





'''simple docstring'''

import copy

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


__lowerCamelCase : Union[str, Any]											=			logging.get_logger(__name__)

__lowerCamelCase : str											=			{
    "ut/deta": "https://huggingface.co/ut/deta/resolve/main/config.json",
}

class       UpperCAmelCase (						_lowercase  ):
		UpperCAmelCase					:	Tuple 			=		'''deta'''
		UpperCAmelCase					:	str 			=		{
		    '''hidden_size''': '''d_model''',
		    '''num_attention_heads''': '''encoder_attention_heads''',
		}




		def __init__(self      :	Tuple							,			A__      :	Optional[int]=None							,			A__      :	Any=9_0_0							,			A__      :	Tuple=2_0_4_8							,			A__      :	Optional[int]=6							,			A__      :	int=2_0_4_8							,			A__      :	str=8							,			A__      :	Union[str, Any]=6							,			A__      :	Dict=1_0_2_4							,			A__      :	Optional[int]=8							,			A__      :	Union[str, Any]=0.0							,			A__      :	Dict=True							,			A__      :	Dict="relu"							,			A__      :	Dict=2_5_6							,			A__      :	int=0.1							,			A__      :	str=0.0							,			A__      :	List[str]=0.0							,			A__      :	str=0.0_2							,			A__      :	Any=1.0							,			A__      :	Dict=True							,			A__      :	str=False							,			A__      :	Tuple="sine"							,			A__      :	Tuple=5							,			A__      :	Any=4							,			A__      :	Tuple=4							,			A__      :	Optional[int]=True							,			A__      :	List[Any]=3_0_0							,			A__      :	List[Any]=True							,			A__      :	Optional[int]=True							,			A__      :	str=1							,			A__      :	str=5							,			A__      :	int=2							,			A__      :	Tuple=1							,			A__      :	List[str]=1							,			A__      :	List[str]=5							,			A__      :	List[Any]=2							,			A__      :	Any=0.1							,			A__      :	Dict=0.2_5							,			**A__      :	Dict							,			)					-> int:
						if backbone_config is None:
										logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."				)
										lowercase         = CONFIG_MAPPING["resnet"](out_features=["stage2", "stage3", "stage4"]				)
						else:
										if isinstance(A__							,			A__				):
														lowercase         = backbone_config.pop("model_type"				)
														lowercase         = CONFIG_MAPPING[backbone_model_type]
														lowercase         = config_class.from_dict(A__				)

						lowercase         = backbone_config
						lowercase         = num_queries
						lowercase         = max_position_embeddings
						lowercase         = d_model
						lowercase         = encoder_ffn_dim
						lowercase         = encoder_layers
						lowercase         = encoder_attention_heads
						lowercase         = decoder_ffn_dim
						lowercase         = decoder_layers
						lowercase         = decoder_attention_heads
						lowercase         = dropout
						lowercase         = attention_dropout
						lowercase         = activation_dropout
						lowercase         = activation_function
						lowercase         = init_std
						lowercase         = init_xavier_std
						lowercase         = encoder_layerdrop
						lowercase         = auxiliary_loss
						lowercase         = position_embedding_type
						# deformable attributes
						lowercase         = num_feature_levels
						lowercase         = encoder_n_points
						lowercase         = decoder_n_points
						lowercase         = two_stage
						lowercase         = two_stage_num_proposals
						lowercase         = with_box_refine
						lowercase         = assign_first_stage
						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
						lowercase         = class_cost
						lowercase         = bbox_cost
						lowercase         = giou_cost
						# Loss coefficients
						lowercase         = mask_loss_coefficient
						lowercase         = dice_loss_coefficient
						lowercase         = bbox_loss_coefficient
						lowercase         = giou_loss_coefficient
						lowercase         = eos_coefficient
						lowercase         = focal_alpha
						super().__init__(is_encoder_decoder=A__							,			**A__				)




		@property
		def 				UpperCAmelCase__   (self      :	List[str]				)					-> int:
						return self.encoder_attention_heads




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





		def 				UpperCAmelCase__   (self      :	Any				)					-> Optional[Any]:
						lowercase         = copy.deepcopy(self.__dict__				)
						lowercase         = self.backbone_config.to_dict()
						lowercase         = self.__class__.model_type
						return output











'''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


__lowerCamelCase : Any											=			{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  ):




		def __init__(self      :	Tuple							,			A__      :	Any				)					-> str:
						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 				UpperCAmelCase__   (self      :	List[str]							,			A__      :	Optional[Any]				)					-> Optional[int]:
						# Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
						lowercase         = self.pool(self.model(A__				)				)
						lowercase         = torch.flatten(A__							,			start_dim=2				)
						lowercase         = out.transpose(1							,			2				).contiguous()
						return out  # BxNx2048




class       UpperCAmelCase (						_lowercase  ):




		def __init__(self      :	int							,			A__      :	Optional[int]							,			A__      :	Optional[Any]							,			A__      :	List[Any]							,			A__      :	Tuple							,			A__      :	Optional[int]				)					-> Union[str, Any]:
						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      :	List[str]				)					-> Dict:
						return len(self.data				)




		def __getitem__(self      :	int							,			A__      :	Union[str, Any]				)					-> List[str]:
						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 				UpperCAmelCase__   (self      :	str				)					-> str:
						lowercase         = Counter()
						for row in self.data:
										label_freqs.update(row["label"]				)
						return label_freqs







def 							UpperCAmelCase_    (	lowerCAmelCase_							):

				"""simple docstring"""






				lowercase         = [len(row["sentence"]							) for row in batch]
				lowercase   ,							lowercase         = len(lowerCAmelCase_							), max(lowerCAmelCase_							)

				lowercase         = torch.zeros(lowerCAmelCase_		,							lowerCAmelCase_		,							dtype=torch.long							)
				lowercase         = torch.zeros(lowerCAmelCase_		,							lowerCAmelCase_		,							dtype=torch.long							)

				for i_batch, (input_row, length) in enumerate(zip(lowerCAmelCase_		,							lowerCAmelCase_							)							):
								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_    (	):

				"""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 							UpperCAmelCase_    (	):

				"""simple docstring"""






				return transforms.Compose(
				    [
				        transforms.Resize(256							),
				        transforms.CenterCrop(224							),
				        transforms.ToTensor(),
				        transforms.Normalize(
				            mean=[0.46_77_70_44, 0.44_53_14_29, 0.40_66_10_17]		,							std=[0.12_22_19_94, 0.12_14_58_35, 0.14_38_04_69]		,							),
				    ]							)




