# -*- coding: utf-8 -*-
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import csv
import os
import logging
import argparse
import random
from tqdm import tqdm, trange

import numpy as np
import torch
from torch.utils.data import TensorDataset, DataLoader, RandomSampler, SequentialSampler

from pytorch_pretrained_bert.tokenization import BertTokenizer
from pytorch_pretrained_bert.modeling import SentenceBert

logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s -   %(message)s', 
                    datefmt = '%m/%d/%Y %H:%M:%S',
                    level = logging.INFO)
logger = logging.getLogger(__name__)


class InputExample(object):
    """A single training/test example for simple sequence classification."""

    def __init__(self, guid, text_a, text_b=None, label=None):
        """Constructs a InputExample.

        Args:
            guid: Unique id for the example.
            text_a: string. The untokenized text of the first sequence. For single
            sequence tasks, only this sequence must be specified.
            text_b: (Optional) string. The untokenized text of the second sequence.
            Only must be specified for sequence pair tasks.
            label: (Optional) string. The label of the example. This should be
            specified for train and dev examples, but not for test examples.
        """
        self.guid = guid
        self.text_a = text_a
        self.text_b = text_b
        self.label = label

class InputFeatures(object):
    """A single set of features of data."""

    def __init__(self, input_ids, input_mask, segment_ids, label_id):
        self.input_ids = input_ids
        self.input_mask = input_mask
        self.segment_ids = segment_ids
        self.label_id = label_id

class DataProcessor(object):
    """Base class for data converters for sequence classification data sets."""

    def get_train_examples(self, data_dir):
        """Gets a collection of `InputExample`s for the train set."""
        raise NotImplementedError()

    def get_dev_examples(self, data_dir):
        """Gets a collection of `InputExample`s for the dev set."""
        raise NotImplementedError()

    def get_labels(self):
        """Gets the list of labels for this data set."""
        raise NotImplementedError()

    @classmethod
    def _read_tsv(cls, input_file, quotechar=None):
        """Reads a tab separated value file."""
        with open(input_file, "r") as f:
            reader = csv.reader(f, delimiter="\t", quotechar=quotechar)
            lines = []
            for line in reader:
                lines.append(line)
            return lines

class MrpcProcessor(DataProcessor):
    """Processor for the MRPC data set (GLUE version)."""

    def get_train_examples(self, data_dir):
        """See base class."""
        logger.info("LOOKING AT {}".format(os.path.join(data_dir, "train.tsv")))
        return self._create_examples(
            self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")

    def get_dev_examples(self, data_dir):
        """See base class."""
        return self._create_examples(
            self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev")

    def get_labels(self):
        """See base class."""
        return ["0", "1"]

    def _create_examples(self, lines, set_type):
        """Creates examples for the training and dev sets."""
        logger.info("Samples: {}".format(len(lines)))
        examples = []
        for (i, line) in enumerate(lines):
            if i == 0:
                continue
            guid = "%s-%s" % (set_type, i)
            text_a = line[3]
            text_b = line[4]
            label = line[0]
            examples.append(
                InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
        return examples

class SentProcessor(object):
    def __init__(self):
        pass

    def get_train_examples(self, data_dir):
        """See base class."""
        logger.info("LOOKING AT {}".format(os.path.join(data_dir, "train.tsv")))
        return self._create_examples(
            self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")

    def get_dev_examples(self, sents):
        # sents: list of sentence
        """See base class."""
        return self._create_examples(sents, "dev")

    def get_labels(self):
        """See base class."""
        return ["0", "1"]

    def _create_examples(self, lines, set_type):
        """Creates examples for the training and dev sets."""
        examples = []
        for (i, line) in enumerate(lines):
            guid = "%s-%s" % (set_type, i)
            text_a = line
            text_b = None
            label = '1'
            examples.append(
                InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
        return examples

class PairProcessor(object):
    def __init__(self):
        pass

    def get_train_examples(self, data_dir):
        """See base class."""
        logger.info("LOOKING AT {}".format(os.path.join(data_dir, "train.tsv")))
        return self._create_examples(
            self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")

    def get_dev_examples(self, sents1, sents2):
        # sents: list of sentence
        """See base class."""
        return self._create_examples(sents1, sents2, "dev")

    def get_labels(self):
        """See base class."""
        return ["0", "1"]

    def _create_examples(self, lines1, lines2, set_type):
        """Creates examples for the training and dev sets."""
        examples = []
        for (i, line) in enumerate(lines1):
            guid = "%s-%s" % (set_type, i)
            text_a = line
            text_b = lines2[i]
            label = '1'
            examples.append(
                InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
        return examples



def _truncate_seq_pair(tokens_a, tokens_b, max_length):
    """Truncates a sequence pair in place to the maximum length."""

