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# Copyright 2025 Arcee AI 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.

from collections.abc import Callable
from typing import Optional

import torch
from torch import nn

from ... import initialization as init
from ...activations import ACT2FN
from ...cache_utils import Cache, DynamicCache
from ...generation import GenerationMixin
from ...integrations import use_kernel_forward_from_hub, use_kernel_func_from_hub, use_kernelized_func
from ...masking_utils import create_causal_mask, create_sliding_window_causal_mask
from ...modeling_layers import GradientCheckpointingLayer
from ...modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, MoeModelOutputWithPast
from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
from ...processing_utils import Unpack
from ...utils import TransformersKwargs, auto_docstring, can_return_tuple
from ...utils.generic import maybe_autocast, merge_with_config_defaults
from ...utils.output_capturing import capture_outputs
from .configuration_afmoe import AfmoeConfig


class AfmoeRotaryEmbedding(nn.Module):
    inv_freq: torch.Tensor  # fix linting for `register_buffer`

    def __init__(self, config: AfmoeConfig, device=None):
        super().__init__()
        self.max_seq_len_cached = config.max_position_embeddings
        self.original_max_seq_len = config.max_position_embeddings

        self.config = config

        self.rope_type = self.config.rope_parameters["rope_type"]
        rope_init_fn: Callable = self.compute_default_rope_parameters
        if self.rope_type != "default":
            rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
        inv_freq, self.attention_scaling = rope_init_fn(self.config, device)

        self.register_buffer("inv_freq", inv_freq, persistent=False)
        self.register_buffer("original_inv_freq", inv_freq.clone(), persistent=False)

    @staticmethod
    def compute_default_rope_parameters(
        config: AfmoeConfig | None = None,
        device: Optional["torch.device"] = None,
        seq_len: int | None = None,
    ) -> tuple["torch.Tensor", float]:
        """
        Computes the inverse frequencies according to the original RoPE implementation
        Args:
            config ([`~transformers.PreTrainedConfig`]):
                The model configuration.
            device (`torch.device`):
                The device to use for initialization of the inverse frequencies.
            seq_len (`int`, *optional*):
                The current sequence length. Unused for this type of RoPE.
        Returns:
            Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the
            post-processing scaling factor applied to the computed cos/sin (unused in this type of RoPE).
        """
        base = config.rope_parameters["rope_theta"]
        dim = getattr(config, "head_dim", None) or config.hidden_size // config.num_attention_heads

        attention_factor = 1.0  # Unused in this type of RoPE

        # Compute the inverse frequencies
        inv_freq = 1.0 / (
            base ** (torch.arange(0, dim, 2, dtype=torch.int64).to(device=device, dtype=torch.float) / dim)
        )
        return inv_freq, attention_factor

    @torch.no_grad()
    @dynamic_rope_update  # power user: used with advanced RoPE types (e.g. dynamic rope)
    def forward(self, x, position_ids):
        inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device)
        position_ids_expanded = position_ids[:, None, :].float()

        device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
        with maybe_autocast(device_type=device_type, enabled=False):  # Force float32
            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
            emb = torch.cat((freqs, freqs), dim=-1)
            cos = emb.cos() * self.attention_scaling
            sin = emb.sin() * self.attention_scaling

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


@use_kernel_forward_from_hub("RMSNorm")
class AfmoeRMSNorm(nn.Module):
    def __init__(self, hidden_size, eps: float = 1e-6) -> None:
        """
        AfmoeRMSNorm is equivalent to T5LayerNorm
        """
        super().__init__()
        self.weight = nn.Parameter(torch.ones(hidden_size))
        self.variance_epsilon = eps

    def forward(self, hidden_states) -> torch.Tensor:
        input_dtype = hidden_states.dtype
        hidden_states = hidden_states.to(torch.float32)
        variance = hidden_states.pow(2).mean(-1, keepdim=True)
        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
        return (self.weight * hidden_states).to(input_dtype)  # main diff with Llama

    def extra_repr(self):
        return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}"


class AfmoeMLP(nn.Module):
    def __init__(self, config, intermediate_size=None):
        super().__init__()
        self.config = config
        self.hidden_size = config.hidden_size
        self.intermediate_size = config.intermediate_size if intermediate_size is None else intermediate_size
        self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
        self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
        self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
        self.act_fn = ACT2FN[config.hidden_act]

    def forward(self, x):
        down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
        return down_proj


class AfmoeTokenChoiceRouter(nn.Module):
    """
    Token-choice top-K router for MoE routing.

