models.py

import inspect
import math
import os
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn
from torch.nn import CrossEntropyLoss
from torch.nn.parameter import Parameter
import torch.nn.functional as F
from transformers.activations import ACT2FN
from transformers.configuration_utils import PretrainedConfig
from transformers.modeling_outputs import (
    BaseModelOutputWithPastAndCrossAttentions,
    BaseModelOutputWithPoolingAndCrossAttentions,
    MaskedLMOutput,
    SequenceClassifierOutput,
)
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from transformers.modeling_utils import PreTrainedModel
from transformers.generation import GenerationMixin
from dataclasses import dataclass
from transformers.utils import ModelOutput
from contextlib import nullcontext

try:
    from transformers.cache_utils import Cache, DynamicCache, EncoderDecoderCache
except ImportError:  # pragma: no cover - compatibility fallback for older Transformers
    class Cache:  # type: ignore[no-redef]
        pass

    class DynamicCache(Cache):  # type: ignore[no-redef]
        def __init__(self, *args, **kwargs):
            super().__init__()

        def get_seq_length(self):
            return 0

    class EncoderDecoderCache(Cache):  # type: ignore[no-redef]
        def __init__(self, self_attention_cache=None, cross_attention_cache=None):
            super().__init__()
            self.self_attention_cache = self_attention_cache
            self.cross_attention_cache = cross_attention_cache
            self.is_updated = {}

        @classmethod
        def from_legacy_cache(cls, past_key_values):
            cache = cls()
            cache.legacy_cache = past_key_values
            return cache

        def get_seq_length(self):
            return 0

try:
    from transformers.modeling_layers import GradientCheckpointingLayer
except ImportError:  # pragma: no cover - compatibility fallback for older Transformers
    class GradientCheckpointingLayer(nn.Module):  # type: ignore[no-redef]
        gradient_checkpointing = False

        def __init__(self, *args, **kwargs):
            super().__init__()

try:
    from transformers.utils import auto_docstring, logging
except ImportError:  # pragma: no cover - compatibility fallback
    from transformers.utils import logging  # type: ignore

    def auto_docstring(*args, **kwargs):
        if args and callable(args[0]) and len(args) == 1 and not kwargs:
            return args[0]

        def _decorator(obj):
            return obj

        return _decorator

try:
    from transformers.utils.deprecation import deprecate_kwarg
except ImportError:  # pragma: no cover - compatibility fallback
    def deprecate_kwarg(*args, **kwargs):
        def _decorator(fn):
            return fn

        return _decorator

try:
    from transformers.utils.hub import cached_file
except ImportError:  # pragma: no cover - compatibility fallback
    from transformers.utils import cached_file  # type: ignore


logger = logging.get_logger(__name__)
_HF_LOAD_KWARGS = {
    "cache_dir", "force_download", "local_files_only",
    "token", "revision", "subfolder", "use_safetensors",
}


_HF_CONFIG_LOAD_KWARGS = {
    "cache_dir",
    "force_download",
    "local_files_only",
    "token",
    "revision",
    "subfolder",
    "proxies",
}

_HF_NON_MODEL_INIT_KWARGS = {
    "trust_remote_code",
    "_from_auto",
    "adapter_kwargs",
}



def l2_norm(input, axis=1, epsilon=1e-12):
    norm = torch.norm(input, 2, axis, True)
    norm = torch.clamp(norm, min=epsilon)  # Avoid zero division
    output = torch.div(input, norm)
    return output

def initialize_linear_kaiming(layer: nn.Linear):
    if isinstance(layer, nn.Linear):
        nn.init.kaiming_uniform_(layer.weight, nonlinearity='linear')
        if layer.bias is not None:
            nn.init.zeros_(layer.bias)


def get_classifier_dropout(config) -> float:
    classifier_dropout = getattr(config, "classifier_dropout", None)
    if classifier_dropout is None:
        classifier_dropout = getattr(config, "hidden_dropout_prob", 0.0)
    return float(classifier_dropout)


def normalize_pooling_attention_mask(
    attention_mask: Optional[torch.Tensor],
) -> Optional[torch.Tensor]:
    """
    Return a boolean keep-mask of shape (batch_size, seq_length).
    Supports:
      - (B, L) masks with 1/0 or bool
      - (B, 1, L)
      - (B, 1, 1, L)
      - additive masks with 0 for keep and negative values for masked positions
    """
    if attention_mask is None:
        return None

    if attention_mask.dim() == 4:
        if attention_mask.size(1) == 1 and attention_mask.size(2) == 1:
            attention_mask = attention_mask[:, 0, 0, :]
        else:
            raise ValueError(f"Unexpected 4D attention_mask shape: {tuple(attention_mask.shape)}")
    elif attention_mask.dim() == 3:
        if attention_mask.size(1) == 1:
            attention_mask = attention_mask[:, 0, :]
        else:
            raise ValueError(f"Unexpected 3D attention_mask shape: {tuple(attention_mask.shape)}")
    elif attention_mask.dim() != 2:
        raise ValueError(f"Unexpected attention_mask shape: {tuple(attention_mask.shape)}")

    if attention_mask.dtype == torch.bool:
        return attention_mask

    if torch.is_floating_point(attention_mask) and (attention_mask < 0).any():
        # HF additive masks: 0 means keep, negative means masked
        return attention_mask == 0

    return attention_mask != 0


def masked_attention_pool(
    sequence_output: torch.Tensor,
    token_scores: torch.Tensor,
    attention_mask: Optional[torch.Tensor] = None,
) -> torch.Tensor:
    keep_mask = normalize_pooling_attention_mask(attention_mask)

    if keep_mask is not None:
        empty_rows = keep_mask.sum(dim=1) == 0
        if empty_rows.any():
            keep_mask = keep_mask.clone()
            keep_mask[empty_rows, 0] = True

        token_scores = token_scores.masked_fill(~keep_mask.unsqueeze(-1), float("-inf"))

    weights = torch.softmax(token_scores.float(), dim=1).to(dtype=sequence_output.dtype)
    pooled_output = torch.sum(weights * sequence_output, dim=1)
    return pooled_output


def apply_chunking_to_forward(forward_fn, chunk_size: int, chunk_dim: int, *input_tensors) -> torch.Tensor:
    """Local copy of the HF utility to reduce cross-version import fragility."""
    if len(input_tensors) == 0:
        raise ValueError(f"{input_tensors} has to be a tuple/list of tensors")

    num_args_in_forward_chunk_fn = len(inspect.signature(forward_fn).parameters)
    if num_args_in_forward_chunk_fn != len(input_tensors):
        raise ValueError(
            f"forward_chunk_fn expects {num_args_in_forward_chunk_fn} arguments, but only {len(input_tensors)} input tensors are given"
        )

    if chunk_size > 0:
        tensor_shape = input_tensors[0].shape[chunk_dim]
        for input_tensor in input_tensors:
            if input_tensor.shape[chunk_dim] != tensor_shape:
                raise ValueError(
                    f"All input tenors have to be of the same shape: {tensor_shape}, found shape {input_tensor.shape[chunk_dim]}"
                )
        if input_tensors[0].shape[chunk_dim] % chunk_size != 0:
            raise ValueError(
                f"The dimension to be chunked {input_tensors[0].shape[chunk_dim]} has to be a multiple of the chunk size {chunk_size}"
            )
        num_chunks = input_tensors[0].shape[chunk_dim] // chunk_size
        input_tensors_chunks = tuple(input_tensor.chunk(num_chunks, dim=chunk_dim) for input_tensor in input_tensors)
        output_chunks = tuple(forward_fn(*input_tensors_chunk) for input_tensors_chunk in zip(*input_tensors_chunks))
        return torch.cat(output_chunks, dim=chunk_dim)

    return forward_fn(*input_tensors)


def prune_linear_layer(layer: nn.Linear, index: torch.LongTensor, dim: int = 0) -> nn.Linear:
    """Local copy of the HF utility to reduce cross-version import fragility."""
    index = index.to(layer.weight.device)
    weight = layer.weight.index_select(dim, index).detach().clone()
    if layer.bias is not None:
        if dim == 1:
            bias = layer.bias.detach().clone()
        else:
            bias = layer.bias[index].detach().clone()
    new_size = list(layer.weight.size())
    new_size[dim] = len(index)
    new_layer = nn.Linear(new_size[1], new_size[0], bias=layer.bias is not None).to(layer.weight.device)
    new_layer.weight.requires_grad = False
    new_layer.weight.copy_(weight.contiguous())
    new_layer.weight.requires_grad = True
    if layer.bias is not None:
        new_layer.bias.requires_grad = False
        new_layer.bias.copy_(bias.contiguous())
        new_layer.bias.requires_grad = True
    return new_layer


def find_pruneable_heads_and_indices(
    heads: list[int], n_heads: int, head_size: int, already_pruned_heads: set[int]
) -> tuple[set[int], torch.LongTensor]:
    """Local copy of the HF utility that was removed from newer Transformers."""
    mask = torch.ones(n_heads, head_size)
    heads = set(heads) - already_pruned_heads
    for head in heads:
        head = head - sum(1 if h < head else 0 for h in already_pruned_heads)
        mask[head] = 0
    mask = mask.view(-1).contiguous().eq(1)
    index = torch.arange(len(mask))[mask].long()
    return heads, index

logger = logging.get_logger(__name__)

def load_tf_weights_in_megatron_bert(model, config, tf_checkpoint_path):
    """Load tf checkpoints in a pytorch model."""
    try:
        import re

