





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_						   =       {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						:    Tuple						,lowerCamelCase__						:    List[Any]       )     ->		Optional[int]:




           '''simple docstring'''


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






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Any						,lowerCamelCase__						:    int       )     ->		Optional[Any]:




           '''simple docstring'''


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

class 		UpperCamelCase__ (      lowerCAmelCase_   ):



     '''simple docstring'''

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




           '''simple docstring'''


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

           SCREAMING_SNAKE_CASE		=						transforms






     def __len__(							self						:    Any       )     ->		Optional[int]:




           '''simple docstring'''


           return len(self.data       )






     def __getitem__(							self						:    List[Any]						,lowerCamelCase__						:    Dict       )     ->		Union[str, Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						torch.LongTensor(self.tokenizer.encode(self.data[index]["""text"""]						,add_special_tokens=lowerCamelCase__       )       )
           SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE		=						sentence[0], sentence[1:-1], sentence[-1]
           SCREAMING_SNAKE_CASE		=						sentence[: self.max_seq_length]

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

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

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






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Any       )     ->		Tuple:




           '''simple docstring'''


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



def    __lowercase   (      _SCREAMING_SNAKE_CASE	) ->   List[str]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						[len(row["""sentence"""]	) for row in batch]
      SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE		=						len(_SCREAMING_SNAKE_CASE	), max(_SCREAMING_SNAKE_CASE	)

      SCREAMING_SNAKE_CASE		=						torch.zeros(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE ,						dtype=torch.long	)
      SCREAMING_SNAKE_CASE		=						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	)	):
            SCREAMING_SNAKE_CASE		=						input_row["""sentence"""]
            SCREAMING_SNAKE_CASE		=						1

      SCREAMING_SNAKE_CASE		=						torch.stack([row["""image"""] for row in batch]	)
      SCREAMING_SNAKE_CASE		=						torch.stack([row["""label"""] for row in batch]	)
      SCREAMING_SNAKE_CASE		=						torch.stack([row["""image_start_token"""] for row in batch]	)
      SCREAMING_SNAKE_CASE		=						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   (      ) ->   List[Any]:


      '''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   (      ) ->   List[str]:


      '''simple docstring'''


      return transforms.Compose(
          [
              transforms.Resize(2_56	),
              transforms.CenterCrop(2_24	),
              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 collections import OrderedDict
from typing import Mapping

from packaging import version

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


SCREAMING_SNAKE_CASE_						   =       logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_						   =       {
    """microsoft/table-transformer-detection""": (
        """https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json"""
    ),
}

class 		UpperCamelCase__ (      lowerCAmelCase_   ):



     '''simple docstring'''

     __snake_case    :	Union[str, Any]			=			"table-transformer"
     __snake_case    :	Union[str, Any]			=			["past_key_values"]
     __snake_case    :	List[Any]			=			{
         "hidden_size": "d_model",
         "num_attention_heads": "encoder_attention_heads",
     }






     def __init__(							self						:    Optional[int]						,lowerCamelCase__						:    Optional[Any]=True						,lowerCamelCase__						:    Optional[Any]=None						,lowerCamelCase__						:    List[Any]=3						,lowerCamelCase__						:    Optional[int]=100						,lowerCamelCase__						:    List[Any]=6						,lowerCamelCase__						:    Dict=2048						,lowerCamelCase__						:    List[Any]=8						,lowerCamelCase__						:    Dict=6						,lowerCamelCase__						:    Dict=2048						,lowerCamelCase__						:    Any=8						,lowerCamelCase__						:    Optional[int]=0.0						,lowerCamelCase__						:    int=0.0						,lowerCamelCase__						:    List[Any]=True						,lowerCamelCase__						:    Optional[int]="relu"						,lowerCamelCase__						:    Tuple=256						,lowerCamelCase__						:    Any=0.1						,lowerCamelCase__						:    Optional[Any]=0.0						,lowerCamelCase__						:    Tuple=0.0						,lowerCamelCase__						:    List[Any]=0.02						,lowerCamelCase__						:    int=1.0						,lowerCamelCase__						:    List[str]=False						,lowerCamelCase__						:    Optional[Any]="sine"						,lowerCamelCase__						:    List[str]="resnet50"						,lowerCamelCase__						:    Optional[Any]=True						,lowerCamelCase__						:    List[str]=False						,lowerCamelCase__						:    int=1						,lowerCamelCase__						:    Dict=5						,lowerCamelCase__						:    Tuple=2						,lowerCamelCase__						:    Union[str, Any]=1						,lowerCamelCase__						:    str=1						,lowerCamelCase__						:    Any=5						,lowerCamelCase__						:    Tuple=2						,lowerCamelCase__						:    str=0.1						,**lowerCamelCase__						:    List[str]						,)     ->		Optional[int]:




