FA-80111 / Typography line breaking / Open access

Drop cap shaped paragraph: initial letter consumption · case 01

The first line measures the initial letter twice.

Verified by executionVariant 1 · 8 checks per implementationDownload source bundle ↓JSON ↗

ROOT CAUSE

The first word keeps its full width although its first letter moved into the cap.

VERIFIED REPAIR

Remove one unit from the first word when a cap is present.

Unsuccessful approach: Trimming even without a cap shortens ordinary paragraphs.

Case contract

Input [word widths, width, cap lines, cap width, gap, indent]. With a drop cap its first letter moves into the cap (first word loses one unit) and the first cap-lines lines are narrowed by cap width + gap; the first-line indent applies only without a cap. Greedy fill with unit spaces; a word too wide for an empty line is set alone. Return [[words on line, used width]].

Why this case matters

Line breaking decides where paragraphs wrap on screen and in print; a wrong decision point shifts every following line.

1 / The failure

Exit 1

"""Failure Map reference implementation. Python standard library only."""
import json

N = 1
observations = []
def solve(x):
    words, width, cap_lines, cap_width, gap, indent = x
    lens = list(words)
    if cap_lines > 0:
        lens[0] = lens[0]
    def avail(li):
        base = width
        if li < cap_lines:
            base -= cap_width + gap
        elif cap_lines == 0 and li == 0:
            base -= indent
        return base
    lines = []
    cur = []
    used = 0
    for w in lens:
        li = len(lines)
        need = w if not cur else used + 1 + w
        if need <= avail(li) or not cur:
            cur.append(w)
            used = need
        else:
            lines.append([len(cur), used])
            cur = [w]
            used = w
    if cur:
        lines.append([len(cur), used])
    return lines
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('three-line drop cap', [[5, 3, 4, 6, 2, 5, 4, 3], 14, 3, 3, 1, 2], [[2, 8], [1, 4], [2, 9], [3, 14]]), ('regression: initial letter consumption', [[7, 1, 4, 6, 4, 3, 7, 5], 13, 2, 5, 2, 3], [[1, 6], [2, 6], [2, 11], [2, 11], [1, 5]]), ('partial-repair probe', [[7, 1, 8, 5], 16, 0, 5, 1, 3], [[2, 9], [2, 14]]), ('partial-repair probe', [[1, 8, 8, 5, 8, 1, 2, 5], 17, 0, 2, 1, 3], [[2, 10], [2, 14], [3, 13], [1, 5]]), ('no cap uses indent', [[5, 3, 4, 6, 2], 12, 0, 3, 1, 3], [[2, 9], [2, 11], [1, 2]]), ('partial-repair probe', [[5, 2, 6, 6, 2, 6, 3, 3, 5, 3], 16, 0, 2, 1, 3], [[2, 8], [3, 16], [3, 14], [2, 9]]), ('partial-repair probe', [[6, 6, 8, 8, 8, 2, 3], 15, 0, 5, 1, 4], [[1, 6], [2, 15], [1, 8], [3, 15]]), ('partial-repair probe', [[1, 2, 6, 7, 1, 8], 20, 0, 3, 2, 2], [[3, 11], [3, 18]])], [('regression: initial letter consumption', [[8, 1, 6, 4, 1, 7, 7], 24, 2, 5, 2, 1], [[3, 16], [3, 14], [1, 7]]), ('regression: initial letter consumption', [[1, 2, 7, 6, 5, 2, 3], 17, 3, 5, 1, 3], [[3, 11], [1, 6], [2, 8], [1, 3]]), ('partial-repair probe', [[1, 8, 7, 5, 7, 8], 15, 0, 2, 2, 3], [[2, 10], [2, 13], [1, 7], [1, 8]]), ('partial-repair probe', [[2, 3, 3, 1, 7, 5, 6, 1, 3, 3, 5], 14, 0, 3, 1, 2], [[4, 12], [2, 13], [3, 12], [2, 9]]), ('three-line drop