""" Train tab for Video Model Studio UI with improved task progress display """ import gradio as gr import logging import os from typing import Dict, Any, List, Optional, Tuple from pathlib import Path from vms.utils import BaseTab from vms.config import ( TRAINING_PRESETS, OUTPUT_PATH, MODEL_TYPES, ASK_USER_TO_DUPLICATE_SPACE, SMALL_TRAINING_BUCKETS, TRAINING_TYPES, DEFAULT_NB_TRAINING_STEPS, DEFAULT_SAVE_CHECKPOINT_EVERY_N_STEPS, DEFAULT_BATCH_SIZE, DEFAULT_CAPTION_DROPOUT_P, DEFAULT_LEARNING_RATE, DEFAULT_LORA_RANK, DEFAULT_LORA_ALPHA, DEFAULT_LORA_RANK_STR, DEFAULT_LORA_ALPHA_STR, DEFAULT_SEED, DEFAULT_NUM_GPUS, DEFAULT_MAX_GPUS, DEFAULT_PRECOMPUTATION_ITEMS, DEFAULT_NB_TRAINING_STEPS, DEFAULT_NB_LR_WARMUP_STEPS, ) logger = logging.getLogger(__name__) class TrainTab(BaseTab): """Train tab for model training""" def __init__(self, app_state): super().__init__(app_state) self.id = "train_tab" self.title = "4️⃣ Train" def create(self, parent=None) -> gr.TabItem: """Create the Train tab UI components""" with gr.TabItem(self.title, id=self.id) as tab: with gr.Row(): with gr.Column(): with gr.Row(): self.components["train_title"] = gr.Markdown("## 0 files available for training (0 bytes)") with gr.Row(): with gr.Column(): self.components["training_preset"] = gr.Dropdown( choices=list(TRAINING_PRESETS.keys()), label="Training Preset", value=list(TRAINING_PRESETS.keys())[0] ) self.components["preset_info"] = gr.Markdown() with gr.Row(): with gr.Column(): self.components["model_type"] = gr.Dropdown( choices=list(MODEL_TYPES.keys()), label="Model Type", value=list(MODEL_TYPES.keys())[0] ) with gr.Column(): self.components["training_type"] = gr.Dropdown( choices=list(TRAINING_TYPES.keys()), label="Training Type", value=list(TRAINING_TYPES.keys())[0] ) with gr.Row(): self.components["model_info"] = gr.Markdown( value=self.get_model_info(list(MODEL_TYPES.keys())[0], list(TRAINING_TYPES.keys())[0]) ) # LoRA specific parameters (will show/hide based on training type) with gr.Row(visible=True) as lora_params_row: self.components["lora_params_row"] = lora_params_row self.components["lora_rank"] = gr.Dropdown( label="LoRA Rank", choices=["16", "32", "64", "128", "256", "512", "1024"], value=DEFAULT_LORA_RANK_STR, type="value" ) self.components["lora_alpha"] = gr.Dropdown( label="LoRA Alpha", choices=["16", "32", "64", "128", "256", "512", "1024"], value=DEFAULT_LORA_ALPHA_STR, type="value" ) with gr.Row(): self.components["train_steps"] = gr.Number( label="Number of Training Steps", value=DEFAULT_NB_TRAINING_STEPS, minimum=1, precision=0 ) self.components["batch_size"] = gr.Number( label="Batch Size", value=1, minimum=1, precision=0 ) with gr.Row(): self.components["learning_rate"] = gr.Number( label="Learning Rate", value=DEFAULT_LEARNING_RATE, minimum=1e-8 ) self.components["save_iterations"] = gr.Number( label="Save checkpoint every N iterations", value=DEFAULT_SAVE_CHECKPOINT_EVERY_N_STEPS, minimum=1, precision=0, info="Model will be saved periodically after these many steps" ) with gr.Row(): self.components["num_gpus"] = gr.Slider( label="Number of GPUs to use", value=DEFAULT_NUM_GPUS, minimum=1, maximum=DEFAULT_MAX_GPUS, step=1, info="Number of GPUs to use for training" ) self.components["precomputation_items"] = gr.Number( label="Precomputation Items", value=DEFAULT_PRECOMPUTATION_ITEMS, minimum=1, precision=0, info="Should be more or less the number of total items (ex: 200 videos), divided by the number of GPUs" ) with gr.Row(): self.components["lr_warmup_steps"] = gr.Number( label="Learning Rate Warmup Steps", value=DEFAULT_NB_LR_WARMUP_STEPS, minimum=0, precision=0, info="Number of warmup steps (typically 20-40% of total training steps)" ) with gr.Column(): with gr.Row(): # Check for existing checkpoints to determine button text has_checkpoints = len(list(OUTPUT_PATH.glob("checkpoint-*"))) > 0 start_text = "Continue Training" if has_checkpoints else "Start Training" self.components["start_btn"] = gr.Button( start_text, variant="primary", interactive=not ASK_USER_TO_DUPLICATE_SPACE ) # Just use stop and pause buttons for now to ensure compatibility