    This router assigns each token to the top-K experts based on sigmoid scores, matching the released checkpoints.
    """

    def __init__(self, config):
        super().__init__()
        self.config = config
        self.top_k = config.num_experts_per_tok
        self.num_experts = config.num_experts
        self.route_scale = config.route_scale
        self.gate = nn.Linear(config.hidden_size, config.num_experts, bias=False)

    def forward(self, hidden_states: torch.Tensor, expert_bias: torch.Tensor):
        _, _, hidden_dim = hidden_states.shape
        hidden_states = hidden_states.view(-1, hidden_dim)

        scores = torch.sigmoid(self.gate(hidden_states).to(torch.float32))

        _, selected_experts = torch.topk(scores + expert_bias, k=self.top_k, dim=1)
        top_scores = scores.gather(dim=1, index=selected_experts)
        denominator = top_scores.sum(dim=-1, keepdim=True) + 1e-20
        top_scores = top_scores / denominator
        top_scores = top_scores * self.route_scale
        return top_scores, selected_experts


class AfmoeExperts(nn.ModuleList):
    """
    Container holding the routed experts.

    This mirrors the Experts pattern used across other MoE models to ease checkpoint conversion.
    """

    def __init__(self, config: AfmoeConfig):
        super().__init__()
        self.top_k = config.num_experts_per_tok
        self.num_experts = config.num_experts
        for _ in range(self.num_experts):
            self.append(AfmoeMLP(config, intermediate_size=config.moe_intermediate_size))

    def forward(
        self, hidden_states: torch.Tensor, selected_experts: torch.Tensor, routing_weights: torch.Tensor
    ) -> torch.Tensor:
        """
        Args:
            hidden_states: (batch, seq, hidden)
            selected_experts: (batch, seq, top_k)
            routing_weights: (batch, seq, top_k)
        """
        batch_size, seq_len, hidden_dim = hidden_states.shape
        if seq_len == 0:
            return hidden_states.new_zeros(batch_size, 0, hidden_dim)
        hidden_states_flat = hidden_states.view(-1, hidden_dim)
        top_k = selected_experts.shape[-1]

        # Map every token routing decision to a unique position so we can process expert by expert.
        token_indices = torch.arange(
            hidden_states_flat.shape[0], device=hidden_states.device, dtype=torch.long
        ).repeat_interleave(top_k)
        expert_indices = selected_experts.reshape(-1)
        routing_weights = routing_weights.reshape(-1)

        sorting = torch.argsort(expert_indices, stable=True)
        token_indices = token_indices[sorting]
        expert_indices = expert_indices[sorting]
        routing_weights = routing_weights[sorting]

        dispatched_tokens = hidden_states_flat.index_select(0, token_indices)
        expert_outputs = torch.zeros_like(dispatched_tokens)

        unique_experts, counts = torch.unique_consecutive(expert_indices, return_counts=True)
        start = 0
        for expert_id, count in zip(unique_experts.tolist(), counts.tolist()):
            if count == 0:
                continue
            end = start + count
            expert_input = dispatched_tokens[start:end]
            expert_output = self[expert_id](expert_input)
            expert_outputs[start:end] = expert_output
            start = end

        weighted_outputs = (expert_outputs.to(torch.float32) * routing_weights.unsqueeze(-1)).to(hidden_states.dtype)
        aggregated = torch.zeros_like(hidden_states_flat)
        scatter_indices = token_indices.unsqueeze(-1).expand_as(weighted_outputs)
        aggregated.scatter_add_(0, scatter_indices, weighted_outputs)
        return aggregated.view(batch_size, seq_len, hidden_dim)


class AfmoeMoE(nn.Module):
    """
    Mixture of Experts (MoE) module for AFMoE.

    This module implements a sparse MoE layer with both shared experts (always active) and
    routed experts (activated based on token-choice routing).
    """

    def __init__(self, config):
        super().__init__()
        self.config = config
        self.router = AfmoeTokenChoiceRouter(config)
        self.shared_experts = AfmoeMLP(config, config.moe_intermediate_size * config.num_shared_experts)
        self.experts = AfmoeExperts(config)
        self.expert_bias = nn.Parameter(torch.zeros(config.num_experts), requires_grad=False)

    def forward(self, hidden_states):
        batch_size, seq_len, hidden_dim = hidden_states.shape
        hidden_states_flat = hidden_states.view(-1, hidden_dim)

