# Copyright 2023-2024 SGLang Team
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""DetokenizerManager is a process that detokenizes the token ids."""

import dataclasses
import logging
import os
import signal
from collections import OrderedDict, defaultdict
from typing import Dict, List, Optional, Tuple, Union

import psutil
import pybase64
import setproctitle
import zmq

from sglang.srt.environ import envs
from sglang.srt.managers.io_struct import (
    BatchEmbeddingOutput,
    BatchMultimodalDecodeReq,
    BatchStrOutput,
    BatchTokenIDOutput,
    FreezeGCReq,
)
from sglang.srt.managers.multi_tokenizer_mixin import MultiHttpWorkerDetokenizerMixin
from sglang.srt.observability.cpu_monitor import start_cpu_monitor_thread
from sglang.srt.server_args import PortArgs, ServerArgs
from sglang.srt.utils import (
    configure_logger,
    freeze_gc,
    kill_itself_when_parent_died,
)
from sglang.srt.utils.hf_transformers_utils import get_tokenizer
from sglang.srt.utils.network import get_zmq_socket
from sglang.srt.utils.watchdog import Watchdog
from sglang.utils import (
    TypeBasedDispatcher,
    find_printable_text,
    get_exception_traceback,
)

logger = logging.getLogger(__name__)

# Maximum number of request states that detokenizer can hold. When exceeded,
# oldest request states will be evicted. Default: 65536 (1<<16).
# For more details, see: https://github.com/sgl-project/sglang/issues/2812
# Use power of 2 values for better memory allocation.
DETOKENIZER_MAX_STATES = int(os.environ.get("SGLANG_DETOKENIZER_MAX_STATES", 1 << 16))


@dataclasses.dataclass
class DecodeStatus:
    """Store the status of incremental decoding."""

    decoded_text: str
    decode_ids: List[int]
    surr_offset: int
    read_offset: int
    # Offset that's sent to tokenizer for incremental update.
    sent_offset: int = 0


class DetokenizerManager(MultiHttpWorkerDetokenizerMixin):
    """DetokenizerManager is a process that detokenizes the token ids."""

    def __init__(
        self,
        server_args: ServerArgs,
        port_args: PortArgs,
    ):
        # Init inter-process communication
        self.init_ipc_channels(port_args)

        # Init tokenizer
        self.init_tokenizer(server_args)

        # Init running status
        self.init_running_status(server_args)

        if server_args.enable_metrics:
            start_cpu_monitor_thread("detokenizer")

        # Init dispatcher
        self.init_request_dispatcher()

    @staticmethod
    def is_health_check_request(rid: Optional[str]) -> bool:
        return isinstance(rid, str) and rid.startswith("HEALTH_CHECK")

    def init_ipc_channels(self, port_args: PortArgs):
        context = zmq.Context(2)
        self.recv_from_scheduler = get_zmq_socket(
            context, zmq.PULL, port_args.detokenizer_ipc_name, True
        )
        self.send_to_tokenizer = get_zmq_socket(
            context, zmq.PUSH, port_args.tokenizer_ipc_name, False
        )

    def init_tokenizer(self, server_args: ServerArgs):
        if server_args.skip_tokenizer_init:
            self.tokenizer = None
        else:
            self.tokenizer = get_tokenizer(
                server_args.tokenizer_path,
                tokenizer_mode=server_args.tokenizer_mode,
                trust_remote_code=server_args.trust_remote_code,
                revision=server_args.revision,
            )

    def init_running_status(self, server_args: ServerArgs):
        self.decode_status = LimitedCapacityDict(capacity=DETOKENIZER_MAX_STATES)
        self.is_dummy = False
        self.is_tool_call_parser_gpt_oss = server_args.tool_call_parser == "gpt-oss"
        self.disable_tokenizer_batch_decode = server_args.disable_tokenizer_batch_decode

        self.soft_watchdog = Watchdog.create(
            debug_name="DetokenizerManager",
            watchdog_timeout=server_args.soft_watchdog_timeout,
            soft=True,
            test_stuck_time=envs.SGLANG_TEST_STUCK_DETOKENIZER.get(),
        )

