

"""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


a		:				List[str]  			=    {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   ,      lowerCAmelCase__   )			->				str:
								super().__init__()
								a   :	Union[str, Any]			=       torchvision.models.resnetaaa(pretrained=lowerCAmelCase__   )
								a   :	Optional[Any]			=       list(model.children()   )[:-2]
								a   :	Tuple			=       nn.Sequential(*lowerCAmelCase__   )
								a   :	Optional[Any]			=       nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]   )






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


class  __UpperCamelCase       (		UpperCamelCase_		):






							def __init__(     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   )			->				Any:
								a   :	Optional[int]			=       [json.loads(lowerCAmelCase__   ) for l in open(lowerCAmelCase__   )]
								a   :	List[str]			=       os.path.dirname(lowerCAmelCase__   )
								a   :	int			=       tokenizer
								a   :	Any			=       labels
								a   :	List[Any]			=       len(lowerCAmelCase__   )
								a   :	Any			=       max_seq_length

								a   :	Optional[Any]			=       transforms






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






							def __getitem__(     self   ,      lowerCAmelCase__   )			->				Tuple:
								a   :	Optional[Any]			=       torch.LongTensor(self.tokenizer.encode(self.data[index]["text"]   ,      add_special_tokens=lowerCAmelCase__   )   )
								a   :	Tuple			=       sentence[0], sentence[1:-1], sentence[-1]
								a   :	Tuple			=       sentence[: self.max_seq_length]

								a   :	Optional[Any]			=       torch.zeros(self.n_classes   )
								a   :	Dict			=       1

								a   :	Optional[int]			=       Image.open(os.path.join(self.data_dir   ,      self.data[index]["img"]   )   ).convert("RGB"   )
								a   :	int			=       self.transforms(lowerCAmelCase__   )

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






							def 					__a     (     self   )			->				Optional[int]:
								a   :	List[Any]			=       Counter()
								for row in self.data:
									label_freqs.update(row["label"]   )
								return label_freqs






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					int      )				->List[str]:






	'''simple docstring'''






	a   :	Any			=       [len(row["sentence"]      ) for row in batch]
	a   :	Optional[int]			=       len(snake_case_      ), max(snake_case_      )

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

	for i_batch, (input_row, length) in enumerate(zip(snake_case_					, snake_case_      )      ):
		a   :	Optional[int]			=       input_row['''sentence''']
		a   :	Dict			=       1

	a   :	Optional[Any]			=       torch.stack([row["image"] for row in batch]      )
	a   :	List[str]			=       torch.stack([row["label"] for row in batch]      )
	a   :	Any			=       torch.stack([row["image_start_token"] for row in batch]      )
	a   :	Optional[int]			=       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    _SCREAMING_SNAKE_CASE	(			)				->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    _SCREAMING_SNAKE_CASE	(			)				->Union[str, Any]:






	'''simple docstring'''






	return transforms.Compose(
	    [
	        transforms.Resize(256      ),
	        transforms.CenterCrop(224      ),
	        transforms.ToTensor(),
	        transforms.Normalize(
	            mean=[0.4677_7044, 0.4453_1429, 0.4066_1017]					, std=[0.1222_1994, 0.1214_5835, 0.1438_0469]					, ),
	    ]      )









"""simple docstring"""




import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
		from .tokenization_albert import AlbertTokenizer
else:
		a		:				Tuple  			=    None

a		:				Any  			=    logging.get_logger(__name__)
a		:				List[Any]  			=    {'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

a		:				str  			=    {
    '''vocab_file''': {
        '''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/spiece.model''',
        '''albert-large-v1''': '''https://huggingface.co/albert-large-v1/resolve/main/spiece.model''',
        '''albert-xlarge-v1''': '''https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model''',
        '''albert-xxlarge-v1''': '''https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model''',
        '''albert-base-v2''': '''https://huggingface.co/albert-base-v2/resolve/main/spiece.model''',
        '''albert-large-v2''': '''https://huggingface.co/albert-large-v2/resolve/main/spiece.model''',
        '''albert-xlarge-v2''': '''https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model''',
        '''albert-xxlarge-v2''': '''https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model''',
    },
    '''tokenizer_file''': {
        '''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/tokenizer.json''',
        '''albert-large-v1''': '''https://huggingface.co/albert-large-v1/resolve/main/tokenizer.json''',
        '''albert-xlarge-v1''': '''https://huggingface.co/albert-xlarge-v1/resolve/main/tokenizer.json''',
        '''albert-xxlarge-v1''': '''https://huggingface.co/albert-xxlarge-v1/resolve/main/tokenizer.json''',
        '''albert-base-v2''': '''https://huggingface.co/albert-base-v2/resolve/main/tokenizer.json''',
        '''albert-large-v2''': '''https://huggingface.co/albert-large-v2/resolve/main/tokenizer.json''',
        '''albert-xlarge-v2''': '''https://huggingface.co/albert-xlarge-v2/resolve/main/tokenizer.json''',
        '''albert-xxlarge-v2''': '''https://huggingface.co/albert-xxlarge-v2/resolve/main/tokenizer.json''',
    },
}

