"""
Data service provider
"""
import json
from typing import List

import pandas as pd

from app.backend.constant import ModelProvider


class DataEngine:

    def __init__(self):
        self.df = self.init_dataframe()

    @property
    def leaderboards(self):
        """
        Get leaderboard data
        """
        with open('./mock_data/leaderboard.json', 'r') as f:
            return json.load(f)

    @property
    def models(self):
        """
        Get models data
        """
        with open('./mock_data/models.json', 'r') as f:
            return json.load(f)

    @property
    def tasks(self):
        """
        Get tasks data
        """
        with open('./mock_data/tasks.json', 'r') as f:
            return json.load(f)

    @property
    def results(self):
        """
        Get results data
        """
        with open('./mock_data/results.json', 'r') as f:
            return json.load(f)

    def init_dataframe(self):
        """
        Initialize DataFrame
        """
        d = {"hello": [123], "world": [456]}
        return pd.DataFrame(d)

    def _check_providers(self, organization: str, providers: List):
        if not providers:
            return True
        if "Others" in providers:
            if organization not in (
                    ModelProvider.OPENAI.value, ModelProvider.COHERE.value, ModelProvider.VOYAGEAI.value):
                return True
        return organization in providers

    def filter_df(self, leaderboard: str, task: str, providers: List, sort_key: str):

        tasks = []
        for lb in self.leaderboards:
            if lb["name"] == leaderboard:
                tasks = lb["tasks"]
                break
        df_list = []
        for t in (filter(lambda x: x.upper() == task.upper(), tasks)):
            datasets = []
            for ta in self.tasks:
                if ta["slug"].upper() == t.upper():
                    datasets = ta["datasets"]
                    break
            for model in self.models:
                if t in model["tasks"] and self._check_providers(model["organization"], providers):

                    for dataset in datasets:
                        results = self.results[dataset]
                        for result in results:
                            if result['model_name'] == model["model_name"]:
                                d = result["results"]
                                d["class"] = result["class"]
                                d["organization"] = result["organization"]
                                d["model_name"] = result["model_name"]
                                df = pd.DataFrame([d])
                                df = df[["class", "organization", "model_name", "ndcg_at_1", "ndcg_at_3", "ndcg_at_5",
                                         "ndcg_at_10",
                                         "ndcg_at_20", "ndcg_at_50", "ndcg_at_100", "recall_at_1", "recall_at_3",
                                         "recall_at_5", "recall_at_10",
                                         "recall_at_20", "recall_at_50", "recall_at_100", "precision_at_1",
                                         "precision_at_3", "precision_at_5",
                                         "precision_at_10", "precision_at_20", "precision_at_50", "precision_at_100"]]
                                df_list.append(df)
            if df_list:
                return pd.concat(df_list).sort_values(by=sort_key.replace("@", '_at_').lower())
            return pd.DataFrame(columns=["class", "organization", "model_name", "ndcg_at_1", "ndcg_at_3", "ndcg_at_5",
                                         "ndcg_at_10",
                                         "ndcg_at_20", "ndcg_at_50", "ndcg_at_100", "recall_at_1", "recall_at_3",
                                         "recall_at_5", "recall_at_10",
                                         "recall_at_20", "recall_at_50", "recall_at_100", "precision_at_1",
                                         "precision_at_3", "precision_at_5",
                                         "precision_at_10", "precision_at_20", "precision_at_50", "precision_at_100"])

    def get_model_result(self, model: dict, task_datasets_map: dict, results: dict):
        """
        get_model_result
        """
        model_class = model["class"]
        model_organization = model["organization"]
        model_model_name = model["model_name"]
        for leaderboard in model["leaderboards"]:
            for task in model["tasks"]:
                for dateset in task_datasets_map.get(task, []):
                    for result in results[dateset]:
                        if result["model_name"] == model_model_name:
                            d_result = result["results"]
                            d_result["class"] = model_class
                            d_result["organization"] = model_organization
                            d_result["model_name"] = model_model_name
                            d_result["leaderboard"] = leaderboard
                            d_result["dateset"] = dateset
                            d_result["task"] = task
                            yield d_result

    def jsons_to_df(self):

        # change leaderboards to task_leaderboard_map
        task_leaderboard_map = {}
        leaderboards = self.leaderboards
        for leaderboard in leaderboards:
            for task in leaderboard["tasks"]:
                task_leaderboard_map[task] = leaderboard["name"]

        # change tasks to task_datasets_map
        task_datasets_map = {}
        for task in self.tasks:
            task_datasets_map[task["slug"]] = task["datasets"]

        df_results_list = []
        results = self.results
        for model in self.models:
            for d_result in self.get_model_result(model, task_datasets_map, results):
                if d_result:
                    df_results_list.append(pd.DataFrame([d_result]))

        if df_results_list:
            df_result = pd.concat(df_results_list)
            return df_result[
                ["leaderboard", "task", "class", "organization", "model_name", "dateset", "ndcg_at_1", "ndcg_at_3",
                 "ndcg_at_5",
                 "ndcg_at_10",
                 "ndcg_at_20", "ndcg_at_50", "ndcg_at_100", "recall_at_1", "recall_at_3",
                 "recall_at_5", "recall_at_10",
                 "recall_at_20", "recall_at_50", "recall_at_100", "precision_at_1",
                 "precision_at_3", "precision_at_5",
                 "precision_at_10", "precision_at_20", "precision_at_50", "precision_at_100"]], leaderboards
        return pd.DataFrame(
            columns=["leaderboard", "task", "class", "organization", "model_name", "dateset", "ndcg_at_1", "ndcg_at_3",
                     "ndcg_at_5",
                     "ndcg_at_10",
                     "ndcg_at_20", "ndcg_at_50", "ndcg_at_100", "recall_at_1", "recall_at_3",
                     "recall_at_5", "recall_at_10",
                     "recall_at_20", "recall_at_50", "recall_at_100", "precision_at_1",
                     "precision_at_3", "precision_at_5",
                     "precision_at_10", "precision_at_20", "precision_at_50", "precision_at_100"]), leaderboards

    def filter_by_providers(self, df_result: pd.DataFrame, providers: List):
        """
        filter_by_providers
        """
        if not providers:
            # providers are empty, return empty
            return df_result[0:0]
        return df_result[df_result['organization'].apply(lambda x: self._check_providers(x, providers))]

    def summarize_dataframe(self):
        """
        Summarize data statistics
        """