from __future__ import annotations

# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
lowercase_     :   Optional[Any] 				=				1_0







def 			A__      (	snake_case_ :		int       ,				snake_case_ :		int       ,				snake_case_ :		list[int]       ,				snake_case_ :		int      ):
 for i in range(snake_case_       ,				snake_case_      ):
  if array[i] == target:
   return i
 return -1







def 			A__      (	snake_case_ :		list[int]       ,				snake_case_ :		int      ):
 SCREAMING_SNAKE_CASE__:   Any=      0
 SCREAMING_SNAKE_CASE__:   int=      len(snake_case_      )
 while left <= right:
  if right - left < precision:
   return lin_search(snake_case_       ,				snake_case_       ,				snake_case_       ,				snake_case_      )

  SCREAMING_SNAKE_CASE__:   List[Any]=      (left + right) // 3 + 1
  SCREAMING_SNAKE_CASE__:   Any=      2 * (left + right) // 3 + 1

  if array[one_third] == target:
   return one_third
  elif array[two_third] == target:
   return two_third

  elif target < array[one_third]:
   SCREAMING_SNAKE_CASE__:   Union[str, Any]=      one_third - 1
  elif array[two_third] < target:
   SCREAMING_SNAKE_CASE__:   List[str]=      two_third + 1

  else:
   SCREAMING_SNAKE_CASE__:   List[str]=      one_third + 1
   SCREAMING_SNAKE_CASE__:   Union[str, Any]=      two_third - 1
 else:
  return -1







def 			A__      (	snake_case_ :		int       ,				snake_case_ :		int       ,				snake_case_ :		list[int]       ,				snake_case_ :		int      ):
 if left < right:
  if right - left < precision:
   return lin_search(snake_case_       ,				snake_case_       ,				snake_case_       ,				snake_case_      )
  SCREAMING_SNAKE_CASE__:   str=      (left + right) // 3 + 1
  SCREAMING_SNAKE_CASE__:   Optional[Any]=      2 * (left + right) // 3 + 1

  if array[one_third] == target:
   return one_third
  elif array[two_third] == target:
   return two_third

  elif target < array[one_third]:
   return rec_ternary_search(snake_case_       ,				one_third - 1       ,				snake_case_       ,				snake_case_      )
  elif array[two_third] < target:
   return rec_ternary_search(two_third + 1       ,				snake_case_       ,				snake_case_       ,				snake_case_      )
  else:
   return rec_ternary_search(one_third + 1       ,				two_third - 1       ,				snake_case_       ,				snake_case_      )
 else:
  return -1


if __name__ == "__main__":
      import doctest

      doctest.testmod()

      lowercase_     :   str 				=				input('Enter numbers separated by comma:\n').strip()
      lowercase_     :   Optional[int] 				=				[int(item.strip()) for item in user_input.split(',')]
      assert collection == sorted(collection), f"List must be ordered.\n{collection}."
      lowercase_     :   List[Any] 				=				int(input('Enter the number to be found in the list:\n').strip())
      lowercase_     :   Tuple 				=				ite_ternary_search(collection, target)
      lowercase_     :   Any 				=				rec_ternary_search(0, len(collection) - 1, collection, target)
      if resulta != -1:
            print(f'''Iterative search: {target} found at positions: {resulta}''')
            print(f'''Recursive search: {target} found at positions: {resulta}''')
      else:
            print('Not found')


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]       ,				),
     ]      )

