from torch.utils import data
import pandas as pd
import random


class AbstractDataset(data.Dataset):
    def __init__(self, file_path, sample_size, tokenizer, type_path, num_samples, input_length, output_length, print_text=False):   
              
        self.dataset =  pd.read_csv(file_path, skiprows=lambda i: i>0 and random.random() > sample_size)
        self.dataset = dataset.dropna()
        
        self.input_length = input_length
        self.tokenizer = tokenizer
        self.output_length = output_length
        self.print_text = print_text
  
    def __len__(self):
        return self.dataset.shape[0]
    
    def clean_text(self, text):
        text = text.replace('\n','')
        text = text.replace('``', '')
        text = text.replace('"', '')
        return text
    
    
    def convert_to_features(self, example_batch):
        # Tokenize contexts and questions (as pairs of inputs)
        
        if self.print_text:
            print("Input Text: ", self.clean_text(example_batch['abstract']))
        
        input_ = self.clean_text(example_batch['abstract'])
        target_ = self.clean_text(example_batch['title'])
        
        source = self.tokenizer.batch_encode_plus([input_], max_length=self.input_length, 
                                                     padding='max_length', truncation=True, return_tensors="pt")
        
        targets = self.tokenizer.batch_encode_plus([target_], max_length=self.output_length, 
                                                     padding='max_length', truncation=True, return_tensors="pt")
    
       
        return source, targets
  
    def __getitem__(self, index):
        source, targets = self.convert_to_features(self.dataset.iloc[index])
        
        source_ids = source["input_ids"].squeeze()
        target_ids = targets["input_ids"].squeeze()

        src_mask    = source["attention_mask"].squeeze()
        target_mask = targets["attention_mask"].squeeze()

        return {"source_ids": source_ids, "source_mask": src_mask, "target_ids": target_ids, "target_mask": target_mask}
31e954ed8d4bd15ab047d05f7ca907562ca25622
<mask token>


class AbstractDataset(data.Dataset):
    <mask token>
    <mask token>
    <mask token>

    def convert_to_features(self, example_batch):
        if self.print_text:
            print('Input Text: ', self.clean_text(example_batch['abstract']))
        input_ = self.clean_text(example_batch['abstract'])
        target_ = self.clean_text(example_batch['title'])
        source = self.tokenizer.batch_encode_plus([input_], max_length=self
            .input_length, padding='max_length', truncation=True,
            return_tensors='pt')
        targets = self.tokenizer.batch_encode_plus([target_], max_length=
            self.output_length, padding='max_length', truncation=True,
            return_tensors='pt')
        return source, targets
    <mask token>

<mask token>


class AbstractDataset(data.Dataset):

    def __init__(self, file_path, sample_size, tokenizer, type_path,
        num_samples, input_length, output_length, print_text=False):
        self.dataset = pd.read_csv(file_path, skiprows=lambda i: i > 0 and 
            random.random() > sample_size)
        self.dataset = dataset.dropna()
        self.input_length = input_length
        self.tokenizer = tokenizer
        self.output_length = output_length
        self.print_text = print_text
    <mask token>

    def clean_text(self, text):
        text = text.replace('\n', '')
        text = text.replace('``', '')
        text = text.replace('"', '')
        return text

    def convert_to_features(self, example_batch):
        if self.print_text:
            print('Input Text: ', self.clean_text(example_batch['abstract']))
        input_ = self.clean_text(example_batch['abstract'])
        target_ = self.clean_text(example_batch['title'])
        source = self.tokenizer.batch_encode_plus([input_], max_length=self
            .input_length, padding='max_length', truncation=True,
            return_tensors='pt')
        targets = self.tokenizer.batch_encode_plus([target_], max_length=
            self.output_length, padding='max_length', truncation=True,
            return_tensors='pt')
        return source, targets

    def __getitem__(self, index):
        source, targets = self.convert_to_features(self.dataset.iloc[index])
        source_ids = source['input_ids'].squeeze()
        target_ids = targets['input_ids'].squeeze()
        src_mask = source['attention_mask'].squeeze()
        target_mask = targets['attention_mask'].squeeze()
        return {'source_ids': source_ids, 'source_mask': src_mask,
            'target_ids': target_ids, 'target_mask': target_mask}

