from __future__ import annotations

import logging
import time
from typing import TYPE_CHECKING, List, Optional, Tuple, Union

import torch

from sglang.srt.disaggregation.utils import DisaggregationMode
from sglang.srt.layers.logits_processor import LogitsProcessorOutput
from sglang.srt.managers.io_struct import (
    AbortReq,
    BatchEmbeddingOutput,
    BatchTokenIDOutput,
)
from sglang.srt.managers.schedule_batch import BaseFinishReason, Req, ScheduleBatch
from sglang.srt.utils.common import ceil_div

if TYPE_CHECKING:
    from sglang.srt.managers.scheduler import (
        EmbeddingBatchResult,
        GenerationBatchResult,
        ScheduleBatch,
        Scheduler,
    )

logger = logging.getLogger(__name__)

DEFAULT_FORCE_STREAM_INTERVAL = 50


class SchedulerOutputProcessorMixin:
    """
    This class implements the output processing logic for Scheduler.
    We put them into a separate file to make the `scheduler.py` shorter.
    """

    def process_batch_result_prefill(
        self: Scheduler,
        batch: ScheduleBatch,
        result: Union[GenerationBatchResult, EmbeddingBatchResult],
    ):
        skip_stream_req = None

        if self.is_generation:
            if result.copy_done is not None:
                result.copy_done.synchronize()

            (
                logits_output,
                next_token_ids,
                extend_input_len_per_req,
                extend_logprob_start_len_per_req,
            ) = (
                result.logits_output,
                result.next_token_ids,
                result.extend_input_len_per_req,
                result.extend_logprob_start_len_per_req,
            )

            # Move next_token_ids and logprobs to cpu
            next_token_ids = next_token_ids.tolist()
            if batch.return_logprob:
                if logits_output.next_token_logprobs is not None:
                    logits_output.next_token_logprobs = (
                        logits_output.next_token_logprobs.tolist()
                    )
                if logits_output.input_token_logprobs is not None:
                    logits_output.input_token_logprobs = tuple(
                        logits_output.input_token_logprobs.tolist()
                    )

            hidden_state_offset = 0

            # Check finish conditions
            logprob_pt = 0

            for i, (req, next_token_id) in enumerate(zip(batch.reqs, next_token_ids)):
                if req.is_retracted:
                    continue

                if self.is_mixed_chunk and self.enable_overlap and req.finished():
                    # Free the one delayed token for the mixed decode batch
                    j = len(batch.out_cache_loc) - len(batch.reqs) + i
                    self.token_to_kv_pool_allocator.free(batch.out_cache_loc[j : j + 1])
                    continue

                if req.is_chunked <= 0:
                    # req output_ids are set here
                    req.output_ids.append(next_token_id)
                    req.check_finished()

                    if req.finished():
                        self.tree_cache.cache_finished_req(req)
                        req.time_stats.completion_time = time.perf_counter()
                    elif not batch.decoding_reqs or req not in batch.decoding_reqs:
                        # This updates radix so others can match
                        self.tree_cache.cache_unfinished_req(req)

                    if batch.return_logprob:
                        assert extend_logprob_start_len_per_req is not None
                        assert extend_input_len_per_req is not None
                        extend_logprob_start_len = extend_logprob_start_len_per_req[i]
                        extend_input_len = extend_input_len_per_req[i]

                        num_input_logprobs = self._calculate_num_input_logprobs(
                            req, extend_input_len, extend_logprob_start_len
                        )

