





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 							__lowercase       (      nn.Module						):







	'''simple docstring'''

	def __init__(self     ,_lowerCamelCase )							->							str:







				'''simple docstring'''





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


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase )							->							Union[str, Any]:







				'''simple docstring'''





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






class 							__lowercase       (      lowercase__						):







	'''simple docstring'''

	def __init__(self     ,_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase )							->							Union[str, Any]:







				'''simple docstring'''





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

				__lowercase										=							transforms


	def __len__(self )							->							Tuple:







				'''simple docstring'''





				return len(self.data )


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







				'''simple docstring'''





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

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


	def 						_UpperCAmelCase     (self )							->							int:







				'''simple docstring'''





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





def    _lowerCAmelCase						(				lowerCamelCase_			:  Optional[Any]				):
			__lowercase										=							[len(row['''sentence''']				) for row in batch]
			__lowercase						,		__lowercase										=							len(SCREAMING_SNAKE_CASE_				), max(SCREAMING_SNAKE_CASE_				)

			__lowercase										=							torch.zeros(SCREAMING_SNAKE_CASE_		,       SCREAMING_SNAKE_CASE_		,       dtype=torch.long				)
			__lowercase										=							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_				)				):
						__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    _lowerCAmelCase						(				):
			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    _lowerCAmelCase						(				):
			return transforms.Compose(
			    [
			        transforms.Resize(2_5_6				),
			        transforms.CenterCrop(2_2_4				),
			        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]		,       ),
			    ]				)












'''simple docstring'''



import warnings

from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor


_SCREAMING_SNAKE_CASE					=    logging.get_logger(__name__)






class 							__lowercase       (      lowerCAmelCase__						):







	'''simple docstring'''

	def __init__(self     ,*_lowerCamelCase     ,**_lowerCamelCase )							->							None:







				'''simple docstring'''





				warnings.warn(
				    '''The class GLPNFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
				    ''' use GLPNImageProcessor instead.'''     ,_lowerCamelCase     ,)
				super().__init__(*_lowerCamelCase     ,**_lowerCamelCase )





