


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

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


if is_sentencepiece_available():
				from .tokenization_ta import TaTokenizer
else:
				_lowercase							:   Tuple						=						None


_lowercase							:   Optional[Any]						=						logging.get_logger(__name__)

_lowercase							:   Any						=						{"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

_lowercase							:   Any						=						{
    "vocab_file": {
        "t5-small": "https://huggingface.co/t5-small/resolve/main/spiece.model",
        "t5-base": "https://huggingface.co/t5-base/resolve/main/spiece.model",
        "t5-large": "https://huggingface.co/t5-large/resolve/main/spiece.model",
        "t5-3b": "https://huggingface.co/t5-3b/resolve/main/spiece.model",
        "t5-11b": "https://huggingface.co/t5-11b/resolve/main/spiece.model",
    },
    "tokenizer_file": {
        "t5-small": "https://huggingface.co/t5-small/resolve/main/tokenizer.json",
        "t5-base": "https://huggingface.co/t5-base/resolve/main/tokenizer.json",
        "t5-large": "https://huggingface.co/t5-large/resolve/main/tokenizer.json",
        "t5-3b": "https://huggingface.co/t5-3b/resolve/main/tokenizer.json",
        "t5-11b": "https://huggingface.co/t5-11b/resolve/main/tokenizer.json",
    },
}


# TODO(PVP) - this should be removed in Transformers v5
_lowercase							:   Dict						=						{
    "t5-small": 512,
    "t5-base": 512,
    "t5-large": 512,
    "t5-3b": 512,
    "t5-11b": 512,
}






class    _UpperCamelCase     (       __snake_case					):

				"""simple docstring"""







				lowerCAmelCase					=			VOCAB_FILES_NAMES
				lowerCAmelCase					=			PRETRAINED_VOCAB_FILES_MAP
				lowerCAmelCase					=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				lowerCAmelCase					=			['input_ids', 'attention_mask']
				lowerCAmelCase					=			TaTokenizer

				lowerCAmelCase					=			[]

				def __init__(    self					, a__=None					, a__=None					, a__="</s>"					, a__="<unk>"					, a__="<pad>"					, a__=100					, a__=None					, **a__					, )						->					int:
					# Add extra_ids to the special token list
					if extra_ids > 0 and additional_special_tokens is None:
						A             =					[f'<extra_id_{i}>' for i in range(a__ )]
					elif extra_ids > 0 and additional_special_tokens is not None:
						# Check that we have the right number of extra special tokens
						A             =					len(set(filter(lambda a__ : bool("""extra_id_""" in str(a__ ) )					, a__ ) ) )
						if extra_tokens != extra_ids:
							raise ValueError(
							    f'Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are'
							    """ provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids"""
							    """ tokens""" )

					super().__init__(
					    a__					, tokenizer_file=a__					, eos_token=a__					, unk_token=a__					, pad_token=a__					, extra_ids=a__					, additional_special_tokens=a__					, **a__					, )

					A             =					vocab_file
					A             =					False if not self.vocab_file else True
					A             =					extra_ids

				@staticmethod
				def   _UpperCAmelCase       (    a__					, a__					, a__ )						->					int:
					if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
						A             =					TaTokenizerFast.max_model_input_sizes[pretrained_model_name_or_path]
						if init_max_model_length is not None and init_max_model_length != max_model_length:
							return init_max_model_length
						elif init_max_model_length is None:
							warnings.warn(
							    """This tokenizer was incorrectly instantiated with a model max length of"""
							    f' {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this'
							    """ behavior is kept to avoid breaking backwards compatibility when padding/encoding with"""
							    """ `truncation is True`.\n- Be aware that you SHOULD NOT rely on"""
							    f' {pretrained_model_name_or_path} automatically truncating your input to'
							    f' {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences'
							    f' longer than {deprecated_max_model_length} you can either instantiate this tokenizer with'
							    """ `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please"""
							    """ instantiate this tokenizer with `model_max_length` set to your preferred value."""					, a__					, )

