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# coding=utf-8
# Copyright 2024 Databricks Mosaic Research 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 Any, Optional, Union

import torch
from torch import nn

from ...activations import ACT2FN
from ...cache_utils import Cache, DynamicCache
from ...generation import GenerationMixin
from ...masking_utils import create_causal_mask
from ...modeling_layers import GradientCheckpointingLayer
from ...modeling_outputs import MoeCausalLMOutputWithPast, 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 check_model_inputs
from .configuration_dbrx import DbrxConfig


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

    def __init__(self, config: DbrxConfig, 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.original_inv_freq = inv_freq

    @staticmethod
    def compute_default_rope_parameters(
        config: Optional[DbrxConfig] = None,
        device: Optional["torch.device"] = None,
        seq_len: Optional[int] = 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 torch.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)


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 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: Optional[torch.Tensor],
    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:
        causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
        attn_weights = attn_weights + causal_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


class DbrxAttention(nn.Module):
    """Modular DBRX attention component that can be reused across different model architectures."""

    def __init__(
        self,
        config,
        layer_idx: Optional[int] = None,
        **kwargs,
    ):
        super().__init__()
        self.config = config
        self.hidden_size = config.d_model
        self.num_heads = config.n_heads
        self.head_dim = self.hidden_size // self.num_heads
        self.max_position_embeddings = config.max_seq_len
        self.layer_idx = layer_idx

        attn_config = config.attn_config
        self.attention_dropout = attn_config.attn_pdrop
        self.clip_qkv = attn_config.clip_qkv
        self.num_key_value_heads = attn_config.kv_n_heads
        self.num_key_value_groups = self.num_heads // self.num_key_value_heads
        self.scaling = self.head_dim**-0.5
        self.rope_theta = attn_config.rope_theta
        self.is_causal = True

        self.Wqkv = nn.Linear(
            self.hidden_size, self.hidden_size + 2 * self.num_key_value_heads * self.head_dim, bias=False
        )
        self.out_proj = nn.Linear(self.hidden_size, self.hidden_size, bias=False)

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

        qkv_states = self.Wqkv(hidden_states)
        min_val = -self.clip_qkv if self.clip_qkv is not None else None
        qkv_states = qkv_states.clamp(min=min_val, max=self.clip_qkv)

        query_states, key_states, value_states = qkv_states.split(
            [
                self.hidden_size,
                self.num_key_value_heads * self.head_dim,
                self.num_key_value_heads * self.head_dim,
            ],
            dim=2,
        )

        query_states = query_states.view(hidden_shape).transpose(1, 2)
        key_states = key_states.view(hidden_shape).transpose(1, 2)
        value_states = value_states.view(hidden_shape).transpose(1, 2)

        cos, sin = position_embeddings
        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)

        if past_key_values is not None:
            # sin and cos are specific to RoPE models; cache_position needed for the static cache
            cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
            key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx, cache_kwargs)

        attention_interface: Callable = eager_attention_forward
        if self.config._attn_implementation != "eager":
            attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]

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

        attn_output = attn_output.reshape(*input_shape, -1).contiguous()
        attn_output = self.out_proj(attn_output)
        return attn_output, attn_weights


class DbrxExpertGLU(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.hidden_size = config.hidden_size
        self.ffn_hidden_size = config.ffn_hidden_size
        self.moe_num_experts = config.moe_num_experts

        self.w1 = nn.Parameter(torch.empty(self.moe_num_experts * self.ffn_hidden_size, self.hidden_size))
        self.v1 = nn.Parameter(torch.empty(self.moe_num_experts * self.ffn_hidden_size, self.hidden_size))
        self.w2 = nn.Parameter(torch.empty(self.moe_num_experts * self.ffn_hidden_size, self.hidden_size))

        act_fn_name = config.ffn_act_fn.get("name", "silu")
        self.activation_fn = ACT2FN[act_fn_name]

