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import torch
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import torch.nn as nn
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from torch.utils.data import DataLoader, Dataset
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import tensorflow as tf
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import numpy as np
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def pad_sequences(sequences, max_seq_len=0):
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"""Pad sequences to max length in sequence."""
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max_seq_len = max(max_seq_len, max(len(sequence) for sequence in sequences))
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padded_sequences = np.zeros((len(sequences), max_seq_len))
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for i, sequence in enumerate(sequences):
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padded_sequences[i][:len(sequence)] = sequence
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return padded_sequences
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class SkimlitDataset(Dataset):
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def __init__(self, text_seq, line_num, total_line):
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self.text_seq = text_seq
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self.line_num_one_hot = line_num
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self.total_line_one_hot = total_line
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def __len__(self):
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return len(self.text_seq)
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def __str__(self):
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return f"<Dataset(N={len(self)})>"
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def __getitem__(self, index):
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X = self.text_seq[index]
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line_num = self.line_num_one_hot[index]
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total_line = self.total_line_one_hot[index]
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return [X, len(X), line_num, total_line]
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def collate_fn(self, batch):
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"""Processing on a batch"""
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batch = np.array(batch)
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text_seq = batch[:,0]
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seq_lens = batch[:, 1]
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line_nums = batch[:, 2]
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total_lines = batch[:, 3]
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pad_text_seq = pad_sequences(sequences=text_seq)
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line_nums = tf.one_hot(line_nums, depth=20)
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total_lines = tf.one_hot(total_lines, depth=24)
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pad_text_seq = torch.LongTensor(pad_text_seq.astype(np.int32))
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seq_lens = torch.LongTensor(seq_lens.astype(np.int32))
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line_nums = torch.tensor(line_nums.numpy())
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total_lines = torch.tensor(total_lines.numpy())
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return pad_text_seq, seq_lens, line_nums, total_lines
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def create_dataloader(self, batch_size, shuffle=False, drop_last=False):
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dataloader = DataLoader(dataset=self, batch_size=batch_size, collate_fn=self.collate_fn, shuffle=shuffle, drop_last=drop_last, pin_memory=True)
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return dataloader
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