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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
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