                        if req.return_logprob:
                            self.add_logprob_return_values(
                                i,
                                req,
                                logprob_pt,
                                next_token_ids,
                                num_input_logprobs,
                                logits_output,
                            )
                        logprob_pt += num_input_logprobs

                    if (
                        req.return_hidden_states
                        and logits_output.hidden_states is not None
                    ):
                        req.hidden_states.append(
                            logits_output.hidden_states[
                                hidden_state_offset : (
                                    hidden_state_offset := hidden_state_offset
                                    + len(req.origin_input_ids)
                                )
                            ]
                            .cpu()
                            .clone()
                            .tolist()
                        )

                    if req.grammar is not None:
                        # FIXME: this try-except block is for handling unexpected xgrammar issue.
                        try:
                            req.grammar.accept_token(next_token_id)
                        except ValueError as e:
                            # Grammar accept_token can raise ValueError if the token is not in the grammar.
                            # This can happen if the grammar is not set correctly or the token is invalid.
                            logger.error(
                                f"Grammar accept_token failed for req {req.rid} with token {next_token_id}: {e}"
                            )
                            self.abort_request(AbortReq(rid=req.rid))
                        req.grammar.finished = req.finished()
                else:
                    # being chunked reqs' prefill is not finished
                    req.is_chunked -= 1
                    # There is only at most one request being currently chunked.
                    # Because this request does not finish prefill,
                    # we don't want to stream the request currently being chunked.
                    skip_stream_req = req

                    # Incrementally update input logprobs.
                    if batch.return_logprob:
                        extend_logprob_start_len = extend_logprob_start_len_per_req[i]
                        extend_input_len = extend_input_len_per_req[i]
                        if extend_logprob_start_len < extend_input_len:
                            # Update input logprobs.
                            num_input_logprobs = self._calculate_num_input_logprobs(
                                req, extend_input_len, extend_logprob_start_len
                            )
                            if req.return_logprob:
                                self.add_input_logprob_return_values(
                                    i,
                                    req,
                                    logits_output,
                                    logprob_pt,
                                    num_input_logprobs,
                                    last_prefill_chunk=False,
                                )
                            logprob_pt += num_input_logprobs

        else:  # embedding or reward model
            embeddings = result.embeddings.tolist()

            # Check finish conditions
            for i, req in enumerate(batch.reqs):
                if req.is_retracted:
                    continue

                req.embedding = embeddings[i]
                if req.is_chunked <= 0:
                    # Dummy output token for embedding models
                    req.output_ids.append(0)
                    req.check_finished()

                    if req.finished():
                        self.tree_cache.cache_finished_req(req)
                    else:
                        self.tree_cache.cache_unfinished_req(req)
                else:
                    # being chunked reqs' prefill is not finished
                    req.is_chunked -= 1

        self.stream_output(batch.reqs, batch.return_logprob, skip_stream_req)

    def _resolve_spec_overlap_token_ids(
        self: Scheduler, result: GenerationBatchResult, batch: ScheduleBatch
    ) -> List[List[int]]:
        """Resolve the padding next token ids for speculative decoding with overlap."""
        assert result.next_token_ids.is_cpu
        assert result.accept_lens.is_cpu
        assert result.allocate_lens.is_cpu

        next_token_ids = result.next_token_ids.tolist()
        accept_lens = result.accept_lens.tolist()
        result.num_accepted_tokens = sum(accept_lens)

        predict_tokens = []
        stride = self.draft_worker.speculative_num_draft_tokens
        for i, req in enumerate(batch.reqs):
            predict_tokens.append(
                next_token_ids[i * stride : i * stride + accept_lens[i]]
            )
            req.spec_verify_ct += 1

        return predict_tokens

    def process_batch_result_decode(
        self: Scheduler,
        batch: ScheduleBatch,
        result: GenerationBatchResult,
    ):
        if result.copy_done is not None:
            result.copy_done.synchronize()

        logits_output, next_token_ids, can_run_cuda_graph = (
            result.logits_output,
            result.next_token_ids,
            result.can_run_cuda_graph,
        )

        if batch.spec_algorithm.is_none():
            next_token_ids = next_token_ids.tolist()
            if batch.return_logprob:
                next_token_logprobs = logits_output.next_token_logprobs.tolist()
        elif batch.is_v2_eagle:
            next_token_ids = self._resolve_spec_overlap_token_ids(result, batch)
            allocate_lens_list = result.allocate_lens.tolist()
            accept_lens_list = result.accept_lens.tolist()

        self.num_generated_tokens += len(batch.reqs)
        if not self.spec_algorithm.is_none():
            self.update_spec_metrics(batch.batch_size(), result.num_accepted_tokens)

        self.token_to_kv_pool_allocator.free_group_begin()

