"""
Handle submissions to the CodeReview Bench leaderboard.
"""

import json
import os
import tempfile
from datetime import datetime
from typing import Dict, List, Tuple

from huggingface_hub import HfApi
from datasets import load_dataset

from src.display.formatting import styled_error, styled_message
from src.envs import RESULTS_DATASET_ID, TOKEN, REPO_ID
from src.leaderboard.processor import process_jsonl_submission, add_entries_to_leaderboard


def validate_submission(file_path: str) -> Tuple[bool, str]:
    """
    Validate a submission file.
    """
    try:
        entries, message = process_jsonl_submission(file_path)
        if not entries:
            return False, message
        return True, "Submission is valid"
    except Exception as e:
        return False, f"Error validating submission: {e}"


def submit_entry_to_hub(entry: Dict, model_name: str, mode: str, version="v0") -> Tuple[bool, str]:
    """
    Submit a model's evaluation entry to the HuggingFace dataset. The entry is uniquely identified by model_name, mode, and version.
    """
    try:
        # Create safe model name for file path
        model_name_safe = model_name.replace("/", "_").replace(" ", "_")
        mode_safe = str(mode).replace("/", "_").replace(" ", "_").lower()

        # Create entry path in entries folder
        entry_path = f"entries/entry_{model_name_safe}_{mode_safe}_{version}.json"

        # Save entry to temporary file
        with tempfile.NamedTemporaryFile(mode='w', suffix='.json', delete=False) as temp_file:
            json.dump(entry, temp_file, indent=2)
            temp_path = temp_file.name

        # Upload file
        api = HfApi(token=TOKEN)
        api.upload_file(
            path_or_fileobj=temp_path,
            path_in_repo=entry_path,
            repo_id=RESULTS_DATASET_ID,
            repo_type="dataset",
            commit_message=f"Add evaluation entry for {model_name} (mode {mode}, version {version})"
        )

        os.unlink(temp_path)
        return True, f"Successfully uploaded evaluation entry for {model_name} (mode {mode})"
    except Exception as e:
        return False, f"Error submitting entry to dataset: {e}"


def submit_leaderboard_to_hub(entries: List[Dict], version="v0") -> Tuple[bool, str]:
    """
    Submit updated leaderboard to the HuggingFace dataset.
    """
    try:
        # Create leaderboard data
        leaderboard_data = {
            "entries": entries,
            "last_updated": datetime.now().isoformat(),
            "version": version
        }

        # Save to temporary file
        with tempfile.NamedTemporaryFile(mode='w', suffix='.json', delete=False) as temp_file:
            json.dump(leaderboard_data, temp_file, indent=2)
            temp_path = temp_file.name

        # Upload file
        api = HfApi(token=TOKEN)
        api.upload_file(
            path_or_fileobj=temp_path,
            path_in_repo=f"leaderboards/leaderboard_{version}.json",
            repo_id=RESULTS_DATASET_ID,
            repo_type="dataset",
            commit_message=f"Update leaderboard for version {version}"
        )

        os.unlink(temp_path)
        return True, "Leaderboard updated successfully"
    except Exception as e:
        return False, f"Error updating leaderboard: {e}"


def process_submission(file_path: str, metadata: Dict, version="v0") -> str:
    """
    Process a submission to the CodeReview Bench leaderboard.
    """
    try:
        # Validate submission
        is_valid, validation_message = validate_submission(file_path)
        if not is_valid:
            return styled_error(validation_message)

        # Process the submission entries
        entries, message = process_jsonl_submission(file_path)
        if not entries:
            return styled_error(f"Failed to process submission: {message}")

        # Upload raw submission file
        model_name = metadata.get("model_name", "unknown")
        model_name_safe = model_name.replace("/", "_").replace(" ", "_")
        
        api = HfApi(token=TOKEN)
        submission_path = f"submissions_{version}/{model_name_safe}_{datetime.now().strftime('%Y%m%d_%H%M%S')}.jsonl"
        api.upload_file(
            path_or_fileobj=file_path,
            path_in_repo=submission_path,
            repo_id=RESULTS_DATASET_ID,
            repo_type="dataset",
            commit_message=f"Add raw submission for {model_name}"
        )

        # Process entries and add metadata
        processed_entries = []
        for entry in entries:
            # Add metadata to entry
            entry.update({
                "model_name": metadata.get("model_name"),
                "model_type": metadata.get("model_type"),
                "review_model_type": str(metadata.get("review_model_type", "custom")).lower(),
                "mode": metadata.get("mode"),
                "base_model": metadata.get("base_model"),
                "revision": metadata.get("revision"),
                "precision": metadata.get("precision"),
                "weight_type": metadata.get("weight_type"),
                "version": version,
                "submission_date": datetime.now().isoformat()
            })
            processed_entries.append(entry)

        # Submit entries to entries folder
        for entry in processed_entries:
            success, message = submit_entry_to_hub(entry, model_name, metadata.get("mode"), version)
            if not success:
                return styled_error(message)

        # Get all entries from HF dataset and update leaderboard
        files = api.list_repo_files(repo_id=RESULTS_DATASET_ID, repo_type="dataset")
        entry_files = [f for f in files if f.startswith("entries/") and f.endswith(f"_{version}.json")]

        all_entries = []
        for entry_file in entry_files:
            try:
                entry_path = api.hf_hub_download(
                    repo_id=RESULTS_DATASET_ID,
                    filename=entry_file,
                    repo_type="dataset",
                )
                with open(entry_path, 'r') as f:
                    entry_data = json.load(f)
                    all_entries.append(entry_data)
            except Exception as e:
                print(f"Error loading entry {entry_file}: {e}")

        # Update leaderboard with all entries
        success, message = submit_leaderboard_to_hub(all_entries, version)
        if not success:
            return styled_error(message)

        return styled_message("Submission successful! Model evaluated and leaderboard updated.")

    except Exception as e:
        return styled_error(f"Error processing submission: {e}")
    finally:
        # Clean up temporary files if they exist
        try:
            if os.path.exists(file_path):
                os.remove(file_path)
        except:
            pass