from __future__ import annotations

import logging
from typing import TYPE_CHECKING

import torch
import triton
import triton.language as tl

from sglang.srt.mem_cache.allocator import SWATokenToKVPoolAllocator
from sglang.srt.mem_cache.base_prefix_cache import BasePrefixCache
from sglang.srt.mem_cache.chunk_cache import ChunkCache, SWAChunkCache
from sglang.srt.mem_cache.memory_pool import HybridReqToTokenPool, ReqToTokenPool
from sglang.srt.server_args import ServerArgs
from sglang.srt.utils import support_triton

if TYPE_CHECKING:
    from sglang.srt.managers.schedule_batch import Req, ScheduleBatch

logger = logging.getLogger(__name__)

GLOBAL_SERVER_ARGS_KEYS = ["attention_backend"]

global_server_args_dict = {k: getattr(ServerArgs, k) for k in GLOBAL_SERVER_ARGS_KEYS}


@triton.jit
def write_req_to_token_pool_triton(
    req_to_token_ptr,  # [max_batch, max_context_len]
    req_pool_indices,
    prefix_tensors,
    pre_lens,
    seq_lens,
    extend_lens,
    out_cache_loc,
    req_to_token_ptr_stride: tl.constexpr,
):
    BLOCK_SIZE: tl.constexpr = 512
    pid = tl.program_id(0)

    req_pool_index = tl.load(req_pool_indices + pid)
    pre_len = tl.load(pre_lens + pid)
    seq_len = tl.load(seq_lens + pid)
    prefix_tensor = tl.load(prefix_tensors + pid).to(tl.pointer_type(tl.int64))

    # write prefix
    num_loop = tl.cdiv(pre_len, BLOCK_SIZE)
    for i in range(num_loop):
        offset = tl.arange(0, BLOCK_SIZE) + i * BLOCK_SIZE
        mask = offset < pre_len
        value = tl.load(prefix_tensor + offset, mask=mask)
        tl.store(
            req_to_token_ptr + req_pool_index * req_to_token_ptr_stride + offset,
            value,
            mask=mask,
        )

    # NOTE: This can be slow for large bs
    cumsum_start = tl.cast(0, tl.int64)
    for i in range(pid):
        cumsum_start += tl.load(extend_lens + i)

    num_loop = tl.cdiv(seq_len - pre_len, BLOCK_SIZE)
    for i in range(num_loop):
        offset = tl.arange(0, BLOCK_SIZE) + i * BLOCK_SIZE
        mask = offset < (seq_len - pre_len)
        value = tl.load(out_cache_loc + cumsum_start + offset, mask=mask)
        tl.store(
            req_to_token_ptr
            + req_pool_index * req_to_token_ptr_stride
            + offset
            + pre_len,
            value,
            mask=mask,
        )


def write_cache_indices(
    out_cache_loc: torch.Tensor,
    req_pool_indices_tensor: torch.Tensor,
    req_pool_indices_cpu: torch.Tensor,
    prefix_lens_tensor: torch.Tensor,
    prefix_lens_cpu: torch.Tensor,
    seq_lens_tensor: torch.Tensor,
    seq_lens_cpu: torch.Tensor,
    extend_lens_tensor: torch.Tensor,
    extend_lens_cpu: torch.Tensor,
    prefix_tensors: list[torch.Tensor],
    req_to_token_pool: ReqToTokenPool,
):
    if support_triton(global_server_args_dict.get("attention_backend")):
        prefix_pointers = torch.tensor(
            [t.data_ptr() for t in prefix_tensors],
            device=req_to_token_pool.device,
        )
        # TODO: some tensors can be reused for ForwardBatchInfo (e.g., extend_lens, cumsum_start)
        write_req_to_token_pool_triton[(req_pool_indices_tensor.shape[0],)](
            req_to_token_pool.req_to_token,
            req_pool_indices_tensor,
            prefix_pointers,
            prefix_lens_tensor,
            seq_lens_tensor,
            extend_lens_tensor,
            out_cache_loc,
            req_to_token_pool.req_to_token.shape[1],
        )
    else:
        pt = 0
        for i in range(req_pool_indices_cpu.shape[0]):
            req_idx = req_pool_indices_cpu[i].item()
            prefix_len = prefix_lens_cpu[i].item()
            seq_len = seq_lens_cpu[i].item()
            extend_len = extend_lens_cpu[i].item()

