data.py

from huggingface_hub import HfFileSystem
import pandas as pd
from utils import logger
from datetime import datetime, timedelta
import threading
import traceback
import json
import re
from typing import List, Tuple, Optional

# 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.
    """
    if link.startswith("sample_"):
        return "9999-99-99"
    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_available_dates() -> List[str]:
    """Get list of available dates from both AMD and NVIDIA datasets."""
    try:
        # Get AMD dates - the path structure is: YYYY-MM-DD/runs/{run_id}/ci_results_run_models_gpu/model_results.json
        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)
        logger.info(f"Found {len(files_amd)} AMD files")
        
        # Get NVIDIA dates - structure is: 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)
        logger.info(f"Found {len(files_nvidia)} NVIDIA files")
        
        # Extract dates from file paths
        amd_dates = set()
        for file_path in files_amd:
            # Pattern to match the date in the AMD path: YYYY-MM-DD/runs/{run_id}/ci_results_run_models_gpu/model_results.json
            pattern = r'transformers_daily_ci/(\d{4}-\d{2}-\d{2})/runs/[^/]+/ci_results_run_models_gpu/model_results\.json'
            match = re.search(pattern, file_path)
            if match:
                amd_dates.add(match.group(1))
            else:
                # Log unmatched paths for debugging
                logger.debug(f"AMD file path didn't match pattern: {file_path}")
        
        # Log a few example AMD file paths for debugging
        if files_amd:
            logger.info(f"Example AMD file paths: {files_amd[:3]}")
        
        nvidia_dates = set()
        for file_path in files_nvidia:
            # Pattern to match the date in the NVIDIA path: YYYY-MM-DD/ci_results_run_models_gpu/model_results.json
            pattern = r'transformers_daily_ci/(\d{4}-\d{2}-\d{2})/ci_results_run_models_gpu/model_results\.json'
            match = re.search(pattern, file_path)
            if match:
                nvidia_dates.add(match.group(1))
        
        logger.info(f"AMD dates: {sorted(amd_dates, reverse=True)[:5]}...")  # Show first 5
        logger.info(f"NVIDIA dates: {sorted(nvidia_dates, reverse=True)[:5]}...")  # Show first 5
        
        # Return intersection of both datasets (dates where both have data)
        common_dates = sorted(amd_dates.intersection(nvidia_dates), reverse=True)
        logger.info(f"Common dates: {len(common_dates)} dates where both AMD and NVIDIA have data")
        
        if common_dates:
            return common_dates[:30]  # Limit to last 30 days for performance
        else:
            # If no real dates available, generate fake dates for the last 7 days
            logger.warning("No real dates available, generating fake dates for demo purposes")
            fake_dates = []
            today = datetime.now()
            for i in range(7):
                date = today - timedelta(days=i)
                fake_dates.append(date.strftime("%Y-%m-%d"))
            return fake_dates
        
    except Exception as e:
        logger.error(f"Error getting available dates: {e}")
        # Generate fake dates when there's an error
        logger.info("Generating fake dates due to error")
        fake_dates = []
        today = datetime.now()
        for i in range(7):
            date = today - timedelta(days=i)
            fake_dates.append(date.strftime("%Y-%m-%d"))
        return fake_dates


def get_data_for_date(target_date: str) -> tuple[pd.DataFrame, str]:
    """Get data for a specific date."""
    try:
        # For AMD, we need to find the specific run file for the date
        # AMD structure: YYYY-MM-DD/runs/{run_id}/ci_results_run_models_gpu/model_results.json
        amd_src = f"hf://datasets/optimum-amd/transformers_daily_ci/{target_date}/runs/*/ci_results_run_models_gpu/model_results.json"
        amd_files = fs.glob(amd_src, refresh=True)
        
        if not amd_files:
            raise FileNotFoundError(f"No AMD data found for date {target_date}")
        
        # Use the first (most recent) run for the date
        amd_file = amd_files[0]
        # Ensure the AMD file path has the hf:// prefix
        if not amd_file.startswith("hf://"):
            amd_file = f"hf://{amd_file}"
        
        # NVIDIA structure: YYYY-MM-DD/ci_results_run_models_gpu/model_results.json
        nvidia_src = f"hf://datasets/hf-internal-testing/transformers_daily_ci/{target_date}/ci_results_run_models_gpu/model_results.json"
        
