import time

from grazie.api.client.chat.prompt import ChatPrompt
from grazie.api.client.endpoints import GrazieApiGatewayUrls
from grazie.api.client.gateway import GrazieApiGatewayClient, GrazieAgent, AuthType
from grazie.api.client.profiles import LLMProfile
from tqdm import tqdm

import config
import hf_data_loader

client = GrazieApiGatewayClient(
    grazie_agent=GrazieAgent(name="commit-rewriting-summary-generation", version="dev"),
    url=GrazieApiGatewayUrls.STAGING,
    auth_type=AuthType.SERVICE,
    grazie_jwt_token=config.GRAZIE_API_JWT_TOKEN
)


def get_example_prompt(start_msg, end_msg):
    return f"""START OF THE EXAMPLE

For following the edited message:
START OF THE EDITED COMMIT MESSAGE 
{end_msg}
END OF THE EDITED COMMIT MESSAGE

You would output the following initial commit message:
START OF THE INITIAL COMMIT MESSAGE
{start_msg}
END OF THE INITIAL COMMIT MESSAGE

END OF THE EXAMPLE"""


def generate_examples():
    manual_df = hf_data_loader.load_raw_rewriting_dataset_as_pandas()[['commit_msg_start', 'commit_msg_end']]
    examples = [
        get_example_prompt(row['commit_msg_start'], row['commit_msg_end'])
        for _, row in manual_df.iterrows()
    ]

    return "\n".join(examples)


EXAMPLES = generate_examples()


def build_prompt(reference, diff):
    return f"""A software developer uses a LLM to generate commit messages.
    
They generated a commit message for the following source code changes:
START OF THE SOURCE CODE CHANGES
{diff}
END OF THE SOURCE CODE CHANGES

After generating the commit message the developer understands that it is not perfect. After making dome changes,
they come up with an edited version of the message. Here is this edited message:
START OF THE COMMIT MESSAGE 
{reference}
END OF THE COMMIT MESSAGE

Your task is to print the initial, LLM-generated commit message. Here are some examples of what you should output:
START OF THE EXAMPLES LIST
{EXAMPLES}
END OF THE EXAMPLES LIST

Print only the initial commit message's text after the 
token "OUTPUT".

OUTPUT"""


def generate_prompt_for_row(row):
    reference = row['reference']
    diff = row['mods']
    return build_prompt(reference, diff)


def generate_initial_msg(prompt):
    commit_msg = client.chat(
        chat=ChatPrompt()
        .add_system("You are a helpful assistant.")
        .add_user(prompt),
        profile=LLMProfile("gpt-4-1106-preview")
    ).content

    return commit_msg


def generate_synthetic_dataset():
    df = hf_data_loader.load_full_commit_dataset_as_pandas()
    df['initial_msg_prompt'] = df.apply(generate_prompt_for_row, axis=1)
    initial_messages_pred = []

    for i, prompt in enumerate(tqdm(df['initial_msg_ prompt'])):
        output = None

        while output is None:
            try:
                output = generate_initial_msg(prompt)
            except:
                time.sleep(0.5)

        assert output is not None
        initial_messages_pred.append(output)

    df['initial_msg_pred'] = initial_messages_pred

    df.to_csv(config.SYNTHETIC_DATASET_ARTIFACT)


if __name__ == '__main__':
    generate_synthetic_dataset()