cap', [[5, 3, 4, 6, 2, 5, 4, 3], 14, 3, 3, 1, 2], [[2, 8], [1, 4], [2, 9], [3, 14]]), ('no cap uses indent', [[5, 3, 4, 6, 2], 12, 0, 3, 1, 3], [[2, 9], [2, 11], [1, 2]]), ('regression: initial letter consumption', [[3, 4, 3, 1, 5, 7, 5, 6, 6, 5, 5], 19, 2, 4, 2, 2], [[4, 13], [2, 13], [3, 19], [2, 11]]), ('partial-repair probe', [[6, 8, 2, 2, 5, 4, 5, 7], 24, 0, 2, 1, 2], [[4, 21], [4, 24]])], [('regression: initial letter consumption', [[2, 6, 7, 8, 2, 8, 5, 5, 1, 1, 5], 12, 3, 2, 1, 1], [[2, 8], [1, 7], [1, 8], [2, 11], [2, 11], [3, 9]]), ('regression: initial letter consumption', [[7, 2, 3, 8, 2, 8, 7], 22, 2, 5, 1, 1], [[3, 13], [2, 11], [2, 16]]), ('partial-repair probe', [[1, 6, 4, 2, 2, 2, 8], 15, 0, 4, 1, 3], [[2, 8], [4, 13], [1, 8]]), ('partial-repair probe', [[8, 1, 7, 5, 5, 4, 4, 8, 7], 22, 0, 5, 1, 2], [[3, 18], [4, 21], [2, 16]]), ('three-line drop cap', [[5, 3, 4, 6, 2, 5, 4, 3], 14, 3, 3, 1, 2], [[2, 8], [1, 4], [2, 9], [3, 14]]), ('no cap uses indent', [[5, 3, 4, 6, 2], 12, 0, 3, 1, 3], [[2, 9], [2, 11], [1, 2]]), ('partial-repair probe', [[3, 7, 4, 7], 24, 0, 2, 2, 4], [[3, 16], [1, 7]]), ('partial-repair probe', [[8, 2, 3, 5, 8, 1, 2], 12, 0, 4, 1, 1], [[2, 11], [2, 9], [2, 10], [1, 2]])], [('regression: initial letter consumption', [[2, 6, 7, 8, 1, 2, 5, 6, 8], 21, 2, 2, 2, 4], [[3, 16], [3, 13], [3, 21]]), ('regression: initial letter consumption', [[7, 6, 8, 6, 7, 6, 3, 5, 3, 6, 4], 13, 2, 4, 1, 1], [[1, 6], [1, 6], [1, 8], [1, 6], [1, 7], [2, 10], [2, 9], [2, 11]]), ('partial-repair probe', [[7, 4, 4, 1], 21, 0, 2, 2, 3], [[3, 17], [1, 1]]), ('partial-repair probe', [[3, 2, 8, 1, 5, 6, 1, 5, 8], 16, 0, 4, 2, 2], [[2, 6], [3, 16], [3, 14], [1, 8]]), ('no cap uses indent', [[5, 3, 4, 6, 2], 12, 0, 3, 1, 3], [[2, 9], [2, 11], [1, 2]]), ('three-line drop cap', [[5, 3, 4, 6, 2, 5, 4, 3], 14, 3, 3, 1, 2], [[2, 8], [1, 4], [2, 9], [3, 14]]), ('partial-repair probe', [[7, 6, 7, 4, 5, 3, 3, 5, 3, 5], 23, 0, 3, 2, 4], [[2, 14], [4, 22], [4, 19]]), ('regression: initial letter consumption', [[5, 4, 1, 4, 5, 5, 2, 7], 21, 3, 2, 1, 2], [[4, 16], [3, 14], [1, 7]])], [('regression: initial letter consumption', [[3, 1, 5, 1, 6], 22, 2, 5, 2, 4], [[4, 12], [1, 6]]), ('regression: initial letter consumption', [[6, 6, 3, 1, 6], 19, 2, 5, 1, 3], [[2, 12], [3, 12]]), ('partial-repair probe', [[3, 5, 6, 1, 6, 8, 2, 6, 1], 15, 0, 2, 2, 3], [[2, 9], [3, 15], [2, 11], [2, 8]]), ('partial-repair probe', [[2, 2, 2, 2, 1, 7, 6, 1, 1, 8, 3, 4], 23, 0, 4, 1, 2], [[6, 21], [5, 23], [1, 4]]), ('three-line drop cap', [[5, 3, 4, 6, 2, 5, 4, 3], 14, 3, 3, 1, 2], [[2, 8], [1, 4], [2, 9], [3, 14]]), ('no cap uses indent', [[5, 3, 4, 6, 2], 12, 0, 3, 1, 3], [[2, 9], [2, 11], [1, 2]]), ('regression: initial letter consumption', [[4, 3, 6, 1, 3, 3, 4, 6], 15, 3, 4, 1, 3], [[2, 7], [2, 8], [2, 7], [2, 11]]), ('regression: initial letter consumption', [[1, 5, 6, 6, 3, 7], 22, 2, 4, 2, 4], [[3, 13], [2, 10], [1, 7]])]]
for label, args, expected in fixtures[N - 1]:
    check(label, solve(args), expected)
print(json.dumps({"observations": observations, "passed": all(x["passed"] for x in observations)}, ensure_ascii=False))
raise SystemExit(0 if all(x["passed"] for x in observations) else 1)