        # Check finish condition
        # NOTE: the length of reqs and next_token_ids don't match if it is spec decoding.
        # We should ignore using next_token_ids for spec decoding cases.
        for i, (req, next_token_id) in enumerate(zip(batch.reqs, next_token_ids)):
            req: Req
            if req.is_retracted:
                continue

            if self.enable_overlap and req.finished():
                indices_to_free = None
                if batch.spec_algorithm.is_eagle():
                    from sglang.srt.speculative.eagle_info import EagleDraftInput

                    end_p = allocate_lens_list[i]
                    start_p = end_p - EagleDraftInput.ALLOC_LEN_PER_DECODE
                    if self.page_size > 1:
                        start_p = ceil_div(start_p, self.page_size) * self.page_size

                    indices_to_free = self.req_to_token_pool.req_to_token[
                        req.req_pool_idx
                    ][start_p:end_p]

                else:
                    if self.page_size == 1:
                        # Free the one extra delayed token
                        indices_to_free = batch.out_cache_loc[i : i + 1]
                    else:
                        if (
                            len(req.origin_input_ids) + len(req.output_ids) - 1
                        ) % self.page_size == 0:
                            # Only free when the extra token is in a new page
                            indices_to_free = batch.out_cache_loc[i : i + 1]

                if indices_to_free is not None:
                    self.token_to_kv_pool_allocator.free(indices_to_free)
                continue

            if batch.spec_algorithm.is_none():
                req.output_ids.append(next_token_id)
            elif batch.is_v2_eagle:
                # Only v2 eagle's output_ids are updated here.
                req.output_ids.extend(next_token_id)

            req.check_finished()
            if req.finished():
                if batch.is_v2_eagle and self.cur_batch.forward_mode.is_extend():
                    # FIXME(lsyin): fix the messy logic here
                    # 1) when not overlap (v2 impl), we free the extra tokens in the req
                    # 2) overlap eagle and the current batch is prefill. This seq will not run extra iteration.
                    start_p = batch.seq_lens_cpu[i] + accept_lens_list[i]
                    end_p = allocate_lens_list[i]

                    if self.page_size > 1:
                        start_p = ceil_div(start_p, self.page_size) * self.page_size

                    indices_to_free = self.req_to_token_pool.req_to_token[
                        req.req_pool_idx
                    ][start_p:end_p]
                    self.token_to_kv_pool_allocator.free(indices_to_free)

                if self.server_args.disaggregation_decode_enable_offload_kvcache:
                    # Asynchronously offload KV cache; cache_finished_req will be called after Device->Host transfer completes
                    if not self.decode_offload_manager.offload_kv_cache(req):
                        self.tree_cache.cache_finished_req(req)
                else:
                    self.tree_cache.cache_finished_req(req)

                req.time_stats.completion_time = time.perf_counter()

            if req.return_logprob and batch.spec_algorithm.is_none():
                # speculative worker handles logprob in speculative decoding
                req.output_token_logprobs_val.append(next_token_logprobs[i])
                req.output_token_logprobs_idx.append(next_token_id)
                if req.top_logprobs_num > 0:
                    req.output_top_logprobs_val.append(
                        logits_output.next_token_top_logprobs_val[i]
                    )
                    req.output_top_logprobs_idx.append(
                        logits_output.next_token_top_logprobs_idx[i]
                    )
                if req.token_ids_logprob is not None:
                    req.output_token_ids_logprobs_val.append(
                        logits_output.next_token_token_ids_logprobs_val[i]
                    )
                    req.output_token_ids_logprobs_idx.append(
                        logits_output.next_token_token_ids_logprobs_idx[i]
                    )

