import torch
import re
from transformers import BertTokenizer
import pandas as pd


if torch.cuda.is_available():
    device = torch.device("cuda")
    print(f'There are {torch.cuda.device_count()} GPU(s) available.')
    print('Device name:', torch.cuda.get_device_name(0))

else:
    print('No GPU available, using the CPU instead.')
    device = torch.device("cpu")


def text_preprocessing(text):
    """
    - Remove entity mentions (eg. '@united')
    - Correct errors (eg. '&' to '&')
    @param    text (str): a string to be processed.
    @return   text (Str): the processed string.
    """
    # Remove '@name'
    text = re.sub(r'(@.*?)[\s]', ' ', text)

    # Replace '&' with '&'
    text = re.sub(r'&', '&', text)

    # Remove trailing whitespace
    text = re.sub(r'\s+', ' ', text).strip()

    return text


model_name = 'bert-base-uncased'
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased', do_lower_case=True)
# Specify `MAX_LEN`
MAX_LEN = 64

def preprocessing_for_bert(data):
    """Perform required preprocessing steps for pretrained BERT.
    @param    data (np.array): Array of texts to be processed.
    @return   input_ids (torch.Tensor): Tensor of token ids to be fed to a model.
    @return   attention_masks (torch.Tensor): Tensor of indices specifying which
                  tokens should be attended to by the model.
    """
    # Create empty lists to store outputs
    input_ids = []
    attention_masks = []

    # For every sentence...
    for sent in data:
        # `encode_plus` will:
        #    (1) Tokenize the sentence
        #    (2) Add the `[CLS]` and `[SEP]` token to the start and end
        #    (3) Truncate/Pad sentence to max length
        #    (4) Map tokens to their IDs
        #    (5) Create attention mask
        #    (6) Return a dictionary of outputs
        encoded_sent = tokenizer.encode_plus(
            text=text_preprocessing(sent),  # Preprocess sentence
            add_special_tokens=True,  # Add `[CLS]` and `[SEP]`
            max_length=MAX_LEN,  # Max length to truncate/pad
            pad_to_max_length=True,  # Pad sentence to max length
            # return_tensors='pt',           # Return PyTorch tensor
            return_attention_mask=True  # Return attention mask
        )

        # Add the outputs to the lists
        input_ids.append(encoded_sent.get('input_ids'))
        attention_masks.append(encoded_sent.get('attention_mask'))

    # Convert lists to tensors
    input_ids = torch.tensor(input_ids)
    attention_masks = torch.tensor(attention_masks)

    return input_ids, attention_masks