# coding=utf-8
"""Inference-only Jamba model."""
from dataclasses import dataclass
from typing import Dict, Iterable, List, Optional, Tuple

import torch
from torch import nn
from torch.nn.parameter import Parameter
from transformers import JambaConfig

from vllm.attention.backends.abstract import AttentionMetadata
from vllm.attention.layer import Attention
from vllm.config import CacheConfig, LoRAConfig, SchedulerConfig
from vllm.distributed import (get_tensor_model_parallel_rank,
                              get_tensor_model_parallel_world_size)
from vllm.model_executor.layers.fused_moe import FusedMoE
from vllm.model_executor.layers.layernorm import RMSNorm
from vllm.model_executor.layers.linear import (ColumnParallelLinear,
                                               MergedColumnParallelLinear,
                                               QKVParallelLinear,
                                               ReplicatedLinear,
                                               RowParallelLinear)
from vllm.model_executor.layers.logits_processor import LogitsProcessor
from vllm.model_executor.layers.mamba.ops.causal_conv1d import (
    causal_conv1d_fn, causal_conv1d_update)
from vllm.model_executor.layers.mamba.ops.mamba_ssm import (
    selective_scan_fn, selective_state_update)
from vllm.model_executor.layers.quantization import QuantizationConfig
from vllm.model_executor.layers.sampler import Sampler, SamplerOutput
from vllm.model_executor.layers.vocab_parallel_embedding import (
    DEFAULT_VOCAB_PADDING_SIZE, ParallelLMHead, VocabParallelEmbedding)
from vllm.model_executor.model_loader.weight_utils import default_weight_loader
from vllm.model_executor.sampling_metadata import SamplingMetadata
from vllm.model_executor.utils import set_weight_attrs
from vllm.sequence import IntermediateTensors
from vllm.worker.model_runner import (_BATCH_SIZES_TO_CAPTURE,
                                      _get_graph_batch_size)

from .interfaces import HasInnerState, SupportsLoRA

KVCache = Tuple[torch.Tensor, torch.Tensor]


@dataclass
class MambaCacheParams:
    is_prompt: bool = False
    conv_state: torch.Tensor = torch.Tensor()
    ssm_state: torch.Tensor = torch.Tensor()


# Adapted from transformers.models.mamba.modeling_mamba.MambaMixer
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 = ColumnParallelLinear(
            input_size=self.conv_kernel_size,
            output_size=self.intermediate_size,
            bias=self.use_conv_bias,
        )
        # unsqueeze to fit conv1d weights shape into the linear weights shape.
        # Can't do this in `weight_loader` since it already exists in
        # `ColumnParallelLinear` and `set_weight_attrs`
        # doesn't allow to override it
        self.conv1d.weight.data = self.conv1d.weight.data.unsqueeze(1)

        self.in_proj = MergedColumnParallelLinear(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 = RowParallelLinear(
            self.intermediate_size,
            self.time_step_rank + self.ssm_state_size * 2,
            bias=False,
        )
        # time step projection (discretization) -
        # In the forward we need to apply dt_proj without the bias,
        # as the bias is added in the selective scan kernel.
        self.dt_proj = ColumnParallelLinear(self.time_step_rank,
                                            self.intermediate_size,
                                            bias=True,
                                            skip_bias_add=True)

        def weight_loader(param: Parameter, loaded_weight: torch.Tensor):
            tp_rank = get_tensor_model_parallel_rank()
            tp_size = get_tensor_model_parallel_world_size()
            param.data.copy_(
                loaded_weight.data.split(loaded_weight.shape[0] // tp_size,
                                         dim=0)[tp_rank])

        def A_weight_loader(param: Parameter, loaded_weight: torch.Tensor):
            weight_loader(param, -torch.exp(loaded_weight.float()))

        tp_size = get_tensor_model_parallel_world_size()
        self.A = nn.Parameter(
            torch.empty(
                self.intermediate_size // tp_size,
                self.ssm_state_size,
                dtype=torch.float32,
            ))
        self.D = nn.Parameter(torch.ones(self.intermediate_size // tp_size))

        set_weight_attrs(self.D, {"weight_loader": weight_loader})
        set_weight_attrs(self.A, {"weight_loader": A_weight_loader})

        self.out_proj = RowParallelLinear(
            self.intermediate_size,
            self.hidden_size,
            bias=self.use_bias,
            input_is_parallel=True,
        )
        self.activation = config.hidden_act

