# coding=utf-8
# Copyright 2024 Google Inc. 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.
""" PyTorch Gemmoe model."""

import math
import warnings
from typing import List, Optional, Tuple, Union

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

from transformers.activations import ACT2FN
from transformers.cache_utils import Cache, DynamicCache, StaticCache
from transformers.modeling_attn_mask_utils import (
    _prepare_4d_causal_attention_mask,
)
from transformers.modeling_outputs import SequenceClassifierOutputWithPast, MoeModelOutputWithPast, MoeCausalLMOutputWithPast
from transformers.modeling_utils import PreTrainedModel
from transformers.pytorch_utils import ALL_LAYERNORM_LAYERS, is_torch_greater_or_equal_than_1_13
from transformers.utils import (
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    is_flash_attn_2_available,
    is_flash_attn_greater_or_equal_2_10,
    logging,
    replace_return_docstrings,
)
from transformers.utils.import_utils import is_torch_fx_available
from .configuration_gemmoe import GemmoeConfig

from math import sqrt as math_sqrt


if is_flash_attn_2_available():
    from flash_attn import flash_attn_func, flash_attn_varlen_func
    from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input  # noqa


# This makes `_prepare_4d_causal_attention_mask` a leaf function in the FX graph.
# It means that the function will not be traced through and simply appear as a node in the graph.
if is_torch_fx_available():
    if not is_torch_greater_or_equal_than_1_13:
        import torch.fx

    _prepare_4d_causal_attention_mask = torch.fx.wrap(_prepare_4d_causal_attention_mask)


logger = logging.get_logger(__name__)

_CONFIG_FOR_DOC = "GemmoeConfig"

def approx_gelu(x):
    return 0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * x**3)))

def _get_unpad_data(attention_mask):
    seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
    indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
    max_seqlen_in_batch = seqlens_in_batch.max().item()
    cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.torch.int32), (1, 0))
    return (
        indices,
        cu_seqlens,
        max_seqlen_in_batch,
    )


class GemmoeRMSNorm(nn.Module):
    def __init__(self, dim: int, eps: float = 1e-6):
        super().__init__()
        self.eps = eps
        self.weight = nn.Parameter(torch.zeros(dim))

    def _norm(self, x):
        return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)

    def forward(self, x):
        output = self._norm(x.float()).type_as(x)
        return output * (self.weight + 1)

ALL_LAYERNORM_LAYERS.append(GemmoeRMSNorm)

class GemmoeRotaryEmbedding(nn.Module):
    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
        super().__init__()
        self.dim = dim
        self.max_position_embeddings = max_position_embeddings
        self.base = base
        self._set_cos_sin_cache(seq_len=max_position_embeddings, device=device, dtype=torch.get_default_dtype())

    def _set_cos_sin_cache(self, seq_len, device, dtype):
        self.max_seq_len_cached = seq_len
        freq_exponents = (2.0 / self.dim) * (
            torch.arange(self.dim // 2, dtype=torch.int64, device="cpu").float()
        )
        timescale = self.base ** freq_exponents
        positions = torch.arange(self.max_seq_len_cached, device="cpu", dtype=torch.int64).float()
        radians_new = positions[..., None] / timescale[None, None, :]
        radians_new = radians_new.squeeze(0)
        emb = torch.cat((radians_new, radians_new), dim=-1)
        cos = emb.cos().to(device=device, non_blocking=True)
        sin = emb.sin().to(device=device, non_blocking=True)
        self.register_buffer("cos_cached", cos, persistent=False)
        self.register_buffer("sin_cached", sin, persistent=False)

    def forward(self, x, position_ids=None, seq_len=None):
        if seq_len is None:
            seq_len = x.size(2)
        if seq_len > self.max_seq_len_cached:
            self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype)
        return (
            self.cos_cached[:seq_len],
            self.sin_cached[:seq_len],
        )

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):
    """Applies Rotary Position Embedding to the query and key tensors."""
    seq_len, dim = q.shape[-2], q.shape[-1]
    cos = cos[:seq_len].view(1, 1, seq_len, dim)
    sin = sin[:seq_len].view(1, 1, seq_len, dim)
    q_embed = (q * cos) + (rotate_half(q) * sin)
    k_embed = (k * cos) + (rotate_half(k) * sin)
    return q_embed, k_embed

class GemmoeMLP(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.config = config
        self.hidden_size = config.hidden_size
        self.intermediate_size = config.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 = approx_gelu

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

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

	See Switch Transformer (https://arxiv.org/abs/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 (Union[`torch.Tensor`, Tuple[torch.Tensor]):
			Logits from the `gate`, should be a tuple of model.config.num_hidden_layers tensors of
			shape [batch_size X sequence_length, num_experts].
		attention_mask (`torch.Tensor`, None):
			The attention_mask used in forward function
			shape [batch_size X sequence_length] if not None.
		num_experts (`int`, *optional*):
			Number of experts

	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, 2, num_experts))
			.reshape(-1, 2, 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

def repeat_kv(self, 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)

class GemmoeAttention(nn.Module):
    """
    Multi-headed attention module for Gemmoe model.

