from __future__ import annotations

from typing import TYPE_CHECKING, List

import torch
import triton
import triton.language as tl

from sglang.srt.layers.attention.flashinfer_backend import (
    create_flashinfer_kv_indices_triton,
)
from sglang.srt.model_executor.forward_batch_info import ForwardMode
from sglang.srt.speculative.build_eagle_tree import build_tree_kernel
from sglang.srt.speculative.spec_info import SpecInfo

if TYPE_CHECKING:
    from sglang.srt.managers.schedule_batch import ScheduleBatch
    from sglang.srt.server_args import ServerArgs


@triton.jit
def eagle_verify_retrive(
    retrive_index,
    accept_mask,
    retrive_cum_len,
    accept_index,
    accept_length,
    extract_index,
    max_len: tl.constexpr,
    draft_token_num: tl.constexpr,
    max_len_upper: tl.constexpr,
):
    pid = tl.program_id(axis=0)

    retrive_end = tl.load(retrive_cum_len + pid + 1)
    retrive_start = tl.load(retrive_cum_len + pid)
    retrive_len = retrive_end - retrive_start
    accept_ptr = accept_mask + retrive_start
    accept_offset = tl.arange(0, draft_token_num)
    accept_load_mask = accept_offset < retrive_len
    accept_len_list = tl.load(
        accept_ptr + accept_offset, mask=accept_load_mask, other=-1
    )

    accept_len = tl.max(accept_len_list)
    max_index = tl.argmax(accept_len_list, axis=0, tie_break_left=True)
    # triton is not support argmax with tie_break_right, so I need implement it by some way
    mask_max = accept_len_list == accept_len

    count_mask = tl.full(shape=[draft_token_num], value=0, dtype=tl.int32)
    count = tl.sum(tl.where(mask_max, 1, count_mask))
    if count > 1:
        index = tl.arange(0, draft_token_num)
        mask_left = index != max_index
        remained_index = tl.where(mask_max and mask_left, index, 0)
        max_index = tl.max(remained_index)

    tl.store(accept_length + pid, accept_len)
    retrive_index_ptr = retrive_index + (retrive_start + max_index) * max_len
    retrive_offset = tl.arange(0, max_len_upper)
    retrive_load_mask = retrive_offset < accept_len + 1
    data = tl.load(retrive_index_ptr + retrive_offset, mask=retrive_load_mask)

    tl.store(
        accept_index + pid * max_len + retrive_offset, data, mask=retrive_load_mask
    )

    extract_load_ptr = accept_index + pid * max_len + accept_len
    if accept_len == max_len - 1:
        extract_data = tl.load(extract_load_ptr - 1)
        tl.store(extract_index + pid * 2, extract_data)
        extract_data = tl.load(extract_load_ptr)
        tl.store(extract_index + pid * 2 + 1, extract_data)

    else:
        extract_data = tl.load(extract_load_ptr)
        tl.store(extract_index + pid * 2, extract_data)


@triton.jit
def create_extend_spec_info(
    verified_id,
    seq_len,
    accept_len,
    accept_len_cum,
    positions,
    new_verified_id,
    accept_len_upper: tl.constexpr,
):
    pid = tl.program_id(axis=0)
    offset = 0 if pid == 0 else tl.load(accept_len_cum + pid - 1)
    seq_length = tl.load(seq_len + pid)
    accept_length = tl.load(accept_len + pid)
    positions_ptr = positions + offset
    data = tl.arange(0, accept_len_upper)
    mask = data < accept_length
    tl.store(positions_ptr + data, seq_length - accept_length + data, mask)

    offset = tl.load(accept_len_cum + pid) - 1
    verified_id_data = tl.load(verified_id + offset)
    tl.store(new_verified_id + pid, verified_id_data)