    def init_request_dispatcher(self):
        self._request_dispatcher = TypeBasedDispatcher(
            [
                (BatchEmbeddingOutput, self.handle_batch_embedding_out),
                (BatchTokenIDOutput, self.handle_batch_token_id_out),
                (BatchMultimodalDecodeReq, self.handle_multimodal_decode_req),
                (FreezeGCReq, self.handle_freeze_gc_req),
            ]
        )

    def event_loop(self):
        """The event loop that handles requests"""
        while True:
            with self.soft_watchdog.disable():
                recv_obj = self.recv_from_scheduler.recv_pyobj()
            output = self._request_dispatcher(recv_obj)
            if output is not None:
                self.send_to_tokenizer.send_pyobj(output)
            self.soft_watchdog.feed()

    def trim_matched_stop(
        self, output: Union[str, List[int]], finished_reason: Dict, no_stop_trim: bool
    ):
        if no_stop_trim or not finished_reason:
            return output

        matched = finished_reason.get("matched", None)
        if not matched:
            return output

        # TODO(lmzheng): handle the case where multiple stop strs are hit

        # Trim stop str.
        if isinstance(matched, str) and isinstance(output, str):
            pos = output.find(matched)
            return output[:pos] if pos != -1 else output

        # Trim stop token.
        if isinstance(matched, int) and isinstance(output, list):
            # 200012 <|call|> is the tool call token and one of eos tokens for gpt-oss model
            if output[-1] == 200012 and self.is_tool_call_parser_gpt_oss:
                return output
            assert len(output) > 0
            # NOTE: We can always assume the last token is the matched stop token
            return output[:-1]
        return output

    def handle_batch_embedding_out(self, recv_obj: BatchEmbeddingOutput):
        # If it is embedding model, no detokenization is needed.
        return recv_obj

    def _grouped_batch_decode(
        self,
        ids_list: List[List[int]],
        skip_list: List[bool],
        space_list: List[bool],
    ) -> List[str]:
        """Batch decode with grouping by (skip_special_tokens, spaces_between_special_tokens)."""

        assert self.tokenizer is not None

        # fast path
        first_skip, first_space = skip_list[0], space_list[0]
        if all(s == first_skip for s in skip_list) and all(
            sp == first_space for sp in space_list
        ):
            return self.tokenizer.batch_decode(
                ids_list,
                skip_special_tokens=first_skip,
                spaces_between_special_tokens=first_space,
            )

        # Group indices by (skip, space) tuple
        groups: Dict[Tuple[bool, bool], List[int]]
        groups = defaultdict(list)
        for idx, (skip, space) in enumerate(zip(skip_list, space_list)):
            groups[(skip, space)].append(idx)

        # Decode each group and collect results
        results: List[str] = [""] * len(ids_list)
        for (skip, space), indices in groups.items():
            decoded = self.tokenizer.batch_decode(
                [ids_list[idx] for idx in indices],
                skip_special_tokens=skip,
                spaces_between_special_tokens=space,
            )
            for idx, text in zip(indices, decoded):
                results[idx] = text

        return results

    def _decode_batch_token_id_output(self, recv_obj: BatchTokenIDOutput):
        bs = len(recv_obj.rids)

        # Initialize decode status
        read_ids, surr_ids = [], []
        for i in range(bs):
            rid = recv_obj.rids[i]
            if rid not in self.decode_status:
                s = DecodeStatus(
                    decoded_text=recv_obj.decoded_texts[i],
                    decode_ids=recv_obj.decode_ids[i],
                    surr_offset=0,
                    read_offset=recv_obj.read_offsets[i],
                )
                if not self.is_health_check_request(rid):
                    # for health check requests, we do not store the decode status
                    self.decode_status[rid] = s
            else:
                s = self.decode_status[rid]
                s.decode_ids.extend(recv_obj.decode_ids[i])

            read_ids.append(
                self.trim_matched_stop(
                    s.decode_ids[s.surr_offset :],
                    recv_obj.finished_reasons[i],
                    recv_obj.no_stop_trim[i],
                )
            )
            surr_ids.append(s.decode_ids[s.surr_offset : s.read_offset])