    Args:
        config (GemmoeConfig): The configuration object for the Gemmoe model.
        layer_idx (Optional[int]): The index of the layer. Default is None.
    """

    def __init__(self, config: GemmoeConfig, layer_idx: Optional[int] = None):
        super().__init__()
        self.config = config
        self.layer_idx = layer_idx
        if layer_idx is None:
            logger.warning_once(
                f"Instantiating {self.__class__.__name__} without passing a `layer_idx` is not recommended and will "
                "lead to errors during the forward call if caching is used. Please make sure to provide a `layer_idx` "
                "when creating this class."
            )
        self.attention_dropout = config.attention_dropout
        self.hidden_size = config.hidden_size
        self.num_heads = config.num_attention_heads
        self.head_dim = config.head_dim
        self.num_key_value_heads = config.num_key_value_heads
        self.num_key_value_groups = self.num_heads // self.num_key_value_heads
        self.max_position_embeddings = config.max_position_embeddings
        self.rope_theta = config.rope_theta
        self.is_causal = True

        if self.hidden_size % self.num_heads != 0:
            raise ValueError(
                f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
                f" and `num_heads`: {self.num_heads})."
            )
        self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=config.attention_bias)
        self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
        self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
        self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
        self.rotary_emb = GemmoeRotaryEmbedding(
				self.head_dim,
				max_position_embeddings=self.max_position_embeddings,
				base=self.rope_theta,
			)

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_value: Optional[Cache] = None,
        output_attentions: bool = False,
        use_cache: bool = False,
        cache_position: Optional[torch.LongTensor] = None,
        **kwargs,
    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
        """
        Forward pass of the attention module.

        Args:
            hidden_states (torch.Tensor): The input hidden states.
            attention_mask (Optional[torch.Tensor]): The attention mask. Default is None.
            position_ids (Optional[torch.LongTensor]): The position IDs. Default is None.
            past_key_value (Optional[Cache]): The past key-value cache. Default is None.
            output_attentions (bool): Whether to output the attention weights. Default is False.
            use_cache (bool): Whether to use caching. Default is False.
            cache_position (Optional[torch.LongTensor]): The cache position. Default is None.
            **kwargs: Additional keyword arguments.

        Returns:
            Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
                - The output hidden states.
                - The attention weights (if `output_attentions=True`).
                - The past key-value cache (if `use_cache=True`).
        """
        bsz, q_len, _ = hidden_states.size()

        query_states = self.q_proj(hidden_states)
        key_states = self.k_proj(hidden_states)
        value_states = self.v_proj(hidden_states)

        query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
        key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
        value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)

        past_key_value = getattr(self, "past_key_value", past_key_value)

        cos, sin = self.rotary_emb(value_states, position_ids, seq_len=None)
        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, None)

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

            key_states = self.repeat_kv(key_states, self.num_key_value_groups)
            value_states = self.repeat_kv(value_states, self.num_key_value_groups)

        attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)

        if attention_mask is not None:  # no matter the length, we just slice it
            if cache_position is not None:
                causal_mask = attention_mask[:, :, cache_position, : key_states.shape[-2]]
            else:
                causal_mask = attention_mask
            attn_weights = attn_weights + causal_mask

        # upcast attention to fp32
        attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
        attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training)

        attn_output = torch.matmul(attn_weights, value_states)

        if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
            raise ValueError(
                f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
                f" {attn_output.size()}"
            )

        attn_output = attn_output.transpose(1, 2).contiguous()
        attn_output = attn_output.view(bsz, q_len, -1)

        attn_output = self.o_proj(attn_output)

        if not output_attentions:
            attn_weights = None

        return attn_output, attn_weights, past_key_value

class GemmoeFlashAttention2(GemmoeAttention):
    """
    Gemmoe flash attention module. This module inherits from `GemmoeAttention` as the weights of the module stays
    untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
    flash attention and deal with padding tokens in case the input contains any of them.
    """
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        # TODO: Remove this attribute once Flash Attention for RoCm is bumped to 2.1.
        self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.LongTensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_value: Optional[Cache] = None,
        output_attentions: bool = False,
        use_cache: bool = False,
        cache_position: Optional[torch.LongTensor] = None,
        **kwargs,
    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
        output_attentions = False

        bsz, q_len, _ = hidden_states.size()

        query_states = self.q_proj(hidden_states)
        key_states = self.k_proj(hidden_states)
        value_states = self.v_proj(hidden_states)

        # Flash attention requires the input to have the shape
        # batch_size x seq_length x head_dim x hidden_dim
        # therefore we just need to keep the original shape
        query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
        key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
        value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)

        cos, sin = self.rotary_emb(value_states, position_ids, seq_len=None)
        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, None)

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

        # TODO: These transpose are quite inefficient but Flash Attention requires the layout [batch_size, sequence_length, num_heads, head_dim]. We would need to refactor the KV cache
        # to be able to avoid many of these transpose/reshape/view.
        query_states = query_states.transpose(1, 2)
        key_states = key_states.transpose(1, 2)
        value_states = value_states.transpose(1, 2)

        dropout_rate = self.attention_dropout if self.training else 0.0

        # In PEFT, usually we cast the layer norms in float32 for training stability reasons
        # therefore the input hidden states gets silently casted in float32. Hence, we need
        # cast them back in the correct dtype just to be sure everything works as expected.
        # This might slowdown training & inference so it is recommended to not cast the LayerNorms
        # in fp32. (GemmoeRMSNorm handles it correctly)
        input_dtype = query_states.dtype
        if input_dtype == torch.float32:
            if torch.is_autocast_enabled():
                target_dtype = torch.get_autocast_gpu_dtype()
            # Handle the case where the model is quantized
            elif hasattr(self.config, "_pre_quantization_dtype"):
                target_dtype = self.config._pre_quantization_dtype
            else:
                target_dtype = self.q_proj.weight.dtype