            req_to_token_pool.write(
                (req_idx, slice(0, prefix_len)),
                prefix_tensors[i],
            )
            req_to_token_pool.write(
                (req_idx, slice(prefix_len, seq_len)),
                out_cache_loc[pt : pt + extend_len],
            )
            pt += extend_len


def get_last_loc(
    req_to_token: torch.Tensor,
    req_pool_indices_tensor: torch.Tensor,
    prefix_lens_tensor: torch.Tensor,
) -> torch.Tensor:
    if (
        global_server_args_dict["attention_backend"] != "ascend"
        and global_server_args_dict["attention_backend"] != "torch_native"
    ):
        impl = get_last_loc_triton
    else:
        impl = get_last_loc_torch

    return impl(req_to_token, req_pool_indices_tensor, prefix_lens_tensor)


def get_last_loc_torch(
    req_to_token: torch.Tensor,
    req_pool_indices_tensor: torch.Tensor,
    prefix_lens_tensor: torch.Tensor,
) -> torch.Tensor:
    return torch.where(
        prefix_lens_tensor > 0,
        req_to_token[req_pool_indices_tensor, prefix_lens_tensor - 1],
        torch.full_like(prefix_lens_tensor, -1),
    )


@triton.jit
def get_last_loc_kernel(
    req_to_token,
    req_pool_indices_tensor,
    prefix_lens_tensor,
    result,
    num_tokens,
    req_to_token_stride,
    BLOCK_SIZE: tl.constexpr,
):
    pid = tl.program_id(0)
    offset = tl.arange(0, BLOCK_SIZE) + pid * BLOCK_SIZE
    mask = offset < num_tokens

    prefix_lens = tl.load(prefix_lens_tensor + offset, mask=mask, other=0)
    req_pool_indices = tl.load(req_pool_indices_tensor + offset, mask=mask, other=0)

    token_mask = prefix_lens > 0
    token_index = req_pool_indices * req_to_token_stride + (prefix_lens - 1)
    tokens = tl.load(req_to_token + token_index, mask=token_mask, other=-1)

    tl.store(result + offset, tokens, mask=mask)


def get_last_loc_triton(
    req_to_token: torch.Tensor,
    req_pool_indices_tensor: torch.Tensor,
    prefix_lens_tensor: torch.Tensor,
) -> torch.Tensor:
    BLOCK_SIZE = 256
    num_tokens = prefix_lens_tensor.shape[0]
    result = torch.empty_like(prefix_lens_tensor)
    grid = (triton.cdiv(num_tokens, BLOCK_SIZE),)

    get_last_loc_kernel[grid](
        req_to_token,
        req_pool_indices_tensor,
        prefix_lens_tensor,
        result,
        num_tokens,
        req_to_token.stride(0),
        BLOCK_SIZE,
    )
    return result


def alloc_token_slots(
    tree_cache: BasePrefixCache,
    num_tokens: int,
    backup_state: bool = False,
):
    allocator = tree_cache.token_to_kv_pool_allocator
    evict_from_tree_cache(tree_cache, num_tokens)

    state = None
    if backup_state:
        state = allocator.backup_state()

    out_cache_loc = allocator.alloc(num_tokens)

    if out_cache_loc is None:
        error_msg = (
            f"Out of memory. Try to lower your batch size.\n"
            f"Try to allocate {num_tokens} tokens.\n"
            f"{available_and_evictable_str(tree_cache)}"
        )
        logger.error(error_msg)
        if tree_cache is not None:
            tree_cache.pretty_print()
        raise RuntimeError(error_msg)

    return (out_cache_loc, state) if backup_state else out_cache_loc


def evict_from_tree_cache(tree_cache: BasePrefixCache | None, num_tokens: int):
    if tree_cache is None:
        return

    if isinstance(tree_cache, (SWAChunkCache, ChunkCache)):
        return

    allocator = tree_cache.token_to_kv_pool_allocator

    # Check if this is a hybrid allocator
    if hasattr(allocator, "full_available_size"):
        # Hybrid allocator
        full_available_size = allocator.full_available_size()
        swa_available_size = allocator.swa_available_size()

        if full_available_size < num_tokens or swa_available_size < num_tokens:
            full_num_tokens = max(0, num_tokens - full_available_size)
            swa_num_tokens = max(0, num_tokens - swa_available_size)
            tree_cache.evict(full_num_tokens, swa_num_tokens)
    else:
        # Standard allocator
        if allocator.available_size() < num_tokens:
            tree_cache.evict(num_tokens)