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

    def forward(self, hidden_states: torch.Tensor,
                attn_metadata: AttentionMetadata, conv_state: torch.Tensor,
                ssm_state: torch.Tensor):

        # 1. Gated MLP's linear projection
        projected_states = self.in_proj(hidden_states)[0].transpose(-2, -1)
        hidden_states, gate = projected_states.chunk(2, dim=-2)

        # 2. Convolution sequence transformation
        conv_weights = self.conv1d.weight.view(self.conv1d.weight.size(0),
                                               self.conv1d.weight.size(2))

        if attn_metadata.query_start_loc is not None \
            and attn_metadata.context_lens_tensor is not None:
            # |---------- N-1 iteration --------|
            # |---------------- N iteration ---------------------|
            # |- tokenA -|......................|-- newTokens ---|
            # |---------- context_len ----------|
            # |-------------------- seq_len ---------------------|
            #                                   |-- query_len ---|
            hidden_states = causal_conv1d_fn(
                hidden_states,
                conv_weights,
                self.conv1d.bias,
                activation=self.activation,
                conv_states=conv_state,
                has_initial_state=attn_metadata.context_lens_tensor > 0,
                query_start_loc=attn_metadata.query_start_loc)
        else:
            hidden_states = causal_conv1d_update(
                hidden_states.transpose(0, 1),
                conv_state,
                conv_weights,
                self.conv1d.bias,
                self.activation,
            )
            hidden_states = hidden_states.transpose(0, 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(-2, -1))[0]

        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.contiguous())
        B = self.b_layernorm(B.contiguous())
        C = self.c_layernorm(C.contiguous())

        discrete_time_step = self.dt_proj(time_step)[0].transpose(-2, -1)
        # 3.c perform the recurrence y ← SSM(A, B, C)(x)
        time_proj_bias = (self.dt_proj.bias.float() if hasattr(
            self.dt_proj, "bias") else None)

        if attn_metadata.query_start_loc is not None \
            and attn_metadata.context_lens_tensor is not None:
            scan_outputs = selective_scan_fn(
                hidden_states,
                ssm_state,
                discrete_time_step,
                self.A,
                B.transpose(-2, -1),
                C.transpose(-2, -1),
                self.D.float(),
                gate,
                time_proj_bias,
                delta_softplus=True,
                has_initial_state=attn_metadata.context_lens_tensor > 0,
                query_start_loc=attn_metadata.query_start_loc)
        else:
            scan_outputs = selective_state_update(
                ssm_state,
                hidden_states.transpose(0, 1),
                discrete_time_step.transpose(0, 1),
                self.A,
                B,
                C,
                self.D,
                gate.transpose(0, 1),
                time_proj_bias,
                dt_softplus=True,
            )
            scan_outputs = scan_outputs.transpose(0, 1)

        # 4. Final linear projection
        contextualized_states = self.out_proj(scan_outputs.transpose(-2,
                                                                     -1))[0]
        return contextualized_states


class JambaMoE(nn.Module):

    def __init__(self,
                 config: JambaConfig,
                 num_experts: Optional[int] = None,
                 top_k: Optional[int] = None,
                 params_dtype: Optional[torch.dtype] = None,
                 tp_size: Optional[int] = None,
                 quant_config: Optional[QuantizationConfig] = None):
        super().__init__()
        self.num_total_experts = num_experts or config.num_experts
        self.top_k = top_k or config.num_experts_per_tok
        self.hidden_size = config.hidden_size
        self.intermediate_size = config.intermediate_size

        if self.num_total_experts > 1:
            self.router = ReplicatedLinear(self.hidden_size,
                                           self.num_total_experts,
                                           bias=False,
                                           quant_config=None,
                                           params_dtype=params_dtype)

        self.experts = FusedMoE(self.num_total_experts,
                                self.top_k,
                                self.hidden_size,
                                self.intermediate_size,
                                tp_size=tp_size,
                                params_dtype=params_dtype,
                                reduce_results=True,
                                renormalize=False,
                                use_grouped_topk=False,
                                quant_config=quant_config)