            logger.warning_once(
                f"The input hidden states seems to be silently casted in float32, this might be related to"
                f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
                f" {target_dtype}."
            )
            query_states = query_states.to(target_dtype)
            key_states = key_states.to(target_dtype)
            value_states = value_states.to(target_dtype)

        attn_output = self._flash_attention_forward(
            query_states, key_states, value_states, attention_mask, q_len, dropout=dropout_rate
        )

        attn_output = attn_output.reshape(bsz, q_len, -1).contiguous()
        attn_output = self.o_proj(attn_output)

        if not output_attentions:
            attn_weights = None

        return attn_output, attn_weights, past_key_value

    def _flash_attention_forward(
        self, query_states, key_states, value_states, attention_mask, query_length, dropout=0.0, softmax_scale=None
    ):
        """
        Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token
        first unpad the input, then computes the attention scores and pad the final attention scores.

        Args:
            query_states (`torch.Tensor`):
                Input query states to be passed to Flash Attention API
            key_states (`torch.Tensor`):
                Input key states to be passed to Flash Attention API
            value_states (`torch.Tensor`):
                Input value states to be passed to Flash Attention API
            attention_mask (`torch.Tensor`):
                The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the
                position of padding tokens and 1 for the position of non-padding tokens.
            dropout (`int`, *optional*):
                Attention dropout
            softmax_scale (`float`, *optional*):
                The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim)
        """
        if not self._flash_attn_uses_top_left_mask:
            causal = self.is_causal
        else:
            # TODO: Remove the `query_length != 1` check once Flash Attention for RoCm is bumped to 2.1. For details, please see the comment in GemmoeFlashAttention2 __init__.
            causal = self.is_causal and query_length != 1

        # Contains at least one padding token in the sequence
        if attention_mask is not None:
            batch_size = query_states.shape[0]
            query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._upad_input(
                query_states, key_states, value_states, attention_mask, query_length
            )
            cu_seqlens_q, cu_seqlens_k = cu_seq_lens
            max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens

            attn_output_unpad = flash_attn_varlen_func(
                query_states,
                key_states,
                value_states,
                cu_seqlens_q=cu_seqlens_q,
                cu_seqlens_k=cu_seqlens_k,
                max_seqlen_q=max_seqlen_in_batch_q,
                max_seqlen_k=max_seqlen_in_batch_k,
                dropout_p=dropout,
                softmax_scale=softmax_scale,
                causal=causal,
            )
            attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length)
        else:
            attn_output = flash_attn_func(
                query_states, key_states, value_states, dropout, softmax_scale=softmax_scale, causal=causal
            )

        return attn_output

    def _upad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length):
        indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask)

        batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape
        key_layer = index_first_axis(
            key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
        )
        value_layer = index_first_axis(
            value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
        )

        if query_length == kv_seq_len:
            query_layer = index_first_axis(
                query_layer.reshape(batch_size * kv_seq_len, self.num_heads, head_dim), indices_k
            )
            cu_seqlens_q = cu_seqlens_k
            max_seqlen_in_batch_q = max_seqlen_in_batch_k
            indices_q = indices_k
        elif query_length == 1:
            max_seqlen_in_batch_q = 1
            cu_seqlens_q = torch.arange(
                batch_size + 1, dtype=torch.int32, device=query_layer.device
            )  # There is a memcpy here, that is very bad.
            indices_q = cu_seqlens_q[:-1]
            query_layer = query_layer.squeeze(1)
        else:
            # The -q_len: slice assumes left padding.
            attention_mask = attention_mask[:, -query_length:]
            query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, attention_mask)

        return (
            query_layer,
            key_layer,
            value_layer,
            indices_q,
            (cu_seqlens_q, cu_seqlens_k),
            (max_seqlen_in_batch_q, max_seqlen_in_batch_k),
        )

class GemmoeSdpaAttention(GemmoeAttention):
    """
    Gemmoe attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
    GemmoeAttention as the weights of the module stays untouched. The only changes are on the forward pass to adapt to
    SDPA API.
    """
    def repeat_kv(self, 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 forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_value: Optional[Cache] = None,
        output_attentions: bool = False,
        use_cache: bool = False,
        cache_position: Optional[torch.LongTensor] = None,
    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
        if output_attentions:
            # TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"` once this is implemented.
            # logger.warning_once(
            "GemmoeModel is using GemmoeSdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to the manual attention implementation, "
            'but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
            # )
            
            return super().forward(
                hidden_states=hidden_states,
                attention_mask=attention_mask,
                position_ids=position_ids,
                past_key_value=past_key_value,
                output_attentions=output_attentions,
                use_cache=use_cache,
                cache_position=cache_position,
			),
            
        bsz, q_len, _ = hidden_states.size()

        query_states = self.q_proj(hidden_states)
        key_states = self.k_proj(hidden_states)
        value_states = self.v_proj(hidden_states)

        query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
        key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
        value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)

        cos, sin = self.rotary_emb(value_states, position_ids, seq_len=None)
        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, None)