self.components["stop_btn"] = gr.Button( "Stop at Last Checkpoint", variant="primary", interactive=False ) self.components["pause_resume_btn"] = gr.Button( "Resume Training", variant="secondary", interactive=False, visible=False ) # Add delete checkpoints button self.components["delete_checkpoints_btn"] = gr.Button( "Delete All Checkpoints", variant="stop", interactive=True ) with gr.Row(): with gr.Column(): self.components["status_box"] = gr.Textbox( label="Training Status", interactive=False, lines=4 ) # Add new component for current task progress self.components["current_task_box"] = gr.Textbox( label="Current Task Progress", interactive=False, lines=3, elem_id="current_task_display" ) with gr.Accordion("See training logs"): self.components["log_box"] = gr.TextArea( label="Finetrainers output (see HF Space logs for more details)", interactive=False, lines=40, max_lines=200, autoscroll=True ) return tab def connect_events(self) -> None: """Connect event handlers to UI components""" # Model type change event def update_model_info(model, training_type): params = self.get_default_params(MODEL_TYPES[model], TRAINING_TYPES[training_type]) info = self.get_model_info(model, training_type) show_lora_params = training_type == list(TRAINING_TYPES.keys())[0] # Show if LoRA Finetune return { self.components["model_info"]: info, self.components["train_steps"]: params["train_steps"], self.components["batch_size"]: params["batch_size"], self.components["learning_rate"]: params["learning_rate"], self.components["save_iterations"]: params["save_iterations"], self.components["lora_params_row"]: gr.Row(visible=show_lora_params) } self.components["model_type"].change( fn=lambda v: self.app.update_ui_state(model_type=v), inputs=[self.components["model_type"]], outputs=[] ).then( fn=update_model_info, inputs=[self.components["model_type"], self.components["training_type"]], outputs=[ self.components["model_info"], self.components["train_steps"], self.components["batch_size"], self.components["learning_rate"], self.components["save_iterations"], self.components["lora_params_row"] ] ) # Training type change event self.components["training_type"].change( fn=lambda v: self.app.update_ui_state(training_type=v), inputs=[self.components["training_type"]], outputs=[] ).then( fn=update_model_info, inputs=[self.components["model_type"], self.components["training_type"]], outputs=[ self.components["model_info"], self.components["train_steps"], self.components["batch_size"], self.components["learning_rate"], self.components["save_iterations"], self.components["lora_params_row"] ] ) # Add in the connect_events() method: self.components["num_gpus"].change( fn=lambda v: self.app.update_ui_state(num_gpus=v), inputs=[self.components["num_gpus"]], outputs=[] ) self.components["precomputation_items"].change( fn=lambda v: self.app.update_ui_state(precomputation_items=v), inputs=[self.components["precomputation_items"]], outputs=[] ) self.components["lr_warmup_steps"].change( fn=lambda v: self.app.update_ui_state(lr_warmup_steps=v), inputs=[self.components["lr_warmup_steps"]], outputs=[] ) # Training parameters change events self.components["lora_rank"].change( fn=lambda v: self.app.update_ui_state(lora_rank=v), inputs=[self.components["lora_rank"]], outputs=[] ) self.components["lora_alpha"].change( fn=lambda v: self.app.update_ui_state(lora_alpha=v), inputs=[self.components["lora_alpha"]], outputs=[] ) self.components["train_steps"].change( fn=lambda v: self.app.update_ui_state(train_steps=v), inputs=[self.components["train_steps"]], outputs=[] ) self.components["batch_size"].change( fn=lambda v: self.app.update_ui_state(batch_size=v), inputs=[self.components["batch_size"]], outputs=[] ) self.components["learning_rate"].change( fn=lambda v: self.app.update_ui_state(learning_rate=v), inputs=[self.components["learning_rate"]], outputs=[] ) self.components["save_iterations"].change( fn=lambda v: self.app.update_ui_state(save_iterations=v), inputs=[self.components["save_iterations"]], outputs=[] ) # Training preset change event self.components["training_preset"].change( fn=lambda v: