
import numpy as np

_A     =  [
    ["a", "b", "c", "d", "e"],
    ["f", "g", "h", "i", "k"],
    ["l", "m", "n", "o", "p"],
    ["q", "r", "s", "t", "u"],
    ["v", "w", "x", "y", "z"],
]





class 			_lowerCAmelCase      :




				def __init__(      self  )       ->   None:
							lowerCAmelCase_      			=			np.array(_UpperCamelCase  )




				def   __a		(      self       ,       _UpperCamelCase  )       ->   np.ndarray:
							lowerCAmelCase_			,						lowerCAmelCase_      			=			np.where(letter == self.SQUARE  )
							lowerCAmelCase_      			=			np.concatenate([indexa + 1, indexa + 1]  )
							return indexes




				def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   str:
							lowerCAmelCase_      			=			self.SQUARE[indexa - 1, indexa - 1]
							return letter




				def   __a		(      self       ,       _UpperCamelCase  )       ->   str:
							lowerCAmelCase_      			=			message.lower()
							lowerCAmelCase_      			=			message.replace(" "       ,       ""  )
							lowerCAmelCase_      			=			message.replace("j"       ,       "i"  )

							lowerCAmelCase_      			=			np.empty((2, len(_UpperCamelCase  ))  )
							for letter_index in range(len(_UpperCamelCase  )  ):
										lowerCAmelCase_      			=			self.letter_to_numbers(message[letter_index]  )

										lowerCAmelCase_      			=			numbers[0]
										lowerCAmelCase_      			=			numbers[1]

							lowerCAmelCase_      			=			first_step.reshape(2 * len(_UpperCamelCase  )  )
							lowerCAmelCase_      			=			""
							for numbers_index in range(len(_UpperCamelCase  )  ):
										lowerCAmelCase_      			=			int(second_step[numbers_index * 2]  )
										lowerCAmelCase_      			=			int(second_step[(numbers_index * 2) + 1]  )
										lowerCAmelCase_      			=			self.numbers_to_letter(_UpperCamelCase       ,       _UpperCamelCase  )
										lowerCAmelCase_      			=			encoded_message + letter

							return encoded_message




				def   __a		(      self       ,       _UpperCamelCase  )       ->   str:
							lowerCAmelCase_      			=			message.lower()
							message.replace(" "       ,       ""  )
							lowerCAmelCase_      			=			np.empty(2 * len(_UpperCamelCase  )  )
							for letter_index in range(len(_UpperCamelCase  )  ):
										lowerCAmelCase_      			=			self.letter_to_numbers(message[letter_index]  )
										lowerCAmelCase_      			=			numbers[0]
										lowerCAmelCase_      			=			numbers[1]

							lowerCAmelCase_      			=			first_step.reshape((2, len(_UpperCamelCase  ))  )
							lowerCAmelCase_      			=			""
							for numbers_index in range(len(_UpperCamelCase  )  ):
										lowerCAmelCase_      			=			int(second_step[0, numbers_index]  )
										lowerCAmelCase_      			=			int(second_step[1, numbers_index]  )
										lowerCAmelCase_      			=			self.numbers_to_letter(_UpperCamelCase       ,       _UpperCamelCase  )
										lowerCAmelCase_      			=			decoded_message + letter

							return decoded_message





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     =  {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 			_lowerCAmelCase      (			nn.Module ):




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




				def   __a		(      self       ,       _UpperCamelCase  )       ->   Dict:
							# Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
							lowerCAmelCase_      			=			self.pool(self.model(_UpperCamelCase  )  )
							lowerCAmelCase_      			=			torch.flatten(_UpperCamelCase       ,       start_dim=2  )
							lowerCAmelCase_      			=			out.transpose(1       ,       2  ).contiguous()
							return out  # BxNx2048





class 			_lowerCAmelCase      (			__a ):




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

							lowerCAmelCase_      			=			transforms




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




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

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

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

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




				def   __a		(      self  )       ->   str:
							lowerCAmelCase_      			=			Counter()
							for row in self.data:
										label_freqs.update(row["label"]  )
							return label_freqs






def      lowerCamelCase__       (       __lowerCAmelCase						:						List[str]       ):




			"""simple docstring"""


			lowerCAmelCase_      			=			[len(row["sentence"]       ) for row in batch]
			lowerCAmelCase_			,						lowerCAmelCase_      			=			len(__lowerCAmelCase       ), max(__lowerCAmelCase       )

			lowerCAmelCase_      			=			torch.zeros(__lowerCAmelCase					,							__lowerCAmelCase					,							dtype=torch.long       )
			lowerCAmelCase_      			=			torch.zeros(__lowerCAmelCase					,							__lowerCAmelCase					,							dtype=torch.long       )

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

			lowerCAmelCase_      			=			torch.stack([row["image"] for row in batch]       )
			lowerCAmelCase_      			=			torch.stack([row["label"] for row in batch]       )
			lowerCAmelCase_      			=			torch.stack([row["image_start_token"] for row in batch]       )
			lowerCAmelCase_      			=			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__       (       ):




			"""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__       (       ):




			"""simple docstring"""


			return transforms.Compose(
			    [
			        transforms.Resize(256       ),
			        transforms.CenterCrop(224       ),
			        transforms.ToTensor(),
			        transforms.Normalize(
			            mean=[0.46_777_044, 0.44_531_429, 0.40_661_017]					,							std=[0.12_221_994, 0.12_145_835, 0.14_380_469]					,							),
			    ]       )



