






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     :							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  __magic_name__   (							nn.Module						):






		def __init__(						self      :     str				,		UpperCamelCase__      :     List[str]      )				->					Union[str, Any]:
								'''simple docstring'''







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







		def SCREAMING_SNAKE_CASE_			(						self      :     List[Any]				,		UpperCamelCase__      :     Union[str, Any]      )				->					Dict:
								'''simple docstring'''







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





class  __magic_name__   (							A__						):






		def __init__(						self      :     str				,		UpperCamelCase__      :     List[Any]				,		UpperCamelCase__      :     Tuple				,		UpperCamelCase__      :     Union[str, Any]				,		UpperCamelCase__      :     Dict				,		UpperCamelCase__      :     Dict      )				->					int:
								'''simple docstring'''







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

								UpperCAmelCase         =     transforms






		def __len__(						self      :     Any      )				->					List[str]:
								'''simple docstring'''







								return len(self.data      )






		def __getitem__(						self      :     int				,		UpperCamelCase__      :     Dict      )				->					Tuple:
								'''simple docstring'''







								UpperCAmelCase         =     torch.LongTensor(self.tokenizer.encode(self.data[index]["text"]				,		add_special_tokens=UpperCamelCase__      )      )
								UpperCAmelCase       ,		UpperCAmelCase       ,		UpperCAmelCase         =     sentence[0], sentence[1:-1], sentence[-1]
								UpperCAmelCase         =     sentence[: self.max_seq_length]

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

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

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







		def SCREAMING_SNAKE_CASE_			(						self      :     str      )				->					Dict:
								'''simple docstring'''







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

def    lowerCamelCase_(lowerCamelCase_    ) ->       List[Any]:
						UpperCAmelCase         =     [len(row["sentence"]    ) for row in batch]
						UpperCAmelCase       ,		UpperCAmelCase         =     len(lowerCamelCase_    ), max(lowerCamelCase_    )

						UpperCAmelCase         =     torch.zeros(lowerCamelCase_    ,   lowerCamelCase_    ,   dtype=torch.long    )
						UpperCAmelCase         =     torch.zeros(lowerCamelCase_    ,   lowerCamelCase_    ,   dtype=torch.long    )

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

						UpperCAmelCase         =     torch.stack([row["image"] for row in batch]    )
						UpperCAmelCase         =     torch.stack([row["label"] for row in batch]    )
						UpperCAmelCase         =     torch.stack([row["image_start_token"] for row in batch]    )
						UpperCAmelCase         =     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_() ->       Dict:
						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_() ->       List[Any]:
						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]    ,   ),
						    ]    )








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:
		__lowerCamelCase     :							Optional[int]  							=				None


__lowerCamelCase     :							int  							=				logging.get_logger(__name__)

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

__lowerCamelCase     :							List[str]  							=				{
    "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
__lowerCamelCase     :							List[str]  							=				{
    "t5-small": 512,
    "t5-base": 512,
    "t5-large": 512,
    "t5-3b": 512,
    "t5-11b": 512,
}





class  __magic_name__   (							A__						):
		lowercase      :       List[str]          =VOCAB_FILES_NAMES
		lowercase      :       int          =PRETRAINED_VOCAB_FILES_MAP
		lowercase      :       Union[str, Any]          =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		lowercase      :       Union[str, Any]          =['''input_ids''', '''attention_mask''']
		lowercase      :       Any          =TaTokenizer

		lowercase      :       List[int]          =[]






		def __init__(						self      :     Optional[Any]				,		UpperCamelCase__      :     List[str]=None				,		UpperCamelCase__      :     List[str]=None				,		UpperCamelCase__      :     Any="</s>"				,		UpperCamelCase__      :     Dict="<unk>"				,		UpperCamelCase__      :     Tuple="<pad>"				,		UpperCamelCase__      :     Tuple=1_00				,		UpperCamelCase__      :     List[Any]=None				,		**UpperCamelCase__      :     int				,		)				->					Optional[Any]:
								'''simple docstring'''







								if extra_ids > 0 and additional_special_tokens is None:
														UpperCAmelCase         =     [F'<extra_id_{i}>' for i in range(UpperCamelCase__      )]
								elif extra_ids > 0 and additional_special_tokens is not None:
														# Check that we have the right number of extra special tokens
														UpperCAmelCase         =     len(set(filter(lambda UpperCamelCase__      : bool("extra_id_" in str(UpperCamelCase__      )      )				,		UpperCamelCase__      )      )      )
														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__(
								    UpperCamelCase__				,		tokenizer_file=UpperCamelCase__				,		eos_token=UpperCamelCase__				,		unk_token=UpperCamelCase__				,		pad_token=UpperCamelCase__				,		extra_ids=UpperCamelCase__				,		additional_special_tokens=UpperCamelCase__				,		**UpperCamelCase__				,		)

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






		@staticmethod
		def SCREAMING_SNAKE_CASE_			(						UpperCamelCase__      :     Optional[int]				,		UpperCamelCase__      :     Dict				,		UpperCamelCase__      :     Union[str, Any]      )				->					Optional[Any]:
								'''simple docstring'''







								if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
														UpperCAmelCase         =     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."				,		UpperCamelCase__				,		)

								return max_model_length






		def SCREAMING_SNAKE_CASE_			(						self      :     Any				,		UpperCamelCase__      :     str				,		UpperCamelCase__      :     Optional[str] = None      )				->					Tuple[str]:
								'''simple docstring'''







								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(UpperCamelCase__      ):
														logger.error(F'Vocabulary path ({save_directory}) should be a directory'      )
														return
								UpperCAmelCase         =     os.path.join(
								    UpperCamelCase__				,		(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]      )

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

								return (out_vocab_file,)






		def SCREAMING_SNAKE_CASE_			(						self      :     List[str]				,		UpperCamelCase__      :     List[int]				,		UpperCamelCase__      :     Optional[List[int]] = None      )				->					List[int]:
								'''simple docstring'''







								UpperCAmelCase         =     token_ids_a + [self.eos_token_id]
								if token_ids_a is None:
														return self.prefix_tokens + token_ids_a
								else:
														UpperCAmelCase         =     token_ids_a + [self.eos_token_id]
														return self.prefix_tokens + token_ids_a + token_ids_a






		def SCREAMING_SNAKE_CASE_			(						self      :     Optional[Any]				,		UpperCamelCase__      :     List[int]				,		UpperCamelCase__      :     Optional[List[int]] = None      )				->					List[int]:
								'''simple docstring'''







								UpperCAmelCase         =     [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 SCREAMING_SNAKE_CASE_			(						self      :     List[Any]      )				->					List[Any]:
								'''simple docstring'''







								return list(
								    set(filter(lambda UpperCamelCase__      : bool(re.search(R"<extra_id_\d+>"				,		UpperCamelCase__      )      ) is not None				,		self.additional_special_tokens      )      )      )







		def SCREAMING_SNAKE_CASE_			(						self      :     str      )				->					Dict:
								'''simple docstring'''







								return [self.convert_tokens_to_ids(UpperCamelCase__      ) for token in self.get_sentinel_tokens()]