        import numpy as np
        import tensorflow as tf
    except ImportError:
        logger.error(
            "Loading a TensorFlow model in PyTorch, requires TensorFlow to be installed. Please see "
            "https://www.tensorflow.org/install/ for installation instructions."
        )
        raise
    tf_path = os.path.abspath(tf_checkpoint_path)
    logger.info(f"Converting TensorFlow checkpoint from {tf_path}")
    # Load weights from TF model
    init_vars = tf.train.list_variables(tf_path)
    names = []
    arrays = []
    for name, shape in init_vars:
        logger.info(f"Loading TF weight {name} with shape {shape}")
        array = tf.train.load_variable(tf_path, name)
        names.append(name)
        arrays.append(array)

    for name, array in zip(names, arrays):
        name = name.split("/")
        # adam_v and adam_m are variables used in AdamWeightDecayOptimizer to calculated m and v
        # which are not required for using pretrained model
        if any(
            n in ["adam_v", "adam_m", "AdamWeightDecayOptimizer", "AdamWeightDecayOptimizer_1", "global_step"]
            for n in name
        ):
            logger.info(f"Skipping {'/'.join(name)}")
            continue
        pointer = model
        for m_name in name:
            if re.fullmatch(r"[A-Za-z]+_\d+", m_name):
                scope_names = re.split(r"_(\d+)", m_name)
            else:
                scope_names = [m_name]
            if scope_names[0] == "kernel" or scope_names[0] == "gamma":
                pointer = getattr(pointer, "weight")
            elif scope_names[0] == "output_bias" or scope_names[0] == "beta":
                pointer = getattr(pointer, "bias")
            elif scope_names[0] == "output_weights":
                pointer = getattr(pointer, "weight")
            elif scope_names[0] == "squad":
                pointer = getattr(pointer, "classifier")
            else:
                try:
                    pointer = getattr(pointer, scope_names[0])
                except AttributeError:
                    logger.info(f"Skipping {'/'.join(name)}")
                    continue
            if len(scope_names) >= 2:
                num = int(scope_names[1])
                pointer = pointer[num]
        if m_name[-11:] == "_embeddings":
            pointer = getattr(pointer, "weight")
        elif m_name == "kernel":
            array = np.transpose(array)
        if pointer.shape != array.shape:
            raise ValueError(f"Pointer shape {pointer.shape} and array shape {array.shape} mismatched")
        logger.info(f"Initialize PyTorch weight {name}")
        pointer.data = torch.from_numpy(array)
    return model
def _extract_base_model_state_dict(
    state_dict: dict[str, torch.Tensor],
    base_prefix: str = "bert",
) -> dict[str, torch.Tensor]:
    prefix = f"{base_prefix}."
    if any(k.startswith(prefix) for k in state_dict.keys()):
        return {k[len(prefix):]: v for k, v in state_dict.items() if k.startswith(prefix)}
    return state_dict


def _split_pretrained_kwargs(kwargs):
    """
    Split kwargs into:
      - config/hub loading kwargs
      - weight file preference kwargs
      - state-dict reading kwargs
      - remaining kwargs (config overrides or model __init__ kwargs)
    """
    kwargs = dict(kwargs)

    for k in _HF_NON_MODEL_INIT_KWARGS:
        kwargs.pop(k, None)

    config_load_kwargs = {
        k: kwargs.pop(k) for k in list(kwargs) if k in _HF_CONFIG_LOAD_KWARGS
    }

    use_safetensors = kwargs.pop("use_safetensors", None)
    weights_only = kwargs.pop("weights_only", True)

    return config_load_kwargs, use_safetensors, weights_only, kwargs

def _resolve_weights_file(
    pretrained_model_name_or_path,
    use_safetensors=None,
    **load_kwargs,
) -> str:
    """
    Resolve a single weight file path from either a local directory or the Hub.

    use_safetensors:
      - True  -> require model.safetensors
      - False -> require pytorch_model.bin
      - None  -> prefer safetensors, then fall back to bin
    """
    pretrained_model_name_or_path = os.fspath(pretrained_model_name_or_path)

    if use_safetensors is True:
        candidates = ("model.safetensors",)
    elif use_safetensors is False:
        candidates = ("pytorch_model.bin",)
    else:
        candidates = ("model.safetensors", "pytorch_model.bin")

    subfolder = load_kwargs.get("subfolder")

    if os.path.isdir(pretrained_model_name_or_path):
        base_dir = (
            os.path.join(pretrained_model_name_or_path, subfolder)
            if subfolder
            else pretrained_model_name_or_path
        )
        for name in candidates:
            path = os.path.join(base_dir, name)
            if os.path.exists(path):
                return path

    for name in candidates:
        try:
            path = cached_file(pretrained_model_name_or_path, name, **load_kwargs)
            if path is not None:
                return path
        except Exception:
            pass

    raise FileNotFoundError(
        f"No checkpoint file found in {pretrained_model_name_or_path!r} "
        f"(candidates: {', '.join(candidates)})"
    )


def _read_state_dict(weights_path, weights_only: bool = True) -> dict[str, torch.Tensor]:
    weights_path = os.fspath(weights_path)

    if weights_path.endswith(".safetensors"):
        from safetensors.torch import load_file as safe_load_file
        return safe_load_file(weights_path, device="cpu")

    try:
        return torch.load(weights_path, map_location="cpu", weights_only=weights_only)
    except TypeError:
        # Older torch versions do not support weights_only
        return torch.load(weights_path, map_location="cpu")

def _autocast_disabled(device_type: str):
    try:
        return torch.amp.autocast(device_type=device_type, enabled=False)
    except (AttributeError, TypeError):
        # older torch fallback
        if device_type == "cuda":
            return torch.cuda.amp.autocast(enabled=False)
        if device_type == "cpu" and hasattr(torch, "cpu") and hasattr(torch.cpu, "amp"):
            return torch.cpu.amp.autocast(enabled=False)
        return nullcontext()
    

class _SafeFromPretrainedMixin:
    """
    Simplified custom-model loader that preserves the useful HF behavior:

      - if config is None or a path/string:
          kwargs matching config fields update the config via
          config_class.from_pretrained(..., return_unused_kwargs=True)

      - remaining kwargs are passed to model __init__

      - supports:
          output_loading_info
          state_dict
          ignore_mismatched_sizes
          use_safetensors
          weights_only

    This is still intentionally much simpler than the full HF loader:
      - no sharded checkpoints
      - no device_map / offload / low_cpu_mem_usage
      - no quantized loaders
      - no tensor parallel / dispatch logic
    """

    @classmethod
    def _adapt_state_dict(cls, state_dict):
        """
        Hook for subclasses that need to rewrite checkpoint keys before loading.
        Example: stripping a leading 'bert.' prefix for base-model-only loads.
        """
        return state_dict

    @staticmethod
    def _filter_keys_with_patterns(keys, patterns):
        if not patterns:
            return list(keys)

        import re

        compiled = [re.compile(p) if isinstance(p, str) else p for p in patterns]
        return [k for k in keys if not any(p.search(k) for p in compiled)]

    @classmethod
    def _resolve_config_and_init_kwargs(
        cls,
        pretrained_model_name_or_path,
        config,
        config_load_kwargs,
        other_kwargs,
    ):
        """
        Mirror HF behavior:
          - config instance: use it directly, pass remaining kwargs to __init__
          - config path / no config: load config and split overrides via return_unused_kwargs=True
        """
        if isinstance(config, PretrainedConfig):
            return config, other_kwargs

        if config is None:
            config_source = pretrained_model_name_or_path
        elif isinstance(config, (str, os.PathLike)):
            config_source = config
        else:
            raise TypeError(
                "`config` must be None, a path-like object, or an instance of PretrainedConfig"
            )

        if config_source is None:
            raise ValueError(
                "You must provide either `pretrained_model_name_or_path` or `config` "
                "to load a configuration."
            )

        config, init_kwargs = cls.config_class.from_pretrained(
            config_source,
            return_unused_kwargs=True,
            **config_load_kwargs,
            **other_kwargs,
        )
        return config, init_kwargs

    @staticmethod
    def _remove_mismatched_keys(model, state_dict):
        """
        Remove keys whose tensor shapes do not match the current model.
        Returns:
          filtered_state_dict, mismatched_keys
        where mismatched_keys is a list of:
          (key, checkpoint_shape, model_shape)
        """
        state_dict = dict(state_dict)
        model_state = model.state_dict()
        mismatched_keys = []

        for key in list(state_dict.keys()):
            if key not in model_state:
                continue

            loaded_value = state_dict[key]
            model_value = model_state[key]

            if not isinstance(loaded_value, torch.Tensor):
                continue
            if not isinstance(model_value, torch.Tensor):
                continue

            if tuple(loaded_value.shape) != tuple(model_value.shape):
                mismatched_keys.append(
                    (key, tuple(loaded_value.shape), tuple(model_value.shape))
                )
                state_dict.pop(key)

        return state_dict, mismatched_keys

    @classmethod
    def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
        output_loading_info = kwargs.pop("output_loading_info", False)
        state_dict = kwargs.pop("state_dict", None)
        config = kwargs.pop("config", None)
        ignore_mismatched_sizes = kwargs.pop("ignore_mismatched_sizes", False)
        strict = kwargs.pop("strict", False)

        config_load_kwargs, use_safetensors, weights_only, other_kwargs = _split_pretrained_kwargs(kwargs)

        # 1) Resolve config and route config overrides correctly
        config, init_kwargs = cls._resolve_config_and_init_kwargs(
            pretrained_model_name_or_path=pretrained_model_name_or_path,
            config=config,
            config_load_kwargs=config_load_kwargs,
            other_kwargs=other_kwargs,
        )

