import asyncio
import logging
import multiprocessing
import time
import warnings
from concurrent.futures import ThreadPoolExecutor
from enum import Enum, auto
from typing import Dict, List, Optional, Tuple, Union

import numpy as np
import rpyc
import torch
from rpyc.utils.classic import obtain
from rpyc.utils.server import ThreadedServer
from sglang.srt.constrained.fsm_cache import FSMCache
from sglang.srt.hf_transformers_utils import get_processor, get_tokenizer
from sglang.srt.managers.io_struct import BatchTokenIDOut, TokenizedGenerateReqInput
from sglang.srt.managers.router.infer_batch import Batch, ForwardMode, Req
from sglang.srt.managers.router.model_runner import ModelRunner
from sglang.srt.managers.router.radix_cache import RadixCache
from sglang.srt.managers.router.scheduler import Scheduler
from sglang.srt.model_config import ModelConfig
from sglang.srt.server_args import PortArgs, ServerArgs
from sglang.srt.utils import (
    get_exception_traceback,
    get_int_token_logit_bias,
    is_multimodal_model,
    set_random_seed,
)

logger = logging.getLogger("model_rpc")


class ModelRpcServer(rpyc.Service):
    def exposed_init_model(
        self,
        tp_rank: int,
        server_args: ServerArgs,
        port_args: PortArgs,
    ):
        server_args, port_args = [obtain(x) for x in [server_args, port_args]]

        # Copy arguments
        self.model_mode = server_args.model_mode
        self.tp_rank = tp_rank
        self.tp_size = server_args.tp_size
        self.schedule_heuristic = server_args.schedule_heuristic

        # Init model and tokenizer
        self.model_config = ModelConfig(
            server_args.model_path, server_args.trust_remote_code
        )
        self.model_runner = ModelRunner(
            self.model_config,
            server_args.mem_fraction_static,
            tp_rank,
            server_args.tp_size,
            port_args.nccl_port,
            server_args.load_format,
            server_args.trust_remote_code,
            server_args.model_mode,
        )
        if is_multimodal_model(server_args.model_path):
            self.processor = get_processor(
                server_args.tokenizer_path,
                tokenizer_mode=server_args.tokenizer_mode,
                trust_remote_code=server_args.trust_remote_code,
            )
            self.tokenizer = self.processor.tokenizer
        else:
            self.tokenizer = get_tokenizer(
                server_args.tokenizer_path,
                tokenizer_mode=server_args.tokenizer_mode,
                trust_remote_code=server_args.trust_remote_code,
            )
        self.eos_token_id = self.tokenizer.eos_token_id
        self.max_total_num_token = self.model_runner.max_total_num_token
        self.max_num_running_seq = self.max_total_num_token // 2
        self.max_prefill_num_token = max(
            self.model_config.context_len, self.max_total_num_token // 6
        )
        self.int_token_logit_bias = torch.tensor(
            get_int_token_logit_bias(self.tokenizer, self.model_config.vocab_size)
        )
        set_random_seed(server_args.random_seed)
        logger.info(
            f"Rank {self.tp_rank}: "
            f"max_total_num_token={self.max_total_num_token}, "
            f"max_prefill_num_token={self.max_prefill_num_token}, "
            f"context_len={self.model_config.context_len}, "
            f"model_mode={self.model_mode}"
        )

        # Init cache
        self.tree_cache = RadixCache(disable="no-cache" in self.model_mode)
        self.scheduler = Scheduler(
            self.schedule_heuristic,
            self.max_num_running_seq,
            self.max_prefill_num_token,
            self.max_total_num_token,
            self.tree_cache,
        )
        self.req_to_token_pool = self.model_runner.req_to_token_pool
        self.token_to_kv_pool = self.model_runner.token_to_kv_pool

        # Init running status
        self.forward_queue: List[Req] = []
        self.running_batch: Batch = None
        self.out_pyobjs = []
        self.decode_forward_ct = 0
        self.stream_interval = server_args.stream_interval

        # Init the FSM cache for constrained generation
        self.regex_fsm_cache = FSMCache(self.tokenizer)

        # Init new token estimation
        self.new_token_ratio = min(0.4 * server_args.schedule_conservativeness, 1.0)
        self.min_new_token_ratio = min(0.2 * server_args.schedule_conservativeness, 1.0)
        self.new_token_ratio_step = (0.0001, 0.05)  # (down, up)

    def exposed_step(self, recv_reqs):
        if self.tp_size != 1:
            recv_reqs = obtain(recv_reqs)

        try:
            # Recv requests
            for recv_req in recv_reqs:
                if isinstance(recv_req, TokenizedGenerateReqInput):
                    self.handle_generate_request(recv_req)
                else:
                    raise ValueError(f"Invalid request: {recv_req}")

