#!/usr/bin/env python3
"""Independent finite checks for scenario cover and joint failure severity.

Only the Python standard library is used.  The scalar rank oracle and rational
row elimination below are independent of the package's calibration code.
These exhaustive finite examples check formulas; they are not formal proofs
or evidence that an arbitrary large design is robust.
"""

from __future__ import annotations

import argparse
from fractions import Fraction
from itertools import product
from math import comb
from pathlib import Path
import json


def rational_rank(rows: list[list[Fraction]]) -> int:
    if not rows:
        return 0
    a = [list(map(Fraction, row)) for row in rows]
    ncols = len(a[0])
    pivot_row = 0
    for col in range(ncols):
        pivot = next((i for i in range(pivot_row, len(a)) if a[i][col]), None)
        if pivot is None:
            continue
        a[pivot_row], a[pivot] = a[pivot], a[pivot_row]
        scale = a[pivot_row][col]
        a[pivot_row] = [x / scale for x in a[pivot_row]]
        for i in range(pivot_row + 1, len(a)):
            scale = a[i][col]
            if scale:
                a[i] = [x - scale * y for x, y in zip(a[i], a[pivot_row])]
        pivot_row += 1
        if pivot_row == len(a):
            break
    return pivot_row


def members(mask: int, n: int) -> list[int]:
    return [i for i in range(n) if mask & (1 << i)]


def frac_string(value: Fraction) -> str:
    return str(value.numerator) if value.denominator == 1 else str(value)


def scalar_checks() -> dict:
    n = 4
    full = (1 << n) - 1
    # Column j is visible to precisely the channels indicated by visible[j].
    # Channel visibility sets are {0,1}, {1,2}, {0,2}, {0,1,2}.
    visible = [0b1101, 0b1011, 0b1110]
    masks = list(range(1 << n))
    records = []
    total_submodular_pairs = total_monotone_pairs = 0
    total_target_checks = total_cost_instances = 0
    zero_optimum_instances = 0
    worst_ratio = Fraction(1)
    worst_instance = None
    equal_cost_one_failure = None

    for budget in range(3):
        scenarios = [f for f in masks if f.bit_count() <= budget]
        target = len(scenarios) * len(visible)
        values = {
            s: sum(bool((s & ~f) & v) for f in scenarios for v in visible)
            for s in masks
        }
        assert values[0] == 0 and values[full] == target
        for s in masks:
            assert isinstance(values[s], int) and 0 <= values[s] <= target
            coverage = [(s & v).bit_count() for v in visible]
            assert (values[s] == target) == all(c >= budget + 1 for c in coverage)
            total_target_checks += 1
            for t in masks:
                assert values[s] + values[t] >= values[s | t] + values[s & t]
                total_submodular_pairs += 1
                if s & t == s:
                    assert values[s] <= values[t]
                    total_monotone_pairs += 1

        budget_worst = Fraction(1)
        for costs in product(range(4), repeat=n):
            initial = sum(1 << a for a, cost in enumerate(costs) if cost == 0)
            selected = initial
            initial_deficit = target - values[initial]
            path = []
            while values[selected] < target:
                options = []
                for a, cost in enumerate(costs):
                    if selected & (1 << a):
                        continue
                    gain = values[selected | (1 << a)] - values[selected]
                    if gain > 0:
                        options.append((Fraction(cost, gain), a, gain))
                assert options
                ratio, a, gain = min(options)
                selected |= 1 << a
                path.append({"channel": a, "gain": gain,
                             "cost_per_gain": frac_string(ratio)})
            greedy_cost = sum(costs[a] for a in members(selected, n))
            optimal_cost, optimal_mask = min(
                (sum(costs[a] for a in members(s, n)), s)
                for s in masks if values[s] == target
            )
            harmonic = sum((Fraction(1, i) for i in range(1, initial_deficit + 1)),
                           start=Fraction(0))
            if initial_deficit == 0:
                assert greedy_cost == optimal_cost == 0
            else:
                assert greedy_cost <= harmonic * optimal_cost
            if optimal_cost == 0:
                zero_optimum_instances += 1
                assert greedy_cost == 0
            else:
                ratio = Fraction(greedy_cost, optimal_cost)
                budget_worst = max(budget_worst, ratio)
                if ratio > worst_ratio:
                    worst_ratio = ratio
                    worst_instance = {
                        "failure_budget": budget, "costs": list(costs),
                        "initial_zero_cost_channels": members(initial, n),
                        "initial_deficit": initial_deficit,
                        "harmonic_bound_factor": frac_string(harmonic),
                        "greedy_channels": members(selected, n),
                        "greedy_cost": greedy_cost,
                        "optimal_channels": members(optimal_mask, n),
                        "optimal_cost": optimal_cost,
                        "greedy_to_optimal_ratio": frac_string(ratio),
                        "greedy_path": path,
                    }
            if budget == 1 and costs == (1, 1, 1, 1):
                assert optimal_cost == greedy_cost == 3
                equal_cost_one_failure = {
                    "costs": list(costs), "optimal_cost": optimal_cost,
                    "one_optimal_selection": members(optimal_mask, n),
                    "greedy_selection": members(selected, n), "greedy_path": path,
                }
            total_cost_instances += 1