def alloc_paged_token_slots_extend(
    tree_cache: BasePrefixCache,
    prefix_lens: torch.Tensor,
    prefix_lens_cpu: torch.Tensor,
    seq_lens: torch.Tensor,
    seq_lens_cpu: torch.Tensor,
    last_loc: torch.Tensor,
    extend_num_tokens: int,
    backup_state: bool = False,
):
    # Over estimate the number of tokens: assume each request needs a new page.
    allocator = tree_cache.token_to_kv_pool_allocator
    num_tokens = extend_num_tokens + len(seq_lens_cpu) * allocator.page_size
    evict_from_tree_cache(tree_cache, num_tokens)

    state = None
    if backup_state:
        state = allocator.backup_state()

    out_cache_loc = allocator.alloc_extend(
        prefix_lens,
        prefix_lens_cpu,
        seq_lens,
        seq_lens_cpu,
        last_loc,
        extend_num_tokens,
    )

    if out_cache_loc is None:
        error_msg = (
            f"Prefill out of memory. Try to lower your batch size.\n"
            f"Try to allocate {extend_num_tokens} tokens.\n"
            f"{available_and_evictable_str(tree_cache)}"
        )
        logger.error(error_msg)
        if tree_cache is not None:
            tree_cache.pretty_print()
        raise RuntimeError(error_msg)

    return (out_cache_loc, state) if backup_state else out_cache_loc


def alloc_req_slots(
    req_to_token_pool: ReqToTokenPool,
    num_reqs: int,
    reqs: list[Req] | None,
) -> list[int]:
    """Allocate request slots from the pool."""
    if isinstance(req_to_token_pool, HybridReqToTokenPool):
        req_pool_indices = req_to_token_pool.alloc(num_reqs, reqs)
    else:
        req_pool_indices = req_to_token_pool.alloc(num_reqs)

    if req_pool_indices is None:
        raise RuntimeError(
            "alloc_req_slots runs out of memory. "
            "Please set a smaller number for `--max-running-requests`. "
            f"{req_to_token_pool.available_size()=}, "
            f"{num_reqs=}, "
        )
    return req_pool_indices


def alloc_for_extend(
    batch: ScheduleBatch,
) -> tuple[torch.Tensor, torch.Tensor, list[int]]:
    """
    Allocate KV cache for extend batch and write to req_to_token_pool.

    Returns:
        out_cache_loc: allocated cache locations
        req_pool_indices_device: request pool indices at a device tensor
        req_pool_indices: request pool indices as list
    """
    # free out-of-window swa tokens
    if isinstance(batch.tree_cache, SWAChunkCache):
        for req, pre_len in zip(batch.reqs, batch.prefix_lens):
            batch.tree_cache.evict_swa(
                req, pre_len, batch.model_config.attention_chunk_size
            )

    bs = len(batch.reqs)
    prefix_tensors = [r.prefix_indices for r in batch.reqs]

    # Create tensors for allocation
    prefix_lens_cpu = torch.tensor(batch.prefix_lens, dtype=torch.int64)
    extend_lens_cpu = torch.tensor(batch.extend_lens, dtype=torch.int64)
    prefix_lens_device = prefix_lens_cpu.to(batch.device, non_blocking=True)
    extend_lens_device = extend_lens_cpu.to(batch.device, non_blocking=True)

    # Allocate req slots
    req_pool_indices = alloc_req_slots(batch.req_to_token_pool, bs, batch.reqs)
    req_pool_indices_cpu = torch.tensor(req_pool_indices, dtype=torch.int64)
    req_pool_indices_device = req_pool_indices_cpu.to(batch.device, non_blocking=True)

    # Allocate KV cache (throws exception on failure)
    if batch.tree_cache.page_size == 1:
        out_cache_loc = alloc_token_slots(batch.tree_cache, batch.extend_num_tokens)
    else:
        # Paged allocation - build last_loc
        last_loc = [
            (
                t[-1:]
                if len(t) > 0
                else torch.tensor([-1], device=batch.tree_cache.device)
            )
            for t in prefix_tensors
        ]
        out_cache_loc = alloc_paged_token_slots_extend(
            tree_cache=batch.tree_cache,
            prefix_lens=prefix_lens_device,
            prefix_lens_cpu=prefix_lens_cpu,
            seq_lens=batch.seq_lens,
            seq_lens_cpu=batch.seq_lens_cpu,
            last_loc=torch.cat(last_loc),
            extend_num_tokens=batch.extend_num_tokens,
        )