    def forward(
        self, x: torch.Tensor, expert_w1: torch.Tensor, expert_v1: torch.Tensor, expert_w2: torch.Tensor
    ) -> torch.Tensor:
        gate_proj = x.matmul(expert_w1)
        up_proj = x.matmul(expert_v1)
        gate_proj = self.activation_fn(gate_proj)
        intermediate_states = gate_proj * up_proj
        down_proj = intermediate_states.matmul(expert_w2.t())
        return down_proj


class DbrxExperts(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.mlp = DbrxExpertGLU(config)
        self.hidden_size = config.hidden_size
        self.ffn_hidden_size = config.ffn_hidden_size
        self.num_experts = config.moe_num_experts

    def forward(
        self,
        hidden_states: torch.Tensor,
        top_k_index: torch.Tensor,
        top_k_weights: torch.Tensor,
    ) -> torch.Tensor:
        batch_size = hidden_states.shape[0]
        hidden_states = hidden_states.reshape(-1, self.ffn_hidden_size)

        next_states = torch.zeros_like(hidden_states, dtype=hidden_states.dtype, device=hidden_states.device)
        with torch.no_grad():
            expert_mask = torch.nn.functional.one_hot(top_k_index, num_classes=self.num_experts)
            expert_mask = expert_mask.permute(2, 1, 0)
            expert_hit = torch.greater(expert_mask.sum(dim=(-1, -2)), 0).nonzero()

        split_expert_shape = (-1, self.ffn_hidden_size, self.hidden_size)
        for expert_idx in expert_hit:
            expert_idx = expert_idx[0]
            with torch.no_grad():
                idx, token_idx = torch.where(expert_mask[expert_idx])
            v1 = self.mlp.v1.view(split_expert_shape)[expert_idx]
            w1 = self.mlp.w1.view(split_expert_shape)[expert_idx]
            w2 = self.mlp.w2.view(split_expert_shape)[expert_idx]
            states = self.mlp(hidden_states[token_idx], w1, v1, w2)
            states = states.view(-1, self.ffn_hidden_size) * top_k_weights[token_idx, idx, None]
            next_states.index_add_(0, token_idx, states)

        next_states = next_states.view(batch_size, -1, self.ffn_hidden_size)
        return next_states


class DbrxRouter(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.hidden_size = config.ffn_hidden_size
        self.moe_jitter_eps = config.moe_jitter_eps
        self.layer = nn.Linear(self.hidden_size, config.moe_num_experts, bias=False)

    def forward(self, hidden_states: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor, torch.LongTensor]:
        if self.training and self.moe_jitter_eps is not None:
            hidden_states *= torch.empty_like(hidden_states).uniform_(
                1.0 - self.moe_jitter_eps, 1.0 + self.moe_jitter_eps
            )
        hidden_states = hidden_states.view(-1, hidden_states.shape[-1])
        router_logits = self.layer(hidden_states)
        return router_logits


class DbrxFFN(nn.Module):
    """Modular DBRX MLP/FFN component with MoE support."""

    def __init__(self, config, **kwargs):
        super().__init__()
        self.router = DbrxRouter(config.ffn_config)
        self.experts = DbrxExperts(config.ffn_config)

        self.moe_normalize_expert_weights = config.ffn_config.moe_normalize_expert_weights
        self.top_k = config.ffn_config.moe_top_k

    def route_tokens_to_experts(self, router_logits):
        router_logits = torch.nn.functional.softmax(router_logits, dim=1, dtype=router_logits.dtype)
        router_top_value, router_indices = torch.topk(router_logits, self.top_k, dim=-1)
        if self.moe_normalize_expert_weights is not None:
            router_top_value = router_top_value / torch.norm(
                router_top_value, p=self.moe_normalize_expert_weights, dim=-1, keepdim=True
            )
        return router_top_value, router_indices

    def forward(self, hidden_states: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
        router_logits = self.router(hidden_states)
        top_k_weights, top_k_index = self.route_tokens_to_experts(router_logits)
        output = self.experts(hidden_states, top_k_index, top_k_weights)
        return output


class DbrxNormAttentionNorm(nn.Module):
    def __init__(self, config: DbrxConfig, layer_idx: Optional[int] = None):
        super().__init__()
        self.layer_idx = layer_idx
        self.resid_pdrop = config.resid_pdrop
        self.norm_1 = nn.LayerNorm(config.d_model, bias=False)
        self.attn = DbrxAttention(
            config=config,
            layer_idx=layer_idx,
        )
        self.norm_2 = nn.LayerNorm(config.d_model, bias=False)