Boundary fixture	Actual	Expected	Outcome
three-line drop cap	`[[2, 9], [1, 4], [2, 9], [3, 14]]`	`[[2, 8], [1, 4], [2, 9], [3, 14]]`	Failed
regression: initial letter consumption	`[[1, 7], [2, 6], [2, 11], [2, 11], [1, 5]]`	`[[1, 6], [2, 6], [2, 11], [2, 11], [1, 5]]`	Failed
partial-repair probe	`[[2, 9], [2, 14]]`	`[[2, 9], [2, 14]]`	Passed
partial-repair probe	`[[2, 10], [2, 14], [3, 13], [1, 5]]`	`[[2, 10], [2, 14], [3, 13], [1, 5]]`	Passed
no cap uses indent	`[[2, 9], [2, 11], [1, 2]]`	`[[2, 9], [2, 11], [1, 2]]`	Passed
partial-repair probe	`[[2, 8], [3, 16], [3, 14], [2, 9]]`	`[[2, 8], [3, 16], [3, 14], [2, 9]]`	Passed
partial-repair probe	`[[1, 6], [2, 15], [1, 8], [3, 15]]`	`[[1, 6], [2, 15], [1, 8], [3, 15]]`	Passed
partial-repair probe	`[[3, 11], [3, 18]]`	`[[3, 11], [3, 18]]`	Passed

SHA-256 / 0c158608dec0a084ad216121a96e05fb9eae6f6b37e3da5f000f539db5d5862f

2 / The unsuccessful fix

Exit 1

"""Failure Map reference implementation. Python standard library only."""
import json

