import json
import logging
import os
from tempfile import TemporaryDirectory
from typing import Dict, List, Optional

import jsonlines
from huggingface_hub import CommitOperationAdd  # type: ignore[import]
from huggingface_hub import Discussion, HfApi, HfFileSystem
from tqdm import tqdm

from .evaluation import METRICS
from .formatting import styled_error, styled_message, styled_warning
from .tasks import TASKS_PRETTY_REVERSE


class AlreadyExists(Exception):
    pass


class SubmissionUploader:
    """Class for adding new files to a dataset on a Hub and opening a PR.

    Heavily influenced by these amazing spaces:
    * https://huggingface.co/spaces/safetensors/convert
    * https://huggingface.co/spaces/gaia-benchmark/leaderboard
    """

    def __init__(self, dataset_id: str):
        self._api = HfApi(token=os.environ["HF_TOKEN"])
        self._fs = HfFileSystem(token=os.environ["HF_TOKEN"])
        self._dataset_id = dataset_id

    def _get_previous_pr(self, pr_title: str) -> Optional[Discussion]:
        """Searches among discussions of dataset repo for a PR with the given title."""
        try:
            discussions = self._api.get_repo_discussions(
                repo_id=self._dataset_id, repo_type="dataset"
            )
        except Exception:
            return None
        for discussion in discussions:
            if (
                discussion.status == "open"
                and discussion.is_pull_request
                and discussion.title == pr_title
            ):
                return discussion
        return None

    def _get_metadata(
        self,
        model_name_pretty: str,
        model_availability: str,
        urls: str,
        context_size: str,
        submitted_by: str,
    ) -> Dict[str, str]:
        return {
            "model_name": model_name_pretty,
            "model_availability": model_availability,
            "urls": urls,
            "context_size": context_size,
            "submitted_by": submitted_by,
        }

    def _upload_predictions(
        self,
        task_id: str,
        model_folder: str,
        filenames: List[str],
    ) -> List[CommitOperationAdd]:
        commit_operations = [
            CommitOperationAdd(
                path_in_repo=f"{task_id}/predictions/{model_folder}/{os.path.basename(filename)}",
                path_or_fileobj=filename,
            )
            for filename in filenames
        ]
        return commit_operations

    def _compute_metrics_for_predictions(
        self, task_id: str, filenames: Optional[List[str]], temp_directory: str
    ) -> None:
        metrics_module = METRICS[task_id]
        assert (
            metrics_module is not None
        ), f"Computing metrics for {task_id} is not supported."
        metrics_module.reset()
        open(os.path.join(temp_directory, "metrics.jsonl"), "w").close()

        # compute the metrics for each submitted file
        for filename in filenames:
            with jsonlines.open(filename, "r") as reader:
                for example in tqdm(
                    reader, desc=f"Computing metrics for {os.path.basename(filename)}"
                ):
                    metrics_module.add_batch(
                        predictions=[example["prediction"]],
                        references=[example["reference"]],
                    )
            computed_metrics = metrics_module.compute()
            metrics_module.reset()
            with jsonlines.open(
                os.path.join(temp_directory, "metrics.jsonl"), "a"
            ) as writer:
                writer.write(computed_metrics)

        # aggregate the metrics over submitted files
        with jsonlines.open(
            os.path.join(temp_directory, "metrics.jsonl"), "r"
        ) as reader:
            metrics_results = [line for line in reader]
        final_metrics_results = {
            key: sum(entry[key] for entry in metrics_results) / len(metrics_results)
            for key in metrics_results[0]
        }
        with open(os.path.join(temp_directory, "final_metrics.json"), "w") as f:
            json.dump(final_metrics_results, f)

    def _upload_results(
        self,
        task_id: str,
        model_folder: str,
        model_name_pretty: str,
        model_availability: str,
        urls: str,
        context_size: str,
        submitted_by: str,
        temp_directory: str,
    ) -> List[CommitOperationAdd]:
        final_results = {}
        with open(os.path.join(temp_directory, "final_metrics.json"), "r") as f:
            metrics = json.load(f)
        final_results.update(metrics)
        metadata_dict = self._get_metadata(
            model_name_pretty=model_name_pretty,
            model_availability=model_availability,
            urls=urls,
            context_size=context_size,
            submitted_by=submitted_by,
        )
        final_results.update(metadata_dict)

        with jsonlines.open(
            os.path.join(temp_directory, "final_results.jsonl"), "w"
        ) as writer:
            writer.write(final_results)

