import numpy as np
import pandas as pd
from pprint import pprint
import argparse
from pytorch_pretrained_bert.tokenization import (BasicTokenizer,
                                                  BertTokenizer, whitespace_tokenize)
import collections
import torch
from torch.utils.data import TensorDataset
from pytorch_pretrained_bert.modeling import BertForQuestionAnswering, BertConfig
import math
from torch.utils.data import (DataLoader, RandomSampler, SequentialSampler,
                              TensorDataset)
# from tqdm import tqdm

from parafinder import ParaFinder
torch.manual_seed(123)

class SquadExample(object):
    """
    A single training/test example for the Squad dataset.
    For examples without an answer, the start and end position are -1.
    """

    def __init__(self,
                 example_id,
                 para_text,
                 qas_id,
                 question_text,
                 doc_tokens,
                 unique_id):
        self.qas_id = qas_id
        self.question_text = question_text
        self.doc_tokens = doc_tokens
        self.example_id = example_id
        self.para_text = para_text
        self.unique_id = unique_id

    def __str__(self):
        return self.__repr__()

    def __repr__(self):
        s = ""
        s += "qas_id: %s" % (self.qas_id)
        s += ", question_text: %s" % (
            self.question_text)
        s += ", doc_tokens: [%s]" % (" ".join(self.doc_tokens))

        return s


### Convert paragraph to tokens and returns question_text
def read_squad_examples(input_data):
    """Read a SQuAD json file into a list of SquadExample."""

    def is_whitespace(c):
        if c == " " or c == "\t" or c == "\r" or c == "\n" or ord(c) == 0x202F:
            return True
        return False

    i = 0
    examples = []
    for entry in input_data:
        example_id = entry['id']
        paragraph_text = entry['text']
        doc_tokens = []
        prev_is_whitespace = True
        for c in paragraph_text:
            if is_whitespace(c):
                prev_is_whitespace = True
            else:
                if prev_is_whitespace:
                    doc_tokens.append(c)
                else:
                    doc_tokens[-1] += c
                prev_is_whitespace = False

        for qa in entry['ques']:
            qas_id = i
            question_text = qa

            example = SquadExample(example_id=example_id,
                                   qas_id=qas_id,
                                   para_text=paragraph_text,
                                   question_text=question_text,
                                   doc_tokens=doc_tokens,
                                   unique_id=i)
            i += 1
            examples.append(example)

    return examples


def _check_is_max_context(doc_spans, cur_span_index, position):
    """Check if this is the 'max context' doc span for the token."""
    best_score = None
    best_span_index = None
    for (span_index, doc_span) in enumerate(doc_spans):
        end = doc_span.start + doc_span.length - 1
        if position < doc_span.start:
            continue
        if position > end:
            continue
        num_left_context = position - doc_span.start
        num_right_context = end - position
        score = min(num_left_context, num_right_context) + 0.01 * doc_span.length
        if best_score is None or score > best_score:
            best_score = score
            best_span_index = span_index

    return cur_span_index == best_span_index


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

    def __init__(self,
                 unique_id,
                 example_index,
                 doc_span_index,
                 tokens,
                 token_is_max_context,
                 token_to_orig_map,
                 input_ids,
                 input_mask,
                 segment_ids):
        self.doc_span_index = doc_span_index
        self.unique_id = unique_id
        self.example_index = example_index
        self.tokens = tokens
        self.token_is_max_context = token_is_max_context
        self.token_to_orig_map = token_to_orig_map
        self.input_ids = input_ids
        self.input_mask = input_mask
        self.segment_ids = segment_ids


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

    features = []
    unique_id = 1
    for (example_index, example) in enumerate(examples):
        query_tokens = tokenizer.tokenize(example.question_text)
        ### Truncate the query if query length > max_query_length..
        if len(query_tokens) > max_query_length:
            query_tokens = query_tokens[0:max_query_length]

        tok_to_orig_index = []
        orig_to_tok_index = []
        all_doc_tokens = []
        for (i, token) in enumerate(example.doc_tokens):
            orig_to_tok_index.append(len(all_doc_tokens))
            sub_tokens = tokenizer.tokenize(token)
            for sub_token in sub_tokens:
                tok_to_orig_index.append(i)
                all_doc_tokens.append(sub_token)