					return max_model_length

				def   _UpperCAmelCase       (    self					, a__					, a__ = 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(a__ ):
						logger.error(f'Vocabulary path ({save_directory}) should be a directory' )
						return
					A             =					os.path.join(
					    a__					, (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )

					if os.path.abspath(self.vocab_file ) != os.path.abspath(a__ ):
						copyfile(self.vocab_file					, a__ )
						logger.info(f'Copy vocab file to {out_vocab_file}' )

					return (out_vocab_file,)

				def   _UpperCAmelCase       (    self					, a__					, a__ = None )						->					List[int]:
					A             =					token_ids_a + [self.eos_token_id]
					if token_ids_a is None:
						return self.prefix_tokens + token_ids_a
					else:
						A             =					token_ids_a + [self.eos_token_id]
						return self.prefix_tokens + token_ids_a + token_ids_a

				def   _UpperCAmelCase       (    self					, a__					, a__ = None )						->					List[int]:
					A             =					[self.eos_token_id]

					if token_ids_a is None:
						return len(token_ids_a + eos ) * [0]
					return len(token_ids_a + eos + token_ids_a + eos ) * [0]

				def   _UpperCAmelCase       (    self )						->					Optional[int]:
					return list(
					    set(filter(lambda a__ : bool(re.search(r"""<extra_id_\d+>"""					, a__ ) ) is not None					, self.additional_special_tokens ) ) )

				def   _UpperCAmelCase       (    self )						->					List[str]:
					return [self.convert_tokens_to_ids(a__ ) for token in self.get_sentinel_tokens()]







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							:   Union[str, 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					):

				"""simple docstring"""







				def __init__(    self					, a__ )						->					Dict:
					super().__init__()
					A             =					torchvision.models.resnetaaa(pretrained=a__ )
					A             =					list(model.children() )[:-2]
					A             =					nn.Sequential(*a__ )
					A             =					nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds] )

				def   _UpperCAmelCase       (    self					, a__ )						->					List[str]:
					# Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
					A             =					self.pool(self.model(a__ ) )
					A             =					torch.flatten(a__					, start_dim=2 )
					A             =					out.transpose(1					, 2 ).contiguous()
					return out  # BxNx2048






class    _UpperCamelCase     (       __snake_case					):

				"""simple docstring"""







				def __init__(    self					, a__					, a__					, a__					, a__					, a__ )						->					Union[str, Any]:
					A             =					[json.loads(a__ ) for l in open(a__ )]
					A             =					os.path.dirname(a__ )
					A             =					tokenizer
					A             =					labels
					A             =					len(a__ )
					A             =					max_seq_length

					A             =					transforms

				def __len__(    self )						->					Tuple:
					return len(self.data )

				def __getitem__(    self					, a__ )						->					Optional[int]:
					A             =					torch.LongTensor(self.tokenizer.encode(self.data[index]["""text"""]					, add_special_tokens=a__ ) )
					A       ,      A       ,      A             =					sentence[0], sentence[1:-1], sentence[-1]
					A             =					sentence[: self.max_seq_length]

					A             =					torch.zeros(self.n_classes )
					A             =					1

					A             =					Image.open(os.path.join(self.data_dir					, self.data[index]["""img"""] ) ).convert("""RGB""" )
					A             =					self.transforms(a__ )

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

				def   _UpperCAmelCase       (    self )						->					Union[str, Any]:
					A             =					Counter()
					for row in self.data:
						label_freqs.update(row["""label"""] )
					return label_freqs





def 	_lowerCAmelCase					( UpperCamelCase__:  int    )			-> List[str]:





	"""simple docstring"""







	A             =					[len(row["""sentence"""]    ) for row in batch]
	A       ,      A             =					len(UpperCamelCase__    ), max(UpperCamelCase__    )

	A             =					torch.zeros(UpperCamelCase__				,       UpperCamelCase__				,       dtype=torch.long    )
	A             =					torch.zeros(UpperCamelCase__				,       UpperCamelCase__				,       dtype=torch.long    )

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

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





	"""simple docstring"""







	return transforms.Compose(
	    [
	        transforms.Resize(2_56    ),
	        transforms.CenterCrop(2_24    ),
	        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]				,       ),
	    ]    )