        return [
            CommitOperationAdd(
                path_in_repo=f"{task_id}/results/{model_folder}.jsonl",
                path_or_fileobj=os.path.join(temp_directory, "final_results.jsonl"),
            )
        ]

    def _verify_arguments(
        self,
        task_pretty: str,
        model_folder: str,
        model_name_pretty: str,
        model_availability: str,
        urls: str,
        context_size: str,
        submitted_by: str,
        filenames: Optional[List[str]],
    ):
        assert (
            task_pretty and task_pretty in TASKS_PRETTY_REVERSE
        ), "Please, select one of the supported tasks."
        assert (
            model_folder
        ), "Please, specify non-empty name for a directory with a model's results."
        assert model_name_pretty, "Please, specify non-empty name for a model."
        assert (
            model_availability
        ), "Please, specify non-empty information about a model's availability."
        assert (
            context_size
        ), "Please, specify non-empty information about a model's context size."
        try:
            _ = int(context_size)
        except:
            raise ValueError(
                "Please, specify a model's context size as an integer (e.g., 16000)."
            )

        assert (
            submitted_by
        ), "Please, specify non-empty information about a submission's author(s)."
        assert filenames, "Please, attach at least one file with predictions."

    def upload_files(
        self,
        task_pretty: str,
        model_folder: str,
        model_name_pretty: str,
        model_availability: str,
        urls: str,
        context_size: str,
        submitted_by: str,
        filenames: Optional[List[str]],
        force: bool = False,
    ) -> str:
        try:
            self._verify_arguments(
                task_pretty=task_pretty,
                model_folder=model_folder,
                model_name_pretty=model_name_pretty,
                model_availability=model_availability,
                urls=urls,
                context_size=context_size,
                submitted_by=submitted_by,
                filenames=filenames,
            )
            pr_title = f"🚀 New submission to {task_pretty} task: {model_name_pretty} with {context_size} context size from {submitted_by}"

            logging.info(f"Start processing {pr_title}")

            task_id = TASKS_PRETTY_REVERSE[task_pretty]

            logging.info("Checking if this request has already been submitted...")
            if not force:
                if model_name_pretty in self._fs.ls(
                    f"datasets/{self._dataset_id}/{task_id}/predictions"
                ) and all(
                    filename
                    in self._fs.ls(
                        f"datasets/{self._dataset_id}/{task_id}/predictions/{model_name_pretty}"
                    )
                    for filename in filenames + ["metadata.json"]
                ):
                    return styled_warning(
                        f"{model_name_pretty} is already present in {self._dataset_id}."
                    )

                prev_pr = self._get_previous_pr(pr_title)
                if prev_pr is not None:
                    url = f"https://huggingface.co/datasets/{self._dataset_id}/discussions/{prev_pr.num}"
                    return styled_warning(
                        f"{self._dataset_id} already has an open PR for this submission: {url}."
                    )

            logging.info("Processing predictions...")
            predictions_commit_operations = self._upload_predictions(
                task_id=task_id,
                model_folder=model_folder,
                filenames=filenames,
            )

            with TemporaryDirectory() as d:
                logging.info("Computing metrics...")
                self._compute_metrics_for_predictions(
                    task_id=task_id, filenames=filenames, temp_directory=str(d)
                )

                logging.info("Processing results...")
                results_commit_operations = self._upload_results(
                    task_id=task_id,
                    model_folder=model_folder,
                    model_name_pretty=model_name_pretty,
                    model_availability=model_availability,
                    urls=urls,
                    context_size=context_size,
                    submitted_by=submitted_by,
                    temp_directory=str(d),
                )

                logging.info("Creating commit...")
                new_pr = self._api.create_commit(
                    repo_id=self._dataset_id,
                    operations=predictions_commit_operations
                    + results_commit_operations,
                    commit_message=pr_title,
                    commit_description=f"""New submission to {task_pretty} task in 🏟️ Long Code Arena benchmark!\n* Model name: {model_name_pretty}\n* Model availability: {model_availability}\n* Context Size: {context_size}\n* Relevant URLs: {urls}\n* Submitted By: {submitted_by}""",
                    create_pr=True,
                    repo_type="dataset",
                )
                return styled_message(f"🎉 PR created at {new_pr.pr_url}.")

        except Exception as e:
            logging.exception(e)
            if str(e):
                return styled_error(f"An exception occurred. Please, try again.\n{e}")
            return styled_error("An exception occurred. Please, try again.")