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# Copyright 2024 Answer.AI, LightOn, and contributors, and the HuggingFace Inc. team. All rights reserved.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import math
from contextlib import nullcontext
from typing import Dict, Optional, Tuple, Union

import torch
import torch.nn.functional as F
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from ...activations import ACT2FN
from ...modeling_attn_mask_utils import _prepare_4d_attention_mask
from ...modeling_outputs import BaseModelOutput, MaskedLMOutput, SequenceClassifierOutput, TokenClassifierOutput
from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS
from ...modeling_utils import PreTrainedModel
from ...utils import (
    add_code_sample_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    is_flash_attn_2_available,
    logging,
)
from ...utils.import_utils import is_triton_available
from .configuration_modernbert import ModernBertConfig


if is_flash_attn_2_available():
    from flash_attn.flash_attn_interface import flash_attn_varlen_qkvpacked_func
    from flash_attn.layers.rotary import RotaryEmbedding
    from flash_attn.ops.triton.rotary import apply_rotary
else:
    RotaryEmbedding = object

logger = logging.get_logger(__name__)

_CHECKPOINT_FOR_DOC = "answerdotai/ModernBERT-base"
_CONFIG_FOR_DOC = "ModernBertConfig"


class ApplyRotaryEmbUnpad(torch.autograd.Function):
    @staticmethod
    def forward(
        ctx,
        qkv,
        cos,
        sin,
        cu_seqlens: Optional[torch.Tensor] = None,
        max_seqlen: Optional[int] = None,
    ):
        # (total_nnz, 3, nheads, headdim)
        qkv = qkv.contiguous()
        total_nnz, _three, _nheads, headdim = qkv.shape
        # We need qkv to be contiguous so that when we reshape to combine (3, nheads) dimensions,
        # we get the same tensor
        # qk = rearrange(qkv[:, :2], "b_s t h d -> b_s (t h) d")
        qk = qkv[:, :2].view(total_nnz, -1, headdim)
        apply_rotary(
            qk,
            cos,
            sin,
            seqlen_offsets=0,
            cu_seqlens=cu_seqlens,
            max_seqlen=max_seqlen,
            interleaved=False,
            inplace=True,
        )

        ctx.save_for_backward(cos, sin, cu_seqlens)
        ctx.max_seqlen = max_seqlen
        return qkv

    @staticmethod
    def backward(ctx, do):
        cos, sin, cu_seqlens = ctx.saved_tensors
        do = do.contiguous()
        total_nnz, _three, _nheads, headdim = do.shape
        # We need dqkv to be contiguous so that when we reshape to combine (3, nheads) dimensions,
        # we get the same tensor
        dqk = do[:, :2].view(total_nnz, -1, headdim)
        apply_rotary(
            dqk,
            cos,
            sin,
            seqlen_offsets=0,
            cu_seqlens=cu_seqlens,
            max_seqlen=ctx.max_seqlen,
            interleaved=False,
            inplace=True,
            conjugate=True,
        )

        return do, None, None, None, None, None, None


def apply_rotary_unpadded(
    qkv,
    cos,
    sin,
    cu_seqlens: Optional[torch.Tensor] = None,
    max_seqlen: Optional[int] = None,
):
    """
    Arguments:
        qkv: (total_nnz, 3, nheads, headdim) - input tensor for packed QKV.
        cos, sin: (seqlen_rotary, rotary_dim / 2)
        interleaved: if True, rotate pairs of even and odd dimensions (GPT-J style) instead
            of 1st half and 2nd half (GPT-NeoX style).
        inplace: if True, apply rotary embedding in-place.
        seqlen_offsets: (batch_size,) or int. Each sequence in x is shifted by this amount.
            Most commonly used in inference when we have KV cache.
        cu_seqlens: (batch + 1,) or None
        max_seqlen: int
    Return:
        out: (total_nnz, dim)
    rotary_dim must be <= headdim
    Apply rotary embedding to the first rotary_dim of x.
    """
    return ApplyRotaryEmbUnpad.apply(qkv, cos, sin, cu_seqlens, max_seqlen)


class ModernBertUnpaddedRotaryEmbedding(RotaryEmbedding):
    """
    The rotary position embeddings applied directly to unpadded sequences.
    """

    def __init__(
        self,
        dim: int,
        base: float = 10000.0,
        max_seqlen: Optional[int] = None,
        device: Optional[torch.device] = None,
        dtype: Optional[torch.dtype] = None,
    ):
        """
        max_seqlen: if max_seqlen, device, and dtype are provided, we precompute the cos_sin_cache
            up to max_seqlen. If the max_seqlen, device, or dtype during training/inference differ,
            the cos_sin_cache wll be recomputed during the forward pass.
        """
        super().__init__(dim=dim, base=base, pos_idx_in_fp32=True, device=device, interleaved=False)
        self.max_seqlen = max_seqlen

        if max_seqlen is not None and device is not None and dtype is not None:
            self._update_cos_sin_cache(max_seqlen, device=device, dtype=dtype)

    def forward(
        self,
        qkv: torch.Tensor,
        cu_seqlens: torch.Tensor,
        max_seqlen: Optional[int] = None,
    ) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
        """
        Apply rotary embedding *inplace* to qkv.
        qkv: (total_nnz, 3, nheads, headdim)
        cu_seqlens: (batch + 1,) cumulative sequence lengths
        max_seqlen: int max seq length in the batch
        """
        if max_seqlen is not None:
            self._update_cos_sin_cache(max_seqlen, device=qkv.device, dtype=qkv.dtype)

        qkv = apply_rotary_unpadded(
            qkv,
            self._cos_cached,
            self._sin_cached,
            cu_seqlens=cu_seqlens,
            max_seqlen=max_seqlen,
        )

        return qkv

    def extra_repr(self) -> str:
        return f"dim={self.dim}, base={self.base}, scale_base={self.scale_base}"


class ModernBertEmbeddings(nn.Module):
    """
    Same as BertEmbeddings with a tiny tweak for positional embeddings indexing.
    """

    def __init__(self, config: ModernBertConfig):
        super().__init__()
        self.config = config
        self.tok_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
        self.norm = nn.LayerNorm(config.hidden_size, eps=config.norm_eps, bias=config.norm_bias)
        self.drop = nn.Dropout(config.embedding_dropout)

