#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
#           This file was automatically generated from src/transformers/models/jamba/modular_jamba.py.
#               Do NOT edit this file manually as any edits will be overwritten by the generation of
#             the file from the modular. If any change should be done, please apply the change to the
#                          modular_jamba.py file directly. One of our CI enforces this.
#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
# Copyright 2024 AI21 Labs Ltd. and the HuggingFace Inc. team. All rights reserved.
#
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
# and OPT implementations in this library. It has been modified from its
# original forms to accommodate minor architectural differences compared
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from collections.abc import Callable
from typing import Any

import torch
from torch import nn

from ... import initialization as init
from ...activations import ACT2FN
from ...generation import GenerationMixin
from ...integrations import (
    lazy_load_kernel,
    use_experts_implementation,
    use_kernel_forward_from_hub,
    use_kernel_func_from_hub,
    use_kernelized_func,
)
from ...masking_utils import create_causal_mask
from ...modeling_layers import GenericForSequenceClassification, GradientCheckpointingLayer
from ...modeling_outputs import MoeCausalLMOutputWithPast, MoeModelOutputWithPast
from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
from ...processing_utils import Unpack
from ...utils import TransformersKwargs, auto_docstring, can_return_tuple, logging
from ...utils.generic import merge_with_config_defaults
from ...utils.import_utils import resolve_internal_import
from ...utils.output_capturing import OutputRecorder, capture_outputs
from .configuration_jamba import JambaConfig


logger = logging.get_logger(__name__)


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

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

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


class HybridMambaAttentionDynamicCache:
    """
    A dynamic cache that can handle both the attention cache (which has a seq_len dimension) and the mamba cache
    (which has a constant shape regardless of seq_len).

    This cache has two sets of lists of tensors: `key_cache` and `value_cache` for attention cache and `conv_states`
    and `ssm_states` for mamba cache. Each of these lists has `num_layers` tensors. The expected shape for each tensor
    For attention layers, `key_cache` and `value_cache` have a shape of `(batch_size, num_heads, seq_len, head_dim)`,
    while `conv_states` and `ssm_states` have a shape of `(batch_size, 0)` (empty tensors).
    For mamba layers, `key_cache` and `value_cache` have a shape of `(batch_size, 0)` (empty tensors),
    while `conv_states` represents the convolution state and has a shape of `(batch_size, d_inner, d_conv)`,
    and `ssm_states` represents the ssm state and has a shape of `(batch_size, d_inner, d_state)`.
    """

    is_compileable = False

    def __init__(self, config, batch_size, dtype=torch.float16, device=None):
        self.dtype = dtype
        self.layers_block_type = config.layers_block_type
        self.has_previous_state = False  # only used by mamba
        intermediate_size = config.mamba_expand * config.hidden_size
        ssm_state_size = config.mamba_d_state
        conv_kernel_size = config.mamba_d_conv
        self.conv_states = []
        self.ssm_states = []
        self.transformer_layers = []
        for i in range(config.num_hidden_layers):
            if self.layers_block_type[i] == "mamba":
                self.conv_states += [
                    torch.zeros(batch_size, intermediate_size, conv_kernel_size, device=device, dtype=dtype)
                ]
                self.ssm_states += [
                    torch.zeros(batch_size, intermediate_size, ssm_state_size, device=device, dtype=dtype)
                ]
            else:
                self.conv_states += [torch.tensor([[]] * batch_size, device=device)]
                self.ssm_states += [torch.tensor([[]] * batch_size, device=device)]
                self.transformer_layers.append(i)

        self.key_cache = [torch.tensor([[]] * batch_size, device=device) for _ in range(config.num_hidden_layers)]
        self.value_cache = [torch.tensor([[]] * batch_size, device=device) for _ in range(config.num_hidden_layers)]

    def __len__(self):
        return len(self.key_cache)

    def __getitem__(self, layer_idx):
        return self.key_cache[layer_idx], self.value_cache[layer_idx]