    # This is a simple heuristic which will always truncate the longer sequence
    # one token at a time. This makes more sense than truncating an equal percent
    # of tokens from each, since if one sequence is very short then each token
    # that's truncated likely contains more information than a longer sequence.
    while True:
        total_length = len(tokens_a) + len(tokens_b)
        if total_length <= max_length:
            break
        if len(tokens_a) > len(tokens_b):
            tokens_a.pop()
        else:
            tokens_b.pop()

def convert_examples_to_features(examples, label_list, max_seq_length, tokenizer):
    """Loads a data file into a list of `InputBatch`s."""

    label_map = {}
    for (i, label) in enumerate(label_list):
        label_map[label] = i


    features = []
    for (ex_index, example) in enumerate(examples):
        tokens_a = tokenizer.tokenize(example.text_a)

        tokens_b = None
        if example.text_b:
            tokens_b = tokenizer.tokenize(example.text_b)

        if tokens_b:
            # Modifies `tokens_a` and `tokens_b` in place so that the total
            # length is less than the specified length.
            # Account for [CLS], [SEP], [SEP] with "- 3"
            _truncate_seq_pair(tokens_a, tokens_b, max_seq_length - 3)
        else:
            # Account for [CLS] and [SEP] with "- 2"
            if len(tokens_a) > max_seq_length - 2:
                tokens_a = tokens_a[0:(max_seq_length - 2)]

        # The convention in BERT is:
        # (a) For sequence pairs:
        #  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
        #  type_ids: 0   0  0    0    0     0       0 0    1  1  1  1   1 1
        # (b) For single sequences:
        #  tokens:   [CLS] the dog is hairy . [SEP]
        #  type_ids: 0   0   0   0  0     0 0
        #
        # Where "type_ids" are used to indicate whether this is the first
        # sequence or the second sequence. The embedding vectors for `type=0` and
        # `type=1` were learned during pre-training and are added to the wordpiece
        # embedding vector (and position vector). This is not *strictly* necessary
        # since the [SEP] token unambigiously separates the sequences, but it makes
        # it easier for the model to learn the concept of sequences.
        #
        # For classification tasks, the first vector (corresponding to [CLS]) is
        # used as as the "sentence vector". Note that this only makes sense because
        # the entire model is fine-tuned.
        tokens = []
        segment_ids = []
        tokens.append("[CLS]")
        segment_ids.append(0)
        for token in tokens_a:
            tokens.append(token)
            segment_ids.append(0)
        tokens.append("[SEP]")
        segment_ids.append(0)

        if tokens_b:
            for token in tokens_b:
                tokens.append(token)
                segment_ids.append(1)
            tokens.append("[SEP]")
            segment_ids.append(1)

        input_ids = tokenizer.convert_tokens_to_ids(tokens)

        # The mask has 1 for real tokens and 0 for padding tokens. Only real
        # tokens are attended to.
        input_mask = [1] * len(input_ids)

        # Zero-pad up to the sequence length.
        while len(input_ids) < max_seq_length:
            input_ids.append(0)
            input_mask.append(0)
            segment_ids.append(0)

        assert len(input_ids) == max_seq_length
        assert len(input_mask) == max_seq_length
        assert len(segment_ids) == max_seq_length

        label_id = label_map[example.label]
        if ex_index < 5:
            logger.info("*** Example ***")
            logger.info("guid: %s" % (example.guid))
            logger.info("tokens: %s" % " ".join(
                    [str(x) for x in tokens]))
            logger.info("input_ids: %s" % " ".join([str(x) for x in input_ids]))
            logger.info("input_mask: %s" % " ".join([str(x) for x in input_mask]))
            logger.info(
                    "segment_ids: %s" % " ".join([str(x) for x in segment_ids]))
            logger.info("label: %s (id = %d)" % (example.label, label_id))

        features.append(
                InputFeatures(input_ids=input_ids,
                              input_mask=input_mask,
                              segment_ids=segment_ids,
                              label_id=label_id))
    return features

def test():
    parser = argparse.ArgumentParser()

    ## Required parameters
    parser.add_argument("--data_dir",
                        default=None,
                        type=str,
                        required=True,
                        help="The input data dir. Should contain the .tsv files (or other data files) for the task.")
    parser.add_argument("--bert_model", default=None, type=str, required=True,
                        help="Bert pre-trained model selected in the list: bert-base-uncased, "
                             "bert-large-uncased, bert-base-cased, bert-base-multilingual, bert-base-chinese.")
    parser.add_argument("--task_name",
                        default=None,
                        type=str,
                        required=True,
                        help="The name of the task to train.")
    parser.add_argument("--output_dir",
                        default=None,
                        type=str,
                        required=True,
                        help="The output directory where the model checkpoints will be written.")