            if req.return_hidden_states and logits_output.hidden_states is not None:
                req.hidden_states.append(
                    logits_output.hidden_states[i].cpu().clone().tolist()
                )

            if req.grammar is not None and batch.spec_algorithm.is_none():
                # FIXME: this try-except block is for handling unexpected xgrammar issue.
                try:
                    req.grammar.accept_token(next_token_id)
                except ValueError as e:
                    # Grammar accept_token can raise ValueError if the token is not in the grammar.
                    # This can happen if the grammar is not set correctly or the token is invalid.
                    logger.error(
                        f"Grammar accept_token failed for req {req.rid} with token {next_token_id}: {e}"
                    )
                    self.abort_request(AbortReq(rid=req.rid))
                req.grammar.finished = req.finished()

        self.stream_output(batch.reqs, batch.return_logprob)
        self.token_to_kv_pool_allocator.free_group_end()

        self.forward_ct_decode = (self.forward_ct_decode + 1) % (1 << 30)
        if (
            self.current_scheduler_metrics_enabled()
            and self.forward_ct_decode % self.server_args.decode_log_interval == 0
        ):
            self.log_decode_stats(can_run_cuda_graph, running_batch=batch)

    def _process_input_token_logprobs(
        self, req: Req, input_token_logprobs: List
    ) -> None:
        """Process input token logprobs values and indices."""
        is_multi_item_scoring = self._is_multi_item_scoring(req)

        # Process logprob values - handle multi-item scoring vs regular requests
        if is_multi_item_scoring:
            # Multi-item scoring: use all logprobs as-is
            req.input_token_logprobs_val = input_token_logprobs
        else:
            # Regular request: add None at start, remove last (sampling token)
            req.input_token_logprobs_val = [None] + input_token_logprobs[:-1]

        # Process logprob indices based on scoring type
        if is_multi_item_scoring:
            # Multi-item scoring: only include delimiter token positions
            relevant_tokens = req.origin_input_ids[req.logprob_start_len :]
            input_token_logprobs_idx = [
                token_id
                for token_id in relevant_tokens
                if token_id == self.server_args.multi_item_scoring_delimiter
            ]
        else:
            # Regular request: include all tokens from logprob_start_len onwards
            input_token_logprobs_idx = req.origin_input_ids[req.logprob_start_len :]

        # Clip padded hash values from image tokens to prevent detokenization errors
        req.input_token_logprobs_idx = [
            x if x < self.model_config.vocab_size - 1 else 0
            for x in input_token_logprobs_idx
        ]

    def _process_input_top_logprobs(self, req: Req) -> None:
        """Process input top logprobs."""
        if req.top_logprobs_num <= 0:
            return

        is_multi_item_scoring = self._is_multi_item_scoring(req)

        # Initialize arrays - multi-item scoring starts empty, others start with None
        req.input_top_logprobs_val = [] if is_multi_item_scoring else [None]
        req.input_top_logprobs_idx = [] if is_multi_item_scoring else [None]

        # Extend arrays with temp values
        for val, idx in zip(
            req.temp_input_top_logprobs_val,
            req.temp_input_top_logprobs_idx,
            strict=True,
        ):
            req.input_top_logprobs_val.extend(val)
            req.input_top_logprobs_idx.extend(idx)

        # Remove last token (sampling token) for non multi-item scoring requests
        if not is_multi_item_scoring:
            req.input_top_logprobs_val.pop()
            req.input_top_logprobs_idx.pop()

        # Clean up temp storage
        req.temp_input_top_logprobs_idx = None
        req.temp_input_top_logprobs_val = None

    def _process_input_token_ids_logprobs(self, req: Req) -> None:
        """Process input token IDs logprobs."""
        if req.token_ids_logprob is None:
            return

        is_multi_item_scoring = self._is_multi_item_scoring(req)

        # Initialize arrays - multi-item scoring starts empty, others start with None
        req.input_token_ids_logprobs_val = [] if is_multi_item_scoring else [None]
        req.input_token_ids_logprobs_idx = [] if is_multi_item_scoring else [None]

