multi-classifier / multi_head_trainer.py

Big cleanup

0dfbd20 over 1 year ago

20.3 kB

from sklearn.metrics import classification_report, precision_recall_fscore_support
from transformers import (
    DebertaV2TokenizerFast,
    EarlyStoppingCallback,
    Trainer,
    TrainingArguments,
)
import logging
import numpy as np
import torch

from multi_head_model import MultiHeadModel, MultiHeadModelConfig

logger = logging.getLogger(__name__)


# ------------------------------------------------------------------------------
# Tokenize with max_length=512, stride=128, and subword alignment
# ------------------------------------------------------------------------------

class ExampleAligner:
    def __init__(self, all_labels, label2id):
        self.all_labels = all_labels
        self.label2id = label2id

    def tokenize_and_align_labels(self, examples):
        """
        For each example, the tokenizer may produce multiple overlapping
        chunks if the tokens exceed 512 subwords. Each chunk will be
        length=512, with a stride=128 for the next chunk.
        We'll align labels so that subwords beyond the first in a token get -100.
        """
        # We rely on is_split_into_words=True because examples["tokens"] is a list of token strings.
        tokenized_batch = tokenizer(
            examples["tokens"],
            is_split_into_words=True,
            max_length=512,
            stride=128,
            truncation=True,
            return_overflowing_tokens=True,
            return_offsets_mapping=False,  # not mandatory for basic alignment
            padding="max_length"
        )

        # The tokenizer returns "overflow_to_sample_mapping", telling us
        # which original example index each chunk corresponds to.
        # If the tokenizer didn't need to create overflows, the key might be missing
        if "overflow_to_sample_mapping" not in tokenized_batch:
            # No overflow => each input corresponds 1:1 with the original example
            sample_map = [i for i in range(len(tokenized_batch["input_ids"]))]
        else:
            sample_map = tokenized_batch["overflow_to_sample_mapping"]

        # We'll build lists for final outputs.
        # For each chunk i, we produce:
        #   "input_ids"[i], "attention_mask"[i], plus per-feature label IDs.
        final_input_ids = []
        final_attention_mask = []
        final_labels_columns = {feat: [] for feat in self.all_labels}  # store one label-sequence per chunk

        for i in range(len(tokenized_batch["input_ids"])):
            # chunk i
            chunk_input_ids = tokenized_batch["input_ids"][i]
            chunk_attn_mask = tokenized_batch["attention_mask"][i]

            original_index = sample_map[i]  # which example in the original batch
            word_ids = tokenized_batch.word_ids(batch_index=i)

            # We'll build label arrays for each feature
            chunk_labels_dict = {}

            for feat_name in self.all_labels:
                # The UD token-level labels for the *original* example
                token_labels = examples[feat_name][original_index]  # e.g. length T
                chunk_label_ids = []

                previous_word_id = None
                for w_id in word_ids:
                    if w_id is None:
                        # special token (CLS, SEP, padding)
                        chunk_label_ids.append(-100)
                    else:
                        # If it's the same word_id as before, it's a subword => label = -100
                        if w_id == previous_word_id:
                            chunk_label_ids.append(-100)
                        else:
                            # New token => use the actual label
                            label_str = token_labels[w_id]
                            label_id = self.label2id[feat_name][label_str]
                            chunk_label_ids.append(label_id)
                    previous_word_id = w_id

                chunk_labels_dict[feat_name] = chunk_label_ids

            final_input_ids.append(chunk_input_ids)
            final_attention_mask.append(chunk_attn_mask)
            for feat_name in self.all_labels:
                final_labels_columns[feat_name].append(chunk_labels_dict[feat_name])

        # Return the new "flattened" set of chunks
        # So the "map" call will expand each example → multiple chunk examples.
        result = {
            "input_ids": final_input_ids,
            "attention_mask": final_attention_mask,
        }
        # We'll store each feature's label IDs in separate columns (e.g. labels_xpos, labels_deprel, etc.)
        for feat_name in self.all_labels:
            result[f"labels_{feat_name}"] = final_labels_columns[feat_name]

        return result

# ------------------------------------------------------------------------------
# Trainer Setup
# ------------------------------------------------------------------------------

class MultiHeadTrainer(Trainer):
    def __init__(self, all_labels, **kwargs):
        self.all_labels = all_labels
        super().__init__(**kwargs)

    def compute_loss(self, model, inputs, return_outputs=False, num_items_in_batch=None):
        # 1) Gather all your per-feature labels from inputs
        _labels_dict = {}
        for feat_name in self.all_labels:
            key = f"labels_{feat_name}"
            if key in inputs:
                _labels_dict[feat_name] = inputs[key]