           '''simple docstring'''


           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."""       )
                       SCREAMING_SNAKE_CASE		=						CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""]       )
                 elif isinstance(lowerCamelCase__						,lowerCamelCase__       ):
                       SCREAMING_SNAKE_CASE		=						backbone_config.get("""model_type"""       )
                       SCREAMING_SNAKE_CASE		=						CONFIG_MAPPING[backbone_model_type]
                       SCREAMING_SNAKE_CASE		=						config_class.from_dict(lowerCamelCase__       )
                 # set timm attributes to None
                 SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE		=						None, None, None

           SCREAMING_SNAKE_CASE		=						use_timm_backbone
           SCREAMING_SNAKE_CASE		=						backbone_config
           SCREAMING_SNAKE_CASE		=						num_channels
           SCREAMING_SNAKE_CASE		=						num_queries
           SCREAMING_SNAKE_CASE		=						d_model
           SCREAMING_SNAKE_CASE		=						encoder_ffn_dim
           SCREAMING_SNAKE_CASE		=						encoder_layers
           SCREAMING_SNAKE_CASE		=						encoder_attention_heads
           SCREAMING_SNAKE_CASE		=						decoder_ffn_dim
           SCREAMING_SNAKE_CASE		=						decoder_layers
           SCREAMING_SNAKE_CASE		=						decoder_attention_heads
           SCREAMING_SNAKE_CASE		=						dropout
           SCREAMING_SNAKE_CASE		=						attention_dropout
           SCREAMING_SNAKE_CASE		=						activation_dropout
           SCREAMING_SNAKE_CASE		=						activation_function
           SCREAMING_SNAKE_CASE		=						init_std
           SCREAMING_SNAKE_CASE		=						init_xavier_std
           SCREAMING_SNAKE_CASE		=						encoder_layerdrop
           SCREAMING_SNAKE_CASE		=						decoder_layerdrop
           SCREAMING_SNAKE_CASE		=						encoder_layers
           SCREAMING_SNAKE_CASE		=						auxiliary_loss
           SCREAMING_SNAKE_CASE		=						position_embedding_type
           SCREAMING_SNAKE_CASE		=						backbone
           SCREAMING_SNAKE_CASE		=						use_pretrained_backbone
           SCREAMING_SNAKE_CASE		=						dilation
           # Hungarian matcher
           SCREAMING_SNAKE_CASE		=						class_cost
           SCREAMING_SNAKE_CASE		=						bbox_cost
           SCREAMING_SNAKE_CASE		=						giou_cost
           # Loss coefficients
           SCREAMING_SNAKE_CASE		=						mask_loss_coefficient
           SCREAMING_SNAKE_CASE		=						dice_loss_coefficient
           SCREAMING_SNAKE_CASE		=						bbox_loss_coefficient
           SCREAMING_SNAKE_CASE		=						giou_loss_coefficient
           SCREAMING_SNAKE_CASE		=						eos_coefficient
           super().__init__(is_encoder_decoder=lowerCamelCase__						,**lowerCamelCase__       )






     @property
     def 				SCREAMING_SNAKE_CASE__  (							self						:    Union[str, Any]       )     ->		int:




           '''simple docstring'''


           return self.encoder_attention_heads






     @property
     def 				SCREAMING_SNAKE_CASE__  (							self						:    Optional[Any]       )     ->		int:




           '''simple docstring'''


           return self.d_model

class 		UpperCamelCase__ (      lowerCAmelCase_   ):



     '''simple docstring'''

     __snake_case    :	int			=			version.parse("1.11"   )






     @property
     def 				SCREAMING_SNAKE_CASE__  (							self						:    Union[str, Any]       )     ->		Mapping[str, Mapping[int, str]]:




           '''simple docstring'''


           return OrderedDict(
               [
                   ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
                   ("""pixel_mask""", {0: """batch"""}),
               ]       )






     @property
     def 				SCREAMING_SNAKE_CASE__  (							self						:    Optional[int]       )     ->		float:




           '''simple docstring'''


           return 1e-5






     @property
     def 				SCREAMING_SNAKE_CASE__  (							self						:    Any       )     ->		int:




           '''simple docstring'''


           return 12


