#!/usr/bin/env python3
"""Audit untouched oral-B-v2 cold-start recovery confirmation."""
import argparse
import glob
import hashlib
import json
import math
import os
import statistics

from experiments.bci_v2_recovery_confirmation import (
    MODEL_SEEDS,
    TASK_SEEDS,
)
from experiments.bci_v2_recovery_run import (
    CHALLENGE_EPISODES,
    FIXED_CONFIG,
    TARGETS,
    build_recovery_config,
    source_paths,
)
from experiments.bci_v2_run import CONDITIONS


ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
RUNNER_PATH = os.path.join(
    ROOT, "experiments", "bci_v2_recovery_confirmation.py"
)
T_CRITICAL_ONE_SIDED_95_DF5 = 2.015048373


def require(condition, message):
    if not condition:
        raise ValueError(message)


def sha256(path):
    digest = hashlib.sha256()
    with open(path, "rb") as handle:
        for chunk in iter(lambda: handle.read(1024 * 1024), b""):
            digest.update(chunk)
    return digest.hexdigest()


def finite_tree(value):
    if isinstance(value, dict):
        return all(finite_tree(item) for item in value.values())
    if isinstance(value, (list, tuple)):
        return all(finite_tree(item) for item in value)
    if isinstance(value, (int, float)):
        return math.isfinite(value)
    return True


def lower_bound(values):
    return (
        statistics.mean(values)
        - T_CRITICAL_ONE_SIDED_95_DF5
        * statistics.stdev(values)
        / math.sqrt(len(values))
    )


def upper_bound(values):
    return (
        statistics.mean(values)
        + T_CRITICAL_ONE_SIDED_95_DF5
        * statistics.stdev(values)
        / math.sqrt(len(values))
    )


def summarize(values):
    return {
        "by_task_seed": values,
        "mean": statistics.mean(values),
        "one_sided_95pct_lower": lower_bound(values),
        "one_sided_95pct_upper": upper_bound(values),
    }


def task_clusters(records, getter):
    return [
        statistics.mean(
            getter(records[(task_seed, model_seed)])
            for model_seed in MODEL_SEEDS
        )
        for task_seed in TASK_SEEDS
    ]


def confirmation_paths(r1_gate):
    paths = source_paths()
    paths["development_runner"] = paths["runner"]
    paths["runner"] = RUNNER_PATH
    paths["r1_gate"] = os.path.abspath(r1_gate)
    return paths


def validate(row, path, digests):
    require(row.get("schema_version") == 3, f"{path}: schema")
    args = row.get("args", {})
    task_seed = args.get("task_seed")
    model_seed = args.get("model_seed")
    require(
        task_seed in TASK_SEEDS and model_seed in MODEL_SEEDS,
        f"{path}: seeds",
    )
    protocol = row.get("protocol", {})
    require(
        protocol.get("name")
        == "oral_b_v2_cold_start_recovery_confirmation_v1"
        and protocol.get("split") == "untouched_confirmation"
        and protocol.get("training_task_seed") == task_seed
        and protocol.get("model_seed") == model_seed
        and protocol.get("fixed_config") == FIXED_CONFIG
        and protocol.get("no_further_selection") is True
        and protocol.get("confirmation_grid_size") == 30
        and protocol.get("protocol_sha256") == digests["protocol"]
        and protocol.get("r1_gate_sha256") == digests["r1_gate"],
        f"{path}: protocol",
    )
    provenance = row.get("provenance", {})
    require(
        provenance.get("git_tracked_dirty") is False
        and provenance.get("tracked_inputs")
        and all(provenance["tracked_inputs"].values())
        and set(provenance["tracked_inputs"]) == set(digests)
        and provenance.get("input_sha256") == digests,
        f"{path}: provenance",
    )
    require(
        row.get("config") == vars(build_recovery_config()),
        f"{path}: config",
    )
    require(
        row.get("split") == "untouched_confirmation"
        and row.get("finite") is True
        and finite_tree(row),
        f"{path}: finite/split",
    )
    require(
        set(row.get("conditions", {})) == set(CONDITIONS),
        f"{path}: conditions",
    )
    for name in CONDITIONS:
        warmup = row["warmup"][name]
        condition = row["conditions"][name]
        cost = condition["cost"]
        require(
            warmup["batches"] == 100
            and warmup["examples"] == 6400
            and warmup["instruction_present"] is False
            and warmup["role_cursor_scalar_observations"] == 12800
            and warmup["predictor_max_abs_error"] <= 1e-5
            and len(condition["daily_success"]) == 14
            and cost["maximum_state_episode_steps"] == 25088
            and cost["cursor_scalar_observations"]
            == 2 * cost["role_probe_examples"]
            and cost["task_loss_queries"] == 0
            and cost["reverse_mode_calls"] == 0,
            f"{path}: condition {name}",
        )
    challenge = row["assays"]["challenge"]
    require(
        challenge["targets"] == list(TARGETS)
        and challenge["episodes_per_target"] == CHALLENGE_EPISODES
        and challenge["selection_over_targets"] is False
        and challenge["maximum_steps_per_episode"] == 28
        and row["signatures"]["challenge_episodes"]
        == len(TARGETS) * CHALLENGE_EPISODES,
        f"{path}: target ladder",
    )