    def update(
        self,
        key_states: torch.Tensor,
        value_states: torch.Tensor,
        layer_idx: int,
        cache_kwargs: dict[str, Any] | None = None,
    ) -> tuple[torch.Tensor, torch.Tensor]:
        # Update the cache
        if self.key_cache[layer_idx].shape[-1] == 0:
            self.key_cache[layer_idx] = key_states
            self.value_cache[layer_idx] = value_states
        else:
            self.key_cache[layer_idx] = torch.cat([self.key_cache[layer_idx], key_states], dim=2)
            self.value_cache[layer_idx] = torch.cat([self.value_cache[layer_idx], value_states], dim=2)

        return self.key_cache[layer_idx], self.value_cache[layer_idx]

    def reorder_cache(self, beam_idx: torch.LongTensor):
        """Reorders the cache for beam search, given the selected beam indices."""
        if self.get_seq_length() > 0:
            for layer_idx in range(len(self.key_cache)):
                device = self.key_cache[layer_idx].device
                self.key_cache[layer_idx] = self.key_cache[layer_idx].index_select(0, beam_idx.to(device))
                device = self.value_cache[layer_idx].device
                self.value_cache[layer_idx] = self.value_cache[layer_idx].index_select(0, beam_idx.to(device))

                device = self.conv_states[layer_idx].device
                self.conv_states[layer_idx] = self.conv_states[layer_idx].index_select(0, beam_idx.to(device))
                device = self.ssm_states[layer_idx].device
                self.ssm_states[layer_idx] = self.ssm_states[layer_idx].index_select(0, beam_idx.to(device))

    def get_mask_sizes(self, cache_position: torch.Tensor, layer_idx: int) -> tuple[int, int]:
        """Return the length and offset of the cache, used to generate the mask"""
        kv_offset = 0
        query_length = cache_position.shape[0]
        kv_length = self.get_seq_length(layer_idx) + query_length
        return kv_length, kv_offset

    def get_seq_length(self, layer_idx: int | None = 0) -> int:
        """Returns the sequence length of the cached states. A layer index can be optionally passed."""
        # take any layer that contains cache and not empty tensor
        layer_idx = self.transformer_layers[0] if layer_idx not in self.transformer_layers else layer_idx
        if len(self.key_cache) <= layer_idx or self.key_cache[layer_idx].shape[-1] == 0:
            return 0
        return self.key_cache[layer_idx].shape[-2]


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


@use_kernel_func_from_hub("rotary_pos_emb")
def apply_rotary_pos_emb(q, k, cos, sin, unsqueeze_dim=1):
    """Applies Rotary Position Embedding to the query and key tensors.

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


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


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

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

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

    return attn_output, attn_weights


@use_kernelized_func(apply_rotary_pos_emb)
class JambaAttention(nn.Module):
    """Multi-headed attention from 'Attention Is All You Need' paper"""

    def __init__(self, config: JambaConfig, layer_idx: int):
        super().__init__()
        self.config = config
        self.layer_idx = layer_idx
        self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
        self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads
        self.scaling = self.head_dim**-0.5
        self.attention_dropout = config.attention_dropout
        self.is_causal = True
        self.q_proj = nn.Linear(config.hidden_size, config.num_attention_heads * self.head_dim, bias=False)
        self.k_proj = nn.Linear(config.hidden_size, config.num_key_value_heads * self.head_dim, bias=False)
        self.v_proj = nn.Linear(config.hidden_size, config.num_key_value_heads * self.head_dim, bias=False)
        self.o_proj = nn.Linear(config.num_attention_heads * self.head_dim, config.hidden_size, bias=False)