    @torch.compile(dynamic=True)
    def compiled_embeddings(self, input_ids: torch.LongTensor) -> torch.Tensor:
        return self.drop(self.norm(self.tok_embeddings(input_ids)))

    def forward(
        self, input_ids: torch.LongTensor = None, inputs_embeds: Optional[torch.Tensor] = None
    ) -> torch.Tensor:
        if inputs_embeds is not None:
            hidden_states = self.drop(self.norm(inputs_embeds))
        else:
            hidden_states = (
                self.compiled_embeddings(input_ids)
                if self.config.reference_compile
                else self.drop(self.norm(self.tok_embeddings(input_ids)))
            )
        return hidden_states


class ModernBertMLP(nn.Module):
    """Applies the GLU at the end of each ModernBERT layer.

    Compared to the default BERT architecture, this block replaces :class:`~transformers.model.bert.modeling_bert.BertIntermediate`
    and :class:`~transformers.model.bert.modeling_bert.SelfOutput` with a single module that has similar functionality.
    """

    def __init__(self, config: ModernBertConfig):
        super().__init__()
        self.config = config
        self.Wi = nn.Linear(config.hidden_size, int(config.intermediate_size) * 2, bias=config.mlp_bias)
        self.act = ACT2FN[config.hidden_activation]
        self.drop = nn.Dropout(config.mlp_dropout)
        self.Wo = nn.Linear(config.intermediate_size, config.hidden_size, bias=config.mlp_bias)

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        input, gate = self.Wi(hidden_states).chunk(2, dim=-1)
        return self.Wo(self.drop(self.act(input) * gate))


class ModernBertRotaryEmbedding(nn.Module):
    def __init__(self, config: ModernBertConfig, dim: int, base: float, device: Optional[torch.device] = None):
        super().__init__()
        # BC: "rope_type" was originally "type"
        if hasattr(config, "rope_scaling") and config.rope_scaling is not None:
            self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type"))
        else:
            self.rope_type = "default"
        self.max_seq_len_cached = config.max_position_embeddings
        self.original_max_seq_len = config.max_position_embeddings

        self.config = config
        self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
        inv_freq, self.attention_scaling = self.rope_init_fn(None, device, dim=dim, base=base)
        self.register_buffer("inv_freq", inv_freq, persistent=False)
        self.original_inv_freq = self.inv_freq

    def _dynamic_frequency_update(self, position_ids, device):
        """
        dynamic RoPE layers should recompute `inv_freq` in the following situations:
        1 - growing beyond the cached sequence length (allow scaling)
        2 - the current sequence length is in the original scale (avoid losing precision with small sequences)
        """
        seq_len = torch.max(position_ids) + 1
        if seq_len > self.max_seq_len_cached:  # growth
            inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device, seq_len=seq_len)
            self.register_buffer("inv_freq", inv_freq, persistent=False)  # TODO joao: may break with compilation
            self.max_seq_len_cached = seq_len

        if seq_len < self.original_max_seq_len and self.max_seq_len_cached > self.original_max_seq_len:  # reset
            # This .to() is needed if the model has been moved to a device after being initialized (because
            # the buffer is automatically moved, but not the original copy)
            self.original_inv_freq = self.original_inv_freq.to(device)
            self.register_buffer("inv_freq", self.original_inv_freq, persistent=False)
            self.max_seq_len_cached = self.original_max_seq_len

    @torch.no_grad()
    def forward(self, x, position_ids):
        if "dynamic" in self.rope_type:
            self._dynamic_frequency_update(position_ids, device=x.device)

        # Core RoPE block
        inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1)
        position_ids_expanded = position_ids[:, None, :].float()
        # Force float32 (see https://github.com/huggingface/transformers/pull/29285)
        device_type = x.device.type
        device_type = device_type if isinstance(device_type, str) and device_type != "mps" else "cpu"
        with torch.autocast(device_type=device_type, enabled=False):
            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
            emb = torch.cat((freqs, freqs), dim=-1)
            cos = emb.cos()
            sin = emb.sin()

        # Advanced RoPE types (e.g. yarn) apply a post-processing scaling factor, equivalent to scaling attention
        cos = cos * self.attention_scaling
        sin = sin * self.attention_scaling

        return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)


def rotate_half(x):
    """Rotates half the hidden dims of the input."""
    x1 = x[..., : x.shape[-1] // 2]
    x2 = x[..., x.shape[-1] // 2 :]
    return torch.cat((-x2, x1), dim=-1)


def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
    """Applies Rotary Position Embedding to the query and key tensors.