        tok_start_position = None
        tok_end_position = None

        max_tokens_for_doc = max_seq_length - len(query_tokens) - 3

        # We can have documents that are longer than the maximum sequence length.
        # To deal with this we do a sliding window approach, where we take chunks
        # of the up to our max length with a stride of `doc_stride`.
        _DocSpan = collections.namedtuple(  # pylint: disable=invalid-name
            "DocSpan", ["start", "length"])
        doc_spans = []
        start_offset = 0
        while start_offset < len(all_doc_tokens):
            length = len(all_doc_tokens) - start_offset
            if length > max_tokens_for_doc:
                length = max_tokens_for_doc
            doc_spans.append(_DocSpan(start=start_offset, length=length))
            if start_offset + length == len(all_doc_tokens):
                break
            start_offset += min(length, doc_stride)

        for (doc_span_index, doc_span) in enumerate(doc_spans):
            tokens = []
            token_to_orig_map = {}
            token_is_max_context = {}
            segment_ids = []
            tokens.append("[CLS]")
            segment_ids.append(0)
            for token in query_tokens:
                tokens.append(token)
                segment_ids.append(0)
            tokens.append("[SEP]")
            segment_ids.append(0)

            for i in range(doc_span.length):
                split_token_index = doc_span.start + i
                token_to_orig_map[len(tokens)] = tok_to_orig_index[split_token_index]

                is_max_context = _check_is_max_context(doc_spans, doc_span_index,
                                                       split_token_index)
                token_is_max_context[len(tokens)] = is_max_context
                tokens.append(all_doc_tokens[split_token_index])
                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

            features.append(InputFeatures(unique_id=unique_id,
                                          example_index=example_index,
                                          doc_span_index=doc_span_index,
                                          tokens=tokens,
                                          token_is_max_context=token_is_max_context,
                                          token_to_orig_map=token_to_orig_map,
                                          input_ids=input_ids,
                                          input_mask=input_mask,
                                          segment_ids=segment_ids))
            unique_id += 1

    return features


def _get_best_indexes(logits, n_best_size):
    """Get the n-best logits from a list."""
    index_and_score = sorted(enumerate(logits), key=lambda x: x[1], reverse=True)
    best_indexes = []
    for i in range(len(index_and_score)):
        if i >= n_best_size:
            break
        best_indexes.append(index_and_score[i][0])

    return best_indexes


def get_final_text(pred_text, orig_text, do_lower_case, verbose_logging=False):
    """Project the tokenized prediction back to the original text."""

    def _strip_spaces(text):
        ns_chars = []
        ns_to_s_map = collections.OrderedDict()
        for (i, c) in enumerate(text):
            if c == " ":
                continue
            ns_to_s_map[len(ns_chars)] = i
            ns_chars.append(c)
        ns_text = "".join(ns_chars)
        return (ns_text, ns_to_s_map)

    tokenizer = BasicTokenizer(do_lower_case=do_lower_case)

    tok_text = " ".join(tokenizer.tokenize(orig_text))

    start_position = tok_text.find(pred_text)
    if start_position == -1:
        if verbose_logging:
            logger.info(
                "Unable to find text: '%s' in '%s'" % (pred_text, orig_text))
        return orig_text
    end_position = start_position + len(pred_text) - 1

    (orig_ns_text, orig_ns_to_s_map) = _strip_spaces(orig_text)
    (tok_ns_text, tok_ns_to_s_map) = _strip_spaces(tok_text)

    if len(orig_ns_text) != len(tok_ns_text):
        if verbose_logging:
            logger.info("Length not equal after stripping spaces: '%s' vs '%s'",
                        orig_ns_text, tok_ns_text)
        return orig_text

    # We then project the characters in `pred_text` back to `orig_text` using
    # the character-to-character alignment.
    tok_s_to_ns_map = {}
    for (i, tok_index) in tok_ns_to_s_map.items():
        tok_s_to_ns_map[tok_index] = i

    orig_start_position = None
    if start_position in tok_s_to_ns_map:
        ns_start_position = tok_s_to_ns_map[start_position]
        if ns_start_position in orig_ns_to_s_map:
            orig_start_position = orig_ns_to_s_map[ns_start_position]

    if orig_start_position is None:
        if verbose_logging:
            logger.info("Couldn't map start position")
        return orig_text

    orig_end_position = None
    if end_position in tok_s_to_ns_map:
        ns_end_position = tok_s_to_ns_map[end_position]
        if ns_end_position in orig_ns_to_s_map:
            orig_end_position = orig_ns_to_s_map[ns_end_position]

    if orig_end_position is None:
        if verbose_logging:
            logger.info("Couldn't map end position")
        return orig_text

    output_text = orig_text[orig_start_position:(orig_end_position + 1)]
    return output_text