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        orig_shape = hidden_states.shape
        hidden_states = hidden_states.view(-1, self.hidden_size)
        # router_logits: (batch * sequence_length, n_experts)
        if self.num_total_experts > 1:
            router_logits, _ = self.router(hidden_states)
        else:
            router_logits = torch.ones((hidden_states.shape[0], 1),
                                       device=hidden_states.device,
                                       dtype=hidden_states.dtype)
        hidden_states = self.experts(hidden_states, router_logits)
        return hidden_states.view(orig_shape)


class JambaMLP(JambaMoE):

    def __init__(self,
                 config: JambaConfig,
                 params_dtype: Optional[torch.dtype] = None,
                 tp_size: Optional[int] = None,
                 quant_config: Optional[QuantizationConfig] = None):
        super().__init__(config,
                         num_experts=1,
                         top_k=1,
                         params_dtype=params_dtype,
                         tp_size=tp_size,
                         quant_config=quant_config)


class JambaMambaDecoderLayer(nn.Module):

    def __init__(self,
                 config: JambaConfig,
                 layer_idx: int,
                 cache_config: Optional[CacheConfig] = None,
                 quant_config: Optional[QuantizationConfig] = None) -> None:
        super().__init__()
        self.layer_idx = layer_idx
        self.config = config
        self.mamba = JambaMambaMixer(config, layer_idx)

        num_experts = config.layers_num_experts[layer_idx]
        ffn_layer_class = JambaMoE if num_experts > 1 else JambaMLP
        self.feed_forward = ffn_layer_class(config, quant_config=quant_config)
        self.input_layernorm = RMSNorm(config.hidden_size,
                                       eps=config.rms_norm_eps)
        self.pre_ff_layernorm = RMSNorm(config.hidden_size,
                                        eps=config.rms_norm_eps)

    def forward(
        self,
        hidden_states: torch.Tensor,
        attn_metadata: AttentionMetadata,
        residual: Optional[torch.Tensor],
        conv_state: torch.Tensor,
        ssm_state: torch.Tensor,
        **kwargs,
    ):
        if residual is None:
            residual = hidden_states
            hidden_states = self.input_layernorm(hidden_states)
        else:
            hidden_states, residual = self.input_layernorm(
                hidden_states, residual)

        hidden_states = self.mamba(hidden_states, attn_metadata, conv_state,
                                   ssm_state)
        # Fully Connected
        hidden_states, residual = self.pre_ff_layernorm(
            hidden_states, residual)
        hidden_states = self.feed_forward(hidden_states)
        return hidden_states, residual


class JambaAttentionDecoderLayer(nn.Module):

    def __init__(
        self,
        config: JambaConfig,
        layer_idx: int,
        cache_config: Optional[CacheConfig] = None,
        quant_config: Optional[QuantizationConfig] = None,
    ) -> None:
        super().__init__()
        self.hidden_size = config.hidden_size
        tp_size = get_tensor_model_parallel_world_size()
        self.total_num_heads = config.num_attention_heads
        assert self.total_num_heads % tp_size == 0
        self.num_heads = self.total_num_heads // tp_size
        self.total_num_kv_heads = config.num_key_value_heads
        if self.total_num_kv_heads >= tp_size:
            # Number of KV heads is greater than TP size, so we partition
            # the KV heads across multiple tensor parallel GPUs.
            assert self.total_num_kv_heads % tp_size == 0
        else:
            # Number of KV heads is less than TP size, so we replicate
            # the KV heads across multiple tensor parallel GPUs.
            assert tp_size % self.total_num_kv_heads == 0
        self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
        self.head_dim = config.hidden_size // self.total_num_heads
        self.q_size = self.num_heads * self.head_dim
        self.kv_size = self.num_kv_heads * self.head_dim
        self.scaling = self.head_dim**-0.5