        # 2) Build model
        model = cls(config, *model_args, **init_kwargs)

        # 3) Read checkpoint if state_dict was not supplied explicitly
        if state_dict is None:
            if pretrained_model_name_or_path is None:
                raise ValueError(
                    "`pretrained_model_name_or_path` cannot be None when `state_dict` is not provided."
                )

            weights_path = _resolve_weights_file(
                pretrained_model_name_or_path,
                use_safetensors=use_safetensors,
                **config_load_kwargs,
            )
            state_dict = _read_state_dict(
                weights_path,
                weights_only=True if weights_only is None else bool(weights_only),
            )

        if not isinstance(state_dict, dict):
            raise TypeError(
                f"`state_dict` must be a dict-like mapping of parameter names to tensors, got {type(state_dict).__name__}"
            )

        # 4) Allow subclasses to rewrite checkpoint keys
        state_dict = cls._adapt_state_dict(dict(state_dict))

        # 5) Optionally drop shape-mismatched tensors
        mismatched_keys = []
        if ignore_mismatched_sizes:
            state_dict, mismatched_keys = cls._remove_mismatched_keys(model, state_dict)

        # 6) Load
        incompatible = model.load_state_dict(state_dict, strict=strict)

        # 7) Re-tie if the model defines tied weights
        if hasattr(model, "tie_weights"):
            model.tie_weights()

        if hasattr(model, "assert_mlm_head_is_valid"):
            model.assert_mlm_head_is_valid()

        model.eval()

        missing_keys = list(incompatible.missing_keys)
        unexpected_keys = list(incompatible.unexpected_keys)

        # Honor standard HF ignore patterns if the class defines them
        missing_keys = cls._filter_keys_with_patterns(
            missing_keys,
            getattr(model, "_keys_to_ignore_on_load_missing", None),
        )
        unexpected_keys = cls._filter_keys_with_patterns(
            unexpected_keys,
            getattr(model, "_keys_to_ignore_on_load_unexpected", None),
        )

        info = {
            "missing_keys": missing_keys,
            "unexpected_keys": unexpected_keys,
            "mismatched_keys": mismatched_keys,
            "error_msgs": [],
        }

        return (model, info) if output_loading_info else model



class MegatronBertConfig(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`MegatronBertModel`]. It is used to instantiate a
    MEGATRON_BERT model according to the specified arguments, defining the model architecture. Instantiating a
    configuration with the defaults will yield a similar configuration to that of the MEGATRON_BERT
    [nvidia/megatron-bert-uncased-345m](https://huggingface.co/nvidia/megatron-bert-uncased-345m) architecture.

    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
    documentation from [`PretrainedConfig`] for more information.


    Args:
        vocab_size (`int`, *optional*, defaults to 29056):
            Vocabulary size of the MEGATRON_BERT model. Defines the number of different tokens that can be represented
            by the `inputs_ids` passed when calling [`MegatronBertModel`].
        hidden_size (`int`, *optional*, defaults to 1024):
            Dimensionality of the encoder layers and the pooler layer.
        num_hidden_layers (`int`, *optional*, defaults to 24):
            Number of hidden layers in the Transformer encoder.
        num_attention_heads (`int`, *optional*, defaults to 16):
            Number of attention heads for each attention layer in the Transformer encoder.
        intermediate_size (`int`, *optional*, defaults to 4096):
            Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
        hidden_act (`str` or `Callable`, *optional*, defaults to `"gelu"`):
            The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
            `"relu"`, `"silu"` and `"gelu_new"` are supported.
        hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
        attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
            The dropout ratio for the attention probabilities.
        max_position_embeddings (`int`, *optional*, defaults to 512):
            The maximum sequence length that this model might ever be used with. Typically set this to something large
            just in case (e.g., 512 or 1024 or 2048).
        type_vocab_size (`int`, *optional*, defaults to 2):
            The vocabulary size of the `token_type_ids` passed when calling [`MegatronBertModel`].
        initializer_range (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        layer_norm_eps (`float`, *optional*, defaults to 1e-12):
            The epsilon used by the layer normalization layers.
        position_embedding_type (`str`, *optional*, defaults to `"absolute"`):
            Type of position embedding. Choose one of `"absolute"`, `"relative_key"`, `"relative_key_query"`. For
            positional embeddings use `"absolute"`. For more information on `"relative_key"`, please refer to
            [Self-Attention with Relative Position Representations (Shaw et al.)](https://huggingface.co/papers/1803.02155).
            For more information on `"relative_key_query"`, please refer to *Method 4* in [Improve Transformer Models
            with Better Relative Position Embeddings (Huang et al.)](https://huggingface.co/papers/2009.13658).
        is_decoder (`bool`, *optional*, defaults to `False`):
            Whether the model is used as a decoder or not. If `False`, the model is used as an encoder.
        use_cache (`bool`, *optional*, defaults to `True`):
            Whether or not the model should return the last key/values attentions (not used by all models). Only
            relevant if `config.is_decoder=True`.

    Examples:

    ```python
    >>> from transformers import MegatronBertConfig, MegatronBertModel

    >>> # Initializing a MEGATRON_BERT google-bert/bert-base-uncased style configuration
    >>> configuration = MegatronBertConfig()

    >>> # Initializing a model (with random weights) from the google-bert/bert-base-uncased style configuration
    >>> model = MegatronBertModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```"""

    model_type = "megatron-bert"

    def __init__(
        self,
        vocab_size=29056,
        hidden_size=1024,
        num_hidden_layers=24,
        num_attention_heads=16,
        intermediate_size=4096,
        hidden_act="gelu",
        hidden_dropout_prob=0.1,
        attention_probs_dropout_prob=0.1,
        max_position_embeddings=512,
        type_vocab_size=2,
        initializer_range=0.02,
        layer_norm_eps=1e-12,
        pad_token_id=0,
        position_embedding_type="absolute",
        use_cache=True,
        is_decoder=False,
        add_cross_attention=False,
        **kwargs,
    ):
        super().__init__(pad_token_id=pad_token_id, **kwargs)

        self.vocab_size = vocab_size
        self.hidden_size = hidden_size
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads
        self.hidden_act = hidden_act
        self.intermediate_size = intermediate_size
        self.hidden_dropout_prob = hidden_dropout_prob
        self.attention_probs_dropout_prob = attention_probs_dropout_prob
        self.max_position_embeddings = max_position_embeddings
        self.type_vocab_size = type_vocab_size
        self.initializer_range = initializer_range
        self.layer_norm_eps = layer_norm_eps
        self.position_embedding_type = position_embedding_type
        self.use_cache = use_cache
        self.is_decoder = is_decoder
        self.add_cross_attention = add_cross_attention



class MegatronBertEmbeddings(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.word_embeddings = nn.Embedding(
            config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id
        )
        self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
        self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)
        self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")

    @staticmethod
    def _make_position_ids(seq_length: int, device: torch.device, past_key_values_length: int = 0):
        return torch.arange(
            past_key_values_length,
            past_key_values_length + seq_length,
            dtype=torch.long,
            device=device,
        ).unsqueeze(0)

    def forward(
        self,
        input_ids=None,
        token_type_ids=None,
        position_ids=None,
        inputs_embeds=None,
        past_key_values_length: int = 0,
    ):
        if input_ids is not None:
            input_shape = input_ids.size()
            device = input_ids.device
        else:
            input_shape = inputs_embeds.size()[:-1]
            device = inputs_embeds.device

        seq_length = input_shape[1]

        if position_ids is None and self.position_embedding_type == "absolute":
            position_ids = self._make_position_ids(
                seq_length, device, past_key_values_length
            )

        if token_type_ids is None:
            token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)

        if inputs_embeds is None:
            inputs_embeds = self.word_embeddings(input_ids)

        embeddings = inputs_embeds + self.token_type_embeddings(token_type_ids)

        if self.position_embedding_type == "absolute":
            embeddings = embeddings + self.position_embeddings(position_ids)

        return self.dropout(embeddings)
    
# Copied from transformers.models.bert.modeling_bert.BertSelfAttention with Bert->MegatronBert

class MegatronBertSelfAttention(nn.Module):
    def __init__(self, config, position_embedding_type=None, layer_idx=None):
        super().__init__()
        if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
            raise ValueError(
                f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention "
                f"heads ({config.num_attention_heads})"
            )

        self.num_attention_heads = config.num_attention_heads
        self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
        self.all_head_size = self.num_attention_heads * self.attention_head_size

        self.query = nn.Linear(config.hidden_size, self.all_head_size)
        self.key = nn.Linear(config.hidden_size, self.all_head_size)
        self.value = nn.Linear(config.hidden_size, self.all_head_size)

        self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
        self.position_embedding_type = position_embedding_type or getattr(
            config, "position_embedding_type", "absolute"
        )
        if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
            self.max_position_embeddings = config.max_position_embeddings
            self.distance_embedding = nn.Embedding(2 * config.max_position_embeddings - 1, self.attention_head_size)

        self.is_decoder = config.is_decoder
        self.layer_idx = layer_idx

    @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.FloatTensor] = None,
        head_mask: Optional[torch.FloatTensor] = None,
        encoder_hidden_states: Optional[torch.FloatTensor] = None,
        past_key_values: Optional[Cache] = None,
        output_attentions: Optional[bool] = False,
        cache_position: Optional[torch.Tensor] = None,
    ) -> tuple[torch.Tensor]:
        batch_size, seq_length, _ = hidden_states.shape
        query_layer = self.query(hidden_states)
        query_layer = query_layer.view(batch_size, -1, self.num_attention_heads, self.attention_head_size).transpose(
            1, 2
        )

        is_updated = False
        is_cross_attention = encoder_hidden_states is not None
        if past_key_values is not None:
            if isinstance(past_key_values, EncoderDecoderCache):
                is_updated = past_key_values.is_updated.get(self.layer_idx)
                if is_cross_attention:
                    # after the first generated id, we can subsequently re-use all key/value_layer from cache
                    curr_past_key_value = past_key_values.cross_attention_cache
                else:
                    curr_past_key_value = past_key_values.self_attention_cache
            else:
                curr_past_key_value = past_key_values

        current_states = encoder_hidden_states if is_cross_attention else hidden_states
        if is_cross_attention and past_key_values is not None and is_updated:
            # reuse k,v, cross_attentions
            key_layer = curr_past_key_value.layers[self.layer_idx].keys
            value_layer = curr_past_key_value.layers[self.layer_idx].values
        else:
            key_layer = self.key(current_states)
            key_layer = key_layer.view(batch_size, -1, self.num_attention_heads, self.attention_head_size).transpose(
                1, 2
            )
            value_layer = self.value(current_states)
            value_layer = value_layer.view(
                batch_size, -1, self.num_attention_heads, self.attention_head_size
            ).transpose(1, 2)

            if past_key_values is not None:
                # save all key/value_layer to cache to be re-used for fast auto-regressive generation
                cache_position = cache_position if not is_cross_attention else None
                key_layer, value_layer = curr_past_key_value.update(
                    key_layer, value_layer, self.layer_idx, {"cache_position": cache_position}
                )
                # set flag that curr layer for cross-attn is already updated so we can re-use in subsequent calls
                if is_cross_attention and isinstance(past_key_values, EncoderDecoderCache):
                    past_key_values.is_updated[self.layer_idx] = True