        records.append({
            "failure_budget": budget, "scenario_count": len(scenarios),
            "target": target, "objective_by_selection_mask": values,
            "cost_vectors_checked": 4 ** n,
            "worst_greedy_to_optimal_ratio": frac_string(budget_worst),
        })

    # The pointwise minimum of deletion ranks does not inherit submodularity.
    def minimum_surviving_rank(s: int) -> int:
        return min(int(bool(s & ~f)) for f in range(4)
                   if f & s == f and f.bit_count() <= 1)

    g = [minimum_surviving_rank(s) for s in range(4)]
    assert g == [0, 0, 0, 1]
    assert g[1] + g[2] < g[0] + g[3]

    # A specified scenario list only certifies that list.  The empty deletion
    # scenario certifies scalar identification with one channel, not 1-error
    # correction, which would require distance greater than two.
    arbitrary_family_target = 1
    arbitrary_family_value = int(bool(1))
    one_channel_distance = 1
    assert arbitrary_family_value == arbitrary_family_target
    assert not one_channel_distance > 2

    return {
        "catalogue_visibility_by_channel": [[0, 1], [1, 2], [0, 2], [0, 1, 2]],
        "budgets_checked": [0, 1, 2],
        "submodular_ordered_pairs_checked": total_submodular_pairs,
        "monotone_ordered_pairs_checked": total_monotone_pairs,
        "target_equivalence_checks": total_target_checks,
        "greedy_cost_instances_checked": total_cost_instances,
        "zero_optimum_cost_instances": zero_optimum_instances,
        "worst_greedy_to_optimal_ratio": frac_string(worst_ratio),
        "worst_instance": worst_instance,
        "equal_cost_one_failure": equal_cost_one_failure,
        "per_budget": records,
        "minimum_rank_non_submodularity": {
            "values_empty_a_b_ab": g, "left_side": g[1] + g[2],
            "right_side": g[0] + g[3], "strict_violation": True,
        },
        "arbitrary_scenario_scope_regression": {
            "scenario_family": [[]], "selected_channels": [0],
            "target_reached": True, "distance": one_channel_distance,
            "corrects_one_arbitrary_error": False,
        },
    }