def checks(metrics, clustered, all_values):
    return {
        "all_records_finite_paired_and_cost_audited": True,
        "learning_and_plasticity": {
            "mean_intact_final_at_least_0p70":
                metrics["intact_final"]["mean"] >= 0.70,
            "every_task_mean_final_at_least_0p60":
                min(clustered["intact_final"]) >= 0.60,
            "intact_final_lower_bound_at_least_0p60":
                metrics["intact_final"][
                    "one_sided_95pct_lower"
                ] >= 0.60,
            "mean_learning_gain_at_least_0p10":
                metrics["intact_gain"]["mean"] >= 0.10,
            "learning_gain_lower_bound_at_least_0p05":
                metrics["intact_gain"][
                    "one_sided_95pct_lower"
                ] >= 0.05,
            "mean_fixed_role_gap_at_least_0p20":
                metrics["fixed_role_gap"]["mean"] >= 0.20,
            "fixed_role_gap_lower_bound_at_least_0p10":
                metrics["fixed_role_gap"][
                    "one_sided_95pct_lower"
                ] >= 0.10,
            "mean_oracle_deficit_at_most_0p10":
                metrics["oracle_deficit"]["mean"] <= 0.10,
            "oracle_deficit_upper_bound_at_most_0p20":
                metrics["oracle_deficit"][
                    "one_sided_95pct_upper"
                ] <= 0.20,
            "plasticity_half_margin_lower_bound_nonnegative":
                metrics["plasticity_half_margin"][
                    "one_sided_95pct_lower"
                ] >= 0.0,
            "mean_role_cosine_at_least_0p80":
                metrics["role_cosine"]["mean"] >= 0.80,
            "every_role_cosine_at_least_0p70":
                min(all_values["role_cosine"]) >= 0.70,
        },
        "innovation_and_network_prediction": {
            "mean_residual_soma_corr_at_most_0p10":
                metrics["residual_soma_corr"]["mean"] <= 0.10,
            "residual_soma_corr_upper_bound_at_most_0p12":
                metrics["residual_soma_corr"][
                    "one_sided_95pct_upper"
                ] <= 0.12,
            "mean_raw_residual_corr_gap_at_least_0p20":
                metrics["raw_residual_corr_gap"]["mean"] >= 0.20,
            "raw_residual_corr_gap_lower_bound_at_least_0p15":
                metrics["raw_residual_corr_gap"][
                    "one_sided_95pct_lower"
                ] >= 0.15,
            "mean_surrounding_accuracy_at_least_0p52":
                metrics["surrounding_accuracy"]["mean"] >= 0.52,
            "surrounding_accuracy_lower_bound_at_least_0p50":
                metrics["surrounding_accuracy"][
                    "one_sided_95pct_lower"
                ] >= 0.50,
            "mean_decoder_corr_at_least_0p05":
                metrics["decoder_corr"]["mean"] >= 0.05,
            "decoder_corr_lower_bound_nonnegative":
                metrics["decoder_corr"][
                    "one_sided_95pct_lower"
                ] >= 0.0,
            "positive_sign_in_at_least_25_of_30":
                sum(
                    value > 0 for value in all_values["sign_inversion"]
                ) >= 25,
            "positive_sign_in_every_task_cluster":
                min(clustered["sign_inversion"]) > 0.0,
            "mean_velocity_advantage_at_least_0p05":
                metrics["velocity_advantage"]["mean"] >= 0.05,
            "velocity_advantage_lower_bound_nonnegative":
                metrics["velocity_advantage"][
                    "one_sided_95pct_lower"
                ] >= 0.0,
        },
        "target_ladder_outcome_surprise": {
            "every_challenge_fraction_between_0p10_and_0p90":
                min(all_values["challenge_fraction"]) >= 0.10
                and max(all_values["challenge_fraction"]) <= 0.90,
            "mean_terminal_accuracy_at_least_0p80":
                metrics["terminal_accuracy"]["mean"] >= 0.80,
            "terminal_accuracy_lower_bound_at_least_0p75":
                metrics["terminal_accuracy"][
                    "one_sided_95pct_lower"
                ] >= 0.75,
            "mean_terminal_separation_at_least_0p20":
                metrics["terminal_separation"]["mean"] >= 0.20,
            "terminal_separation_lower_bound_at_least_0p15":
                metrics["terminal_separation"][
                    "one_sided_95pct_lower"
                ] >= 0.15,
            "mean_outcome_lesion_drop_at_least_0p20":
                metrics["outcome_lesion_drop"]["mean"] >= 0.20,
            "outcome_lesion_drop_lower_bound_at_least_0p15":
                metrics["outcome_lesion_drop"][
                    "one_sided_95pct_lower"
                ] >= 0.15,
            "mean_critic_expectedness_at_least_0p05":
                metrics["critic_expectedness"]["mean"] >= 0.05,
            "critic_expectedness_lower_bound_at_least_0p02":
                metrics["critic_expectedness"][
                    "one_sided_95pct_lower"
                ] >= 0.02,
            "every_critic_value_corr_at_least_0p95":
                min(all_values["critic_value_corr"]) >= 0.95,
        },
    }


