import os

import argparse

import torch

import torch.nn as nn

import pandas as pd

import numpy as np

import utils

from models.bert_labeler import bert_labeler

from bert_tokenizer import tokenize

from transformers import BertTokenizer

from collections import OrderedDict

from datasets.unlabeled_dataset import UnlabeledDataset

from constants import *

from tqdm import tqdm

def collate_fn_no_labels(sample_list):

    """Custom collate function to pad reports in each batch to the max len,

       where the reports have no associated labels

    @param sample_list (List): A list of samples. Each sample is a dictionary with

                               keys 'imp', 'len' as returned by the __getitem__

                               function of ImpressionsDataset

    @returns batch (dictionary): A dictionary with keys 'imp' and 'len' but now

                                 'imp' is a tensor with padding and batch size as the

                                 first dimension. 'len' is a list of the length of 

                                 each sequence in batch

    """

    tensor_list = [s['imp'] for s in sample_list]

    batched_imp = torch.nn.utils.rnn.pad_sequence(tensor_list,

                                                  batch_first=True,

                                                  padding_value=PAD_IDX)

    len_list = [s['len'] for s in sample_list]

    batch = {'imp': batched_imp, 'len': len_list}

    return batch

def load_unlabeled_data(csv_path, batch_size=BATCH_SIZE, num_workers=NUM_WORKERS,

                        shuffle=False):

    """ Create UnlabeledDataset object for the input reports

    @param csv_path (string): path to csv file containing reports

    @param batch_size (int): the batch size. As per the BERT repository, the max batch size

                             that can fit on a TITAN XP is 6 if the max sequence length

                             is 512, which is our case. We have 3 TITAN XP's

    @param num_workers (int): how many worker processes to use to load data

    @param shuffle (bool): whether to shuffle the data or not  

    @returns loader (dataloader): dataloader object for the reports

    """

    collate_fn = collate_fn_no_labels

    dset = UnlabeledDataset(csv_path)

    loader = torch.utils.data.DataLoader(dset, batch_size=batch_size, shuffle=shuffle,

                                         num_workers=NUM_WORKERS, collate_fn=collate_fn)

    return loader

def label(checkpoint_path, csv_path):

    """Labels a dataset of reports

    @param checkpoint_path (string): location of saved model checkpoint 

    @param csv_path (string): location of csv with reports

    @returns y_pred (List[List[int]]): Labels for each of the 14 conditions, per report  

    """

    ld = load_unlabeled_data(csv_path)

    model = bert_labeler()

    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

    if torch.cuda.device_count() > 0: #works even if only 1 GPU available

        print("Using", torch.cuda.device_count(), "GPUs!")

        model = nn.DataParallel(model, device_ids=list(range(torch.cuda.device_count()))) #to utilize multiple GPU's

        model = model.to(device)

        checkpoint = torch.load(checkpoint_path)

        model.load_state_dict(checkpoint['model_state_dict'])

    else:

        checkpoint = torch.load(checkpoint_path, map_location=torch.device('cpu'))

        new_state_dict = OrderedDict()

        for k, v in checkpoint['model_state_dict'].items():

            name = k[7:] # remove `module.`

            new_state_dict[name] = v

        model.load_state_dict(new_state_dict)

    was_training = model.training

    model.eval()

    y_pred = [[] for _ in range(len(CONDITIONS))]

    print("\nBegin report impression labeling. The progress bar counts the # of batches completed:")

    print("The batch size is %d" % BATCH_SIZE)

    with torch.no_grad():

        for i, data in enumerate(tqdm(ld)):

            batch = data['imp'] #(batch_size, max_len)

            batch = batch.to(device)

            src_len = data['len']

            batch_size = batch.shape[0]

            attn_mask = utils.generate_attention_masks(batch, src_len, device)

            out = model(batch, attn_mask)

            for j in range(len(out)):

                curr_y_pred = out[j].argmax(dim=1) #shape is (batch_size)

                y_pred[j].append(curr_y_pred)

        for j in range(len(y_pred)):

            y_pred[j] = torch.cat(y_pred[j], dim=0)

    if was_training:

        model.train()

    y_pred = [t.tolist() for t in y_pred]

    return y_pred

def save_preds(y_pred, csv_path, out_path):

    """Save predictions as out_path/labeled_reports.csv 

    @param y_pred (List[List[int]]): list of predictions for each report

    @param csv_path (string): path to csv containing reports

    @param out_path (string): path to output directory

    """

    y_pred = np.array(y_pred)

    y_pred = y_pred.T

    df = pd.DataFrame(y_pred, columns=CONDITIONS)

    findings = pd.read_csv(csv_path, header=None)[0]

    # dicom was used for labeling training set, but is not available for labeling predictions

    # dicom_ids = pd.read_csv(csv_path)['dicom_id']

    # df['dicom_id'] = dicom_ids.tolist()

    df['findings'] = findings.tolist()

    new_cols = ['findings'] +CONDITIONS #['dicom_id']

    df = df[new_cols]

    df.replace(0, np.nan, inplace=True) #blank class is NaN

    df.replace(3, -1, inplace=True)     #uncertain class is -1

    df.replace(2, 0, inplace=True)      #negative class is 0 

    df.to_csv(out_path, index=False)

if __name__ == '__main__':

    parser = argparse.ArgumentParser(description='Label a csv file containing radiology reports')

    parser.add_argument('-d', '--data', type=str, nargs='?', required=True,

                        help='path to csv containing reports. The reports should be \

                              under the \"Report Impression\" column')

    parser.add_argument('-o', '--output_dir', type=str, nargs='?', required=True,

                        help='path to intended output folder')

    parser.add_argument('-c', '--checkpoint', type=str, nargs='?', required=True,

                        help='path to the pytorch checkpoint')

    args = parser.parse_args()

    csv_path = args.data

    out_path = args.output_dir

    checkpoint_path = args.checkpoint

    y_pred = label(checkpoint_path, csv_path)

    save_preds(y_pred, csv_path, out_path)