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# pylint: skip-file
"""ROUGE metric implementation.

Copy from tf_seq2seq/seq2seq/metrics/rouge.py.
This is a modified and slightly extended verison of
https://github.com/miso-belica/sumy/blob/dev/sumy/evaluation/rouge.py.
"""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals

import itertools
import numpy as np


def _get_ngrams(n, text):
    """Calcualtes n-grams.

    Args:
      n: which n-grams to calculate
      text: An array of tokens

    Returns:
      A set of n-grams
    """
    ngram_set = set()
    text_length = len(text)
    max_index_ngram_start = text_length - n
    for i in range(max_index_ngram_start + 1):
        ngram_set.add(tuple(text[i : i + n]))
    return ngram_set


def _split_into_words(sentences):
    """Splits multiple sentences into words and flattens the result"""
    return list(itertools.chain(*[_.split(" ") for _ in sentences]))


def _get_word_ngrams(n, sentences):
    """Calculates word n-grams for multiple sentences."""
    assert len(sentences) > 0
    assert n > 0

    words = _split_into_words(sentences)
    return _get_ngrams(n, words)


def _len_lcs(x, y):
    """
    Returns the length of the Longest Common Subsequence between sequences x
    and y.
    Source: http://www.algorithmist.com/index.php/Longest_Common_Subsequence

    Args:
      x: sequence of words
      y: sequence of words

    Returns
      integer: Length of LCS between x and y
    """
    table = _lcs(x, y)
    n, m = len(x), len(y)
    return table[n, m]


def _lcs(x, y):
    """
    Computes the length of the longest common subsequence (lcs) between two
    strings. The implementation below uses a DP programming algorithm and runs
    in O(nm) time where n = len(x) and m = len(y).
    Source: http://www.algorithmist.com/index.php/Longest_Common_Subsequence

    Args:
      x: collection of words
      y: collection of words

    Returns:
      Table of dictionary of coord and len lcs
    """
    n, m = len(x), len(y)
    table = dict()
    for i in range(n + 1):
        for j in range(m + 1):
            if i == 0 or j == 0:
                table[i, j] = 0
            elif x[i - 1] == y[j - 1]:
                table[i, j] = table[i - 1, j - 1] + 1
            else:
                table[i, j] = max(table[i - 1, j], table[i, j - 1])
    return table


def _recon_lcs(x, y):
    """
    Returns the Longest Subsequence between x and y.
    Source: http://www.algorithmist.com/index.php/Longest_Common_Subsequence

    Args:
      x: sequence of words
      y: sequence of words

    Returns:
      sequence: LCS of x and y
    """
    i, j = len(x), len(y)
    table = _lcs(x, y)

    def _recon(i, j):
        """private recon calculation"""
        if i == 0 or j == 0:
            return []
        elif x[i - 1] == y[j - 1]:
            return _recon(i - 1, j - 1) + [(x[i - 1], i)]
        elif table[i - 1, j] > table[i, j - 1]:
            return _recon(i - 1, j)
        else:
            return _recon(i, j - 1)

    recon_tuple = tuple(map(lambda x: x[0], _recon(i, j)))
    return recon_tuple


def rouge_n(evaluated_sentences, reference_sentences, n=2):
    """
    Computes ROUGE-N of two text collections of sentences.
    Sourece: http://research.microsoft.com/en-us/um/people/cyl/download/
    papers/rouge-working-note-v1.3.1.pdf

    Args:
      evaluated_sentences: The sentences that have been picked by the summarizer
      reference_sentences: The sentences from the referene set
      n: Size of ngram.  Defaults to 2.