            # Forward
            self.forward_step()
        except Exception:
            logger.error("Exception in ModelRpcClient:\n" + get_exception_traceback())

        # Return results
        ret = self.out_pyobjs
        self.out_pyobjs = []
        return ret

    @torch.inference_mode()
    def forward_step(self):
        new_batch = self.get_new_fill_batch()

        if new_batch is not None:
            # Run new fill batch
            self.forward_fill_batch(new_batch)

            if not new_batch.is_empty():
                if self.running_batch is None:
                    self.running_batch = new_batch
                else:
                    self.running_batch.merge(new_batch)
        else:
            # Run decode batch
            if self.running_batch is not None:
                # Run a few decode batches continuously for reducing overhead
                for _ in range(10):
                    self.forward_decode_batch(self.running_batch)

                    if self.running_batch.is_empty():
                        self.running_batch = None
                        break
            else:
                # check the available size
                available_size = (
                    self.token_to_kv_pool.available_size()
                    + self.tree_cache.evictable_size()
                )
                if available_size != self.max_total_num_token:
                    warnings.warn(
                        "Warning: "
                        f"available_size={available_size}, max_total_num_token={self.max_total_num_token}\n"
                        "KV cache pool leak detected!"
                    )

        if self.running_batch is not None and self.tp_rank == 0:
            if self.decode_forward_ct >= 20:
                self.decode_forward_ct = 0
                num_used = self.max_total_num_token - (
                    self.token_to_kv_pool.available_size()
                    + self.tree_cache.evictable_size()
                )
                logger.info(
                    f"#running-req: {len(self.running_batch.reqs)}, "
                    f"#token: {num_used}, "
                    f"token usage: {num_used / self.max_total_num_token:.2f}, "
                    f"#queue-req: {len(self.forward_queue)}"
                )

    def handle_generate_request(
        self,
        recv_req: TokenizedGenerateReqInput,
    ):
        req = Req(recv_req.rid)
        req.input_ids = recv_req.input_ids
        req.pixel_values = recv_req.pixel_values
        if req.pixel_values is not None:
            pad_value = [
                (recv_req.image_hash) % self.model_config.vocab_size,
                (recv_req.image_hash >> 16) % self.model_config.vocab_size,
                (recv_req.image_hash >> 32) % self.model_config.vocab_size,
                (recv_req.image_hash >> 64) % self.model_config.vocab_size,
            ]
            req.input_ids, req.image_offset = self.model_runner.model.pad_input_ids(
                req.input_ids, pad_value
            )
        req.sampling_params = recv_req.sampling_params
        req.return_normalized_logprob = recv_req.return_normalized_logprob
        req.normalized_logprob_start_len = recv_req.normalized_logprob_start_len
        req.stream = recv_req.stream
        req.tokenizer = self.tokenizer

        # Truncate long prompts
        req.input_ids = req.input_ids[: self.model_config.context_len - 1]
        req.sampling_params.max_new_tokens = min(
            req.sampling_params.max_new_tokens,
            self.model_config.context_len - 1 - len(req.input_ids),
        )
        self.forward_queue.append(req)

    def get_new_fill_batch(self):
        if (
            self.running_batch is not None
            and len(self.running_batch.reqs) > self.max_num_running_seq
        ):
            return None

        for req in self.forward_queue:
            prefix_indices, last_node = self.tree_cache.match_prefix(req.input_ids)
            if req.return_normalized_logprob:
                prefix_indices = prefix_indices[: req.normalized_logprob_start_len]
            req.adjust_input_len = len(req.input_ids) - len(prefix_indices)
            req.prefix_indices = prefix_indices
            req.last_node = last_node

        # Get priority queue
        self.forward_queue = self.scheduler.get_priority_queue(self.forward_queue)

        # Add requests if there is available space
        can_run_list = []
        new_batch_total_tokens = 0
        new_batch_input_tokens = 0
        new_batch_prefix_tokens = 0

        available_size = (
            self.token_to_kv_pool.available_size() + self.tree_cache.evictable_size()
        )
        if self.running_batch:
            available_size -= sum(
                [
                    (r.max_new_tokens() - len(r.output_ids)) * self.new_token_ratio
                    for r in self.running_batch.reqs
                ]
            )

