#!/usr/bin/env python3
"""Build an NL/Graph/Code variant whose durations and targets match Code minutes.

The pinned raw files are read-only inputs.  Generated training and evaluation
files are written to separate directories so the reproduction data remain
unchanged.
"""
from __future__ import annotations

import argparse
import ast
import hashlib
import json
import re
from collections import Counter
from pathlib import Path


FORMS = ("natural", "graph", "python")
SPLITS = ("train", "test")
STEP_RE = re.compile(r"^(Step \d+\..*?)\s+\(([^()]*)\)\s*$", re.MULTILINE)
DICT_RE = re.compile(r"\{[^\n]+\}")
CODE_INPUT_RE = re.compile(r"adj_list = (\{.*\})\nsource = ([^\n]+)\ntarget = ([^\n]+)")
ANSWER_INSTRUCTION_RE = re.compile(
    r"Then, encode your final answer in <answer></answer> \(e\.g\. <answer>1 min</answer>\)"
)
NUMBER_UNIT_RE = re.compile(r"([-+]?\d+(?:\.\d+)?)\s*([A-Za-z]+)")

MINUTES_PER_UNIT = {
    "second": 1 / 60,
    "seconds": 1 / 60,
    "sec": 1 / 60,
    "minute": 1,
    "minutes": 1,
    "min": 1,
    "hour": 60,
    "hours": 60,
    "day": 1440,
    "days": 1440,
    "week": 10080,
    "weeks": 10080,
    # These match the released Code generator: 30-day months, 365-day years.
    "month": 43200,
    "months": 43200,
    "year": 525600,
    "years": 525600,
}


def sha256(path: Path) -> str:
    return hashlib.sha256(path.read_bytes()).hexdigest()


def read_jsonl(path: Path) -> list[dict]:
    return [json.loads(line) for line in path.open(encoding="utf-8")]


def write_jsonl(path: Path, rows: list[dict]) -> None:
    path.write_text(
        "".join(json.dumps(row, ensure_ascii=False) + "\n" for row in rows),
        encoding="utf-8",
    )


def code_number(value: object) -> float:
    number = float(value)
    if not (number >= 0):
        raise ValueError(f"invalid Code target: {value!r}")
    return number


def shown_number(value: float) -> str:
    """Use the same compact decimal style as the released Code weights."""
    return str(int(value)) if value.is_integer() else f"{value:.2f}"


def duration_to_code_minutes(text: str) -> float:
    """Convert one released duration phrase, rounding as the Code generator did."""
    matches = list(NUMBER_UNIT_RE.finditer(text))
    if not matches or " ".join(match.group(0) for match in matches) != " ".join(text.split()):
        raise ValueError(f"unsupported duration: {text!r}")
    total = 0.0
    for match in matches:
        unit = match.group(2)
        if unit not in MINUTES_PER_UNIT:
            raise ValueError(f"unsupported duration unit {unit!r} in {text!r}")
        total += float(match.group(1)) * MINUTES_PER_UNIT[unit]
    return round(total, 2)


def align_answer_prompt(question: str) -> str:
    replacement = (
        "Report the result as a number of minutes. Then, encode only that number "
        "in <answer></answer> (e.g. <answer>1</answer>)"
    )
    question, count = ANSWER_INSTRUCTION_RE.subn(replacement, question)
    if count != 1:
        raise ValueError("expected exactly one NL/Graph answer instruction")
    return question


def convert_natural_question(question: str) -> tuple[str, list[float]]:
    minutes: list[float] = []

    def replace(match: re.Match[str]) -> str:
        value = duration_to_code_minutes(match.group(2))
        minutes.append(value)
        return f"{match.group(1)} ({shown_number(value)} minutes)"

    question, count = STEP_RE.subn(replace, question)
    if count == 0:
        raise ValueError("no NL step durations found")
    return align_answer_prompt(question), minutes


def convert_graph_question(question: str) -> tuple[str, list[float]]:
    dictionaries = list(DICT_RE.finditer(question))
    if len(dictionaries) != 2:
        raise ValueError("expected adjacency and duration dictionaries")
    duration_match = dictionaries[1]
    durations = ast.literal_eval(duration_match.group(0))
    converted = {node: f"{shown_number(duration_to_code_minutes(value))} minutes"
                 for node, value in durations.items()}
    question = question[:duration_match.start()] + repr(converted) + question[duration_match.end():]
    return align_answer_prompt(question), [duration_to_code_minutes(value) for value in durations.values()]


def longest_code_path(question: str) -> float:
    match = CODE_INPUT_RE.search(question)
    if not match:
        raise ValueError("could not parse Code inputs")
    adjacency = ast.literal_eval(match.group(1))
    source, target = ast.literal_eval(match.group(2)), ast.literal_eval(match.group(3))
    visiting: set[object] = set()
    memo: dict[object, float] = {}

