# coding=utf-8
# Copyright 2025 Jingze Shi and the HuggingFace Inc. team. All rights reserved.
#
# The Doge family of small language models is trained by SmallDoge Team.
#
# 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.
"""PyTorch Doge model."""

import math
from collections.abc import Callable
from typing import Optional, Union

import torch
import torch.nn.functional as F
from torch import nn

from ...activations import ACT2FN
from ...cache_utils import Cache
from ...configuration_utils import PreTrainedConfig
from ...integrations.flex_attention import compile_friendly_flex_attention
from ...modeling_layers import GradientCheckpointingLayer
from ...modeling_outputs import MoeCausalLMOutputWithPast, MoeModelOutputWithPast
from ...modeling_rope_utils import RopeParameters, rope_config_validation, standardize_rope_params
from ...modeling_utils import AttentionInterface, PreTrainedModel
from ...processing_utils import Unpack
from ...utils import TransformersKwargs, is_torch_flex_attn_available, logging
from ...utils.generic import OutputRecorder
from ..llama.modeling_llama import (
    LlamaForSequenceClassification,
    LlamaMLP,
    LlamaPreTrainedModel,
    LlamaRMSNorm,
    LlamaRotaryEmbedding,
    apply_rotary_pos_emb,
    eager_attention_forward,
    repeat_kv,
)
from ..mixtral.modeling_mixtral import MixtralForCausalLM, MixtralModel


logger = logging.get_logger(__name__)

if is_torch_flex_attn_available():
    from torch.nn.attention.flex_attention import BlockMask


class DogeConfig(PreTrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`DogeModel`]. It is used to instantiate an Doge
    model according to the specified arguments, defining the model architecture like [SmallDoge/Doge-320M](https://huggingface.co/SmallDoge/Doge-320M).

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

    Args:
        vocab_size (`int`, *optional*, defaults to 32768):
            Vocabulary size of the Doge2 model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`DogeModel`]
        hidden_size (`int`, *optional*, defaults to 1024):
            Dimension of the hidden representations.
        intermediate_size (`int`, *optional*, defaults to 2048):
            Dimension of the MLP representations.
        num_hidden_layers (`int`, *optional*, defaults to 32):
            Number of hidden layers in the Transformer decoder.
        hidden_dropout (`float`, *optional*, defaults to 0.0):
            Dropout probability for each sequence transformation and state transformation module.
        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
            The non-linear activation function (function or string) in the decoder.
        initializer_range (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        rms_norm_eps (`float`, *optional*, defaults to 1e-06):
            The epsilon used by the rms normalization layers.
        use_cache (`bool`, *optional*, defaults to `True`):
            Whether or not the model should return the last key/values attentions (not used by all models). Only
            relevant if `config.is_decoder=True`.
        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
            Whether the model's input and output word embeddings should be tied.
        max_position_embeddings (`int`, *optional*, defaults to 2048):
            The maximum sequence length that this model might ever be used with.
        rope_parameters (`RopeParameters`, *optional*):
            Dictionary containing the configuration parameters for the RoPE embeddings. The dictionaty should contain
            a value for `rope_theta` and optionally parameters used for scaling in case you want to use RoPE
            with longer `max_position_embeddings`.
        num_attention_heads (`int`, *optional*, defaults to 8):
            Number of attention heads for each attention layer in the Transformer decoder.
        num_key_value_heads (`int`, *optional*):
            This is the number of key_value heads that should be used to implement Grouped Query Attention.
            If `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
            `num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used.
            When converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed by meanpooling all the original heads within that group.
            For more details checkout [this paper](https://huggingface.co/papers/2305.13245).
            If it is not specified, will default to `num_attention_heads`.
        attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
            Whether to use a bias in the query, key, value and output projection layers during self-attention.
        attention_dropout (`float`, *optional*, defaults to 0.0):
            The dropout ratio for the attention probabilities.
        mlp_bias (`bool`, *optional*, defaults to `False`):
            Whether to use a bias in up_proj, down_proj and gate_proj layers in the MLP layers.
        sliding_window (`int`, *optional*):
            Sliding window attention window size. If not specified, will default to `None`.
        keep_window_size (`int`, *optional*, defaults to 2048):
            The window size of tokens that are not dynamically masked, and dynamic masking is only performed when the sequence length exceeds this value.
        is_moe (`bool`, *optional*, defaults to `False`):
            Whether to use the Cross Domain Mixture of Experts, if `True`, the MoE will inherit the MLP to initialize.
        num_experts (`int`, *optional*, defaults to 16384):
            Number of routed experts in the model. This is only used when `is_moe=True`.
        num_experts_per_tok (`int`, *optional*, defaults to 64):
            Number of selected experts to route per-token.
        norm_topk_prob (`bool`, *optional*, defaults to `False`):
            Whether to normalize the topk probabilities.
        output_router_logits (`bool`, *optional*, defaults to `False`):
            Whether or not the router logits should be returned by the model. Enabling this will also
            allow the model to output the auxiliary loss, including load balancing loss and router z-loss.
        router_aux_loss_coef (`float`, *optional*, defaults to 0.001):
            The aux loss factor for the total loss.

