


import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


_A			=						logging.get_logger(__name__)

_A			=						{
    'SenseTime/deformable-detr': 'https://huggingface.co/sensetime/deformable-detr/resolve/main/config.json',
    # See all Deformable DETR models at https://huggingface.co/models?filter=deformable-detr
}






class    UpperCAmelCase__	( A_		):



    """simple docstring"""






    UpperCAmelCase__						:		List[Any]              =  "deformable_detr"
    UpperCAmelCase__						:		List[str]              =  {
        "hidden_size": "d_model",
        "num_attention_heads": "encoder_attention_heads",
    }






    def __init__(      self ,       A_=True ,       A_=None ,       A_=3 ,       A_=300 ,       A_=1024 ,       A_=6 ,       A_=1024 ,       A_=8 ,       A_=6 ,       A_=1024 ,       A_=8 ,       A_=0.0 ,       A_=True ,       A_="relu" ,       A_=256 ,       A_=0.1 ,       A_=0.0 ,       A_=0.0 ,       A_=0.02 ,       A_=1.0 ,       A_=True ,       A_=False ,       A_="sine" ,       A_="resnet50" ,       A_=True ,       A_=False ,       A_=4 ,       A_=4 ,       A_=4 ,       A_=False ,       A_=300 ,       A_=False ,       A_=1 ,       A_=5 ,       A_=2 ,       A_=1 ,       A_=1 ,       A_=5 ,       A_=2 ,       A_=0.1 ,       A_=0.25 ,       A_=False ,       **A_ ,       )  ->	Dict:
         if backbone_config is not None and use_timm_backbone:
              raise ValueError('You can\'t specify both `backbone_config` and `use_timm_backbone`.'   )

         if not use_timm_backbone:
              if backbone_config is None:
                   logger.info('`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'   )
                   __UpperCamelCase   							=CONFIG_MAPPING['resnet'](out_features=['stage4']   )
              elif isinstance(A_ ,       A_   ):
                   __UpperCamelCase   							=backbone_config.get('model_type'   )
                   __UpperCamelCase   							=CONFIG_MAPPING[backbone_model_type]
                   __UpperCamelCase   							=config_class.from_dict(A_   )
         __UpperCamelCase   							=use_timm_backbone
         __UpperCamelCase   							=backbone_config
         __UpperCamelCase   							=num_channels
         __UpperCamelCase   							=num_queries
         __UpperCamelCase   							=max_position_embeddings
         __UpperCamelCase   							=d_model
         __UpperCamelCase   							=encoder_ffn_dim
         __UpperCamelCase   							=encoder_layers
         __UpperCamelCase   							=encoder_attention_heads
         __UpperCamelCase   							=decoder_ffn_dim
         __UpperCamelCase   							=decoder_layers
         __UpperCamelCase   							=decoder_attention_heads
         __UpperCamelCase   							=dropout
         __UpperCamelCase   							=attention_dropout
         __UpperCamelCase   							=activation_dropout
         __UpperCamelCase   							=activation_function
         __UpperCamelCase   							=init_std
         __UpperCamelCase   							=init_xavier_std
         __UpperCamelCase   							=encoder_layerdrop
         __UpperCamelCase   							=auxiliary_loss
         __UpperCamelCase   							=position_embedding_type
         __UpperCamelCase   							=backbone
         __UpperCamelCase   							=use_pretrained_backbone
         __UpperCamelCase   							=dilation
         # deformable attributes
         __UpperCamelCase   							=num_feature_levels
         __UpperCamelCase   							=encoder_n_points
         __UpperCamelCase   							=decoder_n_points
         __UpperCamelCase   							=two_stage
         __UpperCamelCase   							=two_stage_num_proposals
         __UpperCamelCase   							=with_box_refine
         if two_stage is True and with_box_refine is False:
              raise ValueError('If two_stage is True, with_box_refine must be True.'   )
         # Hungarian matcher
         __UpperCamelCase   							=class_cost
         __UpperCamelCase   							=bbox_cost
         __UpperCamelCase   							=giou_cost
         # Loss coefficients
         __UpperCamelCase   							=mask_loss_coefficient
         __UpperCamelCase   							=dice_loss_coefficient
         __UpperCamelCase   							=bbox_loss_coefficient
         __UpperCamelCase   							=giou_loss_coefficient
         __UpperCamelCase   							=eos_coefficient
         __UpperCamelCase   							=focal_alpha
         __UpperCamelCase   							=disable_custom_kernels
         super().__init__(is_encoder_decoder=A_ ,       **A_   )






    @property
    def _a (      self   )  ->	int:
         return self.encoder_attention_heads






    @property
    def _a (      self   )  ->	int:
         return self.d_model






    def _a (      self   )  ->	List[str]:
         __UpperCamelCase   							=copy.deepcopy(self.__dict__   )
         if self.backbone_config is not None:
              __UpperCamelCase   							=self.backbone_config.to_dict()
         __UpperCamelCase   							=self.__class__.model_type
         return output









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    UpperCAmelCase__	( nn.Module		):



    """simple docstring"""






    def __init__(      self ,       A_   )  ->	Optional[int]:
         super().__init__()
         __UpperCamelCase   							=torchvision.models.resnetaaa(pretrained=A_   )
         __UpperCamelCase   							=list(model.children()   )[:-2]
         __UpperCamelCase   							=nn.Sequential(*A_   )
         __UpperCamelCase   							=nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]   )






    def _a (      self ,       A_   )  ->	int:
         # Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
         __UpperCamelCase   							=self.pool(self.model(A_   )   )
         __UpperCamelCase   							=torch.flatten(A_ ,       start_dim=2   )
         __UpperCamelCase   							=out.transpose(1 ,       2   ).contiguous()
         return out  # BxNx2048






class    UpperCAmelCase__	( A_		):



    """simple docstring"""






    def __init__(      self ,       A_ ,       A_ ,       A_ ,       A_ ,       A_   )  ->	List[str]:
         __UpperCamelCase   							=[json.loads(A_   ) for l in open(A_   )]
         __UpperCamelCase   							=os.path.dirname(A_   )
         __UpperCamelCase   							=tokenizer
         __UpperCamelCase   							=labels
         __UpperCamelCase   							=len(A_   )
         __UpperCamelCase   							=max_seq_length

         __UpperCamelCase   							=transforms






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






    def __getitem__(      self ,       A_   )  ->	Union[str, Any]:
         __UpperCamelCase   							=torch.LongTensor(self.tokenizer.encode(self.data[index]['text'] ,       add_special_tokens=A_   )   )
         __UpperCamelCase							,				__UpperCamelCase							,				__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(A_   )

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






    def _a (      self   )  ->	List[str]:
         __UpperCamelCase   							=Counter()
         for row in self.data:
              label_freqs.update(row['label']   )
         return label_freqs

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Union[str, Any]    ):
     __UpperCamelCase   							=[len(row['sentence']    ) for row in batch]
     __UpperCamelCase							,				__UpperCamelCase   							=len(SCREAMING_SNAKE_CASE__    ), max(SCREAMING_SNAKE_CASE__    )

     __UpperCamelCase   							=torch.zeros(SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__      ,					dtype=torch.long    )
     __UpperCamelCase   							=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__    )    ):
          __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 						_UpperCAmelCase    (      ):
     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    (      ):
     return transforms.Compose(
         [
             transforms.Resize(2_56    ),
             transforms.CenterCrop(2_24    ),
             transforms.ToTensor(),
             transforms.Normalize(
                 mean=[0.46777044, 0.44531429, 0.40661017]      ,					std=[0.12221994, 0.12145835, 0.14380469]      ,					),
         ]    )