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

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

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

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

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

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


class JambaMambaMixer(nn.Module):
    """
    Compute ∆, A, B, C, and D the state space parameters and compute the `contextualized_states`.
    A, D are input independent (see Mamba paper [1] Section 3.5.2 "Interpretation of A" for why A isn't selective)
    ∆, B, C are input-dependent (this is a key difference between Mamba and the linear time invariant S4,
    and is why Mamba is called **selective** state spaces)
    """

    def __init__(self, config: JambaConfig, layer_idx):
        super().__init__()
        self.config = config
        self.layer_idx = layer_idx
        self.hidden_size = config.hidden_size
        self.ssm_state_size = config.mamba_d_state
        self.conv_kernel_size = config.mamba_d_conv
        self.intermediate_size = config.mamba_expand * config.hidden_size
        self.time_step_rank = config.mamba_dt_rank
        self.use_conv_bias = config.mamba_conv_bias
        self.use_bias = config.mamba_proj_bias
        self.conv1d = nn.Conv1d(
            in_channels=self.intermediate_size,
            out_channels=self.intermediate_size,
            bias=self.use_conv_bias,
            kernel_size=self.conv_kernel_size,
            groups=self.intermediate_size,
            padding=self.conv_kernel_size - 1,
        )

        self.activation = config.hidden_act
        self.act = ACT2FN[config.hidden_act]

        # projection of the input hidden states
        self.in_proj = nn.Linear(self.hidden_size, self.intermediate_size * 2, bias=self.use_bias)
        # selective projection used to make dt, B and C input dependent
        self.x_proj = nn.Linear(self.intermediate_size, self.time_step_rank + self.ssm_state_size * 2, bias=False)
        # time step projection (discretization)
        self.dt_proj = nn.Linear(self.time_step_rank, self.intermediate_size, bias=True)

        # S4D real initialization. These are not discretized!
        # The core is to load them, compute the discrete states, then write the updated state. Keeps the memory bounded
        A = torch.arange(1, self.ssm_state_size + 1)[None, :]
        A = A.expand(self.intermediate_size, -1).contiguous()

        self.A_log = nn.Parameter(torch.log(A))
        self.D = nn.Parameter(torch.ones(self.intermediate_size))
        self.out_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=self.use_bias)

        self.dt_layernorm = JambaRMSNorm(self.time_step_rank, eps=config.rms_norm_eps)
        self.b_layernorm = JambaRMSNorm(self.ssm_state_size, eps=config.rms_norm_eps)
        self.c_layernorm = JambaRMSNorm(self.ssm_state_size, eps=config.rms_norm_eps)

        global causal_conv1d_update, causal_conv1d_fn
        causal_conv1d = lazy_load_kernel("causal-conv1d")
        causal_conv1d_update = getattr(causal_conv1d, "causal_conv1d_update", None)
        causal_conv1d_fn = getattr(causal_conv1d, "causal_conv1d_fn", None)

        global selective_state_update, mamba_inner_fn, selective_scan_fn
        mamba_ssm = lazy_load_kernel("mamba-ssm")
        selective_state_update = resolve_internal_import(
            mamba_ssm, chained_path="ops.triton.selective_state_update.selective_state_update"
        )
        selective_scan_fn = getattr(mamba_ssm, "selective_scan_fn", None)
        mamba_inner_fn = getattr(mamba_ssm, "mamba_inner_fn", None)

        global is_fast_path_available
        is_fast_path_available = all(
            (selective_state_update, selective_scan_fn, causal_conv1d_fn, causal_conv1d_update, mamba_inner_fn)
        )

        if not is_fast_path_available:
            logger.warning_once(
                "The fast path is not available because on of `(selective_state_update, selective_scan_fn, causal_conv1d_fn, causal_conv1d_update, mamba_inner_fn)`"
                " is None. To install follow https://github.com/state-spaces/mamba/#installation and https://github.com/Dao-AILab/causal-conv1d."
            )

    def cuda_kernels_forward(
        self,
        hidden_states: torch.Tensor,
        cache_params: HybridMambaAttentionDynamicCache | None = None,
        attention_mask: torch.LongTensor | None = None,
    ):
        batch_size, seq_len, _ = hidden_states.shape
        use_precomputed_states = (
            cache_params is not None
            and cache_params.has_previous_state
            and seq_len == 1
            and cache_params.conv_states[self.layer_idx].shape[0]
            == cache_params.ssm_states[self.layer_idx].shape[0]
            == batch_size
        )
        # 1. Gated MLP's linear projection
        projected_states = self.in_proj(hidden_states).transpose(1, 2)

