# Path: dsegmenter/evaluation/segmentation.py
# def get_untyped_masses(tree):
#     """ Extracts the untyped segmentation masses from a tree.
# 
#     Extracts a segmentation of the leaves of a tree, such that each
#     start or end of a child node triggers a boundary. The segmentation
#     is returned as a list of segments lengths.
# 
#     >>> from nltk.tree import Tree
#     >>> get_untyped_masses(Tree.fromstring('(A x x x)'))
#     [3]
#     >>> get_untyped_masses(Tree.fromstring('(A x (B x (C x)))'))
#     [1, 1, 1]
#     >>> get_untyped_masses(Tree.fromstring('(A (B (C x) x) x)'))
#     [1, 1, 1]
#     >>> get_untyped_masses(Tree.fromstring('(A (B x) (C x) (D x))'))
#     [1, 1, 1]
#     >>> get_untyped_masses(Tree.fromstring('(A x (B x (C x) x) x)'))
#     [1, 1, 1, 1, 1]
#     """
#     return list(m for m, _c in get_typed_masses(tree))
# 
# def get_typed_nonoverlapping_spans(tree):
#     """ Extracts non-overlapping typed spans from a tree.
# 
#     Returns a linearly ordered list of pre-terminal nodes as span
#     tuples (position, length, label).
# 
#     >>> from nltk.tree import Tree
#     >>> get_typed_nonoverlapping_spans(Tree.fromstring('(A x x x)'))
#     ([(0, 3, 'A')], 3)
#     >>> get_typed_nonoverlapping_spans(Tree.fromstring('(A x (B x (C x)))'))
#     ([(0, 1, 'A'), (1, 1, 'B'), (2, 1, 'C')], 3)
#     >>> get_typed_nonoverlapping_spans(Tree.fromstring('(A (B (C x) x) x)'))
#     ([(0, 1, 'C'), (1, 1, 'B'), (2, 1, 'A')], 3)
#     >>> get_typed_nonoverlapping_spans(
#     ...     Tree.fromstring('(A (B x) (C x) (D x))'))
#     ([(0, 1, 'B'), (1, 1, 'C'), (2, 1, 'D')], 3)
#     >>> get_typed_nonoverlapping_spans(
#     ...     Tree.fromstring('(A x (B x (C x) x) x)'))
#     ([(0, 1, 'A'), (1, 1, 'B'), (2, 1, 'C'), (3, 1, 'B'), (4, 1, 'A')], 5)
#     """
#     typed_masses = get_typed_masses(tree)
#     spans = list(typed_masses_to_spans(typed_masses))
#     last_start, last_length, _ = spans[-1]
#     return spans, last_start + last_length


import os
import math
import subprocess
import segeval
import parseval
from itertools import chain
from collections import Counter
from .segmentation import get_untyped_masses, get_typed_nonoverlapping_spans

def metric_f1(forest1, forest2):
    p = parseval.precision(forest1, forest2)
    r = parseval.recall(forest1, forest2)
    score = f1(p, r) * 100
    return score


def metric_lf1(forest1, forest2):
    lp = parseval.labeled_precision(forest1, forest2)
    lr = parseval.labeled_recall(forest1, forest2)
    score = f1(lp, lr) * 100
    return score


def export_to_dk_agree_unit(annotator, spans, offset=0, typed=True):
    for start, length, cat in spans:
        if not typed:
            cat = None
        yield (offset + start,
               length,
               annotator,
               cat)


def metric_alpha_unit(forest1, forest2, typed=True):
    r = []
    total_length = 0
    # extract spans
    for tree1, tree2 in zip(forest1, forest2):

# Path: dsegmenter/evaluation/segmentation.py
# def get_untyped_masses(tree):
#     """ Extracts the untyped segmentation masses from a tree.
# 
#     Extracts a segmentation of the leaves of a tree, such that each
#     start or end of a child node triggers a boundary. The segmentation
#     is returned as a list of segments lengths.
# 
#     >>> from nltk.tree import Tree
#     >>> get_untyped_masses(Tree.fromstring('(A x x x)'))
#     [3]
#     >>> get_untyped_masses(Tree.fromstring('(A x (B x (C x)))'))
#     [1, 1, 1]
#     >>> get_untyped_masses(Tree.fromstring('(A (B (C x) x) x)'))
#     [1, 1, 1]
#     >>> get_untyped_masses(Tree.fromstring('(A (B x) (C x) (D x))'))
#     [1, 1, 1]
#     >>> get_untyped_masses(Tree.fromstring('(A x (B x (C x) x) x)'))
#     [1, 1, 1, 1, 1]
#     """
#     return list(m for m, _c in get_typed_masses(tree))
# 
# def get_typed_nonoverlapping_spans(tree):
#     """ Extracts non-overlapping typed spans from a tree.
# 
#     Returns a linearly ordered list of pre-terminal nodes as span
#     tuples (position, length, label).
# 
#     >>> from nltk.tree import Tree
#     >>> get_typed_nonoverlapping_spans(Tree.fromstring('(A x x x)'))
#     ([(0, 3, 'A')], 3)
#     >>> get_typed_nonoverlapping_spans(Tree.fromstring('(A x (B x (C x)))'))
#     ([(0, 1, 'A'), (1, 1, 'B'), (2, 1, 'C')], 3)
#     >>> get_typed_nonoverlapping_spans(Tree.fromstring('(A (B (C x) x) x)'))
#     ([(0, 1, 'C'), (1, 1, 'B'), (2, 1, 'A')], 3)
#     >>> get_typed_nonoverlapping_spans(
#     ...     Tree.fromstring('(A (B x) (C x) (D x))'))
#     ([(0, 1, 'B'), (1, 1, 'C'), (2, 1, 'D')], 3)
#     >>> get_typed_nonoverlapping_spans(
#     ...     Tree.fromstring('(A x (B x (C x) x) x)'))
#     ([(0, 1, 'A'), (1, 1, 'B'), (2, 1, 'C'), (3, 1, 'B'), (4, 1, 'A')], 5)
#     """
#     typed_masses = get_typed_masses(tree)
#     spans = list(typed_masses_to_spans(typed_masses))
#     last_start, last_length, _ = spans[-1]
#     return spans, last_start + last_length


# Path: dsegmenter/evaluation/metrics.py
import os
import math
import subprocess
import segeval
import parseval
from itertools import chain
from collections import Counter
from .segmentation import get_untyped_masses, get_typed_nonoverlapping_spans


def metric_f1(forest1, forest2):
    p = parseval.precision(forest1, forest2)
    r = parseval.recall(forest1, forest2)
    score = f1(p, r) * 100
    return score


def metric_lf1(forest1, forest2):
    lp = parseval.labeled_precision(forest1, forest2)
    lr = parseval.labeled_recall(forest1, forest2)
    score = f1(lp, lr) * 100
    return score


def export_to_dk_agree_unit(annotator, spans, offset=0, typed=True):
    for start, length, cat in spans:
        if not typed:
            cat = None
        yield (offset + start,
               length,
               annotator,
               cat)


def metric_alpha_unit(forest1, forest2, typed=True):
    r = []
    total_length = 0
    # extract spans
    for tree1, tree2 in zip(forest1, forest2):

        spans1, length1 = get_typed_nonoverlapping_spans(tree1)