



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


SCREAMING_SNAKE_CASE_: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 SCREAMING_SNAKE_CASE__					(		nn.Module   ):




    '''simple docstring'''






    def __init__(	self,		lowerCamelCase__		):
        super().__init__()
        A			:	Optional[int]										=       torchvision.models.resnetaaa(pretrained=lowerCamelCase__		)
        A			:	List[Any]										=       list(model.children()		)[:-2]
        A			:	Tuple										=       nn.Sequential(*lowerCamelCase__		)
        A			:	Dict										=       nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]		)






    def       _lowerCAmelCase (	self,		lowerCamelCase__		):
        # Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
        A			:	Dict										=       self.pool(self.model(lowerCamelCase__		)		)
        A			:	List[str]										=       torch.flatten(lowerCamelCase__,		start_dim=2		)
        A			:	str										=       out.transpose(1,		2		).contiguous()
        return out  # BxNx2048






class SCREAMING_SNAKE_CASE__					(		SCREAMING_SNAKE_CASE__   ):




    '''simple docstring'''






    def __init__(	self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		):
        A			:	Optional[Any]										=       [json.loads(lowerCamelCase__		) for l in open(lowerCamelCase__		)]
        A			:	str										=       os.path.dirname(lowerCamelCase__		)
        A			:	List[str]										=       tokenizer
        A			:	Any										=       labels
        A			:	Optional[int]										=       len(lowerCamelCase__		)
        A			:	str										=       max_seq_length

        A			:	List[Any]										=       transforms






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






    def __getitem__(	self,		lowerCamelCase__		):
        A			:	Any										=       torch.LongTensor(self.tokenizer.encode(self.data[index]["""text"""],		add_special_tokens=lowerCamelCase__		)		)
        A					,							A					,							A			:	str										=       sentence[0], sentence[1:-1], sentence[-1]
        A			:	Union[str, Any]										=       sentence[: self.max_seq_length]

        A			:	str										=       torch.zeros(self.n_classes		)
        A			:	Union[str, Any]										=       1

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

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






    def       _lowerCAmelCase (	self		):
        A			:	Any										=       Counter()
        for row in self.data:
            label_freqs.update(row["""label"""]		)
        return label_freqs




def      __UpperCamelCase     (  _lowerCAmelCase		)	->  List[str]:



    """simple docstring"""






    A			:	int										=       [len(row["""sentence"""]		) for row in batch]
    A					,							A			:	Dict										=       len(_lowerCAmelCase		), max(_lowerCAmelCase		)

    A			:	Dict										=       torch.zeros(_lowerCAmelCase				,					_lowerCAmelCase				,					dtype=torch.long		)
    A			:	Optional[Any]										=       torch.zeros(_lowerCAmelCase				,					_lowerCAmelCase				,					dtype=torch.long		)

    for i_batch, (input_row, length) in enumerate(zip(_lowerCAmelCase				,					_lowerCAmelCase		)		):
        A			:	str										=       input_row["""sentence"""]
        A			:	List[Any]										=       1

    A			:	Optional[int]										=       torch.stack([row["""image"""] for row in batch]		)
    A			:	List[Any]										=       torch.stack([row["""label"""] for row in batch]		)
    A			:	List[str]										=       torch.stack([row["""image_start_token"""] for row in batch]		)
    A			:	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      __UpperCamelCase     (  )	->  List[Any]:



    """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      __UpperCamelCase     (  )	->  Dict:



    """simple docstring"""






    return transforms.Compose(
        [
            transforms.Resize(256		),
            transforms.CenterCrop(224		),
            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




def      __UpperCamelCase     (  _lowerCAmelCase		)	->  list[int]:



    """simple docstring"""






    A			:	Tuple										=       2
    A			:	List[Any]										=       []
    while i * i <= n:
        if n % i:
            i += 1
        else:
            n //= i
            factors.append(_lowerCAmelCase		)
    if n > 1:
        factors.append(_lowerCAmelCase		)
    return factors


if __name__ == "__main__":
     import doctest

     doctest.testmod()