    ```python
    >>> from transformers import DogeConfig, DogeModel

    >>> # Initializing a Doge-320M style configuration
    >>> configuration = DogeConfig()

    >>> # Initializing a model from the Doge-320M style configuration
    >>> model = DogeModel(configuration)

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

    model_type = "doge"
    keys_to_ignore_at_inference = ["past_key_values"]
    # Default tensor parallel plan for base model `DogeModel`
    base_model_tp_plan = {
        "layers.*.self_attn.q_proj": "colwise",
        "layers.*.self_attn.k_proj": "colwise",
        "layers.*.self_attn.v_proj": "colwise",
        "layers.*.self_attn.dt_proj": "rowwise",
        "layers.*.self_attn.o_proj": "rowwise",
        "layers.*.input_layernorm.weight": "sequence_parallel",
        "layers.*.input_residual.weight": "sequence_parallel",
        "layers.*.post_attention_layernorm.weight": "sequence_parallel",
        "layers.*.post_attention_residual.weight": "sequence_parallel",
        "norm.weight": "sequence_parallel",
        "layers.*.mlp.gate_proj": "colwise",
        "layers.*.mlp.up_proj": "colwise",
        "layers.*.mlp.down_proj": "rowwise",
        "layers.*.mlp.router_gate": "colwise_rep",
        "layers.*.mlp.down_embed": "rowwise_rep",
        "layers.*.mlp.up_embed": "rowwise_rep",
    }
    base_model_pp_plan = {
        "embed_tokens": (["input_ids"], ["inputs_embeds"]),
        "layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
        "norm": (["hidden_states"], ["hidden_states"]),
    }

    def __init__(
        self,
        vocab_size: Optional[int] = 32768,
        hidden_size: Optional[int] = 1024,
        intermediate_size: Optional[int] = 2048,
        num_hidden_layers: Optional[int] = 32,
        hidden_dropout: Optional[float] = 0.0,
        hidden_act: Optional[str] = "silu",
        initializer_range: Optional[float] = 0.02,
        rms_norm_eps: Optional[int] = 1e-06,
        use_cache: Optional[bool] = True,
        tie_word_embeddings: Optional[bool] = False,
        max_position_embeddings: Optional[int] = 2048,
        rope_parameters: Optional[RopeParameters | dict[RopeParameters]] = None,
        num_attention_heads: Optional[int] = 8,
        num_key_value_heads: Optional[int] = None,
        attention_bias: Optional[bool] = False,
        attention_dropout: Optional[float] = 0.0,
        mlp_bias: Optional[bool] = False,
        sliding_window: Optional[int] = None,
        keep_window_size: Optional[int] = 2048,
        is_moe: Optional[bool] = False,
        num_experts: Optional[int] = 16384,
        num_experts_per_tok: Optional[int] = 64,
        norm_topk_prob: Optional[bool] = False,
        output_router_logits: Optional[bool] = False,
        router_aux_loss_coef: Optional[float] = 0.001,
        **kwargs,
    ):
        self.vocab_size = vocab_size
        self.hidden_size = hidden_size
        self.intermediate_size = intermediate_size
        self.num_hidden_layers = num_hidden_layers

