






'''simple docstring'''
from importlib import import_module

from .logging import get_logger


a_ 		=       get_logger(__name__)






class    __SCREAMING_SNAKE_CASE					:




 def __init__(					self		:							Optional[Any]    ,					__lowercase		:							str    ,					__lowercase		:							Union[str, Any]=None					)		->  Optional[Any]:
       SCREAMING_SNAKE_CASE__   : Tuple													=attrs or []
       if module is not None:
             for key in module.__dict__:
                   if key in attrs or not key.startswith('''__'''					):
                         setattr(self    ,					__SCREAMING_SNAKE_CASE    ,					getattr(__SCREAMING_SNAKE_CASE    ,					__SCREAMING_SNAKE_CASE					)					)
       SCREAMING_SNAKE_CASE__   : Union[str, Any]													=module._original_module if isinstance(__SCREAMING_SNAKE_CASE    ,					_PatchedModuleObj					) else module






class    __SCREAMING_SNAKE_CASE					:
 snake_case_     							=							[]




 def __init__(					self		:							Optional[Any]    ,					__lowercase		:							Any    ,					__lowercase		:							str    ,					__lowercase		:							Tuple    ,					__lowercase		:							Dict=None					)		->  Optional[int]:
       SCREAMING_SNAKE_CASE__   : List[Any]													=obj
       SCREAMING_SNAKE_CASE__   : Any													=target
       SCREAMING_SNAKE_CASE__   : Dict													=new
       SCREAMING_SNAKE_CASE__   : Union[str, Any]													=target.split('''.'''					)[0]
       SCREAMING_SNAKE_CASE__   : Optional[Any]													={}
       SCREAMING_SNAKE_CASE__   : Tuple													=attrs or []




 def __enter__(					self		:							Optional[Any]					)		->  str:
       *SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__   : Dict													=self.target.split('''.'''					)

       # Patch modules:
       # it's used to patch attributes of submodules like "os.path.join";
       # in this case we need to patch "os" and "os.path"

       for i in range(len(__SCREAMING_SNAKE_CASE					)					):
             try:
                   SCREAMING_SNAKE_CASE__   : Optional[Any]													=import_module('''.'''.join(submodules[: i + 1]					)					)
             except ModuleNotFoundError:
                   continue
             # We iterate over all the globals in self.obj in case we find "os" or "os.path"
             for attr in self.obj.__dir__():
                   SCREAMING_SNAKE_CASE__   : Optional[int]													=getattr(self.obj    ,					__SCREAMING_SNAKE_CASE					)
                   # We don't check for the name of the global, but rather if its value *is* "os" or "os.path".
                   # This allows to patch renamed modules like "from os import path as ospath".
                   if obj_attr is submodule or (
                       (isinstance(__SCREAMING_SNAKE_CASE    ,					_PatchedModuleObj					) and obj_attr._original_module is submodule)
                   ):
                         SCREAMING_SNAKE_CASE__   : Dict													=obj_attr
                         # patch at top level
                         setattr(self.obj    ,					__SCREAMING_SNAKE_CASE    ,					_PatchedModuleObj(__SCREAMING_SNAKE_CASE    ,					attrs=self.attrs					)					)
                         SCREAMING_SNAKE_CASE__   : int													=getattr(self.obj    ,					__SCREAMING_SNAKE_CASE					)
                         # construct lower levels patches
                         for key in submodules[i + 1 :]:
                               setattr(__SCREAMING_SNAKE_CASE    ,					__SCREAMING_SNAKE_CASE    ,					_PatchedModuleObj(getattr(__SCREAMING_SNAKE_CASE    ,					__SCREAMING_SNAKE_CASE    ,					__SCREAMING_SNAKE_CASE					)    ,					attrs=self.attrs					)					)
                               SCREAMING_SNAKE_CASE__   : Dict													=getattr(__SCREAMING_SNAKE_CASE    ,					__SCREAMING_SNAKE_CASE					)
                         # finally set the target attribute
                         setattr(__SCREAMING_SNAKE_CASE    ,					__SCREAMING_SNAKE_CASE    ,					self.new					)

             # Patch attribute itself:
             # it's used for builtins like "open",
             # and also to patch "os.path.join" we may also need to patch "join"
             # itself if it was imported as "from os.path import join".