        # Decode token ids to strings
        if not self.disable_tokenizer_batch_decode:
            if not self.is_dummy:
                # Run normal batch decode
                surr_texts = self._grouped_batch_decode(
                    surr_ids,
                    recv_obj.skip_special_tokens,
                    recv_obj.spaces_between_special_tokens,
                )
                read_texts = self._grouped_batch_decode(
                    read_ids,
                    recv_obj.skip_special_tokens,
                    recv_obj.spaces_between_special_tokens,
                )
            else:
                # If it is dummy weights, just return dummy strings to prevent potential detokenization edge cases
                surr_texts = ["dog" for _ in surr_ids]
                read_texts = ["cat" for _ in read_ids]
        else:
            # Do not use batch decode to prevent some detokenization edge cases (e.g., gpt-oss).
            surr_texts = [
                self.tokenizer.decode(
                    surr, skip_special_tokens=skip, spaces_between_special_tokens=space
                )
                for surr, skip, space in zip(
                    surr_ids,
                    recv_obj.skip_special_tokens,
                    recv_obj.spaces_between_special_tokens,
                )
            ]
            read_texts = [
                self.tokenizer.decode(
                    read, skip_special_tokens=skip, spaces_between_special_tokens=space
                )
                for read, skip, space in zip(
                    read_ids,
                    recv_obj.skip_special_tokens,
                    recv_obj.spaces_between_special_tokens,
                )
            ]

        # Incremental decoding
        output_strs = []
        for i in range(bs):
            rid = recv_obj.rids[i]
            if self.is_health_check_request(rid):
                s = DecodeStatus(
                    decoded_text=recv_obj.decoded_texts[i],
                    decode_ids=recv_obj.decode_ids[i],
                    surr_offset=0,
                    read_offset=recv_obj.read_offsets[i],
                )
            else:
                try:
                    s = self.decode_status[rid]
                except KeyError:
                    raise RuntimeError(
                        f"Decode status not found for request {rid}. "
                        "It may be due to the request being evicted from the decode status due to memory pressure. "
                        "Please increase the maximum number of requests by setting "
                        "the SGLANG_DETOKENIZER_MAX_STATES environment variable to a bigger value than the default value. "
                        f"The current value is {DETOKENIZER_MAX_STATES}. "
                        "For more details, see: https://github.com/sgl-project/sglang/issues/2812"
                    )
            new_text = read_texts[i][len(surr_texts[i]) :]
            if recv_obj.finished_reasons[i] is None:
                # Streaming chunk: update the decode status
                if len(new_text) > 0 and not new_text.endswith("�"):
                    s.decoded_text = s.decoded_text + new_text
                    s.surr_offset = s.read_offset
                    s.read_offset = len(s.decode_ids)
                    new_text = ""
                else:
                    new_text = find_printable_text(new_text)
            else:
                if rid in self.decode_status:
                    del self.decode_status[rid]

            output_str = self.trim_matched_stop(
                s.decoded_text + new_text,
                recv_obj.finished_reasons[i],
                recv_obj.no_stop_trim[i],
            )
            # Incrementally send text.
            incremental_output = output_str[s.sent_offset :]
            s.sent_offset = len(output_str)
            output_strs.append(incremental_output)

        return output_strs

    def _extract_routed_experts(
        self, recv_obj: BatchTokenIDOutput
    ) -> list[str | None] | None:
        routed_experts = None
        if recv_obj.routed_experts is not None:
            routed_experts = [
                (
                    pybase64.b64encode(routed_experts.numpy().tobytes()).decode("utf-8")
                    if routed_experts is not None
                    else None
                )
                for routed_experts in recv_obj.routed_experts
            ]
        return routed_experts

