




'''simple docstring'''





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


UpperCamelCase_						:      Tuple       =						{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        _a    (						nn.Module							):






     def __init__(			self ,_SCREAMING_SNAKE_CASE   )							->      Union[str, Any]:
          super().__init__()
          _snake_case							=				torchvision.models.resnetaaa(pretrained=_SCREAMING_SNAKE_CASE   )
          _snake_case							=				list(model.children()   )[:-2]
          _snake_case							=				nn.Sequential(*_SCREAMING_SNAKE_CASE   )
          _snake_case							=				nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]   )






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE   )							->      Any:
          # Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
          _snake_case							=				self.pool(self.model(_SCREAMING_SNAKE_CASE   )   )
          _snake_case							=				torch.flatten(_SCREAMING_SNAKE_CASE ,start_dim=2   )
          _snake_case							=				out.transpose(1 ,2   ).contiguous()
          return out  # BxNx2048







class        _a    (						__lowerCAmelCase							):






     def __init__(			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )							->      Optional[Any]:
          _snake_case							=				[json.loads(_SCREAMING_SNAKE_CASE   ) for l in open(_SCREAMING_SNAKE_CASE   )]
          _snake_case							=				os.path.dirname(_SCREAMING_SNAKE_CASE   )
          _snake_case							=				tokenizer
          _snake_case							=				labels
          _snake_case							=				len(_SCREAMING_SNAKE_CASE   )
          _snake_case							=				max_seq_length

          _snake_case							=				transforms






     def __len__(			self   )							->      Tuple:
          return len(self.data   )






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

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

          _snake_case							=				Image.open(os.path.join(self.data_dir ,self.data[index]["img"]   )   ).convert("RGB"   )
          _snake_case							=				self.transforms(_SCREAMING_SNAKE_CASE   )

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






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







def      __a     (       _UpperCamelCase:   Dict   )						->			int:

     """simple docstring"""




     _snake_case							=				[len(row["sentence"]   ) for row in batch]
     _snake_case							, _snake_case							=				len(_UpperCamelCase   ), max(_UpperCamelCase   )

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

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

     _snake_case							=				torch.stack([row["image"] for row in batch]   )
     _snake_case							=				torch.stack([row["label"] for row in batch]   )
     _snake_case							=				torch.stack([row["image_start_token"] for row in batch]   )
     _snake_case							=				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     (       )						->			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      __a     (       )						->			Any:

     """simple docstring"""




     return transforms.Compose(
         [
             transforms.Resize(256   ),
             transforms.CenterCrop(224   ),
             transforms.ToTensor(),
             transforms.Normalize(
                 mean=[0.46777044, 0.44531429, 0.40661017] ,						std=[0.12221994, 0.12145835, 0.14380469] ,						),
         ]   )








'''simple docstring'''





import fire

from utils import calculate_rouge, save_json





def      __a     (       _UpperCamelCase:   Tuple ,						_UpperCamelCase:   Optional[int] ,						_UpperCamelCase:   Optional[int]=None ,						**_UpperCamelCase:   Any   )						->			Optional[Any]:

     """simple docstring"""




     _snake_case							=				[x.strip() for x in open(_UpperCamelCase   ).readlines()]
     _snake_case							=				[x.strip() for x in open(_UpperCamelCase   ).readlines()][: len(_UpperCamelCase   )]
     _snake_case							=				calculate_rouge(_UpperCamelCase ,						_UpperCamelCase ,						**_UpperCamelCase   )
     if save_path is not None:
          save_json(_UpperCamelCase ,						_UpperCamelCase ,						indent=_UpperCamelCase   )
     return metrics  # these print nicely


if __name__ == "__main__":
   fire.Fire(calculate_rouge_path)