self.app.update_ui_state(training_preset=v), inputs=[self.components["training_preset"]], outputs=[] ).then( fn=self.update_training_params, inputs=[self.components["training_preset"]], outputs=[ self.components["model_type"], self.components["training_type"], self.components["lora_rank"], self.components["lora_alpha"], self.components["train_steps"], self.components["batch_size"], self.components["learning_rate"], self.components["save_iterations"], self.components["preset_info"], self.components["lora_params_row"], self.components["num_gpus"], self.components["precomputation_items"], self.components["lr_warmup_steps"] ] ) # Training control events self.components["start_btn"].click( fn=self.handle_training_start, inputs=[ self.components["training_preset"], self.components["model_type"], self.components["training_type"], self.components["lora_rank"], self.components["lora_alpha"], self.components["train_steps"], self.components["batch_size"], self.components["learning_rate"], self.components["save_iterations"], self.app.tabs["manage_tab"].components["repo_id"] ], outputs=[ self.components["status_box"], self.components["log_box"] ] ) # Use simplified event handlers for pause/resume and stop third_btn = self.components["delete_checkpoints_btn"] if "delete_checkpoints_btn" in self.components else self.components["pause_resume_btn"] self.components["pause_resume_btn"].click( fn=self.handle_pause_resume, outputs=[ self.components["status_box"], self.components["log_box"], self.components["current_task_box"], self.components["start_btn"], self.components["stop_btn"], third_btn ] ) self.components["stop_btn"].click( fn=self.handle_stop, outputs=[ self.components["status_box"], self.components["log_box"], self.components["current_task_box"], self.components["start_btn"], self.components["stop_btn"], third_btn ] ) # Add an event handler for delete_checkpoints_btn self.components["delete_checkpoints_btn"].click( fn=lambda: self.app.training.delete_all_checkpoints(), outputs=[self.components["status_box"]] ) def handle_training_start( self, preset, model_type, training_type, lora_rank, lora_alpha, train_steps, batch_size, learning_rate, save_iterations, repo_id, progress=gr.Progress() ): """Handle training start with proper log parser reset and checkpoint detection""" # Safely reset log parser if it exists if hasattr(self.app, 'log_parser') and self.app.log_parser is not None: self.app.log_parser.reset() else: logger.warning("Log parser not initialized, creating a new one") from ..utils import TrainingLogParser self.app.log_parser = TrainingLogParser() # Initialize progress #progress(0, desc="Initializing training") # Check for latest checkpoint checkpoints = list(OUTPUT_PATH.glob("checkpoint-*")) resume_from = None if checkpoints: # Find the latest checkpoint latest_checkpoint = max(checkpoints, key=os.path.getmtime) resume_from = str(latest_checkpoint) logger.info(f"Found checkpoint at {resume_from}, will resume training") #progress(0.05, desc=f"Resuming from checkpoint {Path(resume_from).name}") else: #progress(0.05, desc="Starting new training run") pass # Convert model_type display name to internal name model_internal_type = MODEL_TYPES.get(model_type) if not model_internal_type: logger.error(f"Invalid model type: {model_type}") return f"Error: Invalid model type '{model_type}'", "Model type not recognized" # Convert training_type display name to internal name training_internal_type = TRAINING_TYPES.get(training_type) if not training_internal_type: logger.error(f"Invalid training type: {training_type}") return f"Error: Invalid training type '{training_type}'", "Training type not recognized" # Get other parameters from UI form num_gpus = int(self.components["num_gpus"].value) precomputation_items = int(self.components["precomputation_items"].value) lr_warmup_steps = int(self.components["lr_warmup_steps"].value) # Progress update #progress(0.1, desc="Preparing dataset") # Start training (it will automatically use the checkpoint if provided) try: return