        # Get routing decisions
        top_scores, selected_experts = self.router(hidden_states, self.expert_bias)
        top_scores = top_scores.view(batch_size, seq_len, self.config.num_experts_per_tok)
        selected_experts = selected_experts.view(batch_size, seq_len, self.config.num_experts_per_tok)

        # Process through shared experts
        shared_output = self.shared_experts(hidden_states_flat).view(batch_size, seq_len, hidden_dim)
        routed_output = self.experts(hidden_states, selected_experts, top_scores)
        return shared_output + routed_output


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)


@use_kernel_func_from_hub("rotary_pos_emb")
def apply_rotary_pos_emb(q, k, cos, sin, 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.
        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 repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
    """
    This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
    num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
    """
    batch, num_key_value_heads, slen, head_dim = hidden_states.shape
    if n_rep == 1:
        return hidden_states
    hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
    return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)


def eager_attention_forward(
    module: nn.Module,
    query: torch.Tensor,
    key: torch.Tensor,
    value: torch.Tensor,
    attention_mask: torch.Tensor | None,
    scaling: float,
    dropout: float = 0.0,
    **kwargs: Unpack[TransformersKwargs],
):
    key_states = repeat_kv(key, module.num_key_value_groups)
    value_states = repeat_kv(value, module.num_key_value_groups)

    attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling
    if attention_mask is not None:
        attn_weights = attn_weights + attention_mask

    attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
    attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
    attn_output = torch.matmul(attn_weights, value_states)
    attn_output = attn_output.transpose(1, 2).contiguous()

    return attn_output, attn_weights


@use_kernelized_func(apply_rotary_pos_emb)
class AfmoeAttention(nn.Module):
    """
    Multi-headed attention module with optional sliding window and gating.

    This attention mechanism supports both full attention and sliding window attention,
    and includes Q/K normalization and gating of the output. It inherits from [`LlamaAttention`] to minimize the amount
    of custom logic we need to maintain.
    """

    def __init__(self, config: AfmoeConfig, layer_idx: int):
        super().__init__()
        self.config = config
        self.layer_idx = layer_idx
        self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
        self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads
        self.scaling = self.head_dim**-0.5
        self.attention_dropout = config.attention_dropout
        self.is_causal = True

        self.q_proj = nn.Linear(
            config.hidden_size, config.num_attention_heads * self.head_dim, bias=config.attention_bias
        )
        self.k_proj = nn.Linear(
            config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
        )
        self.v_proj = nn.Linear(
            config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
        )
        self.o_proj = nn.Linear(
            config.num_attention_heads * self.head_dim, config.hidden_size, bias=config.attention_bias
        )
        # Parent LlamaAttention already sets: layer_idx, num_heads, num_key_value_heads, num_key_value_groups, head_dim
        # We only add AFMoE-specific attributes
        self.is_local_attention = config.layer_types[layer_idx] == "sliding_attention"
        self.sliding_window = config.sliding_window if self.is_local_attention else None

        self.q_norm = AfmoeRMSNorm(self.head_dim, eps=config.rms_norm_eps)
        self.k_norm = AfmoeRMSNorm(self.head_dim, eps=config.rms_norm_eps)
        self.gate_proj = nn.Linear(config.hidden_size, config.num_attention_heads * self.head_dim, bias=False)

    def forward(
        self,
        hidden_states: torch.Tensor,
        position_embeddings: tuple[torch.Tensor, torch.Tensor],
        attention_mask: torch.Tensor | None,
        past_key_value: Cache | None = None,
        cache_position: torch.LongTensor | None = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> tuple[torch.Tensor, torch.Tensor]:
        input_shape = hidden_states.shape[:-1]
        hidden_shape = (*input_shape, -1, self.head_dim)

        query_states = self.q_proj(hidden_states).view(hidden_shape)
        key_states = self.k_proj(hidden_states).view(hidden_shape)
        value_states = self.v_proj(hidden_states).view(hidden_shape)
        gate_states = self.gate_proj(hidden_states)

        query_states = self.q_norm(query_states).transpose(1, 2)
        key_states = self.k_norm(key_states).transpose(1, 2)
        value_states = value_states.transpose(1, 2)

        if self.is_local_attention:
            cos, sin = position_embeddings
            query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)

        if past_key_value is not None:
            cache_kwargs = {"cache_position": cache_position}
            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)

        attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface(
            self.config._attn_implementation, eager_attention_forward
        )

        output, attn_weights = attention_interface(
            self,
            query_states,
            key_states,
            value_states,
            attention_mask=attention_mask,
            dropout=0.0 if not self.training else self.attention_dropout,
            scaling=self.scaling,
            sliding_window=self.sliding_window,
            **kwargs,
        )

        output = output.view(*input_shape, -1).contiguous()
        output = output * torch.sigmoid(gate_states)
        attn_output = self.o_proj(output)
        return attn_output, attn_weights


class AfmoeDecoderLayer(GradientCheckpointingLayer):
    """
    AFMoE decoder layer with dual normalization.

