data.py

from huggingface_hub import HfFileSystem
import pandas as pd
from utils import logger
from datetime import datetime
import threading
import traceback
import json
import re

# NOTE: if caching is an issue, try adding `use_listings_cache=False`
fs = HfFileSystem()

IMPORTANT_MODELS = [
    "auto",
    "bert",  # old but dominant (encoder only)
    "gpt2",  # old (decoder)
    "t5",  # old (encoder-decoder)
    "modernbert",  # (encoder only)
    "vit",  # old (vision) - fixed comma
    "clip",  # old but dominant (vision)
    "detr",  # objection detection, segmentation (vision)
    "table-transformer",  # objection detection (visioin) - maybe just detr?
    "got_ocr2",  # ocr (vision)
    "whisper",  # old but dominant (audio)
    "wav2vec2",  # old (audio)
    "llama",  # new and dominant (meta)
    "gemma3",  # new (google)
    "qwen2",  # new (Alibaba)
    "mistral3",  # new (Mistral) - added missing comma
    "qwen2_5_vl",  # new (vision)
    "llava",  # many models from it (vision)
    "smolvlm",  # new (video)
    "internvl",  # new (video)
    "gemma3n",  # new (omnimodal models)
    "qwen2_5_omni",  # new (omnimodal models)
]

KEYS_TO_KEEP = [
    "success_amd",
    "success_nvidia",
    "skipped_amd",
    "skipped_nvidia",
    "failed_multi_no_amd",
    "failed_multi_no_nvidia",
    "failed_single_no_amd",
    "failed_single_no_nvidia",
    "failures_amd",
    "failures_nvidia",
    "job_link_amd",
    "job_link_nvidia",
]


def log_dataframe_link(link: str) -> str:
    """
    Adds the link to the dataset in the logs, modifies it to get a clockable link and then returns the date of the 
    report.
    """
    logger.info(f"Reading df located at {link}")
    # Make sure the links starts with an http adress
    if link.startswith("hf://"):
        link = "https://huggingface.co/" + link.removeprefix("hf://")
    # Pattern to match transformers_daily_ci followed by any path, then a date (YYYY-MM-DD format)
    pattern = r'transformers_daily_ci(.*?)/(\d{4}-\d{2}-\d{2})'
    match = re.search(pattern, link)
    # Failure case: 
    if not match:
        logger.error("Could not find transformers_daily_ci and.or date in the link")
        return "9999-99-99"
    # Replace the path between with blob/main
    path_between = match.group(1)
    link = link.replace("transformers_daily_ci" + path_between, "transformers_daily_ci/blob/main")
    logger.info(f"Link to data source: {link}")
    # Return the date
    return match.group(2)

def infer_latest_update_msg(date_df_amd: str, date_df_nvidia: str) -> str:
    # Early return if one of the dates is invalid
    if date_df_amd.startswith("9999") and date_df_nvidia.startswith("9999"):
        return "could not find last update time"
    # Warn if dates are not the same
    if date_df_amd != date_df_nvidia:
        logger.warning(f"Different dates found: {date_df_amd} (AMD) vs {date_df_nvidia} (NVIDIA)")
    # Take the latest date and format it
    try:
        latest_date = max(date_df_amd, date_df_nvidia)
        yyyy, mm, dd = latest_date.split("-")
        return f"last updated {mm}/{dd}/{yyyy}"
    except Exception as e:
        logger.error(f"When trying to infer latest date, got error {e}")
        return "could not find last update time"

def read_one_dataframe(json_path: str, device_label: str) -> tuple[pd.DataFrame, str]:
    df_upload_date = log_dataframe_link(json_path)
    df = pd.read_json(json_path, orient="index")
    df.index.name = "model_name"
    df[f"failed_multi_no_{device_label}"] = df["failures"].apply(lambda x: len(x["multi"]) if "multi" in x else 0)
    df[f"failed_single_no_{device_label}"] = df["failures"].apply(lambda x: len(x["single"]) if "single" in x else 0)
    return df, df_upload_date

def get_distant_data() -> tuple[pd.DataFrame, str]:
    # Retrieve AMD dataframe
    amd_src = "hf://datasets/optimum-amd/transformers_daily_ci/**/runs/**/ci_results_run_models_gpu/model_results.json"
    files_amd = sorted(fs.glob(amd_src, refresh=True), reverse=True)
    df_amd, date_df_amd = read_one_dataframe(f"hf://{files_amd[0]}", "amd")
    # Retrieve NVIDIA dataframe, which pattern should be:
    # hf://datasets/hf-internal-testing`/transformers_daily_ci/raw/main/YYYY-MM-DD/ci_results_run_models_gpu/model_results.json 
    nvidia_src = "hf://datasets/hf-internal-testing/transformers_daily_ci/*/ci_results_run_models_gpu/model_results.json"
    files_nvidia = sorted(fs.glob(nvidia_src, refresh=True), reverse=True)
    # NOTE: should this be removeprefix instead of lstrip?
    nvidia_path = files_nvidia[0].lstrip('datasets/hf-internal-testing/transformers_daily_ci/')
    nvidia_path = "https://huggingface.co/datasets/hf-internal-testing/transformers_daily_ci/raw/main/" + nvidia_path
    df_nvidia, date_df_nvidia = read_one_dataframe(nvidia_path, "nvidia")
    # Infer and format the latest df date
    latest_update_msg = infer_latest_update_msg(date_df_amd, date_df_nvidia)
    # Join both dataframes
    joined = df_amd.join(df_nvidia, rsuffix="_nvidia", lsuffix="_amd", how="outer")
    joined = joined[KEYS_TO_KEEP]
    joined.index = joined.index.str.replace("^models_", "", regex=True)
    # Fitler out all but important models
    important_models_lower = [model.lower() for model in IMPORTANT_MODELS]
    filtered_joined = joined[joined.index.str.lower().isin(important_models_lower)]
    # Warn for ach missing important models
    for model in IMPORTANT_MODELS:
        if model not in filtered_joined.index:
            print(f"[WARNING] Model {model} was missing from index.")            
    return filtered_joined, latest_update_msg