@triton.jit
def assign_req_to_token_pool(
    req_pool_indices,
    req_to_token,
    start_offset,
    end_offset,
    out_cache_loc,
    pool_len: tl.constexpr,
    bs_upper: tl.constexpr,
):
    BLOCK_SIZE: tl.constexpr = 32
    pid = tl.program_id(axis=0)
    kv_start = tl.load(start_offset + pid)
    kv_end = tl.load(end_offset + pid)
    token_pool = req_to_token + tl.load(req_pool_indices + pid) * pool_len

    length_offset = tl.arange(0, bs_upper)
    start = tl.load(start_offset + length_offset, mask=length_offset < pid)
    end = tl.load(end_offset + length_offset, mask=length_offset < pid)
    out_offset = tl.sum(end - start, axis=0)

    out_cache_ptr = out_cache_loc + out_offset

    save_offset = tl.arange(0, BLOCK_SIZE) + kv_start
    load_offset = tl.arange(0, BLOCK_SIZE)

    num_loop = tl.cdiv(kv_end - kv_start, BLOCK_SIZE)
    for _ in range(num_loop):
        mask = save_offset < kv_end
        data = tl.load(out_cache_ptr + load_offset, mask=mask)
        tl.store(token_pool + save_offset, data, mask=mask)
        save_offset += BLOCK_SIZE
        load_offset += BLOCK_SIZE


@triton.jit
def generate_draft_decode_kv_indices(
    req_pool_indices,
    req_to_token,
    paged_kernel_lens,
    kv_indices,
    iters: tl.constexpr,
    topk: tl.constexpr,
    pool_len: tl.constexpr,
    bs_upper: tl.constexpr,
    iter_upper: tl.constexpr,
):
    BLOCK_SIZE: tl.constexpr = 128
    bid = tl.program_id(axis=0)
    topk_id = tl.program_id(axis=1)

    load_offset = tl.arange(0, bs_upper)
    seq_lens = tl.load(paged_kernel_lens + load_offset, mask=load_offset < bid)
    seq_len = tl.load(paged_kernel_lens + bid)
    cum_seq_len = tl.sum(seq_lens)

    kv_offset = cum_seq_len * topk + bid * iters * topk + topk_id * (seq_len + iters)
    kv_ptr = kv_indices + kv_offset
    token_pool_ptr = req_to_token + tl.load(req_pool_indices + bid) * pool_len

    kv_offset = tl.arange(0, BLOCK_SIZE)
    num_loop = tl.cdiv(seq_len, BLOCK_SIZE)
    for _ in range(num_loop):
        mask = kv_offset < seq_len
        data = tl.load(token_pool_ptr + kv_offset, mask=mask)
        tl.store(kv_ptr + kv_offset, data, mask=mask)
        kv_offset += BLOCK_SIZE

    extend_offset = tl.arange(0, iter_upper)
    extend_data = tl.load(
        token_pool_ptr + seq_len + tl.arange(0, iter_upper) * topk + topk_id,
        mask=extend_offset < iters,
    )
    tl.store(kv_ptr + seq_len + extend_offset, extend_data, mask=extend_offset < iters)


class EAGLEDraftInput(SpecInfo):
    def __init__(self):
        self.prev_mode = ForwardMode.DECODE

        self.scores: torch.Tensor = None
        self.score_list: List[torch.Tensor] = []
        self.token_list: List[torch.Tensor] = []
        self.origin_score_list: List[torch.Tensor] = []  # used for sampling
        self.parents_list: List[torch.Tensor] = []
        self.cache_list: List[torch.Tenor] = []
        self.iter = 0

        # shape: (b, hidden_size)
        self.hidden_states: torch.Tensor = None
        # shape: (b,)
        self.verified_id: torch.Tensor = None
        # shape: (b, vocab_size)
        self.sample_output: torch.Tensor = None

        self.positions: torch.Tensor = None
        self.accept_length: torch.Tensor = None
        self.accept_length_cpu: List[int] = None

    def load_server_args(self, server_args: ServerArgs):
        self.topk: int = server_args.speculative_eagle_topk
        self.num_verify_token: int = server_args.speculative_num_draft_tokens
        self.spec_steps = server_args.speculative_num_steps

    def prepare_for_extend(self, batch: ScheduleBatch):
        req_pool_indices = batch.alloc_req_slots(len(batch.reqs))
        out_cache_loc = batch.alloc_token_slots(batch.input_ids.numel())
        batch.out_cache_loc = out_cache_loc

        pt = 0
        for i, req in enumerate(batch.reqs):
            req.req_pool_idx = req_pool_indices[i]
            pre_len, seq_len = len(req.prefix_indices), len(req.fill_ids)
            assert seq_len - pre_len == req.extend_input_len