N = 1
observations = []
def solve(x):
    words, width, cap_lines, cap_width, gap, indent = x
    lens = list(words)
    if cap_lines >= 0:
        lens[0] = lens[0] - 1
    def avail(li):
        base = width
        if li < cap_lines:
            base -= cap_width + gap
        elif cap_lines == 0 and li == 0:
            base -= indent
        return base
    lines = []
    cur = []
    used = 0
    for w in lens:
        li = len(lines)
        need = w if not cur else used + 1 + w
        if need <= avail(li) or not cur:
            cur.append(w)
            used = need
        else:
            lines.append([len(cur), used])
            cur = [w]
            used = w
    if cur:
        lines.append([len(cur), used])
    return lines
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('three-line drop cap', [[5, 3, 4, 6, 2, 5, 4, 3], 14, 3, 3, 1, 2], [[2, 8], [1, 4], [2, 9], [3, 14]]), ('regression: initial letter consumption', [[7, 1, 4, 6, 4, 3, 7, 5], 13, 2, 5, 2, 3], [[1, 6], [2, 6], [2, 11], [2, 11], [1, 5]]), ('partial-repair probe', [[7, 1, 8, 5], 16, 0, 5, 1, 3], [[2, 9], [2, 14]]), ('partial-repair probe', [[1, 8, 8, 5, 8, 1, 2, 5], 17, 0, 2, 1, 3], [[2, 10], [2, 14], [3, 13], [1, 5]]), ('no cap uses indent', [[5, 3, 4, 6, 2], 12, 0, 3, 1, 3], [[2, 9], [2, 11], [1, 2]]), ('partial-repair probe', [[5, 2, 6, 6, 2, 6, 3, 3, 5, 3], 16, 0, 2, 1, 3], [[2, 8], [3, 16], [3, 14], [2, 9]]), ('partial-repair probe', [[6, 6, 8, 8, 8, 2, 3], 15, 0, 5, 1, 4], [[1, 6], [2, 15], [1, 8], [3, 15]]), ('partial-repair probe', [[1, 2, 6, 7, 1, 8], 20, 0, 3, 2, 2], [[3, 11], [3, 18]])], [('regression: initial letter consumption', [[8, 1, 6, 4, 1, 7, 7], 24, 2, 5, 2, 1], [[3, 16], [3, 14], [1, 7]]), ('regression: initial letter consumption', [[1, 2, 7, 6, 5, 2, 3], 17, 3, 5, 1, 3], [[3, 11], [1, 6], [2, 8], [1, 3]]), ('partial-repair probe', [[1, 8, 7, 5, 7, 8], 15, 0, 2, 2, 3], [[2, 10], [2, 13], [1, 7], [1, 8]]), ('partial-repair probe', [[2, 3, 3, 1, 7, 5, 6, 1, 3, 3, 5], 14, 0, 3, 1, 2], [[4, 12], [2, 13], [3, 12], [2, 9]]), ('three-line drop cap', [[5, 3, 4, 6, 2, 5, 4, 3], 14, 3, 3, 1, 2], [[2, 8], [1, 4], [2, 9], [3, 14]]), ('no cap uses indent', [[5, 3, 4, 6, 2], 12, 0, 3, 1, 3], [[2, 9], [2, 11], [1, 2]]), ('regression: initial letter consumption', [[3, 4, 3, 1, 5, 7, 5, 6, 6, 5, 5], 19, 2, 4, 2, 2], [[4, 13], [2, 13], [3, 19], [2, 11]]), ('partial-repair probe', [[6, 8, 2, 2, 5, 4, 5, 7], 24, 0, 2, 1, 2], [[4, 21], [4, 24]])], [('regression: initial letter consumption', [[2, 6, 7, 8, 2, 8, 5, 5, 1, 1, 5], 12, 3, 2, 1, 1], [[2, 8], [1, 7], [1, 8], [2, 11], [2, 11], [3, 9]]), ('regression: initial letter consumption', [[7, 2, 3, 8, 2, 8, 7], 22, 2, 5, 1, 1], [[3, 13], [2, 11], [2, 16]]), ('partial-repair probe', [[1, 6, 4, 2, 2, 2, 8], 15, 0, 4, 1, 3], [[2, 8], [4, 13], [1, 8]]), ('partial-repair probe', [[8, 1, 7, 5, 5, 4, 4, 8, 7], 22, 0, 5, 1, 2], [[3, 18], [4, 21], [2, 16]]), ('three-line drop cap', [[5, 3, 4, 6, 2, 5, 4, 3], 14, 3, 3, 1, 2], [[2, 8], [1, 4], [2, 9], [3, 14]]), ('no cap uses indent', [[5, 3, 4, 6, 2], 12, 0, 3, 1, 3], [[2, 9], [2, 11], [1, 2]]), ('partial-repair probe', [[3, 7, 4, 7], 24, 0, 2, 2, 4], [[3, 16], [1, 7]]), ('partial-repair probe', [[8, 2, 3, 5, 8, 1, 2], 12, 0, 4, 1, 1], [[2, 11], [2, 9], [2, 10], [1, 2]])], [('regression: initial letter consumption', [[2, 6, 7, 8, 1, 2, 5, 6, 8], 21, 2, 2, 2, 4], [[3, 16], [3, 13], [3, 21]]), ('regression: initial letter consumption', [[7, 6, 8, 6, 7, 6, 3, 5, 3, 6, 4], 13, 2, 4, 1, 1], [[1, 6], [1, 6], [1, 8], [1, 6], [1, 7], [2, 10], [2, 9], [2, 11]]), ('partial-repair probe', [[7, 4, 4, 1], 21, 0, 2, 2, 3], [[3, 17], [1, 1]]), ('partial-repair probe', [[3, 2, 8, 1, 5, 6, 1, 5, 8], 16, 0, 4, 2, 2], [[2, 6], [3, 16], [3, 14], [1, 8]]), ('no cap uses indent', [[5, 3, 4, 6, 2], 12, 0, 3, 1, 3], [[2, 9], [2, 11], [1, 2]]), ('three-line drop cap', [[5, 3, 4, 6, 2, 5, 4, 3], 14, 3, 3, 1, 2], [[2, 8], [1, 4], [2, 9], [3, 14]]), ('partial-repair probe', [[7, 6, 7, 4, 5, 3, 3, 5, 3, 5], 23, 0, 3, 2, 4], [[2, 14], [4, 22], [4, 19]]), ('regression: initial letter consumption', [[5, 4, 1, 4, 5, 5, 2, 7], 21, 3, 2, 1, 2], [[4, 16], [3, 14], [1, 7]])], [('regression: initial letter consumption', [[3, 1, 5, 1, 6], 22, 2, 5, 2, 4], [[4, 12], [1, 6]]), ('regression: initial letter consumption', [[6, 6, 3, 1, 6], 19, 2, 5, 1, 3], [[2, 12], [3, 12]]), ('partial-repair probe', [[3, 5, 6, 1, 6, 8, 2, 6, 1], 15, 0, 2, 2, 3], [[2, 9], [3, 15], [2, 11], [2, 8]]), ('partial-repair probe', [[2, 2, 2, 2, 1, 7, 6, 1, 1, 8, 3, 4], 23, 0, 4, 1, 2], [[6, 21], [5, 23], [1, 4]]), ('three-line drop cap', [[5, 3, 4, 6, 2, 5, 4, 3], 14, 3, 3, 1, 2], [[2, 8], [1, 4], [2, 9], [3, 14]]), ('no cap uses indent', [[5, 3, 4, 6, 2], 12, 0, 3, 1, 3], [[2, 9], [2, 11], [1, 2]]), ('regression: initial letter consumption', [[4, 3, 6, 1, 3, 3, 4, 6], 15, 3, 4, 1, 3], [[2, 7], [2, 8], [2, 7], [2, 11]]), ('regression: initial letter consumption', [[1, 5, 6, 6, 3, 7], 22, 2, 4, 2, 4], [[3, 13], [2, 10], [1, 7]])]]
for label, args, expected in fixtures[N - 1]:
    check(label, solve(args), expected)
print(json.dumps({"observations": observations, "passed": all(x["passed"] for x in observations)}, ensure_ascii=False))
raise SystemExit(0 if all(x["passed"] for x in observations) else 1)

