



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_								=     {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 				_snake_case			(					nn.Module):




				def __init__(						self					:						List[Any],					__lowercase					:						List[Any]						):
							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 							A__      (						self					:						Union[str, Any],					__lowercase					:						Dict						):
							# Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
							lowercase__					  =				self.pool(self.model(__lowercase						)						)
							lowercase__					  =				torch.flatten(__lowercase,					start_dim=2						)
							lowercase__					  =				out.transpose(1,					2						).contiguous()
							return out  # BxNx2048
class 				_snake_case			(					lowercase__):




				def __init__(						self					:						Any,					__lowercase					:						List[Any],					__lowercase					:						Optional[Any],					__lowercase					:						Tuple,					__lowercase					:						Optional[Any],					__lowercase					:						Optional[int]						):
							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					:						str						):
							return len(self.data						)




				def __getitem__(						self					:						List[str],					__lowercase					:						Tuple						):
							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 							A__      (						self					:						Any						):
							lowercase__					  =				Counter()
							for row in self.data:
										label_freqs.update(row["label"]						)
							return label_freqs



def __lowerCAmelCase (     SCREAMING_SNAKE_CASE_  ):
			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(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 json
from typing import Iterator, List, Union

from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.processors import TemplateProcessing
class 				_snake_case			(					lowercase__):




				def __init__(						self					:						Optional[Any],					__lowercase					:						str = "▁",					__lowercase					:						bool = True,					__lowercase					:						Union[str, AddedToken] = "<unk>",					__lowercase					:						Union[str, AddedToken] = "</s>",					__lowercase					:						Union[str, AddedToken] = "<pad>",					):
							lowercase__					  =				{
							    "pad": {"id": 0, "token": pad_token},
							    "eos": {"id": 1, "token": eos_token},
							    "unk": {"id": 2, "token": unk_token},
							}

							lowercase__					  =				[None] * len(self.special_tokens						)
							for token_dict in self.special_tokens.values():
										lowercase__					  =				token_dict["token"]

							lowercase__					  =				Tokenizer(Unigram()						)

							lowercase__					  =				normalizers.Sequence(
							    [
							        normalizers.Nmt(),
							        normalizers.NFKC(),
							        normalizers.Replace(Regex(" {2,}"						),					" "						),
							        normalizers.Lowercase(),
							    ]						)
							lowercase__					  =				pre_tokenizers.Sequence(
							    [
							        pre_tokenizers.Metaspace(replacement=__lowercase,					add_prefix_space=__lowercase						),
							        pre_tokenizers.Digits(individual_digits=__lowercase						),
							        pre_tokenizers.Punctuation(),
							    ]						)
							lowercase__					  =				decoders.Metaspace(replacement=__lowercase,					add_prefix_space=__lowercase						)

							lowercase__					  =				TemplateProcessing(
							    single=F'''$A {self.special_tokens["eos"]["token"]}''',					special_tokens=[(self.special_tokens["eos"]["token"], self.special_tokens["eos"]["id"])],					)

							lowercase__					  =				{
							    "model": "SentencePieceUnigram",
							    "replacement": replacement,
							    "add_prefix_space": add_prefix_space,
							}

							super().__init__(__lowercase,					__lowercase						)




				def 							A__      (						self					:						Union[str, Any],					__lowercase					:						Union[str, List[str]],					__lowercase					:						int = 8000,					__lowercase					:						bool = True,					):
							lowercase__					  =				trainers.UnigramTrainer(
							    vocab_size=__lowercase,					special_tokens=self.special_tokens_list,					show_progress=__lowercase,					)

							if isinstance(__lowercase,					__lowercase						):
										lowercase__					  =				[files]
							self._tokenizer.train(__lowercase,					trainer=__lowercase						)

							self.add_unk_id()




				def 							A__      (						self					:						List[Any],					__lowercase					:						Union[Iterator[str], Iterator[Iterator[str]]],					__lowercase					:						int = 8000,					__lowercase					:						bool = True,					):
							lowercase__					  =				trainers.UnigramTrainer(
							    vocab_size=__lowercase,					special_tokens=self.special_tokens_list,					show_progress=__lowercase,					)

							self._tokenizer.train_from_iterator(__lowercase,					trainer=__lowercase						)

							self.add_unk_id()




				def 							A__      (						self					:						str						):
							lowercase__					  =				json.loads(self._tokenizer.to_str()						)

							lowercase__					  =				self.special_tokens["unk"]["id"]

							lowercase__					  =				Tokenizer.from_str(json.dumps(__lowercase						)						)