       if submodules:  # if it's an attribute of a submodule like "os.path.join"
             try:
                   SCREAMING_SNAKE_CASE__   : List[Any]													=getattr(import_module('''.'''.join(__SCREAMING_SNAKE_CASE					)					)    ,					__SCREAMING_SNAKE_CASE					)
             except (AttributeError, ModuleNotFoundError):
                   return
             # We iterate over all the globals in self.obj in case we find "os.path.join"
             for attr in self.obj.__dir__():
                   # We don't check for the name of the global, but rather if its value *is* "os.path.join".
                   # This allows to patch renamed attributes like "from os.path import join as pjoin".
                   if getattr(self.obj    ,					__SCREAMING_SNAKE_CASE					) is attr_value:
                         SCREAMING_SNAKE_CASE__   : Tuple													=getattr(self.obj    ,					__SCREAMING_SNAKE_CASE					)
                         setattr(self.obj    ,					__SCREAMING_SNAKE_CASE    ,					self.new					)
       elif target_attr in globals()["__builtins__"]:  # if it'a s builtin like "open"
             SCREAMING_SNAKE_CASE__   : str													=globals()['''__builtins__'''][target_attr]
             setattr(self.obj    ,					__SCREAMING_SNAKE_CASE    ,					self.new					)
       else:
             raise RuntimeError(F"Tried to patch attribute {target_attr} instead of a submodule."					)




 def __exit__(					self		:							List[Any]    ,					*__lowercase		:							List[Any]					)		->  Union[str, Any]:
       for attr in list(self.original					):
             setattr(self.obj    ,					__SCREAMING_SNAKE_CASE    ,					self.original.pop(__SCREAMING_SNAKE_CASE					)					)




 def    __magic_name__		(					self		:							Tuple					)		->  List[Any]:
       self.__enter__()
       self._active_patches.append(self					)




 def    __magic_name__		(					self		:							Union[str, Any]					)		->  List[str]:
       try:
             self._active_patches.remove(self					)
       except ValueError:
             # If the patch hasn't been started this will fail
             return None

       return self.__exit__()







"""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


_SCREAMING_SNAKE_CASE					:				List[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      a						(					nn.Module		):






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






    def    UpperCamelCase       (       self     :							List[Any]       ,						__SCREAMING_SNAKE_CASE     :							Any					)     ->		Any:
      # Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
      lowerCamelCase_         =						self.pool(self.model(__SCREAMING_SNAKE_CASE					)					)
      lowerCamelCase_         =						torch.flatten(__SCREAMING_SNAKE_CASE       ,						start_dim=2					)
      lowerCamelCase_         =						out.transpose(1       ,						2					).contiguous()
      return out  # BxNx2048






class      a						(					__snake_case		):






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

      lowerCamelCase_         =						transforms






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






    def __getitem__(       self     :							Optional[Any]       ,						__SCREAMING_SNAKE_CASE     :							List[str]					)     ->		List[str]:
      lowerCamelCase_         =						torch.LongTensor(self.tokenizer.encode(self.data[index]['text']       ,						add_special_tokens=__SCREAMING_SNAKE_CASE					)					)
      lowerCamelCase_     ,	lowerCamelCase_     ,	lowerCamelCase_         =						sentence[0], sentence[1:-1], sentence[-1]
      lowerCamelCase_         =						sentence[: self.max_seq_length]

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

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

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






    def    UpperCamelCase       (       self     :							Dict					)     ->		Dict:
      lowerCamelCase_         =						Counter()
      for row in self.data:
        label_freqs.update(row['label']					)
      return label_freqs







def      lowerCamelCase__       (							_lowerCamelCase       :		Union[str, Any] )      ->				str:
  lowerCamelCase_         =						[len(row['sentence'] ) for row in batch]
  lowerCamelCase_     ,	lowerCamelCase_         =						len(_lowerCamelCase ), max(_lowerCamelCase )

  lowerCamelCase_         =						torch.zeros(_lowerCamelCase		,				_lowerCamelCase		,				dtype=torch.long )
  lowerCamelCase_         =						torch.zeros(_lowerCamelCase		,				_lowerCamelCase		,				dtype=torch.long )

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

  lowerCamelCase_         =						torch.stack([row['image'] for row in batch] )
  lowerCamelCase_         =						torch.stack([row['label'] for row in batch] )
  lowerCamelCase_         =						torch.stack([row['image_start_token'] for row in batch] )
  lowerCamelCase_         =						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__       (							)      ->				List[str]:
  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__       (							)      ->				Union[str, Any]:
  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]		,				),
      ] )