        # 2) Remove them so they don't get passed incorrectly to the model
        for key in list(inputs.keys()):
            if key.startswith("labels_"):
                del inputs[key]

        # 3) Call model(...) with _labels_dict
        outputs = model(**inputs, labels_dict=_labels_dict)
        # 'outputs' is (loss, logits_dict) in training/eval mode
        loss, logits_dict = outputs

        # Optional: if your special param is used upstream for some logic,
        # you can handle it here or pass it along. For example:
        if num_items_in_batch is not None:
            # ... do something if needed ...
            pass

        if return_outputs:
            # Return (loss, logits_dict) so Trainer sees logits_dict as predictions
            return loss, logits_dict
        else:
            return loss

    def prediction_step(self, model, inputs, prediction_loss_only=False, ignore_keys=None):
        # 1) gather the "labels_xxx" columns
        _labels_dict = {}
        for feat_name in self.all_labels:
            key = f"labels_{feat_name}"
            if key in inputs:
                _labels_dict[feat_name] = inputs[key]
                del inputs[key]

        # 2) forward pass without those keys
        with torch.no_grad():
            outputs = model(**inputs, labels_dict=_labels_dict)

        loss, logits_dict = outputs  # you are returning (loss, dict-of-arrays)

        if prediction_loss_only:
            return (loss, None, None)

        # The trainer expects a triple: (loss, predictions, labels)
        #   - 'predictions' can be the dictionary
        #   - 'labels' can be the dictionary of label IDs
        return loss, logits_dict, _labels_dict


def multi_head_classification_reports(logits_dict, labels_dict, id2label_dict):
    """
    For each head, generate a classification report (precision, recall, f1, etc. per class).
    Return them as a dict: {head_name: "string report"}.
    :param logits_dict: dict of {head_name: np.array(batch_size, seq_len, num_classes)}
    :param labels_dict: dict of {head_name: np.array(batch_size, seq_len)}
    :param id2label_dict: dict of {head_name: {id: label_str}}
    :return: A dict of classification-report strings, one per head.
    """
    reports = {}

    for head_name, logits in logits_dict.items():
        if head_name not in labels_dict:
            continue

        predictions = np.argmax(logits, axis=-1)
        valid_preds, valid_labels = [], []
        for pred_seq, label_seq in zip(predictions, labels_dict[head_name]):
            for p, lab in zip(pred_seq, label_seq):
                if lab != -100:
                    valid_preds.append(p)
                    valid_labels.append(lab)

        if len(valid_preds) == 0:
            reports[head_name] = "No valid predictions."
            continue

        # Convert numeric IDs to string labels
        valid_preds_str = [id2label_dict[head_name][p] for p in valid_preds]
        valid_labels_str = [id2label_dict[head_name][l] for l in valid_labels]

        # Generate the per-class classification report
        report_str = classification_report(
            valid_labels_str,
            valid_preds_str,
            zero_division=0
        )
        reports[head_name] = report_str

    return reports


def multi_head_compute_metrics(logits_dict, labels_dict):
    """
    For each head (e.g. xpos, deprel, Case, etc.), computes:
      - Accuracy
      - Precision (macro/micro)
      - Recall (macro/micro)
      - F1 (macro/micro)
    :param logits_dict: dict of {head_name: np.array of shape (batch_size, seq_len, num_classes)}
    :param labels_dict: dict of {head_name: np.array of shape (batch_size, seq_len)}
    :return: A dict with aggregated metrics. Keys prefixed by head_name, e.g. "xpos_accuracy", "xpos_f1_macro", etc.
    """
    # We'll accumulate metrics in one big dictionary, keyed by "<head>_<metric>"
    results = {}

    for head_name, logits in logits_dict.items():
        if head_name not in labels_dict:
            # In case there's a mismatch or a head we didn't provide labels for
            continue

        # (batch_size, seq_len, num_classes)
        predictions = np.argmax(logits, axis=-1)  # => (batch_size, seq_len)

        # Flatten ignoring positions where label == -100
        valid_preds, valid_labels = [], []
        for pred_seq, label_seq in zip(predictions, labels_dict[head_name]):
            for p, lab in zip(pred_seq, label_seq):
                if lab != -100:
                    valid_preds.append(p)
                    valid_labels.append(lab)

        valid_preds = np.array(valid_preds)
        valid_labels = np.array(valid_labels)

        if len(valid_preds) == 0:
            # No valid data for this head—skip
            continue