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

        key_states = self.repeat_kv(key_states, self.num_key_value_groups)
        value_states = self.repeat_kv(value_states, self.num_key_value_groups)

        causal_mask = attention_mask
        if attention_mask is not None and cache_position is not None:
            causal_mask = causal_mask[:, :, cache_position, : key_states.shape[-2]]
            
            # Cast query, key, and value states to the same dtype (bf16)
            query_states = query_states.to(dtype=torch.bfloat16)
            key_states = key_states.to(dtype=torch.bfloat16)
            value_states = value_states.to(dtype=torch.bfloat16)

        # SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with custom attn_mask,
        # Reference: https://github.com/pytorch/pytorch/issues/112577.
        if query_states.device.type == "cuda" and causal_mask is not None:
            query_states = query_states.contiguous()
            key_states = key_states.contiguous()
            value_states = value_states.contiguous()

        attn_output = torch.nn.functional.scaled_dot_product_attention(
            query_states,
            key_states,
            value_states,
            attn_mask=causal_mask,
            dropout_p=self.attention_dropout if self.training else 0.0,
        )

        attn_output = attn_output.transpose(1, 2).contiguous()
        attn_output = attn_output.view(bsz, q_len, -1)
        attn_output = self.o_proj(attn_output)

        return attn_output, None, past_key_value

GEMMOE_ATTENTION_CLASSES = {
	"eager": GemmoeAttention,
	"flash_attention_2": GemmoeFlashAttention2,
	"sdpa": GemmoeSdpaAttention,
	}

class GemmoeBlockSparseTop2MLP(nn.Module):
    def __init__(self, config: GemmoeConfig):
        super().__init__()
        self.ffn_dim = config.intermediate_size
        self.hidden_dim = config.hidden_size
        self.w1 = nn.Linear(self.hidden_dim, self.ffn_dim, bias=False)
        self.w2 = nn.Linear(self.ffn_dim, self.hidden_dim, bias=False)
        self.w3 = nn.Linear(self.hidden_dim, self.ffn_dim, bias=False)
        self.act_fn = ACT2FN[config.hidden_act]

    def forward(self, hidden_states):
        current_hidden_states = self.act_fn(self.w1(hidden_states)) * self.w3(hidden_states)
        current_hidden_states = self.w2(current_hidden_states)
        return current_hidden_states


class GemmoeSparseMoeBlock(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.hidden_dim = config.hidden_size
        self.ffn_dim = config.intermediate_size
        self.num_experts = config.num_local_experts
        self.top_k = 2

        # gating
        self.gate = nn.Linear(self.hidden_dim, self.num_experts, bias=False)

        self.experts = nn.ModuleList([GemmoeBlockSparseTop2MLP(config) for _ in range(self.num_experts)])

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        hidden_states = hidden_states.to(self.gate.weight.device)
        batch_size, sequence_length, hidden_dim = hidden_states.shape
        hidden_states = hidden_states.view(-1, hidden_dim)

        # router_logits: (batch * sequence_length, n_experts)
        router_logits = self.gate(hidden_states)
        routing_weights = F.softmax(router_logits, dim=1, dtype=torch.float32)
        top_routing_weights, selected_experts = torch.topk(routing_weights, self.top_k, dim=-1)
        top_routing_weights /= top_routing_weights.sum(dim=-1, keepdim=True)

        # we cast back to the input dtype
        top_routing_weights = top_routing_weights.to(hidden_states.dtype)

        final_hidden_states = torch.zeros(
            (batch_size * sequence_length, hidden_dim), dtype=hidden_states.dtype, device=hidden_states.device
        )

        # Loop over all available experts in the model and perform the computation on each expert
        for expert_idx in range(self.num_experts):
            expert_layer = self.experts[expert_idx]
            token_indices = (selected_experts == expert_idx).any(dim=-1).nonzero(as_tuple=True)[0]

            if token_indices.numel() == 0:
                continue

            current_state = hidden_states[token_indices]
            current_hidden_states = expert_layer(current_state)

            # Multiply the output hidden states by `top_routing_weights` on the corresponding tokens
            expert_indices = (selected_experts[token_indices] == expert_idx).nonzero(as_tuple=True)[1]
            current_hidden_states *= top_routing_weights[token_indices, expert_indices, None]

            final_hidden_states.index_add_(0, token_indices, current_hidden_states)

        final_hidden_states = final_hidden_states.reshape(batch_size, sequence_length, hidden_dim)
        return final_hidden_states, router_logits

    
class GemmoeDecoderLayer(nn.Module):
    """
    Decoder layer for the Gemmoe model.