        # Process temp values - convert tensors to lists and extend arrays
        for val, idx in zip(
            req.temp_input_token_ids_logprobs_val,
            req.temp_input_token_ids_logprobs_idx,
            strict=True,
        ):
            val_list = val.tolist() if isinstance(val, torch.Tensor) else val
            req.input_token_ids_logprobs_val.extend(
                val_list if isinstance(val_list, list) else [val_list]
            )
            req.input_token_ids_logprobs_idx.extend(idx)

        # Remove last token (sampling token) for non multi-item scoring requests
        if not is_multi_item_scoring:
            req.input_token_ids_logprobs_val.pop()
            req.input_token_ids_logprobs_idx.pop()

        # Clean up temp storage
        req.temp_input_token_ids_logprobs_idx = None
        req.temp_input_token_ids_logprobs_val = None

    def _calculate_relevant_tokens_len(self, req: Req) -> int:
        """Calculate the expected length of logprob arrays based on whether multi-item scoring is enabled.

        For multi-item scoring, only delimiter positions have logprobs.
        For regular requests, all positions from logprob_start_len onwards have logprobs.
        """
        is_multi_item_scoring = self._is_multi_item_scoring(req)

        if is_multi_item_scoring:
            # Multi-item scoring: count delimiter tokens from logprob_start_len onwards
            relevant_tokens = req.origin_input_ids[req.logprob_start_len :]
            return sum(
                1
                for token_id in relevant_tokens
                if token_id == self.server_args.multi_item_scoring_delimiter
            )
        else:
            # Regular request: all tokens from logprob_start_len onwards
            return len(req.origin_input_ids) - req.logprob_start_len

    def _calculate_num_input_logprobs(
        self, req: Req, extend_input_len: int, extend_logprob_start_len: int
    ) -> int:
        """Calculate the number of input logprobs based on whether multi-item scoring is enabled.

        For multi-item scoring, only delimiter positions have logprobs.
        For regular requests, all positions in the range have logprobs.
        """
        is_multi_item_scoring = self._is_multi_item_scoring(req)

        if is_multi_item_scoring:
            # Multi-item scoring: count delimiter tokens in the relevant portion
            relevant_tokens = req.origin_input_ids[
                extend_logprob_start_len:extend_input_len
            ]
            return sum(
                1
                for token_id in relevant_tokens
                if token_id == self.server_args.multi_item_scoring_delimiter
            )
        else:
            # Regular request: all tokens in the range
            return extend_input_len - extend_logprob_start_len

    def _is_multi_item_scoring(self, req: Req) -> bool:
        """Check if request uses multi-item scoring.

        Multi-item scoring applies to prefill-only requests when a delimiter
        token is configured. In this mode, only positions containing the
        delimiter token receive logprobs.
        """
        return req.is_prefill_only and self.server_args.multi_item_scoring_delimiter

    def add_input_logprob_return_values(
        self: Scheduler,
        i: int,
        req: Req,
        output: LogitsProcessorOutput,
        logprob_pt: int,
        num_input_logprobs: int,
        last_prefill_chunk: bool,  # If True, it means prefill is finished.
    ):
        """Incrementally add input logprobs to `req`.

        Args:
            i: The request index in a batch.
            req: The request. Input logprobs inside req are modified as a
                consequence of the API
            fill_ids: The prefill ids processed.
            output: Logit processor output that's used to compute input logprobs
            last_prefill_chunk: True if it is the last prefill (when chunked).
                Some of input logprob operation should only happen at the last
                prefill (e.g., computing input token logprobs).
        """
        assert output.input_token_logprobs is not None
        if req.input_token_logprobs is None:
            req.input_token_logprobs = []
        if req.temp_input_top_logprobs_val is None:
            req.temp_input_top_logprobs_val = []
        if req.temp_input_top_logprobs_idx is None:
            req.temp_input_top_logprobs_idx = []
        if req.temp_input_token_ids_logprobs_val is None:
            req.temp_input_token_ids_logprobs_val = []
        if req.temp_input_token_ids_logprobs_idx is None:
            req.temp_input_token_ids_logprobs_idx = []

        if req.input_token_logprobs_val is not None:
            # The input logprob has been already computed. It only happens
            # upon retract.
            if req.top_logprobs_num > 0:
                assert req.input_token_logprobs_val is not None
            return