    def handle_batch_token_id_out(self, recv_obj: BatchTokenIDOutput):
        # If handling idle batch, set output_strs to [].
        output_strs = (
            self._decode_batch_token_id_output(recv_obj)
            if len(recv_obj.rids) > 0
            else []
        )
        routed_experts = self._extract_routed_experts(recv_obj)

        return BatchStrOutput(
            rids=recv_obj.rids,
            http_worker_ipcs=recv_obj.http_worker_ipcs,
            finished_reasons=recv_obj.finished_reasons,
            output_strs=output_strs,
            output_ids=recv_obj.output_ids,
            prompt_tokens=recv_obj.prompt_tokens,
            completion_tokens=recv_obj.completion_tokens,
            cached_tokens=recv_obj.cached_tokens,
            cached_tokens_details=recv_obj.cached_tokens_details,
            spec_verify_ct=recv_obj.spec_verify_ct,
            spec_accepted_tokens=recv_obj.spec_accepted_tokens,
            spec_acceptance_histogram=recv_obj.spec_acceptance_histogram,
            input_token_logprobs_val=recv_obj.input_token_logprobs_val,
            input_token_logprobs_idx=recv_obj.input_token_logprobs_idx,
            output_token_logprobs_val=recv_obj.output_token_logprobs_val,
            output_token_logprobs_idx=recv_obj.output_token_logprobs_idx,
            input_top_logprobs_val=recv_obj.input_top_logprobs_val,
            input_top_logprobs_idx=recv_obj.input_top_logprobs_idx,
            output_top_logprobs_val=recv_obj.output_top_logprobs_val,
            output_top_logprobs_idx=recv_obj.output_top_logprobs_idx,
            input_token_ids_logprobs_val=recv_obj.input_token_ids_logprobs_val,
            input_token_ids_logprobs_idx=recv_obj.input_token_ids_logprobs_idx,
            output_token_ids_logprobs_val=recv_obj.output_token_ids_logprobs_val,
            output_token_ids_logprobs_idx=recv_obj.output_token_ids_logprobs_idx,
            output_token_entropy_val=recv_obj.output_token_entropy_val,
            output_hidden_states=recv_obj.output_hidden_states,
            routed_experts=routed_experts,
            customized_info=recv_obj.customized_info,
            placeholder_tokens_idx=None,
            placeholder_tokens_val=None,
            retraction_counts=recv_obj.retraction_counts,
            token_steps=recv_obj.token_steps,
            load=recv_obj.load,
            dp_ranks=recv_obj.dp_ranks,
            time_stats=recv_obj.time_stats,
        )

    def handle_multimodal_decode_req(self, recv_obj: BatchMultimodalDecodeReq):
        raise NotImplementedError()

    def handle_freeze_gc_req(self, recv_req: FreezeGCReq):
        freeze_gc("Detokenizer Manager")
        return None


class LimitedCapacityDict(OrderedDict):
    def __init__(self, capacity: int, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.capacity = capacity

    def __setitem__(self, key, value):
        if len(self) >= self.capacity:
            # Remove the oldest element (first item in the dict)
            self.popitem(last=False)
        # Set the new item
        super().__setitem__(key, value)


def run_detokenizer_process(
    server_args: ServerArgs,
    port_args: PortArgs,
    detokenizer_manager_class=DetokenizerManager,
):
    kill_itself_when_parent_died()
    setproctitle.setproctitle("sglang::detokenizer")
    configure_logger(server_args)
    parent_process = psutil.Process().parent()

    manager = None
    try:
        manager = detokenizer_manager_class(server_args, port_args)
        if server_args.tokenizer_worker_num == 1:
            manager.event_loop()
        else:
            manager.multi_http_worker_event_loop()
    except Exception:
        traceback = get_exception_traceback()
        logger.error(f"DetokenizerManager hit an exception: {traceback}")
        if manager is not None:
            manager.maybe_clear_socket_mapping()
        parent_process.send_signal(signal.SIGQUIT)