    Args:
        config (GemmoeConfig): The configuration object for the Gemmoe model.
        layer_idx (int): The index of the layer.
    """
    def __init__(self, config: GemmoeConfig, layer_idx: int):
        super().__init__()
        self.hidden_size = config.hidden_size
        self.self_attn = GEMMOE_ATTENTION_CLASSES[config._attn_implementation](config=config, layer_idx=layer_idx)
        self.mlp = GemmoeMLP(config)
        self.block_sparse_moe = GemmoeSparseMoeBlock(config)
        self.input_layernorm = GemmoeRMSNorm(config.hidden_size, eps=config.rms_norm_eps) 
        self.post_attention_layernorm = GemmoeRMSNorm(config.hidden_size, eps=config.rms_norm_eps)

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_value: Optional[Tuple[torch.Tensor]] = None,
        output_attentions: Optional[bool] = False,
        output_router_logits: Optional[bool] = False,
        use_cache: Optional[bool] = False,
        cache_position: Optional[torch.LongTensor] = None,
        **kwargs,
    ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
        """
        Args:
            hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
            attention_mask (`torch.FloatTensor`, *optional*):
                attention mask of size `(batch_size, sequence_length)` if flash attention is used or `(batch_size, 1,
                query_sequence_length, key_sequence_length)` if default attention is used.
            output_attentions (`bool`, *optional*):
                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
                returned tensors for more detail.
            use_cache (`bool`, *optional*):
                If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
                (see `past_key_values`).
            past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
            output_router_logits (`bool`, *optional*):
                Whether or not to return the logits of all the routers. They are useful for computing the router loss, and
                should not be returned during inference.
        """
        if "padding_mask" in kwargs:
            warnings.warn(
                "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
            )

        residual = hidden_states
        hidden_states = self.input_layernorm(hidden_states)

        # Self Attention
        hidden_states, self_attn_weights, present_key_value = self.self_attn(
            hidden_states=hidden_states,
            attention_mask=attention_mask,
            position_ids=position_ids,
            past_key_value=past_key_value,
            output_attentions=output_attentions,
            use_cache=use_cache,
            cache_position=cache_position,
            **kwargs,
        )
        hidden_states = residual + hidden_states

        # Fully Connected
        residual = hidden_states
        hidden_states = self.post_attention_layernorm(hidden_states)
        hidden_states, router_logits = self.block_sparse_moe(hidden_states)
        hidden_states = residual + hidden_states
    

        outputs = (hidden_states,)

        if output_attentions:
            outputs += (self_attn_weights,)

        if use_cache:
            outputs += (present_key_value,)

        if output_router_logits:
            outputs += (router_logits,)

        return outputs

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

@add_start_docstrings(
"The bare Gemmoe Model outputting raw hidden-states without any specific head on top.",
GEMMOE_START_DOCSTRING,
)

class GemmoePreTrainedModel(PreTrainedModel):
	config_class = GemmoeConfig
	base_model_prefix = "model"
	supports_gradient_checkpointing = True
	_keep_in_fp32_modules = ["inv_freq", "rotary_emb", "cos_cached", "sin_cached"]
	_no_split_modules = ["GemmoeDecoderLayer"]
	_skip_keys_device_placement = ["past_key_values", "causal_mask"]
	_supports_flash_attn_2 = True
	_supports_sdpa = True
	_supports_cache_class = True
 
	def _init_weights(self, 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_()

	def _setup_cache(self, cache_cls, max_batch_size, max_cache_len: Optional[int] = None):
		if self.config._attn_implementation == "flash_attention_2" and cache_cls == StaticCache:
			raise ValueError(
				"`static` cache implementation is not compatible with `attn_implementation==flash_attention_2` "
				"make sure to use `sdpa` in the mean time, and open an issue at https://github.com/huggingface/transformers"
			)
		if max_cache_len > self.model.causal_mask.shape[-1] or self.device != self.model.causal_mask.device:
			causal_mask = torch.full((max_cache_len, max_cache_len), fill_value=1, device=self.device)
			self.register_buffer("causal_mask", torch.triu(causal_mask, diagonal=1), persistent=False)

		for layer in self.model.layers:
			weights = layer.self_attn.o_proj.weight
			layer.self_attn.past_key_value = cache_cls(
				self.config, max_batch_size, max_cache_len, device=weights.device, dtype=weights.dtype
			)

	def _reset_cache(self):
		for layer in self.model.layers:
			layer.self_attn.past_key_value = None

GEMMOE_INPUTS_DOCSTRING = r"""
Args:
input_ids (torch.LongTensor of shape (batch_size, sequence_length)):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
it.
"""

@add_start_docstrings(
"The bare Gemmoe Model outputting raw hidden-states without any specific head on top.",
GEMMOE_START_DOCSTRING,
)

class GemmoeModel(GemmoePreTrainedModel):
	"""
	Transformer decoder consisting of config.num_hidden_layers layers. Each layer is a [GemmoeDecoderLayer]Args:
		config: GemmoeConfig
	"""


	def __init__(self, config: GemmoeConfig):
		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(
			[GemmoeDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
		)
		self.norm = GemmoeRMSNorm(config.hidden_size, eps=config.rms_norm_eps)

		self.gradient_checkpointing = False

		# Register a causal mask to separate causal and padding mask creation. Merging happens in the attention class.
		# NOTE: This is not friendly with TorchScript, ONNX, ExportedProgram serialization for very large `max_position_embeddings`.
		causal_mask = torch.full(
			(config.max_position_embeddings, config.max_position_embeddings), fill_value=True, dtype=torch.bool
		)
		self.register_buffer("causal_mask", torch.triu(causal_mask, diagonal=1), persistent=False)

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

	def get_input_embeddings(self):
		return self.embed_tokens

	def set_input_embeddings(self, value):
		self.embed_tokens = value

	@add_start_docstrings_to_model_forward(GEMMOE_INPUTS_DOCSTRING)
	@replace_return_docstrings(output_type=MoeModelOutputWithPast, config_class=_CONFIG_FOR_DOC)
	def forward(
		self,
		input_ids: torch.LongTensor = None,
		attention_mask: Optional[torch.Tensor] = None,
		position_ids: Optional[torch.LongTensor] = None,
		past_key_values: Optional[List[torch.FloatTensor]] = None,
		inputs_embeds: Optional[torch.FloatTensor] = None,
		use_cache: Optional[bool] = None,
		output_attentions: Optional[bool] = None,
		output_hidden_states: Optional[bool] = None,
		output_router_logits: Optional[bool] = None,
		return_dict: Optional[bool] = None,
		cache_position: Optional[torch.LongTensor] = None,
	) -> Union[Tuple, MoeModelOutputWithPast]:
		"""
		Forward pass of the sequence classification model.