def get_sample_data() -> tuple[pd.DataFrame, str]:
    # Retrieve sample dataframes
    df_amd, _ = read_one_dataframe("sample_amd.json", "amd")
    df_nvidia, _ = read_one_dataframe("sample_nvidia.json", "nvidia")
    # Join both dataframes
    joined = df_amd.join(df_nvidia, rsuffix="_nvidia", lsuffix="_amd", how="outer")
    joined = joined[KEYS_TO_KEEP]
    joined.index = joined.index.str.replace("^models_", "", regex=True)
    # Fitler out all but important models
    important_models_lower = [model.lower() for model in IMPORTANT_MODELS]
    filtered_joined = joined[joined.index.str.lower().isin(important_models_lower)]
    # Prefix all model names with "sample_"
    filtered_joined.index = "sample_" + filtered_joined.index
    return filtered_joined, "sample data was loaded"

def safe_extract(row: pd.DataFrame, key: str) -> int:
    return int(row.get(key, 0)) if pd.notna(row.get(key, 0)) else 0

def extract_model_data(row: pd.Series) -> tuple[dict[str, int], dict[str, int], int, int, int, int]:
    """Extract and process model data from DataFrame row."""
    # Handle missing values and get counts directly from dataframe
    success_nvidia = safe_extract(row, "success_nvidia")
    success_amd = safe_extract(row, "success_amd")

    skipped_nvidia = safe_extract(row, "skipped_nvidia")
    skipped_amd = safe_extract(row, "skipped_amd")
    
    failed_multi_amd = safe_extract(row, 'failed_multi_no_amd')
    failed_multi_nvidia = safe_extract(row, 'failed_multi_no_nvidia')
    failed_single_amd = safe_extract(row, 'failed_single_no_amd')
    failed_single_nvidia = safe_extract(row, 'failed_single_no_nvidia')
    # Calculate total failures
    total_failed_amd = failed_multi_amd + failed_single_amd
    total_failed_nvidia = failed_multi_nvidia + failed_single_nvidia
    # Create stats dictionaries directly from dataframe values
    amd_stats = {
        'passed': success_amd,
        'failed': total_failed_amd,
        'skipped': skipped_amd,
        'error': 0     # Not available in this dataset
    }
    nvidia_stats = {
        'passed': success_nvidia,
        'failed': total_failed_nvidia,
        'skipped': skipped_nvidia,
        'error': 0     # Not available in this dataset
    }
    return amd_stats, nvidia_stats, failed_multi_amd, failed_single_amd, failed_multi_nvidia, failed_single_nvidia



class CIResults:

    def __init__(self):
        self.df = pd.DataFrame()
        self.available_models = []
        self.latest_update_msg = ""

    def load_data(self) -> None:
        """Load data from the data source."""
        # Try loading the distant data, and fall back on sample data for local tinkering
        try:
            logger.info("Loading distant data...")
            new_df, latest_update_msg = get_distant_data()
            self.latest_update_msg = latest_update_msg
        except Exception as e:
            error_msg = [
                "Loading data failed:",
                "-" * 120,
                traceback.format_exc(),
                "-" * 120,
                "Falling back on sample data."
            ]
            logger.error("\n".join(error_msg))
            new_df, latest_update_msg = get_sample_data()
            self.latest_update_msg = latest_update_msg
        # Update attributes
        self.df = new_df
        self.available_models = new_df.index.tolist()
        # Log and return distant load status
        logger.info(f"Data loaded successfully: {len(self.available_models)} models")
        logger.info(f"Models: {self.available_models[:5]}{'...' if len(self.available_models) > 5 else ''}")
        logger.info(f"Latest update message: {self.latest_update_msg}")
        # Log a preview of the df
        msg = {}
        for model in self.available_models[:3]:
            msg[model] = {}
            for col in self.df.columns:
                value = self.df.loc[model, col]
                if not isinstance(value, int):
                    value = str(value)
                    if len(value) > 10:
                        value = value[:10] + "..."
                msg[model][col] = value
        logger.info(json.dumps(msg, indent=4))

    def schedule_data_reload(self):
        """Schedule the next data reload."""
        def reload_data():
            self.load_data()
            # Schedule the next reload in 15 minutes (900 seconds)
            timer = threading.Timer(900.0, reload_data)
            timer.daemon = True  # Dies when main thread dies
            timer.start()
            logger.info("Next data reload scheduled in 15 minutes")

        # Start the first reload timer
        timer = threading.Timer(900.0, reload_data)
        timer.daemon = True
        timer.start()
        logger.info("Data auto-reload scheduled every 15 minutes")