            if pre_len > 0:
                batch.req_to_token_pool.req_to_token[req.req_pool_idx][
                    :pre_len
                ] = req.prefix_indices

            batch.req_to_token_pool.req_to_token[req.req_pool_idx, pre_len:seq_len] = (
                out_cache_loc[pt : pt + req.extend_input_len]
            )

            pt += req.extend_input_len

        # TODO: support batching inputs
        assert len(batch.extend_lens) == 1
        batch.input_ids = torch.concat((batch.input_ids[1:], self.verified_id))

    def filter_batch(
        self,
        new_indices: torch.Tensor,
    ):
        self.sample_output = self.sample_output[: len(new_indices)]
        self.hidden_states = self.hidden_states[: len(new_indices)]
        self.verified_id = self.verified_id[: len(new_indices)]

    def prepare_for_decode(self, batch: ScheduleBatch):
        prob = self.sample_output  # shape: (b * top_k, vocab) or (b, vocab)
        top = torch.topk(prob, self.topk, dim=-1)
        topk_index, topk_p = (
            top.indices,
            top.values,
        )  # shape: (b * top_k, top_k) or (b, top_k)

        if self.prev_mode.is_decode():
            scores = torch.mul(
                self.scores.unsqueeze(2), topk_p.reshape(-1, self.topk, self.topk)
            )  # (b, topk, 1) x (b, topk ,topk) -> (b, topk, topk)
            topk_cs = torch.topk(
                scores.flatten(start_dim=1), self.topk, dim=-1
            )  # (b, topk)
            topk_cs_index, topk_cs_p = topk_cs.indices, topk_cs.values

            selected_input_index = topk_cs_index.flatten() // self.topk + torch.arange(
                0, batch.batch_size() * self.topk, step=self.topk, device="cuda"
            ).repeat_interleave(self.topk)

            batch.spec_info.hidden_states = batch.spec_info.hidden_states[
                selected_input_index, :
            ]

            topk_index = topk_index.reshape(-1, self.topk**2)
            batch.input_ids = torch.gather(
                topk_index, index=topk_cs_index, dim=1
            ).flatten()
            batch.out_cache_loc = batch.alloc_token_slots(len(batch.input_ids))

            self.scores = topk_cs_p
            self.score_list.append(scores)  # (b, topk, topk)
            self.token_list.append(topk_index)  # (b, topk * topk)
            self.origin_score_list.append(topk_p.reshape(topk_index.shape))
            self.parents_list.append(
                topk_cs_index + (self.topk**2 * (self.iter - 1) + self.topk)
            )  # shape: (b, topk)
        else:
            # ForwardMode.EXTEND or ForwardMode.DRAFT_EXTEND
            batch.spec_info.hidden_states = (
                batch.spec_info.hidden_states.repeat_interleave(self.topk, dim=0)
            )

            batch.input_ids = topk_index.flatten()
            batch.out_cache_loc = batch.alloc_token_slots(topk_index.numel())

            self.scores = topk_p  # shape: (b, topk)
            self.score_list.append(topk_p.unsqueeze(1))  # shape: (b, 1, topk)
            self.token_list.append(topk_index)  # shape: (b, topk)
            self.origin_score_list.append(topk_p)
            self.parents_list.append(
                torch.arange(-1, self.topk, dtype=torch.long, device="cuda")
                .unsqueeze(0)
                .repeat(self.scores.shape[0], 1)
            )  # shape: (b, topk + 1)
        self.cache_list.append(batch.out_cache_loc)
        self.positions = (
            batch.seq_lens[:, None]
            + torch.full(
                [1, self.topk], fill_value=self.iter, device="cuda", dtype=torch.long
            )
        ).flatten()

        bs = len(batch.seq_lens)
        assign_req_to_token_pool[(bs,)](
            batch.req_pool_indices,
            batch.req_to_token_pool.req_to_token,
            batch.seq_lens + self.topk * self.iter,
            batch.seq_lens + self.topk * (self.iter + 1),
            batch.out_cache_loc,
            batch.req_to_token_pool.req_to_token.shape[1],
            triton.next_power_of_2(bs),
        )
        self.iter += 1

