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 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") 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: data_dict[eval_dim.value.col_name] = self.results[eval_dim.value.metric] 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