self.app.training.start_training( model_internal_type, lora_rank, lora_alpha, train_steps, batch_size, learning_rate, save_iterations, repo_id, preset_name=preset, training_type=training_internal_type, resume_from_checkpoint=resume_from, num_gpus=num_gpus, precomputation_items=precomputation_items, lr_warmup_steps=lr_warmup_steps, progress=progress ) except Exception as e: logger.exception("Error starting training") return f"Error starting training: {str(e)}", f"Exception: {str(e)}\n\nCheck the logs for more details." def get_model_info(self, model_type: str, training_type: str) -> str: """Get information about the selected model type and training method""" if model_type == "HunyuanVideo": base_info = """### HunyuanVideo - Required VRAM: ~48GB minimum - Recommended batch size: 1-2 - Typical training time: 2-4 hours - Default resolution: 49x512x768""" if training_type == "LoRA Finetune": return base_info + "\n- Required VRAM: ~18GB minimum\n- Default LoRA rank: 128 (~400 MB)" else: return base_info + "\n- Required VRAM: ~48GB minimum\n- **Full finetune not recommended due to VRAM requirements**" elif model_type == "LTX-Video": base_info = """### LTX-Video - Recommended batch size: 1-4 - Typical training time: 1-3 hours - Default resolution: 49x512x768""" if training_type == "LoRA Finetune": return base_info + "\n- Required VRAM: ~18GB minimum\n- Default LoRA rank: 128 (~400 MB)" else: return base_info + "\n- Required VRAM: ~21GB minimum\n- Full model size: ~8GB" elif model_type == "Wan-2.1-T2V": base_info = """### Wan-2.1-T2V - Recommended batch size: ? - Typical training time: ? hours - Default resolution: 49x512x768""" if training_type == "LoRA Finetune": return base_info + "\n- Required VRAM: ?GB minimum\n- Default LoRA rank: 32 (~120 MB)" else: return base_info + "\n- **Full finetune not recommended due to VRAM requirements**" # Default fallback return f"### {model_type}\nPlease check documentation for VRAM requirements and recommended settings." def get_default_params(self, model_type: str, training_type: str) -> Dict[str, Any]: """Get default training parameters for model type""" # Find preset that matches model type and training type matching_presets = [ preset for preset_name, preset in TRAINING_PRESETS.items() if preset["model_type"] == model_type and preset["training_type"] == training_type ] if matching_presets: # Use the first matching preset preset = matching_presets[0] return { "train_steps": preset.get("train_steps", DEFAULT_NB_TRAINING_STEPS), "batch_size": preset.get("batch_size", DEFAULT_BATCH_SIZE), "learning_rate": preset.get("learning_rate", DEFAULT_LEARNING_RATE), "save_iterations": preset.get("save_iterations", DEFAULT_SAVE_CHECKPOINT_EVERY_N_STEPS), "lora_rank": preset.get("lora_rank", DEFAULT_LORA_RANK_STR), "lora_alpha": preset.get("lora_alpha", DEFAULT_LORA_ALPHA_STR) } # Default fallbacks if model_type == "hunyuan_video": return { "train_steps": DEFAULT_NB_TRAINING_STEPS, "batch_size": DEFAULT_BATCH_SIZE, "learning_rate": 2e-5, "save_iterations": DEFAULT_SAVE_CHECKPOINT_EVERY_N_STEPS, "lora_rank": DEFAULT_LORA_RANK_STR, "lora_alpha": DEFAULT_LORA_ALPHA_STR } elif model_type == "ltx_video": return { "train_steps": DEFAULT_NB_TRAINING_STEPS, "batch_size": DEFAULT_BATCH_SIZE, "learning_rate": DEFAULT_LEARNING_RATE, "save_iterations": DEFAULT_SAVE_CHECKPOINT_EVERY_N_STEPS, "lora_rank": DEFAULT_LORA_RANK_STR, "lora_alpha": DEFAULT_LORA_ALPHA_STR } elif model_type == "wan": return { "train_steps": DEFAULT_NB_TRAINING_STEPS, "batch_size": DEFAULT_BATCH_SIZE, "learning_rate": 5e-5, "save_iterations": DEFAULT_SAVE_CHECKPOINT_EVERY_N_STEPS, "lora_rank": "32", "lora_alpha": "32" } else: # Generic defaults return { "train_steps": DEFAULT_NB_TRAINING_STEPS, "batch_size": DEFAULT_BATCH_SIZE, "learning_rate": DEFAULT_LEARNING_RATE, "save_iterations": DEFAULT_SAVE_CHECKPOINT_EVERY_N_STEPS, "lora_rank": DEFAULT_LORA_RANK_STR, "lora_alpha": DEFAULT_LORA_ALPHA_STR } def update_training_params(self, preset_name: str) -> Tuple: """Update UI components based on selected preset while preserving custom settings""" preset = TRAINING_PRESETS[preset_name] # Load current UI state to check if user has customized values current_state = self.app.load_ui_values() # Find