        # Take the dot product between "query" and "key" to get the raw attention scores.
        attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))

        if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
            query_length, key_length = query_layer.shape[2], key_layer.shape[2]
            if past_key_values is not None:
                position_ids_l = torch.tensor(key_length - 1, dtype=torch.long, device=hidden_states.device).view(
                    -1, 1
                )
            else:
                position_ids_l = torch.arange(query_length, dtype=torch.long, device=hidden_states.device).view(-1, 1)
            position_ids_r = torch.arange(key_length, dtype=torch.long, device=hidden_states.device).view(1, -1)
            distance = position_ids_l - position_ids_r

            positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1)
            positional_embedding = positional_embedding.to(dtype=query_layer.dtype)  # fp16 compatibility

            if self.position_embedding_type == "relative_key":
                relative_position_scores = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
                attention_scores = attention_scores + relative_position_scores
            elif self.position_embedding_type == "relative_key_query":
                relative_position_scores_query = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
                relative_position_scores_key = torch.einsum("bhrd,lrd->bhlr", key_layer, positional_embedding)
                attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key

        attention_scores = attention_scores / math.sqrt(self.attention_head_size)
        if attention_mask is not None:
            # Apply the attention mask is (precomputed for all layers in MegatronBertModel forward() function)
            attention_scores = attention_scores + attention_mask

        # Normalize the attention scores to probabilities.
        attention_probs = nn.functional.softmax(attention_scores, dim=-1)

        # This is actually dropping out entire tokens to attend to, which might
        # seem a bit unusual, but is taken from the original Transformer paper.
        attention_probs = self.dropout(attention_probs)

        # Mask heads if we want to
        if head_mask is not None:
            attention_probs = attention_probs * head_mask

        context_layer = torch.matmul(attention_probs, value_layer)

        context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
        new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
        context_layer = context_layer.view(new_context_layer_shape)

        return context_layer, attention_probs


# Based transformers.models.bert.modeling_bert.BertSelfOutput. Moved LayerNorm to MegatronBertAttention below.

class MegatronBertSelfOutput(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)

    def forward(self, hidden_states: torch.Tensor, residual: torch.Tensor) -> torch.Tensor:
        hidden_states = self.dense(hidden_states)
        hidden_states = self.dropout(hidden_states)
        return residual + hidden_states


# Based transformers.models.bert.modeling_bert.BertAttention. Added LayerNorm.

class MegatronBertAttention(nn.Module):
    def __init__(self, config, layer_idx=None):
        super().__init__()
        self.ln = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        self.self = MegatronBertSelfAttention(config, layer_idx=layer_idx)
        self.output = MegatronBertSelfOutput(config)
        self.pruned_heads = set()

    def prune_heads(self, heads):
        if len(heads) == 0:
            return
        heads, index = find_pruneable_heads_and_indices(
            heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads
        )

        # Prune linear layers
        self.self.query = prune_linear_layer(self.self.query, index)
        self.self.key = prune_linear_layer(self.self.key, index)
        self.self.value = prune_linear_layer(self.self.value, index)
        self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)

        # Update hyper params and store pruned heads
        self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
        self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads
        self.pruned_heads = self.pruned_heads.union(heads)

    @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.FloatTensor] = None,
        head_mask: Optional[torch.FloatTensor] = None,
        encoder_hidden_states: Optional[torch.FloatTensor] = None,
        past_key_values: Optional[Cache] = None,
        output_attentions: Optional[bool] = False,
        cache_position: Optional[torch.Tensor] = None,
    ) -> tuple[torch.Tensor]:
        ln_outputs = self.ln(hidden_states)
        self_outputs = self.self(
            ln_outputs,
            attention_mask=attention_mask,
            head_mask=head_mask,
            encoder_hidden_states=encoder_hidden_states,
            past_key_values=past_key_values,
            output_attentions=output_attentions,
            cache_position=cache_position,
        )
        attention_output = self.output(self_outputs[0], hidden_states)
        outputs = (attention_output,) + self_outputs[1:]  # add attentions if we output them
        return outputs


# Copied from transformers.models.bert.modeling_bert.BertIntermediate with Bert->MegatronBert

class MegatronBertIntermediate(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
        if isinstance(config.hidden_act, str):
            self.intermediate_act_fn = ACT2FN[config.hidden_act]
        else:
            self.intermediate_act_fn = config.hidden_act

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        hidden_states = self.dense(hidden_states)
        hidden_states = self.intermediate_act_fn(hidden_states)
        return hidden_states


# Based on transformers.models.bert.modeling_bert.BertOutput. Moved LayerNorm to MegatronBertLayer below.

class MegatronBertOutput(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)

    def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
        hidden_states = self.dense(hidden_states)
        hidden_states = self.dropout(hidden_states)
        return input_tensor + hidden_states


# Based on transformers.models.bert.modeling_bert.BertLayer. Added LayerNorm.

class MegatronBertLayer(GradientCheckpointingLayer):
    def __init__(self, config, layer_idx=None):
        super().__init__()
        self.chunk_size_feed_forward = config.chunk_size_feed_forward
        self.seq_len_dim = 1
        self.attention = MegatronBertAttention(config, layer_idx=layer_idx)
        self.is_decoder = config.is_decoder
        self.add_cross_attention = config.add_cross_attention
        if self.add_cross_attention:
            if not self.is_decoder:
                raise TypeError(f"{self} should be used as a decoder model if cross attention is added")
            self.crossattention = MegatronBertAttention(config, layer_idx=layer_idx)
        self.ln = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        self.intermediate = MegatronBertIntermediate(config)
        self.output = MegatronBertOutput(config)

    @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.FloatTensor] = None,
        head_mask: Optional[torch.FloatTensor] = None,
        encoder_hidden_states: Optional[torch.FloatTensor] = None,
        encoder_attention_mask: Optional[torch.FloatTensor] = None,
        past_key_values: Optional[Cache] = None,
        output_attentions: Optional[bool] = False,
        cache_position: Optional[torch.Tensor] = None,
    ) -> tuple[torch.Tensor]:
        # decoder uni-directional self-attention cached key/values tuple is at positions 1,2
        self_attention_outputs = self.attention(
            hidden_states,
            attention_mask=attention_mask,
            head_mask=head_mask,
            output_attentions=output_attentions,
            past_key_values=past_key_values,
            cache_position=cache_position,
        )
        attention_output = self_attention_outputs[0]
        outputs = self_attention_outputs[1:]  # add self attentions if we output attention weights

        if self.is_decoder and encoder_hidden_states is not None:
            if not hasattr(self, "crossattention"):
                raise AttributeError(
                    f"If `encoder_hidden_states` are passed, {self} has to be instantiated with cross-attention layers"
                    " by setting `config.add_cross_attention=True`"
                )

            cross_attention_outputs = self.crossattention(
                attention_output,
                attention_mask=encoder_attention_mask,
                head_mask=head_mask,
                encoder_hidden_states=encoder_hidden_states,
                past_key_values=past_key_values,
                output_attentions=output_attentions,
                cache_position=cache_position,
            )
            attention_output = cross_attention_outputs[0]
            outputs = outputs + cross_attention_outputs[1:]  # add cross attentions if we output attention weights

        layer_output = apply_chunking_to_forward(
            self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output
        )
        return (layer_output,) + outputs

    def feed_forward_chunk(self, attention_output):
        ln_output = self.ln(attention_output)
        intermediate_output = self.intermediate(ln_output)
        layer_output = self.output(intermediate_output, attention_output)
        return layer_output

class MegatronBertEncoder(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.config = config
        self.layer = nn.ModuleList([MegatronBertLayer(config, layer_idx=i) for i in range(config.num_hidden_layers)])

        # The final layer norm. We removed the 1st LN, moved LN to each hidden layer and this one
        # is simply the final LN (Transformer's BERT has it attached to each hidden layer).
        self.ln = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        self.gradient_checkpointing = False

