src/leaderboard/read_evals.py

import glob
import json
import os
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


@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, ...
    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
    energy_score: str = "NA" # energy consumption in kWh, "NA" if not available

    @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_general")

        # Handle case where config_general is None
        if config is None:
            # Set default values
            precision = Precision.Unknown
            org_and_model = data.get("model_name", "Unknown/Unknown")
            if not isinstance(org_and_model, str):
                org_and_model = "Unknown/Unknown"
            revision = "main"
        else:
            # Precision
            precision = Precision.from_str(config.get("model_dtype"))
            # Get revision
            revision = config.get("model_sha", "")
            # Get model and org
            org_and_model = config.get("model_name", config.get("model_args", None))
            
        if isinstance(org_and_model, str):
            org_and_model = org_and_model.split("/", 1)
        else:
            org_and_model = ["Unknown", "Unknown"]

        # Already handled above

        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)

        # Use a safe default for model_sha if config is None
        model_sha = "main"
        if config is not None:
            model_sha = config.get("model_sha", "main")
            
        still_on_hub, _, model_config = is_model_on_hub(
            full_model, model_sha, 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 = {}
        
        # Check if results key exists in the data
        if "results" not in data:
            # If no results, set all benchmarks to None
            for task in Tasks:
                task = task.value
                results[task.benchmark] = None
        else:
            # Process results normally
            for task in Tasks:
                task = task.value

                # We average all scores of a given metric (not all metrics are present in all files)
                # Handle metrics that could be None in the JSON
                metric_values = []
                
                # Define the expected metric name and alternative names for each benchmark
                expected_metric = task.metric
                alternative_metrics = []
                
                print(f"Processing benchmark: {task.benchmark}, expected metric: {expected_metric}")
                
                # Set up alternative metric names based on the benchmark
                if task.benchmark == "custom|folio:logical_reasoning|0":
                    if expected_metric != "folio_em":
                        alternative_metrics = ["folio_em"]
                elif task.benchmark == "custom|telecom:qna|0":
                    if expected_metric != "telecom_qna_em":
                        alternative_metrics = ["telecom_qna_em"]
                elif task.benchmark == "custom|3gpp:tsg|0":
                    if expected_metric != "em":
                        alternative_metrics = ["em"]
                elif task.benchmark == "custom|math:problem_solving|0":
                    if expected_metric != "math_metric":
                        alternative_metrics = ["math_metric"]
                elif task.benchmark == "custom|spider:text2sql|0":
                    if expected_metric != "sql_metric":
                        alternative_metrics = ["sql_metric"]
                
                # Check for results with the benchmark name
                for k, v in data["results"].items():
                    if task.benchmark == k:
                        # Try the expected metric name first
                        metric_value = v.get(expected_metric)
                        
                        # If not found, try alternative metric names
                        if metric_value is None:
                            for alt_metric in alternative_metrics:
                                if alt_metric in v:
                                    metric_value = v.get(alt_metric)
                                    break
                        
                        if metric_value is not None:
                            metric_values.append(metric_value)
                            print(f"Found metric value for {task.benchmark}: {metric_value}")
                
                accs = np.array([v for v in metric_values if v is not None])
                if len(accs) == 0:
                    # Also check the "all" section for metrics
                    if "all" in data["results"]:
                        all_results = data["results"]["all"]
                        
                        print(f"Checking 'all' section for {task.benchmark}, available keys: {list(all_results.keys())}")
                        
                        # Try the expected metric name first
                        metric_value = all_results.get(expected_metric)
                        
                        # If not found, try alternative metric names
                        if metric_value is None:
                            for alt_metric in alternative_metrics:
                                if alt_metric in all_results:
                                    metric_value = all_results.get(alt_metric)
                                    print(f"Found alternative metric {alt_metric} in 'all' section")
                                    break
                        
                        if metric_value is not None:
                            accs = np.array([metric_value])
                            print(f"Found metric value in 'all' section for {task.benchmark}: {metric_value}")
                        else:
                            results[task.benchmark] = None
                            continue
                    else:
                        results[task.benchmark] = None
                        continue

                mean_acc = np.mean(accs) * 100.0
                results[task.benchmark] = mean_acc
                print(f"Final result for {task.benchmark}: {mean_acc}")
            
        # Extract energy score if available
        energy_score = "NA"
        if "energy_metrics" in data and data["energy_metrics"] is not None and data["energy_metrics"].get("enabled", False):
            total_energy = data["energy_metrics"].get("total_energy", 0)
            if total_energy > 0:
                energy_score = f"{total_energy:.5f}"

        return self(
            eval_name=result_key,
            full_model=full_model,
            org=org,
            model=model,
            results=results,
            precision=precision,
            revision=revision,
            still_on_hub=still_on_hub,
            architecture=architecture,
            energy_score=energy_score
        )

    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.architecture = request.get("architectures", "Unknown") # delete later
            self.status = request.get("status", "FAILED")
        except Exception:
            self.status = "FAILED"
            print(f'Could not find request file for {self.org}/{self.model} with "precision:{self.precision.value.name},model_type:{self.model_type}",license:{self.license},status:{self.status}')

    def to_dict(self):
        """Converts the Eval Result to a dict compatible with our dataframe display"""
        available_metrics = [v for v in self.results.values() if v is not None]
        average = sum(available_metrics) / len([v for v in available_metrics if v is not None]) if available_metrics else None

        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.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.energy_score.name: self.energy_score,
        }

        print(f"\nConverting to dict for model: {self.full_model}")
        for task in Tasks:
            result = self.results.get(task.value.benchmark)
            print(f"  Task: {task.value.col_name}, Benchmark: {task.value.benchmark}, Result: {result}")
            data_dict[task.value.col_name] = "NA" if result is None else round(result, 2)

        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):
        # 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:
        try:
            # Creation of result
            print(f"\nProcessing file: {model_result_filepath}")
            eval_result = EvalResult.init_from_json_file(model_result_filepath)
            
            # Skip entries with Unknown/Unknown model name
            if eval_result.full_model == "Unknown/Unknown":
                print(f"Skipping invalid result file: {model_result_filepath}")
                continue
            
            print(f"Model: {eval_result.full_model}")
            print(f"Results before update_with_request_file:")
            for benchmark, value in eval_result.results.items():
                print(f"  {benchmark}: {value}")
                
            eval_result.update_with_request_file(requests_path)
            
            print(f"Results after update_with_request_file:")
            for benchmark, value in eval_result.results.items():
                print(f"  {benchmark}: {value}")

            # Store results of same eval together
            eval_name = eval_result.eval_name
            if eval_name in eval_results.keys():
                print(f"Updating existing results for {eval_name}")
                eval_results[eval_name].results.update({k: v for k, v in eval_result.results.items() if v is not None})
            else:
                print(f"Adding new results for {eval_name}")
                eval_results[eval_name] = eval_result
        except Exception as e:
            print(f"Error processing result file {model_result_filepath}: {str(e)}")
            continue

    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