Boundary fixture	Actual	Expected	Outcome
three-line drop cap	`[[2, 8], [1, 4], [2, 9], [3, 14]]`	`[[2, 8], [1, 4], [2, 9], [3, 14]]`	Passed
regression: initial letter consumption	`[[1, 6], [2, 6], [2, 11], [2, 11], [1, 5]]`	`[[1, 6], [2, 6], [2, 11], [2, 11], [1, 5]]`	Passed
partial-repair probe	`[[2, 8], [2, 14]]`	`[[2, 9], [2, 14]]`	Failed
partial-repair probe	`[[2, 9], [2, 14], [3, 13], [1, 5]]`	`[[2, 10], [2, 14], [3, 13], [1, 5]]`	Failed
no cap uses indent	`[[2, 8], [2, 11], [1, 2]]`	`[[2, 9], [2, 11], [1, 2]]`	Failed
partial-repair probe	`[[2, 7], [3, 16], [3, 14], [2, 9]]`	`[[2, 8], [3, 16], [3, 14], [2, 9]]`	Failed
partial-repair probe	`[[1, 5], [2, 15], [1, 8], [3, 15]]`	`[[1, 6], [2, 15], [1, 8], [3, 15]]`	Failed
partial-repair probe	`[[4, 18], [2, 10]]`	`[[3, 11], [3, 18]]`	Failed