        self.hidden_dropout = hidden_dropout
        self.hidden_act = hidden_act
        self.initializer_range = initializer_range
        self.rms_norm_eps = rms_norm_eps
        self.use_cache = use_cache

        self.max_position_embeddings = max_position_embeddings
        self.num_attention_heads = num_attention_heads
        self.num_key_value_heads = num_key_value_heads
        self.attention_bias = attention_bias
        self.attention_dropout = attention_dropout
        self.mlp_bias = mlp_bias
        self.sliding_window = sliding_window
        self.keep_window_size = keep_window_size
        self.is_moe = is_moe
        self.num_experts = num_experts
        self.num_experts_per_tok = num_experts_per_tok
        self.norm_topk_prob = norm_topk_prob
        self.output_router_logits = output_router_logits
        self.router_aux_loss_coef = router_aux_loss_coef
        # Try to set `rope_scaling` if available, otherwise use `rope_parameters`
        rope_scaling = kwargs.pop("rope_scaling", None)
        self.rope_parameters = rope_scaling or rope_parameters

        # Validate the correctness of rotary position embeddings parameters
        rope_theta = kwargs.get("rope_theta", 10000.0)
        standardize_rope_params(self, rope_theta=rope_theta)
        rope_config_validation(self)

        # for backward compatibility
        if num_key_value_heads is None:
            self.num_key_value_heads = num_attention_heads

        super().__init__(
            tie_word_embeddings=tie_word_embeddings,
            **kwargs,
        )


class DogeRMSNorm(LlamaRMSNorm):
    pass


class DogeRotaryEmbedding(LlamaRotaryEmbedding):
    pass


def flex_attention_forward(
    module: nn.Module,
    query: torch.Tensor,
    key: torch.Tensor,
    value: torch.Tensor,
    attention_mask: Union[torch.Tensor, "BlockMask"],
    scaling: Optional[float] = None,
    softcap: Optional[float] = None,
    **kwargs,
) -> tuple[torch.Tensor, torch.Tensor]:
    block_mask = None
    causal_mask = None
    if isinstance(attention_mask, BlockMask):
        block_mask = attention_mask
    else:
        causal_mask = attention_mask

    if causal_mask is not None:
        causal_mask = causal_mask[:, :, :, : key.shape[-2]]

    def score_mod(score, batch_idx, head_idx, q_idx, kv_idx):
        if softcap is not None:
            score = softcap * torch.tanh(score / softcap)
        if causal_mask is not None:
            score = score + causal_mask[batch_idx][head_idx][q_idx][kv_idx]
        return score

    attn_output, attention_weights = compile_friendly_flex_attention(
        query,
        key,
        value,
        score_mod=score_mod,
        block_mask=block_mask,
        enable_gqa=True,
        scale=scaling,
        # Last time checked on PyTorch == 2.5.1: Flex Attention always computes the lse regardless.
        # For simplification, we thus always return it as no additional computations are introduced.
        return_lse=True,
    )
    # lse is returned in float32
    attention_weights = attention_weights.to(value.dtype)
    attn_output = attn_output.transpose(1, 2).contiguous()

    return attn_output, attention_weights


ALL_ATTENTION_FUNCTIONS = AttentionInterface()
ALL_ATTENTION_FUNCTIONS["doge_flex_attention"] = flex_attention_forward


class DogeAttention(nn.Module):
    def __init__(self, config: DogeConfig, layer_idx: Optional[int] = None):
        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.keep_window_size = config.keep_window_size

        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
        )
        # dynamic mask for the QK^T attention weights matrix
        self.A = nn.Parameter(torch.zeros(config.num_key_value_heads))
        self.dt_proj = nn.Linear(
            config.num_key_value_heads * self.head_dim, config.num_key_value_heads, bias=config.attention_bias
        )
        self.o_proj = nn.Linear(
            config.num_attention_heads * self.head_dim, config.hidden_size, bias=config.attention_bias
        )
        self.q_norm = DogeRMSNorm(self.head_dim, eps=config.rms_norm_eps)
        self.k_norm = DogeRMSNorm(self.head_dim, eps=config.rms_norm_eps)