    # Write to req_to_token_pool
    write_cache_indices(
        out_cache_loc,
        req_pool_indices_device,
        req_pool_indices_cpu,
        prefix_lens_device,
        prefix_lens_cpu,
        batch.seq_lens,
        batch.seq_lens_cpu,
        extend_lens_device,
        extend_lens_cpu,
        prefix_tensors,
        batch.req_to_token_pool,
    )

    return out_cache_loc, req_pool_indices_device, req_pool_indices


def alloc_paged_token_slots_decode(
    tree_cache: BasePrefixCache,
    seq_lens: torch.Tensor,
    seq_lens_cpu: torch.Tensor,
    last_loc: torch.Tensor,
    token_per_req: int = 1,
) -> torch.Tensor:
    """Allocate paged KV cache for decode batch."""
    allocator = tree_cache.token_to_kv_pool_allocator
    # Over estimate the number of tokens: assume each request needs a new page.
    num_tokens = len(seq_lens) * allocator.page_size
    evict_from_tree_cache(tree_cache, num_tokens)

    out_cache_loc = allocator.alloc_decode(seq_lens, seq_lens_cpu, last_loc)

    if out_cache_loc is None:
        error_msg = (
            f"Decode out of memory. Try to lower your batch size.\n"
            f"Try to allocate {len(seq_lens) * token_per_req} tokens.\n"
            f"{available_and_evictable_str(tree_cache)}"
        )
        logger.error(error_msg)
        if tree_cache is not None:
            tree_cache.pretty_print()
        raise RuntimeError(error_msg)

    return out_cache_loc


def alloc_for_decode(batch: ScheduleBatch, token_per_req: int) -> torch.Tensor:
    """
    Allocate KV cache for decode batch and write to req_to_token_pool.

    Returns:
        out_cache_loc: allocated cache locations
    """
    if isinstance(batch.tree_cache, SWAChunkCache):
        for req in batch.reqs:
            batch.tree_cache.evict_swa(
                req, req.seqlen - 1, batch.model_config.attention_chunk_size
            )

    bs = batch.seq_lens.shape[0]

    if batch.tree_cache.page_size == 1:
        # Non-paged allocation
        out_cache_loc = alloc_token_slots(batch.tree_cache, bs * token_per_req)
    else:
        # Paged allocation
        last_loc = batch.req_to_token_pool.req_to_token[
            batch.req_pool_indices, batch.seq_lens - 1
        ]
        seq_lens_next = batch.seq_lens + token_per_req
        out_cache_loc = alloc_paged_token_slots_decode(
            tree_cache=batch.tree_cache,
            seq_lens=seq_lens_next,
            seq_lens_cpu=batch.seq_lens_cpu + token_per_req,
            last_loc=last_loc,
            token_per_req=token_per_req,
        )

    # Write to req_to_token_pool
    if batch.model_config.is_encoder_decoder:
        locs = batch.encoder_lens + batch.seq_lens
    else:
        locs = batch.seq_lens.clone()

    batch.req_to_token_pool.write(
        (batch.req_pool_indices, locs), out_cache_loc.to(torch.int32)
    )

    return out_cache_loc


def available_and_evictable_str(tree_cache) -> str:
    token_to_kv_pool_allocator = tree_cache.token_to_kv_pool_allocator
    if isinstance(token_to_kv_pool_allocator, SWATokenToKVPoolAllocator):
        full_available_size = token_to_kv_pool_allocator.full_available_size()
        swa_available_size = token_to_kv_pool_allocator.swa_available_size()
        full_evictable_size = tree_cache.full_evictable_size()
        swa_evictable_size = tree_cache.swa_evictable_size()
        return (
            f"Available full tokens: {full_available_size + full_evictable_size} ({full_available_size=} + {full_evictable_size=})\n"
            f"Available swa tokens: {swa_available_size + swa_evictable_size} ({swa_available_size=} + {swa_evictable_size=})\n"
            f"Full LRU list evictable size: {tree_cache.full_lru_list_evictable_size()}\n"
            f"SWA LRU list evictable size: {tree_cache.swa_lru_list_evictable_size()}\n"
        )
    else:
        available_size = token_to_kv_pool_allocator.available_size()
        evictable_size = tree_cache.evictable_size()
        return f"Available tokens: {available_size + evictable_size} ({available_size=} + {evictable_size=})\n"