    Args:
        q (`torch.Tensor`): The query tensor.
        k (`torch.Tensor`): The key tensor.
        cos (`torch.Tensor`): The cosine part of the rotary embedding.
        sin (`torch.Tensor`): The sine part of the rotary embedding.
        position_ids (`torch.Tensor`, *optional*):
            Deprecated and unused.
        unsqueeze_dim (`int`, *optional*, defaults to 1):
            The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
            sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
            that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
            k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
            cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
            the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
    Returns:
        `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
    """
    cos = cos.unsqueeze(unsqueeze_dim)
    sin = sin.unsqueeze(unsqueeze_dim)
    q_embed = (q * cos) + (rotate_half(q) * sin)
    k_embed = (k * cos) + (rotate_half(k) * sin)
    return q_embed, k_embed


def eager_attention_forward(
    module: "ModernBertAttention",
    qkv: torch.Tensor,
    attention_mask: torch.Tensor,
    sliding_window_mask: torch.Tensor,
    position_ids: Optional[torch.LongTensor],
    local_attention: Tuple[int, int],
    bs: int,
    dim: int,
    output_attentions: Optional[bool] = False,
    **_kwargs,
) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]:
    # qkv: [batch_size, seqlen, 3, nheads, headdim]
    cos, sin = module.rotary_emb(qkv, position_ids=position_ids)
    query, key, value = qkv.transpose(3, 1).unbind(dim=2)
    # query, key, value: [batch_size, heads, seq_len, head_dim]
    query, key = apply_rotary_pos_emb(query, key, cos, sin)

    scale = module.head_dim**-0.5
    attn_weights = torch.matmul(query, key.transpose(2, 3)) * scale

    if local_attention != (-1, -1):
        attention_mask = sliding_window_mask

    attn_weights = attn_weights + attention_mask

    # upcast attention to fp32
    attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
    attn_weights = nn.functional.dropout(attn_weights, p=module.attention_dropout, training=module.training)
    attn_output = torch.matmul(attn_weights, value)
    attn_output = attn_output.transpose(1, 2).contiguous()
    attn_output = attn_output.view(bs, -1, dim)
    if output_attentions:
        return (attn_output, attn_weights)
    return (attn_output,)


def flash_attention_forward(
    module: "ModernBertAttention",
    qkv: torch.Tensor,
    rotary_emb: ModernBertUnpaddedRotaryEmbedding,
    cu_seqlens: torch.Tensor,
    max_seqlen: int,
    local_attention: Tuple[int, int],
    bs: int,
    dim: int,
    target_dtype: torch.dtype = torch.bfloat16,
    **_kwargs,
) -> Tuple[torch.Tensor]:
    # (total_seqlen, 3, nheads, headdim)
    qkv = rotary_emb(qkv, cu_seqlens=cu_seqlens, max_seqlen=max_seqlen)

    convert_dtype = qkv.dtype not in (torch.float16, torch.bfloat16)
    if convert_dtype:
        # FA2 implementation only supports fp16 and bf16. If FA2 is supported,
        # bfloat16 must be supported as of FA2 2.5.7. (Turing GPUs not supported)
        orig_dtype = qkv.dtype
        qkv = qkv.to(target_dtype)

        attn = flash_attn_varlen_qkvpacked_func(
            qkv,
            cu_seqlens=cu_seqlens,
            max_seqlen=max_seqlen,
            dropout_p=module.attention_dropout if module.training else 0.0,
            deterministic=module.deterministic_flash_attn,
            window_size=local_attention,
        )
        attn = attn.to(orig_dtype)  # type: ignore
    else:
        attn = flash_attn_varlen_qkvpacked_func(
            qkv,
            cu_seqlens=cu_seqlens,
            max_seqlen=max_seqlen,
            dropout_p=module.attention_dropout if module.training else 0.0,
            deterministic=module.deterministic_flash_attn,
            window_size=local_attention,
        )
    return (attn.view(bs, dim),)


def sdpa_attention_forward(
    module: "ModernBertAttention",
    qkv: torch.Tensor,
    attention_mask: torch.Tensor,
    sliding_window_mask: torch.Tensor,
    position_ids: Optional[torch.LongTensor],
    local_attention: Tuple[int, int],
    bs: int,
    dim: int,
    **_kwargs,
) -> Tuple[torch.Tensor]:
    # qkv: [batch_size, seqlen, 3, nheads, headdim]
    cos, sin = module.rotary_emb(qkv, position_ids=position_ids)
    query, key, value = qkv.transpose(3, 1).unbind(dim=2)
    # query, key, value: [batch_size, heads, seq_len, head_dim]
    query, key = apply_rotary_pos_emb(query, key, cos, sin)

    if local_attention != (-1, -1):
        attention_mask = sliding_window_mask

    attn_output = (
        F.scaled_dot_product_attention(
            query,
            key,
            value,
            dropout_p=module.attention_dropout if module.training else 0.0,
            attn_mask=attention_mask,
        )
        .transpose(1, 2)
        .contiguous()
    )
    attn_output = attn_output.view(bs, -1, dim)
    return (attn_output,)


MODERNBERT_ATTENTION_FUNCTION = {
    "flash_attention_2": flash_attention_forward,
    "eager": eager_attention_forward,
    "sdpa": sdpa_attention_forward,
}


class ModernBertAttention(nn.Module):
    """Performs multi-headed self attention on a batch of unpadded sequences.

    If Flash Attention 2 is installed, this module uses Flash Attention to improve throughput.
    If Flash Attention 2 is not installed, the implementation will use PyTorch's SDPA kernel,
    which requires padding and unpadding inputs, adding some overhead.

