import glob
import json
import os
from tqdm import tqdm
from dataclasses import dataclass

import dateutil

# import numpy as np

from src.display.formatting import make_clickable_model
from src.display.utils import AutoEvalColumn, ModelType, Tasks, Precision, WeightType
from src.submission.check_validity import is_model_on_hub

from typing import Optional


def is_float(string):
    try:
        float(string)
        return True
    except ValueError:
        return False


@dataclass
class EvalResult:
    # Also see src.display.utils.AutoEvalColumn for what will be displayed.
    eval_name: str  # org_model_precision (uid)
    full_model: str  # org/model (path on hub)
    org: str
    model: str
    revision: str  # commit hash, "" if main
    results: dict
    precision: Precision = Precision.Unknown
    model_type: ModelType = ModelType.Unknown  # Pretrained, fine tuned, ...
    weight_type: WeightType = WeightType.Original  # Original or Adapter
    architecture: str = "Unknown"  # From config file
    license: str = "?"
    likes: int = 0
    num_params: int = 0
    date: str = ""  # submission date of request file
    still_on_hub: bool = False
    inference_framework: str = "Unknown"

    @staticmethod
    def init_from_json_file(json_filepath, is_backend: bool = False):
        """Inits the result from the specific model result file"""
        with open(json_filepath) as fp:
            data = json.load(fp)

        # We manage the legacy config format
        config = data.get("config", data.get("config_general", None))

        # Precision
        precision = Precision.from_str(config.get("model_dtype"))

        # Get model and org
        org_and_model = config.get("model_name", config.get("model_args", None))
        org_and_model = org_and_model.split("/", 1)

        # Get inference framework
        inference_framework = config.get("inference_framework", "Unknown")

        if len(org_and_model) == 1:
            org = None
            model = org_and_model[0]
            result_key = f"{model}_{precision.value.name}"
        else:
            org = org_and_model[0]
            model = org_and_model[1]
            result_key = f"{org}_{model}_{precision.value.name}"
        full_model = "/".join(org_and_model)

        still_on_hub, error, model_config = is_model_on_hub(
            full_model, config.get("model_sha", "main"), trust_remote_code=True, test_tokenizer=False
        )
        architecture = "?"
        if model_config is not None:
            architectures = getattr(model_config, "architectures", None)
            if architectures:
                architecture = ";".join(architectures)

        # Extract results available in this file (some results are split in several files)

        # data['results'] is {'nq_open': {'em': 0.24293628808864265, 'em_stderr': 0.007138697341112125}}

        results = {}
        for benchmark, benchmark_results in data["results"].items():
            if benchmark not in results:
                results[benchmark] = {}

            for metric, value in benchmark_results.items():
                to_add = True
                if "_stderr" in metric:
                    to_add = False
                if "alias" in metric:
                    to_add = False

                if "," in metric:
                    metric = metric.split(",")[0]
                metric = metric.replace("exact_match", "em")

                if to_add is True:
                    multiplier = 100.0
                    if "GPU" in metric:
                        results[benchmark][metric] = value
                        continue
                    if "precision" in metric:
                        results[benchmark][metric] = value
                        continue

                    if "rouge" in metric and "truthful" not in benchmark:
                        multiplier = 1.0
                    if "squad" in benchmark:
                        multiplier = 1.0
                    if "time" in metric:
                        multiplier = 1.0
                    if "throughput" in metric:
                        multiplier = 1.0
                    if "batch_" in metric or "Mem" in metric or "Util" in metric:
                        multiplier = 1


                    # print('RESULTS', data['results'])
                    # print('XXX', benchmark, metric, value, multiplier)
                    results[benchmark][metric] = value * multiplier

        res = EvalResult(
            eval_name=result_key,
            full_model=full_model,
            org=org,
            model=model,
            results=results,
            precision=precision,
            revision=config.get("model_sha", ""),
            still_on_hub=still_on_hub,
            architecture=architecture,
            inference_framework=inference_framework,
        )

        return res

    def update_with_request_file(self, requests_path):
        """Finds the relevant request file for the current model and updates info with it"""
        request_file = get_request_file_for_model(requests_path, self.full_model, self.precision.value.name)

        try:
            with open(request_file, "r") as f:
                request = json.load(f)

            self.model_type = ModelType.from_str(request.get("model_type", ""))
            self.weight_type = WeightType[request.get("weight_type", "Original")]
            self.license = request.get("license", "?")
            self.likes = request.get("likes", 0)
            self.num_params = request.get("params", 0)
            self.date = request.get("submitted_time", "")
            self.inference_framework = request.get("inference_framework", "Unknown")
        except Exception as e:
            print(f"Could not find request file for {self.org}/{self.model} -- path: {requests_path} -- {e}")

    def is_complete(self) -> bool:
        for task in Tasks:
            if task.value.benchmark not in self.results:
                return False
        return True

    def to_dict(self):
        """Converts the Eval Result to a dict compatible with our dataframe display"""