        self.qkv_proj = QKVParallelLinear(
            config.hidden_size,
            self.head_dim,
            self.total_num_heads,
            self.total_num_kv_heads,
            bias=False,
            quant_config=quant_config,
        )
        self.o_proj = RowParallelLinear(self.total_num_heads * self.head_dim,
                                        config.hidden_size,
                                        bias=False,
                                        quant_config=quant_config)

        self.attn = Attention(
            self.num_heads,
            self.head_dim,
            self.scaling,
            num_kv_heads=self.num_kv_heads,
            cache_config=cache_config,
        )

        num_experts = config.layers_num_experts[layer_idx]
        ffn_layer_class = JambaMoE if num_experts > 1 else JambaMLP
        self.feed_forward = ffn_layer_class(config, quant_config=quant_config)
        self.input_layernorm = RMSNorm(config.hidden_size,
                                       eps=config.rms_norm_eps)
        self.pre_ff_layernorm = RMSNorm(config.hidden_size,
                                        eps=config.rms_norm_eps)

    def self_attention(
        self,
        positions: torch.Tensor,
        hidden_states: torch.Tensor,
        kv_cache: torch.Tensor,
        attn_metadata: AttentionMetadata,
        **kwargs,
    ) -> torch.Tensor:
        qkv, _ = self.qkv_proj(hidden_states)
        q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
        attn_output = self.attn(q, k, v, kv_cache, attn_metadata)
        output, _ = self.o_proj(attn_output)
        return output

    def forward(
        self,
        positions: torch.Tensor,
        hidden_states: torch.Tensor,
        kv_cache: torch.Tensor,
        attn_metadata: AttentionMetadata,
        residual: Optional[torch.Tensor],
        **kwargs,
    ):
        if residual is None:
            residual = hidden_states
            hidden_states = self.input_layernorm(hidden_states)
        else:
            hidden_states, residual = self.input_layernorm(
                hidden_states, residual)

        hidden_states = self.self_attention(
            positions=positions,
            hidden_states=hidden_states,
            kv_cache=kv_cache,
            attn_metadata=attn_metadata,
        )
        # Fully Connected
        hidden_states, residual = self.pre_ff_layernorm(
            hidden_states, residual)
        hidden_states = self.feed_forward(hidden_states)
        return hidden_states, residual


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


class JambaModel(nn.Module):

    def __init__(
        self,
        config: JambaConfig,
        quant_config: Optional[QuantizationConfig] = None,
        cache_config: Optional[CacheConfig] = None,
        lora_config: Optional[LoRAConfig] = None,
    ) -> None:
        super().__init__()
        self.config = config
        self.padding_idx = config.pad_token_id
        lora_vocab = ((lora_config.lora_extra_vocab_size *
                       (lora_config.max_loras or 1)) if lora_config else 0)
        self.vocab_size = config.vocab_size + lora_vocab
        self.org_vocab_size = config.vocab_size

        self.embed_tokens = VocabParallelEmbedding(
            self.vocab_size,
            config.hidden_size,
            org_num_embeddings=config.vocab_size,
        )

        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,
                            cache_config=cache_config,
                            quant_config=quant_config))
        self.layers = nn.ModuleList(decoder_layers)
        self.final_layernorm = RMSNorm(config.hidden_size,
                                       eps=config.rms_norm_eps)