        # Read dataframes - try each platform independently
        df_amd = pd.DataFrame()
        df_nvidia = pd.DataFrame()
        
        try:
            df_amd, _ = read_one_dataframe(amd_file, "amd")
            logger.info(f"Successfully loaded AMD data for {target_date}")
        except Exception as e:
            logger.warning(f"Failed to load AMD data for {target_date}: {e}")
        
        try:
            df_nvidia, _ = read_one_dataframe(nvidia_src, "nvidia")
            logger.info(f"Successfully loaded NVIDIA data for {target_date}")
        except Exception as e:
            logger.warning(f"Failed to load NVIDIA data for {target_date}: {e}")
        
        # If both failed, return empty dataframe
        if df_amd.empty and df_nvidia.empty:
            logger.warning(f"No data available for either platform on {target_date}")
            return pd.DataFrame(), target_date
        
        # Join both dataframes (outer join to include data from either platform)
        if not df_amd.empty and not df_nvidia.empty:
            joined = df_amd.join(df_nvidia, rsuffix="_nvidia", lsuffix="_amd", how="outer")
        elif not df_amd.empty:
            joined = df_amd.copy()
        else:
            joined = df_nvidia.copy()
        
        joined = joined[KEYS_TO_KEEP]
        joined.index = joined.index.str.replace("^models_", "", regex=True)
        
        # Filter 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)]
        
        return filtered_joined, target_date
        
    except Exception as e:
        logger.error(f"Error getting data for date {target_date}: {e}")
        # Return empty dataframe instead of sample data for historical functionality
        return pd.DataFrame(), target_date


def get_historical_data(start_date: str, end_date: str, sample_data = False) -> pd.DataFrame:
    """Get historical data for a date range."""
    if sample_data:
        return get_fake_historical_data(start_date, end_date)
    try:
        start_dt = datetime.strptime(start_date, "%Y-%m-%d")
        end_dt = datetime.strptime(end_date, "%Y-%m-%d")
        
        historical_data = []
        current_dt = start_dt
        
        while current_dt <= end_dt:
            date_str = current_dt.strftime("%Y-%m-%d")
            try:
                df, _ = get_data_for_date(date_str)
                # Only add non-empty dataframes
                if not df.empty:
                    df['date'] = date_str
                    historical_data.append(df)
                    logger.info(f"Loaded data for {date_str}")
                else:
                    logger.warning(f"No data available for {date_str}")
            except Exception as e:
                logger.warning(f"Could not load data for {date_str}: {e}")
            
            current_dt += timedelta(days=1)
        
        # Combine all dataframes
        combined_df = pd.concat(historical_data, ignore_index=False)
        return combined_df
        
    except Exception as e:
        logger.error(f"Error getting historical data: {e}")
        # Fall back to fake data when there's an error
        logger.info("Falling back to fake historical data due to error")
        return get_fake_historical_data(start_date, end_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 get_fake_historical_data(start_date: str, end_date: str) -> pd.DataFrame:
    """Generate fake historical data for a date range when real data loading fails."""
    try:
        start_dt = datetime.strptime(start_date, "%Y-%m-%d")
        end_dt = datetime.strptime(end_date, "%Y-%m-%d")
        
        # Generate fake data for each date in the range
        historical_data = []
        current_dt = start_dt
        
        # Get base sample data to use as template
        sample_df, _ = get_sample_data()
        
        while current_dt <= end_dt:
            date_str = current_dt.strftime("%Y-%m-%d")
            
            # Create a copy of sample data for this date with some random variations
            date_df = sample_df.copy()
            date_df['date'] = date_str
            
            # Add some random variation to make it look more realistic
            import random
            for idx in date_df.index:
                # Vary the success/failure counts slightly (±20%)
                for col in ['success_amd', 'success_nvidia', 'skipped_amd', 'skipped_nvidia']:
                    if col in date_df.columns:
                        original_val = date_df.loc[idx, col]
                        if pd.notna(original_val) and original_val > 0:
                            variation = random.uniform(0.8, 1.2)
                            date_df.loc[idx, col] = max(0, int(original_val * variation))
                