    def forward(
        self,
        hidden_states: torch.Tensor,
        position_embeddings: torch.LongTensor,
        attention_mask: Optional[torch.Tensor] = None,
        past_key_values: Optional[Cache] = None,
        cache_position: Optional[torch.LongTensor] = None,
        **kwargs: Any,
    ) -> tuple[torch.Tensor, torch.Tensor]:
        residual_states = hidden_states
        hidden_states = self.norm_1(hidden_states).to(hidden_states.dtype)

        hidden_states, _ = self.attn(
            hidden_states=hidden_states,
            attention_mask=attention_mask,
            position_embeddings=position_embeddings,
            past_key_values=past_key_values,
            cache_position=cache_position,
            **kwargs,
        )

        hidden_states = nn.functional.dropout(hidden_states, p=self.resid_pdrop, training=self.training)
        hidden_states = hidden_states + residual_states

        residual_states = hidden_states
        hidden_states = self.norm_2(hidden_states).to(hidden_states.dtype)

        return residual_states, hidden_states


class DbrxBlock(GradientCheckpointingLayer):
    def __init__(self, config: DbrxConfig, layer_idx: int):
        super().__init__()
        self.hidden_size = config.d_model
        self.resid_pdrop = config.resid_pdrop
        self.layer_idx = layer_idx
        self.norm_attn_norm = DbrxNormAttentionNorm(
            config=config,
            layer_idx=layer_idx,
        )
        self.ffn = DbrxFFN(config=config)

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        position_embeddings: Optional[torch.LongTensor] = None,
        past_key_values: Optional[Cache] = None,
        cache_position: Optional[torch.LongTensor] = None,
        **kwargs: Any,
    ):
        resid_states, hidden_states = self.norm_attn_norm(
            hidden_states=hidden_states,
            attention_mask=attention_mask,
            position_embeddings=position_embeddings,
            past_key_values=past_key_values,
            cache_position=cache_position,
            **kwargs,
        )

        hidden_states = self.ffn(hidden_states)
        hidden_states = nn.functional.dropout(hidden_states, p=self.resid_pdrop, training=self.training)
        hidden_states = resid_states + hidden_states
        return hidden_states


class DbrxPreTrainedModel(PreTrainedModel):
    config: DbrxConfig
    base_model_prefix = "transformer"
    supports_gradient_checkpointing = True
    _no_split_modules = ["DbrxBlock"]
    _skip_keys_device_placement = ["past_key_values"]
    _supports_flex_attn = True
    _supports_attention_backend = True
    _supports_flash_attn = True
    _supports_sdpa = True
    _can_compile_fullgraph = False  # MoE models don't work with torch.compile (`torch.where(condition)` not supported)
    _can_record_outputs = {
        "hidden_states": DbrxBlock,
        "attentions": DbrxAttention,
    }

    def _init_weights(self, module: nn.Module):
        std = self.config.initializer_range
        if isinstance(module, nn.Linear):
            module.weight.data.normal_(mean=0.0, std=std)
            if module.bias is not None:
                module.bias.data.zero_()
        elif isinstance(module, nn.Embedding):
            module.weight.data.normal_(mean=0.0, std=std)
            if module.padding_idx is not None:
                module.weight.data[module.padding_idx].zero_()
        elif isinstance(module, nn.LayerNorm):
            module.weight.data.fill_(1.0)
            if module.bias is not None:
                module.bias.data.zero_()
        elif isinstance(module, DbrxExpertGLU):
            module.w1.data.normal_(mean=0.0, std=std)
            module.v1.data.normal_(mean=0.0, std=std)
            module.w2.data.normal_(mean=0.0, std=std)


@auto_docstring
class DbrxModel(DbrxPreTrainedModel):
    """Transformer decoder consisting of *config.num_hidden_layers*. Each layer is a [`DbrxBlock`] layer.