        # Overall token-level accuracy
        accuracy = (valid_preds == valid_labels).mean()

        # Macro average => treat each class equally
        precision_macro, recall_macro, f1_macro, _ = precision_recall_fscore_support(
            valid_labels, valid_preds, average="macro", zero_division=0
        )

        # Micro average => aggregate across all classes
        precision_micro, recall_micro, f1_micro, _ = precision_recall_fscore_support(
            valid_labels, valid_preds, average="micro", zero_division=0
        )

        results[f"{head_name}_accuracy"] = accuracy
        results[f"{head_name}_precision_macro"] = precision_macro
        results[f"{head_name}_recall_macro"] = recall_macro
        results[f"{head_name}_f1_macro"] = f1_macro
        results[f"{head_name}_precision_micro"] = precision_micro
        results[f"{head_name}_recall_micro"] = recall_micro
        results[f"{head_name}_f1_micro"] = f1_micro

    return results


def multi_head_compute_metrics_aggregate_f1(logits_dict, labels_dict):
    results = multi_head_compute_metrics(logits_dict, labels_dict)  # your existing function

    # Grab all keys that end with "_f1_macro"
    f1_keys = [k for k in results.keys() if k.endswith("_f1_macro")]
    if not f1_keys:
        # fallback in case no F1 keys exist
        final_f1 = 0.0
    else:
        final_f1 = np.mean([results[k] for k in f1_keys])

    final_dict = {"f1_macro": final_f1}
    # Optionally keep all others for logging
    final_dict.update(results)
    return final_dict


def compute_metrics_for_trainer(eval_pred):
    # This is the HF Trainer signature: eval_pred is usually (logits, labels) or (predictions, label_ids)
    logits_dict, labels_dict = eval_pred.predictions, eval_pred.label_ids
    return multi_head_compute_metrics_aggregate_f1(logits_dict, labels_dict)


if __name__ == "__main__":
    from datasets import DatasetDict, load_from_disk
    import argparse
    import logging.config

    from utils import default_logging_config, get_torch_device, get_uniq_training_labels, show_examples

    arg_parser = argparse.ArgumentParser(description="Train multi-task model.")
    arg_parser.add_argument("-A", "--accumulation-steps", help="Gradient accumulation steps.",
                            action="store", type=int, default=12)
    arg_parser.add_argument("--data-only", help='Show training data info and exit.',
                            action="store_true", default=False)
    arg_parser.add_argument("--data-path", help="Load training dataset from specified path.",
                            action="store", default="./training_data")
    arg_parser.add_argument("-E", "--train-epochs", help="Number of epochs to train for.",
                            action="store", type=int, default=10)
    arg_parser.add_argument("-V", "--eval-batch-size", help="Per device eval batch size.",
                            action="store", type=int, default=2)
    arg_parser.add_argument("--from-base", help="Load a base model.",
                            action="store", default=None,
                            choices=[
                                "microsoft/deberta-v3-base",  # Requires --deberta-v3
                                "microsoft/deberta-v3-large",  # Requires --deberta-v3
                                # More?
                            ])
    arg_parser.add_argument("-L", "--learning-rate", help="Learning rate.",
                            action="store", type=float, default=2e-5)
    arg_parser.add_argument("--mini", help='Train model using small subset of examples for pipeline testing.',
                            action="store_true", default=False)
    arg_parser.add_argument("--save-path", help="Save final model to specified path.",
                            action="store", default="./final")
    arg_parser.add_argument("--show", help="Show examples: <split>/<col>/<label>/<count>",
                            action="store", default=None)
    arg_parser.add_argument("--train", help='Train model using loaded examples.',
                            action="store_true", default=False)
    arg_parser.add_argument("-T", "--train-batch-size", help="Per device train batch size.",
                            action="store", type=int, default=8)
    args = arg_parser.parse_args()
    logging.config.dictConfig(default_logging_config)
    logger.info(f"Args {args}")

    # ------------------------------------------------------------------------------
    # Load dataset and show examples for manual inspection
    # ------------------------------------------------------------------------------

    loaded_dataset = load_from_disk(args.data_path)
    show_examples(loaded_dataset, args.show)