		Args:
			input_ids: Input token IDs.
			attention_mask: Attention mask.
			position_ids: Position IDs.
			past_key_values: Past key-value pairs.
			inputs_embeds: Input embeddings.
			labels: Labels for sequence classification.
			use_cache: Whether to use cache.
			output_attentions: Whether to output attentions.
			output_hidden_states: Whether to output hidden states.
			return_dict: Whether to return a dictionary or tuple.

		Returns:
			Output of the sequence classification model.
		"""
		output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
		output_hidden_states = (
			output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
		)
		output_router_logits = (
			output_router_logits if output_router_logits is not None else self.config.output_router_logits
		)
		use_cache = use_cache if use_cache is not None else self.config.use_cache
		return_dict = return_dict if return_dict is not None else self.config.use_return_dict

		if (input_ids is None) ^ (inputs_embeds is not None):
			raise ValueError(
				"You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one"
			)

		if self.gradient_checkpointing and self.training and use_cache:
			logger.warning_once(
				"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`."
			)
			use_cache = False

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

		past_seen_tokens = 0
		if use_cache:  # kept for BC (cache positions)
			if not isinstance(past_key_values, StaticCache):
				past_key_values = DynamicCache.from_legacy_cache(past_key_values)
			past_seen_tokens = past_key_values.get_seq_length()

		if cache_position is None:
			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 = self._update_causal_mask(attention_mask, inputs_embeds)

		hidden_states = inputs_embeds

		# Normalize
		scale_factor = torch.tensor(math_sqrt(self.config.hidden_size), dtype=hidden_states.dtype)
		hidden_states = hidden_states * scale_factor
		# Decoder layers
		all_hidden_states = () if output_hidden_states else None
		all_self_attns = () if output_attentions else None
		all_router_logits = () if output_router_logits else None
		next_decoder_cache = None

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

			if self.gradient_checkpointing and self.training:
				layer_outputs = self._gradient_checkpointing_func(
					decoder_layer.__call__,
					hidden_states,
					causal_mask,
					position_ids,
					past_key_values,
					output_attentions,
					output_router_logits,
					use_cache,
					cache_position,
				)
			else:
				layer_outputs = decoder_layer(
					hidden_states,
					attention_mask=causal_mask,
					position_ids=position_ids,
					past_key_value=past_key_values,
					output_attentions=output_attentions,
					output_router_logits=output_router_logits,
					use_cache=use_cache,
					cache_position=cache_position,
				)

			hidden_states = layer_outputs[0]
			if use_cache:
				next_decoder_cache = layer_outputs[2 if output_attentions else 1]
			if output_attentions:
				all_self_attns += (layer_outputs[1],)
			if output_router_logits:
				all_router_logits += (layer_outputs[-1],)

		hidden_states = self.norm(hidden_states)

		# Add hidden states from the last decoder layer
		if output_hidden_states:
			all_hidden_states += (hidden_states,)

		next_cache = None
		if use_cache:
			next_cache = (
				next_decoder_cache.to_legacy_cache() if isinstance(next_decoder_cache, Cache) else next_decoder_cache
			)

		if not return_dict:
			return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns, all_router_logits] if v is not None)

		return MoeModelOutputWithPast(
			last_hidden_state=hidden_states,
			past_key_values=next_cache,
			hidden_states=all_hidden_states,
			attentions=all_self_attns,
			router_logits=all_router_logits
		)

	def _update_causal_mask(self, attention_mask, input_tensor):
		"""
		Update the causal mask based on the attention mask and input tensor.

		Args:
			attention_mask (torch.Tensor): The attention mask.
			input_tensor (torch.Tensor): The input tensor.

		Returns:
			torch.Tensor: The updated causal mask.
		"""

		if self.config._attn_implementation == "flash_attention_2":
			if attention_mask is not None and 0.0 in attention_mask:
				return attention_mask
			return None

		batch_size, seq_length = input_tensor.shape[:2]
		dtype = input_tensor.dtype
		device = input_tensor.device

		# support going beyond cached `max_position_embedding`
		if seq_length > self.causal_mask.shape[-1]:
			logger.info(f"Resizing causal mask buffer from {self.causal_mask.shape[-1]} to {2 * self.causal_mask.shape[-1]}")
			causal_mask = torch.full((2 * self.causal_mask.shape[-1], 2 * self.causal_mask.shape[-1]), fill_value=1)
			self.register_buffer("causal_mask", torch.triu(causal_mask, diagonal=1), persistent=False)

