extract_eb_alfred_mixed_case_diff_steps.py

#!/usr/bin/env python3
"""
Extract divergent steps for aligned EB-ALFRED mixed-task cases.

For each aligned success/failure case pair, the script finds the most similar
success run and failure run by maximizing their shared action prefix length.
It then saves only the steps from the first divergence onward for both sides.
"""

from __future__ import annotations

import argparse
import copy
import json
from pathlib import Path
from typing import Any


DEFAULT_SUCCESS_INPUT = Path(
    "/data/Top_Spcae/ICML_2026/Dataset/eb-alfred_mixed_tasks_success_cases.json"
)
DEFAULT_FAILURE_INPUT = Path(
    "/data/Top_Spcae/ICML_2026/Dataset/eb-alfred_mixed_tasks_failure_cases.json"
)
DEFAULT_OUTPUT = Path(
    "/data/Top_Spcae/ICML_2026/Dataset/eb-alfred_mixed_tasks_diff_steps.json"
)


def parse_args() -> argparse.Namespace:
    parser = argparse.ArgumentParser(
        description="Extract divergent steps from aligned EB-ALFRED mixed-task cases."
    )
    parser.add_argument(
        "--success-input",
        type=Path,
        default=DEFAULT_SUCCESS_INPUT,
        help=f"Path to aligned success cases JSON. Default: {DEFAULT_SUCCESS_INPUT}",
    )
    parser.add_argument(
        "--failure-input",
        type=Path,
        default=DEFAULT_FAILURE_INPUT,
        help=f"Path to aligned failure cases JSON. Default: {DEFAULT_FAILURE_INPUT}",
    )
    parser.add_argument(
        "--output",
        type=Path,
        default=DEFAULT_OUTPUT,
        help=f"Path to output JSON. Default: {DEFAULT_OUTPUT}",
    )
    return parser.parse_args()


def action_sequence(run: dict[str, Any]) -> list[str]:
    actions: list[str] = []
    for step in run.get("trajectory", []) or []:
        executable_plan = step.get("executable_plan", []) or []
        if executable_plan:
            actions.append(str(executable_plan[0]["action"][1]))
    return actions


def shared_prefix_len(a: list[str], b: list[str]) -> int:
    prefix = 0
    for left, right in zip(a, b):
        if left != right:
            break
        prefix += 1
    return prefix


def choose_best_pair(
    success_case: dict[str, Any], failure_case: dict[str, Any]
) -> tuple[dict[str, Any], dict[str, Any], int]:
    best_success_run: dict[str, Any] | None = None
    best_failure_run: dict[str, Any] | None = None
    best_prefix = -1
    best_success_len = -1
    best_failure_len = -1

    for success_run in success_case.get("runs", []):
        success_actions = action_sequence(success_run)
        for failure_run in failure_case.get("runs", []):
            failure_actions = action_sequence(failure_run)
            prefix = shared_prefix_len(success_actions, failure_actions)

            # Prefer longer shared prefixes. On ties, prefer shorter total tails.
            success_len = len(success_actions)
            failure_len = len(failure_actions)
            is_better = (
                prefix > best_prefix
                or (
                    prefix == best_prefix
                    and success_len + failure_len < best_success_len + best_failure_len
                )
            )
            if is_better:
                best_success_run = success_run
                best_failure_run = failure_run
                best_prefix = prefix
                best_success_len = success_len
                best_failure_len = failure_len

    if best_success_run is None or best_failure_run is None:
        raise ValueError(
            f"Could not find a valid run pair for case {success_case.get('case_index')}"
        )

    return best_success_run, best_failure_run, best_prefix


def annotate_diff_steps(
    trajectory_steps: list[dict[str, Any]],
    prefix: int,
    trajectory_outcome: str,
    selected_model: str | None,
) -> list[dict[str, Any]]:
    annotated_steps: list[dict[str, Any]] = []
    trajectory_success = 1.0 if trajectory_outcome == "success" else 0.0
    for original_step_index, step in enumerate(trajectory_steps[prefix:], start=prefix):
        annotated_step = copy.deepcopy(step)
        executable_plan = annotated_step.get("executable_plan", []) or []
        for executable in executable_plan:
            executable["trajectory_outcome"] = trajectory_outcome
            executable["trajectory_success"] = trajectory_success
            executable["selected_model"] = selected_model
            executable["original_step_index_0based"] = original_step_index
            executable["original_step_index_1based"] = original_step_index + 1
        annotated_step["trajectory_outcome"] = trajectory_outcome
        annotated_step["trajectory_success"] = trajectory_success
        annotated_step["selected_model"] = selected_model
        annotated_step["original_step_index_0based"] = original_step_index
        annotated_step["original_step_index_1based"] = original_step_index + 1
        annotated_steps.append(annotated_step)
    return annotated_steps