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.FloatTensor] = None,
        head_mask: Optional[torch.FloatTensor] = None,
        encoder_hidden_states: Optional[torch.FloatTensor] = None,
        encoder_attention_mask: Optional[torch.FloatTensor] = None,
        past_key_values: Optional[Cache] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = False,
        output_hidden_states: Optional[bool] = False,
        return_dict: Optional[bool] = True,
        cache_position: Optional[torch.Tensor] = None,
    ) -> Union[tuple, BaseModelOutputWithPastAndCrossAttentions]:
        if self.gradient_checkpointing and self.training:
            if use_cache:
                logger.warning_once(
                    "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
                )
                use_cache = False
        if use_cache and past_key_values is None:
            past_key_values = EncoderDecoderCache(DynamicCache(config=self.config), DynamicCache(config=self.config))
        if use_cache and isinstance(past_key_values, tuple):
            logger.warning_once(
                "Passing a tuple of `past_key_values` is deprecated and will be removed in Transformers v4.58.0. "
                "You should pass an instance of `EncoderDecoderCache` instead, e.g. "
                "`past_key_values=EncoderDecoderCache.from_legacy_cache(past_key_values)`."
            )
            past_key_values = EncoderDecoderCache.from_legacy_cache(past_key_values)

        all_hidden_states = () if output_hidden_states else None
        all_self_attentions = () if output_attentions else None
        all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None

        for i, layer_module in enumerate(self.layer):
            if output_hidden_states:
                all_hidden_states = all_hidden_states + (hidden_states,)

            layer_head_mask = head_mask[i] if head_mask is not None else None

            layer_outputs = layer_module(
                hidden_states,
                attention_mask,
                layer_head_mask,
                encoder_hidden_states,
                encoder_attention_mask,
                past_key_values,
                output_attentions,
                cache_position,
            )

            # Because we moved the layer-norm at the end of the hidden layer, we have non-normali-
            # zed data here. If that's really needed, we must apply LN to match Transformer's BERT.

            hidden_states = layer_outputs[0]
            if output_attentions:
                all_self_attentions = all_self_attentions + (layer_outputs[1],)
                if self.config.add_cross_attention:
                    all_cross_attentions = all_cross_attentions + (layer_outputs[2],)

        # Finalize the hidden states.
        hidden_states = self.ln(hidden_states)

        if output_hidden_states:
            all_hidden_states = all_hidden_states + (hidden_states,)

        if not return_dict:
            return tuple(
                v
                for v in [
                    hidden_states,
                    past_key_values,
                    all_hidden_states,
                    all_self_attentions,
                    all_cross_attentions,
                ]
                if v is not None
            )
        return BaseModelOutputWithPastAndCrossAttentions(
            last_hidden_state=hidden_states,
            past_key_values=past_key_values,
            hidden_states=all_hidden_states,
            attentions=all_self_attentions,
            cross_attentions=all_cross_attentions,
        )


# Copied from transformers.models.bert.modeling_bert.BertPooler with Bert->MegatronBert

class MegatronBertPooler(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
        self.activation = nn.Tanh()

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        # We "pool" the model by simply taking the hidden state corresponding
        # to the first token.
        first_token_tensor = hidden_states[:, 0]
        pooled_output = self.dense(first_token_tensor)
        pooled_output = self.activation(pooled_output)
        return pooled_output


# Copied from transformers.models.bert.modeling_bert.BertPredictionHeadTransform with Bert->MegatronBert

class MegatronBertPredictionHeadTransform(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
        if isinstance(config.hidden_act, str):
            self.transform_act_fn = ACT2FN[config.hidden_act]
        else:
            self.transform_act_fn = config.hidden_act
        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        hidden_states = self.dense(hidden_states)
        hidden_states = self.transform_act_fn(hidden_states)
        hidden_states = self.LayerNorm(hidden_states)
        return hidden_states


# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->MegatronBert

class MegatronBertLMPredictionHead(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.transform = MegatronBertPredictionHeadTransform(config)

        # The output weights are the same as the input embeddings, but there is
        # an output-only bias for each token.
        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)

        self.bias = nn.Parameter(torch.zeros(config.vocab_size))

        # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
        self.decoder.bias = self.bias

    def _tie_weights(self):
        self.decoder.bias = self.bias

    def forward(self, hidden_states):
        hidden_states = self.transform(hidden_states)
        hidden_states = self.decoder(hidden_states)
        return hidden_states


# Copied from transformers.models.bert.modeling_bert.BertOnlyMLMHead with Bert->MegatronBert

class MegatronBertOnlyMLMHead(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.predictions = MegatronBertLMPredictionHead(config)

    def forward(self, sequence_output: torch.Tensor) -> torch.Tensor:
        prediction_scores = self.predictions(sequence_output)
        return prediction_scores

#@auto_docstring
class MegatronBertPreTrainedModel(PreTrainedModel):
    config_class = MegatronBertConfig
    load_tf_weights = load_tf_weights_in_megatron_bert
    base_model_prefix = "bert"
    supports_gradient_checkpointing = True

    def _init_weights(self, module):
        """Initialize the weights"""
        if isinstance(module, (nn.Linear, nn.Embedding)):
            # Slightly different from the TF version which uses truncated_normal for initialization
            # cf https://github.com/pytorch/pytorch/pull/5617
            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
            if hasattr(module, "bias") and module.bias is not None:
                module.bias.data.zero_()
        elif isinstance(module, nn.LayerNorm):
            module.bias.data.zero_()
            module.weight.data.fill_(1.0)
        elif isinstance(module, MegatronBertLMPredictionHead):
            module.bias.data.zero_()

#@auto_docstring

class MegatronBertModel(MegatronBertPreTrainedModel):
    _no_split_modules = ["MegatronBertEmbeddings", "MegatronBertLayer"]

    def __init__(self, config, add_pooling_layer=False):
        super().__init__(config)
        self.config = config
        self.gradient_checkpointing = False
        self.embeddings = MegatronBertEmbeddings(config)
        self.encoder = MegatronBertEncoder(config)
        self.pooler = MegatronBertPooler(config) if add_pooling_layer else None
        self.post_init()

    def get_input_embeddings(self):
        return self.embeddings.word_embeddings

    def set_input_embeddings(self, value):
        self.embeddings.word_embeddings = value

    def _prune_heads(self, heads_to_prune):
        """
        Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
        class PreTrainedModel
        """
        for layer, heads in heads_to_prune.items():
            self.encoder.layer[layer].attention.prune_heads(heads)

    #@auto_docstring
    def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        attention_mask: Optional[torch.FloatTensor] = None,
        token_type_ids: Optional[torch.LongTensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        head_mask: Optional[torch.FloatTensor] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        encoder_hidden_states: Optional[torch.FloatTensor] = None,
        encoder_attention_mask: Optional[torch.FloatTensor] = None,
        past_key_values: Optional[Cache] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        cache_position: Optional[torch.Tensor] = None,
    ) -> Union[tuple, BaseModelOutputWithPoolingAndCrossAttentions]:
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        return_dict = return_dict if return_dict is not None else self.config.return_dict

        if self.config.is_decoder:
            use_cache = use_cache if use_cache is not None else self.config.use_cache
        else:
            use_cache = False

        if input_ids is not None and inputs_embeds is not None:
            raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
        elif input_ids is not None:
            self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
            input_shape = input_ids.size()
        elif inputs_embeds is not None:
            input_shape = inputs_embeds.size()[:-1]
        else:
            raise ValueError("You have to specify either input_ids or inputs_embeds")

        batch_size, seq_length = input_shape
        device = input_ids.device if input_ids is not None else inputs_embeds.device

        past_key_values_length = 0
        if past_key_values is not None:
            past_key_values_length = (
                past_key_values[0][0].shape[-2]
                if not isinstance(past_key_values, Cache)
                else past_key_values.get_seq_length()
            )

        if attention_mask is None:
            attention_mask = torch.ones(((batch_size, seq_length + past_key_values_length)), device=device)
        if token_type_ids is None:
            token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)

        # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
        # ourselves in which case we just need to make it broadcastable to all heads.
        extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape)

        # If a 2D or 3D attention mask is provided for the cross-attention
        # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
        if self.config.is_decoder and encoder_hidden_states is not None:
            encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size()
            encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)
            if encoder_attention_mask is None:
                encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device)
            encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
        else:
            encoder_extended_attention_mask = None

        # Prepare head mask if needed
        # 1.0 in head_mask indicate we keep the head
        # attention_probs has shape bsz x n_heads x N x N
        # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
        # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
        # head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)

        embedding_output = self.embeddings(
            input_ids=input_ids,
            position_ids=position_ids,
            token_type_ids=token_type_ids,
            inputs_embeds=inputs_embeds,
            past_key_values_length=past_key_values_length,
        )
        encoder_outputs = self.encoder(
            embedding_output,
            attention_mask=extended_attention_mask,
            # head_mask=head_mask,
            encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=encoder_extended_attention_mask,
            past_key_values=past_key_values,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            cache_position=cache_position,
        )
        sequence_output = encoder_outputs[0]
        pooled_output = self.pooler(sequence_output) if self.pooler is not None else None

        if not return_dict:
            return (sequence_output, pooled_output) + encoder_outputs[1:]

        return BaseModelOutputWithPoolingAndCrossAttentions(
            last_hidden_state=sequence_output,
            pooler_output=pooled_output,
            past_key_values=encoder_outputs.past_key_values,
            hidden_states=encoder_outputs.hidden_states,
            attentions=encoder_outputs.attentions,
            cross_attentions=encoder_outputs.cross_attentions,
        )


@auto_docstring(
    custom_intro="""
    MegatronBert Model with two heads on top as done during the pretraining: a `masked language modeling` head and a
    `next sentence prediction (classification)` head.
    """
)

#@auto_docstring
class MegatronBertForMaskedLM(MegatronBertPreTrainedModel, GenerationMixin):
    _tied_weights_keys = {
        "cls.predictions.decoder.weight": "bert.embeddings.word_embeddings.weight",
        "cls.predictions.decoder.bias": "cls.predictions.bias",
    }

    def __init__(self, config):
        super().__init__(config)

        if config.is_decoder:
            logger.warning(
                "If you want to use `MegatronBertForMaskedLM` make sure "
                "`config.is_decoder=False` for bi-directional self-attention."
            )

        self.bert = MegatronBertModel(config, add_pooling_layer=False)
        self.cls = MegatronBertOnlyMLMHead(config)