    See `forward` method for additional details.
    """

    def __init__(self, config: ModernBertConfig, layer_id: Optional[int] = None):
        super().__init__()
        self.config = config
        self.layer_id = layer_id

        if config.hidden_size % config.num_attention_heads != 0:
            raise ValueError(
                f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention heads ({config.num_attention_heads})"
            )

        self.attention_dropout = config.attention_dropout
        self.deterministic_flash_attn = config.deterministic_flash_attn
        self.num_heads = config.num_attention_heads
        self.head_dim = config.hidden_size // config.num_attention_heads
        self.all_head_size = self.head_dim * self.num_heads
        self.Wqkv = nn.Linear(config.hidden_size, 3 * self.all_head_size, bias=config.attention_bias)

        if layer_id % config.global_attn_every_n_layers != 0:
            self.local_attention = (config.local_attention // 2, config.local_attention // 2)
        else:
            self.local_attention = (-1, -1)

        rope_theta = config.global_rope_theta
        max_position_embeddings = config.max_position_embeddings
        if self.local_attention != (-1, -1):
            if config.local_rope_theta is not None:
                rope_theta = config.local_rope_theta
            max_position_embeddings = config.local_attention

        if config._attn_implementation == "flash_attention_2":
            self.rotary_emb = ModernBertUnpaddedRotaryEmbedding(
                dim=self.head_dim, max_seqlen=max_position_embeddings, base=rope_theta
            )
        else:
            self.rotary_emb = ModernBertRotaryEmbedding(config=config, dim=self.head_dim, base=rope_theta)

        self.Wo = nn.Linear(config.hidden_size, config.hidden_size, bias=config.attention_bias)
        self.out_drop = nn.Dropout(config.attention_dropout) if config.attention_dropout > 0.0 else nn.Identity()
        self.pruned_heads = set()

    def forward(
        self,
        hidden_states: torch.Tensor,
        output_attentions: Optional[bool] = False,
        **kwargs,
    ) -> torch.Tensor:
        qkv = self.Wqkv(hidden_states)

        bs = hidden_states.shape[0]
        if self.config._attn_implementation == "flash_attention_2":
            qkv = qkv.view(-1, 3, self.num_heads, self.head_dim)
        else:
            qkv = qkv.view(bs, -1, 3, self.num_heads, self.head_dim)

        attn_outputs = MODERNBERT_ATTENTION_FUNCTION[self.config._attn_implementation](
            self,
            qkv=qkv,
            rotary_emb=self.rotary_emb,
            local_attention=self.local_attention,
            bs=bs,
            dim=self.all_head_size,
            output_attentions=output_attentions,
            **kwargs,
        )
        hidden_states = attn_outputs[0]
        hidden_states = self.out_drop(self.Wo(hidden_states))

        return (hidden_states,) + attn_outputs[1:]  # add attentions if outputted


class ModernBertEncoderLayer(nn.Module):
    def __init__(self, config: ModernBertConfig, layer_id: Optional[int] = None):
        super().__init__()
        self.config = config
        if layer_id == 0:
            self.attn_norm = nn.Identity()
        else:
            self.attn_norm = nn.LayerNorm(config.hidden_size, eps=config.norm_eps, bias=config.norm_bias)
        self.attn = ModernBertAttention(config=config, layer_id=layer_id)
        self.mlp_norm = nn.LayerNorm(config.hidden_size, eps=config.norm_eps, bias=config.norm_bias)
        self.mlp = ModernBertMLP(config)

    @torch.compile(dynamic=True)
    def compiled_mlp(self, hidden_states: torch.Tensor) -> torch.Tensor:
        return self.mlp(self.mlp_norm(hidden_states))

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        sliding_window_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        cu_seqlens: Optional[torch.Tensor] = None,
        max_seqlen: Optional[int] = None,
        output_attentions: Optional[bool] = False,
    ) -> torch.Tensor:
        attn_outputs = self.attn(
            self.attn_norm(hidden_states),
            attention_mask=attention_mask,
            sliding_window_mask=sliding_window_mask,
            position_ids=position_ids,
            cu_seqlens=cu_seqlens,
            max_seqlen=max_seqlen,
            output_attentions=output_attentions,
        )
        hidden_states = hidden_states + attn_outputs[0]
        mlp_output = (
            self.compiled_mlp(hidden_states)
            if self.config.reference_compile
            else self.mlp(self.mlp_norm(hidden_states))
        )
        hidden_states = hidden_states + mlp_output

        return (hidden_states,) + attn_outputs[1:]  # add attentions if outputted


MODERNBERT_START_DOCSTRING = r"""
    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
    etc.)

    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
    and behavior.

    Parameters:
        config ([`ModernBertConfig`]):
            Model configuration class with all the parameters of the model. Initializing with a config file does not
            load the weights associated with the model, only the configuration. Check out the
            [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""


@add_start_docstrings(
    "The bare ModernBert Model outputting raw hidden-states without any specific head on top.",
    MODERNBERT_START_DOCSTRING,
)
class ModernBertPreTrainedModel(PreTrainedModel):
    config_class = ModernBertConfig
    base_model_prefix = "model"
    supports_gradient_checkpointing = True
    _no_split_modules = ["ModernBertEmbeddings", "ModernBertEncoderLayer"]
    _supports_flash_attn_2 = True
    _supports_sdpa = True
    _supports_flex_attn = False

    def _init_weights(self, module: nn.Module):
        cutoff_factor = self.config.initializer_cutoff_factor
        if cutoff_factor is None:
            cutoff_factor = 3

        def init_weight(module: nn.Module, std: float):
            nn.init.trunc_normal_(
                module.weight,
                mean=0.0,
                std=std,
                a=-cutoff_factor * std,
                b=cutoff_factor * std,
            )

            if isinstance(module, nn.Linear):
                if module.bias is not None:
                    nn.init.zeros_(module.bias)

        stds = {
            "in": self.config.initializer_range,
            "out": self.config.initializer_range / math.sqrt(2.0 * self.config.num_hidden_layers),
            "embedding": self.config.initializer_range,
            "final_out": self.config.hidden_size**-0.5,
        }

        if isinstance(module, ModernBertEmbeddings):
            init_weight(module.tok_embeddings, stds["embedding"])
        elif isinstance(module, ModernBertMLP):
            init_weight(module.Wi, stds["in"])
            init_weight(module.Wo, stds["out"])
        elif isinstance(module, ModernBertAttention):
            init_weight(module.Wqkv, stds["in"])
            init_weight(module.Wo, stds["out"])
        elif isinstance(module, ModernBertPredictionHead):
            init_weight(module.dense, stds["out"])
        elif isinstance(module, ModernBertForMaskedLM):
            init_weight(module.decoder, stds["out"])
        elif isinstance(module, (ModernBertForSequenceClassification, ModernBertForTokenClassification)):
            init_weight(module.classifier, stds["final_out"])