        for req in self.forward_queue:
            if req.return_normalized_logprob:
                # Need at least two tokens to compute normalized logprob
                if req.adjust_input_len < 2:
                    delta = 2 - req.adjust_input_len
                    req.adjust_input_len += delta
                    req.prefix_indices = req.prefix_indices[:-delta]
                    if req.image_offset is not None:
                        req.image_offset += delta
            if req.adjust_input_len == 0 and req.max_new_tokens() > 0:
                # Need at least one token to compute logits
                req.adjust_input_len = 1
                req.prefix_indices = req.prefix_indices[:-1]
                if req.image_offset is not None:
                    req.image_offset += 1

            if (
                req.adjust_input_len + req.max_new_tokens() + new_batch_total_tokens
                < available_size
                and req.adjust_input_len + new_batch_input_tokens
                < self.max_prefill_num_token
            ):
                delta = self.tree_cache.inc_ref_counter(req.last_node)
                available_size += delta

                if not (
                    req.adjust_input_len + req.max_new_tokens() + new_batch_total_tokens
                    < available_size
                ):
                    delta = self.tree_cache.dec_ref_counter(req.last_node)
                    available_size += delta
                else:
                    self.token_to_kv_pool.add_refs(req.prefix_indices)
                    can_run_list.append(req)
                    new_batch_total_tokens += (
                        req.adjust_input_len + req.max_new_tokens()
                    )
                    new_batch_input_tokens += req.adjust_input_len

        if len(can_run_list) == 0:
            return None

        if self.tp_rank == 0:
            logger.info(
                f"new fill batch. #seq: {len(can_run_list)}. "
                f"#cached_token: {sum(len(x.prefix_indices) for x in can_run_list)}. "
                f"#new_token: {new_batch_input_tokens}. "
                f"#remaining_req: {len(self.forward_queue) - len(can_run_list)}. "
                f"#running_req: {0 if self.running_batch is None else len(self.running_batch.reqs)}"
            )

        new_batch = Batch.init_new(
            can_run_list,
            self.req_to_token_pool,
            self.token_to_kv_pool,
            self.tree_cache,
        )
        self.forward_queue = [x for x in self.forward_queue if x not in can_run_list]
        return new_batch

    def forward_fill_batch(self, batch: Batch):
        # Build batch tensors
        batch.prepare_for_extend(
            self.model_config.vocab_size, self.int_token_logit_bias
        )

        # init the regex fsm before first sampling
        for req in batch.reqs:
            if req.sampling_params.regex is not None:
                req.regex_fsm_state = 0
                req.regex_fsm = self.regex_fsm_cache.get_fsm(req.sampling_params.regex)

        if batch.extend_num_tokens != 0:
            # Forward
            logits, normalized_logprobs = self.model_runner.forward(
                batch, ForwardMode.EXTEND, batch.return_normalized_logprob
            )
            # print("extend logits", logits)
            if normalized_logprobs is not None:
                normalized_logprobs = normalized_logprobs.cpu().tolist()

            next_token_ids, next_token_probs = batch.sample(logits)
            next_token_ids = next_token_ids.cpu().tolist()
        else:
            next_token_ids = [self.tokenizer.eos_token_id] * len(batch.reqs)
            normalized_logprobs = None

        # Check finish condition
        reqs = batch.reqs
        for i in range(len(reqs)):
            reqs[i].output_ids = [next_token_ids[i]]
            reqs[i].check_finished()

            if normalized_logprobs is not None:
                reqs[i].normalized_logprob = normalized_logprobs[i]

        self.handle_finished_requests(batch)

    def forward_decode_batch(self, batch: Batch):
        # check if decode out of memory
        if not batch.check_decode_mem():
            old_ratio = self.new_token_ratio
            self.new_token_ratio = min(old_ratio + self.new_token_ratio_step[1], 1.0)

            retracted_reqs = batch.retract_decode()
            logger.info(
                "decode out of memory happened, "
                f"#retracted_reqs: {len(retracted_reqs)}, "
                f"#new_token_ratio: {old_ratio:.4f} -> {self.new_token_ratio:.4f}"
            )
            self.forward_queue.extend(retracted_reqs)
        else:
            self.new_token_ratio = max(
                self.new_token_ratio - self.new_token_ratio_step[0],
                self.min_new_token_ratio,
            )

        # Update batch tensors
        self.decode_forward_ct += 1
        batch.prepare_for_decode()

        # Forward
        logits = self.model_runner.forward(batch, ForwardMode.DECODE)
        next_token_ids, next_token_probs = batch.sample(logits)
        next_token_ids = next_token_ids.cpu().tolist()

