






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


UpperCamelCase        =				{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        lowerCAmelCase_				(      nn.Module	):







				"""simple docstring"""




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







				def 				__a   ( self     :int  ,     lowerCamelCase__     :Union[str, Any]     ):
					# Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
					UpperCamelCase__ :Dict						=      self.pool(self.model(lowerCamelCase__     )     )
					UpperCamelCase__ :str						=      torch.flatten(lowerCamelCase__  ,     start_dim=2     )
					UpperCamelCase__ :List[Any]						=      out.transpose(1  ,     2     ).contiguous()
					return out  # BxNx2048

class        lowerCAmelCase_				(      lowercase	):







				"""simple docstring"""




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

					UpperCamelCase__ :Tuple						=      transforms







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







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

					UpperCamelCase__ :Union[str, Any]						=      torch.zeros(self.n_classes     )
					UpperCamelCase__ :str						=      1

					UpperCamelCase__ :Tuple						=      Image.open(os.path.join(self.data_dir  ,     self.data[index]["""img"""]     )     ).convert("""RGB"""     )
					UpperCamelCase__ :Optional[Any]						=      self.transforms(lowerCamelCase__     )

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







				def 				__a   ( self     :List[str]     ):
					UpperCamelCase__ :Dict						=      Counter()
					for row in self.data:
						label_freqs.update(row["""label"""]     )
					return label_freqs




def 				A		(					lowercase__  :  Tuple  )		-> Union[str, Any]:
	UpperCamelCase__ :Tuple						=      [len(row["""sentence"""]  ) for row in batch]
	UpperCamelCase__				,		UpperCamelCase__ :int						=      len(lowercase__  ), max(lowercase__  )

	UpperCamelCase__ :Union[str, Any]						=      torch.zeros(lowercase__ ,		lowercase__ ,		dtype=torch.long  )
	UpperCamelCase__ :List[str]						=      torch.zeros(lowercase__ ,		lowercase__ ,		dtype=torch.long  )

	for i_batch, (input_row, length) in enumerate(zip(lowercase__ ,		lowercase__  )  ):
		UpperCamelCase__ :Optional[Any]						=      input_row["""sentence"""]
		UpperCamelCase__ :Any						=      1

	UpperCamelCase__ :Any						=      torch.stack([row["""image"""] for row in batch]  )
	UpperCamelCase__ :Optional[int]						=      torch.stack([row["""label"""] for row in batch]  )
	UpperCamelCase__ :Optional[int]						=      torch.stack([row["""image_start_token"""] for row in batch]  )
	UpperCamelCase__ :Any						=      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:
	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		(					)		-> Tuple:
	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] ,		),
	    ]  )






'''simple docstring'''




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


__UpperCAmelCase            =       logging.get_logger(__name__)

__UpperCAmelCase            =       {
    """naver-clova-ix/donut-base""": """https://huggingface.co/naver-clova-ix/donut-base/resolve/main/config.json""",
    # See all Donut models at https://huggingface.co/models?filter=donut-swin
}




class      UpperCamelCase__					(					lowercase_			):




					"""simple docstring"""
					SCREAMING_SNAKE_CASE__				=	'''donut-swin'''

					SCREAMING_SNAKE_CASE__				=	{
					    '''num_attention_heads''': '''num_heads''',
					    '''num_hidden_layers''': '''num_layers''',
					}


					def __init__(						self				:			str      ,						lowerCamelCase_				:			Union[str, Any]=2_24      ,						lowerCamelCase_				:			Union[str, Any]=4      ,						lowerCamelCase_				:			Optional[Any]=3      ,						lowerCamelCase_				:			Dict=96      ,						lowerCamelCase_				:			int=[2, 2, 6, 2]      ,						lowerCamelCase_				:			Optional[Any]=[3, 6, 12, 24]      ,						lowerCamelCase_				:			List[str]=7      ,						lowerCamelCase_				:			List[str]=4.0      ,						lowerCamelCase_				:			Optional[Any]=True      ,						lowerCamelCase_				:			List[str]=0.0      ,						lowerCamelCase_				:			Dict=0.0      ,						lowerCamelCase_				:			Dict=0.1      ,						lowerCamelCase_				:			Union[str, Any]="gelu"      ,						lowerCamelCase_				:			List[str]=False      ,						lowerCamelCase_				:			Optional[int]=0.02      ,						lowerCamelCase_				:			Any=1e-5      ,						**lowerCamelCase_				:			Union[str, Any]      ,						):

							'''simple docstring'''



							super().__init__(**lowerCamelCase_						)

							SCREAMING_SNAKE_CASE :				int		=			image_size
							SCREAMING_SNAKE_CASE :				Dict		=			patch_size
							SCREAMING_SNAKE_CASE :				str		=			num_channels
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			embed_dim
							SCREAMING_SNAKE_CASE :				Optional[int]		=			depths
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			len(lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Optional[int]		=			num_heads
							SCREAMING_SNAKE_CASE :				Optional[int]		=			window_size
							SCREAMING_SNAKE_CASE :				Optional[Any]		=			mlp_ratio
							SCREAMING_SNAKE_CASE :				Dict		=			qkv_bias
							SCREAMING_SNAKE_CASE :				Optional[int]		=			hidden_dropout_prob
							SCREAMING_SNAKE_CASE :				Tuple		=			attention_probs_dropout_prob
							SCREAMING_SNAKE_CASE :				Dict		=			drop_path_rate
							SCREAMING_SNAKE_CASE :				List[str]		=			hidden_act
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			use_absolute_embeddings
							SCREAMING_SNAKE_CASE :				Tuple		=			layer_norm_eps
							SCREAMING_SNAKE_CASE :				Optional[Any]		=			initializer_range
							# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
							# this indicates the channel dimension after the last stage of the model
							SCREAMING_SNAKE_CASE :				str		=			int(embed_dim * 2 ** (len(lowerCamelCase_						) - 1)						)