        # Initialize weights and apply final processing
        self.post_init()
        self._force_tie_mlm_head()

    def get_input_embeddings(self):
        return self.bert.get_input_embeddings()

    def set_input_embeddings(self, value):
        self.bert.set_input_embeddings(value)
        self._force_tie_mlm_head()

    def _force_tie_mlm_head(self):
        self.cls.predictions.decoder.weight = self.bert.embeddings.word_embeddings.weight
        self.cls.predictions._tie_weights()

    def tie_weights(self, missing_keys: Optional[set[str]] = None, recompute_mapping: bool = True, **kwargs):
        # Transformers v5 calls tie_weights(recompute_mapping=False) during post_init/init_weights.
        # Keep the signature compatible with both v4 and v5, but force the exact tying behavior we need.
        self._force_tie_mlm_head()

    def assert_mlm_head_is_valid(self):
        in_w = self.bert.embeddings.word_embeddings.weight
        out_w = self.cls.predictions.decoder.weight
        out_b = self.cls.predictions.decoder.bias
        ref_b = self.cls.predictions.bias

        if in_w.data_ptr() != out_w.data_ptr():
            raise RuntimeError("MLM decoder.weight is not tied to input embeddings.")
        if out_b is None or out_b.data_ptr() != ref_b.data_ptr():
            raise RuntimeError("MLM decoder.bias is not tied to cls.predictions.bias.")

    def get_output_embeddings(self):
        return self.cls.predictions.decoder

    def set_output_embeddings(self, new_embeddings):
        self.cls.predictions.decoder = new_embeddings
        self.cls.predictions.bias = new_embeddings.bias

    #@auto_docstring
    def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        attention_mask: Optional[torch.FloatTensor] = None,
        token_type_ids: Optional[torch.LongTensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        head_mask: Optional[torch.FloatTensor] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        encoder_hidden_states: Optional[torch.FloatTensor] = None,
        encoder_attention_mask: Optional[torch.FloatTensor] = None,
        labels: Optional[torch.LongTensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[tuple, MaskedLMOutput]:
        r"""
        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
            Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
            config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the
            loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`
        """

        return_dict = return_dict if return_dict is not None else self.config.return_dict

        outputs = self.bert(
            input_ids,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            position_ids=position_ids,
            head_mask=head_mask,
            inputs_embeds=inputs_embeds,
            encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=encoder_attention_mask,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        sequence_output = outputs[0]
        prediction_scores = self.cls(sequence_output)

        masked_lm_loss = None
        if labels is not None:
            loss_fct = CrossEntropyLoss()  # -100 index = padding token
            masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))

        if not return_dict:
            output = (prediction_scores,) + outputs[2:]
            return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output

        return MaskedLMOutput(
            loss=masked_lm_loss,
            logits=prediction_scores,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )

    def prepare_inputs_for_generation(self, input_ids, attention_mask=None, **model_kwargs):
        input_shape = input_ids.shape
        effective_batch_size = input_shape[0]

        #  add a dummy token
        if self.config.pad_token_id is None:
            raise ValueError("The PAD token should be defined for generation")
        attention_mask = torch.cat([attention_mask, attention_mask.new_zeros((attention_mask.shape[0], 1))], dim=-1)
        dummy_token = torch.full(
            (effective_batch_size, 1), self.config.pad_token_id, dtype=torch.long, device=input_ids.device
        )
        input_ids = torch.cat([input_ids, dummy_token], dim=1)

        return {"input_ids": input_ids, "attention_mask": attention_mask}


# Previous codes

class ProkBertConfig(MegatronBertConfig):
    model_type = "prokbert"

    attribute_map = {
        "num_class_labels": "num_labels",
        "curricular_num_labels": "num_labels",
        "classification_dropout_rate": "classifier_dropout",
        "curriculum_hidden_size": "curricular_embedding_size",
        "curricular_face_m": "curricular_margin",
        "curricular_face_s": "curricular_scale",
    }

    def __init__(
        self,
        kmer: int = 6,
        shift: int = 1,
        num_labels: int = 2,
        problem_type: str | None = None,
        classifier_dropout: float | None = None,
        classifier_pooling: str = "attention",
        classifier_mlp_hidden_size: int | None = None,
        classifier_head_type: str = "linear",
        curricular_margin: float = 0.5,
        curricular_scale: float = 64.0,
        curricular_embedding_size: int | None = None,
        **kwargs,
    ):
        legacy_num_class_labels = kwargs.pop("num_class_labels", None)
        legacy_curricular_num_labels = kwargs.pop("curricular_num_labels", None)
        legacy_dropout = kwargs.pop("classification_dropout_rate", None)
        legacy_proj = kwargs.pop("curriculum_hidden_size", None)
        legacy_margin = kwargs.pop("curricular_face_m", None)
        legacy_scale = kwargs.pop("curricular_face_s", None)
        kwargs.pop("bert_base_model", None)

        if legacy_num_class_labels is not None:
            num_labels = legacy_num_class_labels
        if legacy_curricular_num_labels is not None:
            num_labels = legacy_curricular_num_labels            
        loaded_id2label = kwargs.get("id2label")
        if loaded_id2label is not None:
            num_labels = len(loaded_id2label)                        
        if classifier_dropout is None and legacy_dropout is not None:
            classifier_dropout = legacy_dropout
        if curricular_embedding_size is None and legacy_proj not in (None, -1):
            curricular_embedding_size = legacy_proj
        if legacy_margin is not None:
            curricular_margin = legacy_margin
        if legacy_scale is not None:
            curricular_scale = legacy_scale

        super().__init__(num_labels=num_labels, problem_type=problem_type, **kwargs)

        self.kmer = kmer
        self.shift = shift

        self.classifier_dropout = classifier_dropout
        self.classifier_pooling = classifier_pooling
        self.classifier_mlp_hidden_size = classifier_mlp_hidden_size
        self.classifier_head_type = classifier_head_type

        self.curricular_margin = curricular_margin
        self.curricular_scale = curricular_scale
        self.curricular_embedding_size = curricular_embedding_size

        if self.classifier_pooling not in {"cls", "mean", "attention"}:
            raise ValueError(f"Unsupported classifier_pooling={self.classifier_pooling}")
        if self.classifier_head_type not in {"linear", "mlp", "curricular"}:
            raise ValueError(f"Unsupported classifier_head_type={self.classifier_head_type}")

        
class ProkBertPreTrainedModel(MegatronBertPreTrainedModel):
    """
    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
    models.
    """

    config_class = ProkBertConfig
    base_model_prefix = "bert"
    supports_gradient_checkpointing = True

    def _init_weights(self, module):
        """Initialize the weights"""
        if isinstance(module, (nn.Linear, nn.Embedding)):
            # Slightly different from the TF version which uses truncated_normal for initialization
            # cf https://github.com/pytorch/pytorch/pull/5617
            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
        elif isinstance(module, nn.LayerNorm):
            module.bias.data.zero_()
            module.weight.data.fill_(1.0)
        if isinstance(module, nn.Linear) and module.bias is not None:
            module.bias.data.zero_()


class ProkBertModel(_SafeFromPretrainedMixin, MegatronBertModel):
    config_class = ProkBertConfig

    def __init__(self, config: ProkBertConfig, **kwargs):
        if not isinstance(config, ProkBertConfig):
            raise ValueError(
                f"Expected `ProkBertConfig`, got {config.__class__.__module__}.{config.__class__.__name__}"
            )
        super().__init__(config, **kwargs)
        self.config = config


    @classmethod
    def _adapt_state_dict(cls, state_dict):
        return _extract_base_model_state_dict(state_dict, base_prefix="bert")

    @classmethod
    def test_from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
        config = kwargs.pop("config", None)
        add_pooling_layer = kwargs.pop("add_pooling_layer", False)

        # ignored here on purpose; this loader bypasses HF v5 from_pretrained internals
        kwargs.pop("output_loading_info", None)
        kwargs.pop("ignore_mismatched_sizes", None)
        kwargs.pop("state_dict", None)

        if config is None:
            config = cls.config_class.from_pretrained(pretrained_model_name_or_path, **kwargs)

        model = cls(config, add_pooling_layer=add_pooling_layer)

        weights_path = _resolve_weights_file(pretrained_model_name_or_path)
        raw_state_dict = _read_state_dict(weights_path)

        # ProkBERT checkpoint is MLM-style; encoder lives under `bert.`
        state_dict = _extract_base_model_state_dict(raw_state_dict, base_prefix="bert")

        missing, unexpected = model.load_state_dict(state_dict, strict=False)

        allowed_missing = set()
        if add_pooling_layer:
            allowed_missing.update({"pooler.dense.weight", "pooler.dense.bias"})

        bad_missing = [k for k in missing if k not in allowed_missing]

        if bad_missing or unexpected:
            raise RuntimeError(
                f"Checkpoint mismatch.\nMissing: {bad_missing}\nUnexpected: {unexpected}"
            )

        model.eval()
        return model


class ProkBertForMaskedLM(_SafeFromPretrainedMixin, MegatronBertForMaskedLM):
    config_class = ProkBertConfig

    def __init__(self, config: ProkBertConfig, **kwargs):
        if not isinstance(config, ProkBertConfig):
            raise ValueError(
                f"Expected `ProkBertConfig`, got "
                f"{config.__class__.__module__}.{config.__class__.__name__}"
            )

        super().__init__(config, **kwargs)
        self.config = config
        # One should check if it is a prper prokbert config, if not crafting one.