SHA-256 / afc8f4adc8489904db706b33f355fabce81bb5a1c5c1239edd1122f8d74a0216

3 / The verified repair

Exit 0

"""Failure Map reference implementation. Python standard library only."""
import json

N = 1
observations = []
def solve(x):
    words, width, cap_lines, cap_width, gap, indent = x
    lens = list(words)
    if cap_lines > 0:
        lens[0] = lens[0] - 1
    def avail(li):
        base = width
        if li < cap_lines:
            base -= cap_width + gap
        elif cap_lines == 0 and li == 0:
            base -= indent
        return base
    lines = []
    cur = []
    used = 0
    for w in lens:
        li = len(lines)
        need = w if not cur else used + 1 + w
        if need <= avail(li) or not cur:
            cur.append(w)
            used = need
        else:
            lines.append([len(cur), used])
            cur = [w]
            used = w
    if cur:
        lines.append([len(cur), used])
    return lines
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('three-line drop cap', [[5, 3, 4, 6, 2, 5, 4, 3], 14, 3, 3, 1, 2], [[2, 8], [1, 4], [2, 9], [3, 14]]), ('regression: initial letter consumption', [[7, 1, 4, 6, 4, 3, 7, 5], 13, 2, 5, 2, 3], [[1, 6], [2, 6], [2, 11], [2, 11], [1, 5]]), ('partial-repair probe', [[7, 1, 8, 5], 16, 0, 5, 1, 3], [[2, 9], [2, 14]]), ('partial-repair probe', [[1, 8, 8, 5, 8, 1, 2, 5], 17, 0, 2, 1, 3], [[2, 10], [2, 14], [3, 13], [1, 5]]), ('no cap uses indent', [[5, 3, 4, 6, 2], 12, 0, 3, 1, 3], [[2, 9], [2, 11], [1, 2]]), ('partial-repair probe', [[5, 2, 6, 6, 2, 6, 3, 3, 5, 3], 16, 0, 2, 1, 3], [[2, 8], [3, 16], [3, 14], [2, 9]]), ('partial-repair probe', [[6, 6, 8, 8, 8, 2, 3], 15, 0, 5, 1, 4], [[1, 6], [2, 15], [1, 8], [3, 15]]), ('partial-repair probe', [[1, 2, 6, 7, 1, 8], 20, 0, 3, 2, 2], [[3, 11], [3, 18]])], [('regression: initial letter consumption', [[8, 1, 6, 4, 1, 7, 7], 24, 2, 5, 2, 1], [[3, 16], [3, 14], [1, 7]]), ('regression: initial letter consumption', [[1, 2, 7, 6, 5, 2, 3], 17, 3, 5, 1, 3], [[3, 11], [1, 6], [2, 8], [1, 3]]), ('partial-repair probe', [[1, 8, 7, 5, 7, 8], 15, 0, 2, 2, 3], [[2, 10], [2, 13], [1, 7], [1, 8]]), ('partial-repair probe', [[2, 3, 3, 1, 7, 5, 6, 1, 3, 3, 5], 14, 0, 3, 1, 2], [[4, 12], [2, 13], [3, 12], [2, 9]]), ('three-line drop cap', [[5, 3, 4, 6, 2, 5, 4, 3], 14, 3, 3, 1, 2], [[2, 8], [1, 4], [2, 9], [3, 14]]), ('no cap uses indent', [[5, 3, 4, 6, 2], 12, 0, 3, 1, 3], [[2, 9], [2, 11], [1, 2]]), ('regression: initial letter consumption', [[3, 4, 3, 1, 5, 7, 5, 6, 6, 5, 5], 19, 2, 4, 2, 2], [[4, 13], [2, 13], [3, 19], [2, 11]]), ('partial-repair probe', [[6, 8, 2, 2, 5, 4, 5, 7], 24, 0, 2, 1, 2], [[4, 21], [4, 24]])], [('regression: initial letter consumption', [[2, 6, 7, 8, 2, 8, 5, 5, 1, 1, 5], 