# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Official evaluation script for SQuAD version 2.0.

In addition to basic functionality, we also compute additional statistics and
plot precision-recall curves if an additional na_prob.json file is provided.
This file is expected to map question ID's to the model's predicted probability
that a question is unanswerable.
"""
import collections
import json
import math

from paddlenlp.metrics.bleu import BLEU
from paddlenlp.metrics.rouge import RougeL


def compute_predictions(
    all_examples, all_features, all_results, n_best_size, max_answer_length, do_lower_case, verbose, tokenizer
):
    """Write final predictions to the json file and log-odds of null if needed."""

    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

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

    preds_for_eval = collections.OrderedDict()
    preds_for_test = []

    print(len(unique_id_to_result))
    for (example_index, example) in enumerate(all_examples):
        features = example_index_to_features[example_index]

        prelim_predictions = []
        # keep track of the minimum score of null start+end of position 0
        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)
            # if we could have irrelevant answers, get the min score of irrelevant

            for start_index in start_indexes:
                for end_index in end_indexes:
                    # We could hypothetically create invalid predictions, e.g., predict
                    # that the start of the span is in the question. We throw out all
                    # invalid predictions.
                    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)

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

        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, tokenizer, verbose)
                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 we didn't inlude the empty option in the n-best, inlcude it

        # In very rare edge cases we could have no valid predictions. So we
        # just create a nonce prediction in this case to avoid failure.
        if not nbest:
            nbest.append(_NbestPrediction(text="empty", 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
                else:
                    best_non_null_entry = _NbestPrediction(text="empty", start_logit=0.0, end_logit=0.0)

        preds_for_eval[example.qas_id] = best_non_null_entry.text

        preds_for_test.append(
            {
                "yesno_answers": [],
                "question": example.question_text,
                "question_type": example.question_type,
                "answers": [best_non_null_entry.text],
                "question_id": example.qas_id,
            }
        )

    return preds_for_eval, preds_for_test


def get_final_text(pred_text, orig_text, tokenizer, verbose):
    """Project the tokenized prediction back to the original text."""

    # When we created the data, we kept track of the alignment between original
    # (whitespace tokenized) tokens and our WordPiece tokenized tokens. So
    # now `orig_text` contains the span of our original text corresponding to the
    # span that we predicted.
    #
    # However, `orig_text` may contain extra characters that we don't want in
    # our prediction.
    #
    # For example, let's say:
    #   pred_text = steve smith
    #   orig_text = Steve Smith's
    #
    # We don't want to return `orig_text` because it contains the extra "'s".
    #
    # We don't want to return `pred_text` because it's already been normalized
    # (the SQuAD eval script also does punctuation stripping/lower casing but
    # our tokenizer does additional normalization like stripping accent
    # characters).
    #
    # What we really want to return is "Steve Smith".
    #
    # Therefore, we have to apply a semi-complicated alignment heruistic between
    # `pred_text` and `orig_text` to get a character-to-charcter alignment. This
    # can fail in certain cases in which case we just return `orig_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)

    # We first tokenize `orig_text`, strip whitespace from the result
    # and `pred_text`, and check if they are the same length. If they are
    # NOT the same length, the heuristic has failed. If they are the same
    # length, we assume the characters are one-to-one aligned.

    tok_text = " ".join(tokenizer.basic_tokenizer.tokenize(orig_text))
    start_position = tok_text.find(pred_text)
    if start_position == -1:
        if verbose:
            print("Unable to find text: '%s' in '%s'" % (pred_text, tok_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:
            print("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:
            print("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:
            print("Couldn't map end position")
        return orig_text

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


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


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 normalize(s):
    """
    Normalize strings to space joined chars.
    Args:
        s: a list of strings.
    Returns:
        A list of normalized strings.
    """
    if not s:
        return s
    normalized = []
    for ss in s:
        tokens = [c for c in list(ss) if len(c.strip()) != 0]
        norm_s = "".join(tokens)
        norm_s = norm_s.replace("，", ",")
        norm_s = norm_s.replace("。", ".")
        norm_s = norm_s.replace("！", "!")
        norm_s = norm_s.replace("？", "?")
        norm_s = norm_s.replace("；", ";")
        norm_s = norm_s.replace("（", "(").replace("）", ")")
        norm_s = norm_s.replace("【", "[").replace("】", "]")
        norm_s = norm_s.replace("“", '"').replace("“", '"')
        normalized.append(norm_s)
    return normalized


def dureader_evaluate(examples, preds):
    bleu_eval = BLEU(4)
    rouge_eval = RougeL()

    for example in examples:
        qid = example.qas_id
        if qid not in preds:
            print("Missing prediction for %s" % qid)
            continue
        pred_answers = preds[qid]
        pred_answers = normalize([pred_answers])[0]
        ref_answers = example.orig_answer_text
        if not ref_answers:
            continue
        ref_answers = normalize(ref_answers)

        bleu_eval.add_inst(pred_answers, ref_answers)
        rouge_eval.add_inst(pred_answers, ref_answers)

    bleu4 = bleu_eval.score()
    rouge_l = rouge_eval.score()
    metrics = {"ROUGE-L": round(rouge_l * 100, 2), "BLEU-4": round(bleu4 * 100, 2)}

    print(json.dumps(metrics).encode("utf8"))
