# 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
from typing import Dict, List, Union

import psutil
import setproctitle
import zmq

from sglang.srt.managers.io_struct import (
    BatchEmbeddingOutput,
    BatchMultimodalDecodeReq,
    BatchMultimodalOutput,
    BatchStrOutput,
    BatchTokenIDOutput,
    FreezeGCReq,
    MultiTokenizerRegisterReq,
)
from sglang.srt.managers.multi_tokenizer_mixin import MultiHttpWorkerDetokenizerMixin
from sglang.srt.server_args import PortArgs, ServerArgs
from sglang.srt.utils import (
    configure_logger,
    freeze_gc,
    get_zmq_socket,
    kill_itself_when_parent_died,
)
from sglang.srt.utils.hf_transformers_utils import get_tokenizer
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
        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
        )

        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,
            )

        self.decode_status = LimitedCapacityDict(capacity=DETOKENIZER_MAX_STATES)
        self.is_dummy = server_args.load_format == "dummy"

        self._request_dispatcher = TypeBasedDispatcher(
            [
                (BatchEmbeddingOutput, self.handle_batch_embedding_out),
                (BatchTokenIDOutput, self.handle_batch_token_id_out),
                (BatchMultimodalDecodeReq, self.handle_multimodal_decode_req),
                (MultiTokenizerRegisterReq, lambda x: x),
                (FreezeGCReq, self.handle_freeze_gc_req),
            ]
        )

        self.is_tool_call_parser_gpt_oss = server_args.tool_call_parser == "gpt-oss"

    def event_loop(self):
        """The event loop that handles requests"""
        while True:
            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)

    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
            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 handle_batch_token_id_out(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],
                )
                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])

        # TODO(lmzheng): handle skip_special_tokens/spaces_between_special_tokens per request
        surr_texts = self.tokenizer.batch_decode(
            surr_ids,
            skip_special_tokens=recv_obj.skip_special_tokens[0],
            spaces_between_special_tokens=recv_obj.spaces_between_special_tokens[0],
        )
        read_texts = self.tokenizer.batch_decode(
            read_ids,
            skip_special_tokens=recv_obj.skip_special_tokens[0],
            spaces_between_special_tokens=recv_obj.spaces_between_special_tokens[0],
        )

        # Incremental decoding
        output_strs = []
        for i in range(bs):
            try:
                s = self.decode_status[recv_obj.rids[i]]
            except KeyError:
                raise RuntimeError(
                    f"Decode status not found for request {recv_obj.rids[i]}. "
                    "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)

            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 BatchStrOutput(
            rids=recv_obj.rids,
            finished_reasons=recv_obj.finished_reasons,
            output_strs=output_strs,
            output_ids=recv_obj.decode_ids,
            prompt_tokens=recv_obj.prompt_tokens,
            completion_tokens=recv_obj.completion_tokens,
            cached_tokens=recv_obj.cached_tokens,
            spec_verify_ct=recv_obj.spec_verify_ct,
            spec_accepted_tokens=recv_obj.spec_accepted_tokens,
            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_hidden_states=recv_obj.output_hidden_states,
            placeholder_tokens_idx=None,
            placeholder_tokens_val=None,
        )

    def handle_multimodal_decode_req(self, recv_obj: BatchMultimodalDecodeReq):
        outputs = self.tokenizer.detokenize(recv_obj)
        return BatchMultimodalOutput(
            rids=recv_obj.rids,
            finished_reasons=recv_obj.finished_reasons,
            outputs=outputs,
            prompt_tokens=recv_obj.prompt_tokens,
            completion_tokens=recv_obj.completion_tokens,
            cached_tokens=recv_obj.cached_tokens,
            placeholder_tokens_idx=None,
            placeholder_tokens_val=None,
        )

    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,
):
    kill_itself_when_parent_died()
    setproctitle.setproctitle("sglang::detokenizer")
    configure_logger(server_args)
    parent_process = psutil.Process().parent()

    try:
        manager = DetokenizerManager(server_args, port_args)
        if server_args.tokenizer_worker_num > 1:
            manager.multi_http_worker_event_loop()
        else:
            manager.event_loop()
    except Exception:
        manager.maybe_clear_socket_mapping()
        traceback = get_exception_traceback()
        logger.error(f"DetokenizerManager hit an exception: {traceback}")
        parent_process.send_signal(signal.SIGQUIT)
