src/leaderboard/read_evals.py

import glob
import json
import math
import os
from dataclasses import dataclass

import dateutil
import numpy as np
from typing import Dict, Union

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


@dataclass
class EvalResult:
    """Represents one full evaluation. Built from a combination of the result and request file for a given run.
    """
    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[str, Union[float, int]]  # float o int
    average_CPS: float
    is_5fewshot: bool
    fewshot_symbol: FewShotType = FewShotType.Unknown
    weight_type: WeightType = 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")

        #average_CPS = f"{data.get('average_CPS'):.2f}"
        # Get average_CPS
        average_CPS = float(data.get('average_CPS', 0.0))  # 0.0 come valore di default
        # Get number of fewshot
        fewshot = config.get("num_fewshot", False)

        try:
            if fewshot == "5":
                is_5fewshot = True
            else:
                is_5fewshot = False
        except ValueError:
            is_5fewshot = False
        # Determine the few-shot type (ZS or FS) based on num_fewshot
        fewshot_symbol = FewShotType.from_num_fewshot(is_5fewshot)  # Use the new

        # Determine the number of parameters of the models
        num_params = int(0)
        num_params_billion = config.get("num_params_billion")
        if num_params_billion is not None:
            num_params = math.ceil(num_params_billion)

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

        if len(org_and_model) == 1:
            org = None
            model = org_and_model[0]
            #result_key = f"{model}_{precision.value.name}"
            result_key = f"{model}_{is_5fewshot}"
        else:
            org = org_and_model[0]
            model = org_and_model[1]
            #result_key = f"{org}_{model}_{precision.value.name}"
            result_key = f"{org}_{model}_{is_5fewshot}"
        full_model = "/".join(org_and_model)

        still_on_hub, _, 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 the results of the models
        results = {}
        for task in Tasks:
            task = task.value

            for k, v in data["tasks"].items():
                if task.benchmark[:-2] == k:
                    if "Best Prompt Id" in task.col_name:
                        results[task.benchmark] = int(v[task.metric_type][-1:])
                    else:
                        #results[task.benchmark] = f"{v[task.metric_type]:.2f}"  # Ensure two decimals for display
                        results[task.benchmark] = float(v[task.metric_type])
                        #value = float(v[task.metric_type])
                        #results[task.benchmark] = round(value, 2)  # Arrotonda a 2 decimali

        return self(
            eval_name=result_key,
            full_model=full_model,
            org=org,
            model=model,
            results=results,
            average_CPS=average_CPS,
            fewshot_symbol=fewshot_symbol,
            is_5fewshot=is_5fewshot,
            revision= config.get("model_sha", ""),
            still_on_hub=still_on_hub,
            architecture=architecture,
            num_params=num_params
        )

    '''
    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", "")
        except Exception:
            print(f"Could not find request file for {self.org}/{self.model} with precision 
    '''

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

        fewshot_symbol = (
            self.fewshot_symbol.value.symbol if isinstance(self.fewshot_symbol, FewShotType) else "❓"
        )

        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.model_type.name: self.model_type.value.name if self.model_type else "Unknown",
            #AutoEvalColumn.model_type_symbol.name: self.model_type.value.symbol if self.model_type else "Unknown",
            AutoEvalColumn.fewshot_symbol.name: fewshot_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.revision.name: self.revision,
            AutoEvalColumn.average.name: average,
            AutoEvalColumn.is_5fewshot.name: self.is_5fewshot,
            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,
        }

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

        return data_dict


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 = []

    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 model_result_filepaths:
        # Creation of result
        eval_result = EvalResult.init_from_json_file(model_result_filepath)
        #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