12, 3, 2, 1, 1], [[2, 8], [1, 7], [1, 8], [2, 11], [2, 11], [3, 9]]), ('regression: initial letter consumption', [[7, 2, 3, 8, 2, 8, 7], 22, 2, 5, 1, 1], [[3, 13], [2, 11], [2, 16]]), ('partial-repair probe', [[1, 6, 4, 2, 2, 2, 8], 15, 0, 4, 1, 3], [[2, 8], [4, 13], [1, 8]]), ('partial-repair probe', [[8, 1, 7, 5, 5, 4, 4, 8, 7], 22, 0, 5, 1, 2], [[3, 18], [4, 21], [2, 16]]), ('three-line drop cap', [[5, 3, 4, 6, 2, 5, 4, 3], 14, 3, 3, 1, 2], [[2, 8], [1, 4], [2, 9], [3, 14]]), ('no cap uses indent', [[5, 3, 4, 6, 2], 12, 0, 3, 1, 3], [[2, 9], [2, 11], [1, 2]]), ('partial-repair probe', [[3, 7, 4, 7], 24, 0, 2, 2, 4], [[3, 16], [1, 7]]), ('partial-repair probe', [[8, 2, 3, 5, 8, 1, 2], 12, 0, 4, 1, 1], [[2, 11], [2, 9], [2, 10], [1, 2]])], [('regression: initial letter consumption', [[2, 6, 7, 8, 1, 2, 5, 6, 8], 21, 2, 2, 2, 4], [[3, 16], [3, 13], [3, 21]]), ('regression: initial letter consumption', [[7, 6, 8, 6, 7, 6, 3, 5, 3, 6, 4], 13, 2, 4, 1, 1], [[1, 6], [1, 6], [1, 8], [1, 6], [1, 7], [2, 10], [2, 9], [2, 11]]), ('partial-repair probe', [[7, 4, 4, 1], 21, 0, 2, 2, 3], [[3, 17], [1, 1]]), ('partial-repair probe', [[3, 2, 8, 1, 5, 6, 1, 5, 8], 16, 0, 4, 2, 2], [[2, 6], [3, 16], [3, 14], [1, 8]]), ('no cap uses indent', [[5, 3, 4, 6, 2], 12, 0, 3, 1, 3], [[2, 9], [2, 11], [1, 2]]), ('three-line drop cap', [[5, 3, 4, 6, 2, 5, 4, 3], 14, 3, 3, 1, 2], [[2, 8], [1, 4], [2, 9], [3, 14]]), ('partial-repair probe', [[7, 6, 7, 4, 5, 3, 3, 5, 3, 5], 23, 0, 3, 2, 4], [[2, 14], [4, 22], [4, 19]]), ('regression: initial letter consumption', [[5, 4, 1, 4, 5, 5, 2, 7], 21, 3, 2, 1, 2], [[4, 16], [3, 14], [1, 7]])], [('regression: initial letter consumption', [[3, 1, 5, 1, 6], 22, 2, 5, 2, 4], [[4, 12], [1, 6]]), ('regression: initial letter consumption', [[6, 6, 3, 1, 6], 19, 2, 5, 1, 3], [[2, 12], [3, 12]]), ('partial-repair probe', [[3, 5, 6, 1, 6, 8, 2, 6, 1], 15, 0, 2, 2, 3], [[2, 9], [3, 15], [2, 11], [2, 8]]), ('partial-repair probe', [[2, 2, 2, 2, 1, 7, 6, 1, 1, 8, 3, 4], 23, 0, 4, 1, 2], [[6, 21], [5, 23], [1, 4]]), ('three-line drop cap', [[5, 3, 4, 6, 2, 5, 4, 3], 14, 3, 3, 1, 2], [[2, 8], [1, 4], [2, 9], [3, 14]]), ('no cap uses indent', [[5, 3, 4, 6, 2], 12, 0, 3, 1, 3], [[2, 9], [2, 11], [1, 2]]), ('regression: initial letter consumption', [[4, 3, 6, 1, 3, 3, 4, 6], 15, 3, 4, 1, 3], [[2, 7], [2, 8], [2, 7], [2, 11]]), ('regression: initial letter consumption', [[1, 5, 6, 6, 3, 7], 22, 2, 4, 2, 4], [[3, 13], [2, 10], [1, 7]])]]
for label, args, expected in fixtures[N - 1]:
    check(label, solve(args), expected)
print(json.dumps({"observations": observations, "passed": all(x["passed"] for x in observations)}, ensure_ascii=False))
raise SystemExit(0 if all(x["passed"] for x in observations) else 1)