    @classmethod
    def _autoset_attn_implementation(
        cls,
        config,
        use_flash_attention_2: bool = False,
        torch_dtype: Optional[torch.dtype] = None,
        device_map: Optional[Union[str, Dict[str, int]]] = None,
        check_device_map: bool = True,
    ):
        # If the user didn't specify anything, try to use flash_attention_2 if available.
        # Otherwise we fall back to the default SDPA -> Eager from the super() method.
        # ModernBert's FA2 implementation correctly handles non-fp16/bf16 dtypes, we don't
        # need the FA2 warning for non-fp16/bf16 dtypes so we set fp16 for the FA2 check.
        if config._attn_implementation_internal is None:
            config._attn_implementation_internal = "flash_attention_2"
            try:
                return cls._check_and_enable_flash_attn_2(
                    config,
                    torch_dtype=torch.float16,
                    device_map=device_map,
                    hard_check_only=False,
                    check_device_map=check_device_map,
                )
            except (ValueError, ImportError):
                config._attn_implementation_internal = None
        return super()._autoset_attn_implementation(
            config,
            use_flash_attention_2=use_flash_attention_2,
            torch_dtype=torch.float16,
            device_map=device_map,
            check_device_map=check_device_map,
        )

    def _maybe_set_compile(self):
        if self.config.reference_compile is False:
            return

        if hasattr(self, "hf_device_map") and len(self.hf_device_map) > 1:
            if self.config.reference_compile:
                logger.warning_once(
                    "If `accelerate` split the model across devices, `torch.compile` will not work. "
                    "Falling back to non-compiled mode."
                )
            self.config.reference_compile = False

        if self.device.type == "mps":
            if self.config.reference_compile:
                logger.warning_once(
                    "Compiling the model with `torch.compile` and using a `torch.mps` device is not supported. "
                    "Falling back to non-compiled mode."
                )
            self.config.reference_compile = False

        if self.device.type == "cpu":
            if self.config.reference_compile:
                logger.warning_once(
                    "Compiling the model with `torch.compile` and using a `torch.cpu` device is not supported. "
                    "Falling back to non-compiled mode."
                )
            self.config.reference_compile = False

        if self.config.reference_compile is None:
            self.config.reference_compile = is_triton_available()

    def resize_token_embeddings(self, *args, **kwargs):
        model_embeds = super().resize_token_embeddings(*args, **kwargs)

        if self.config.reference_compile in {True, None}:
            if self.config.reference_compile:
                logger.warning_once(
                    "Resizing token embeddings with `torch.compile` is not supported. Falling back to non-compiled mode."
                )
            self.config.reference_compile = False

        return model_embeds


def _unpad_modernbert_input(
    inputs: torch.Tensor,
    attention_mask: torch.Tensor,
    position_ids: Optional[torch.Tensor] = None,
    labels: Optional[torch.Tensor] = None,
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, int, Optional[torch.Tensor], Optional[torch.Tensor]]:
    """
    Remove padding from input sequences.

    Args:
        inputs: (batch, seqlen, ...) or (batch, seqlen)
        attention_mask: (batch, seqlen), bool / int, 1 means valid and 0 means not valid.
        position_ids: (batch, seqlen), int, position ids
        labels: (batch, seqlen), int, labels

    Returns:
        unpadded_inputs: (total_nnz, ...), where total_nnz = number of tokens selected in attention_mask.
        indices: (total_nnz)
        cu_seqlens: (batch + 1), the cumulative sequence lengths
        max_seqlen_in_batch: int
        unpadded_position_ids: (total_nnz) or None
        unpadded_labels: (total_nnz) or None
    """
    seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
    indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
    max_seqlen_in_batch = int(seqlens_in_batch.max().item())
    cu_seqlens = torch.nn.functional.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.int32), (1, 0))

    if inputs.dim() == 2:
        unpadded_inputs = inputs.flatten()[indices]
    else:
        batch, seqlen, *rest = inputs.shape
        shape = batch * seqlen
        unpadded_inputs = inputs.view(shape, *rest)[indices]

    unpadded_position_ids = position_ids.flatten()[indices] if position_ids is not None else None
    unpadded_labels = labels.flatten()[indices] if labels is not None else None

    return unpadded_inputs, indices, cu_seqlens, max_seqlen_in_batch, unpadded_position_ids, unpadded_labels


def _pad_modernbert_output(
    inputs: torch.Tensor,
    indices: torch.Tensor,
    batch: int,
    seqlen: int,
) -> torch.Tensor:
    """
    Add padding to sequences.

    Args:
        inputs: (total_nnz, ...) or (total_nnz,), where total_nnz = number of tokens selected in attention_mask.
        indices: (total_nnz)
        batch: int, batch size
        seqlen: int, max sequence length

    Returns:
        padded_inputs: (batch, seqlen, ...) or (batch, seqlen)
    """
    if inputs.dim() == 1:
        output = torch.zeros(batch * seqlen, dtype=inputs.dtype, device=inputs.device)
        output[indices] = inputs
        padded_inputs = output.view(batch, seqlen)
    else:
        _, *rest = inputs.shape
        output = torch.zeros(batch * seqlen, *rest, dtype=inputs.dtype, device=inputs.device)
        output[indices] = inputs
        padded_inputs = output.view(batch, seqlen, *rest)

    return padded_inputs


MODERNBERT_INPUTS_DOCSTRING = r"""
    Args:
        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
            Indices of input sequence tokens in the vocabulary. With Flash Attention 2.0, padding will be ignored
            by default should you provide it.