        # Important for the performance.
        assert isinstance(output.input_token_logprobs, tuple)
        input_token_logprobs: Tuple[int] = output.input_token_logprobs
        input_token_logprobs = input_token_logprobs[
            logprob_pt : logprob_pt + num_input_logprobs
        ]
        req.input_token_logprobs.extend(input_token_logprobs)

        if req.top_logprobs_num > 0:
            req.temp_input_top_logprobs_val.append(output.input_top_logprobs_val[i])
            req.temp_input_top_logprobs_idx.append(output.input_top_logprobs_idx[i])

        if req.token_ids_logprob is not None:
            req.temp_input_token_ids_logprobs_val.append(
                output.input_token_ids_logprobs_val[i]
            )
            req.temp_input_token_ids_logprobs_idx.append(
                output.input_token_ids_logprobs_idx[i]
            )

        if last_prefill_chunk:
            input_token_logprobs = req.input_token_logprobs
            req.input_token_logprobs = None
            assert req.input_token_logprobs_val is None
            assert req.input_token_logprobs_idx is None
            assert req.input_top_logprobs_val is None
            assert req.input_top_logprobs_idx is None

            # Process all input logprob types using helper functions
            self._process_input_token_logprobs(req, input_token_logprobs)
            self._process_input_top_logprobs(req)

            self._process_input_token_ids_logprobs(req)

            if req.return_logprob:
                relevant_tokens_len = self._calculate_relevant_tokens_len(req)
                assert len(req.input_token_logprobs_val) == relevant_tokens_len
                assert len(req.input_token_logprobs_idx) == relevant_tokens_len
                if req.top_logprobs_num > 0:
                    assert len(req.input_top_logprobs_val) == relevant_tokens_len
                    assert len(req.input_top_logprobs_idx) == relevant_tokens_len
                if req.token_ids_logprob is not None:
                    assert len(req.input_token_ids_logprobs_val) == relevant_tokens_len
                    assert len(req.input_token_ids_logprobs_idx) == relevant_tokens_len

    def add_logprob_return_values(
        self: Scheduler,
        i: int,
        req: Req,
        pt: int,
        next_token_ids: List[int],
        num_input_logprobs: int,
        output: LogitsProcessorOutput,
    ):
        """Attach logprobs to the return values."""
        if output.next_token_logprobs is not None:
            req.output_token_logprobs_val.append(output.next_token_logprobs[i])
            req.output_token_logprobs_idx.append(next_token_ids[i])

        # Only add input logprobs if there are input tokens to process
        # Note: For prefill-only requests with default logprob_start_len, this will be 0,
        # meaning we only compute output logprobs (which is the intended behavior)
        if num_input_logprobs > 0:
            self.add_input_logprob_return_values(
                i, req, output, pt, num_input_logprobs, last_prefill_chunk=True
            )
        else:
            self._initialize_empty_logprob_containers(req)

        if req.top_logprobs_num > 0:
            req.output_top_logprobs_val.append(output.next_token_top_logprobs_val[i])
            req.output_top_logprobs_idx.append(output.next_token_top_logprobs_idx[i])

        if (
            req.token_ids_logprob is not None
            and output.next_token_token_ids_logprobs_val is not None
        ):
            # Convert GPU tensor to list if needed
            logprobs_val = output.next_token_token_ids_logprobs_val[i]
            if isinstance(logprobs_val, torch.Tensor):
                logprobs_val = logprobs_val.tolist()
            req.output_token_ids_logprobs_val.append(logprobs_val)
            req.output_token_ids_logprobs_idx.append(
                output.next_token_token_ids_logprobs_idx[i]
            )

        return num_input_logprobs

    def _initialize_empty_logprob_containers(self, req: Req) -> None:
        """
        Initialize logprob fields to empty lists if unset.

