



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


a_								=       {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						,					__UpperCAmelCase					):



    '''simple docstring'''
    super().__init__()
    __lowerCamelCase    				=     torchvision.models.resnetaaa(pretrained=__UpperCAmelCase					)
    __lowerCamelCase    				=     list(model.children()					)[:-2]
    __lowerCamelCase    				=     nn.Sequential(*__UpperCAmelCase					)
    __lowerCamelCase    				=     nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]					)




 def 							lowerCamelCase   (    self						,					__UpperCAmelCase					):



    '''simple docstring'''
    # Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
    __lowerCamelCase    				=     self.pool(self.model(__UpperCAmelCase					)					)
    __lowerCamelCase    				=     torch.flatten(__UpperCAmelCase						,					start_dim=2					)
    __lowerCamelCase    				=     out.transpose(1						,					2					).contiguous()
    return out  # BxNx2048



class 			__lowerCAmelCase  (						lowerCAmelCase__ ):




 def __init__(    self						,					__UpperCAmelCase						,					__UpperCAmelCase						,					__UpperCAmelCase						,					__UpperCAmelCase						,					__UpperCAmelCase					):



    '''simple docstring'''
    __lowerCamelCase    				=     [json.loads(__UpperCAmelCase					) for l in open(__UpperCAmelCase					)]
    __lowerCamelCase    				=     os.path.dirname(__UpperCAmelCase					)
    __lowerCamelCase    				=     tokenizer
    __lowerCamelCase    				=     labels
    __lowerCamelCase    				=     len(__UpperCAmelCase					)
    __lowerCamelCase    				=     max_seq_length

    __lowerCamelCase    				=     transforms




 def __len__(    self					):



    '''simple docstring'''
    return len(self.data					)




 def __getitem__(    self						,					__UpperCAmelCase					):



    '''simple docstring'''
    __lowerCamelCase    				=     torch.LongTensor(self.tokenizer.encode(self.data[index]['''text''']						,					add_special_tokens=__UpperCAmelCase					)					)
    __lowerCamelCase				,__lowerCamelCase				,__lowerCamelCase    				=     sentence[0], sentence[1:-1], sentence[-1]
    __lowerCamelCase    				=     sentence[: self.max_seq_length]

    __lowerCamelCase    				=     torch.zeros(self.n_classes					)
    __lowerCamelCase    				=     1

    __lowerCamelCase    				=     Image.open(os.path.join(self.data_dir						,					self.data[index]['''img''']					)					).convert('''RGB'''					)
    __lowerCamelCase    				=     self.transforms(__UpperCAmelCase					)

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




 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    __lowerCamelCase    				=     Counter()
    for row in self.data:
       label_freqs.update(row['''label''']					)
    return label_freqs





def 		a__      (		_UpperCamelCase      :			Any				):
   __lowerCamelCase    				=     [len(row['''sentence''']				) for row in batch]
   __lowerCamelCase				,__lowerCamelCase    				=     len(_UpperCamelCase				), max(_UpperCamelCase				)

   __lowerCamelCase    				=     torch.zeros(_UpperCamelCase			,_UpperCamelCase			,dtype=torch.long				)
   __lowerCamelCase    				=     torch.zeros(_UpperCamelCase			,_UpperCamelCase			,dtype=torch.long				)

   for i_batch, (input_row, length) in enumerate(zip(_UpperCamelCase			,_UpperCamelCase				)				):
      __lowerCamelCase    				=     input_row['''sentence''']
      __lowerCamelCase    				=     1

   __lowerCamelCase    				=     torch.stack([row['''image'''] for row in batch]				)
   __lowerCamelCase    				=     torch.stack([row['''label'''] for row in batch]				)
   __lowerCamelCase    				=     torch.stack([row['''image_start_token'''] for row in batch]				)
   __lowerCamelCase    				=     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(2_56				),
           transforms.CenterCrop(2_24				),
           transforms.ToTensor(),
           transforms.Normalize(
               mean=[0.46_777_044, 0.44_531_429, 0.40_661_017]			,std=[0.12_221_994, 0.12_145_835, 0.14_380_469]			,),
       ]				)











from __future__ import annotations

from math import ceil, floor, sqrt





def 		a__      (		_UpperCamelCase      :			int = 2_00_00_00				):
   __lowerCamelCase    				=     [0]
   __lowerCamelCase    				=     42

   for idx in range(1			,ceil(sqrt(target * 2				) * 1.1				)				):
      triangle_numbers.append(triangle_numbers[-1] + idx				)

   # we want this to be as close as possible to target
   __lowerCamelCase    				=     0
   # the area corresponding to the grid that gives the product closest to target
   __lowerCamelCase    				=     0
   # an estimate of b, using the quadratic formula
   __lowerCamelCase    				=     42
   # the largest integer less than b_estimate
   __lowerCamelCase    				=     42
   # the largest integer less than b_estimate
   __lowerCamelCase    				=     42
   # the triangle number corresponding to b_floor
   __lowerCamelCase    				=     42
   # the triangle number corresponding to b_ceil
   __lowerCamelCase    				=     42

   for idx_a, triangle_a in enumerate(triangle_numbers[1:]			,1				):
      __lowerCamelCase    				=     (-1 + sqrt(1 + 8 * target / triangle_a				)) / 2
      __lowerCamelCase    				=     floor(_UpperCamelCase				)
      __lowerCamelCase    				=     ceil(_UpperCamelCase				)
      __lowerCamelCase    				=     triangle_numbers[b_floor]
      __lowerCamelCase    				=     triangle_numbers[b_ceil]

      if abs(target - triangle_b_first_guess * triangle_a				) < abs(
          target - best_product				):
         __lowerCamelCase    				=     triangle_b_first_guess * triangle_a
         __lowerCamelCase    				=     idx_a * b_floor

      if abs(target - triangle_b_second_guess * triangle_a				) < abs(
          target - best_product				):
         __lowerCamelCase    				=     triangle_b_second_guess * triangle_a
         __lowerCamelCase    				=     idx_a * b_ceil

   return area


if __name__ == "__main__":
  print(f"{solution() = }")