            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.

            [What are input IDs?](../glossary#input-ids)
        attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

            - 1 for tokens that are **not masked**,
            - 0 for tokens that are **masked**.

            [What are attention masks?](../glossary#attention-mask)

            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.

            If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
            and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
            information on the default strategy.

            - 1 indicates the head is **not masked**,
            - 0 indicates the head is **masked**.
        sliding_window_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
            Mask to avoid performing attention on padding or far-away tokens. In ModernBert, only every few layers
            perform global attention, while the rest perform local attention. This mask is used to avoid attending to
            far-away tokens in the local attention layers when not using Flash Attention.
        position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
            config.n_positions - 1]`.

            [What are position IDs?](../glossary#position-ids)
        inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
            model's internal embedding lookup matrix.
        indices (`torch.Tensor` of shape `(total_unpadded_tokens,)`, *optional*):
            Indices of the non-padding tokens in the input sequence. Used for unpadding the output.
        cu_seqlens (`torch.Tensor` of shape `(batch + 1,)`, *optional*):
            Cumulative sequence lengths of the input sequences. Used to index the unpadded tensors.
        max_seqlen (`int`, *optional*):
            Maximum sequence length in the batch excluding padding tokens. Used to unpad input_ids and pad output tensors.
        batch_size (`int`, *optional*):
            Batch size of the input sequences. Used to pad the output tensors.
        seq_len (`int`, *optional*):
            Sequence length of the input sequences including padding tokens. Used to pad the output tensors.
        output_attentions (`bool`, *optional*):
            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
            tensors for more detail.
        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""


@add_start_docstrings(
    "The bare ModernBert Model outputting raw hidden-states without any specific head on top.",
    MODERNBERT_START_DOCSTRING,
)
class ModernBertModel(ModernBertPreTrainedModel):
    def __init__(self, config: ModernBertConfig):
        super().__init__(config)
        self.config = config
        self.embeddings = ModernBertEmbeddings(config)
        self.layers = nn.ModuleList(
            [ModernBertEncoderLayer(config, layer_id) for layer_id in range(config.num_hidden_layers)]
        )
        self.final_norm = nn.LayerNorm(config.hidden_size, eps=config.norm_eps, bias=config.norm_bias)
        self.gradient_checkpointing = False
        self.post_init()

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

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

    @add_start_docstrings_to_model_forward(MODERNBERT_INPUTS_DOCSTRING)
    @add_code_sample_docstrings(
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=BaseModelOutput,
        config_class=_CONFIG_FOR_DOC,
    )
    def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        sliding_window_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        inputs_embeds: Optional[torch.Tensor] = None,
        indices: Optional[torch.Tensor] = None,
        cu_seqlens: Optional[torch.Tensor] = None,
        max_seqlen: Optional[int] = None,
        batch_size: Optional[int] = None,
        seq_len: Optional[int] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple[torch.Tensor, ...], BaseModelOutput]:
        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.use_return_dict

        if (input_ids is None) ^ (inputs_embeds is not None):
            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")

        all_hidden_states = () if output_hidden_states else None
        all_self_attentions = () if output_attentions else None

        self._maybe_set_compile()

        if input_ids is not None:
            self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)

        if batch_size is None and seq_len is None:
            if inputs_embeds is not None:
                batch_size, seq_len = inputs_embeds.shape[:2]
            else:
                batch_size, seq_len = input_ids.shape[:2]
        device = input_ids.device if input_ids is not None else inputs_embeds.device

        if attention_mask is None:
            attention_mask = torch.ones((batch_size, seq_len), device=device, dtype=torch.bool)

        repad = False
        if self.config._attn_implementation == "flash_attention_2":
            if indices is None and cu_seqlens is None and max_seqlen is None:
                repad = True
                if inputs_embeds is None:
                    with torch.no_grad():
                        input_ids, indices, cu_seqlens, max_seqlen, *_ = _unpad_modernbert_input(
                            inputs=input_ids, attention_mask=attention_mask
                        )
                else:
                    inputs_embeds, indices, cu_seqlens, max_seqlen, *_ = _unpad_modernbert_input(
                        inputs=inputs_embeds, attention_mask=attention_mask
                    )
        else:
            if position_ids is None:
                position_ids = torch.arange(seq_len, device=device).unsqueeze(0)

            attention_mask, sliding_window_mask = self._update_attention_mask(
                attention_mask, output_attentions=output_attentions
            )

        hidden_states = self.embeddings(input_ids=input_ids, inputs_embeds=inputs_embeds)

        for encoder_layer in self.layers:
            if output_hidden_states:
                all_hidden_states = all_hidden_states + (hidden_states,)

            if self.gradient_checkpointing and self.training:
                layer_outputs = self._gradient_checkpointing_func(
                    encoder_layer.__call__,
                    hidden_states,
                    attention_mask,
                    sliding_window_mask,
                    position_ids,
                    cu_seqlens,
                    max_seqlen,
                    output_attentions,
                )
            else:
                layer_outputs = encoder_layer(
                    hidden_states,
                    attention_mask=attention_mask,
                    sliding_window_mask=sliding_window_mask,
                    position_ids=position_ids,
                    cu_seqlens=cu_seqlens,
                    max_seqlen=max_seqlen,
                    output_attentions=output_attentions,
                )
            hidden_states = layer_outputs[0]
            if output_attentions and len(layer_outputs) > 1:
                all_self_attentions = all_self_attentions + (layer_outputs[1],)

        if output_hidden_states:
            all_hidden_states = all_hidden_states + (hidden_states,)

        hidden_states = self.final_norm(hidden_states)

        if repad:
            hidden_states = _pad_modernbert_output(
                inputs=hidden_states, indices=indices, batch=batch_size, seqlen=seq_len
            )
            if all_hidden_states is not None:
                all_hidden_states = tuple(
                    _pad_modernbert_output(inputs=hs, indices=indices, batch=batch_size, seqlen=seq_len)
                    for hs in all_hidden_states
                )

