priority_queue.is_empty():
  return dist, parent

 # initialization
 SCREAMING_SNAKE_CASE__:   Union[str, Any]=      priority_queue.extract_min()
 SCREAMING_SNAKE_CASE__:   int=      0
 for neighbour in graph.connections[node]:
  if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
   SCREAMING_SNAKE_CASE__:   Tuple=      dist[node] + graph.connections[node][neighbour]
   priority_queue.update_key(SCREAMING_SNAKE_CASE_       ,				dist[neighbour]      )
   SCREAMING_SNAKE_CASE__:   Dict=      node

    # running prim's algorithm
 while not priority_queue.is_empty():
  SCREAMING_SNAKE_CASE__:   List[str]=      priority_queue.extract_min()
  for neighbour in graph.connections[node]:
   if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
    SCREAMING_SNAKE_CASE__:   Dict=      dist[node] + graph.connections[node][neighbour]
    priority_queue.update_key(SCREAMING_SNAKE_CASE_       ,				dist[neighbour]      )
    SCREAMING_SNAKE_CASE__:   List[Any]=      node
 return dist, parent


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]       ,				),
     ]      )