    ## Other parameters
    parser.add_argument("--max_seq_length",
                        default=128,
                        type=int,
                        help="The maximum total input sequence length after WordPiece tokenization. \n"
                             "Sequences longer than this will be truncated, and sequences shorter \n"
                             "than this will be padded.")
    parser.add_argument("--do_train",
                        default=False,
                        action='store_true',
                        help="Whether to run training.")
    parser.add_argument("--do_eval",
                        default=False,
                        action='store_true',
                        help="Whether to run eval on the dev set.")
    parser.add_argument("--do_lower_case",
                        default=False,
                        action='store_true',
                        help="Set this flag if you are using an uncased model.")
    parser.add_argument("--train_batch_size",
                        default=32,
                        type=int,
                        help="Total batch size for training.")
    parser.add_argument("--eval_batch_size",
                        default=8,
                        type=int,
                        help="Total batch size for eval.")
    parser.add_argument("--learning_rate",
                        default=5e-5,
                        type=float,
                        help="The initial learning rate for Adam.")
    parser.add_argument("--num_train_epochs",
                        default=3.0,
                        type=float,
                        help="Total number of training epochs to perform.")
    parser.add_argument("--warmup_proportion",
                        default=0.1,
                        type=float,
                        help="Proportion of training to perform linear learning rate warmup for. "
                             "E.g., 0.1 = 10%% of training.")
    parser.add_argument("--no_cuda",
                        default=False,
                        action='store_true',
                        help="Whether not to use CUDA when available")
    parser.add_argument("--local_rank",
                        type=int,
                        default=-1,
                        help="local_rank for distributed training on gpus")
    parser.add_argument('--seed', 
                        type=int, 
                        default=42,
                        help="random seed for initialization")
    parser.add_argument('--gradient_accumulation_steps',
                        type=int,
                        default=1,
                        help="Number of updates steps to accumulate before performing a backward/update pass.")                       
    parser.add_argument('--optimize_on_cpu',
                        default=False,
                        action='store_true',
                        help="Whether to perform optimization and keep the optimizer averages on CPU")
    parser.add_argument('--fp16',
                        default=False,
                        action='store_true',
                        help="Whether to use 16-bit float precision instead of 32-bit")
    parser.add_argument('--loss_scale',
                        type=float, default=128,
                        help='Loss scaling, positive power of 2 values can improve fp16 convergence.')

    args = parser.parse_args()
    device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
    n_gpu = torch.cuda.device_count()

    args.train_batch_size = int(args.train_batch_size / args.gradient_accumulation_steps)

    random.seed(args.seed)
    np.random.seed(args.seed)
    torch.manual_seed(args.seed)
    if n_gpu > 0:
        torch.cuda.manual_seed_all(args.seed)
    os.makedirs(args.output_dir, exist_ok=True)

    task_name = args.task_name.lower()
    processor = SentProcessor()
    tokenizer = BertTokenizer.from_pretrained(args.bert_model, do_lower_case=args.do_lower_case)

    train_examples = None
    num_train_steps = None

    # Prepare model

    model = SentenceBert.from_pretrained(args.bert_model)
    model.to(device)
    
    # input
    sents = ['who343 do3+.. you 看like?','who do you like', 'who do you like most']
    max_seq_length = 15
    label_list = processor.get_labels()
    eval_examples = processor.get_dev_examples(sents=sents)
    for e in eval_examples:
        print('----------------', e.text_a)
    eval_features = convert_examples_to_features(
        eval_examples, label_list, max_seq_length, tokenizer)
    input_ids = torch.tensor([f.input_ids for f in eval_features], dtype=torch.long)
    input_mask = torch.tensor([f.input_mask for f in eval_features], dtype=torch.long)
    segment_ids = torch.tensor([f.segment_ids for f in eval_features], dtype=torch.long)
    label_ids = torch.tensor([f.label_id for f in eval_features], dtype=torch.long)

    print(input_ids.size(),  input_mask.size(),  segment_ids.size())
    input_ids = input_ids.to(device)
    input_mask = input_mask.to(device)
    segment_ids = segment_ids.to(device)
    representation = model(input_ids, segment_ids, input_mask) # 1,768
    print(torch.mean(representation,1))