    def visit(node: object) -> float:
        if node == target:
            return 0.0
        if node in memo:
            return memo[node]
        if node in visiting:
            raise ValueError("Code graph is cyclic")
        visiting.add(node)
        candidates = [float(weight) + visit(next_node)
                      for next_node, weight in adjacency.get(node, [])]
        visiting.remove(node)
        if not candidates:
            raise ValueError(f"Code target is unreachable from node {node!r}")
        memo[node] = max(candidates)
        return memo[node]

    return visit(source)


def assert_graph_code_equivalent(graph_question: str, code_question: str) -> None:
    """Check directed structure and rounded node weights up to a node renaming."""
    dictionaries = DICT_RE.findall(graph_question)
    graph_adj = ast.literal_eval(dictionaries[0])
    graph_labels = ast.literal_eval(dictionaries[1])
    match = CODE_INPUT_RE.search(code_question)
    if not match:
        raise ValueError("could not parse Code inputs")
    code_adj = ast.literal_eval(match.group(1))
    code_source, code_target = ast.literal_eval(match.group(2)), ast.literal_eval(match.group(3))

    graph_nodes = set(graph_adj) | {node for targets in graph_adj.values() for node in targets}
    code_nodes = set(code_adj) | {node for targets in code_adj.values() for node, _ in targets}
    graph_in = Counter(node for targets in graph_adj.values() for node in targets)
    code_in = Counter(node for targets in code_adj.values() for node, _ in targets)
    incoming_weights: dict[object, set[float]] = {node: set() for node in code_nodes}
    for targets in code_adj.values():
        for node, weight in targets:
            incoming_weights[node].add(float(weight))
    if any(len(weights) > 1 for weights in incoming_weights.values()):
        raise ValueError("a Code node has inconsistent incoming weights")

    graph_attr = {node: 0.0 if node in {"START", "END"}
                  else duration_to_code_minutes(graph_labels[node]) for node in graph_nodes}
    code_attr = {}
    for node in code_nodes:
        if node in {code_source, code_target}:
            code_attr[node] = 0.0
        elif not incoming_weights[node]:
            raise ValueError(f"non-source Code node {node!r} has no incoming edge")
        else:
            code_attr[node] = next(iter(incoming_weights[node]))

    graph_edges = {(source, target) for source, targets in graph_adj.items() for target in targets}
    code_edges = {(source, target) for source, targets in code_adj.items() for target, _ in targets}
    if len(graph_nodes) != len(code_nodes) or len(graph_edges) != len(code_edges):
        raise ValueError("Graph/Code node or edge counts differ")

    def signature(node: object, adjacency: dict, indegree: Counter, attrs: dict) -> tuple:
        return (attrs[node], indegree[node], len(adjacency.get(node, [])))

    candidates = {
        node: [other for other in code_nodes
               if signature(node, graph_adj, graph_in, graph_attr)
               == signature(other, code_adj, code_in, code_attr)]
        for node in graph_nodes
    }
    candidates["START"] = [code_source]
    candidates["END"] = [code_target]
    if any(not values for values in candidates.values()):
        raise ValueError("Graph/Code node signatures differ")

    mapping: dict[object, object] = {}
    used: set[object] = set()

    def search() -> bool:
        if len(mapping) == len(graph_nodes):
            return {(mapping[a], mapping[b]) for a, b in graph_edges} == code_edges
        remaining = [node for node in graph_nodes if node not in mapping]
        node = min(remaining, key=lambda item: sum(candidate not in used for candidate in candidates[item]))
        for candidate in candidates[node]:
            if candidate in used:
                continue
            if any(((node, other) in graph_edges) != ((candidate, mapped) in code_edges)
                   or ((other, node) in graph_edges) != ((mapped, candidate) in code_edges)
                   for other, mapped in mapping.items()):
                continue
            mapping[node] = candidate
            used.add(candidate)
            if search():
                return True
            used.remove(candidate)
            del mapping[node]
        return False

    if not search():
        raise ValueError("Graph and Code are not isomorphic after minute conversion")


def build_split(raw_dir: Path, split: str) -> tuple[dict[str, list[dict]], dict[str, list[dict]], dict]:
    raw = {form: read_jsonl(raw_dir / f"asynchow_{form}_{split}.jsonl") for form in FORMS}
    lengths = {form: len(rows) for form, rows in raw.items()}
    if len(set(lengths.values())) != 1:
        raise ValueError(f"unaligned {split} lengths: {lengths}")

    training = {form: [] for form in FORMS}
    evaluation = {form: [] for form in FORMS}
    changed_release_targets = 0
    rounded_targets = 0
    for source_row, (natural, graph, code) in enumerate(zip(*(raw[form] for form in FORMS))):
        code_target = code_number(code["answer"])
        calculated = longest_code_path(code["question"])
        if abs(calculated - code_target) > 1e-6:
            raise ValueError(
                f"{split}:{source_row}: Code graph gives {calculated}, label is {code_target}"
            )
        natural_question, natural_minutes = convert_natural_question(natural["question"])
        graph_question, graph_minutes = convert_graph_question(graph["question"])
        if Counter(natural_minutes) != Counter(graph_minutes):
            raise ValueError(f"{split}:{source_row}: converted NL/Graph durations differ")
        try:
            assert_graph_code_equivalent(graph["question"], code["question"])
        except ValueError as error:
            raise ValueError(f"{split}:{source_row}: {error}") from error