    This layer applies self-attention followed by either a dense MLP or MoE block,
    with dual normalization (pre and post) around each component.
    """

    def __init__(self, config: AfmoeConfig, layer_idx: int):
        super().__init__()
        self.hidden_size = config.hidden_size
        self.layer_idx = layer_idx

        self.self_attn = AfmoeAttention(config=config, layer_idx=layer_idx)
        self.attention_type = config.layer_types[layer_idx]

        # Dual normalization for attention
        self.input_layernorm = AfmoeRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.post_attention_layernorm = AfmoeRMSNorm(config.hidden_size, eps=config.rms_norm_eps)

        # Dual normalization for FFN
        self.pre_mlp_layernorm = AfmoeRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.post_mlp_layernorm = AfmoeRMSNorm(config.hidden_size, eps=config.rms_norm_eps)

        # MoE or dense FFN
        self.moe_enabled = layer_idx >= config.num_dense_layers
        if self.moe_enabled:
            self.mlp = AfmoeMoE(config)
        else:
            self.mlp = AfmoeMLP(config)

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: torch.Tensor | None = None,
        position_ids: torch.LongTensor | None = None,
        past_key_value: Cache | None = None,
        use_cache: bool | None = None,
        cache_position: torch.LongTensor | None = None,
        position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> torch.FloatTensor:
        residual = hidden_states

        # Self Attention with dual normalization
        hidden_states = self.input_layernorm(hidden_states)
        hidden_states, _ = self.self_attn(
            hidden_states=hidden_states,
            attention_mask=attention_mask,
            position_ids=position_ids,
            past_key_value=past_key_value,
            use_cache=use_cache,
            cache_position=cache_position,
            position_embeddings=position_embeddings,
            **kwargs,
        )
        hidden_states = self.post_attention_layernorm(hidden_states)
        hidden_states = residual + hidden_states

        # FFN with dual normalization
        residual = hidden_states
        hidden_states = self.pre_mlp_layernorm(hidden_states)
        hidden_states = self.mlp(hidden_states)
        hidden_states = self.post_mlp_layernorm(hidden_states)

        hidden_states = residual + hidden_states
        return hidden_states


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

    config: AfmoeConfig
    base_model_prefix = "model"
    _no_split_modules = ["AfmoeDecoderLayer"]
    _skip_keys_device_placement = ["past_key_values"]
    _can_record_outputs = {
        "hidden_states": AfmoeDecoderLayer,
        "attentions": AfmoeAttention,
    }
    _keep_in_fp32_modules = [
        "input_layernorm",
        "post_attention_layernorm",
        "pre_mlp_layernorm",
        "post_mlp_layernorm",
        "q_norm",
        "k_norm",
        "norm",
        "expert_bias",
    ]
    _supports_sdpa = True
    _supports_flash_attn = True
    _supports_flex_attn = True
    _supports_attention_backend = True
    supports_gradient_checkpointing = True

    def _init_weights(self, module):
        """Initialize the weights"""
        super()._init_weights(module)
        if isinstance(module, AfmoeTokenChoiceRouter):
            init.zeros_(module.gate.weight)
        elif isinstance(module, AfmoeMoE):
            init.zeros_(module.expert_bias)


@auto_docstring
class AfmoeModel(AfmoePreTrainedModel):
    """
    Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`AfmoeDecoderLayer`]

    Args:
        config: AfmoeConfig
    """

    def __init__(self, config: AfmoeConfig):
        super().__init__(config)
        self.padding_idx = config.pad_token_id
        self.vocab_size = config.vocab_size

        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
        self.layers = nn.ModuleList(
            [AfmoeDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
        )
        self.norm = AfmoeRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.rotary_emb = AfmoeRotaryEmbedding(config=config)
        self.gradient_checkpointing = False

        self.post_init()

