





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


lowercase_          =		{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				):


   def __init__(   self      :    Tuple      ,					a      :    Tuple	)->       Optional[int]:





     """simple docstring"""







     super().__init__()
     lowercase__           =  torchvision.models.resnetaaa(pretrained=a	)
     lowercase__           =  list(model.children()	)[:-2]
     lowercase__           =  nn.Sequential(*a	)
     lowercase__           =  nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]	)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Tuple      ,					a      :    Optional[int]	)->       Optional[Any]:





     """simple docstring"""







     lowercase__           =  self.pool(self.model(a	)	)
     lowercase__           =  torch.flatten(a      ,					start_dim=2	)
     lowercase__           =  out.transpose(1      ,					2	).contiguous()
     return out  # BxNx2048






class     SCREAMING_SNAKE_CASE       (UpperCAmelCase				):


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





     """simple docstring"""







     lowercase__           =  [json.loads(a	) for l in open(a	)]
     lowercase__           =  os.path.dirname(a	)
     lowercase__           =  tokenizer
     lowercase__           =  labels
     lowercase__           =  len(a	)
     lowercase__           =  max_seq_length

     lowercase__           =  transforms


   def __len__(   self      :    int	)->       str:





     """simple docstring"""







     return len(self.data	)


   def __getitem__(   self      :    Any      ,					a      :    List[Any]	)->       int:





     """simple docstring"""







     lowercase__           =  torch.LongTensor(self.tokenizer.encode(self.data[index]['text']      ,					add_special_tokens=a	)	)
     lowercase__      ,   lowercase__      ,   lowercase__           =  sentence[0], sentence[1:-1], sentence[-1]
     lowercase__           =  sentence[: self.max_seq_length]

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

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

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


   def 	SCREAMING_SNAKE_CASE_							(   self      :    int	)->       Dict:





     """simple docstring"""







     lowercase__           =  Counter()
     for row in self.data:
       label_freqs.update(row['label']	)
     return label_freqs






def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		)    ->       List[Any]:
  lowercase__           =  [len(row['sentence']		) for row in batch]
  lowercase__      ,   lowercase__           =  len(_SCREAMING_SNAKE_CASE		), max(_SCREAMING_SNAKE_CASE		)

  lowercase__           =  torch.zeros(_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						dtype=torch.long		)
  lowercase__           =  torch.zeros(_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						dtype=torch.long		)

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

  lowercase__           =  torch.stack([row['image'] for row in batch]		)
  lowercase__           =  torch.stack([row['label'] for row in batch]		)
  lowercase__           =  torch.stack([row['image_start_token'] for row in batch]		)
  lowercase__           =  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  ()    ->       int:
  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  ()    ->       int:
  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]		,						),
      ]		)









from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


lowercase_          =		{
    """configuration_efficientformer""": [
        """EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """EfficientFormerConfig""",
    ]
}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase_          =		["""EfficientFormerImageProcessor"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase_          =		[
          """EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """EfficientFormerForImageClassification""",
          """EfficientFormerForImageClassificationWithTeacher""",
          """EfficientFormerModel""",
          """EfficientFormerPreTrainedModel""",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase_          =		[
          """TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """TFEfficientFormerForImageClassification""",
          """TFEfficientFormerForImageClassificationWithTeacher""",
          """TFEfficientFormerModel""",
          """TFEfficientFormerPreTrainedModel""",
      ]

if TYPE_CHECKING:
      from .configuration_efficientformer import EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, EfficientFormerConfig

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .image_processing_efficientformer import EfficientFormerImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_efficientformer import (
                EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                EfficientFormerForImageClassification,
                EfficientFormerForImageClassificationWithTeacher,
                EfficientFormerModel,
                EfficientFormerPreTrainedModel,
            )
      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_efficientformer import (
                TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFEfficientFormerForImageClassification,
                TFEfficientFormerForImageClassificationWithTeacher,
                TFEfficientFormerModel,
                TFEfficientFormerPreTrainedModel,
            )

else:
      import sys

      lowercase_          =		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