        # We can't use `mamba_inner_fn` even if in training and without cache params because we have the
        # inner layernorms which isn't supported by this fused kernel
        hidden_states, gate = projected_states.chunk(2, dim=1)

        if attention_mask is not None:
            hidden_states = hidden_states * attention_mask.unsqueeze(1)

        # 2. Convolution sequence transformation
        conv_weights = self.conv1d.weight.view(self.conv1d.weight.size(0), self.conv1d.weight.size(2))
        if use_precomputed_states:
            hidden_states = causal_conv1d_update(
                hidden_states.squeeze(-1),
                cache_params.conv_states[self.layer_idx],
                conv_weights,
                self.conv1d.bias,
                self.activation,
            )
            hidden_states = hidden_states.unsqueeze(-1)
        else:
            if cache_params is not None:
                conv_states = nn.functional.pad(hidden_states, (self.conv_kernel_size - hidden_states.shape[-1], 0))
                cache_params.conv_states[self.layer_idx].copy_(conv_states)
            hidden_states = causal_conv1d_fn(hidden_states, conv_weights, self.conv1d.bias, activation=self.activation)

        if attention_mask is not None:
            hidden_states = hidden_states * attention_mask.unsqueeze(1)

        # 3. State Space Model sequence transformation
        # 3.a. input varying initialization of time_step, B and C
        ssm_parameters = self.x_proj(hidden_states.transpose(1, 2))
        time_step, B, C = torch.split(
            ssm_parameters, [self.time_step_rank, self.ssm_state_size, self.ssm_state_size], dim=-1
        )

        time_step = self.dt_layernorm(time_step)
        B = self.b_layernorm(B)
        C = self.c_layernorm(C)

        # Here we need to apply dt_proj without the bias, as the bias is added in the selective scan kernel.
        # This is a hack to apply dt_proj while still using the forward pass of `torch.nn.Linear`, which is needed
        # in order to make quantization work. Quantization code replaces `torch.nn.Linear` layers with quantized
        # linear layers, and requires to call the forward pass directly.
        # Quantized model can't work with the original code:
        # ```discrete_time_step = self.dt_proj.weight @ time_step.transpose(1, 2)```
        time_proj_bias = self.dt_proj.bias.data
        with torch.no_grad():
            self.dt_proj.bias.data = torch.zeros_like(self.dt_proj.bias.data)
        discrete_time_step = self.dt_proj(time_step).transpose(1, 2)
        with torch.no_grad():
            self.dt_proj.bias.data = time_proj_bias

        A = -torch.exp(self.A_log.float())
        # 3.c perform the recurrence y ← SSM(A, B, C)(x)
        time_proj_bias = time_proj_bias.float() if time_proj_bias is not None else None
        if use_precomputed_states:
            scan_outputs = selective_state_update(
                cache_params.ssm_states[self.layer_idx],
                hidden_states[..., 0],
                discrete_time_step[..., 0],
                A,
                B[:, 0],
                C[:, 0],
                self.D,
                gate[..., 0],
                time_proj_bias,
                dt_softplus=True,
            ).unsqueeze(-1)
        else:
            scan_outputs, ssm_state = selective_scan_fn(
                hidden_states,
                discrete_time_step,
                A,
                B.transpose(1, 2),
                C.transpose(1, 2),
                self.D.float(),
                gate,
                time_proj_bias,
                delta_softplus=True,
                return_last_state=True,
            )
            if ssm_state is not None and cache_params is not None:
                cache_params.ssm_states[self.layer_idx].copy_(ssm_state)

