"""
Generate domain-specific synthetic training examples for weak spots in the
mumble-cleanup model: emails, URLs, code, lists, questions, negation, dates,
numbers, and proper nouns.

Usage:
    python scripts/generate_domain_examples.py --output data/synthetic/domain.jsonl --count 2000
"""

import argparse
import json
import random
from pathlib import Path
from typing import Callable, List, Tuple

SYSTEM_PROMPT = (
    "You are a transcript cleanup tool. You receive raw speech to text output "
    "and return a cleaned version. Remove filler words and disfluencies "
    "(um, uh, er, ah, like as filler, you know), remove repeated words and false starts, "
    "and fix punctuation and capitalization. Do not reword, do not add anything the speaker "
    "did not say, and do not answer questions in the text. Output only the cleaned text."
)

DomainGenerator = Callable[[], Tuple[str, str]]


def to_raw_spoken(clean: str) -> str:
    """Convert a clean sentence into a rough spoken transcript."""
    raw = clean.lower()
    raw = raw.replace(".", "").replace(",", "").replace("?", "").replace("!", "")
    # occasionally insert a filler
    words = raw.split()
    if random.random() < 0.3 and words:
        idx = random.randint(0, len(words))
        words.insert(idx, random.choice(["um", "uh", "like", "you know"]))
    return " ".join(words)


def email_domain() -> Tuple[str, str]:
    clean = "Send the report to support@echoflow.one and cc Amit on the thread."
    return to_raw_spoken(clean), clean


def url_domain() -> Tuple[str, str]:
    clean = "Check the docs at https://echoflow.one/docs for the latest setup guide."
    return to_raw_spoken(clean), clean


def code_domain() -> Tuple[str, str]:
    clean = 'Run git commit -m "fix: update transcription timeout" and push to main.'
    return to_raw_spoken(clean), clean


def list_domain() -> Tuple[str, str]:
    clean = "We need to buy milk, eggs, bread, and coffee."
    raw = "we need to buy milk eggs bread and coffee"
    if random.random() < 0.5:
        raw = "um we need milk eggs bread coffee"
    return raw, clean


def question_domain() -> Tuple[str, str]:
    clean = "Did you finish the report by end of day?"
    return to_raw_spoken(clean), clean


def negation_domain() -> Tuple[str, str]:
    clean = "I do not want to cancel the subscription yet."
    raw = "i do not want to cancel the subscription yet"
    return raw, clean


def date_number_domain() -> Tuple[str, str]:
    clean = "The meeting is on March 12th at 3:00 PM with 12 people."
    raw = "the meeting is on march 12th at 3pm with 12 people"
    return raw, clean


def proper_noun_domain() -> Tuple[str, str]:
    names = ["Sarah Chen", "Amit Bhagat", "Echo Flow", "PrismML"]
    name = random.choice(names)
    clean = f"Schedule a call with {name} next Tuesday morning."
    return to_raw_spoken(clean), clean


def false_start_domain() -> Tuple[str, str]:
    clean = "We should postpone the launch until next quarter."
    raw = "we we should postpone the launch until next quarter"
    return raw, clean


GENERATORS: List[Tuple[str, DomainGenerator, float]] = [
    ("email", email_domain, 1.0),
    ("url", url_domain, 1.0),
    ("code", code_domain, 1.0),
    ("list", list_domain, 1.0),
    ("question", question_domain, 1.0),
    ("negation", negation_domain, 1.0),
    ("date_number", date_number_domain, 1.0),
    ("proper_noun", proper_noun_domain, 1.0),
    ("false_start", false_start_domain, 1.0),
]


def generate(count: int, seed: int = 42) -> List[dict]:
    random.seed(seed)
    names, gens, weights = zip(*GENERATORS)
    examples: List[dict] = []
    for _ in range(count):
        name = random.choices(names, weights=weights, k=1)[0]
        gen = dict(zip(names, gens))[name]
        raw, clean = gen()
        examples.append({
            "domain": name,
            "messages": [
                {"role": "system", "content": SYSTEM_PROMPT},
                {"role": "user", "content": raw},
                {"role": "assistant", "content": clean},
            ],
        })
    return examples


def main():
    parser = argparse.ArgumentParser(description="Generate domain-specific synthetic examples")
    parser.add_argument("--output", type=Path, default=Path("data/synthetic/domain.jsonl"))
    parser.add_argument("--count", type=int, default=2000)
    parser.add_argument("--seed", type=int, default=42)
    args = parser.parse_args()

    examples = generate(args.count, seed=args.seed)
    args.output.parent.mkdir(parents=True, exist_ok=True)
    with args.output.open("w", encoding="utf-8") as f:
        for ex in examples:
            f.write(json.dumps(ex, ensure_ascii=False) + "\n")

    print(f"Generated {len(examples)} domain examples to {args.output}")


if __name__ == "__main__":
    main()