
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__							:							Optional[int]									=			{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    ):

							"""simple docstring"""






							def __init__(  self     ,	SCREAMING_SNAKE_CASE_			)->   List[str]:





													'''simple docstring'''






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



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_			)->   List[Any]:





													'''simple docstring'''






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






class        SCREAMING_SNAKE_CASE__    (							SCREAMING_SNAKE_CASE_    ):

							"""simple docstring"""






							def __init__(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			)->   List[Any]:





													'''simple docstring'''






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

													__UpperCamelCase       =			transforms



							def __len__(  self			)->   List[Any]:





													'''simple docstring'''






													return len(self.data			)



							def __getitem__(  self     ,	SCREAMING_SNAKE_CASE_			)->   Union[str, Any]:





													'''simple docstring'''






													__UpperCamelCase       =			torch.LongTensor(self.tokenizer.encode(self.data[index]['''text''']     ,	add_special_tokens=SCREAMING_SNAKE_CASE_			)			)
													__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(SCREAMING_SNAKE_CASE_			)

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



							def     A__		(  self			)->   Optional[int]:





													'''simple docstring'''






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





def 	A_ (							snake_case							:   Dict )      ->    Any:



						'''simple docstring'''

						__UpperCamelCase       =			[len(row['''sentence'''] ) for row in batch]
						__UpperCamelCase  ,							__UpperCamelCase       =			len(snake_case ), max(snake_case )

						__UpperCamelCase       =			torch.zeros(snake_case ,      snake_case ,      dtype=torch.long )
						__UpperCamelCase       =			torch.zeros(snake_case ,      snake_case ,      dtype=torch.long )

						for i_batch, (input_row, length) in enumerate(zip(snake_case ,      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 	A_ (							)      ->    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 	A_ (							)      ->    Dict:



						'''simple docstring'''

						return transforms.Compose(
						    [
						        transforms.Resize(256 ),
						        transforms.CenterCrop(224 ),
						        transforms.ToTensor(),
						        transforms.Normalize(
						            mean=[0.46777044, 0.44531429, 0.40661017] ,      std=[0.12221994, 0.12145835, 0.14380469] ,      ),
						    ] )






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


lowercase__							:							Tuple									=			logging.get_logger(__name__)

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






class        SCREAMING_SNAKE_CASE__    (							SCREAMING_SNAKE_CASE_    ):

							"""simple docstring"""






							_snake_case     		=  'table-transformer'
							_snake_case     		=  ['past_key_values']
							_snake_case     		=  {
							    'hidden_size': 'd_model',
							    'num_attention_heads': 'encoder_attention_heads',
							}



							def __init__(  self     ,	SCREAMING_SNAKE_CASE_=True     ,	SCREAMING_SNAKE_CASE_=None     ,	SCREAMING_SNAKE_CASE_=3     ,	SCREAMING_SNAKE_CASE_=100     ,	SCREAMING_SNAKE_CASE_=6     ,	SCREAMING_SNAKE_CASE_=2048     ,	SCREAMING_SNAKE_CASE_=8     ,	SCREAMING_SNAKE_CASE_=6     ,	SCREAMING_SNAKE_CASE_=2048     ,	SCREAMING_SNAKE_CASE_=8     ,	SCREAMING_SNAKE_CASE_=0.0     ,	SCREAMING_SNAKE_CASE_=0.0     ,	SCREAMING_SNAKE_CASE_=True     ,	SCREAMING_SNAKE_CASE_="relu"     ,	SCREAMING_SNAKE_CASE_=256     ,	SCREAMING_SNAKE_CASE_=0.1     ,	SCREAMING_SNAKE_CASE_=0.0     ,	SCREAMING_SNAKE_CASE_=0.0     ,	SCREAMING_SNAKE_CASE_=0.0_2     ,	SCREAMING_SNAKE_CASE_=1.0     ,	SCREAMING_SNAKE_CASE_=False     ,	SCREAMING_SNAKE_CASE_="sine"     ,	SCREAMING_SNAKE_CASE_="resnet50"     ,	SCREAMING_SNAKE_CASE_=True     ,	SCREAMING_SNAKE_CASE_=False     ,	SCREAMING_SNAKE_CASE_=1     ,	SCREAMING_SNAKE_CASE_=5     ,	SCREAMING_SNAKE_CASE_=2     ,	SCREAMING_SNAKE_CASE_=1     ,	SCREAMING_SNAKE_CASE_=1     ,	SCREAMING_SNAKE_CASE_=5     ,	SCREAMING_SNAKE_CASE_=2     ,	SCREAMING_SNAKE_CASE_=0.1     ,	**SCREAMING_SNAKE_CASE_     ,	)->   Tuple:





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

													__UpperCamelCase       =			use_timm_backbone
													__UpperCamelCase       =			backbone_config
													__UpperCamelCase       =			num_channels
													__UpperCamelCase       =			num_queries
													__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       =			decoder_layerdrop
													__UpperCamelCase       =			encoder_layers
													__UpperCamelCase       =			auxiliary_loss
													__UpperCamelCase       =			position_embedding_type
													__UpperCamelCase       =			backbone
													__UpperCamelCase       =			use_pretrained_backbone
													__UpperCamelCase       =			dilation
													# 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
													super().__init__(is_encoder_decoder=SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_			)



							@property
							def     A__		(  self			)->   int:





													'''simple docstring'''






													return self.encoder_attention_heads



							@property
							def     A__		(  self			)->   int:





													'''simple docstring'''






													return self.d_model






class        SCREAMING_SNAKE_CASE__    (							SCREAMING_SNAKE_CASE_    ):

							"""simple docstring"""






							_snake_case     		=  version.parse('1.11'    )



							@property
							def     A__		(  self			)->   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     A__		(  self			)->   float:





													'''simple docstring'''






													return 1E-5



							@property
							def     A__		(  self			)->   int:





													'''simple docstring'''






													return 12