    ## ------------------------------------------------------------------------------
    ## Instantiate model and tokenizer
    ## ------------------------------------------------------------------------------

    if args.from_base:
        # Convert label analysis data into label sets for each head
        ALL_LABELS = {col: list(vals) for col, vals in get_uniq_training_labels(loaded_dataset).items()}
        LABEL2ID = {
            feat_name: {label: i for i, label in enumerate(ALL_LABELS[feat_name])}
            for feat_name in ALL_LABELS
        }
        ID2LABEL = {
            feat_name: {i: label for label, i in LABEL2ID[feat_name].items()}
            for feat_name in LABEL2ID
        }
        # Each head's number of labels:
        NUM_LABELS_DICT = {k: len(v) for k, v in ALL_LABELS.items()}
        model_name_or_path = args.from_base
        multi_head_model = MultiHeadModel.from_pretrained(
            model_name_or_path,
            config=MultiHeadModelConfig.from_pretrained(
                model_name_or_path,
                num_labels_dict=NUM_LABELS_DICT,
                label_maps=ALL_LABELS
            )
        )
    else:
        model_name_or_path = args.save_path
        # For evaluation, always load the saved checkpoint without overriding the config.
        multi_head_model = MultiHeadModel.from_pretrained(model_name_or_path)
        # EXTREMELY IMPORTANT!
        # Override the label mapping based on the stored config to ensure consistency with training time ordering.
        ALL_LABELS = multi_head_model.config.label_maps
        LABEL2ID = {feat: {label: i for i, label in enumerate(ALL_LABELS[feat])} for feat in ALL_LABELS}
        ID2LABEL = {feat: {i: label for label, i in LABEL2ID[feat].items()} for feat in LABEL2ID}
    logger.info(f"using {model_name_or_path}")

    # Check if GPU is usable
    device = get_torch_device()
    multi_head_model.to(device)

    tokenizer = DebertaV2TokenizerFast.from_pretrained(
        model_name_or_path,
        add_prefix_space=True,
    )

    # ------------------------------------------------------------------------------
    # Shuffle, (optionally) sample, and tokenize final merged dataset
    # ------------------------------------------------------------------------------

    if args.mini:
        loaded_dataset = DatasetDict({
            "train": loaded_dataset["train"].shuffle(seed=42).select(range(1000)),
            "validation": loaded_dataset["validation"].shuffle(seed=42).select(range(100)),
            "test": loaded_dataset["test"].shuffle(seed=42).select(range(100)),
        })

    # remove_columns => remove old "text", "tokens", etc. so we keep only model inputs
    example_aligner = ExampleAligner(ALL_LABELS, LABEL2ID)
    tokenized_dataset = loaded_dataset.map(
        example_aligner.tokenize_and_align_labels,
        batched=True,
        remove_columns=loaded_dataset["train"].column_names,
    )

    # ------------------------------------------------------------------------------
    # Train the model!
    # ------------------------------------------------------------------------------

    trainer = MultiHeadTrainer(
        ALL_LABELS,
        model=multi_head_model,
        args=TrainingArguments(
            # Evaluate less frequently or keep the same
            eval_strategy="steps",
            save_strategy="steps",

            load_best_model_at_end=True,
            metric_for_best_model="f1_macro",
            greater_is_better=True,

            num_train_epochs=args.train_epochs,
            learning_rate=args.learning_rate,

            output_dir="training_output",
            overwrite_output_dir=True,
            remove_unused_columns=False,  # important to keep the labels_xxx columns

            logging_dir="training_logs",
            logging_steps=100,

            # Effective batch size = train_batch_size x gradient_accumulation_steps
            per_device_eval_batch_size=args.eval_batch_size,
            per_device_train_batch_size=args.train_batch_size,
            gradient_accumulation_steps=args.accumulation_steps,

            warmup_ratio=0.1,
            # Try between 0.001 and 0.1. Higher weight decay can prevent overfitting, but too high a value can
            # hurt performance.
            weight_decay=0.01,
        ),
        train_dataset=tokenized_dataset["train"],
        eval_dataset=tokenized_dataset["validation"],
        compute_metrics=compute_metrics_for_trainer,
        callbacks=[EarlyStoppingCallback(early_stopping_patience=3)]  # Add early stopping
    )

    if args.train:
        trainer.train()
        trainer.evaluate()
        trainer.save_model(args.save_path)
        tokenizer.save_pretrained(args.save_path)