    #tmp_eval_loss = model(input_ids, segment_ids, input_mask, label_ids)

class BertEncoding():

    def __init__(self):
        self.device = torch.device("cuda" if torch.cuda.is_available()  else "cpu")
        self.processor = SentProcessor()
        bert_path = '/home/liuxg/.pytorch_pretrained_bert'
        self.tokenizer = BertTokenizer.from_pretrained(bert_path, do_lower_case=True)

        # Prepare model
        self.model = SentenceBert.from_pretrained(bert_path)
        self.model.to(self.device)
        self.model.eval()

    def get_encoding(self, sents, max_seq_length=15):
        label_list = self.processor.get_labels()
        eval_examples = self.processor.get_dev_examples(sents=sents)
        # for e in eval_examples:
        #     print('----------------', e.text_a)
        eval_features = convert_examples_to_features(
            eval_examples, label_list, max_seq_length, self.tokenizer)
        input_ids = torch.tensor([f.input_ids for f in eval_features], dtype=torch.long)
        input_mask = torch.tensor([f.input_mask for f in eval_features], dtype=torch.long)
        segment_ids = torch.tensor([f.segment_ids for f in eval_features], dtype=torch.long)
        label_ids = torch.tensor([f.label_id for f in eval_features], dtype=torch.long)

        input_ids = input_ids.to(self.device)
        input_mask = input_mask.to(self.device)
        segment_ids = segment_ids.to(self.device)
        _,representation = self.model(input_ids, segment_ids, input_mask) # 1,768

        return representation.detach()

    def get_representation(self, sents, max_seq_length=15):
        label_list = self.processor.get_labels()
        eval_examples = self.processor.get_dev_examples(sents=sents)
        # for e in eval_examples:
        #     print('----------------', e.text_a)
        eval_features = convert_examples_to_features(
            eval_examples, label_list, max_seq_length, self.tokenizer)
        input_ids = torch.tensor([f.input_ids for f in eval_features], dtype=torch.long)
        input_mask = torch.tensor([f.input_mask for f in eval_features], dtype=torch.long)
        segment_ids = torch.tensor([f.segment_ids for f in eval_features], dtype=torch.long)
        label_ids = torch.tensor([f.label_id for f in eval_features], dtype=torch.long)

        input_ids = input_ids.to(self.device)
        input_mask = input_mask.to(self.device)
        segment_ids = segment_ids.to(self.device)
        representation,_ = self.model(input_ids, segment_ids, input_mask) # 1,768

        return representation.detach()


class BertSimilarity():

    def __init__(self):
        self.device = torch.device("cuda" if torch.cuda.is_available()  else "cpu")
        self.processor = PairProcessor()
        bert_path = '/home/liuxg/.pytorch_pretrained_bert'
        self.tokenizer = BertTokenizer.from_pretrained(bert_path, do_lower_case=True)

        # Prepare model
        self.model = SentenceBert.from_pretrained(bert_path)
        self.model.to(self.device)
        self.model.eval()

    def get_encoding(self, sents1, sents2, max_seq_length=33):
        label_list = self.processor.get_labels()
        eval_examples = self.processor.get_dev_examples(sents1=sents1, sents2 = sents2)
        # for e in eval_examples:
        #     print('----------------', e.text_a)
        eval_features = convert_examples_to_features(
            eval_examples, label_list, max_seq_length, self.tokenizer)
        input_ids = torch.tensor([f.input_ids for f in eval_features], dtype=torch.long)
        input_mask = torch.tensor([f.input_mask for f in eval_features], dtype=torch.long)
        segment_ids = torch.tensor([f.segment_ids for f in eval_features], dtype=torch.long)
        label_ids = torch.tensor([f.label_id for f in eval_features], dtype=torch.long)

        input_ids = input_ids.to(self.device)
        input_mask = input_mask.to(self.device)
        segment_ids = segment_ids.to(self.device)
        representation = self.model(input_ids, segment_ids, input_mask) # 1,768

        return representation.detach()


if __name__ == "__main__":

    sents = ['who343 do3+.. you 看like?','who do you like', 'who do you like most']
    sents1 = ['what do you believe','why are you so pretty']
    sents2 = ['which computer do you like', 'what app are you most using']
    bertencoding = BertSimilarity()
    rep = bertencoding.get_encoding(sents1,sents2)
    print(rep.size())