    Args:
        config ([`DbrxConfig`]): Model configuration class with all 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.
    """

    def __init__(self, config: DbrxConfig):
        super().__init__(config)
        self.padding_idx = config.pad_token_id
        self.vocab_size = config.vocab_size
        self.emb_pdrop = config.emb_pdrop
        self.rotary_emb = DbrxRotaryEmbedding(config)
        self.wte = nn.Embedding(config.vocab_size, config.d_model, self.padding_idx)
        self.blocks = nn.ModuleList([DbrxBlock(config, layer_idx) for layer_idx in range(config.n_layers)])
        self.norm_f = nn.LayerNorm(config.d_model, bias=False)
        self.gradient_checkpointing = False

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

    def get_input_embeddings(self) -> nn.Embedding:
        return self.wte

    def set_input_embeddings(self, value: nn.Embedding):
        self.wte = value

    @check_model_inputs()
    @auto_docstring
    def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_values: Optional[Cache] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        use_cache: Optional[bool] = None,
        cache_position: Optional[torch.LongTensor] = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> 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.wte(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)

        causal_mask = create_causal_mask(
            config=self.config,
            input_embeds=inputs_embeds,
            attention_mask=attention_mask,
            cache_position=cache_position,
            past_key_values=past_key_values,
            position_ids=position_ids,
        )

        hidden_states = inputs_embeds

        # create position embeddings to be shared across the decoder layers
        position_embeddings = self.rotary_emb(hidden_states, position_ids)

        for decoder_layer in self.blocks[: self.config.num_hidden_layers]:
            hidden_states = decoder_layer(
                hidden_states,
                position_embeddings=position_embeddings,
                attention_mask=causal_mask,
                position_ids=position_ids,
                past_key_values=past_key_values,
                use_cache=use_cache,
                cache_position=cache_position,
                **kwargs,
            )

        hidden_states = self.norm_f(hidden_states)

        return MoeModelOutputWithPast(  # only diff with Mistral is the output type, we need MoE
            last_hidden_state=hidden_states,
            past_key_values=past_key_values,
        )


def load_balancing_loss_func(
    gate_logits: Union[torch.Tensor, tuple[torch.Tensor], None],
    num_experts: Optional[int] = None,
    top_k=2,
    attention_mask: Optional[torch.Tensor] = None,
) -> Union[torch.Tensor, int]:
    r"""
    Computes auxiliary load balancing loss as in Switch Transformer - implemented in Pytorch.

    See Switch Transformer (https://huggingface.co/papers/2101.03961) for more details. This function implements the loss
    function presented in equations (4) - (6) of the paper. It aims at penalizing cases where the routing between
    experts is too unbalanced.

    Args:
        gate_logits:
            Logits from the `gate`, should be a tuple of model.config.num_hidden_layers tensors of
            shape [batch_size X sequence_length, num_experts].
        num_experts:
            Number of experts
        top_k:
            The number of experts to route per-token, can be also interpreted as the `top-k` routing
            parameter.
        attention_mask (`torch.Tensor`, *optional*):
            The attention_mask used in forward function
            shape [batch_size X sequence_length] if not None.

    Returns:
        The auxiliary loss.
    """
    if gate_logits is None or not isinstance(gate_logits, tuple):
        return 0

    if isinstance(gate_logits, tuple):
        compute_device = gate_logits[0].device
        concatenated_gate_logits = torch.cat([layer_gate.to(compute_device) for layer_gate in gate_logits], dim=0)

    routing_weights = torch.nn.functional.softmax(concatenated_gate_logits, dim=-1)

    _, selected_experts = torch.topk(routing_weights, top_k, dim=-1)

    expert_mask = torch.nn.functional.one_hot(selected_experts, num_experts)

    if attention_mask is None:
        # Compute the percentage of tokens routed to each experts
        tokens_per_expert = torch.mean(expert_mask.float(), dim=0)

        # Compute the average probability of routing to these experts
        router_prob_per_expert = torch.mean(routing_weights, dim=0)
    else:
        batch_size, sequence_length = attention_mask.shape
        num_hidden_layers = concatenated_gate_logits.shape[0] // (batch_size * sequence_length)

