





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    _SCREAMING_SNAKE_CASE						(	nn.Module    ):







 '''simple docstring'''

 def __init__(self				:   Union[str, Any]       , UpperCAmelCase_				:   Optional[int])       ->Dict:



   '''simple docstring'''



   super().__init__()
   lowerCamelCase__:      int			  =torchvision.models.resnetaaa(pretrained=UpperCAmelCase_)
   lowerCamelCase__:      Optional[int]			  =list(model.children())[:-2]
   lowerCamelCase__:      Optional[int]			  =nn.Sequential(*UpperCAmelCase_)
   lowerCamelCase__:      Dict			  =nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds])







 def 							SCREAMING_SNAKE_CASE_					(self				:   List[Any]       , UpperCAmelCase_				:   Dict)       ->str:



   '''simple docstring'''



   lowerCamelCase__:      Dict			  =self.pool(self.model(UpperCAmelCase_))
   lowerCamelCase__:      str			  =torch.flatten(UpperCAmelCase_       , start_dim=2)
   lowerCamelCase__:      int			  =out.transpose(1       , 2).contiguous()
   return out  # BxNx2048



class    _SCREAMING_SNAKE_CASE						(	__SCREAMING_SNAKE_CASE    ):







 '''simple docstring'''

 def __init__(self				:   List[str]       , UpperCAmelCase_				:   Dict       , UpperCAmelCase_				:   str       , UpperCAmelCase_				:   Tuple       , UpperCAmelCase_				:   Optional[int]       , UpperCAmelCase_				:   Dict)       ->Any:



   '''simple docstring'''



   lowerCamelCase__:      Dict			  =[json.loads(UpperCAmelCase_) for l in open(UpperCAmelCase_)]
   lowerCamelCase__:      Tuple			  =os.path.dirname(UpperCAmelCase_)
   lowerCamelCase__:      Any			  =tokenizer
   lowerCamelCase__:      List[str]			  =labels
   lowerCamelCase__:      int			  =len(UpperCAmelCase_)
   lowerCamelCase__:      Optional[Any]			  =max_seq_length

   lowerCamelCase__:      str			  =transforms

 def __len__(self				:   List[Any])       ->Union[str, Any]:



   '''simple docstring'''



   return len(self.data)

 def __getitem__(self				:   Tuple       , UpperCAmelCase_				:   Dict)       ->int:



   '''simple docstring'''



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

   lowerCamelCase__:      str			  =torch.zeros(self.n_classes)
   lowerCamelCase__:      str			  =1

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

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







 def 							SCREAMING_SNAKE_CASE_					(self				:   List[Any])       ->Tuple:



   '''simple docstring'''



   lowerCamelCase__:      Dict			  =Counter()
   for row in self.data:
     label_freqs.update(row["label"])
   return label_freqs





def      lowerCAmelCase_ (       __a		)     ->		Any:







  """simple docstring"""





  lowerCamelCase__:      Tuple			  =[len(row["sentence"]		) for row in batch]
  lowerCamelCase__  ,					lowerCamelCase__:      Optional[int]			  =len(__a		), max(__a		)

  lowerCamelCase__:      List[str]			  =torch.zeros(__a	,    __a	,    dtype=torch.long		)
  lowerCamelCase__:      Optional[int]			  =torch.zeros(__a	,    __a	,    dtype=torch.long		)

  for i_batch, (input_row, length) in enumerate(zip(__a	,    __a		)		):
    lowerCamelCase__:      Optional[int]			  =input_row["sentence"]
    lowerCamelCase__:      Dict			  =1

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







  """simple docstring"""





  return transforms.Compose(
      [
          transforms.Resize(256		),
          transforms.CenterCrop(224		),
          transforms.ToTensor(),
          transforms.Normalize(
              mean=[0.4_6_7_7_7_0_4_4, 0.4_4_5_3_1_4_2_9, 0.4_0_6_6_1_0_1_7]	,    std=[0.1_2_2_2_1_9_9_4, 0.1_2_1_4_5_8_3_5, 0.1_4_3_8_0_4_6_9]	,    ),
      ]		)








import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import ClassLabel, Features, Image
from .base import TaskTemplate



@dataclass(frozen=__SCREAMING_SNAKE_CASE    )
class    _SCREAMING_SNAKE_CASE						(	__SCREAMING_SNAKE_CASE    ):







 '''simple docstring'''
 lowercase_		=			field(default="image-classification"    ,							metadata={"include_in_asdict_even_if_is_default": True}    )
 lowercase_		=			Features({"image": Image()}    )
 lowercase_		=			Features({"labels": ClassLabel}    )
 lowercase_		=			"image"
 lowercase_		=			"labels"

 def 							SCREAMING_SNAKE_CASE_					(self				:   Tuple       , UpperCAmelCase_				:   Union[str, Any])       ->Tuple:



   '''simple docstring'''



   if self.label_column not in features:
     raise ValueError(F"""Column {self.label_column} is not present in features.""")
   if not isinstance(features[self.label_column]       , UpperCAmelCase_):
     raise ValueError(F"""Column {self.label_column} is not a ClassLabel.""")
   lowerCamelCase__:      List[Any]			  =copy.deepcopy(self)
   lowerCamelCase__:      Optional[int]			  =self.label_schema.copy()
   lowerCamelCase__:      int			  =features[self.label_column]
   lowerCamelCase__:      int			  =label_schema
   return task_template







 @property
 def 							SCREAMING_SNAKE_CASE_					(self				:   Dict)       ->Dict[str, str]:



   '''simple docstring'''



   return {
       self.image_column: "image",
       self.label_column: "labels",
   }