    # ------------------------------------------------------------------------------
    # Evaluate the model!
    # ------------------------------------------------------------------------------

    pred_output = trainer.predict(tokenized_dataset["test"])
    pred_logits_dict = pred_output.predictions
    pred_labels_dict = pred_output.label_ids
    id2label_dict = ID2LABEL  # from earlier definitions

    reports = multi_head_classification_reports(pred_logits_dict, pred_labels_dict, id2label_dict)
    for head_name, rstr in reports.items():
        print(f"----- {head_name} classification report -----")
        print(rstr)

	from sklearn.metrics import classification_report, precision_recall_fscore_support
	from transformers import (
	DebertaV2TokenizerFast,
	EarlyStoppingCallback,
	Trainer,
	TrainingArguments,
	)
	import logging
	import numpy as np
	import torch

	from multi_head_model import MultiHeadModel, MultiHeadModelConfig

	logger = logging.getLogger(__name__)


	# ------------------------------------------------------------------------------
	# Tokenize with max_length=512, stride=128, and subword alignment
	# ------------------------------------------------------------------------------

	class ExampleAligner:
	def __init__(self, all_labels, label2id):
	self.all_labels = all_labels
	self.label2id = label2id

	def tokenize_and_align_labels(self, examples):
	"""
	For each example, the tokenizer may produce multiple overlapping
	chunks if the tokens exceed 512 subwords. Each chunk will be
	length=512, with a stride=128 for the next chunk.
	We'll align labels so that subwords beyond the first in a token get -100.
	"""
	# We rely on is_split_into_words=True because examples["tokens"] is a list of token strings.
	tokenized_batch = tokenizer(
	examples["tokens"],
	is_split_into_words=True,
	max_length=512,
	stride=128,
	truncation=True,
	return_overflowing_tokens=True,
	return_offsets_mapping=False, # not mandatory for basic alignment
	padding="max_length"
	)

	# The tokenizer returns "overflow_to_sample_mapping", telling us
	# which original example index each chunk corresponds to.
	# If the tokenizer didn't need to create overflows, the key might be missing
	if "overflow_to_sample_mapping" not in tokenized_batch:
	# No overflow => each input corresponds 1:1 with the original example
	sample_map = [i for i in range(len(tokenized_batch["input_ids"]))]
	else:
	sample_map = tokenized_batch["overflow_to_sample_mapping"]

	# We'll build lists for final outputs.
	# For each chunk i, we produce:
	# "input_ids"[i], "attention_mask"[i], plus per-feature label IDs.
	final_input_ids = []
	final_attention_mask = []
	final_labels_columns = {feat: [] for feat in self.all_labels} # store one label-sequence per chunk

	for i in range(len(tokenized_batch["input_ids"])):
	# chunk i
	chunk_input_ids = tokenized_batch["input_ids"][i]
	chunk_attn_mask = tokenized_batch["attention_mask"][i]

	original_index = sample_map[i] # which example in the original batch
	word_ids = tokenized_batch.word_ids(batch_index=i)

	# We'll build label arrays for each feature
	chunk_labels_dict = {}

	for feat_name in self.all_labels:
	# The UD token-level labels for the original example
	token_labels = examples[feat_name][original_index] # e.g. length T
	chunk_label_ids = []

	previous_word_id = None
	for w_id in word_ids:
	if w_id is None:
	# special token (CLS, SEP, padding)
	chunk_label_ids.append(-100)
	else:
	# If it's the same word_id as before, it's a subword => label = -100
	if w_id == previous_word_id:
	chunk_label_ids.append(-100)
	else:
	# New token => use the actual label
	label_str = token_labels[w_id]
	label_id = self.label2id[feat_name][label_str]
	chunk_label_ids.append(label_id)
	previous_word_id = w_id

	chunk_labels_dict[feat_name] = chunk_label_ids

	final_input_ids.append(chunk_input_ids)
	final_attention_mask.append(chunk_attn_mask)
	for feat_name in self.all_labels:
	final_labels_columns[feat_name].append(chunk_labels_dict[feat_name])

	# Return the new "flattened" set of chunks
	# So the "map" call will expand each example → multiple chunk examples.
	result = {
	"input_ids": final_input_ids,
	"attention_mask": final_attention_mask,
	}
	# We'll store each feature's label IDs in separate columns (e.g. labels_xpos, labels_deprel, etc.)
	for feat_name in self.all_labels:
	result[f"labels_{feat_name}"] = final_labels_columns[feat_name]

	return result

	# ------------------------------------------------------------------------------
	# Trainer Setup
	# ------------------------------------------------------------------------------

	class MultiHeadTrainer(Trainer):
	def __init__(self, all_labels, **kwargs):
	self.all_labels = all_labels
	super().__init__(**kwargs)

	def compute_loss(self, model, inputs, return_outputs=False, num_items_in_batch=None):
	# 1) Gather all your per-feature labels from inputs
	_labels_dict = {}
	for feat_name in self.all_labels:
	key = f"labels_{feat_name}"
	if key in inputs:
	_labels_dict[feat_name] = inputs[key]