        # breakpoint()
        # average = sum([v for v in self.results.values() if v is not None]) / len(Tasks)

        data_dict = {
            "eval_name": self.eval_name,  # not a column, just a save name,
            AutoEvalColumn.precision.name: self.precision.value.name,
            AutoEvalColumn.model_type.name: self.model_type.value.name,
            AutoEvalColumn.model_type_symbol.name: self.model_type.value.symbol,
            AutoEvalColumn.weight_type.name: self.weight_type.value.name,
            AutoEvalColumn.architecture.name: self.architecture,
            AutoEvalColumn.model.name: make_clickable_model(self.full_model),
            AutoEvalColumn.dummy.name: self.full_model,
            AutoEvalColumn.revision.name: self.revision,
            # AutoEvalColumn.average.name: average,
            AutoEvalColumn.license.name: self.license,
            AutoEvalColumn.likes.name: self.likes,
            AutoEvalColumn.params.name: self.num_params,
            AutoEvalColumn.still_on_hub.name: self.still_on_hub,
            AutoEvalColumn.inference_framework.name: self.inference_framework,
        }

        for task in Tasks:
            if task.value.benchmark in self.results:
                data_dict[task.value.col_name] = self.results[task.value.benchmark]

        return data_dict


def get_request_file_for_model(requests_path, model_name, precision):
    """Selects the correct request file for a given model. Only keeps runs tagged as FINISHED and RUNNING"""
    request_files = os.path.join(
        requests_path,
        f"{model_name}_eval_request_*.json",
    )
    request_files = glob.glob(request_files)

    # Select correct request file (precision)
    request_file = ""
    request_files = sorted(request_files, reverse=True)

    for tmp_request_file in request_files:
        with open(tmp_request_file, "r") as f:
            req_content = json.load(f)
            if req_content["precision"] == precision.split(".")[-1]:
                request_file = tmp_request_file
    return request_file


def get_request_file_for_model_open_llm(requests_path, model_name, precision):
    """Selects the correct request file for a given model. Only keeps runs tagged as FINISHED"""
    request_files = os.path.join(
        requests_path,
        f"{model_name}_eval_request_*.json",
    )
    request_files = glob.glob(request_files)

    # Select correct request file (precision)
    request_file = ""
    request_files = sorted(request_files, reverse=True)
    for tmp_request_file in request_files:
        with open(tmp_request_file, "r") as f:
            req_content = json.load(f)
            if req_content["status"] in ["FINISHED"] and req_content["precision"] == precision.split(".")[-1]:
                request_file = tmp_request_file
    return request_file


def update_model_type_with_open_llm_request_file(result, open_llm_requests_path):
    """Finds the relevant request file for the current model and updates info with it"""
    request_file = get_request_file_for_model_open_llm(
        open_llm_requests_path, result.full_model, result.precision.value.name
    )

    if request_file:
        try:
            with open(request_file, "r") as f:
                request = json.load(f)
            open_llm_model_type = request.get("model_type", "Unknown")
            if open_llm_model_type != "Unknown":
                result.model_type = ModelType.from_str(open_llm_model_type)
        except Exception as e:
            pass
    return result


def get_raw_eval_results(results_path: str, requests_path: str, is_backend: bool = False) -> list[EvalResult]:
    """From the path of the results folder root, extract all needed info for results"""
    model_result_filepaths = []

    for root, _, files in os.walk(results_path):
        # We should only have json files in model results
        if len(files) == 0 or any([not f.endswith(".json") for f in files]):
            continue

        # Sort the files by date
        try:
            files.sort(key=lambda x: x.removesuffix(".json").removeprefix("results_")[:-7])
        except dateutil.parser._parser.ParserError:
            files = [files[-1]]

        for file in files:
            model_result_filepaths.append(os.path.join(root, file))

    eval_results = {}
    for model_result_filepath in tqdm(model_result_filepaths, desc="reading model_result_filepaths"):
        # Creation of result
        eval_result = EvalResult.init_from_json_file(model_result_filepath, is_backend=is_backend)
        eval_result.update_with_request_file(requests_path)
        # Store results of same eval together
        eval_name = eval_result.eval_name
        if eval_name in eval_results.keys():
            eval_results[eval_name].results.update({k: v for k, v in eval_result.results.items() if v is not None})
        else:
            eval_results[eval_name] = eval_result

    results = []
    for v in eval_results.values():
        results.append(v)

    return results