the display name that maps to our model type model_display_name = next( key for key, value in MODEL_TYPES.items() if value == preset["model_type"] ) # Find the display name that maps to our training type training_display_name = next( key for key, value in TRAINING_TYPES.items() if value == preset["training_type"] ) # Get preset description for display description = preset.get("description", "") # Get max values from buckets buckets = preset["training_buckets"] max_frames = max(frames for frames, _, _ in buckets) max_height = max(height for _, height, _ in buckets) max_width = max(width for _, _, width in buckets) bucket_info = f"\nMaximum video size: {max_frames} frames at {max_width}x{max_height} resolution" info_text = f"{description}{bucket_info}" # Check if LoRA params should be visible show_lora_params = preset["training_type"] == "lora" # Use preset defaults but preserve user-modified values if they exist lora_rank_val = current_state.get("lora_rank") if current_state.get("lora_rank") != preset.get("lora_rank", DEFAULT_LORA_RANK_STR) else preset.get("lora_rank", DEFAULT_LORA_RANK_STR) lora_alpha_val = current_state.get("lora_alpha") if current_state.get("lora_alpha") != preset.get("lora_alpha", DEFAULT_LORA_ALPHA_STR) else preset.get("lora_alpha", DEFAULT_LORA_ALPHA_STR) train_steps_val = current_state.get("train_steps") if current_state.get("train_steps") != preset.get("train_steps", DEFAULT_NB_TRAINING_STEPS) else preset.get("train_steps", DEFAULT_NB_TRAINING_STEPS) batch_size_val = current_state.get("batch_size") if current_state.get("batch_size") != preset.get("batch_size", DEFAULT_BATCH_SIZE) else preset.get("batch_size", DEFAULT_BATCH_SIZE) learning_rate_val = current_state.get("learning_rate") if current_state.get("learning_rate") != preset.get("learning_rate", DEFAULT_LEARNING_RATE) else preset.get("learning_rate", DEFAULT_LEARNING_RATE) save_iterations_val = current_state.get("save_iterations") if current_state.get("save_iterations") != preset.get("save_iterations", DEFAULT_SAVE_CHECKPOINT_EVERY_N_STEPS) else preset.get("save_iterations", DEFAULT_SAVE_CHECKPOINT_EVERY_N_STEPS) num_gpus_val = current_state.get("num_gpus") if current_state.get("num_gpus") != preset.get("num_gpus", DEFAULT_NUM_GPUS) else preset.get("num_gpus", DEFAULT_NUM_GPUS) precomputation_items_val = current_state.get("precomputation_items") if current_state.get("precomputation_items") != preset.get("precomputation_items", DEFAULT_PRECOMPUTATION_ITEMS) else preset.get("precomputation_items", DEFAULT_PRECOMPUTATION_ITEMS) lr_warmup_steps_val = current_state.get("lr_warmup_steps") if current_state.get("lr_warmup_steps") != preset.get("lr_warmup_steps", DEFAULT_NB_LR_WARMUP_STEPS) else preset.get("lr_warmup_steps", DEFAULT_NB_LR_WARMUP_STEPS) # Return values in the same order as the output components return ( model_display_name, training_display_name, lora_rank_val, lora_alpha_val, train_steps_val, batch_size_val, learning_rate_val, save_iterations_val, info_text, gr.Row(visible=show_lora_params), num_gpus_val, precomputation_items_val, lr_warmup_steps_val ) def get_latest_status_message_and_logs(self) -> Tuple[str, str, str]: """Get latest status message, log content, and status code in a safer way""" state = self.app.training.get_status() logs = self.app.training.get_logs() # Check if training process died unexpectedly training_died = False if state["status"] == "training" and not self.app.training.is_training_running(): state["status"] = "error" state["message"] = "Training process terminated unexpectedly." training_died = True # Look for error in logs error_lines = [] for line in logs.splitlines(): if "Error:" in line or "Exception:" in line or "Traceback" in line: error_lines.append(line) if error_lines: state["message"] += f"\n\nPossible error: {error_lines[-1]}" # Ensure log parser is initialized if not hasattr(self.app, 'log_parser') or self.app.log_parser is None: from ..utils import TrainingLogParser self.app.log_parser = TrainingLogParser() logger.info("Initialized missing log parser") # Parse new log lines if logs and not training_died: last_state = None for