		# We use the current dtype to avoid any overflows
		min_dtype = torch.finfo(dtype).min
		causal_mask = self.causal_mask[None, None, :, :].repeat(batch_size, 1, 1, 1).to(dtype) * min_dtype
		causal_mask = causal_mask.to(dtype=dtype, device=device)
		
		if attention_mask is not None and attention_mask.dim() == 2:
			mask_length = attention_mask.shape[-1]
			padding_mask = causal_mask[..., :mask_length].eq(0.0) * attention_mask[:, None, None, :].eq(0.0)
			causal_mask[..., :mask_length] = causal_mask[..., :mask_length].masked_fill(padding_mask, min_dtype)
		
		if self.config._attn_implementation == "sdpa" and attention_mask is not None:
			# TODO: For dynamo, rather use a check on fullgraph=True once this is possible (https://github.com/pytorch/pytorch/pull/120400).
			is_tracing = (
				torch.jit.is_tracing()
				or isinstance(input_tensor, torch.fx.Proxy)
				or (hasattr(torch, "_dynamo") and torch._dynamo.is_compiling())
			)
			
			if not is_tracing and torch.any(attention_mask != 1):
				# Attend to all tokens in masked rows from the causal_mask, for example the relevant first rows when
				# using left padding. This is required by
				# F.scaled_dot_product_attention memory-efficient attention path.
				# Details: https://github.com/pytorch/pytorch/issues/110213
				causal_mask = causal_mask.mul(~torch.all(causal_mask == min_dtype, dim=-1, keepdim=True)).to(dtype)

		return causal_mask

class GemmoeForCausalLM(GemmoePreTrainedModel):
    r"""
    The Gemmoe Model transformer with a language modeling head on top for causal language modeling (CLM).

    Args:
        config (GemmoeConfig): The configuration object for the Gemmoe model.

    Example usage:
    ```python
    >>> from transformers import AutoTokenizer, GemmoeForCausalLM

    >>> model = GemmoeForCausalLM.from_pretrained("google/gemmoe-7b")
    >>> tokenizer = AutoTokenizer.from_pretrained("google/gemmoe-7b")

    >>> prompt = "What is your favorite condiment?"
    >>> 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]
    "What is your favorite condiment?"
    ```
    """
    _tied_weights_keys = ["lm_head.weight"]

    def __init__(self, config):
        super().__init__(config)
        self.model = GemmoeModel(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.router_aux_loss_coef
        self.num_experts = 8
        self.num_experts_per_tok = config.num_experts_per_tok

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

    def get_input_embeddings(self):
        return self.model.embed_tokens

    def set_input_embeddings(self, value):
        self.model.embed_tokens = value

    def get_output_embeddings(self):
        return self.lm_head

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

    def set_decoder(self, decoder):
        self.model = decoder

    def get_decoder(self):
        return self.model

    @add_start_docstrings_to_model_forward(GEMMOE_INPUTS_DOCSTRING)
    @replace_return_docstrings(output_type=MoeCausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
    def forward(
        self,
        input_ids: torch.LongTensor = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_values: Optional[List[torch.FloatTensor]] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        labels: Optional[torch.LongTensor] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        output_router_logits: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        cache_position: Optional[torch.LongTensor] = None,
    ) -> Union[Tuple, MoeCausalLMOutputWithPast]:
        r"""
        Args:
            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]`.

        Returns:

        Example:

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

        >>> model = GemmoeForCausalLM.from_pretrained("google/gemmoe-7b")
        >>> tokenizer = AutoTokenizer.from_pretrained("google/gemmoe-7b")

        >>> prompt = "What is your favorite condiment?"
        >>> 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]
        "What is your favorite condiment?"
        ```"""
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_router_logits = (
            output_router_logits if output_router_logits is not None else getattr(self.config, "output_router_logits", False)
        )
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        outputs = 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,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            output_router_logits=output_router_logits,
            return_dict=return_dict,
            cache_position=cache_position,
        )

        hidden_states = outputs[0]
        logits = self.lm_head(hidden_states)
        logits = logits.float()
        
        loss = None
        if labels is not None:
            shift_logits = logits[..., :-1, :].contiguous()
            shift_labels = labels[..., 1:].contiguous()
            loss_fct = CrossEntropyLoss()
            shift_logits = shift_logits.view(-1, self.config.vocab_size)
            shift_labels = shift_labels.view(-1)
            shift_labels = shift_labels.to(shift_logits.device)
            loss = loss_fct(shift_logits, shift_labels)

        aux_loss = None
        if output_router_logits:
            router_logits = outputs.router_logits if return_dict else outputs[-1]
            if router_logits is not None:
                aux_loss = load_balancing_loss_func(
                    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)