a		:				str  			=    {
    '''albert-base-v1''': 512,
    '''albert-large-v1''': 512,
    '''albert-xlarge-v1''': 512,
    '''albert-xxlarge-v1''': 512,
    '''albert-base-v2''': 512,
    '''albert-large-v2''': 512,
    '''albert-xlarge-v2''': 512,
    '''albert-xxlarge-v2''': 512,
}

a		:				Union[str, Any]  			=    '''▁'''


class  __UpperCamelCase       (		a__		):
							lowerCamelCase     :      Union[str, Any]         =VOCAB_FILES_NAMES
							lowerCamelCase     :      Dict         =PRETRAINED_VOCAB_FILES_MAP
							lowerCamelCase     :      List[Any]         =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowerCamelCase     :      List[Any]         =AlbertTokenizer






							def __init__(     self   ,      lowerCAmelCase__=None   ,      lowerCAmelCase__=None   ,      lowerCAmelCase__=True   ,      lowerCAmelCase__=True   ,      lowerCAmelCase__=False   ,      lowerCAmelCase__="[CLS]"   ,      lowerCAmelCase__="[SEP]"   ,      lowerCAmelCase__="<unk>"   ,      lowerCAmelCase__="[SEP]"   ,      lowerCAmelCase__="<pad>"   ,      lowerCAmelCase__="[CLS]"   ,      lowerCAmelCase__="[MASK]"   ,      **lowerCAmelCase__   ,      )			->				Union[str, Any]:
								# Mask token behave like a normal word, i.e. include the space before it and
								# is included in the raw text, there should be a match in a non-normalized sentence.
								a   :	Optional[int]			=       (
								    AddedToken(lowerCAmelCase__   ,      lstrip=lowerCAmelCase__   ,      rstrip=lowerCAmelCase__   ,      normalized=lowerCAmelCase__   )
								    if isinstance(lowerCAmelCase__   ,      lowerCAmelCase__   )
								    else mask_token
								)

								super().__init__(
								    lowerCAmelCase__   ,      tokenizer_file=lowerCAmelCase__   ,      do_lower_case=lowerCAmelCase__   ,      remove_space=lowerCAmelCase__   ,      keep_accents=lowerCAmelCase__   ,      bos_token=lowerCAmelCase__   ,      eos_token=lowerCAmelCase__   ,      unk_token=lowerCAmelCase__   ,      sep_token=lowerCAmelCase__   ,      pad_token=lowerCAmelCase__   ,      cls_token=lowerCAmelCase__   ,      mask_token=lowerCAmelCase__   ,      **lowerCAmelCase__   ,      )

								a   :	Dict			=       do_lower_case
								a   :	Any			=       remove_space
								a   :	Optional[Any]			=       keep_accents
								a   :	List[str]			=       vocab_file
								a   :	Optional[Any]			=       False if not self.vocab_file else True






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = None   )			->				List[int]:
								a   :	Optional[Any]			=       [self.sep_token_id]
								a   :	int			=       [self.cls_token_id]
								if token_ids_a is None:
									return cls + token_ids_a + sep
								return cls + token_ids_a + sep + token_ids_a + sep






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = None   )			->				List[int]:
								a   :	Optional[Any]			=       [self.sep_token_id]
								a   :	int			=       [self.cls_token_id]

								if token_ids_a is None:
									return len(cls + token_ids_a + sep   ) * [0]
								return len(cls + token_ids_a + sep   ) * [0] + len(token_ids_a + sep   ) * [1]






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = None   )			->				Tuple[str]:
								if not self.can_save_slow_tokenizer:
									raise ValueError(
									    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
									    "tokenizer."   )

								if not os.path.isdir(lowerCAmelCase__   ):
									logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""   )
									return
								a   :	Dict			=       os.path.join(
								    lowerCAmelCase__   ,      (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]   )

								if os.path.abspath(self.vocab_file   ) != os.path.abspath(lowerCAmelCase__   ):
									copyfile(self.vocab_file   ,      lowerCAmelCase__   )

								return (out_vocab_file,)


