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