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

        query_states = self.q_norm(self.q_proj(hidden_states).view(hidden_shape)).transpose(1, 2)
        key_states = self.k_norm(self.k_proj(hidden_states).view(hidden_shape)).transpose(1, 2)
        value_states = self.v_proj(hidden_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)

        # calculate dynamic mask from value_states
        dt_states = self.dt_proj(
            value_states.transpose(1, 2).reshape(value_states.shape[0], value_states.shape[-2], -1)
        )
        dt_states = torch.exp(self.A * F.softplus(dt_states)).transpose(-1, -2)
        attn_mask = self.prepare_dynamic_mask(
            hidden_states=hidden_states,
            dt_states=dt_states,
            keep_window_size=self.keep_window_size,
            attention_mask=attention_mask,
        )
        attn_mask = repeat_kv(attn_mask, self.num_key_value_groups)

        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=attn_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.o_proj(attn_output)
        return attn_output, attn_weights

    def prepare_dynamic_mask(
        self,
        hidden_states: torch.Tensor,
        dt_states: torch.Tensor,
        keep_window_size: int = 2048,
        attention_mask: Optional[torch.Tensor] = None,
    ):
        """
        The core idea of DMA is to calculate the dynamic attention mask to mask the tokens that should be masked, so as to form sparse attention.

        Combine `dt_states` with `attention_mask` to generate the final `attn_mask`.

        Args:
            hidden_states (`torch.Tensor`): The input hidden_states, used to determine the minimum value of the current input precision.
            dt_states (`torch.Tensor`): dt_states of shape `(batch_size, num_heads, key_sequence_length)`.
            keep_window_size (`int`): The window size of tokens that are not dynamically masked, and dynamic masking is only performed when the sequence length exceeds this value.
            attention_mask (`torch.Tensor`, *optional*): attention mask of shape `(batch_size, 1, query_sequence_length, key_sequence_length)`.
        """
        min_dtype = torch.finfo(hidden_states.dtype).min
        dtype = hidden_states.dtype
        attn_mask = dt_states[:, :, None, :].expand(
            -1, -1, hidden_states.shape[1], -1
        )  # [batch_size, num_heads, query_len, key_len]
        if attention_mask is not None and not isinstance(attention_mask, BlockMask):
            if attention_mask.dtype == torch.bool:
                dtype = hidden_states.dtype
                attention_mask = torch.where(
                    attention_mask, torch.tensor(0.0, device=attention_mask.device, dtype=dtype), min_dtype
                )
            attn_mask = attn_mask.masked_fill(attention_mask[:, :, :, : attn_mask.shape[-1]] != 0, min_dtype)
        if attn_mask.shape[-1] > keep_window_size:
            active_mask = torch.zeros_like(attn_mask, dtype=dtype, device=attn_mask.device)
            topk_indices = torch.topk(attn_mask, keep_window_size, dim=-1, largest=True, sorted=False).indices
            active_mask = active_mask.scatter(-1, topk_indices, 1.0)
            attn_mask = attn_mask.masked_fill(active_mask == 0.0, min_dtype)
        return attn_mask


class DogeMLP(LlamaMLP):
    pass


class DogeCDMoE(nn.Module):
    def __init__(self, config: DogeConfig):
        super().__init__()
        self.hidden_size = config.hidden_size
        self.intermediate_size = config.intermediate_size
        self.act_fn = ACT2FN[config.hidden_act]

        self.num_experts = config.num_experts
        self.num_keys = math.floor(math.sqrt(self.num_experts))
        self.top_k = config.num_experts_per_tok
        self.norm_topk_prob = config.norm_topk_prob

        # shared expert
        self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=config.mlp_bias)
        self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=config.mlp_bias)
        self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=config.mlp_bias)

        # router gate for retrieval experts
        self.router_gate = nn.Linear(self.hidden_size, self.num_keys * 2, bias=False)

        # routed experts
        self.down_embed = nn.Embedding(self.num_experts, self.hidden_size)
        self.up_embed = nn.Embedding(self.num_experts, self.hidden_size)

    def forward(
        self,
        hidden_states: torch.Tensor,
        **kwargs,
    ) -> torch.Tensor:
        bsz, seq_len, _ = hidden_states.shape