def build_diff_case(
    success_case: dict[str, Any], failure_case: dict[str, Any]
) -> dict[str, Any]:
    keys_to_match = ["case_index", "trajectory_id", "eval_set", "episode_id", "instruction"]
    for key in keys_to_match:
        if success_case.get(key) != failure_case.get(key):
            raise ValueError(
                f"Mismatched aligned cases for key '{key}': "
                f"{success_case.get(key)!r} != {failure_case.get(key)!r}"
            )

    success_run, failure_run, prefix = choose_best_pair(success_case, failure_case)
    success_actions = action_sequence(success_run)
    failure_actions = action_sequence(failure_run)

    success_diff_steps = annotate_diff_steps(
        success_run.get("trajectory", []) or [],
        prefix,
        "success",
        success_run.get("model_name"),
    )
    failure_diff_steps = annotate_diff_steps(
        failure_run.get("trajectory", []) or [],
        prefix,
        "failure",
        failure_run.get("model_name"),
    )

    success_first_diff_action = success_actions[prefix] if prefix < len(success_actions) else None
    failure_first_diff_action = failure_actions[prefix] if prefix < len(failure_actions) else None

    return {
        "case_index": success_case["case_index"],
        "trajectory_id": success_case["trajectory_id"],
        "eval_set": success_case["eval_set"],
        "episode_id": success_case["episode_id"],
        "instruction": success_case["instruction"],
        "paired_success_run_count": success_case["paired_success_run_count"],
        "paired_failure_run_count": success_case["paired_failure_run_count"],
        "selected_success_model": success_run.get("model_name"),
        "selected_failure_model": failure_run.get("model_name"),
        "selected_success_input": success_run.get("input"),
        "selected_failure_input": failure_run.get("input"),
        "selected_success_num_steps": success_run.get("num_steps"),
        "selected_failure_num_steps": failure_run.get("num_steps"),
        "shared_prefix_len": prefix,
        "shared_prefix_actions": success_actions[:prefix],
        "first_diff_step_index_0based": prefix,
        "first_diff_step_index_1based": prefix + 1,
        "first_diff_success_action": success_first_diff_action,
        "first_diff_failure_action": failure_first_diff_action,
        "success_diff_num_steps": len(success_diff_steps),
        "failure_diff_num_steps": len(failure_diff_steps),
        "success_diff_actions": success_actions[prefix:],
        "failure_diff_actions": failure_actions[prefix:],
        "success_diff_steps": success_diff_steps,
        "failure_diff_steps": failure_diff_steps,
    }


def main() -> None:
    args = parse_args()

    with args.success_input.open("r", encoding="utf-8") as f:
        success_payload = json.load(f)
    with args.failure_input.open("r", encoding="utf-8") as f:
        failure_payload = json.load(f)

    success_cases = success_payload["cases"]
    failure_cases = failure_payload["cases"]
    if len(success_cases) != len(failure_cases):
        raise ValueError(
            f"Case count mismatch: {len(success_cases)} success cases vs "
            f"{len(failure_cases)} failure cases"
        )

    diff_cases = [
        build_diff_case(success_case, failure_case)
        for success_case, failure_case in zip(success_cases, failure_cases)
    ]

    output_payload = {
        "success_input_file": str(args.success_input),
        "failure_input_file": str(args.failure_input),
        "selection_rule": (
            "For each aligned case, choose the success/failure run pair with the "
            "longest shared action prefix; extract only the divergent tail steps."
        ),
        "num_cases": len(diff_cases),
        "cases": diff_cases,
    }

    args.output.parent.mkdir(parents=True, exist_ok=True)
    with args.output.open("w", encoding="utf-8") as f:
        json.dump(output_payload, f, ensure_ascii=False, indent=2)

    print(f"Aligned cases processed: {len(diff_cases)}")
    print(f"Saved divergent-step file to: {args.output}")


if __name__ == "__main__":
    main()