    Returns:
      A tuple (f1, precision, recall) for ROUGE-N

    Raises:
      ValueError: raises exception if a param has len <= 0
    """
    if len(evaluated_sentences) <= 0 or len(reference_sentences) <= 0:
        raise ValueError("Collections must contain at least 1 sentence.")

    evaluated_ngrams = _get_word_ngrams(n, evaluated_sentences)
    reference_ngrams = _get_word_ngrams(n, reference_sentences)
    reference_count = len(reference_ngrams)
    evaluated_count = len(evaluated_ngrams)

    # Gets the overlapping ngrams between evaluated and reference
    overlapping_ngrams = evaluated_ngrams.intersection(reference_ngrams)
    overlapping_count = len(overlapping_ngrams)

    # Handle edge case. This isn't mathematically correct, but it's good enough
    if evaluated_count == 0:
        precision = 0.0
    else:
        precision = overlapping_count / evaluated_count

    if reference_count == 0:
        recall = 0.0
    else:
        recall = overlapping_count / reference_count

    f1_score = 2.0 * ((precision * recall) / (precision + recall + 1e-8))

    # return overlapping_count / reference_count
    return f1_score, precision, recall


def _f_p_r_lcs(llcs, m, n):
    """
    Computes the LCS-based F-measure score
    Source: http://research.microsoft.com/en-us/um/people/cyl/download/papers/
    rouge-working-note-v1.3.1.pdf

    Args:
      llcs: Length of LCS
      m: number of words in reference summary
      n: number of words in candidate summary

    Returns:
      Float. LCS-based F-measure score
    """
    r_lcs = llcs / m
    p_lcs = llcs / n
    beta = p_lcs / (r_lcs + 1e-12)
    num = (1 + (beta ** 2)) * r_lcs * p_lcs
    denom = r_lcs + ((beta ** 2) * p_lcs)
    f_lcs = num / (denom + 1e-12)
    return f_lcs, p_lcs, r_lcs


def rouge_l_sentence_level(evaluated_sentences, reference_sentences):
    """
    Computes ROUGE-L (sentence level) of two text collections of sentences.
    http://research.microsoft.com/en-us/um/people/cyl/download/papers/
    rouge-working-note-v1.3.1.pdf

    Calculated according to:
    R_lcs = LCS(X,Y)/m
    P_lcs = LCS(X,Y)/n
    F_lcs = ((1 + beta^2)*R_lcs*P_lcs) / (R_lcs + (beta^2) * P_lcs)

    where:
    X = reference summary
    Y = Candidate summary
    m = length of reference summary
    n = length of candidate summary

    Args:
      evaluated_sentences: The sentences that have been picked by the summarizer
      reference_sentences: The sentences from the referene set

    Returns:
      A float: F_lcs

    Raises:
      ValueError: raises exception if a param has len <= 0
    """
    if len(evaluated_sentences) <= 0 or len(reference_sentences) <= 0:
        raise ValueError("Collections must contain at least 1 sentence.")
    reference_words = _split_into_words(reference_sentences)
    evaluated_words = _split_into_words(evaluated_sentences)
    m = len(reference_words)
    n = len(evaluated_words)
    lcs = _len_lcs(evaluated_words, reference_words)
    return _f_p_r_lcs(lcs, m, n)


def _union_lcs(evaluated_sentences, reference_sentence):
    """
    Returns LCS_u(r_i, C) which is the LCS score of the union longest common
    subsequence between reference sentence ri and candidate summary C. For example
    if r_i= w1 w2 w3 w4 w5, and C contains two sentences: c1 = w1 w2 w6 w7 w8 and
    c2 = w1 w3 w8 w9 w5, then the longest common subsequence of r_i and c1 is
    "w1 w2" and the longest common subsequence of r_i and c2 is "w1 w3 w5". The
    union longest common subsequence of r_i, c1, and c2 is "w1 w2 w3 w5" and
    LCS_u(r_i, C) = 4/5.