    def prepare_extend_after_decode(self, batch: ScheduleBatch):
        batch.out_cache_loc = batch.alloc_token_slots(self.verified_id.numel())
        accept_length_cpu = batch.spec_info.accept_length_cpu
        batch.extend_lens = [x + 1 for x in accept_length_cpu]
        batch.seq_lens = batch.spec_info.seq_lens_for_draft_extend
        seq_lens_cpu = batch.seq_lens.tolist()

        pt = 0
        i = 0
        for req in batch.reqs:
            if req.finished():
                continue
            # assert seq_len - pre_len == req.extend_input_len
            input_len = batch.extend_lens[i]
            seq_len = seq_lens_cpu[i]
            batch.req_to_token_pool.req_to_token[req.req_pool_idx][
                seq_len - input_len : seq_len
            ] = batch.out_cache_loc[pt : pt + input_len]
            pt += input_len
            i += 1
        assert pt == batch.out_cache_loc.shape[0]

        self.positions = torch.empty_like(self.verified_id)
        new_verified_id = torch.empty_like(self.accept_length, dtype=torch.long)
        self.accept_length.add_(1)

        create_extend_spec_info[(self.accept_length.numel(),)](
            self.verified_id,
            batch.seq_lens,
            self.accept_length,
            torch.cumsum(self.accept_length, axis=0, dtype=torch.int),
            self.positions,
            new_verified_id,
            triton.next_power_of_2(self.spec_steps + 1),
        )

        batch.seq_lens_sum = sum(seq_lens_cpu)
        batch.input_ids = self.verified_id
        self.verified_id = new_verified_id

    def prepare_for_verify(self, batch: ScheduleBatch):
        score_list = torch.cat(self.score_list, dim=1).flatten(
            1
        )  # b, n, topk; n= 1+(self.iter-1)*self.topk
        ss_token_list = torch.cat(
            self.token_list, dim=1
        )  # b, (self.topk+(self.iter-1)*self.topk)
        origin_token_list = torch.cat(self.origin_score_list, dim=1)
        top_scores = torch.topk(score_list, self.num_verify_token - 1, dim=-1)
        top_scores_index = top_scores.indices
        top_scores_index = torch.sort(top_scores_index).values

        draft_tokens = torch.gather(ss_token_list, index=top_scores_index, dim=1)
        scores = torch.gather(origin_token_list, index=top_scores_index, dim=1)
        draft_tokens = torch.cat((self.verified_id.unsqueeze(1), draft_tokens), dim=1)
        parent_list = torch.cat(self.parents_list[:-1], dim=1)

        tree_mask, position, retrive_index, retrive_cum_len = build_tree_kernel(
            parent_list,
            top_scores_index,
            batch.seq_lens,
            self.topk,
            self.iter - 1,
            self.num_verify_token,
        )

        return EagleVerifyInput(
            draft_tokens.flatten(),
            scores.flatten(),
            tree_mask,
            position,
            retrive_index,
            retrive_cum_len,
            self.num_verify_token,
        )

    def generate_attn_arg_decode(
        self,
        req_pool_indices: torch.Tensor,
        paged_kernel_lens: torch.Tensor,
        req_to_token: torch.Tensor,
    ):
        seq_num = req_pool_indices.numel()
        bs = self.topk * req_pool_indices.numel()
        seq_len = self.positions.reshape(-1).contiguous()

        cum_kv_seq_len = torch.zeros((bs + 1,), dtype=torch.int32, device="cuda")
        cum_kv_seq_len[1:] = torch.cumsum(seq_len + 1, dim=0)
        total_len = torch.sum(paged_kernel_lens).item()

        kv_indices = torch.empty(
            (total_len * self.topk + seq_num * self.iter * self.topk,),
            dtype=torch.int32,
            device="cuda",
        )

        generate_draft_decode_kv_indices[(req_pool_indices.numel(), self.topk)](
            req_pool_indices,
            req_to_token,
            paged_kernel_lens,
            kv_indices,
            self.iter,
            self.topk,
            req_to_token.shape[1],
            triton.next_power_of_2(seq_num),
            triton.next_power_of_2(self.spec_steps),
        )
        return bs, kv_indices, cum_kv_seq_len

    def clear_draft_cache(self, batch):
        draft_cache = torch.cat(self.cache_list, dim=0)
        batch.token_to_kv_pool.free(draft_cache)

