





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							:			Union[str, Any]         =			{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	):
     '''simple docstring'''


     def __init__(    self				,     snake_case_							):
         super().__init__()
         _A     =							torchvision.models.resnetaaa(pretrained=snake_case_							)
         _A     =							list(model.children()							)[:-2]
         _A     =							nn.Sequential(*snake_case_							)
         _A     =							nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]							)




     def        lowerCAmelCase__		(    self				,     snake_case_							):
         # Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
         _A     =							self.pool(self.model(snake_case_							)							)
         _A     =							torch.flatten(snake_case_				,     start_dim=2							)
         _A     =							out.transpose(1				,     2							).contiguous()
         return out  # BxNx2048







class      lowerCamelCase(						__snake_case	):
     '''simple docstring'''


     def __init__(    self				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_							):
         _A     =							[json.loads(snake_case_							) for l in open(snake_case_							)]
         _A     =							os.path.dirname(snake_case_							)
         _A     =							tokenizer
         _A     =							labels
         _A     =							len(snake_case_							)
         _A     =							max_seq_length

         _A     =							transforms




     def __len__(    self							):
         return len(self.data							)




     def __getitem__(    self				,     snake_case_							):
         _A     =							torch.LongTensor(self.tokenizer.encode(self.data[index]['text']				,     add_special_tokens=snake_case_							)							)
         _A,			_A,			_A     =							sentence[0], sentence[1:-1], sentence[-1]
         _A     =							sentence[: self.max_seq_length]

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

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

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




     def        lowerCAmelCase__		(    self							):
         _A     =							Counter()
         for row in self.data:
             label_freqs.update(row['label']							)
         return label_freqs





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE				)							-> Any:




    """simple docstring"""




    _A     =							[len(row['sentence']				) for row in batch]
    _A,			_A     =							len(_SCREAMING_SNAKE_CASE				), max(_SCREAMING_SNAKE_CASE				)

    _A     =							torch.zeros(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     dtype=torch.long				)
    _A     =							torch.zeros(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     dtype=torch.long				)

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

    _A     =							torch.stack([row['image'] for row in batch]				)
    _A     =							torch.stack([row['label'] for row in batch]				)
    _A     =							torch.stack([row['image_start_token'] for row in batch]				)
    _A     =							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 				__lowerCAmelCase(       )							-> Dict:




    """simple docstring"""




    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 				__lowerCAmelCase(       )							-> str:




    """simple docstring"""




    return transforms.Compose(
        [
            transforms.Resize(256				),
            transforms.CenterCrop(224				),
            transforms.ToTensor(),
            transforms.Normalize(
                mean=[0.4677_7044, 0.4453_1429, 0.4066_1017]							,     std=[0.1222_1994, 0.1214_5835, 0.1438_0469]							,     ),
        ]				)












from collections import defaultdict
from math import ceil, sqrt





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE = 1_000_000							,     _SCREAMING_SNAKE_CASE = 10				)							-> int:




    """simple docstring"""




    _A     =							defaultdict(_SCREAMING_SNAKE_CASE				)

    for outer_width in range(3							,     (t_limit // 4) + 2				):
        if outer_width * outer_width > t_limit:
            _A     =							max(
                ceil(sqrt(outer_width * outer_width - t_limit				)				)							,     1				)
        else:
            _A     =							1

        hole_width_lower_bound += (outer_width - hole_width_lower_bound) % 2

        for hole_width in range(_SCREAMING_SNAKE_CASE							,     outer_width - 1							,     2				):
            count[outer_width * outer_width - hole_width * hole_width] += 1

    return sum(1 for n in count.values() if 1 <= n <= 10				)


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