        # get routing logits with router gate
        router_logits = self.router_gate(hidden_states).view(2, bsz * seq_len, -1)

        # get experts with the highest routing logits
        (scores_x, scores_y), (indices_x, indices_y) = router_logits.topk(self.num_keys, dim=-1)
        all_scores = scores_x.unsqueeze(-1) + scores_y.unsqueeze(-2)
        all_indices = indices_x.unsqueeze(-1) * self.num_keys + indices_y.unsqueeze(-2)
        all_scores = all_scores.view(*all_scores.shape[:-2], -1)
        all_indices = all_indices.view(*all_indices.shape[:-2], -1)
        scores, position_indices = all_scores.topk(self.top_k, dim=-1)
        indices = all_indices.gather(-1, position_indices)
        routing_weights = F.softmax(scores, dim=-1)
        if self.norm_topk_prob:
            routing_weights /= routing_weights.sum(dim=-1, keepdim=True)

        # mix routed experts states with shared expert states
        down_embed = self.down_embed(indices)
        up_embed = self.up_embed(indices)
        experts_weights = torch.matmul(down_embed, hidden_states.view(bsz * seq_len, -1, 1)).view(bsz * seq_len, -1)
        experts_weights = self.act_fn(experts_weights) * routing_weights
        experts_states = torch.matmul(experts_weights.view(bsz * seq_len, 1, -1), up_embed).view(bsz, seq_len, -1)
        hidden_states = self.down_proj(self.act_fn(self.gate_proj(hidden_states)) * self.up_proj(hidden_states))
        hidden_states = hidden_states + experts_states
        return hidden_states, router_logits


class DogeDecoderLayer(GradientCheckpointingLayer):
    def __init__(self, config: DogeConfig, layer_idx: Optional[int] = None):
        super().__init__()
        self.hidden_dropout = config.hidden_dropout

        self.input_layernorm = DogeRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.self_attn = DogeAttention(config=config, layer_idx=layer_idx)
        self.input_residual = nn.Parameter(torch.ones(config.hidden_size))

        self.post_attention_layernorm = DogeRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.mlp = DogeMLP(config) if not config.is_moe else DogeCDMoE(config)
        self.post_attention_residual = nn.Parameter(torch.ones(config.hidden_size))

    def forward(
        self,
        hidden_states: torch.Tensor,
        position_embeddings: Optional[tuple[torch.Tensor, torch.Tensor]] = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_values: Optional[Cache] = None,
        use_cache: Optional[bool] = False,
        cache_position: Optional[torch.LongTensor] = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> tuple[torch.FloatTensor, Optional[tuple[torch.FloatTensor, torch.FloatTensor]]]:
        # sequence transformation
        residual = hidden_states
        hidden_states = self.input_layernorm(hidden_states)
        hidden_states, self_attn_weights = self.self_attn(
            hidden_states=hidden_states,
            position_embeddings=position_embeddings,
            attention_mask=attention_mask,
            position_ids=position_ids,
            past_key_values=past_key_values,
            use_cache=use_cache,
            cache_position=cache_position,
            **kwargs,
        )
        hidden_states = F.dropout(hidden_states, p=self.hidden_dropout, training=self.training)
        hidden_states = self.input_residual * residual + hidden_states

        # state transformation
        residual = hidden_states
        hidden_states = self.post_attention_layernorm(hidden_states)
        hidden_states = self.mlp(hidden_states)
        hidden_states = F.dropout(hidden_states, p=self.hidden_dropout, training=self.training)
        hidden_states = self.post_attention_residual * residual + hidden_states

        return hidden_states


class DogePreTrainedModel(LlamaPreTrainedModel):
    _supports_flash_attn = False
    _can_compile_fullgraph = False
    _can_record_outputs = {
        "router_logits": OutputRecorder(DogeCDMoE, index=1),
        "hidden_states": DogeDecoderLayer,
        "attentions": DogeAttention,
    }

    def _init_weights(self, module):
        """Initialize the weights"""
        PreTrainedModel._init_weights(self, module)
        if isinstance(module, DogeAttention):
            if hasattr(module, "A"):
                module.A.data.zero_()
        elif isinstance(module, DogeDecoderLayer):
            if hasattr(module, "input_residual"):
                module.input_residual.data.fill_(1.0)
            if hasattr(module, "post_attention_residual"):
                module.post_attention_residual.data.fill_(1.0)