    def generate_attn_arg_prefill(
        self,
        req_pool_indices: torch.Tensor,
        paged_kernel_lens: torch.Tensor,
        req_to_token: torch.Tensor,
    ):
        bs = self.accept_length.numel()
        qo_indptr = torch.zeros((bs + 1,), dtype=torch.int32, device="cuda")
        qo_indptr[1:] = torch.cumsum(self.accept_length, dim=0)

        cum_kv_seq_len = torch.zeros((bs + 1,), dtype=torch.int32, device="cuda")
        cum_kv_seq_len[1:] = torch.cumsum(paged_kernel_lens, dim=0)
        kv_indices = torch.empty(cum_kv_seq_len[-1], dtype=torch.int32, device="cuda")

        create_flashinfer_kv_indices_triton[(bs,)](
            req_to_token,
            req_pool_indices,
            paged_kernel_lens,
            cum_kv_seq_len,
            None,
            kv_indices,
            req_to_token.size(1),
        )

        return kv_indices, cum_kv_seq_len, qo_indptr, None

    def merge_batch(self, spec_info: EAGLEDraftInput):
        if self.hidden_states is None:
            self.hidden_states = spec_info.hidden_states
            self.verified_id = spec_info.verified_id
            self.sample_output = spec_info.sample_output
            self.prev_mode = spec_info.prev_mode
            return
        if spec_info.hidden_states is None:
            return
        self.hidden_states = torch.cat(
            [self.hidden_states, spec_info.hidden_states], axis=0
        )
        self.verified_id = torch.cat([self.verified_id, spec_info.verified_id], axis=0)
        self.sample_output = torch.cat([self.sample_output, spec_info.sample_output])


class EagleVerifyInput(SpecInfo):
    def __init__(
        self,
        draft_token: torch.Tensor,
        draft_score: torch.Tensor,
        tree_mask: torch.Tensor,
        positions: torch.Tensor,
        retrive_index: torch.Tensor,
        retrive_cum_len: torch.Tensor,
        draft_token_num: int,
    ):
        self.draft_token = draft_token
        self.draft_score = draft_score
        self.custom_mask = tree_mask
        self.positions = positions
        self.retrive_index = retrive_index
        self.retrive_cum_len = retrive_cum_len
        self.draft_token_num = draft_token_num

    def prepare_for_verify(self, batch: ScheduleBatch):
        batch.input_ids = self.draft_token
        batch.out_cache_loc = batch.alloc_token_slots(batch.input_ids.numel())
        bs = batch.seq_lens.numel()
        assign_req_to_token_pool[(bs,)](
            batch.req_pool_indices,
            batch.req_to_token_pool.req_to_token,
            batch.seq_lens,
            batch.seq_lens + self.draft_token_num,
            batch.out_cache_loc,
            batch.req_to_token_pool.req_to_token.shape[1],
            triton.next_power_of_2(bs),
        )

    def generate_attn_arg_prefill(
        self,
        req_pool_indices: torch.Tensor,
        paged_kernel_lens: torch.Tensor,
        req_to_token: torch.Tensor,
    ):
        batch_size = len(req_pool_indices)
        qo_indptr = torch.arange(
            0,
            (1 + batch_size) * self.draft_token_num,
            step=self.draft_token_num,
            dtype=torch.int32,
            device="cuda",
        )

        cum_kv_seq_len = torch.zeros(
            (batch_size + 1,), dtype=torch.int32, device="cuda"
        )

        paged_kernel_lens = paged_kernel_lens + self.draft_token_num
        cum_kv_seq_len[1:] = torch.cumsum(paged_kernel_lens, dim=0)

        kv_indices = torch.empty(cum_kv_seq_len[-1], dtype=torch.int32, device="cuda")

        create_flashinfer_kv_indices_triton[(batch_size,)](
            req_to_token,
            req_pool_indices,
            paged_kernel_lens,
            cum_kv_seq_len,
            None,
            kv_indices,
            req_to_token.size(1),
        )
        return kv_indices, cum_kv_seq_len, qo_indptr, self.custom_mask