	# 2) Remove them so they don't get passed incorrectly to the model
	for key in list(inputs.keys()):
	if key.startswith("labels_"):
	del inputs[key]

	# 3) Call model(...) with _labels_dict
	outputs = model(**inputs, labels_dict=_labels_dict)
	# 'outputs' is (loss, logits_dict) in training/eval mode
	loss, logits_dict = outputs

	# Optional: if your special param is used upstream for some logic,
	# you can handle it here or pass it along. For example:
	if num_items_in_batch is not None:
	# ... do something if needed ...
	pass

	if return_outputs:
	# Return (loss, logits_dict) so Trainer sees logits_dict as predictions
	return loss, logits_dict
	else:
	return loss

	def prediction_step(self, model, inputs, prediction_loss_only=False, ignore_keys=None):
	# 1) gather the "labels_xxx" columns
	_labels_dict = {}
	for feat_name in self.all_labels:
	key = f"labels_{feat_name}"
	if key in inputs:
	_labels_dict[feat_name] = inputs[key]
	del inputs[key]

	# 2) forward pass without those keys
	with torch.no_grad():
	outputs = model(**inputs, labels_dict=_labels_dict)

	loss, logits_dict = outputs # you are returning (loss, dict-of-arrays)

	if prediction_loss_only:
	return (loss, None, None)

	# The trainer expects a triple: (loss, predictions, labels)
	# - 'predictions' can be the dictionary
	# - 'labels' can be the dictionary of label IDs
	return loss, logits_dict, _labels_dict


	def multi_head_classification_reports(logits_dict, labels_dict, id2label_dict):
	"""
	For each head, generate a classification report (precision, recall, f1, etc. per class).
	Return them as a dict: {head_name: "string report"}.
	:param logits_dict: dict of {head_name: np.array(batch_size, seq_len, num_classes)}
	:param labels_dict: dict of {head_name: np.array(batch_size, seq_len)}
	:param id2label_dict: dict of {head_name: {id: label_str}}
	:return: A dict of classification-report strings, one per head.
	"""
	reports = {}

	for head_name, logits in logits_dict.items():
	if head_name not in labels_dict:
	continue

	predictions = np.argmax(logits, axis=-1)
	valid_preds, valid_labels = [], []
	for pred_seq, label_seq in zip(predictions, labels_dict[head_name]):
	for p, lab in zip(pred_seq, label_seq):
	if lab != -100:
	valid_preds.append(p)
	valid_labels.append(lab)

	if len(valid_preds) == 0:
	reports[head_name] = "No valid predictions."
	continue

	# Convert numeric IDs to string labels
	valid_preds_str = [id2label_dict[head_name][p] for p in valid_preds]
	valid_labels_str = [id2label_dict[head_name][l] for l in valid_labels]

	# Generate the per-class classification report
	report_str = classification_report(
	valid_labels_str,
	valid_preds_str,
	zero_division=0
	)
	reports[head_name] = report_str

	return reports


	def multi_head_compute_metrics(logits_dict, labels_dict):
	"""
	For each head (e.g. xpos, deprel, Case, etc.), computes:
	- Accuracy
	- Precision (macro/micro)
	- Recall (macro/micro)
	- F1 (macro/micro)

	:param logits_dict: dict of {head_name: np.array of shape (batch_size, seq_len, num_classes)}
	:param labels_dict: dict of {head_name: np.array of shape (batch_size, seq_len)}
	:return: A dict with aggregated metrics. Keys prefixed by head_name, e.g. "xpos_accuracy", "xpos_f1_macro", etc.
	"""
	# We'll accumulate metrics in one big dictionary, keyed by "<head>_<metric>"
	results = {}

	for head_name, logits in logits_dict.items():
	if head_name not in labels_dict:
	# In case there's a mismatch or a head we didn't provide labels for
	continue

	# (batch_size, seq_len, num_classes)
	predictions = np.argmax(logits, axis=-1) # => (batch_size, seq_len)

	# Flatten ignoring positions where label == -100
	valid_preds, valid_labels = [], []
	for pred_seq, label_seq in zip(predictions, labels_dict[head_name]):
	for p, lab in zip(pred_seq, label_seq):
	if lab != -100:
	valid_preds.append(p)
	valid_labels.append(lab)

	valid_preds = np.array(valid_preds)
	valid_labels = np.array(valid_labels)

	if len(valid_preds) == 0:
	# No valid data for this head—skip
	continue

	# Overall token-level accuracy
	accuracy = (valid_preds == valid_labels).mean()

	# Macro average => treat each class equally
	precision_macro, recall_macro, f1_macro, _ = precision_recall_fscore_support(
	valid_labels, valid_preds, average="macro", zero_division=0
	)