class DogeModel(MixtralModel):
    pass


def load_balancing_loss_func(
    gate_logits: Union[torch.Tensor, tuple[torch.Tensor], None],
    num_experts: Optional[int] = None,
    num_keys: Optional[int] = None,
    top_k: int = 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 `router_gate`, should be a tuple of model.config.num_hidden_layers tensors of
            shape [2, batch_size * sequence_length, num_keys].
        num_experts:
            Number of experts
        num_keys:
            Number of keys
        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

    compute_dtype = gate_logits[0].dtype
    compute_device = gate_logits[0].device
    all_expert_indices = []
    all_routing_weights = []

    for layer_gate_logits in gate_logits:
        layer_gate_logits = layer_gate_logits.to(compute_device)

        (scores_x, scores_y), (indices_x, indices_y) = layer_gate_logits.topk(num_keys, dim=-1)

        all_scores = scores_x.unsqueeze(-1) + scores_y.unsqueeze(-2)
        all_indices = indices_x.unsqueeze(-1) * num_keys + indices_y.unsqueeze(-2)
        all_scores = all_scores.view(*all_scores.shape[:-2], -1)
        all_indices = all_indices.view(*all_indices.shape[:-2], -1)

        _, position_indices = all_scores.topk(top_k, dim=-1)
        expert_indices = all_indices.gather(-1, position_indices)

        routing_weights = F.softmax(all_scores, dim=-1)

        all_expert_indices.append(expert_indices)
        all_routing_weights.append(routing_weights)
    all_expert_indices = torch.cat(all_expert_indices, dim=0)
    all_routing_weights = torch.cat(all_routing_weights, dim=0)

    if attention_mask is None:
        # Compute the percentage of tokens routed to each experts
        all_expert_indices = all_expert_indices.view(-1)
        tokens_per_expert = torch.zeros(num_experts, dtype=compute_dtype, device=compute_device)
        pad = torch.ones_like(all_expert_indices, dtype=compute_dtype, device=compute_device)
        tokens_per_expert = tokens_per_expert.scatter_add_(0, all_expert_indices, pad) / all_expert_indices.shape[0]

        # Compute the average probability of routing to these experts
        router_prob_per_expert = torch.mean(all_routing_weights, dim=0)
    else:
        batch_size, sequence_length = attention_mask.shape
        num_hidden_layers = len(gate_logits)

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

        # Compute the percentage of tokens routed to each experts
        tokens_per_expert = torch.zeros(num_experts, dtype=compute_dtype, device=compute_device)
        pad = torch.ones_like(all_expert_indices, dtype=compute_dtype, device=compute_device)
        tokens_per_expert = tokens_per_expert.scatter_add_(0, all_expert_indices, pad) / torch.sum(
            expert_attention_mask
        )

        # 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(all_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)
    return overall_loss * num_experts


class DogeForCausalLM(MixtralForCausalLM):
    def __init__(self, config):
        super().__init__(config)
        self.model = DogeModel(config)
        self.num_experts = config.num_experts

    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,
        cache_position: Optional[torch.LongTensor] = None,
        logits_to_keep: Union[int, torch.Tensor] = 0,
        output_router_logits: Optional[bool] = None,
        **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, DogeForCausalLM

        >>> model = DogeForCausalLM.from_pretrained("SmallDoge/Doge-320M")
        >>> tokenizer = AutoTokenizer.from_pretrained("SmallDoge/Doge-320M")

        >>> 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.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, labels, self.vocab_size, **kwargs)

        aux_loss = None
        if output_router_logits:
            aux_loss = load_balancing_loss_func(
                outputs.router_logits,
                self.num_experts,
                math.floor(math.sqrt(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,
        )


class DogeForSequenceClassification(LlamaForSequenceClassification):
    pass


__all__ = [
    "DogeConfig",
    "DogeForCausalLM",
    "DogeModel",
    "DogePreTrainedModel",
    "DogeForSequenceClassification",
]