    def verify(self, batch: ScheduleBatch, logits_output: torch.Tensor) -> torch.Tensor:
        predict = torch.argmax(logits_output.next_token_logits, dim=-1)
        predict = torch.cat(
            [predict, torch.full([1], -1, dtype=torch.long, device="cuda")], dim=-1
        )
        draft_token = torch.cat(
            [self.draft_token, torch.full([1], -1, dtype=torch.long, device="cuda")],
            dim=-1,
        )
        target_predict = predict[self.retrive_index]
        candidates = draft_token[self.retrive_index]
        # logits = logits_output.next_token_logits[self.retrive_index]
        # target_predict = torch.argmax(logits[:, :-1], dim=-1)
        accept_mask = candidates[:, 1:] == target_predict[:, :-1]
        accept_mask = (torch.cumprod(accept_mask, dim=1)).sum(dim=1)
        bs = self.retrive_cum_len.numel() - 1

        max_draft_len = self.retrive_index.shape[-1]
        accept_index = torch.full(
            (bs, max_draft_len), -1, dtype=torch.long, device="cuda"
        )
        accept_length = torch.empty((bs,), dtype=torch.int, device="cuda")
        extract_index = torch.full((bs * 2,), 0, dtype=torch.int, device="cuda")
        eagle_verify_retrive[(bs,)](
            self.retrive_index.contiguous(),
            accept_mask.contiguous(),
            self.retrive_cum_len,
            accept_index,
            accept_length,
            extract_index,
            max_draft_len,
            self.draft_token_num,
            triton.next_power_of_2(max_draft_len),
        )

        draft_input = EAGLEDraftInput()
        new_accept_index = []
        unfinished_index = []
        finished_extend_len = {}  # {rid:accept_length + 1}
        accept_index_cpu = accept_index.tolist()
        predict_cpu = predict.tolist()
        has_finished = False

        # iterate every accepted token and check if req has finished after append the token
        # should be checked BEFORE free kv cache slots
        for i, (req, accept_index_row) in enumerate(zip(batch.reqs, accept_index_cpu)):
            new_accept_index_ = []
            for j, idx in enumerate(accept_index_row):
                if idx == -1:
                    break
                id = predict_cpu[idx]
                # if not found_finished:
                req.output_ids.append(id)
                finished_extend_len[req.rid] = j + 1
                req.check_finished()
                if req.finished():
                    has_finished = True
                    # set all tokens after finished token to -1 and break
                    accept_index[i, j + 1 :] = -1
                    break
                else:
                    new_accept_index_.append(idx)
            if not req.finished():
                new_accept_index.extend(new_accept_index_)
                unfinished_index.append(i)
        accept_length = (accept_index != -1).sum(dim=1) - 1

        accept_index = accept_index[accept_index != -1]
        accept_length_cpu = accept_length.tolist()
        verified_id = predict[accept_index]

        evict_mask = torch.full_like(self.draft_token, True, dtype=torch.bool)
        evict_mask[accept_index] = False
        mem_need_free_idx = batch.out_cache_loc[evict_mask]
        batch.token_to_kv_pool.free(mem_need_free_idx)
        assign_req_to_token_pool[(bs,)](
            batch.req_pool_indices,
            batch.req_to_token_pool.req_to_token,
            batch.seq_lens,
            batch.seq_lens + accept_length + 1,
            batch.out_cache_loc[accept_index],
            batch.req_to_token_pool.req_to_token.shape[1],
            triton.next_power_of_2(bs),
        )
        batch.seq_lens.add_(accept_length + 1)

        if len(new_accept_index) > 0:
            new_accept_index = torch.tensor(new_accept_index, device="cuda")
            draft_input.verified_id = predict[new_accept_index]
            draft_input.hidden_states = batch.spec_info.hidden_states[new_accept_index]
            draft_input.accept_length = accept_length[unfinished_index]
            draft_input.accept_length_cpu = [
                accept_length_cpu[i] for i in unfinished_index
            ]
            if has_finished:
                draft_input.seq_lens_for_draft_extend = batch.seq_lens[unfinished_index]
            else:
                draft_input.seq_lens_for_draft_extend = batch.seq_lens

        logits_output.next_token_logits = logits_output.next_token_logits[accept_index]
        return (
            draft_input,
            logits_output,
            verified_id,
            finished_extend_len,
            accept_length_cpu,
        )