Boundary fixture	Actual	Expected	Outcome
three-line drop cap	`[[2, 8], [1, 4], [2, 9], [3, 14]]`	`[[2, 8], [1, 4], [2, 9], [3, 14]]`	Passed
regression: initial letter consumption	`[[1, 6], [2, 6], [2, 11], [2, 11], [1, 5]]`	`[[1, 6], [2, 6], [2, 11], [2, 11], [1, 5]]`	Passed
partial-repair probe	`[[2, 9], [2, 14]]`	`[[2, 9], [2, 14]]`	Passed
partial-repair probe	`[[2, 10], [2, 14], [3, 13], [1, 5]]`	`[[2, 10], [2, 14], [3, 13], [1, 5]]`	Passed
no cap uses indent	`[[2, 9], [2, 11], [1, 2]]`	`[[2, 9], [2, 11], [1, 2]]`	Passed
partial-repair probe	`[[2, 8], [3, 16], [3, 14], [2, 9]]`	`[[2, 8], [3, 16], [3, 14], [2, 9]]`	Passed
partial-repair probe	`[[1, 6], [2, 15], [1, 8], [3, 15]]`	`[[1, 6], [2, 15], [1, 8], [3, 15]]`	Passed
partial-repair probe	`[[3, 11], [3, 18]]`	`[[3, 11], [3, 18]]`	Passed

SHA-256 / 4d5d6bccc37cf5991b0fade8460243c855e8e12c62da218899cf514dab1d5973

Verification & scope

A deterministic toy typesetting model with integer widths and a stipulated rule set; it does not claim conformance to any engine. This reproducer isolates one failure mechanism. Results cover the supplied fixtures. Variants within a family share a test contract and should remain grouped when constructing evaluation splits. Related mechanisms with a shared evaluation_group must also remain together; these controlled models are not independent production incidents.

Observations recorded using Python 3.12.14 at 2026-09-29T14:49:50.893641+00:00.

Case digest / 89ffdae90f9b7738c0475a132272615229b70fb64f2e0e60e853c142ecfa72ef