    @classmethod
    def _adapt_state_dict(cls, state_dict):
        state_dict = dict(state_dict)

        emb_w = "bert.embeddings.word_embeddings.weight"
        dec_w = "cls.predictions.decoder.weight"
        mlm_b = "cls.predictions.bias"
        dec_b = "cls.predictions.decoder.bias"

        if dec_w not in state_dict and emb_w in state_dict:
            state_dict[dec_w] = state_dict[emb_w]
        if emb_w not in state_dict and dec_w in state_dict:
            state_dict[emb_w] = state_dict[dec_w]

        if dec_b not in state_dict and mlm_b in state_dict:
            state_dict[dec_b] = state_dict[mlm_b]
        if mlm_b not in state_dict and dec_b in state_dict:
            state_dict[mlm_b] = state_dict[dec_b]

        return state_dict


class ProkBertForSequenceClassification(_SafeFromPretrainedMixin, ProkBertPreTrainedModel):
    """
    Default ProkBERT sequence classifier:
      - padding-safe masked attention pooling
      - neutral pooling init (uniform over non-masked tokens at step 0)
      - simple dropout + linear classifier head
    """
    config_class = ProkBertConfig
    base_model_prefix = "bert"

    def __init__(self, config: ProkBertConfig):
        super().__init__(config)
        self.config = config
        self.num_labels = int(config.num_labels)

        self.bert = ProkBertModel(config, add_pooling_layer=False)

        # Keep the old module name for checkpoint compatibility.
        self.weighting_layer = nn.Linear(self.config.hidden_size, 1)
        self.dropout = nn.Dropout(get_classifier_dropout(self.config))
        self.classifier = nn.Linear(self.config.hidden_size, self.num_labels)

        self.post_init()

        # Neutral pooling init: uniform over valid tokens at the beginning of training.
        with torch.no_grad():
            nn.init.zeros_(self.weighting_layer.weight)
            if self.weighting_layer.bias is not None:
                nn.init.zeros_(self.weighting_layer.bias)

    def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        token_type_ids: Optional[torch.LongTensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        inputs_embeds: Optional[torch.Tensor] = None,
        labels: Optional[torch.Tensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple, SequenceClassifierOutput]:
        return_dict = return_dict if return_dict is not None else self.config.return_dict

        outputs = self.bert(
            input_ids,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            position_ids=position_ids,
            inputs_embeds=inputs_embeds,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        sequence_output = outputs[0]  # (B, L, H)

        token_scores = self.weighting_layer(sequence_output)  # (B, L, 1)
        pooled_output = masked_attention_pool(
            sequence_output=sequence_output,
            token_scores=token_scores,
            attention_mask=attention_mask,
        )

        pooled_output = self.dropout(pooled_output)
        logits = self.classifier(pooled_output)

        loss = None
        if labels is not None:
            if self.config.problem_type is None:
                if self.num_labels == 1:
                    self.config.problem_type = "regression"
                elif labels.dtype in (
                    torch.int8,
                    torch.int16,
                    torch.int32,
                    torch.int64,
                    torch.long,
                    torch.uint8,
                ):
                    self.config.problem_type = "single_label_classification"
                else:
                    self.config.problem_type = "multi_label_classification"

            if self.config.problem_type == "regression":
                loss_fct = nn.MSELoss()
                if self.num_labels == 1:
                    loss = loss_fct(logits.squeeze(), labels.squeeze())
                else:
                    loss = loss_fct(logits, labels)
            elif self.config.problem_type == "single_label_classification":
                loss_fct = nn.CrossEntropyLoss()
                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
            elif self.config.problem_type == "multi_label_classification":
                loss_fct = nn.BCEWithLogitsLoss()
                loss = loss_fct(logits, labels.float())
            else:
                raise ValueError(f"Unsupported problem_type: {self.config.problem_type}")

        if not return_dict:
            output = (logits,) + outputs[2:]
            return ((loss,) + output) if loss is not None else output

        return SequenceClassifierOutput(
            loss=loss,
            logits=logits,
            hidden_states=getattr(outputs, "hidden_states", None),
            attentions=getattr(outputs, "attentions", None),
        )
    
@dataclass
class CurricularSequenceClassifierOutput(ModelOutput):
    loss: Optional[torch.FloatTensor] = None
    logits: Optional[torch.FloatTensor] = None
    embeddings: Optional[torch.FloatTensor] = None
    hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
    attentions: Optional[Tuple[torch.FloatTensor, ...]] = None


class CurricularFace(nn.Module):
    def __init__(self, in_features, out_features, m=0.5, s=64.0, ema_alpha=0.01):
        super().__init__()
        self.in_features = in_features
        self.out_features = out_features
        self.m = float(m)
        self.s = float(s)
        self.ema_alpha = float(ema_alpha)

        self.cos_m = math.cos(self.m)
        self.sin_m = math.sin(self.m)
        self.threshold = math.cos(math.pi - self.m)
        self.mm = math.sin(math.pi - self.m) * self.m

        # keep checkpoint compatibility: same shape as before
        self.kernel = Parameter(torch.empty(in_features, out_features))
        self.register_buffer("t", torch.zeros(1, dtype=torch.float32))
        self.reset_parameters()

    def reset_parameters(self):
        nn.init.xavier_uniform_(self.kernel)
        self.t.zero_()

    def cosine(self, embeddings: torch.Tensor) -> torch.Tensor:
        # entire angular-margin block starts from fp32 cosine similarities
        # one cast at the entrance; do not keep re-casting inside
        with _autocast_disabled(embeddings.device.type):
            x = F.normalize(embeddings.float(), p=2.0, dim=1, eps=1e-12)
            w = F.normalize(self.kernel.float(), p=2.0, dim=0, eps=1e-12)
            cos_theta = F.linear(x, w.t()).clamp(-1.0, 1.0)
        return cos_theta  # fp32

    def inference_logits(self, embeddings: torch.Tensor) -> torch.Tensor:
        return self.cosine(embeddings) * self.s

    def margin_logits_from_cosine(
        self,
        cos_theta: torch.Tensor,
        labels: torch.LongTensor,
        update_t: bool = False,
    ) -> torch.Tensor:
        labels = labels.reshape(-1).long()

        # (B, 1)
        target = cos_theta.gather(1, labels.unsqueeze(1))

        sin_theta = torch.sqrt((1.0 - target.square()).clamp(min=0.0))
        cos_theta_m = target * self.cos_m - sin_theta * self.sin_m

        hard_mask = cos_theta > cos_theta_m
        final_target = torch.where(
            target > self.threshold,
            cos_theta_m,
            target - self.mm,
        )

        # update running t only in training
        if update_t:
            with torch.no_grad():
                target_mean = target.mean().to(dtype=self.t.dtype).view_as(self.t)
                self.t.lerp_(target_mean, self.ema_alpha)

        # keep everything in one dtype; no masked indexed assignment
        t = self.t.to(device=cos_theta.device, dtype=cos_theta.dtype)
        adjusted = torch.where(hard_mask, cos_theta * (t + cos_theta), cos_theta)
        adjusted = adjusted.scatter(1, labels.unsqueeze(1), final_target)

        return adjusted * self.s

    def training_logits(
        self,
        embeddings: torch.Tensor,
        labels: torch.LongTensor,
        update_t: bool = False,
    ) -> torch.Tensor:
        cos_theta = self.cosine(embeddings)
        return self.margin_logits_from_cosine(cos_theta, labels, update_t=update_t)
    
    

class ProkBertForCurricularClassification(_SafeFromPretrainedMixin, ProkBertPreTrainedModel):
    config_class = ProkBertConfig
    base_model_prefix = "bert"

    def __init__(self, config: ProkBertConfig):
        super().__init__(config)
        self.config = config
        self.num_labels = int(config.num_labels)

        self.bert = ProkBertModel(config, add_pooling_layer=False)
        self.weighting_layer = nn.Linear(self.config.hidden_size, 1)
        self.dropout = nn.Dropout(get_classifier_dropout(self.config))

        use_projection = self.config.curricular_embedding_size not in (None, -1)
        proj_dim = self.config.hidden_size if not use_projection else int(self.config.curricular_embedding_size)
        self.linear = nn.Linear(self.config.hidden_size, proj_dim) if use_projection else nn.Identity()

        self.curricular_face = CurricularFace(
            in_features=proj_dim,
            out_features=self.num_labels,
            m=float(self.config.curricular_margin),
            s=float(self.config.curricular_scale),
        )
        self.loss_fct = nn.CrossEntropyLoss()

        self.post_init()

        with torch.no_grad():
            nn.init.zeros_(self.weighting_layer.weight)
            if self.weighting_layer.bias is not None:
                nn.init.zeros_(self.weighting_layer.bias)
            if isinstance(self.linear, nn.Linear):
                initialize_linear_kaiming(self.linear)

    def _pool_sequence_output(
        self,
        sequence_output: torch.Tensor,
        attention_mask: Optional[torch.Tensor],
    ) -> torch.Tensor:
        pooling = self.config.classifier_pooling

        if pooling == "cls":
            return sequence_output[:, 0]

        if pooling == "mean":
            keep_mask = normalize_pooling_attention_mask(attention_mask)
            if keep_mask is None:
                return sequence_output.mean(dim=1)

            empty_rows = keep_mask.sum(dim=1) == 0
            if empty_rows.any():
                keep_mask = keep_mask.clone()
                keep_mask[empty_rows, 0] = True

            mask = keep_mask.unsqueeze(-1).to(dtype=sequence_output.dtype)
            return (sequence_output * mask).sum(dim=1) / mask.sum(dim=1).clamp(min=1.0)

        if pooling == "attention":
            token_scores = self.weighting_layer(sequence_output)
            return masked_attention_pool(
                sequence_output=sequence_output,
                token_scores=token_scores,
                attention_mask=attention_mask,
            )

        raise ValueError(f"Unsupported classifier_pooling={pooling!r}")

