from typing import List
import argparse
import json
import os
import random
import openai

from datasets import Dataset, load_dataset

from multi_token.constants import ROLE_ASSISTANT, ROLE_USER

DATASET_ARGS = dict(
    path="mozilla-foundation/common_voice_15_0", name="en", split="train"
)

PROMPT = """
You are helping train a voice audio assistant that can take speech inputs and output text.

Here is the speech you can hear:
{captions}

{question}

Include the question and answer.
"""

QUESTIONS = [
    "Ask a question about the content of the audio.",
    "Ask a complex question about the content of the audio.",
    "Ask a complex question that is relevant to the content of the audio, for example, asking about background knowledge of the things in the speech. Do not ask about uncertain details.",
    "Ask a complex question that is relevant to the content of the audio, for example, asking about the events referred to in the audio. Do not ask about uncertain details.",
    "Ask a question about the tone of the audio.",
    "Ask to paraphrase the audio in a certain way.",
    "Ask about your thoughts on the audio.",
    "Ask what is said in the audio.",
    "Ask about what could be said next in the audio.",
    "If the audio could be question, ask to answer the question in the audio. If it does not, ask to answer a question only answered by listening to the audio.",
]

OPENAI_TOOLS = [
    {
        "type": "function",
        "function": {
            "name": "create_chat",
            "description": "Create a training example",
            "parameters": {
                "type": "object",
                "properties": {
                    "question": {
                        "type": "string",
                        "description": "The question, must be provided",
                    },
                    "answer": {
                        "type": "string",
                        "description": "The answer to the question, must be provided",
                    },
                },
                "required": ["question", "answer"],
            },
        },
    }
]


def _build_convo(idx, row) -> List:
    client = openai.Client()

    captions = [row["sentence"]]
    speech_audios = [{"dataset_args": DATASET_ARGS, "idx": idx}]

    captions_text = "\n".join([f'Caption: "{cap}"' for i, cap in enumerate(captions)])
    prompt = PROMPT.format(
        captions=captions_text, question=random.choice(QUESTIONS)
    ).strip()

    completion = client.chat.completions.create(
        model="gpt-3.5-turbo-1106",
        messages=[{"role": "system", "content": prompt}],
        tools=OPENAI_TOOLS,
        tool_choice={"type": "function", "function": {"name": "create_chat"}},
    )
    resp = json.loads(completion.choices[0].message.tool_calls[0].function.arguments)
    if "answer" not in resp:
        print(resp)
    q = resp["question"]
    a = resp["answer"]

    if random.choice([True, False]):
        q = "<speech>" * len(captions) + " " + q
    else:
        q = q + " " + "<speech>" * len(captions)

    example = {
        "speech_audios": speech_audios,
        "messages": [
            {
                "role": ROLE_USER,
                "content": q,
            },
            {
                "role": ROLE_ASSISTANT,
                "content": a,
            },
        ],
    }
    return example


def main(args):
    data = load_dataset(**DATASET_ARGS)
    data_idxs = list(range(len(data)))

    os.makedirs(args.cache_folder, exist_ok=True)

    def gen(seeds):
        r = random.Random(seeds[0] + 10)
        cache = open(
            os.path.join(args.cache_folder, f"gpt-cache.{seeds[0]}.jsonl"), "a"
        )
        i = 0
        while i < len(seeds):
            selected_idx = r.sample(data_idxs, k=1)[0]
            selected_row = data[selected_idx]
            try:
                example = _build_convo(selected_idx, selected_row)
                cache.write(json.dumps(example) + "\n")
                yield example
                i += 1
            except Exception as e:
                print(e)
                continue

        cache.close()

    ds = Dataset.from_generator(
        gen,
        num_proc=args.num_proc,
        gen_kwargs={"seeds": list(range(args.num_examples))},
    )
    ds.save_to_disk(args.output_folder)


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "-o",
        "--output_folder",
        type=str,
        default="/data/whisper-gpt-common_voice_15_0-finetune",
    )
    parser.add_argument(
        "-c",
        "--cache_folder",
        type=str,
        default="/data/whisper-gpt-common_voice_15_0-finetune-cache",
    )
    parser.add_argument("-n", "--num_examples", type=int, default=300_000)
    parser.add_argument("-p", "--num_proc", type=int, default=10)
    args = parser.parse_args()
    main(args)