_PrelimPrediction = collections.namedtuple(  # pylint: disable=invalid-name
    "PrelimPrediction",
    ["feature_index", "start_index", "end_index", "start_logit", "end_logit"])


def _compute_softmax(scores):
    """Compute softmax probability over raw logits."""
    if not scores:
        return []

    max_score = None
    for score in scores:
        if max_score is None or score > max_score:
            max_score = score

    exp_scores = []
    total_sum = 0.0
    for score in scores:
        x = math.exp(score - max_score)
        exp_scores.append(x)
        total_sum += x

    probs = []
    for score in exp_scores:
        probs.append(score / total_sum)
    return probs


_NbestPrediction = collections.namedtuple(  # pylint: disable=invalid-name
    "NbestPrediction", ["text", "start_logit", "end_logit"])


def predict(examples, all_features, all_results, max_answer_length):
    n_best_size = 10

    ### Adding index to feature ###
    example_index_to_features = collections.defaultdict(list)
    for feature in all_features:
        example_index_to_features[feature.example_index].append(feature)

    unique_id_to_result = {}
    for result in all_results:
        unique_id_to_result[result.unique_id] = result

    all_predictions = collections.OrderedDict()

    for example in examples:
        index = 0
        features = example_index_to_features[example.unique_id]
        prelim_predictions = []

        for (feature_index, feature) in enumerate(features):
            result = unique_id_to_result[feature.unique_id]
            start_indexes = _get_best_indexes(result.start_logits, n_best_size)
            end_indexes = _get_best_indexes(result.end_logits, n_best_size)
            for start_index in start_indexes:
                for end_index in end_indexes:
                    #### we remove the indexes which are invalid @
                    if start_index >= len(feature.tokens):
                        continue
                    if end_index >= len(feature.tokens):
                        continue
                    if start_index not in feature.token_to_orig_map:
                        continue
                    if end_index not in feature.token_to_orig_map:
                        continue
                    if not feature.token_is_max_context.get(start_index, False):
                        continue
                    if end_index < start_index:
                        continue
                    length = end_index - start_index + 1
                    if length > max_answer_length:
                        continue

                    prelim_predictions.append(
                        _PrelimPrediction(
                            feature_index=feature_index,
                            start_index=start_index,
                            end_index=end_index,
                            start_logit=result.start_logits[start_index],
                            end_logit=result.end_logits[end_index]))

        prelim_predictions = sorted(
            prelim_predictions,
            key=lambda x: (x.start_logit + x.end_logit),
            reverse=True)

        seen_predictions = {}
        nbest = []
        for pred in prelim_predictions:
            if len(nbest) >= n_best_size:
                break

            feature = features[pred.feature_index]
            if pred.start_index > 0:  # this is a non-null prediction
                tok_tokens = feature.tokens[pred.start_index:(pred.end_index + 1)]
                orig_doc_start = feature.token_to_orig_map[pred.start_index]
                orig_doc_end = feature.token_to_orig_map[pred.end_index]
                orig_tokens = example.doc_tokens[orig_doc_start:(orig_doc_end + 1)]
                tok_text = " ".join(tok_tokens)

                # De-tokenize WordPieces that have been split off.
                tok_text = tok_text.replace(" ##", "")
                tok_text = tok_text.replace("##", "")

                # Clean whitespace
                tok_text = tok_text.strip()
                tok_text = " ".join(tok_text.split())
                orig_text = " ".join(orig_tokens)

                final_text = get_final_text(tok_text, orig_text, True)
                if final_text in seen_predictions:
                    continue

                seen_predictions[final_text] = True
            else:
                final_text = ""
                seen_predictions[final_text] = True

            nbest.append(
                _NbestPrediction(
                    text=final_text,
                    start_logit=pred.start_logit,
                    end_logit=pred.end_logit))

        if not nbest:
            nbest.append(
                _NbestPrediction(text="No result found", start_logit=0.0, end_logit=0.0))

        assert len(nbest) >= 1

        total_scores = []
        best_non_null_entry = None
        for entry in nbest:
            total_scores.append(entry.start_logit + entry.end_logit)
            if not best_non_null_entry:
                if entry.text:
                    best_non_null_entry = entry

        probs = _compute_softmax(total_scores)
        nbest_json = []
        for (i, entry) in enumerate(nbest):
            output = collections.OrderedDict()
            output["text"] = entry.text
            output["probability"] = probs[i]
            output["start_logit"] = entry.start_logit
            output["end_logit"] = entry.end_logit
            nbest_json.append(output)

        assert len(nbest_json) >= 1
        all_predictions[example] = (nbest_json[0]["text"], nbest_json[0]["probability"])
        index = +1
    return all_predictions


