from openai import AsyncOpenAI
import os
import pytest


@pytest.mark.asyncio
async def test_openai_fine_tuning():
    """
    [PROD Test] Ensures logprobs are returned correctly
    """
    client = AsyncOpenAI(api_key="sk-1234", base_url="http://0.0.0.0:4000")

    # file_name = "openai_batch_completions.jsonl"
    # _current_dir = os.path.dirname(os.path.abspath(__file__))
    # file_path = os.path.join(_current_dir, file_name)

    # response = await client.files.create(
    #     extra_body={"custom_llm_provider": "azure"},
    #     file=open(file_path, "rb"),
    #     purpose="fine-tune",
    # )

    # print("response from files.create: {}".format(response))

    # # create fine tuning job

    # ft_job = await client.fine_tuning.jobs.create(
    #     model="gpt-35-turbo-1106",
    #     training_file=response.id,
    #     extra_body={"custom_llm_provider": "azure"},
    # )

    # print("response from ft job={}".format(ft_job))

    # # response from example endpoint
    # assert ft_job.id == "file-abc123"

    # get fine tuning job
    # specific_ft_job = await client.fine_tuning.jobs.retrieve(
    #     fine_tuning_job_id="123",
    #     extra_body={"custom_llm_provider": "azure"},
    # )

    # list all fine tuning jobs
    list_ft_jobs = await client.fine_tuning.jobs.list(
        extra_query={"custom_llm_provider": "azure"}
    )

    # cancel specific fine tuning job
    cancel_ft_job = await client.fine_tuning.jobs.cancel(
        fine_tuning_job_id="123",
        extra_body={"custom_llm_provider": "azure"},
    )