src/leaderboard/read_evals.py

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

import dateutil
import numpy as np

from src.display.formatting import make_clickable_model, make_contamination_red
from src.display.utils import AutoEvalColumn, EvalDimensions#, ModelType, Precision, WeightType
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
    #precision: Precision = Precision.Unknown
    #model_type: ModelType = ModelType.Unknown # Pretrained, fine tuned, ...
    model_source: str = "" # HF, API, ...
    model_category: str = "" #Nano, Small, Medium, Large
    #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")

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

        # Get model and org
        org_and_model = config.get("model", config.get("model_args", None))
        print("******* org_and_model **********", config)
        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}
        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, _, 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)
        results = {}

        results_obj = data.get("results")
        print(results_obj)
        results["average_score"] = results_obj.get("average_score")
        results["speed"] = results_obj.get("speed")
        results["contamination_score"] = results_obj.get("contamination_score")

        scores_by_category = results_obj.get("scores_by_category")

        for category_obj in scores_by_category:
            category = category_obj["category"]
            average_score = category_obj["average_score"]
            results[category.lower()] = average_score

            
        

        return self(
            eval_name=result_key,
            full_model=full_model,
            org=org,
            model=model,
            model_source=config.get("model_source", ""),
            model_category=config.get("model_category", ""),
            #num_params=config.get("params", 0),
            #license=config.get("license", "?"),
            #likes=config.get("likes", -1),
            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 = 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.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 {self.precision.value.name}

    def to_dict(self):
        """Converts the Eval Result to a dict compatible with our dataframe display"""
        average_score = self.results["average_score"]
        data_dict = {
            "eval_name": self.eval_name,  # not a column, just a save name,
            #AutoEvalColumn.precision.name: self.precision.value.name,
            AutoEvalColumn.model_source.name: self.model_source,
            AutoEvalColumn.model_category.name: self.model_category,
            #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.revision.name: self.revision,
            AutoEvalColumn.average_score.name: average_score,
            #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 eval_dim in EvalDimensions:
            dimension_name = eval_dim.value.col_name
            try:
                dimension_value = self.results[eval_dim.value.metric]
            except KeyError:
                dimension_value = 0

            if dimension_name == "Contamination Score":
                dimension_value = 0 if dimension_value < 0 else round(dimension_value,2)
            
            data_dict[dimension_name] = dimension_value

        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 = []

    for root, _, files in os.walk(results_path):
        print("HERE",files)
        # We should only have json files in model results ##we allow HTML files
        #if len(files) == 0 or any([not f.endswith(".json") for f in files]):
        #    continue
        files = [f 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 as e:
            print("Error",e)
            files = [files[-1]]
        print(files)
        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 = []
    #print(eval_results.values())
    for v in eval_results.values():
        try:
            print(v.to_dict())
            v.to_dict() # we test if the dict version is complete
            results.append(v)
        except KeyError:  # not all eval values present
            print("Key error in eval result, skipping")
            
            print(v)
            print(v.to_dict())
            continue

    print(results)
    return results


def get_model_answers_html_file(results_path, model_name):

    model_org,model_name_only = model_name.split("/")
    model_answers_prefix = f"{results_path}/{model_org}/"

    html_file_content = "EMPTY"
    download_file_path = "https://huggingface.co/spaces/silma-ai/Arabic-LLM-Broad-Leaderboard/raw/main/"

    for root, _, files in os.walk(model_answers_prefix):

        for file_name in files:

            if file_name.startswith(f"{model_name_only}_abb_benchmark_answers_"):

                file_path = os.path.join(root, file_name)

                with open(file_path, "r") as f:
                    
                    html_file_content = f.read()
                    download_file_path = download_file_path + file_path.replace("./", "")
                    break

    return html_file_content,download_file_path