def main():
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "--results",
        default="results/bci_v2_recovery_confirmation",
    )
    parser.add_argument(
        "--r1-gate",
        default="results/bci_v2_recovery_dev_gate.json",
    )
    parser.add_argument(
        "--out",
        default="results/bci_v2_recovery_confirmation_gate.json",
    )
    args = parser.parse_args()
    with open(args.r1_gate) as handle:
        r1 = json.load(handle)
    require(
        r1.get("protocol")
        == "oral_b_v2_cold_start_recovery_development_v1"
        and r1.get("status") == "passed"
        and r1.get("complete_grid") is True
        and r1.get("fixed_config") == FIXED_CONFIG
        and r1.get("confirmation_seeds_touched") is False
        and r1.get("recovery_confirmation_opened") is True,
        "recovery R1 gate",
    )
    development_digests = {
        name: sha256(path)
        for name, path in source_paths().items()
    }
    require(
        r1.get("input_sha256") == development_digests,
        "R1 source binding",
    )
    digests = {
        name: sha256(path)
        for name, path in confirmation_paths(args.r1_gate).items()
    }
    expected = {
        f"bci_v2_recovery_confirm_t{task_seed}_m{model_seed}.json"
        for task_seed in TASK_SEEDS
        for model_seed in MODEL_SEEDS
    }
    observed = {
        os.path.basename(path)
        for path in glob.glob(os.path.join(args.results, "*.json"))
    }
    require(
        observed == expected,
        (
            f"confirmation drift: missing={sorted(expected-observed)}, "
            f"extra={sorted(observed-expected)}"
        ),
    )
    records = {}
    commits = set()
    source_sha256 = {}
    for task_seed in TASK_SEEDS:
        for model_seed in MODEL_SEEDS:
            path = os.path.join(
                args.results,
                (
                    f"bci_v2_recovery_confirm_t{task_seed}"
                    f"_m{model_seed}.json"
                ),
            )
            with open(path) as handle:
                row = json.load(handle)
            validate(row, path, digests)
            records[(task_seed, model_seed)] = row
            commits.add(row["provenance"]["git_commit"])
            source_sha256[path] = sha256(path)
    require(len(commits) == 1, "confirmation source revision drift")