        # 4. Final linear projection
        contextualized_states = self.out_proj(scan_outputs.transpose(1, 2))

        return contextualized_states

    # fmt: off
    def slow_forward(self, input_states, cache_params: HybridMambaAttentionDynamicCache | None = None, attention_mask: torch.LongTensor | None = None):
        batch_size, seq_len, _ = input_states.shape
        dtype = input_states.dtype
        # 1. Gated MLP's linear projection
        projected_states = self.in_proj(input_states).transpose(1, 2)
        hidden_states, gate = projected_states.chunk(2, dim=1)

        if attention_mask is not None:
            hidden_states = hidden_states * attention_mask.unsqueeze(1)

        use_cache = isinstance(cache_params, HybridMambaAttentionDynamicCache)
        # 2. Convolution sequence transformation
        if use_cache and cache_params.ssm_states[self.layer_idx].shape[0] == batch_size:
            if self.training:
                # In training mode, we don't want to perform in-place operations on ssm_state so we can compute the backwards pass
                ssm_state = cache_params.ssm_states[self.layer_idx].clone()
            else:
                ssm_state = cache_params.ssm_states[self.layer_idx]

            ssm_state = ssm_state.to(hidden_states.device)

            if cache_params.has_previous_state and seq_len == 1 and \
                    cache_params.conv_states[self.layer_idx].shape[0] == batch_size:
                conv_state = cache_params.conv_states[self.layer_idx]
                conv_state = torch.roll(conv_state, shifts=-1, dims=-1)
                conv_state[:, :, -1] = hidden_states[:, :, 0]
                cache_params.conv_states[self.layer_idx] = conv_state
                hidden_states = torch.sum(conv_state * self.conv1d.weight[:, 0, :], dim=-1)
                if self.use_conv_bias:
                    hidden_states += self.conv1d.bias
                hidden_states = self.act(hidden_states).to(dtype).unsqueeze(-1)
            else:
                conv_state = nn.functional.pad(
                    hidden_states,
                    (self.conv_kernel_size - hidden_states.shape[-1], 0)
                )
                cache_params.conv_states[self.layer_idx] = conv_state
                hidden_states = self.act(self.conv1d(hidden_states)[..., :seq_len])
        else:
            ssm_state = torch.zeros(
                (batch_size, self.intermediate_size, self.ssm_state_size),
                device=hidden_states.device, dtype=dtype
            )
            hidden_states = self.act(self.conv1d(hidden_states)[..., :seq_len])

        if attention_mask is not None:
            hidden_states = hidden_states * attention_mask.unsqueeze(1)

        # 3. State Space Model sequence transformation
        # 3.a. Selection:  [batch, seq_len, self.time_step_rank + self.ssm_state_size * 2]
        ssm_parameters = self.x_proj(hidden_states.transpose(1, 2))
        time_step, B, C = torch.split(
            ssm_parameters, [self.time_step_rank, self.ssm_state_size, self.ssm_state_size], dim=-1
        )

        time_step = self.dt_layernorm(time_step)
        B = self.b_layernorm(B)
        C = self.c_layernorm(C)

        discrete_time_step = self.dt_proj(time_step)
        discrete_time_step = nn.functional.softplus(discrete_time_step).transpose(1, 2)

        # 3.b. Discretization: B and C to [batch, seq_len, intermediate_size, ssm_state_size] (SRAM)
        A = -torch.exp(self.A_log.float())
        discrete_A = torch.exp(A[None, :, None, :] * discrete_time_step[:, :, :, None])
        discrete_B = discrete_time_step[:, :, :, None] * B[:, None, :, :].float()
        deltaB_u = discrete_B * hidden_states[:, :, :, None].float()
        # 3.c perform the recurrence y ← SSM(A, B, C)(x)
        scan_outputs = []
        for i in range(seq_len):
            ssm_state = discrete_A[:, :, i, :] * ssm_state + deltaB_u[:, :, i, :]
            scan_output = torch.matmul(ssm_state.to(dtype), C[:, i, :].unsqueeze(-1))
            scan_outputs.append(scan_output[:, :, 0])
        scan_output = torch.stack(scan_outputs, dim=-1)
        scan_output = scan_output + (hidden_states * self.D[None, :, None])
        scan_output = (scan_output * self.act(gate))

        if use_cache:
            cache_params.ssm_states[self.layer_idx] = ssm_state

        # 4. Final linear projection
        contextualized_states = self.out_proj(scan_output.transpose(1, 2))
        return contextualized_states
    # fmt: on