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

    def _update_attention_mask(self, attention_mask: torch.Tensor, output_attentions: bool) -> torch.Tensor:
        if output_attentions:
            if self.config._attn_implementation == "sdpa":
                logger.warning_once(
                    "Outputting attentions is only supported with the 'eager' attention implementation, "
                    'not with "sdpa". Falling back to `attn_implementation="eager"`.'
                )
                self.config._attn_implementation = "eager"
            elif self.config._attn_implementation != "eager":
                logger.warning_once(
                    "Outputting attentions is only supported with the eager attention implementation, "
                    f'not with {self.config._attn_implementation}. Consider setting `attn_implementation="eager"`.'
                    " Setting `output_attentions=False`."
                )

        global_attention_mask = _prepare_4d_attention_mask(attention_mask, self.dtype)

        # Create position indices
        rows = torch.arange(global_attention_mask.shape[2]).unsqueeze(0)
        # Calculate distance between positions
        distance = torch.abs(rows - rows.T)

        # Create sliding window mask (1 for positions within window, 0 outside)
        window_mask = (
            (distance <= self.config.local_attention // 2).unsqueeze(0).unsqueeze(0).to(attention_mask.device)
        )
        # Combine with existing mask
        sliding_window_mask = global_attention_mask.masked_fill(window_mask.logical_not(), torch.finfo(self.dtype).min)

        return global_attention_mask, sliding_window_mask


class ModernBertPredictionHead(nn.Module):
    def __init__(self, config: ModernBertConfig):
        super().__init__()
        self.config = config
        self.dense = nn.Linear(config.hidden_size, config.hidden_size, config.classifier_bias)
        self.act = ACT2FN[config.classifier_activation]
        self.norm = nn.LayerNorm(config.hidden_size, eps=config.norm_eps, bias=config.norm_bias)

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        return self.norm(self.act(self.dense(hidden_states)))


@add_start_docstrings(
    "The ModernBert Model with a decoder head on top that is used for masked language modeling.",
    MODERNBERT_START_DOCSTRING,
)
class ModernBertForMaskedLM(ModernBertPreTrainedModel):
    _tied_weights_keys = ["decoder.weight"]

    def __init__(self, config: ModernBertConfig):
        super().__init__(config)
        self.config = config
        self.model = ModernBertModel(config)
        self.head = ModernBertPredictionHead(config)
        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=config.decoder_bias)

        self.sparse_prediction = self.config.sparse_prediction
        self.sparse_pred_ignore_index = self.config.sparse_pred_ignore_index

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

    def get_output_embeddings(self):
        return self.decoder

    def set_output_embeddings(self, new_embeddings: nn.Linear):
        self.decoder = new_embeddings

    @torch.compile(dynamic=True)
    def compiled_head(self, output: torch.Tensor) -> torch.Tensor:
        return self.decoder(self.head(output))

    @add_start_docstrings_to_model_forward(MODERNBERT_INPUTS_DOCSTRING)
    @add_code_sample_docstrings(
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=MaskedLMOutput,
        config_class=_CONFIG_FOR_DOC,
    )
    def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        sliding_window_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.Tensor] = None,
        inputs_embeds: Optional[torch.Tensor] = None,
        labels: Optional[torch.Tensor] = None,
        indices: Optional[torch.Tensor] = None,
        cu_seqlens: Optional[torch.Tensor] = None,
        max_seqlen: Optional[int] = None,
        batch_size: Optional[int] = None,
        seq_len: Optional[int] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        **kwargs,
    ) -> Union[Tuple[torch.Tensor], MaskedLMOutput]:
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        self._maybe_set_compile()

        if self.config._attn_implementation == "flash_attention_2":
            if indices is None and cu_seqlens is None and max_seqlen is None:
                if batch_size is None and seq_len is None:
                    if inputs_embeds is not None:
                        batch_size, seq_len = inputs_embeds.shape[:2]
                    else:
                        batch_size, seq_len = input_ids.shape[:2]
                device = input_ids.device if input_ids is not None else inputs_embeds.device

                if attention_mask is None:
                    attention_mask = torch.ones((batch_size, seq_len), device=device, dtype=torch.bool)

                if inputs_embeds is None:
                    with torch.no_grad():
                        input_ids, indices, cu_seqlens, max_seqlen, position_ids, labels = _unpad_modernbert_input(
                            inputs=input_ids, attention_mask=attention_mask, position_ids=position_ids, labels=labels
                        )
                else:
                    inputs_embeds, indices, cu_seqlens, max_seqlen, position_ids, labels = _unpad_modernbert_input(
                        inputs=inputs_embeds, attention_mask=attention_mask, position_ids=position_ids, labels=labels
                    )

        outputs = self.model(
            input_ids=input_ids,
            attention_mask=attention_mask,
            sliding_window_mask=sliding_window_mask,
            position_ids=position_ids,
            inputs_embeds=inputs_embeds,
            indices=indices,
            cu_seqlens=cu_seqlens,
            max_seqlen=max_seqlen,
            batch_size=batch_size,
            seq_len=seq_len,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )
        last_hidden_state = outputs[0]

        if self.sparse_prediction and labels is not None:
            # flatten labels and output first
            labels = labels.view(-1)
            last_hidden_state = last_hidden_state.view(labels.shape[0], -1)