	# Micro average => aggregate across all classes
	precision_micro, recall_micro, f1_micro, _ = precision_recall_fscore_support(
	valid_labels, valid_preds, average="micro", zero_division=0
	)

	results[f"{head_name}_accuracy"] = accuracy
	results[f"{head_name}_precision_macro"] = precision_macro
	results[f"{head_name}_recall_macro"] = recall_macro
	results[f"{head_name}_f1_macro"] = f1_macro
	results[f"{head_name}_precision_micro"] = precision_micro
	results[f"{head_name}_recall_micro"] = recall_micro
	results[f"{head_name}_f1_micro"] = f1_micro

	return results


	def multi_head_compute_metrics_aggregate_f1(logits_dict, labels_dict):
	results = multi_head_compute_metrics(logits_dict, labels_dict) # your existing function

	# Grab all keys that end with "_f1_macro"
	f1_keys = [k for k in results.keys() if k.endswith("_f1_macro")]
	if not f1_keys:
	# fallback in case no F1 keys exist
	final_f1 = 0.0
	else:
	final_f1 = np.mean([results[k] for k in f1_keys])

	final_dict = {"f1_macro": final_f1}
	# Optionally keep all others for logging
	final_dict.update(results)
	return final_dict


	def compute_metrics_for_trainer(eval_pred):
	# This is the HF Trainer signature: eval_pred is usually (logits, labels) or (predictions, label_ids)
	logits_dict, labels_dict = eval_pred.predictions, eval_pred.label_ids
	return multi_head_compute_metrics_aggregate_f1(logits_dict, labels_dict)


	if __name__ == "__main__":
	from datasets import DatasetDict, load_from_disk
	import argparse
	import logging.config

	from utils import default_logging_config, get_torch_device, get_uniq_training_labels, show_examples

	arg_parser = argparse.ArgumentParser(description="Train multi-task model.")
	arg_parser.add_argument("-A", "--accumulation-steps", help="Gradient accumulation steps.",
	action="store", type=int, default=12)
	arg_parser.add_argument("--data-only", help='Show training data info and exit.',
	action="store_true", default=False)
	arg_parser.add_argument("--data-path", help="Load training dataset from specified path.",
	action="store", default="./training_data")
	arg_parser.add_argument("-E", "--train-epochs", help="Number of epochs to train for.",
	action="store", type=int, default=10)
	arg_parser.add_argument("-V", "--eval-batch-size", help="Per device eval batch size.",
	action="store", type=int, default=2)
	arg_parser.add_argument("--from-base", help="Load a base model.",
	action="store", default=None,
	choices=[
	"microsoft/deberta-v3-base", # Requires --deberta-v3
	"microsoft/deberta-v3-large", # Requires --deberta-v3
	# More?
	])
	arg_parser.add_argument("-L", "--learning-rate", help="Learning rate.",
	action="store", type=float, default=2e-5)
	arg_parser.add_argument("--mini", help='Train model using small subset of examples for pipeline testing.',
	action="store_true", default=False)
	arg_parser.add_argument("--save-path", help="Save final model to specified path.",
	action="store", default="./final")
	arg_parser.add_argument("--show", help="Show examples: <split>/<col>/<label>/<count>",
	action="store", default=None)
	arg_parser.add_argument("--train", help='Train model using loaded examples.',
	action="store_true", default=False)
	arg_parser.add_argument("-T", "--train-batch-size", help="Per device train batch size.",
	action="store", type=int, default=8)
	args = arg_parser.parse_args()
	logging.config.dictConfig(default_logging_config)
	logger.info(f"Args {args}")

	# ------------------------------------------------------------------------------
	# Load dataset and show examples for manual inspection
	# ------------------------------------------------------------------------------

	loaded_dataset = load_from_disk(args.data_path)
	show_examples(loaded_dataset, args.show)