    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
        kv_caches: List[torch.Tensor],
        attn_metadata: AttentionMetadata,
        conv_state: torch.Tensor,
        ssm_state: torch.Tensor,
    ) -> torch.Tensor:
        hidden_states = self.embed_tokens(input_ids)
        residual = None

        for i in range(len(self.layers)):
            layer = self.layers[i]
            kv_cache = None
            current_ssm_state = None
            current_conv_state = None
            if isinstance(layer, JambaAttentionDecoderLayer):
                kv_cache = kv_caches[(i - self.config.attn_layer_offset) //
                                     self.config.attn_layer_period]
            if isinstance(layer, JambaMambaDecoderLayer):
                current_state_layer = i - (1 +
                                           (i - self.config.attn_layer_offset)
                                           // self.config.attn_layer_period)
                current_ssm_state = ssm_state[current_state_layer]
                current_conv_state = conv_state[current_state_layer]

            hidden_states, residual = layer(
                positions=positions,
                hidden_states=hidden_states,
                kv_cache=kv_cache,
                attn_metadata=attn_metadata,
                residual=residual,
                conv_state=current_conv_state,
                ssm_state=current_ssm_state,
            )
        hidden_states, _ = self.final_layernorm(hidden_states, residual)
        return hidden_states


class JambaForCausalLM(nn.Module, HasInnerState, SupportsLoRA):
    packed_modules_mapping = {
        "qkv_proj": [
            "q_proj",
            "k_proj",
            "v_proj",
        ],
    }

    # LoRA specific attributes
    supported_lora_modules = [
        "qkv_proj",
        "o_proj",
        "embed_tokens",
        "lm_head",
    ]
    embedding_modules = {
        "embed_tokens": "input_embeddings",
        "lm_head": "output_embeddings",
    }
    embedding_padding_modules = ["lm_head"]

    def __init__(
        self,
        config: JambaConfig,
        cache_config: Optional[CacheConfig] = None,
        quant_config: Optional[QuantizationConfig] = None,
        lora_config: Optional[LoRAConfig] = None,
        scheduler_config: Optional[SchedulerConfig] = None,
    ) -> None:
        assert not cache_config.enable_prefix_caching, \
            "Jamba currently does not support prefix caching"

        super().__init__()
        self.config = config
        self.scheduler_config = scheduler_config
        self.model = JambaModel(config,
                                cache_config=cache_config,
                                quant_config=quant_config,
                                lora_config=lora_config)
        self.unpadded_vocab_size = config.vocab_size
        if lora_config:
            self.unpadded_vocab_size += lora_config.lora_extra_vocab_size
        self.lm_head = ParallelLMHead(
            self.unpadded_vocab_size,
            config.hidden_size,
            org_num_embeddings=config.vocab_size,
            padding_size=DEFAULT_VOCAB_PADDING_SIZE
            # We need bigger padding if using lora for kernel
            # compatibility
            if not lora_config else lora_config.lora_vocab_padding_size,
        )
        # Used to track and store by the Mamba cache between steps.
        self.mamba_cache: Tuple[torch.Tensor, torch.Tensor] = tuple()
        # Maps between the request id and a dict that maps between the seq_id
        # and its index inside the self.mamba_cache
        self.mamba_cache_indices_mapping: Dict[str, Dict[int, int]] = {}
        self.logits_processor = LogitsProcessor(self.unpadded_vocab_size,
                                                config.vocab_size)
        self.sampler = Sampler()

    def forward(self,
                input_ids: torch.Tensor,
                positions: torch.Tensor,
                kv_caches: List[KVCache],
                attn_metadata: AttentionMetadata,
                intermediate_tensors: Optional[IntermediateTensors] = None,
                **kwargs):
        if not self.mamba_cache:
            self._prepare_mamba_cache()

        if "seqlen_agnostic_capture_inputs" not in kwargs:
            # We get here only on Prefill/Eager mode runs
            request_ids_to_seq_ids = kwargs["request_ids_to_seq_ids"]
            finished_requests_ids = kwargs["finished_requests_ids"]
            mamba_cache = self._release_finished_and_prepare_mamba_cache(
                finished_requests_ids, request_ids_to_seq_ids)
        else:
            # CUDA graph capturing runs
            mamba_cache = kwargs["seqlen_agnostic_capture_inputs"]

        hidden_states = self.model(input_ids, positions, kv_caches,
                                   attn_metadata, mamba_cache[0],
                                   mamba_cache[1])
        return hidden_states