RawResult = collections.namedtuple("RawResult",
                                   ["unique_id", "start_logits", "end_logits"])


def main():
    parser = argparse.ArgumentParser()
    parser.add_argument("--paragraph", default=None, type=str)
    parser.add_argument("--question", default=None, type=str)
    parser.add_argument("--model", default=None, type=str)
    parser.add_argument("--max_seq_length", default=384, type=int)
    parser.add_argument("--doc_stride", default=128, type=int)
    parser.add_argument("--max_query_length", default=64, type=int)
    parser.add_argument("--config_file", default=None, type=str)
    parser.add_argument("--max_answer_length", default=30, type=int)

    args = parser.parse_args()
    para_file = args.paragraph
    question_file = args.question
    model_path = args.model
    device = torch.device("cuda")

    ### Raeding paragraph
    # f = open(para_file, 'r')
    # para = f.read()
    # f.close()

    ## Reading question
    #     f = open(ques_file, 'r')
    #     ques = f.read()
    #     f.close()

    # para_list = para.split('\n\n')
    f = open(para_file, "rb")
    para = f.read()
    para = para.decode('windows-1252')
    para = para.strip("\n").replace("\r", " ").replace("\n", "")
    #print(para)

    # print(para)
    f.close()

    f_ = open(question_file, "r")
    question = f_.read()
    question = question.split("\n")
    while "" in question:
        question.remove("")
    for q in question:
        q = q.strip("\n")
    f_.close()
    input_data = []
    pfinder = ParaFinder(para)
    i = 0
    for q in question:
        closest_para = pfinder.closestParagraph(q)
        paragraphs = {}
        paragraphs["id"] = i
        paragraphs["text"] = closest_para
        paragraphs["ques"] = [q]
        i += 1
        input_data.append(paragraphs)

    # print(input_data)
    ## input_data is a list of dictionary which has a paragraph and questions

    examples = read_squad_examples(input_data)
    tokenizer = BertTokenizer.from_pretrained('bert-base-uncased', do_lower_case=True)

    eval_features = convert_examples_to_features(
        examples=examples,
        tokenizer=tokenizer,
        max_seq_length=args.max_seq_length,
        doc_stride=args.doc_stride,
        max_query_length=args.max_query_length)

    all_input_ids = torch.tensor([f.input_ids for f in eval_features], dtype=torch.long)
    all_input_mask = torch.tensor([f.input_mask for f in eval_features], dtype=torch.long)
    all_segment_ids = torch.tensor([f.segment_ids for f in eval_features], dtype=torch.long)
    all_example_index = torch.arange(all_input_ids.size(0), dtype=torch.long)

    ### Loading Pretrained model for QnA
    config = BertConfig(args.config_file)
    model = BertForQuestionAnswering(config)
    model.load_state_dict(torch.load(model_path, map_location=device))
    model.to(device)

    pred_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_example_index)
    # Run prediction for full data
    pred_sampler = SequentialSampler(pred_data)
    pred_dataloader = DataLoader(pred_data, sampler=pred_sampler, batch_size=9)

    predictions = []
    for input_ids, input_mask, segment_ids, example_indices in pred_dataloader:
        input_ids = input_ids.to(device)
        input_mask = input_mask.to(device)
        segment_ids = segment_ids.to(device)

        with torch.no_grad():
            batch_start_logits, batch_end_logits = model(input_ids, segment_ids, input_mask)

        features = []
        example = []
        all_results = []

        for i, example_index in enumerate(example_indices):
            start_logits = batch_start_logits[i].detach().cpu().tolist()
            end_logits = batch_end_logits[i].detach().cpu().tolist()
            feature = eval_features[example_index.item()]
            unique_id = int(feature.unique_id)
            features.append(feature)
            all_results.append(RawResult(unique_id=unique_id,
                                         start_logits=start_logits,
                                         end_logits=end_logits))

        output = predict(examples, features, all_results, args.max_answer_length)
        predictions.append(output)

    ### For printing the results ####
    index = None
    for example in examples:
        if index != example.example_id:
            # print(example.para_text)
            index = example.example_id
            # print('\n')
            # print(colored('***********Question and Answers *************', 'red'))

        ques_text = example.question_text
        print(ques_text)
        prediction, prob = predictions[math.floor(example.unique_id / 12)][example]
        if prob > 0.35:
            print(prediction)
            #print(type(prediction))
        else:
            print("No result found")
        # print('\n')
    ## prediction is the answer of the question


if __name__ == "__main__":
    main()