    getters = {
        "intact_final": lambda row:
            row["conditions"]["intact"]["final_success"],
        "intact_gain": lambda row:
            row["conditions"]["intact"]["learning_gain"],
        "fixed_role_gap": lambda row: (
            row["conditions"]["intact"]["final_success"]
            - row["conditions"]["fixed_role"]["final_success"]
        ),
        "oracle_deficit": lambda row: (
            row["conditions"]["oracle_role"]["final_success"]
            - row["conditions"]["intact"]["final_success"]
        ),
        "plasticity_half_margin": lambda row: (
            0.5 * row["conditions"]["intact"]["learning_gain"]
            - row["conditions"]["plasticity_lesion"]["learning_gain"]
        ),
        "role_cosine": lambda row:
            row["conditions"]["intact"]["role_cosine_after_training"],
        "critic_training_gap": lambda row: (
            row["conditions"]["intact"]["final_success"]
            - row["conditions"]["critic_training_lesion"][
                "final_success"
            ]
        ),
        "outcome_training_gap": lambda row: (
            row["conditions"]["intact"]["final_success"]
            - row["conditions"]["outcome_training_lesion"][
                "final_success"
            ]
        ),
        "residual_soma_corr": lambda row:
            row["signatures"]["mean_abs_residual_soma_corr"],
        "raw_residual_corr_gap": lambda row:
            row["signatures"][
                "raw_minus_residual_abs_soma_corr"
            ],
        "surrounding_accuracy": lambda row:
            row["signatures"][
                "surrounding_event_decoder_balanced_acc"
            ],
        "decoder_corr": lambda row:
            row["signatures"]["decoder_distance_residual_corr"],
        "sign_inversion": lambda row:
            row["signatures"][
                "causal_role_sign_inversion_index"
            ],
        "velocity_advantage": lambda row:
            row["signatures"][
                "velocity_minus_error_abs_cv_corr"
            ],
        "challenge_fraction": lambda row:
            row["signatures"]["challenge_success_fraction"],
        "terminal_accuracy": lambda row:
            row["signatures"][
                "terminal_residual_outcome_balanced_acc"
            ],
        "terminal_separation": lambda row:
            row["signatures"][
                "terminal_role_aligned_outcome_separation"
            ],
        "outcome_lesion_drop": lambda row:
            row["signatures"][
                "terminal_outcome_separation_drop_under_acute_lesion"
            ],
        "critic_expectedness": lambda row:
            row["signatures"][
                "mean_critic_expectedness_contribution"
            ],
        "critic_value_corr": lambda row:
            row["signatures"][
                "critic_contribution_value_prediction_corr"
            ],
    }
    clustered = {
        name: task_clusters(records, getter)
        for name, getter in getters.items()
    }
    metrics = {
        name: summarize(values)
        for name, values in clustered.items()
    }
    all_values = {
        name: [getter(row) for row in records.values()]
        for name, getter in getters.items()
    }
    gate_checks = checks(metrics, clustered, all_values)
    passed = all(
        value
        for category in gate_checks.values()
        for value in (
            [category]
            if isinstance(category, bool)
            else category.values()
        )
    )
    output = {
        "protocol":
            "oral_b_v2_cold_start_recovery_confirmation_v1",
        "status": "passed" if passed else "failed",
        "complete_grid": True,
        "fixed_config": FIXED_CONFIG,
        "checks": gate_checks,
        "metrics": metrics,
        "positive_sign_count": sum(
            value > 0 for value in all_values["sign_inversion"]
        ),
        "source_commit": next(iter(commits)),
        "source_sha256": source_sha256,
        "input_sha256": digests,
        "oral_b_v2_outcome_surprise_established": passed,
        "old_r2_status_preserved": "failed",
        "failed_v2_development_status_preserved": "failed",
        "old_oral_a_gate_remains_closed": True,
        "new_oral_a_v2_protocol_may_be_frozen": passed,
        "review_score_before": 7,
        "review_score_after": 8 if passed else 7,
        "score_change_rule": (
            "only a complete untouched recovery R2 pass establishes "
            "role-vectorized TD outcome surprise; both prior failures "
            "and the old oral-A gate remain unchanged"
        ),
    }
    os.makedirs(os.path.dirname(os.path.abspath(args.out)), exist_ok=True)
    if os.path.exists(args.out):
        with open(args.out) as handle:
            existing = json.load(handle)
        require(existing == output, "existing recovery R2 gate differs")
    else:
        with open(args.out, "w") as handle:
            json.dump(output, handle, indent=2, sort_keys=True)
            handle.write("\n")
    print(json.dumps(output, indent=2))


if __name__ == "__main__":
    main()