        This is needed for prefill-only requests where the normal initialization
        flow might be bypassed, but downstream code expects these fields to be lists.
        """
        if req.input_token_logprobs_val is None:
            req.input_token_logprobs_val = []
        if req.input_token_logprobs_idx is None:
            req.input_token_logprobs_idx = []
        if req.input_top_logprobs_val is None:
            req.input_top_logprobs_val = []
        if req.input_top_logprobs_idx is None:
            req.input_top_logprobs_idx = []
        if req.input_token_ids_logprobs_val is None:
            req.input_token_ids_logprobs_val = []
        if req.input_token_ids_logprobs_idx is None:
            req.input_token_ids_logprobs_idx = []

    def stream_output(
        self: Scheduler,
        reqs: List[Req],
        return_logprob: bool,
        skip_req: Optional[Req] = None,
    ):
        """Stream the output to detokenizer."""
        if self.is_generation:
            self.stream_output_generation(reqs, return_logprob, skip_req)
        else:  # embedding or reward model
            self.stream_output_embedding(reqs)

    def stream_output_generation(
        self: Scheduler,
        reqs: List[Req],
        return_logprob: bool,
        skip_req: Optional[Req] = None,
    ):
        rids = []
        finished_reasons: List[BaseFinishReason] = []

        decoded_texts = []
        decode_ids_list = []
        read_offsets = []
        output_ids = []

        skip_special_tokens = []
        spaces_between_special_tokens = []
        no_stop_trim = []
        prompt_tokens = []
        completion_tokens = []
        cached_tokens = []
        spec_verify_ct = []
        spec_accepted_tokens = []
        output_hidden_states = None

        if return_logprob:
            input_token_logprobs_val = []
            input_token_logprobs_idx = []
            output_token_logprobs_val = []
            output_token_logprobs_idx = []
            input_top_logprobs_val = []
            input_top_logprobs_idx = []
            output_top_logprobs_val = []
            output_top_logprobs_idx = []
            input_token_ids_logprobs_val = []
            input_token_ids_logprobs_idx = []
            output_token_ids_logprobs_val = []
            output_token_ids_logprobs_idx = []
        else:
            input_token_logprobs_val = input_token_logprobs_idx = (
                output_token_logprobs_val
            ) = output_token_logprobs_idx = input_top_logprobs_val = (
                input_top_logprobs_idx
            ) = output_top_logprobs_val = output_top_logprobs_idx = (
                input_token_ids_logprobs_val
            ) = input_token_ids_logprobs_idx = output_token_ids_logprobs_val = (
                output_token_ids_logprobs_idx
            ) = None

        for req in reqs:
            if req is skip_req:
                continue

            # Multimodal partial stream chunks break the detokenizer, so drop aborted requests here.
            if self.model_config.is_multimodal_gen and req.to_abort:
                continue

            if req.finished():
                if req.finished_output:
                    # With the overlap schedule, a request will try to output twice and hit this line twice
                    # because of the one additional delayed token. This "continue" prevented the dummy output.
                    continue
                req.finished_output = True
                should_output = True
            else:
                if req.stream:
                    stream_interval = (
                        req.sampling_params.stream_interval or self.stream_interval
                    )