    def forward(
        self,
        hidden_states,
        cache_params: HybridMambaAttentionDynamicCache | None = None,
        attention_mask: torch.LongTensor | None = None,
    ):
        if self.config.use_mamba_kernels and (
            not is_fast_path_available or "cuda" not in self.x_proj.weight.device.type
        ):
            logger.warning_once(
                "Fast Mamba kernels are not available. Make sure that they are installed "
                "and that the mamba module is on a CUDA device. Turning off the fast path "
                "`config.use_mamba_kernels=False` and falling back to the slow path."
            )
            self.config.use_mamba_kernels = False

        if self.config.use_mamba_kernels:
            return self.cuda_kernels_forward(hidden_states, cache_params, attention_mask)
        return self.slow_forward(hidden_states, cache_params, attention_mask)


class JambaMLP(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 = ACT2FN[config.hidden_act]

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


@use_experts_implementation
class JambaExperts(nn.Module):
    """Collection of expert weights stored as 3D tensors."""

    def __init__(self, config: JambaConfig):
        super().__init__()
        self.num_experts = config.num_local_experts
        self.hidden_dim = config.hidden_size
        self.intermediate_dim = config.intermediate_size
        self.gate_up_proj = nn.Parameter(torch.empty(self.num_experts, 2 * self.intermediate_dim, self.hidden_dim))
        self.down_proj = nn.Parameter(torch.empty(self.num_experts, self.hidden_dim, self.intermediate_dim))
        self.act_fn = ACT2FN[config.hidden_act]

    def forward(
        self,
        hidden_states: torch.Tensor,
        top_k_index: torch.Tensor,
        top_k_weights: torch.Tensor,
    ) -> torch.Tensor:
        final_hidden_states = torch.zeros_like(hidden_states)
        with torch.no_grad():
            expert_mask = torch.nn.functional.one_hot(top_k_index, num_classes=self.num_experts)
            expert_mask = expert_mask.permute(2, 1, 0)
            expert_hit = torch.greater(expert_mask.sum(dim=(-1, -2)), 0).nonzero()

        for expert_idx in expert_hit:
            expert_idx = expert_idx[0]
            if expert_idx == self.num_experts:
                continue
            top_k_pos, token_idx = torch.where(expert_mask[expert_idx])
            current_state = hidden_states[token_idx]
            gate, up = nn.functional.linear(current_state, self.gate_up_proj[expert_idx]).chunk(2, dim=-1)
            current_hidden_states = self.act_fn(gate) * up
            current_hidden_states = nn.functional.linear(current_hidden_states, self.down_proj[expert_idx])
            current_hidden_states = current_hidden_states * top_k_weights[token_idx, top_k_pos, None]
            final_hidden_states.index_add_(0, token_idx, current_hidden_states.to(final_hidden_states.dtype))

        return final_hidden_states


class JambaSparseMoeBlock(nn.Module):
    """
    This implementation is
    strictly equivalent to standard MoE with full capacity (no
    dropped tokens). It's faster since it formulates MoE operations
    in terms of block-sparse operations to accommodate imbalanced
    assignments of tokens to experts, whereas standard MoE either
    (1) drop tokens at the cost of reduced performance or (2) set
    capacity factor to number of experts and thus waste computation
    and memory on padding.
    """

    def __init__(self, config: JambaConfig):
        super().__init__()
        self.hidden_dim = config.hidden_size
        self.ffn_dim = config.intermediate_size
        self.num_experts = config.num_experts
        self.top_k = config.num_experts_per_tok

        self.router = nn.Linear(self.hidden_dim, self.num_experts, bias=False)
        self.experts = JambaExperts(config)

    def route_tokens_to_experts(self, hidden_states, router_logits):
        routing_weights = torch.nn.functional.softmax(router_logits, dim=-1, dtype=torch.float)
        top_k_weights, top_k_index = torch.topk(routing_weights, self.top_k, dim=-1)
        return top_k_index, top_k_weights.to(hidden_states.dtype)