        # Check finish condition
        reqs = batch.reqs
        for i in range(len(reqs)):
            reqs[i].output_ids.append(next_token_ids[i])
            reqs[i].check_finished()

        self.handle_finished_requests(batch)

    def handle_finished_requests(self, batch: Batch):
        output_rids = []
        output_tokens = []
        output_hit_stop_str = []
        output_skip_special_tokens = []
        output_meta_info = []
        output_finished = []
        finished_indices = []
        unfinished_indices = []
        for i, req in enumerate(batch.reqs):
            if req.finished:
                finished_indices.append(i)
            else:
                unfinished_indices.append(i)

            if req.finished or (
                req.stream and self.decode_forward_ct % self.stream_interval == 0
            ):
                output_rids.append(req.rid)
                output_tokens.append(req.output_ids)
                output_hit_stop_str.append(req.hit_stop_str)
                output_skip_special_tokens.append(
                    req.sampling_params.skip_special_tokens
                )
                meta_info = {
                    "prompt_tokens": len(req.input_ids),
                    "completion_tokens": len(req.output_ids),
                }
                if req.return_normalized_logprob:
                    meta_info["normalized_logprob"] = req.normalized_logprob
                output_meta_info.append(meta_info)
                output_finished.append(req.finished)

        # Send to detokenizer
        if output_rids:
            self.out_pyobjs.append(
                BatchTokenIDOut(
                    output_rids,
                    output_tokens,
                    output_hit_stop_str,
                    output_skip_special_tokens,
                    output_meta_info,
                    output_finished,
                )
            )

        # Remove finished reqs
        if finished_indices:
            # Update radix cache
            req_pool_indices_cpu = batch.req_pool_indices.cpu().tolist()
            for i in finished_indices:
                req = batch.reqs[i]
                req_pool_idx = req_pool_indices_cpu[i]
                token_ids = tuple(req.input_ids + req.output_ids)
                seq_len = len(token_ids) - 1
                indices = self.req_to_token_pool.req_to_token[req_pool_idx, :seq_len]
                prefix_len = self.tree_cache.insert(
                    token_ids[:seq_len], indices.clone()
                )

                self.token_to_kv_pool.free(indices[:prefix_len])
                self.req_to_token_pool.free(req_pool_idx)
                self.tree_cache.dec_ref_counter(req.last_node)

            # Update batch tensors
            if unfinished_indices:
                batch.filter_batch(unfinished_indices)
            else:
                batch.reqs = []


class ModelRpcClient:
    def __init__(self, server_args: ServerArgs, port_args: PortArgs):
        tp_size = server_args.tp_size

        if tp_size == 1:
            # Init model
            self.model_server = ModelRpcServer()
            self.model_server.exposed_init_model(0, server_args, port_args)

            # Wrap functions
            def async_wrap(f):
                async def _func(*args, **kwargs):
                    return f(*args, **kwargs)

                return _func

            self.step = async_wrap(self.model_server.exposed_step)
        else:
            with ThreadPoolExecutor(tp_size) as executor:
                # Launch model processes
                rets = executor.map(start_model_process, port_args.model_rpc_ports)
                self.model_servers = [x[0] for x in rets]
                self.procs = [x[1] for x in rets]

                # Init model
                def init_model(i):
                    return self.model_servers[i].init_model(i, server_args, port_args)

                rets = [obtain(x) for x in executor.map(init_model, range(tp_size))]

            # Wrap functions
            def async_wrap(func_name):
                fs = [rpyc.async_(getattr(m, func_name)) for m in self.model_servers]

                async def _func(*args, **kwargs):
                    tasks = [f(*args, **kwargs) for f in fs]
                    await asyncio.gather(*[asyncio.to_thread(t.wait) for t in tasks])
                    return obtain(tasks[0].value)

                return _func

            self.step = async_wrap("step")


def start_model_process(port):
    def _init_service(port):
        t = ThreadedServer(
            ModelRpcServer(),
            port=port,
            protocol_config={"allow_pickle": True, "sync_request_timeout": 600},
        )
        t.start()

    proc = multiprocessing.Process(target=_init_service, args=(port,))
    proc.start()
    time.sleep(1)

    repeat_count = 0
    while repeat_count < 20:
        try:
            con = rpyc.connect(
                "localhost",
                port,
                config={"allow_pickle": True, "sync_request_timeout": 600},
            )
            break
        except ConnectionRefusedError:
            time.sleep(1)
        repeat_count += 1
    if repeat_count == 20:
        raise RuntimeError("init rpc env error!")

    assert proc.is_alive()
    return con.root, proc