<mask token>


class AbstractDataset(data.Dataset):

    def __init__(self, file_path, sample_size, tokenizer, type_path,
        num_samples, input_length, output_length, print_text=False):
        self.dataset = pd.read_csv(file_path, skiprows=lambda i: i > 0 and 
            random.random() > sample_size)
        self.dataset = dataset.dropna()
        self.input_length = input_length
        self.tokenizer = tokenizer
        self.output_length = output_length
        self.print_text = print_text

    def __len__(self):
        return self.dataset.shape[0]

    def clean_text(self, text):
        text = text.replace('\n', '')
        text = text.replace('``', '')
        text = text.replace('"', '')
        return text

    def convert_to_features(self, example_batch):
        if self.print_text:
            print('Input Text: ', self.clean_text(example_batch['abstract']))
        input_ = self.clean_text(example_batch['abstract'])
        target_ = self.clean_text(example_batch['title'])
        source = self.tokenizer.batch_encode_plus([input_], max_length=self
            .input_length, padding='max_length', truncation=True,
            return_tensors='pt')
        targets = self.tokenizer.batch_encode_plus([target_], max_length=
            self.output_length, padding='max_length', truncation=True,
            return_tensors='pt')
        return source, targets

    def __getitem__(self, index):
        source, targets = self.convert_to_features(self.dataset.iloc[index])
        source_ids = source['input_ids'].squeeze()
        target_ids = targets['input_ids'].squeeze()
        src_mask = source['attention_mask'].squeeze()
        target_mask = targets['attention_mask'].squeeze()
        return {'source_ids': source_ids, 'source_mask': src_mask,
            'target_ids': target_ids, 'target_mask': target_mask}

from torch.utils import data
import pandas as pd
import random


class AbstractDataset(data.Dataset):

    def __init__(self, file_path, sample_size, tokenizer, type_path,
        num_samples, input_length, output_length, print_text=False):
        self.dataset = pd.read_csv(file_path, skiprows=lambda i: i > 0 and 
            random.random() > sample_size)
        self.dataset = dataset.dropna()
        self.input_length = input_length
        self.tokenizer = tokenizer
        self.output_length = output_length
        self.print_text = print_text

    def __len__(self):
        return self.dataset.shape[0]

    def clean_text(self, text):
        text = text.replace('\n', '')
        text = text.replace('``', '')
        text = text.replace('"', '')
        return text

    def convert_to_features(self, example_batch):
        if self.print_text:
            print('Input Text: ', self.clean_text(example_batch['abstract']))
        input_ = self.clean_text(example_batch['abstract'])
        target_ = self.clean_text(example_batch['title'])
        source = self.tokenizer.batch_encode_plus([input_], max_length=self
            .input_length, padding='max_length', truncation=True,
            return_tensors='pt')
        targets = self.tokenizer.batch_encode_plus([target_], max_length=
            self.output_length, padding='max_length', truncation=True,
            return_tensors='pt')
        return source, targets

    def __getitem__(self, index):
        source, targets = self.convert_to_features(self.dataset.iloc[index])
        source_ids = source['input_ids'].squeeze()
        target_ids = targets['input_ids'].squeeze()
        src_mask = source['attention_mask'].squeeze()
        target_mask = targets['attention_mask'].squeeze()
        return {'source_ids': source_ids, 'source_mask': src_mask,
            'target_ids': target_ids, 'target_mask': target_mask}

from torch.utils import data
import pandas as pd
import random


class AbstractDataset(data.Dataset):
    def __init__(self, file_path, sample_size, tokenizer, type_path, num_samples, input_length, output_length, print_text=False):   
              
        self.dataset =  pd.read_csv(file_path, skiprows=lambda i: i>0 and random.random() > sample_size)
        self.dataset = dataset.dropna()
        
        self.input_length = input_length
        self.tokenizer = tokenizer
        self.output_length = output_length
        self.print_text = print_text
  
    def __len__(self):
        return self.dataset.shape[0]
    
    def clean_text(self, text):
        text = text.replace('\n','')
        text = text.replace('``', '')
        text = text.replace('"', '')
        return text
    
    
    def convert_to_features(self, example_batch):
        # Tokenize contexts and questions (as pairs of inputs)
        
        if self.print_text:
            print("Input Text: ", self.clean_text(example_batch['abstract']))
        
        input_ = self.clean_text(example_batch['abstract'])
        target_ = self.clean_text(example_batch['title'])
        
        source = self.tokenizer.batch_encode_plus([input_], max_length=self.input_length, 
                                                     padding='max_length', truncation=True, return_tensors="pt")
        
        targets = self.tokenizer.batch_encode_plus([target_], max_length=self.output_length, 
                                                     padding='max_length', truncation=True, return_tensors="pt")
    
       
        return source, targets
  
    def __getitem__(self, index):
        source, targets = self.convert_to_features(self.dataset.iloc[index])
        
        source_ids = source["input_ids"].squeeze()
        target_ids = targets["input_ids"].squeeze()

        src_mask    = source["attention_mask"].squeeze()
        target_mask = targets["attention_mask"].squeeze()

        return {"source_ids": source_ids, "source_mask": src_mask, "target_ids": target_ids, "target_mask": target_mask}