


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


lowerCAmelCase__      		= {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 ,						__lowerCamelCase)			->				Optional[Any]:
            super().__init__()
            _A      :       Any				    = torchvision.models.resnetaaa(pretrained=__lowerCamelCase)
            _A      :       Union[str, Any]				    = list(model.children())[:-2]
            _A      :       str				    = nn.Sequential(*__lowerCamelCase)
            _A      :       Any				    = nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds])


     def  _lowerCamelCase (						self ,						__lowerCamelCase)			->				str:
            # Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
            _A      :       str				    = self.pool(self.model(__lowerCamelCase))
            _A      :       int				    = torch.flatten(__lowerCamelCase ,						start_dim=2)
            _A      :       List[Any]				    = out.transpose(1 ,						2).contiguous()
            return out  # BxNx2048







class      lowerCAmelCase__       (				a):




     '''simple docstring'''


     def __init__(						self ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase)			->				List[str]:
            _A      :       Dict				    = [json.loads(__lowerCamelCase) for l in open(__lowerCamelCase)]
            _A      :       Union[str, Any]				    = os.path.dirname(__lowerCamelCase)
            _A      :       Optional[Any]				    = tokenizer
            _A      :       Any				    = labels
            _A      :       str				    = len(__lowerCamelCase)
            _A      :       Optional[int]				    = max_seq_length

            _A      :       int				    = transforms


     def __len__(						self)			->				Optional[Any]:
            return len(self.data)


     def __getitem__(						self ,						__lowerCamelCase)			->				List[Any]:
            _A      :       str				    = torch.LongTensor(self.tokenizer.encode(self.data[index]["text"] ,						add_special_tokens=__lowerCamelCase))
            _A    ,			_A    ,			_A      :       List[str]				    = sentence[0], sentence[1:-1], sentence[-1]
            _A      :       int				    = sentence[: self.max_seq_length]

            _A      :       Any				    = torch.zeros(self.n_classes)
            _A      :       Tuple				    = 1

            _A      :       List[Any]				    = Image.open(os.path.join(self.data_dir ,						self.data[index]["img"])).convert("RGB")
            _A      :       Optional[Any]				    = self.transforms(__lowerCamelCase)

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


     def  _lowerCamelCase (						self)			->				Union[str, Any]:
            _A      :       Any				    = Counter()
            for row in self.data:
                   label_freqs.update(row["label"])
            return label_freqs




def 							_UpperCAmelCase				(UpperCamelCase__     :  List[Any]						):
       _A      :       List[str]				    = [len(row["sentence"]						) for row in batch]
       _A    ,			_A      :       Union[str, Any]				    = len(UpperCamelCase__						), max(UpperCamelCase__						)

       _A      :       int				    = torch.zeros(UpperCamelCase__				,							UpperCamelCase__				,							dtype=torch.long						)
       _A      :       Tuple				    = torch.zeros(UpperCamelCase__				,							UpperCamelCase__				,							dtype=torch.long						)

       for i_batch, (input_row, length) in enumerate(zip(UpperCamelCase__				,							UpperCamelCase__						)						):
              _A      :       Tuple				    = input_row["sentence"]
              _A      :       List[Any]				    = 1

       _A      :       Optional[int]				    = torch.stack([row["image"] for row in batch]						)
       _A      :       Optional[Any]				    = torch.stack([row["label"] for row in batch]						)
       _A      :       List[Any]				    = torch.stack([row["image_start_token"] for row in batch]						)
       _A      :       Dict				    = 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 							_UpperCAmelCase				():
       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 							_UpperCAmelCase				():
       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]				,							),
           ]						)






def 		__A		(	__lowerCamelCase							,			__lowerCamelCase    )      ->       str:
	if not isinstance(__lowerCamelCase							,			__lowerCamelCase    ):
		raise ValueError("""iterations must be defined as integers"""    )
	if not isinstance(__lowerCamelCase							,			__lowerCamelCase    ) or not number >= 1:
		raise ValueError(
		    """starting number must be
                         and integer and be more than 0"""    )
	if not iterations >= 1:
		raise ValueError("""Iterations must be done more than 0 times to play FizzBuzz"""    )

	a										=							""""""
	while number <= iterations:
		if number % 3 == 0:
			out += "Fizz"
		if number % 5 == 0:
			out += "Buzz"
		if 0 not in (number % 3, number % 5):
			out += str(__lowerCamelCase    )

		# print(out)
		number += 1
		out += " "
	return out


if __name__ == "__main__":
		import doctest

		doctest.testmod()

