


"""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


__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 						_A  (  nn.Module  ):







    """simple docstring"""




    def __init__(					self				:    Dict				, __UpperCAmelCase				:    str):
      super().__init__()
      a						:		int      							=			torchvision.models.resnetaaa(pretrained=__UpperCAmelCase)
      a						:		List[str]      							=			list(model.children())[:-2]
      a						:		Optional[int]      							=			nn.Sequential(*__UpperCAmelCase)
      a						:		Union[str, Any]      							=			nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds])






    def 	__snake_case							(					self				:    str				, __UpperCAmelCase				:    Tuple):
      # Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
      a						:		List[Any]      							=			self.pool(self.model(__UpperCAmelCase))
      a						:		Dict      							=			torch.flatten(__UpperCAmelCase				, start_dim=2)
      a						:		List[str]      							=			out.transpose(1				, 2).contiguous()
      return out  # BxNx2048






class 						_A  (  _a  ):







    """simple docstring"""




    def __init__(					self				:    int				, __UpperCAmelCase				:    Optional[int]				, __UpperCAmelCase				:    Any				, __UpperCAmelCase				:    Optional[Any]				, __UpperCAmelCase				:    Tuple				, __UpperCAmelCase				:    Optional[int]):
      a						:		Dict      							=			[json.loads(__UpperCAmelCase) for l in open(__UpperCAmelCase)]
      a						:		Any      							=			os.path.dirname(__UpperCAmelCase)
      a						:		Optional[Any]      							=			tokenizer
      a						:		Tuple      							=			labels
      a						:		int      							=			len(__UpperCAmelCase)
      a						:		Optional[Any]      							=			max_seq_length

      a						:		Union[str, Any]      							=			transforms






    def __len__(					self				:    Optional[int]):
      return len(self.data)






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

      a						:		Optional[Any]      							=			torch.zeros(self.n_classes)
      a						:		int      							=			1

      a						:		Union[str, Any]      							=			Image.open(os.path.join(self.data_dir				, self.data[index]["img"])).convert("RGB")
      a						:		str      							=			self.transforms(__UpperCAmelCase)

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






    def 	__snake_case							(					self				:    Optional[int]):
      a						:		int      							=			Counter()
      for row in self.data:
        label_freqs.update(row["label"])
      return label_freqs






def      lowercase			(				A_       )->      Union[str, Any]:
  '''simple docstring'''







  a						:		Any      							=			[len(row["sentence"]       ) for row in batch]
  a							,		a						:		Any      							=			len(A_       ), max(A_       )

  a						:		Optional[Any]      							=			torch.zeros(A_  ,   A_  ,   dtype=torch.long       )
  a						:		List[str]      							=			torch.zeros(A_  ,   A_  ,   dtype=torch.long       )

  for i_batch, (input_row, length) in enumerate(zip(A_  ,   A_       )       ):
    a						:		Any      							=			input_row["sentence"]
    a						:		List[Any]      							=			1

  a						:		List[str]      							=			torch.stack([row["image"] for row in batch]       )
  a						:		int      							=			torch.stack([row["label"] for row in batch]       )
  a						:		Any      							=			torch.stack([row["image_start_token"] for row in batch]       )
  a						:		str      							=			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      lowercase			(				)->      Dict:
  '''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      lowercase			(				)->      Any:
  '''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]  ,   ),
      ]       )










"""simple docstring"""







from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowercase    					=      logging.get_logger(__name__)

__lowercase    					=      {
    """facebook/dpr-ctx_encoder-single-nq-base""": (
        """https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/config.json"""
    ),
    """facebook/dpr-question_encoder-single-nq-base""": (
        """https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/config.json"""
    ),
    """facebook/dpr-reader-single-nq-base""": (
        """https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/config.json"""
    ),
    """facebook/dpr-ctx_encoder-multiset-base""": (
        """https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/config.json"""
    ),
    """facebook/dpr-question_encoder-multiset-base""": (
        """https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/config.json"""
    ),
    """facebook/dpr-reader-multiset-base""": (
        """https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/config.json"""
    ),
}






class 						_A  (  _a  ):







    """simple docstring"""




    UpperCAmelCase       :							int	=       """dpr"""






    def __init__(					self				:    List[Any]				, __UpperCAmelCase				:    int=30522				, __UpperCAmelCase				:    Union[str, Any]=768				, __UpperCAmelCase				:    Dict=12				, __UpperCAmelCase				:    List[str]=12				, __UpperCAmelCase				:    Any=3072				, __UpperCAmelCase				:    Optional[int]="gelu"				, __UpperCAmelCase				:    Any=0.1				, __UpperCAmelCase				:    Union[str, Any]=0.1				, __UpperCAmelCase				:    str=512				, __UpperCAmelCase				:    List[str]=2				, __UpperCAmelCase				:    Tuple=0.02				, __UpperCAmelCase				:    List[str]=1e-12				, __UpperCAmelCase				:    List[str]=0				, __UpperCAmelCase				:    str="absolute"				, __UpperCAmelCase				:    int = 0				, **__UpperCAmelCase				:    Tuple				, ):
      super().__init__(pad_token_id=__UpperCAmelCase				, **__UpperCAmelCase)

      a						:		List[Any]      							=			vocab_size
      a						:		Optional[Any]      							=			hidden_size
      a						:		Union[str, Any]      							=			num_hidden_layers
      a						:		Dict      							=			num_attention_heads
      a						:		int      							=			hidden_act
      a						:		Any      							=			intermediate_size
      a						:		Any      							=			hidden_dropout_prob
      a						:		Dict      							=			attention_probs_dropout_prob
      a						:		Any      							=			max_position_embeddings
      a						:		Union[str, Any]      							=			type_vocab_size
      a						:		Optional[Any]      							=			initializer_range
      a						:		Dict      							=			layer_norm_eps
      a						:		int      							=			projection_dim
      a						:		str      							=			position_embedding_type