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

        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,
        )

    def prepare_inputs_for_generation(
        self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
    ):
        past_length = 0
        if past_key_values is not None:
            if isinstance(past_key_values, Cache):
                cache_length = past_key_values.get_seq_length()
                past_length = past_key_values.seen_tokens
                max_cache_length = past_key_values.get_max_length()
            else:
                cache_length = past_length = past_key_values[0][0].shape[2]
                max_cache_length = None

            if attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1]:
                input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :]
            elif past_length < input_ids.shape[1]:
                input_ids = input_ids[:, past_length:]
            
            if (
                max_cache_length is not None
                and attention_mask is not None
                and cache_length + input_ids.shape[1] > max_cache_length
            ):
                attention_mask = attention_mask[:, -max_cache_length:]

        position_ids = kwargs.get("position_ids", None)
        if attention_mask is not None and position_ids is None:
            position_ids = attention_mask.long().cumsum(-1) - 1
            position_ids.masked_fill_(attention_mask == 0, 1)
            if past_key_values:
                position_ids = position_ids[:, -input_ids.shape[1] :]

        if self.generation_config.cache_implementation == "static":
            cache_position = kwargs.get("cache_position", None)
            if cache_position is None:
                past_length = 0
            else:
                past_length = cache_position[-1] + 1
            input_ids = input_ids[:, past_length:]
            position_ids = position_ids[:, past_length:]

        cache_position = torch.arange(past_length, past_length + position_ids.shape[-1], device=position_ids.device)

        if inputs_embeds is not None and past_key_values is None:
            model_inputs = {"inputs_embeds": inputs_embeds}
        else:
            model_inputs = {"input_ids": input_ids.contiguous()}

        model_inputs.update(
            {
                "position_ids": position_ids.contiguous(),
                "cache_position": cache_position,
                "past_key_values": past_key_values,
                "use_cache": kwargs.get("use_cache"),
                "attention_mask": attention_mask,
            }
        )

        return model_inputs

    @staticmethod
    def _reorder_cache(past_key_values, beam_idx):
        reordered_past = ()
        for layer_past in past_key_values:
            reordered_past += (
                tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),
            )
        return reordered_past
@add_start_docstrings(
    """
    The Gemmoe Model transformer with a sequence classification head on top (linear layer).
    [`GemmoeForSequenceClassification`] uses the last token in order to do the classification, as other causal models
    (e.g. GPT-2) do.

    Since it does classification on the last token, it requires to know the position of the last token. If a
    `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
    no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
    padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
    each row of the batch).
    """,
    GEMMOE_START_DOCSTRING,
)

class GemmoeForSequenceClassification(GemmoePreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.num_labels = config.num_labels
        self.model = GemmoeModel(config)
        self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
        # Initialize weights and apply final processing
        self.post_init()

    def get_input_embeddings(self):
        return self.model.embed_tokens

    def set_input_embeddings(self, value):
        self.model.embed_tokens = value

    @add_start_docstrings_to_model_forward(GEMMOE_INPUTS_DOCSTRING)
    @replace_return_docstrings(output_type=SequenceClassifierOutputWithPast, config_class=_CONFIG_FOR_DOC)
    def forward(
        self,
        input_ids: torch.LongTensor = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_values: Optional[List[torch.FloatTensor]] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        labels: Optional[torch.LongTensor] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple, SequenceClassifierOutputWithPast]:
        """
        Forward pass of the sequence classification model.

        Args:
            input_ids (torch.LongTensor, optional): Input token IDs.
            attention_mask (torch.Tensor, optional): Attention mask.
            position_ids (torch.LongTensor, optional): Position IDs.
            past_key_values (List[torch.FloatTensor], optional): Past key-value pairs.
            inputs_embeds (torch.FloatTensor, optional): Input embeddings.
            labels (torch.LongTensor, optional): Labels for sequence classification.
            use_cache (bool, optional): Whether to use cache.
            output_attentions (bool, optional): Whether to output attentions.
            output_hidden_states (bool, optional): Whether to output hidden states.
            return_dict (bool, optional): Whether to return a dictionary or tuple.

        Returns:
            Union[Tuple, SequenceClassifierOutputWithPast]: Output of the sequence classification model.
        """
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        transformer_outputs = self.model(
            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_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )
        hidden_states = transformer_outputs[0]
        logits = self.score(hidden_states)

        if input_ids is not None:
            batch_size = input_ids.shape[0]
        else:
            batch_size = inputs_embeds.shape[0]

        if self.config.pad_token_id is None and batch_size != 1:
            raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
        if self.config.pad_token_id is None:
            sequence_lengths = -1
        else:
            if input_ids is not None:
                # if no pad token found, use modulo instead of reverse indexing for ONNX compatibility
                sequence_lengths = torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1
                sequence_lengths = sequence_lengths % input_ids.shape[-1]
                sequence_lengths = sequence_lengths.to(logits.device)
            else:
                sequence_lengths = -1

        pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]

        loss = None
        if labels is not None:
            labels = labels.to(logits.device)
            if self.config.problem_type is None:
                if self.num_labels == 1:
                    self.config.problem_type = "regression"
                elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
                    self.config.problem_type = "single_label_classification"
                else:
                    self.config.problem_type = "multi_label_classification"

            if self.config.problem_type == "regression":
                loss_fct = MSELoss()
                if self.num_labels == 1:
                    loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
                else:
                    loss = loss_fct(pooled_logits, labels)
            elif self.config.problem_type == "single_label_classification":
                loss_fct = CrossEntropyLoss()
                loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
            elif self.config.problem_type == "multi_label_classification":
                loss_fct = BCEWithLogitsLoss()
                loss = loss_fct(pooled_logits, labels)

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

        return SequenceClassifierOutputWithPast(
            loss=loss,
            logits=pooled_logits,
            past_key_values=transformer_outputs.past_key_values,
            hidden_states=transformer_outputs.hidden_states,
            attentions=transformer_outputs.attentions,
        )