import csv
import os
from datetime import datetime
from typing import Optional, Union

import gradio as gr
from huggingface_hub import HfApi, Repository

from onnx_export import convert
from gradio_huggingfacehub_search import HuggingfaceHubSearch
from apscheduler.schedulers.background import BackgroundScheduler

DATASET_REPO_URL = "https://huggingface.co/datasets/optimum/exporters"
DATA_FILENAME = "data.csv"
DATA_FILE = os.path.join("data", DATA_FILENAME)

HF_TOKEN = os.environ.get("HF_WRITE_TOKEN")

DATADIR = "exporters_data"

repo: Optional[Repository] = None
# if HF_TOKEN:
#     repo = Repository(local_dir=DATADIR, clone_from=DATASET_REPO_URL, token=HF_TOKEN)


def onnx_export(
    model_id: str, task: str, opset: Union[int, str], oauth_token: gr.OAuthToken
) -> str:
    if oauth_token.token is None:
        return "You must be logged in to use this space"

    if not model_id:
        return f"### Invalid input 🐞 Please specify a model name, got {model_id}"

    try:
        if opset == "":
            opset = None
        else:
            opset = int(opset)

        api = HfApi(token=oauth_token.token)

        error, commit_info = convert(api=api, model_id=model_id, task=task, opset=opset)
        if error != "0":
            return error

        print("[commit_info]", commit_info)

        # save in a private dataset
        if repo is not None:
            repo.git_pull(rebase=True)
            with open(os.path.join(DATADIR, DATA_FILE), "a") as csvfile:
                writer = csv.DictWriter(
                    csvfile, fieldnames=["model_id", "pr_url", "time"]
                )
                writer.writerow(
                    {
                        "model_id": model_id,
                        "pr_url": commit_info.pr_url,
                        "time": str(datetime.now()),
                    }
                )
            commit_url = repo.push_to_hub()
            print("[dataset]", commit_url)

        pr_revision = commit_info.pr_revision.replace("/", "%2F")

        return f"#### Success 🔥 Yay! This model was successfully exported and a PR was open using your token, here: [{commit_info.pr_url}]({commit_info.pr_url}). If you would like to use the exported model without waiting for the PR to be approved, head to https://huggingface.co/{model_id}/tree/{pr_revision}"
    except Exception as e:
        return f"#### Error: {e}"


TTILE_IMAGE = """
<div
    style="
        display: block;
        margin-left: auto;
        margin-right: auto;
        width: 50%;
    "
>
<img src="https://huggingface.co/spaces/optimum/exporters/resolve/main/clean_hf_onnx.png"/>
</div>
"""

TITLE = """
<div
    style="
        display: inline-flex;
        align-items: center;
        text-align: center;
        max-width: 1400px;
        gap: 0.8rem;
        font-size: 2.2rem;
    "
>
<h1 style="font-weight: 900; margin-bottom: 10px; margin-top: 10px;">
    Export transformers model to ONNX with 🤗 Optimum exporters 🏎️
</h1>
</div>
"""

# for some reason https://huggingface.co/settings/tokens is not showing as a link by default?
DESCRIPTION = """
This Space allows you to automatically export 🤗 transformers, diffusers, timm and sentence-transformers PyTorch models hosted on the Hugging Face Hub to [ONNX](https://onnx.ai/). It opens a PR on the target model, and it is up to the owner of the original model
to merge the PR to allow people to leverage the ONNX standard to share and use the model on a wide range of devices!

Once exported, the model can, for example, be used in the [🤗 Optimum](https://huggingface.co/docs/optimum/) library closely following the transformers API.
Check out [this guide](https://huggingface.co/docs/optimum/main/en/onnxruntime/usage_guides/models) to see how!

Note: in case the model to export is larger than 2 GB, it will be saved in a subfolder called `onnx/`. To load it from Optimum, the argument `subfolder="onnx"` should be provided.
"""

with gr.Blocks() as demo:
    gr.Markdown("You must be logged to use this space")
    gr.LoginButton(min_width=250)

    gr.HTML(TTILE_IMAGE)
    gr.HTML(TITLE)

    with gr.Row():
        with gr.Column(scale=50):
            gr.Markdown(DESCRIPTION)

        with gr.Column(scale=50):
            input_model = HuggingfaceHubSearch(
                label="Hub model ID",
                placeholder="Search for model ID on the hub",
                search_type="model",
            )
            input_task = gr.Textbox(
                value="auto",
                max_lines=1,
                label='Task (can be left to "auto", will be automatically inferred)',
            )
            onnx_opset = gr.Textbox(
                placeholder="for example 14, can be left blank",
                max_lines=1,
                label="ONNX opset (optional, can be left blank)",
            )
            btn = gr.Button("Export to ONNX")
            output = gr.Markdown(label="Output")

    btn.click(
        fn=onnx_export,
        inputs=[input_model, input_task, onnx_opset],
        outputs=output,
    )


def restart_space():
    HfApi().restart_space(repo_id="onnx/export", token=HF_TOKEN, factory_reboot=True)


scheduler = BackgroundScheduler()
scheduler.add_job(restart_space, "interval", seconds=21600)
scheduler.start()

demo.launch()