    @auto_docstring
    @merge_with_config_defaults
    @capture_outputs
    def forward(
        self,
        input_ids: torch.LongTensor | None = None,
        attention_mask: torch.Tensor | None = None,
        inputs_embeds: torch.FloatTensor | None = None,
        position_ids: torch.LongTensor | None = None,
        past_key_values: Cache | None = None,
        cache_position: torch.LongTensor | None = None,
        use_cache: bool | None = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> tuple | MoeModelOutputWithPast:
        if (input_ids is None) ^ (inputs_embeds is not None):
            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")

        if use_cache and past_key_values is None:
            past_key_values = DynamicCache(config=self.config)

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

        if cache_position is None:
            past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
            cache_position = torch.arange(
                past_seen_tokens,
                past_seen_tokens + inputs_embeds.shape[1],
                device=inputs_embeds.device,
            )
        if position_ids is None:
            position_ids = cache_position.unsqueeze(0)

        # It may already have been prepared by e.g. `generate`
        if not isinstance(causal_mask_mapping := attention_mask, dict):
            mask_kwargs = {
                "config": self.config,
                "inputs_embeds": inputs_embeds,
                "attention_mask": attention_mask,
                "cache_position": cache_position,
                "past_key_values": past_key_values,
            }
            causal_mask_mapping = {
                "full_attention": create_causal_mask(**mask_kwargs),
                "sliding_attention": create_sliding_window_causal_mask(**mask_kwargs),
            }

        hidden_states = inputs_embeds

        # Apply muP input scaling if enabled
        if self.config.mup_enabled:
            hidden_states = hidden_states * (self.config.hidden_size**0.5)

        position_embeddings = self.rotary_emb(hidden_states, position_ids)

        for decoder_layer in self.layers:
            hidden_states = decoder_layer(
                hidden_states,
                attention_mask=causal_mask_mapping[decoder_layer.attention_type],
                position_ids=position_ids,
                past_key_value=past_key_values,
                use_cache=use_cache,
                cache_position=cache_position,
                position_embeddings=position_embeddings,
                **kwargs,
            )

        hidden_states = self.norm(hidden_states)
        return MoeModelOutputWithPast(
            last_hidden_state=hidden_states,
            past_key_values=past_key_values if use_cache else None,
        )


@auto_docstring
class AfmoeForCausalLM(AfmoePreTrainedModel, GenerationMixin):
    _tied_weights_keys = {"lm_head.weight": "model.embed_tokens.weight"}
    _tp_plan = {"lm_head": "colwise_gather_output"}
    _pp_plan = {"lm_head": (["hidden_states"], ["logits"])}

    def __init__(self, config):
        super().__init__(config)
        self.model = AfmoeModel(config)
        self.vocab_size = config.vocab_size
        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
        self.post_init()

    @can_return_tuple
    @auto_docstring
    def forward(
        self,
        input_ids: torch.LongTensor | None = None,
        attention_mask: torch.Tensor | None = None,
        position_ids: torch.LongTensor | None = None,
        past_key_values: Cache | None = None,
        inputs_embeds: torch.FloatTensor | None = None,
        labels: torch.LongTensor | None = None,
        use_cache: bool | None = None,
        cache_position: torch.LongTensor | None = None,
        logits_to_keep: int | torch.Tensor = 0,
        **kwargs: Unpack[TransformersKwargs],
    ) -> CausalLMOutputWithPast:
        r"""
        Example:

        ```python
        >>> from transformers import AutoTokenizer, AfmoeForCausalLM

        >>> model = AfmoeForCausalLM.from_pretrained("meta-afmoe/Afmoe-2-7b-hf")
        >>> tokenizer = AutoTokenizer.from_pretrained("meta-afmoe/Afmoe-2-7b-hf")

        >>> prompt = "Hey, are you conscious? Can you talk to me?"
        >>> inputs = tokenizer(prompt, return_tensors="pt")

        >>> # Generate
        >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
        >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
        "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
        ```"""
        outputs: BaseModelOutputWithPast = self.model(
            input_ids=input_ids,
            attention_mask=attention_mask,
            position_ids=position_ids,
            past_key_values=past_key_values,
            inputs_embeds=inputs_embeds,
            use_cache=use_cache,
            cache_position=cache_position,
            **kwargs,
        )

        hidden_states = outputs.last_hidden_state
        # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
        slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
        logits = self.lm_head(hidden_states[:, slice_indices, :])

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

        return CausalLMOutputWithPast(
            loss=loss,
            logits=logits,
            past_key_values=outputs.past_key_values,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )


__all__ = ["AfmoeForCausalLM", "AfmoeModel", "AfmoePreTrainedModel"]