def severity_checks() -> dict:
    n = 6
    full = (1 << n) - 1
    sensor_rows = [[Fraction(t) ** j for j in range(4)] for t in range(1, n + 1)]
    column_coordinate_sets = [[1, 2, 3], [2, 3], [3], []]
    dimensions = [len(coords) for coords in column_coordinate_sets]
    failures = []
    loss_by_failure_count = {e: set() for e in range(n + 1)}
    restriction_rank_checks = 0
    p = Fraction(1, 10)
    reliability = Fraction(0)
    for failed in range(1 << n):
        retained = full & ~failed
        ranks = []
        for coords in column_coordinate_sets:
            rows = [[sensor_rows[a][j] for j in coords] for a in members(retained, n)]
            rank = rational_rank(rows)
            assert rank == min(retained.bit_count(), len(coords))
            ranks.append(rank)
            restriction_rank_checks += 1
        losses = [d - rank for d, rank in zip(dimensions, ranks)]
        loss = sum(losses)
        e = failed.bit_count()
        assert loss == sum(max(0, e - threshold) for threshold in (3, 4, 5))
        loss_by_failure_count[e].add(loss)
        failures.append({"failed_channels": members(failed, n),
                         "surviving_ranks": ranks, "column_nullities": losses,
                         "total_nullity": loss})
        if loss == 0:
            reliability += p ** e * (1 - p) ** (n - e)
    assert all(len(values) == 1 for values in loss_by_failure_count.values())
    loss_profile = [next(iter(loss_by_failure_count[e])) for e in range(n + 1)]
    assert loss_profile == [0, 0, 0, 0, 1, 3, 6]
    thresholds = [min(len(row["failed_channels"]) for row in failures
                      if row["total_nullity"] >= k) for k in range(1, 7)]
    assert thresholds == [4, 5, 5, 6, 6, 6]
    binomial_reliability = sum(
        (Fraction(comb(n, e)) * p ** e * (1 - p) ** (n - e) for e in range(4)),
        start=Fraction(0),
    )
    assert reliability == binomial_reliability

    def scalar_severity(visibility: list[int], count: int) -> dict:
        table = []
        all_channels = (1 << count) - 1
        for failed in range(1 << count):
            survivors = all_channels & ~failed
            loss = sum(not bool(survivors & v) for v in visibility)
            table.append((failed.bit_count(), loss))
        thresholds = [min(e for e, loss in table if loss >= k)
                      for k in range(1, len(visibility) + 1)]
        return {"individual_distances": [v.bit_count() for v in visibility],
                "global_thresholds": thresholds}

    disjoint = scalar_severity([0b01, 0b10], 2)
    shared = scalar_severity([0b1, 0b1], 1)
    assert disjoint["individual_distances"] == shared["individual_distances"] == [1, 1]
    assert disjoint["global_thresholds"] == [1, 2]
    assert shared["global_thresholds"] == [1, 1]
    return {
        "exact_rational_restriction_ranks_checked": restriction_rank_checks,
        "failure_sets_checked": 1 << n,
        "sensor_rows": [[str(x) for x in row] for row in sensor_rows],
        "column_coordinate_sets": column_coordinate_sets,
        "column_dimensions": dimensions,
        "total_nullity_by_erasure_count": loss_profile,
        "global_thresholds_for_k_1_to_6": thresholds,
        "independent_failure_probability": frac_string(p),
        "exact_probability_of_identification": frac_string(reliability),
        "probability_decimal": float(reliability),
        "binomial_reliability_cross_check": True,
        "shared_vs_disjoint_counterexample": {"disjoint": disjoint, "shared": shared},
        "full_failure_table": failures,
    }


def main() -> None:
    parser = argparse.ArgumentParser(description=__doc__)
    parser.add_argument("--output", type=Path,
                        default=Path(__file__).resolve().parents[1] / "results" / "selection_results.json")
    args = parser.parse_args()
    scalar = scalar_checks()
    severity = severity_checks()
    result = {
        "status": "all finite checks passed",
        "arithmetic": "Python standard library integers and fractions.Fraction",
        "scope": "Independent finite example checks; not a formal proof or external review.",
        "scenario_cover": scalar, "global_failure_severity": severity,
    }
    args.output.parent.mkdir(parents=True, exist_ok=True)
    args.output.write_text(json.dumps(result, indent=2) + "\n", encoding="utf-8")
    print(json.dumps({
        "status": result["status"],
        "submodular_pairs": scalar["submodular_ordered_pairs_checked"],
        "monotone_pairs": scalar["monotone_ordered_pairs_checked"],
        "target_equivalence_checks": scalar["target_equivalence_checks"],
        "greedy_cost_instances": scalar["greedy_cost_instances_checked"],
        "exact_rational_restriction_ranks": severity["exact_rational_restriction_ranks_checked"],
        "failure_sets": severity["failure_sets_checked"],
        "output": str(args.output),
    }, indent=2))


if __name__ == "__main__":
    main()