import os
import pandas as pd 
import numpy as np
import pickle 
import torch
import tqdm

from transformers import BertTokenizer, BertForSequenceClassification, CamembertTokenizer, CamembertForSequenceClassification
from torch.utils.data import TensorDataset, DataLoader, SequentialSampler


def generate_dataloader(tokenizer, sentences, batch_size = 8, max_len = 512):

    # Tokenize all of the sentences and map the tokens to thier word IDs.
    input_ids_test = []
    # For every sentence...
    for sent in sentences:
        # `encode` will:
        #   (1) Tokenize the sentence.
        #   (2) Prepend the `[CLS]` token to the start.
        #   (3) Append the `[SEP]` token to the end.
        #   (4) Map tokens to their IDs.
        encoded_sent = tokenizer.encode(
                            sent,                      # Sentence to encode.
                            add_special_tokens = True, # Add '[CLS]' and '[SEP]'
                            # This function also supports truncation and conversion
                            # to pytorch tensors, but I need to do padding, so I
                            # can't use these features.
                            #max_length = max_len,          # Truncate all sentences.
                            #return_tensors = 'pt',     # Return pytorch tensors.
                    )
        input_ids_test.append(encoded_sent)

    # Pad our input tokens
    padded_test = []
    for i in input_ids_test:
        if len(i) > max_len:
            padded_test.extend([i[:max_len]])
        else:
            padded_test.extend([i + [0] * (max_len - len(i))])
    input_ids_test = np.array(padded_test)

    # Create attention masks
    attention_masks = []

    # Create a mask of 1s for each token followed by 0s for padding
    for seq in input_ids_test:
        seq_mask = [float(i>0) for i in seq]
        attention_masks.append(seq_mask)

    # Convert to tensors.
    inputs = torch.tensor(input_ids_test)
    masks = torch.tensor(attention_masks)
    #set batch size

    # Create the DataLoader.
    data = TensorDataset(inputs, masks)
    prediction_sampler = SequentialSampler(data)

    return DataLoader(data, sampler=prediction_sampler, batch_size=batch_size)


def predict(model, dataloader, device):

    # Put model in evaluation mode
    model.eval()

    # Tracking variables
    predictions_test , true_labels = [], []
    pred_labels_ = []
    # Predict
    for batch in dataloader:
    # Add batch to GPU
        batch = tuple(t.to(device) for t in batch)

        # Unpack the inputs from the dataloader
        b_input_ids, b_input_mask = batch

        # Telling the model not to compute or store gradients, saving memory and
        # speeding up prediction
        with torch.no_grad():
            # Forward pass, calculate logit predictions
            outputs = model(b_input_ids, token_type_ids=None,
                            attention_mask=b_input_mask)
        logits = outputs[0]
        #print(logits)

        # Move logits and labels to CPU ???
        logits = logits.detach().cpu().numpy()
        #print(logits)

        # Store predictions and true labels
        predictions_test.append(logits)

        pred_labels = []
        
        for i in range(len(predictions_test)):
            # The predictions for this batch are a 2-column ndarray (one column for "0"
            # and one column for "1"). Pick the label with the highest value and turn this
            # in to a list of 0s and 1s.
            pred_labels_i = np.argmax(predictions_test[i], axis=1).flatten()
            pred_labels.append(pred_labels_i)

    pred_labels_ += [item for sublist in pred_labels for item in sublist]
    return pred_labels_


def text_folder_to_dataframe(path):

  data = []
  # id,tome,filename,nb_words,content,domain

  for tome in sorted(os.listdir(path)):
    try:
        for article in sorted(os.listdir(path + "/" + tome)):
            filename = article[:-4]
            id = tome + filename

            if article[-4:] == ".txt":
                with open(path + "/" + tome + "/" + article) as f:
                    content = f.read()

                    data.append([id, tome, filename, content, len(content.split(' '))])
    except NotADirectoryError:
        pass
  return pd.DataFrame(data, columns=['id', 'tome', 'filename', 'content', 'nb_words'])


if __name__ == '__main__':

  # If there's a GPU available...
  if torch.cuda.is_available():    
      # Tell PyTorch to use the GPU.    
      device = torch.device("cuda")
      gpu_name = "cuda"
      print('There are %d GPU(s) available.' % torch.cuda.device_count())
      print('We will use the GPU:', torch.cuda.get_device_name(0))
  # for MacOS
  elif torch.backends.mps.is_available() and torch.backends.mps.is_built():
      device = torch.device("mps")
      gpu_name = "mps"
      print('We will use the GPU')
  else:
      device = torch.device("cpu")
      gpu_name = "cpu"
      print('No GPU available, using the CPU instead.')


  #############
  ## Load data
  print("* Load data")

  input_path = "/Users/lmoncla/Documents/Data/Corpus/LGE/Text"
  path = "../"

  df_LGE = text_folder_to_dataframe(input_path)
  #df_LGE = pd.read_csv(path + "data/LGE_withContent.tsv", sep="\t")
  data_LGE = df_LGE["content"].values

  #df_LGE.head()
  #df_LGE.shape

  #############
  ## Load model
  print("* Load model")

  model_name = "bert-base-multilingual-cased"
  #model_name = "camembert-base"
  model_path = path + "models/model_" + model_name + "_s10000.pt"

  if model_name == 'bert-base-multilingual-cased' :
    print('Loading Bert Tokenizer...')
    tokenizer = BertTokenizer.from_pretrained(model_name)
  elif model_name == 'camembert-base':
      print('Loading Camembert Tokenizer...')
      tokenizer = CamembertTokenizer.from_pretrained(model_name)

  data_loader = generate_dataloader(tokenizer, data_LGE)

  model = BertForSequenceClassification.from_pretrained(model_path).to(gpu_name)

  #############
  ## Predict
  print("* Predict")

  pred = predict(model, data_loader, device)

  encoder_filename = "models/label_encoder.pkl"
  with open(path+encoder_filename, 'rb') as file:
        encoder = pickle.load(file)
  
  p2 = list(encoder.inverse_transform(pred))

  df_LGE['domain'] = p2


  #############
  ## Save results
  filepath = path + "results_LGE/metadata-withContent.csv"
  print("* Save results: ", filepath)
  df_LGE.to_csv(filepath, sep="\,")

  df_LGE.drop(columns=['content'], inplace=True)
  filepath = path + "results_LGE/metadata.csv"
  print("* Save results: ", filepath)
  df_LGE.to_csv(filepath, sep="\,")