import glob
import json
import os
from dataclasses import dataclass

import numpy as np
import dateutil

import src.display.formatting as formatting
import src.display.utils as utils
import src.submission.check_validity as check_validity


@dataclass
class EvalResult:
    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: utils.Precision = utils.Precision.Unknown
    model_type: utils.ModelType = utils.ModelType.Unknown  # Pretrained, fine tuned, ...
    weight_type: utils.WeightType = utils.WeightType.Original  # Original or Adapter
    architecture: str = "Unknown"
    license: str = "?"
    likes: int = 0
    num_params: int = 0
    date: str = ""  # submission date of request file
    still_on_hub: bool = False

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

        config = data.get("config")

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

        # Get model and org
        full_model = config.get("model_name", config.get("model_args", None))
        org, model = full_model.split("/", 1) if "/" in full_model else (None, full_model)

        if org:
            result_key = f"{org}_{model}_{precision.value.name}"
        else:
            result_key = f"{model}_{precision.value.name}"

        still_on_hub, _, model_config = check_validity.is_model_on_hub(
            full_model, config.get("model_sha", "main"), trust_remote_code=True,
            test_tokenizer=False)

        if model_config:
            architecture = ";".join(getattr(model_config, "architectures", ["?"]))
        else:
            architecture = "?"

        # Extract results available in this file (some results are split in several files)
        results = {}
        for task in utils.Tasks:
            task = task.value

            # We average all scores of a given metric (not all metrics are present in all files)
            accs = np.array([v.get(task.metric, None) for k, v in data["results"].items() if task.benchmark == k])

            results[task.benchmark] = accs 

        return self(
            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
        )

    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 = utils.ModelType.from_str(request.get("model_type", ""))
            self.weight_type = utils.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", "")
        except FileNotFoundError:
            print(f"Could not find request file for {self.org}/{self.model}")
        except json.JSONDecodeError:
            print(f"Error decoding JSON in request file for {self.org}/{self.model}")

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

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

        for task in utils.Tasks:
            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"""
    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 get_raw_eval_results(results_path: str, requests_path: str) -> list[EvalResult]:
    """From the path of the results folder root, extract all needed info for results"""
    model_result_filepaths = []
    print("results_path", results_path)
    for root, _, files in os.walk(results_path):
        # We should only have json files in model results
        print("file",files)

        # if not files or any([not f.endswith(".json") for f in files]):
            
        #     continue
        for f in files:
            if f.endswith(".json"):

        # 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]]

                model_result_filepaths.extend([os.path.join(root, f)])
    print("model_result_filepaths", model_result_filepaths)
    # exit()
    eval_results = {}
    for model_result_filepath in model_result_filepaths:
        # Creation of result
        eval_result = EvalResult.init_from_json_file(model_result_filepath)
        print("request_path:",requests_path)
        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():
        try:
            v.to_dict() # we test if the dict version is complete
            results.append(v)
        except KeyError:  # not all eval values present
            continue

    return results