        target = f"<answer>{code_target}</answer>"
        questions = {
            "natural": natural_question,
            "graph": graph_question,
            "python": code["question"],
        }
        for form in FORMS:
            training[form].append({"instruction": questions[form], "input": "", "output": target})
            evaluation[form].append({
                "source_row": source_row,
                "question": questions[form],
                "answer": code_target,
                "source_answer": raw[form][source_row]["answer"],
            })

        # Audit how often Code's rounded target differs from the exact released range.
        endpoints = []
        for value in re.findall(r"datetime\.timedelta\(([^)]*)\)", natural["answer"]):
            fields = {key: float(number) for key, number in re.findall(
                r"(days|seconds|microseconds)\s*=\s*([-+]?\d+(?:\.\d+)?)", value
            )}
            endpoints.append((fields.get("days", 0) * 86400 + fields.get("seconds", 0)
                              + fields.get("microseconds", 0) / 1e6) / 60)
        if len(endpoints) != 2:
            raise ValueError(f"{split}:{source_row}: invalid released answer interval")
        if not (endpoints[0] <= code_target <= endpoints[1]):
            changed_release_targets += 1
        if all(abs(code_target - endpoint) > 1e-9 for endpoint in endpoints):
            rounded_targets += 1

    audit = {
        "examples": lengths["natural"],
        "code_target_outside_released_nl_graph_interval": changed_release_targets,
        "code_target_differs_from_both_exact_interval_endpoints": rounded_targets,
    }
    return training, evaluation, audit


def main() -> None:
    parser = argparse.ArgumentParser()
    parser.add_argument("--root", type=Path, default=Path(__file__).parent)
    parser.add_argument("--output-dir", type=Path)
    parser.add_argument("--eval-dir", type=Path)
    args = parser.parse_args()
    root = args.root.resolve()
    raw_dir = root / "raw"
    output_dir = (args.output_dir or root / "data_code_aligned_minutes").resolve()
    eval_dir = (args.eval_dir or root / "eval_code_aligned_minutes").resolve()
    if output_dir == raw_dir or eval_dir == raw_dir:
        raise ValueError("refusing to write generated data into raw/")
    output_dir.mkdir(parents=True, exist_ok=True)
    eval_dir.mkdir(parents=True, exist_ok=True)

    manifest = {
        "variant": "code_aligned_minutes",
        "source": "fangru-lin/procedure_generalization_llm",
        "source_commit": "d9bf3485cd41c1050d33471d922c826f474efec1",
        "policy": {
            "input_units": "NL and Graph durations converted to minutes and rounded to two decimals like Code",
            "targets": "all three forms use the released Code numeric target",
            "months": "30 days",
            "years": "365 days",
            "raw_files_modified": False,
        },
        "raw_sha256": {},
        "splits": {},
    }
    for path in sorted(raw_dir.glob("asynchow_*_*.jsonl")):
        manifest["raw_sha256"][path.name] = sha256(path)

    all_training: dict[str, list[dict]] = {form: [] for form in FORMS}
    for split in SPLITS:
        training, evaluation, audit = build_split(raw_dir, split)
        manifest["splits"][split] = audit
        for form in FORMS:
            if split == "train":
                all_training[form] = training[form]
            write_jsonl(eval_dir / f"asynchow_{form}_{split}.jsonl", evaluation[form])
        print(f"{split}: {audit}")

    dataset_info = {}
    for form in FORMS:
        name = f"asynchow_{form}_code_minutes"
        file_name = f"{name}_train.json"
        (output_dir / file_name).write_text(
            json.dumps(all_training[form], ensure_ascii=False, indent=2) + "\n", encoding="utf-8"
        )
        dataset_info[name] = {
            "file_name": file_name,
            "columns": {"prompt": "instruction", "query": "input", "response": "output"},
        }
    (output_dir / "dataset_info.json").write_text(
        json.dumps(dataset_info, ensure_ascii=False, indent=2) + "\n", encoding="utf-8"
    )
    manifest["generated_sha256"] = {
        str(path.relative_to(root)): sha256(path)
        for directory in (output_dir, eval_dir)
        for path in sorted(directory.glob("*"))
        if path.is_file() and path != output_dir / "manifest.json"
    }
    (output_dir / "manifest.json").write_text(
        json.dumps(manifest, ensure_ascii=False, indent=2) + "\n", encoding="utf-8"
    )
    print(f"training data: {output_dir}")
    print(f"evaluation data: {eval_dir}")


if __name__ == "__main__":
    main()