            # then filter out the non-masked tokens
            mask_tokens = labels != self.sparse_pred_ignore_index
            last_hidden_state = last_hidden_state[mask_tokens]
            labels = labels[mask_tokens]

        logits = (
            self.compiled_head(last_hidden_state)
            if self.config.reference_compile
            else self.decoder(self.head(last_hidden_state))
        )

        loss = None
        if labels is not None:
            loss = self.loss_function(logits, labels, vocab_size=self.config.vocab_size)

        if self.config._attn_implementation == "flash_attention_2":
            with nullcontext() if self.config.repad_logits_with_grad or labels is None else torch.no_grad():
                logits = _pad_modernbert_output(inputs=logits, indices=indices, batch=batch_size, seqlen=seq_len)

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

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


@add_start_docstrings(
    "The ModernBert Model with a sequence classification head on top that performs pooling.",
    MODERNBERT_START_DOCSTRING,
)
class ModernBertForSequenceClassification(ModernBertPreTrainedModel):
    def __init__(self, config: ModernBertConfig):
        super().__init__(config)
        self.num_labels = config.num_labels
        self.config = config

        self.model = ModernBertModel(config)
        self.head = ModernBertPredictionHead(config)
        self.drop = torch.nn.Dropout(config.classifier_dropout)
        self.classifier = nn.Linear(config.hidden_size, config.num_labels)

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

    @add_start_docstrings_to_model_forward(MODERNBERT_INPUTS_DOCSTRING)
    @add_code_sample_docstrings(
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=SequenceClassifierOutput,
        config_class=_CONFIG_FOR_DOC,
    )
    def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        sliding_window_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.Tensor] = None,
        inputs_embeds: Optional[torch.Tensor] = None,
        labels: Optional[torch.Tensor] = None,
        indices: Optional[torch.Tensor] = None,
        cu_seqlens: Optional[torch.Tensor] = None,
        max_seqlen: Optional[int] = None,
        batch_size: Optional[int] = None,
        seq_len: Optional[int] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        **kwargs,
    ) -> Union[Tuple[torch.Tensor], SequenceClassifierOutput]:
        r"""
        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
        """
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        self._maybe_set_compile()

        outputs = self.model(
            input_ids=input_ids,
            attention_mask=attention_mask,
            sliding_window_mask=sliding_window_mask,
            position_ids=position_ids,
            inputs_embeds=inputs_embeds,
            indices=indices,
            cu_seqlens=cu_seqlens,
            max_seqlen=max_seqlen,
            batch_size=batch_size,
            seq_len=seq_len,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )
        last_hidden_state = outputs[0]

        if self.config.classifier_pooling == "cls":
            last_hidden_state = last_hidden_state[:, 0]
        elif self.config.classifier_pooling == "mean":
            last_hidden_state = (last_hidden_state * attention_mask.unsqueeze(-1)).sum(dim=1) / attention_mask.sum(
                dim=1, keepdim=True
            )

        pooled_output = self.head(last_hidden_state)
        pooled_output = self.drop(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 self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
                    self.config.problem_type = "single_label_classification"
                else:
                    self.config.problem_type = "multi_label_classification"

            if self.config.problem_type == "regression":
                loss_fct = 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 = CrossEntropyLoss()
                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
            elif self.config.problem_type == "multi_label_classification":
                loss_fct = BCEWithLogitsLoss()
                loss = loss_fct(logits, labels)

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

        return SequenceClassifierOutput(
            loss=loss,
            logits=logits,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )


@add_start_docstrings(
    "The ModernBert Model with a token classification head on top, e.g. for Named Entity Recognition (NER) tasks.",
    MODERNBERT_START_DOCSTRING,
)
class ModernBertForTokenClassification(ModernBertPreTrainedModel):
    def __init__(self, config: ModernBertConfig):
        super().__init__(config)
        self.num_labels = config.num_labels

        self.model = ModernBertModel(config)
        self.head = ModernBertPredictionHead(config)
        self.drop = torch.nn.Dropout(config.classifier_dropout)
        self.classifier = nn.Linear(config.hidden_size, config.num_labels)

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

    @add_start_docstrings_to_model_forward(MODERNBERT_INPUTS_DOCSTRING)
    @add_code_sample_docstrings(
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=TokenClassifierOutput,
        config_class=_CONFIG_FOR_DOC,
    )
    def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        sliding_window_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.Tensor] = None,
        inputs_embeds: Optional[torch.Tensor] = None,
        labels: Optional[torch.Tensor] = None,
        indices: Optional[torch.Tensor] = None,
        cu_seqlens: Optional[torch.Tensor] = None,
        max_seqlen: Optional[int] = None,
        batch_size: Optional[int] = None,
        seq_len: Optional[int] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple[torch.Tensor], TokenClassifierOutput]:
        r"""
        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
            Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.
        """
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        self._maybe_set_compile()

        outputs = self.model(
            input_ids=input_ids,
            attention_mask=attention_mask,
            sliding_window_mask=sliding_window_mask,
            position_ids=position_ids,
            inputs_embeds=inputs_embeds,
            indices=indices,
            cu_seqlens=cu_seqlens,
            max_seqlen=max_seqlen,
            batch_size=batch_size,
            seq_len=seq_len,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )
        last_hidden_state = outputs[0]

        last_hidden_state = self.head(last_hidden_state)
        last_hidden_state = self.drop(last_hidden_state)
        logits = self.classifier(last_hidden_state)

        loss = None
        if labels is not None:
            loss_fct = CrossEntropyLoss()
            loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))

        if not return_dict:
            output = (logits,) + outputs[1:]
            return ((loss,) + output) if loss is not None else output

        return TokenClassifierOutput(
            loss=loss,
            logits=logits,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )


__all__ = [
    "ModernBertModel",
    "ModernBertPreTrainedModel",
    "ModernBertForMaskedLM",
    "ModernBertForSequenceClassification",
    "ModernBertForTokenClassification",
]