    def _compute_embeddings(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        token_type_ids: Optional[torch.LongTensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        inputs_embeds: Optional[torch.Tensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        apply_dropout: bool = True,
    ) -> tuple[torch.Tensor, BaseModelOutputWithPoolingAndCrossAttentions]:
        outputs = self.bert(
            input_ids=input_ids,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            position_ids=position_ids,
            inputs_embeds=inputs_embeds,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=True,
        )

        pooled_output = self._pool_sequence_output(
            outputs.last_hidden_state,
            attention_mask,
        )

        if apply_dropout:
            pooled_output = self.dropout(pooled_output)

        embeddings = self.linear(pooled_output)
        return embeddings, outputs

    def encode(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        token_type_ids: Optional[torch.LongTensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        inputs_embeds: Optional[torch.Tensor] = None,
        normalize: bool = True,
    ) -> torch.Tensor:
        # deterministic embedding extraction: no dropout
        embeddings, _ = self._compute_embeddings(
            input_ids=input_ids,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            position_ids=position_ids,
            inputs_embeds=inputs_embeds,
            apply_dropout=False,
        )
        return l2_norm(embeddings, axis=1) if normalize else embeddings

    def deprecated_curricular_inference_logits(self, embeddings: torch.Tensor) -> torch.Tensor:
        embeddings = l2_norm(embeddings, axis=1)
        kernel_norm = l2_norm(self.curricular_face.kernel, axis=0)
        cos_theta = torch.mm(embeddings, kernel_norm).clamp(-1.0, 1.0)
        return cos_theta * self.curricular_face.s
    

    def _curricular_inference_logits(self, embeddings: torch.Tensor) -> torch.Tensor:
        return self.curricular_face.inference_logits(embeddings)


    def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        token_type_ids: Optional[torch.LongTensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        head_mask: Optional[torch.FloatTensor] = None,  # kept for compatibility
        inputs_embeds: Optional[torch.FloatTensor] = None,
        labels: Optional[torch.LongTensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        return_embeddings: bool = False,
        normalize_embeddings: bool = True,
    ) -> Union[Tuple, CurricularSequenceClassifierOutput]:
        return_dict = return_dict if return_dict is not None else self.config.return_dict

        embeddings, outputs = self._compute_embeddings(
            input_ids=input_ids,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            position_ids=position_ids,
            inputs_embeds=inputs_embeds,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            apply_dropout=self.training,
        )

        exported_embeddings = None
        if return_embeddings:
            exported_embeddings = (
                l2_norm(embeddings, axis=1) if normalize_embeddings else embeddings
            )

        # compute cosine once in fp32
        cos_theta = self.curricular_face.cosine(embeddings)

        # always return label-free prediction logits
        logits = cos_theta * self.curricular_face.s

        loss = None
        if labels is not None:
            labels = labels.view(-1).long()
            train_logits = self.curricular_face.margin_logits_from_cosine(
                cos_theta,
                labels,
                update_t=self.training,  # do not mutate t in eval
            )
            loss = self.loss_fct(train_logits, labels)

        if not return_dict:
            out = (logits,)
            if return_embeddings:
                out = out + (exported_embeddings,)
            if output_hidden_states:
                out = out + (outputs.hidden_states,)
            if output_attentions:
                out = out + (outputs.attentions,)
            return ((loss,) + out) if loss is not None else out

        return CurricularSequenceClassifierOutput(
            loss=loss,
            logits=logits,
            embeddings=exported_embeddings,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )


class ProkBertForSequenceClassificationExt(_SafeFromPretrainedMixin, ProkBertPreTrainedModel):
    """
    Extensions vs. baseline ProkBertForSequenceClassification:
      - Fixes attention-pooling bug by masking PAD positions using attention_mask
      - Neutral pooling init: weighting_layer starts at zero => uniform pooling over non-masked tokens
      - LN + MLP head on pooled embedding
      - Temperature-controlled attention pooling with learnable temperature (scalar)
    """
    config_class = ProkBertConfig
    base_model_prefix = "bert"

    def __init__(self, config):
        super().__init__(config)
        self.config = config

        self.bert = ProkBertModel(config)

        # Attention pooling (token-wise scalar score)
        self.weighting_layer = nn.Linear(self.config.hidden_size, 1)

        # Learnable temperature for pooling: temperature = exp(log_temperature), clamped
        self.log_temperature = nn.Parameter(torch.zeros(()))  # scalar, starts at 0 => temperature=1
        self.temperature_min = float(getattr(config, "pool_temperature_min", 0.1))
        self.temperature_max = float(getattr(config, "pool_temperature_max", 10.0))

        # MLP head on pooled embedding
        eps = float(getattr(config, "layer_norm_eps", 1e-12))
        drop_p = float(getattr(config, "classification_dropout_rate", 0.1))
        hidden_size = int(self.config.hidden_size)
        mlp_hidden = int(getattr(config, "classifier_mlp_hidden_size", max(1, hidden_size // 2)))

        self.mlp_ln = nn.LayerNorm(hidden_size, eps=eps)
        self.mlp_dropout = nn.Dropout(drop_p)
        self.mlp_fc1 = nn.Linear(hidden_size, mlp_hidden)
        self.mlp_act = nn.GELU()
        self.mlp_fc2 = nn.Linear(mlp_hidden, int(self.config.num_class_labels))

        # Loss
        if int(self.config.num_class_labels) == 1:
            self.loss_fct = nn.MSELoss()
        else:
            self.loss_fct = nn.CrossEntropyLoss()

        self.post_init()

        # --- Custom init for "neutral" pooling + slightly conservative output layer ---
        self._init_ext_head()

    def _init_ext_head(self):
        # Make pooling start neutral: scores = 0 => uniform softmax over non-masked tokens
        with torch.no_grad():
            nn.init.zeros_(self.weighting_layer.weight)
            nn.init.zeros_(self.weighting_layer.bias)

        # Optional: make final classifier layer a bit smaller (reduces early overconfidence)
        init_range = float(getattr(self.config, "initializer_range", 0.02))
        with torch.no_grad():
            nn.init.normal_(self.mlp_fc2.weight, mean=0.0, std=init_range * 0.1)
            nn.init.zeros_(self.mlp_fc2.bias)

    def _get_temperature(self, device: torch.device) -> torch.Tensor:
        # Keep temperature positive and within a reasonable range
        t = torch.exp(self.log_temperature.to(device=device))
        return torch.clamp(t, min=self.temperature_min, max=self.temperature_max)

    @staticmethod
    def _normalize_attention_mask(attention_mask: torch.Tensor) -> torch.Tensor:
        """
        Convert attention_mask to shape (B, L) boolean mask where True means "keep token".
        Handles common shapes: (B, L), (B, 1, 1, L), (B, 1, L).
        """
        if attention_mask is None:
            return None

        mask = attention_mask
        # Common HF forms
        if mask.dim() == 4:
            # (B, 1, 1, L) -> (B, L)
            mask = mask.squeeze(1).squeeze(1)
        elif mask.dim() == 3:
            # (B, 1, L) -> (B, L)
            mask = mask.squeeze(1)

        # Convert to bool: treat >0 as keep
        mask = mask > 0
        return mask

    def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        token_type_ids: Optional[torch.LongTensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        head_mask: Optional[torch.Tensor] = None,
        inputs_embeds: Optional[torch.Tensor] = None,
        labels: Optional[torch.LongTensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple, SequenceClassifierOutput]:

        return_dict = return_dict if return_dict is not None else self.config.return_dict

        outputs = self.bert(
            input_ids,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            position_ids=position_ids,
            head_mask=head_mask,
            inputs_embeds=inputs_embeds,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        sequence_output = outputs[0]  # (B, L, H)

        # --- Temperature-controlled attention pooling with PAD-masking ---
        scores = self.weighting_layer(sequence_output)  # (B, L, 1)

        # Apply temperature (smooth if temperature > 1, sharper if < 1)
        temperature = self._get_temperature(device=scores.device)
        scores = scores / temperature

        # Mask out padding tokens (pooling bug fix)
        keep_mask = self._normalize_attention_mask(attention_mask)  # (B, L) bool or None
        if keep_mask is not None:
            # Guard: if an example is fully masked (shouldn't happen), keep first token to avoid NaNs
            if (keep_mask.sum(dim=1) == 0).any():
                keep_mask = keep_mask.clone()
                keep_mask[(keep_mask.sum(dim=1) == 0), 0] = True

            scores = scores.masked_fill(~keep_mask.unsqueeze(-1), float("-inf"))

        # Softmax in fp32 for stability, then cast back
        weights = torch.softmax(scores.float(), dim=1).to(dtype=sequence_output.dtype)  # (B, L, 1)

        pooled_output = torch.sum(weights * sequence_output, dim=1)  # (B, H)

        # --- LN + MLP head ---
        x = self.mlp_ln(pooled_output)
        x = self.mlp_dropout(x)
        x = self.mlp_fc1(x)
        x = self.mlp_act(x)
        x = self.mlp_dropout(x)
        logits = self.mlp_fc2(x)

        loss = None
        if labels is not None:
            if int(self.config.num_class_labels) == 1:
                loss = self.loss_fct(logits.view(-1), labels.view(-1).float())
            else:
                loss = self.loss_fct(logits.view(-1, int(self.config.num_class_labels)), labels.view(-1))

        if not return_dict:
            # outputs: (last_hidden_state, pooled_output, hidden_states, attentions) in most BERT-like models
            out = (logits,) + outputs[2:]
            return ((loss,) + out) if loss is not None else out

        return SequenceClassifierOutput(
            loss=loss,
            logits=logits,
            hidden_states=getattr(outputs, "hidden_states", None),
            attentions=getattr(outputs, "attentions", None),
        )