import gc
import json
import logging
import os
from datetime import datetime
from typing import Dict, List

import torch
import wandb
from datasets import Dataset
from tqdm import tqdm

from src.language_model.language_model_abstraction import LanguageModel
from src.task.task import Task
from src.task.task_factory import tasks_factory


class ModelEvaluator:
    """
    The model evaluator acts as a pipeline for evaluating models on tasks available from tasks_factory.
    """

    def __init__(self):
        self.last_predictions = {}

        self.last_metrics = {}

        self.last_model_name = None

    def compute_metrics(self) -> Dict:
        """
        Compute metrics over the last tested model's predictions,
        must have called one the evaluate functions before or loaded predictions with load_predictions_from_file.
        """
        metrics = []

        for task_dict in tqdm(
            self.last_predictions["tasks"], desc="Computing metrics: "
        ):
            task_name, preds = list(task_dict.items())[0]
            if not preds:
                warning_message = f"Task '{task_name}' ignored due to no predictions"
                logging.warning(warning_message)
                continue
            try:
                tasks = tasks_factory([task_name])
                task = tasks[0]
                metric_score, warning = task.compute(preds)
            except Exception as e:
                error_message = f"Error while calculating metrics'{task_name}' : {e}"
                logging.error(error_message)
                continue
            metric_name = task.metric_name
            task_entry = {
                task_name: {
                    metric_name: {**metric_score, f"{metric_name}_warning": warning}
                }
            }
            metrics.append(task_entry)
            wandb.log({f"{task_name}.{metric_name}": {**metric_score}})

        self.last_metrics = metrics
        metrics = self.last_predictions
        metrics["tasks"] = self.last_metrics
        self.last_metrics = metrics
        return metrics

    def load_predictions_from_file(self, file_path: str) -> None:
        """
        Load predictions from file to compute metrics :param file_path:path to the predictions file.
        """

        try:
            with open(file_path, "r", encoding="utf-8") as f:
                self.last_predictions = json.load(f)
        except FileNotFoundError:
            error = f"File not found: {file_path}"
            logging.error(error)
            self.last_predictions = None
        except json.JSONDecodeError:
            error = f"Invalid JSON in file: {file_path}"
            logging.error(error)
            self.last_predictions = None

    def save_metrics(self, save_path):
        """
        Saves computed metrics to a json file.
        :param save_path : the path to which the json file will be saved.
        """
        if self.last_metrics is None:
            logging.info("No metrics saved")
            return None
        return self.save_object(
            save_path,
            self.last_metrics,
            f"{self.last_model_name.replace('/', '_')}_metrics.json",
        )

    def evaluate(self, model: LanguageModel, tasks: List[Task]):
        """
        Evaluates a given model on the given tasks.
        :param model : the model that will infer on the given tasks.
        :param tasks : the tasks to be evaluated on.
        """
        return self.evaluate_subset(model, tasks)

    def evaluate_subset(
        self, model: LanguageModel, tasks: List[Task], subset_size=None
    ) -> Dict:
        """
        Evaluates a given model on the given tasks, but only on a given size.
        :param model : the model that will infer on the given tasks.
        :param tasks : the tasks to be evaluated on.
        :param subset_size : the size of the subset to be evaluated.
        """
        predictions = []
        for task in tasks:
            info_log = (
                f"-----Doing task '{task.task_name}' with model '{model.name}-----'."
            )
            logging.info(info_log)
            try:
                if subset_size is None:
                    prompts = task.dataset.prompts[:]
                else:
                    prompts = task.dataset.prompts[:subset_size]

                evaluate_dataset = Dataset.from_dict({"text": prompts})

                task_predictions = model.predict(
                    evaluation_dataset=evaluate_dataset, task=task
                )

                task_predictions = {task.task_name: task_predictions}
                predictions.append(task_predictions)

            except Exception as e:
                error_message = f"Task '{task.task_name}' has failed : {e}"
                logging.error(error_message)
                wandb.log({task.task_name: "Failed"})
                continue
            finally:
                # Memory cleaning
                if "evaluate_dataset" in locals():
                    del evaluate_dataset
                if "prompts" in locals():
                    del prompts
                torch.cuda.empty_cache()
                gc.collect()
            wandb.log({task.task_name: "Success"})
        logging.info("Finished evaluating tasks.")
        self.last_predictions = {
            "model_name": model.name,
            "model_url": f"https://huggingface.co/{model.name}",
            "tasks": predictions,
        }
        self.last_model_name = model.name
        return self.last_predictions

    def save_results(self, save_path):
        """
        Saves inferred metrics to a json file.
        :param save_path : the path to which the json file will be saved.
        """

        if self.last_model_name is None:
            logging.error("Please evaluate before saving results")
            return None
        date_time_stamp = datetime.now().strftime("%Y%m%d-%H%M")
        return self.save_object(
            save_path,
            self.last_predictions,
            f"{self.last_model_name.replace('/', '_')}_{date_time_stamp}.json",
        )

    def save_object(self, save_dir_path, saved_object, filename):
        """
        Utility method to save the given object into a json file.
        """
        os.makedirs(save_dir_path, exist_ok=True)
        full_path = os.path.join(save_dir_path, filename)
        if os.path.isfile(full_path):
            logging.info("Appending results to previous results file.")
            try:
                with open(full_path, "r", encoding="utf-8") as f:
                    data = json.load(f)
                data.get("tasks").extend(saved_object.get("tasks"))
                with open(full_path, "w", encoding="utf-8") as f:
                    json.dump(data, f, indent=2)
                info_message = f"Results saved to {save_dir_path}"
                logging.info(info_message)
            except Exception as e:
                error_message = f"Failed to save object: {e}"
                logging.error(error_message)
        else:
            try:
                with open(full_path, "w", encoding="utf-8") as f:
                    json.dump(saved_object, f, indent=2)
                info_message = f"Results saved to {save_dir_path}"
                logging.info(info_message)
            except Exception as e:
                error_message = f"Failed to save object: {e}"
                logging.error(error_message)
        return full_path