    def _swap_mamba_cache(self, from_index: int, to_index: int):
        assert len(self.mamba_cache) > 0
        for cache_t in self.mamba_cache:
            cache_t[:, [to_index,from_index]] = \
             cache_t[:, [from_index,to_index]]

    def _copy_mamba_cache(self, from_index: int, to_index: int):
        assert len(self.mamba_cache) > 0
        for cache_t in self.mamba_cache:
            cache_t[:, to_index].copy_(cache_t[:, from_index],
                                       non_blocking=True)

    def _move_out_if_already_occupied(self, index: int,
                                      all_occupied_indices: List[int]):
        if index in all_occupied_indices:
            first_free_index = self._first_free_index_in_mamba_cache()
            # In case occupied, move the occupied to a new empty block
            self._move_cache_index_and_mappings(from_index=index,
                                                to_index=first_free_index)

    def _assign_seq_id_to_mamba_cache_in_specific_dest(self, cur_rid: str,
                                                       seq_id: int,
                                                       destination_index: int):
        """
        Assign (req_id,seq_id) pair to a `destination_index` index, if
        already occupied, move the occupying index to a free index.
        """
        all_occupied_indices = self._get_all_occupied_indices()
        if cur_rid not in self.mamba_cache_indices_mapping:
            self._move_out_if_already_occupied(
                index=destination_index,
                all_occupied_indices=all_occupied_indices)
            self.mamba_cache_indices_mapping[cur_rid] = {
                seq_id: destination_index
            }
        elif seq_id not in (seq_ids2indices :=
                            self.mamba_cache_indices_mapping[cur_rid]):
            # parallel sampling , where n > 1, assume prefill have
            # already happened now we only need to copy the already
            # existing cache into the siblings seq_ids caches
            self._move_out_if_already_occupied(
                index=destination_index,
                all_occupied_indices=all_occupied_indices)
            index_exists = list(seq_ids2indices.values())[0]
            # case of decoding n>1, copy prefill cache to decoding indices
            self._copy_mamba_cache(from_index=index_exists,
                                   to_index=destination_index)
            self.mamba_cache_indices_mapping[cur_rid][
                seq_id] = destination_index
        else:
            # already exists
            cache_index_already_exists = self.mamba_cache_indices_mapping[
                cur_rid][seq_id]
            if cache_index_already_exists != destination_index:
                # In case the seq id already exists but not in
                # the right destination, swap it with what's occupying it
                self._swap_pair_indices_and_mappings(
                    from_index=cache_index_already_exists,
                    to_index=destination_index)

    def _prepare_current_run_mamba_cache(
            self, request_ids_to_seq_ids: Dict[str, list[int]],
            finished_requests_ids: List[str]
    ) -> Tuple[torch.Tensor, torch.Tensor]:
        running_indices = []
        request_ids_to_seq_ids_flatten = [
            (req_id, seq_id)
            for req_id, seq_ids in request_ids_to_seq_ids.items()
            for seq_id in seq_ids
        ]
        batch_size = len(request_ids_to_seq_ids_flatten)
        for dest_index, (request_id,
                         seq_id) in enumerate(request_ids_to_seq_ids_flatten):
            if request_id in finished_requests_ids:
                # Do not allocate cache index for requests that run
                # and finish right after
                continue
            self._assign_seq_id_to_mamba_cache_in_specific_dest(
                request_id, seq_id, dest_index)
            running_indices.append(dest_index)

        self._clean_up_first_bs_blocks(batch_size, running_indices)
        conv_state = self.mamba_cache[0][:, :batch_size]
        temporal_state = self.mamba_cache[1][:, :batch_size]

        return (conv_state, temporal_state)

    def _get_all_occupied_indices(self):
        return [
            cache_idx
            for seq_ids2indices in self.mamba_cache_indices_mapping.values()
            for cache_idx in seq_ids2indices.values()
        ]