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        batch_size, sequence_length, hidden_dim = hidden_states.shape
        hidden_states = hidden_states.view(-1, hidden_dim)
        router_logits = self.router(hidden_states)
        top_k_index, top_k_weights = self.route_tokens_to_experts(hidden_states, router_logits)
        hidden_states = self.experts(hidden_states, top_k_index, top_k_weights)
        hidden_states = hidden_states.reshape(batch_size, sequence_length, hidden_dim)
        return hidden_states


class JambaAttentionDecoderLayer(GradientCheckpointingLayer):
    def __init__(self, config: JambaConfig, layer_idx: int):
        super().__init__()
        num_experts = config.layers_num_experts[layer_idx] if config.layers_num_experts else 1
        self.self_attn = JambaAttention(config, layer_idx)

        ffn_layer_class = JambaSparseMoeBlock if num_experts > 1 else JambaMLP
        self.feed_forward = ffn_layer_class(config)
        self.input_layernorm = JambaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.pre_ff_layernorm = JambaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: torch.Tensor | None = None,
        position_ids: torch.LongTensor | None = None,
        past_key_values: HybridMambaAttentionDynamicCache | None = None,
        use_cache: bool | None = False,
        cache_position: torch.LongTensor | None = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> torch.FloatTensor:
        residual = hidden_states
        hidden_states = self.input_layernorm(hidden_states)
        hidden_states, _ = self.self_attn(
            hidden_states=hidden_states,
            attention_mask=attention_mask,
            position_ids=position_ids,
            past_key_values=past_key_values,
            use_cache=use_cache,
            cache_position=cache_position,
            **kwargs,
        )
        hidden_states = residual + hidden_states
        residual = hidden_states
        hidden_states = self.pre_ff_layernorm(hidden_states)
        hidden_states = self.feed_forward(hidden_states)
        hidden_states = residual + hidden_states
        return hidden_states


class JambaMambaDecoderLayer(GradientCheckpointingLayer):
    def __init__(self, config: JambaConfig, layer_idx: int):
        super().__init__()
        num_experts = config.layers_num_experts[layer_idx] if config.layers_num_experts else 1
        self.mamba = JambaMambaMixer(config=config, layer_idx=layer_idx)
        ffn_layer_class = JambaSparseMoeBlock if num_experts > 1 else JambaMLP
        self.feed_forward = ffn_layer_class(config)
        self.input_layernorm = JambaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.pre_ff_layernorm = JambaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: torch.Tensor | None = None,
        position_ids: torch.LongTensor | None = None,
        past_key_values: HybridMambaAttentionDynamicCache | None = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> torch.FloatTensor:
        residual = hidden_states
        hidden_states = self.input_layernorm(hidden_states)
        hidden_states = self.mamba(
            hidden_states=hidden_states,
            cache_params=past_key_values,
            attention_mask=attention_mask,
        )
        hidden_states = residual + hidden_states
        residual = hidden_states
        hidden_states = self.pre_ff_layernorm(hidden_states)
        hidden_states = self.feed_forward(hidden_states)
        hidden_states = residual + hidden_states
        return hidden_states


class JambaPreTrainedModel(PreTrainedModel):
    config: JambaConfig
    base_model_prefix = "model"
    supports_gradient_checkpointing = True
    _no_split_modules = ["JambaAttentionDecoderLayer", "JambaMambaDecoderLayer"]
    _skip_keys_device_placement = "past_key_values"
    _supports_flash_attn = True
    _supports_sdpa = True
    _is_stateful = True
    _can_record_outputs = {
        "hidden_states": [JambaAttentionDecoderLayer, JambaMambaDecoderLayer],
        "attentions": JambaAttention,
        "router_logits": OutputRecorder(nn.Linear, layer_name="router"),
    }

    @torch.no_grad()
    def _init_weights(self, module):
        super()._init_weights(module)
        if isinstance(module, JambaMambaMixer):
            A = torch.arange(1, module.ssm_state_size + 1)[None, :]
            A = A.expand(module.intermediate_size, -1).contiguous()
            init.copy_(module.A_log, torch.log(A))
            init.ones_(module.D)
        elif isinstance(module, JambaExperts):
            init.normal_(module.gate_up_proj, mean=0.0, std=self.config.initializer_range)
            init.normal_(module.down_proj, mean=0.0, std=self.config.initializer_range)