    Args:
      evaluated_sentences: The sentences that have been picked by the summarizer
      reference_sentence: One of the sentences in the reference summaries

    Returns:
      float: LCS_u(r_i, C)

    ValueError:
      Raises exception if a param has len <= 0
    """
    if len(evaluated_sentences) <= 0:
        raise ValueError("Collections must contain at least 1 sentence.")

    lcs_union = set()
    reference_words = _split_into_words([reference_sentence])
    combined_lcs_length = 0
    for eval_s in evaluated_sentences:
        evaluated_words = _split_into_words([eval_s])
        lcs = set(_recon_lcs(reference_words, evaluated_words))
        combined_lcs_length += len(lcs)
        lcs_union = lcs_union.union(lcs)

    union_lcs_count = len(lcs_union)
    union_lcs_value = union_lcs_count / combined_lcs_length
    return union_lcs_value


def rouge_l_summary_level(evaluated_sentences, reference_sentences):
    """
    Computes ROUGE-L (summary level) of two text collections of sentences.
    http://research.microsoft.com/en-us/um/people/cyl/download/papers/
    rouge-working-note-v1.3.1.pdf

    Calculated according to:
    R_lcs = SUM(1, u)[LCS<union>(r_i,C)]/m
    P_lcs = SUM(1, u)[LCS<union>(r_i,C)]/n
    F_lcs = ((1 + beta^2)*R_lcs*P_lcs) / (R_lcs + (beta^2) * P_lcs)

    where:
    SUM(i,u) = SUM from i through u
    u = number of sentences in reference summary
    C = Candidate summary made up of v sentences
    m = number of words in reference summary
    n = number of words in candidate summary

    Args:
      evaluated_sentences: The sentences that have been picked by the summarizer
      reference_sentence: One of the sentences in the reference summaries

    Returns:
      A float: F_lcs

    Raises:
      ValueError: raises exception if a param has len <= 0
    """
    if len(evaluated_sentences) <= 0 or len(reference_sentences) <= 0:
        raise ValueError("Collections must contain at least 1 sentence.")

    # total number of words in reference sentences
    m = len(_split_into_words(reference_sentences))

    # total number of words in evaluated sentences
    n = len(_split_into_words(evaluated_sentences))

    union_lcs_sum_across_all_references = 0
    for ref_s in reference_sentences:
        union_lcs_sum_across_all_references += _union_lcs(evaluated_sentences, ref_s)
    return _f_p_r_lcs(union_lcs_sum_across_all_references, m, n)


def rouge(hypotheses, references):
    """Calculates average rouge scores for a list of hypotheses and
    references"""

    # Filter out hyps that are of 0 length
    # hyps_and_refs = zip(hypotheses, references)
    # hyps_and_refs = [_ for _ in hyps_and_refs if len(_[0]) > 0]
    # hypotheses, references = zip(*hyps_and_refs)

    # Calculate ROUGE-1 F1, precision, recall scores
    rouge_1 = [rouge_n([hyp], [ref], 1) for hyp, ref in zip(hypotheses, references)]
    rouge_1_f, rouge_1_p, rouge_1_r = map(np.mean, zip(*rouge_1))

    # Calculate ROUGE-2 F1, precision, recall scores
    rouge_2 = [rouge_n([hyp], [ref], 2) for hyp, ref in zip(hypotheses, references)]
    rouge_2_f, rouge_2_p, rouge_2_r = map(np.mean, zip(*rouge_2))

    # Calculate ROUGE-L F1, precision, recall scores
    rouge_l = [rouge_l_sentence_level([hyp], [ref]) for hyp, ref in zip(hypotheses, references)]
    rouge_l_f, rouge_l_p, rouge_l_r = map(np.mean, zip(*rouge_l))

    return {
        "rouge_1/f_score": rouge_1_f,
        "rouge_1/r_score": rouge_1_r,
        "rouge_1/p_score": rouge_1_p,
        "rouge_2/f_score": rouge_2_f,
        "rouge_2/r_score": rouge_2_r,
        "rouge_2/p_score": rouge_2_p,
        "rouge_l/f_score": rouge_l_f,
        "rouge_l/r_score": rouge_l_r,
        "rouge_l/p_score": rouge_l_p,
    }