    def _clean_up_first_bs_blocks(self, batch_size: int,
                                  indices_for_current_run: List[int]):
        # move out all of the occupied but currently not running blocks
        # outside of the first n blocks
        destination_indices = range(batch_size)
        max_possible_batch_size = self.mamba_cache[0].shape[1]
        for destination_index in destination_indices:
            if destination_index in self._get_all_occupied_indices() and  \
               destination_index not in indices_for_current_run:
                # move not running indices outside of the batch
                all_other_indices = list(
                    range(batch_size, max_possible_batch_size))
                first_avail_index = self._first_free_index_in_mamba_cache(
                    all_other_indices)
                self._swap_indices(from_index=destination_index,
                                   to_index=first_avail_index)

    def _move_cache_index_and_mappings(self, from_index: int, to_index: int):
        self._copy_mamba_cache(from_index=from_index, to_index=to_index)
        self._update_mapping_index(from_index=from_index, to_index=to_index)

    def _swap_pair_indices_and_mappings(self, from_index: int, to_index: int):
        self._swap_mamba_cache(from_index=from_index, to_index=to_index)
        self._swap_mapping_index(from_index=from_index, to_index=to_index)

    def _swap_mapping_index(self, from_index: int, to_index: int):
        for seq_ids2index in self.mamba_cache_indices_mapping.values():
            for seq_id, index in seq_ids2index.items():
                if from_index == index:
                    seq_ids2index.update({seq_id: to_index})
                elif to_index == index:
                    seq_ids2index.update({seq_id: from_index})

    def _update_mapping_index(self, from_index: int, to_index: int):
        for seq_ids2index in self.mamba_cache_indices_mapping.values():
            for seq_id, index in seq_ids2index.items():
                if from_index == index:
                    seq_ids2index.update({seq_id: to_index})
                    return

    def _release_finished_and_prepare_mamba_cache(
            self, finished_requests_ids,
            request_ids_to_seq_ids) -> Tuple[torch.Tensor, torch.Tensor]:
        self._release_mamba_cache(finished_requests_ids)
        return self._prepare_current_run_mamba_cache(request_ids_to_seq_ids,
                                                     finished_requests_ids)

    def copy_inputs_before_cuda_graphs(self, input_buffers, **kwargs):
        """
        Copy the relevant Mamba cache into the CUDA graph input buffer 
        that was provided during the capture runs 
        (JambaForCausalLM.mamba_gc_cache_buffer). 
        """
        self._release_finished_and_prepare_mamba_cache(
            kwargs["finished_requests_ids"], kwargs["request_ids_to_seq_ids"])

    def get_seqlen_agnostic_capture_inputs(self, batch_size: int):
        """
        Provide the CUDA graph capture runs with a buffer in adjusted size.
        The buffer is used to maintain the Mamba Cache during the CUDA graph 
        replay runs.
        """
        return tuple(buffer[:, :batch_size] for buffer in self.mamba_cache)

    def _release_mamba_cache(self, finished_seq_groups_req_ids: List[str]):
        for req_id in finished_seq_groups_req_ids:
            if req_id in self.mamba_cache_indices_mapping:
                self.mamba_cache_indices_mapping.pop(req_id)

    def _first_free_index_in_mamba_cache(
            self, indices_range: Optional[List[int]] = None) -> int:
        assert self.mamba_cache is not None
        if indices_range is None:
            max_possible_batch_size = self.mamba_cache[0].shape[1]
            indices_range = list(range(max_possible_batch_size))
        all_occupied_indices = self._get_all_occupied_indices()
        for i in indices_range:
            if i not in all_occupied_indices:
                return i
        raise Exception("Couldn't find a free spot in the mamba cache! This"
                        "should never happen")

    def _get_mamba_cache_shape(
            self
    ) -> Tuple[Optional[Tuple[int, int]], Optional[Tuple[int, int]]]:
        world_size = get_tensor_model_parallel_world_size()
        hidden_size = self.config.hidden_size
        conv_state_shape = (
            self.config.mamba_expand * hidden_size // world_size,
            self.config.mamba_d_conv - 1,
        )
        temporal_state_shape = (
            self.config.mamba_expand * self.config.hidden_size // world_size,
            self.config.mamba_d_state,
        )
        return conv_state_shape, temporal_state_shape