                # Vary failure counts more dramatically to show trends
                for col in ['failed_multi_no_amd', 'failed_multi_no_nvidia', 'failed_single_no_amd', 'failed_single_no_nvidia']:
                    if col in date_df.columns:
                        original_val = date_df.loc[idx, col]
                        if pd.notna(original_val):
                            # Sometimes have more failures, sometimes fewer
                            variation = random.uniform(0.5, 2.0)
                            date_df.loc[idx, col] = max(0, int(original_val * variation))
            
            historical_data.append(date_df)
            current_dt += timedelta(days=1)
        
        if not historical_data:
            logger.warning("No fake historical data generated")
            return pd.DataFrame()
        
        # Combine all dataframes
        combined_df = pd.concat(historical_data, ignore_index=False)
        logger.info(f"Generated fake historical data: {len(combined_df)} records from {start_date} to {end_date}")
        return combined_df
        
    except Exception as e:
        logger.error(f"Error generating fake historical data: {e}")
        return pd.DataFrame()

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 = ""
        self.available_dates = []
        self.historical_df = pd.DataFrame()
        self.all_historical_data = pd.DataFrame()  # Store all historical data at startup
        self.sample_data = False

    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
            self.available_dates = get_available_dates()
            logger.info(f"Available dates: {len(self.available_dates)} dates")
            if self.available_dates:
                logger.info(f"Date range: {self.available_dates[-1]} to {self.available_dates[0]}")
            else:
                logger.warning("No available dates found")
                self.available_dates = []
        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))
            self.sample_data = True
            new_df, latest_update_msg = get_sample_data()
            self.latest_update_msg = latest_update_msg
            self.available_dates = None
        
        # Update attributes
        self.df = new_df
        self.available_models = new_df.index.tolist()
        
        # Load all historical data at startup
        self.load_all_historical_data()
        
        # 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 load_all_historical_data(self) -> None:
        """Load all available historical data at startup."""
        try:
            if not self.available_dates:
                # Generate fake dates when no real dates are available
                fake_dates = []
                today = datetime.now()
                for i in range(7):
                    date = today - timedelta(days=i)
                    fake_dates.append(date.strftime("%Y-%m-%d"))
                self.available_dates = fake_dates
                logger.info(f"No available dates found, generated {len(self.available_dates)} sample dates.")
                
            logger.info(f"Loading all historical data for {len(self.available_dates)} dates...")
            start_date = self.available_dates[-1]  # Oldest date
            end_date = self.available_dates[0]     # Newest date
            
            self.all_historical_data = get_historical_data(start_date, end_date, self.sample_data)
            logger.info(f"All historical data loaded: {len(self.all_historical_data)} records")
        except Exception as e:
            logger.error(f"Error loading all historical data: {e}")
            self.all_historical_data = pd.DataFrame()

    def load_historical_data(self, start_date: str, end_date: str) -> None:
        """Load historical data for a date range from pre-loaded data."""
        try:
            logger.info(f"Filtering historical data from {start_date} to {end_date}")
            
            if self.all_historical_data.empty:
                logger.warning("No pre-loaded historical data available")
                self.historical_df = pd.DataFrame()
                return
            
            # Filter the pre-loaded data by date range
            start_dt = datetime.strptime(start_date, "%Y-%m-%d")
            end_dt = datetime.strptime(end_date, "%Y-%m-%d")
            
            # Filter data within the date range
            filtered_data = []
            for date_str in self.all_historical_data['date'].unique():
                date_dt = datetime.strptime(date_str, "%Y-%m-%d")
                if start_dt <= date_dt <= end_dt:
                    date_data = self.all_historical_data[self.all_historical_data['date'] == date_str]
                    filtered_data.append(date_data)
            
            if filtered_data:
                self.historical_df = pd.concat(filtered_data, ignore_index=False)
                logger.info(f"Historical data filtered: {len(self.historical_df)} records for {start_date} to {end_date}")
            else:
                self.historical_df = pd.DataFrame()
                logger.warning(f"No historical data found for date range {start_date} to {end_date}")
                
        except Exception as e:
            logger.error(f"Error filtering historical data: {e}")
            self.historical_df = pd.DataFrame()

    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")