




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







      def 	UpperCAmelCase_						(			self   :Any ,      lowerCamelCase   :List[str]      )     ->			Any:
       # Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
       UpperCAmelCase__							 =						self.pool(self.model(snake_case_      )      )
       UpperCAmelCase__							 =						torch.flatten(snake_case_ ,      start_dim=2      )
       UpperCAmelCase__							 =						out.transpose(1 ,      2      ).contiguous()
       return out  # BxNx2048



class 			_UpperCamelCase      (				_snake_case		):
      def __init__(			self   :Dict ,      lowerCamelCase   :Optional[int] ,      lowerCamelCase   :Optional[int] ,      lowerCamelCase   :List[Any] ,      lowerCamelCase   :List[str] ,      lowerCamelCase   :Tuple      )     ->			str:
       UpperCAmelCase__							 =						[json.loads(snake_case_      ) for l in open(snake_case_      )]
       UpperCAmelCase__							 =						os.path.dirname(snake_case_      )
       UpperCAmelCase__							 =						tokenizer
       UpperCAmelCase__							 =						labels
       UpperCAmelCase__							 =						len(snake_case_      )
       UpperCAmelCase__							 =						max_seq_length

       UpperCAmelCase__							 =						transforms
      def __len__(			self   :Optional[Any]      )     ->			str:
       return len(self.data      )
      def __getitem__(			self   :Optional[Any] ,      lowerCamelCase   :Tuple      )     ->			Optional[Any]:
       UpperCAmelCase__							 =						torch.LongTensor(self.tokenizer.encode(self.data[index]["text"] ,      add_special_tokens=snake_case_      )      )
       UpperCAmelCase__							 =						sentence[0], sentence[1:-1], sentence[-1]
       UpperCAmelCase__							 =						sentence[: self.max_seq_length]

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

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

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







      def 	UpperCAmelCase_						(			self   :Tuple      )     ->			Any:
       UpperCAmelCase__							 =						Counter()
       for row in self.data:
        label_freqs.update(row["label"]      )
       return label_freqs



def 						lowerCAmelCase		(		_lowerCAmelCase							:   Tuple	)     ->   int:




 """simple docstring"""







 UpperCAmelCase__							 =						[len(row["sentence"]	) for row in batch]
 UpperCAmelCase__							 =						len(_lowerCAmelCase	), max(_lowerCAmelCase	)

 UpperCAmelCase__							 =						torch.zeros(_lowerCAmelCase						,							_lowerCAmelCase						,							dtype=torch.long	)
 UpperCAmelCase__							 =						torch.zeros(_lowerCAmelCase						,							_lowerCAmelCase						,							dtype=torch.long	)

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

 UpperCAmelCase__							 =						torch.stack([row["image"] for row in batch]	)
 UpperCAmelCase__							 =						torch.stack([row["label"] for row in batch]	)
 UpperCAmelCase__							 =						torch.stack([row["image_start_token"] for row in batch]	)
 UpperCAmelCase__							 =						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[int]:




 """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		(		)     ->   Tuple:




 """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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_lowerCAmelCase						:   Optional[int]		=   {
    "configuration_maskformer": ["MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "MaskFormerConfig"],
    "configuration_maskformer_swin": ["MaskFormerSwinConfig"],
}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase						:   Optional[int]		=   ["MaskFormerFeatureExtractor"]
  _lowerCAmelCase						:   Dict		=   ["MaskFormerImageProcessor"]


try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase						:   int		=   [
      "MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
      "MaskFormerForInstanceSegmentation",
      "MaskFormerModel",
      "MaskFormerPreTrainedModel",
  ]
  _lowerCAmelCase						:   List[str]		=   [
      "MaskFormerSwinBackbone",
      "MaskFormerSwinModel",
      "MaskFormerSwinPreTrainedModel",
  ]

if TYPE_CHECKING:
  from .configuration_maskformer import MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskFormerConfig
  from .configuration_maskformer_swin import MaskFormerSwinConfig

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_maskformer import MaskFormerFeatureExtractor
    from .image_processing_maskformer import MaskFormerImageProcessor
  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_maskformer import (
        MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        MaskFormerForInstanceSegmentation,
        MaskFormerModel,
        MaskFormerPreTrainedModel,
    )
    from .modeling_maskformer_swin import (
        MaskFormerSwinBackbone,
        MaskFormerSwinModel,
        MaskFormerSwinPreTrainedModel,
    )


else:
  import sys

  _lowerCAmelCase						:   str		=   _LazyModule(__name__, globals()["__file__"], _import_structure)