ALL_DECODER_LAYER_TYPES = {"attention": JambaAttentionDecoderLayer, "mamba": JambaMambaDecoderLayer}


@auto_docstring
class JambaModel(JambaPreTrainedModel):
    def __init__(self, config: JambaConfig):
        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)
        decoder_layers = []
        for i in range(config.num_hidden_layers):
            layer_class = ALL_DECODER_LAYER_TYPES[config.layers_block_type[i]]
            decoder_layers.append(layer_class(config, layer_idx=i))
        self.layers = nn.ModuleList(decoder_layers)

        self.final_layernorm = JambaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)

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

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

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

        if use_cache and past_key_values is None:
            past_key_values = HybridMambaAttentionDynamicCache(
                config=self.config,
                batch_size=inputs_embeds.shape[0],
                dtype=inputs_embeds.dtype,
                device=inputs_embeds.device,
            )

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

        if position_ids is None:
            position_ids = cache_position.unsqueeze(0)

        causal_mask = create_causal_mask(
            config=self.config,
            inputs_embeds=inputs_embeds,
            attention_mask=attention_mask,
            cache_position=cache_position,
            past_key_values=past_key_values,
            position_ids=position_ids,
        )
        mamba_mask = self._update_mamba_mask(attention_mask, cache_position)
        hidden_states = inputs_embeds
        for decoder_layer in self.layers:
            layer_mask = mamba_mask if isinstance(decoder_layer, JambaMambaDecoderLayer) else causal_mask

            hidden_states = decoder_layer(
                hidden_states,
                attention_mask=layer_mask,
                position_ids=position_ids,
                past_key_values=past_key_values,
                use_cache=use_cache,
                cache_position=cache_position,
                **kwargs,
            )

        hidden_states = self.final_layernorm(hidden_states)

        if past_key_values and not past_key_values.has_previous_state:
            past_key_values.has_previous_state = True

        return MoeModelOutputWithPast(
            last_hidden_state=hidden_states,
            past_key_values=past_key_values,
        )

    def _update_mamba_mask(self, attention_mask, cache_position):
        """
        No need for zeroing states when
            1. Cached forward
            2. Attending to all inputs
        """
        mamba_mask = attention_mask
        if (cache_position is not None and cache_position[0] > 0) or (
            attention_mask is not None and torch.all(attention_mask == 1)
        ):
            mamba_mask = None
        return mamba_mask


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

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

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

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

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

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

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

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

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

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

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

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

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

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

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


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

    def __init__(self, config: JambaConfig):
        super().__init__(config)
        self.model = JambaModel(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 = config.num_experts
        self.num_experts_per_tok = config.num_experts_per_tok

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

    @can_return_tuple
    @auto_docstring
    def forward(
        self,
        input_ids: torch.LongTensor | None = None,
        attention_mask: torch.Tensor | None = None,
        position_ids: torch.LongTensor | None = None,
        past_key_values: HybridMambaAttentionDynamicCache | None = None,
        inputs_embeds: torch.FloatTensor | None = None,
        labels: torch.LongTensor | None = None,
        use_cache: bool | None = None,
        output_router_logits: bool | None = None,
        cache_position: torch.LongTensor | None = None,
        logits_to_keep: int | torch.Tensor = 0,
        **kwargs: Unpack[TransformersKwargs],
    ) -> MoeCausalLMOutputWithPast:
        r"""
        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
            Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
            config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
            (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.

        Example:

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

        >>> model = JambaForCausalLM.from_pretrained("ai21labs/Jamba-v0.1")
        >>> tokenizer = AutoTokenizer.from_pretrained("ai21labs/Jamba-v0.1")

        >>> 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,
            output_router_logits=output_router_logits,
            cache_position=cache_position,
            **kwargs,
        )

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

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

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

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


class JambaForSequenceClassification(GenericForSequenceClassification, JambaPreTrainedModel):
    pass


__all__ = ["JambaForCausalLM", "JambaForSequenceClassification", "JambaModel", "JambaPreTrainedModel"]