line in logs.splitlines(): try: state_update = self.app.log_parser.parse_line(line) if state_update: last_state = state_update except Exception as e: logger.error(f"Error parsing log line: {str(e)}") continue if last_state: ui_updates = self.update_training_ui(last_state) state["message"] = ui_updates.get("status_box", state["message"]) # Parse status for training state if "completed" in state["message"].lower(): state["status"] = "completed" elif "error" in state["message"].lower(): state["status"] = "error" elif "failed" in state["message"].lower(): state["status"] = "error" elif "stopped" in state["message"].lower(): state["status"] = "stopped" # Add the current task info if available if hasattr(self.app, 'log_parser') and self.app.log_parser is not None: state["current_task"] = self.app.log_parser.get_current_task_display() return (state["status"], state["message"], logs) def get_status_updates(self): """Get status updates for text components (no variant property)""" status, message, logs = self.get_latest_status_message_and_logs() # Get current task if available current_task = "" if hasattr(self.app, 'log_parser') and self.app.log_parser is not None: current_task = self.app.log_parser.get_current_task_display() return message, logs, current_task def get_button_updates(self): """Get button updates (with variant property)""" status, _, _ = self.get_latest_status_message_and_logs() # Add checkpoints detection has_checkpoints = len(list(OUTPUT_PATH.glob("checkpoint-*"))) > 0 is_training = status in ["training", "initializing"] is_completed = status in ["completed", "error", "stopped"] start_text = "Continue Training" if has_checkpoints else "Start Training" # Create button updates start_btn = gr.Button( value=start_text, interactive=not is_training, variant="primary" if not is_training else "secondary" ) stop_btn = gr.Button( value="Stop at Last Checkpoint", interactive=is_training, variant="primary" if is_training else "secondary" ) # Add delete_checkpoints_btn or pause_resume_btn if "delete_checkpoints_btn" in self.components: third_btn = gr.Button( "Delete All Checkpoints", interactive=has_checkpoints and not is_training, variant="stop" ) else: third_btn = gr.Button( "Resume Training" if status == "paused" else "Pause Training", interactive=(is_training or status == "paused") and not is_completed, variant="secondary", visible=False ) return start_btn, stop_btn, third_btn def update_training_ui(self, training_state: Dict[str, Any]): """Update UI components based on training state""" updates = {} # Update status box with high-level information status_text = [] if training_state["status"] != "idle": status_text.extend([ f"Status: {training_state['status']}", f"Progress: {training_state['progress']}", f"Step: {training_state['current_step']}/{training_state['total_steps']}", f"Time elapsed: {training_state['elapsed']}", f"Estimated remaining: {training_state['remaining']}", "", f"Current loss: {training_state['step_loss']}", f"Learning rate: {training_state['learning_rate']}", f"Gradient norm: {training_state['grad_norm']}", f"Memory usage: {training_state['memory']}" ]) if training_state["error_message"]: status_text.append(f"\nError: {training_state['error_message']}") updates["status_box"] = "\n".join(status_text) # Add current task information to the dedicated box if training_state.get("current_task"): updates["current_task_box"] = training_state["current_task"] else: updates["current_task_box"] = "No active task" if training_state["status"] != "training" else "Waiting for task information..." return updates def handle_pause_resume(self): """Handle pause/resume button click""" status, _, _ = self.get_latest_status_message_and_logs() if status == "paused": self.app.training.resume_training() else: self.app.training.pause_training() # Return the updates separately for text and buttons return (*self.get_status_updates(), *self.get_button_updates()) def handle_stop(self): """Handle stop button click""" self.app.training.stop_training() # Return the updates separately for text and buttons return (*self.get_status_updates(), *self.get_button_updates())