        # Compute the mask that masks all padding tokens as 0 with the same shape of expert_mask
        expert_attention_mask = (
            attention_mask[None, :, :, None, None]
            .expand((num_hidden_layers, batch_size, sequence_length, top_k, num_experts))
            .reshape(-1, top_k, num_experts)
            .to(compute_device)
        )

        # Compute the percentage of tokens routed to each experts
        tokens_per_expert = torch.sum(expert_mask.float() * expert_attention_mask, dim=0) / torch.sum(
            expert_attention_mask, dim=0
        )

        # Compute the mask that masks all padding tokens as 0 with the same shape of tokens_per_expert
        router_per_expert_attention_mask = (
            attention_mask[None, :, :, None]
            .expand((num_hidden_layers, batch_size, sequence_length, num_experts))
            .reshape(-1, num_experts)
            .to(compute_device)
        )

        # Compute the average probability of routing to these experts
        router_prob_per_expert = torch.sum(routing_weights * router_per_expert_attention_mask, dim=0) / torch.sum(
            router_per_expert_attention_mask, dim=0
        )

    overall_loss = torch.sum(tokens_per_expert * router_prob_per_expert.unsqueeze(0))
    return overall_loss * num_experts


class DbrxForCausalLM(DbrxPreTrainedModel, GenerationMixin):
    _tied_weights_keys = ["lm_head.weight"]
    _tp_plan = {"lm_head": "colwise_rep"}
    _pp_plan = {"lm_head": (["hidden_states"], ["logits"])}

    def __init__(self, config: DbrxConfig):
        super().__init__(config)
        self.transformer = DbrxModel(config)
        self.vocab_size = config.vocab_size
        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
        self.router_aux_loss_coef = config.ffn_config.moe_loss_weight
        self.num_experts = config.ffn_config.moe_num_experts
        self.num_experts_per_tok = config.ffn_config.moe_top_k
        self.post_init()

    def get_input_embeddings(self) -> nn.Embedding:
        return self.transformer.get_input_embeddings()

    def set_input_embeddings(self, value: nn.Embedding):
        self.transformer.set_input_embeddings(value)

    def get_output_embeddings(self) -> nn.Linear:
        return self.lm_head

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

    def set_decoder(self, decoder: DbrxModel):
        self.transformer = decoder

    def get_decoder(self) -> DbrxModel:
        return self.transformer

    @can_return_tuple
    @auto_docstring
    def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_values: Optional[Cache] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        labels: Optional[torch.LongTensor] = None,
        use_cache: Optional[bool] = None,
        output_router_logits: Optional[bool] = None,
        cache_position: Optional[torch.LongTensor] = None,
        logits_to_keep: Union[int, torch.Tensor] = 0,
        **kwargs: Unpack[TransformersKwargs],
    ) -> MoeCausalLMOutputWithPast:
        r"""
        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
            Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
            config.vocab_size]` or -100 (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]`.

        Example:

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

        >> model = DbrxForCausalLM.from_pretrained("databricks/dbrx-instruct")
        >> tokenizer = AutoTokenizer.from_pretrained("databricks/dbrx-instruct")

        >> 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."
        ```
        """
        output_router_logits = (
            output_router_logits if output_router_logits is not None else self.config.output_router_logits
        )

        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
        outputs: MoeModelOutputWithPast = self.transformer(
            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,
            output_router_logits=output_router_logits,
            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, labels, self.vocab_size, **kwargs)

        aux_loss = None
        if output_router_logits:
            aux_loss = load_balancing_loss_func(
                outputs.router_logits,
                self.num_experts,
                self.num_experts_per_tok,
                attention_mask,
            )
            if labels is not None:
                loss += self.router_aux_loss_coef * aux_loss.to(loss.device)  # make sure to reside in the same device

        return MoeCausalLMOutputWithPast(
            loss=loss,
            aux_loss=aux_loss,
            logits=logits,
            past_key_values=outputs.past_key_values,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
            router_logits=outputs.router_logits,
        )


__all__ = ["DbrxForCausalLM", "DbrxModel", "DbrxPreTrainedModel"]