    def _prepare_mamba_cache(self):
        dtype = self.lm_head.weight.dtype
        layers_type = self.config.layers_block_type
        mamba_layers = sum(
            [layer_type == "mamba" for layer_type in layers_type])
        max_batch_size = (_get_graph_batch_size(
            self.scheduler_config.max_num_seqs) if self.scheduler_config else
                          max(_BATCH_SIZES_TO_CAPTURE) + 2)
        conv_state_shape, temporal_state_shape = self._get_mamba_cache_shape()
        assert conv_state_shape is not None and temporal_state_shape is not None

        self.mamba_cache = (torch.empty(size=(mamba_layers, max_batch_size) +
                                        conv_state_shape,
                                        dtype=dtype,
                                        device="cuda"),
                            torch.empty(size=(mamba_layers, max_batch_size) +
                                        temporal_state_shape,
                                        dtype=dtype,
                                        device="cuda"))

    def compute_logits(
        self,
        hidden_states: torch.Tensor,
        sampling_metadata: SamplingMetadata,
    ) -> Optional[torch.Tensor]:
        logits = self.logits_processor(self.lm_head, hidden_states,
                                       sampling_metadata)
        return logits

    def sample(
        self,
        logits: Optional[torch.Tensor],
        sampling_metadata: SamplingMetadata,
    ) -> Optional[SamplerOutput]:
        next_tokens = self.sampler(logits, sampling_metadata)
        return next_tokens

    def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
        stacked_params_mapping = [
            # (param_name, shard_name, shard_id)
            ("qkv_proj", "q_proj", "q"),
            ("qkv_proj", "k_proj", "k"),
            ("qkv_proj", "v_proj", "v"),
        ]

        # Params for weights, fp8 weight scales, fp8 activation scales
        # (param_name, weight_name, expert_id, shard_id)
        expert_params_mapping = FusedMoE.make_expert_params_mapping(
            ckpt_gate_proj_name="gate_proj",
            ckpt_down_proj_name="down_proj",
            ckpt_up_proj_name="up_proj",
            num_experts=self.config.num_experts)

        params_dict = dict(self.named_parameters())
        for name, loaded_weight in weights:
            if "rotary_emb.inv_freq" in name:
                continue

            if "A_log" in name:
                name = name.replace("A_log", "A")

            if ".self_attn." in name:
                name = name.replace(".self_attn", "")

            if "feed_forward" in name and not _is_moe_layer(name):
                ## map MLP layers to expert with ID=0
                name = name.replace("feed_forward", "feed_forward.experts.0")

            for param_name, weight_name, shard_id in stacked_params_mapping:
                if weight_name not in name:
                    continue
                if 'experts' in name:
                    continue
                name = name.replace(weight_name, param_name)
                # Skip loading extra bias for GPTQ models.
                if name.endswith(".bias") and name not in params_dict:
                    continue
                param = params_dict[name]
                weight_loader = param.weight_loader
                weight_loader(param, loaded_weight, shard_id)
                break
            else:
                for (
                        param_name,
                        weight_name,
                        expert_id,
                        shard_id,
                ) in expert_params_mapping:
                    if weight_name not in name:
                        continue

                    name = name.replace(weight_name, param_name)
                    param = params_dict[name]
                    weight_loader = param.weight_loader
                    weight_loader(param,
                                  loaded_weight,
                                  name,
                                  shard_id=shard_id,
                                  expert_id=expert_id)
                    break
                else:
                    # Skip loading extra bias for GPTQ models.
                    if name.endswith(".bias") and name not in params_dict:
                        continue

                    param = params_dict[name]
                    weight_loader = getattr(param, "weight_loader",
                                            default_weight_loader)
                    weight_loader(param, loaded_weight)


def _is_moe_layer(name: str):
    return any(
        [experts_name in name for experts_name in [
            "experts",
            "router",
        ]])