	## ------------------------------------------------------------------------------
	## Instantiate model and tokenizer
	## ------------------------------------------------------------------------------

	if args.from_base:
	# Convert label analysis data into label sets for each head
	ALL_LABELS = {col: list(vals) for col, vals in get_uniq_training_labels(loaded_dataset).items()}
	LABEL2ID = {
	feat_name: {label: i for i, label in enumerate(ALL_LABELS[feat_name])}
	for feat_name in ALL_LABELS
	}
	ID2LABEL = {
	feat_name: {i: label for label, i in LABEL2ID[feat_name].items()}
	for feat_name in LABEL2ID
	}
	# Each head's number of labels:
	NUM_LABELS_DICT = {k: len(v) for k, v in ALL_LABELS.items()}
	model_name_or_path = args.from_base
	multi_head_model = MultiHeadModel.from_pretrained(
	model_name_or_path,
	config=MultiHeadModelConfig.from_pretrained(
	model_name_or_path,
	num_labels_dict=NUM_LABELS_DICT,
	label_maps=ALL_LABELS
	)
	)
	else:
	model_name_or_path = args.save_path
	# For evaluation, always load the saved checkpoint without overriding the config.
	multi_head_model = MultiHeadModel.from_pretrained(model_name_or_path)
	# EXTREMELY IMPORTANT!
	# Override the label mapping based on the stored config to ensure consistency with training time ordering.
	ALL_LABELS = multi_head_model.config.label_maps
	LABEL2ID = {feat: {label: i for i, label in enumerate(ALL_LABELS[feat])} for feat in ALL_LABELS}
	ID2LABEL = {feat: {i: label for label, i in LABEL2ID[feat].items()} for feat in LABEL2ID}
	logger.info(f"using {model_name_or_path}")

	# Check if GPU is usable
	device = get_torch_device()
	multi_head_model.to(device)

	tokenizer = DebertaV2TokenizerFast.from_pretrained(
	model_name_or_path,
	add_prefix_space=True,
	)

	# ------------------------------------------------------------------------------
	# Shuffle, (optionally) sample, and tokenize final merged dataset
	# ------------------------------------------------------------------------------

	if args.mini:
	loaded_dataset = DatasetDict({
	"train": loaded_dataset["train"].shuffle(seed=42).select(range(1000)),
	"validation": loaded_dataset["validation"].shuffle(seed=42).select(range(100)),
	"test": loaded_dataset["test"].shuffle(seed=42).select(range(100)),
	})

	# remove_columns => remove old "text", "tokens", etc. so we keep only model inputs
	example_aligner = ExampleAligner(ALL_LABELS, LABEL2ID)
	tokenized_dataset = loaded_dataset.map(
	example_aligner.tokenize_and_align_labels,
	batched=True,
	remove_columns=loaded_dataset["train"].column_names,
	)

	# ------------------------------------------------------------------------------
	# Train the model!
	# ------------------------------------------------------------------------------

	trainer = MultiHeadTrainer(
	ALL_LABELS,
	model=multi_head_model,
	args=TrainingArguments(
	# Evaluate less frequently or keep the same
	eval_strategy="steps",
	save_strategy="steps",

	load_best_model_at_end=True,
	metric_for_best_model="f1_macro",
	greater_is_better=True,

	num_train_epochs=args.train_epochs,
	learning_rate=args.learning_rate,

	output_dir="training_output",
	overwrite_output_dir=True,
	remove_unused_columns=False, # important to keep the labels_xxx columns

	logging_dir="training_logs",
	logging_steps=100,

	# Effective batch size = train_batch_size x gradient_accumulation_steps
	per_device_eval_batch_size=args.eval_batch_size,
	per_device_train_batch_size=args.train_batch_size,
	gradient_accumulation_steps=args.accumulation_steps,

	warmup_ratio=0.1,
	# Try between 0.001 and 0.1. Higher weight decay can prevent overfitting, but too high a value can
	# hurt performance.
	weight_decay=0.01,
	),
	train_dataset=tokenized_dataset["train"],
	eval_dataset=tokenized_dataset["validation"],
	compute_metrics=compute_metrics_for_trainer,
	callbacks=[EarlyStoppingCallback(early_stopping_patience=3)] # Add early stopping
	)

	if args.train:
	trainer.train()
	trainer.evaluate()
	trainer.save_model(args.save_path)
	tokenizer.save_pretrained(args.save_path)

	# ------------------------------------------------------------------------------
	# Evaluate the model!
	# ------------------------------------------------------------------------------

	pred_output = trainer.predict(tokenized_dataset["test"])
	pred_logits_dict = pred_output.predictions
	pred_labels_dict = pred_output.label_ids
	id2label_dict = ID2LABEL # from earlier definitions

	reports = multi_head_classification_reports(pred_logits_dict, pred_labels_dict, id2label_dict)
	for head_name, rstr in reports.items():
	print(f"----- {head_name} classification report -----")
	print(rstr)