                    # origin stream_interval logic
                    should_output = (
                        len(req.output_ids) % stream_interval == 1
                        if not self.model_config.is_multimodal_gen
                        and stream_interval > 1
                        else len(req.output_ids) % stream_interval == 0
                    )

                    if should_output:
                        # check_match_stop_str_prefix if  tail_str's suffix match stop_str prefix
                        should_output &= not req.check_match_stop_str_prefix()
                else:
                    should_output = (
                        len(req.output_ids) % DEFAULT_FORCE_STREAM_INTERVAL == 0
                        if not self.model_config.is_multimodal_gen
                        else False
                    )

            if should_output:
                send_token_offset = req.send_token_offset
                send_output_token_logprobs_offset = (
                    req.send_output_token_logprobs_offset
                )
                rids.append(req.rid)
                finished_reasons.append(
                    req.finished_reason.to_json() if req.finished_reason else None
                )
                decoded_texts.append(req.decoded_text)
                decode_ids, read_offset = req.init_incremental_detokenize()

                if self.model_config.is_multimodal_gen:
                    decode_ids_list.append(decode_ids)
                else:
                    decode_ids_list.append(decode_ids[req.send_decode_id_offset :])

                req.send_decode_id_offset = len(decode_ids)
                read_offsets.append(read_offset)
                output_ids.append(req.output_ids[send_token_offset:])
                req.send_token_offset = len(req.output_ids)
                skip_special_tokens.append(req.sampling_params.skip_special_tokens)
                spaces_between_special_tokens.append(
                    req.sampling_params.spaces_between_special_tokens
                )
                no_stop_trim.append(req.sampling_params.no_stop_trim)
                prompt_tokens.append(len(req.origin_input_ids))
                completion_tokens.append(len(req.output_ids))
                cached_tokens.append(req.cached_tokens)

                if not self.spec_algorithm.is_none():
                    spec_verify_ct.append(req.spec_verify_ct)
                    spec_accepted_tokens.append(req.spec_accepted_tokens)

                if return_logprob:
                    if (
                        req.return_logprob
                        and not req.input_logprob_sent
                        # Decode server does not send input logprobs
                        and self.disaggregation_mode != DisaggregationMode.DECODE
                    ):
                        input_token_logprobs_val.append(req.input_token_logprobs_val)
                        input_token_logprobs_idx.append(req.input_token_logprobs_idx)
                        input_top_logprobs_val.append(req.input_top_logprobs_val)
                        input_top_logprobs_idx.append(req.input_top_logprobs_idx)
                        input_token_ids_logprobs_val.append(
                            req.input_token_ids_logprobs_val
                        )
                        input_token_ids_logprobs_idx.append(
                            req.input_token_ids_logprobs_idx
                        )
                        req.input_logprob_sent = True
                    else:
                        input_token_logprobs_val.append([])
                        input_token_logprobs_idx.append([])
                        input_top_logprobs_val.append([])
                        input_top_logprobs_idx.append([])
                        input_token_ids_logprobs_val.append([])
                        input_token_ids_logprobs_idx.append([])

                    if req.return_logprob:
                        output_token_logprobs_val.append(
                            req.output_token_logprobs_val[
                                send_output_token_logprobs_offset:
                            ]
                        )
                        output_token_logprobs_idx.append(
                            req.output_token_logprobs_idx[
                                send_output_token_logprobs_offset:
                            ]
                        )
                        output_top_logprobs_val.append(
                            req.output_top_logprobs_val[
                                send_output_token_logprobs_offset:
                            ]
                        )
                        output_top_logprobs_idx.append(
                            req.output_top_logprobs_idx[
                                send_output_token_logprobs_offset:
                            ]
                        )
                        output_token_ids_logprobs_val.append(
                            req.output_token_ids_logprobs_val[
                                send_output_token_logprobs_offset:
                            ]
                        )
                        output_token_ids_logprobs_idx.append(
                            req.output_token_ids_logprobs_idx[
                                send_output_token_logprobs_offset:
                            ]
                        )
                        req.send_output_token_logprobs_offset = len(
                            req.output_token_logprobs_val
                        )
                    else:
                        output_token_logprobs_val.append([])
                        output_token_logprobs_idx.append([])
                        output_top_logprobs_val.append([])
                        output_top_logprobs_idx.append([])
                        output_token_ids_logprobs_val.append([])
                        output_token_ids_logprobs_idx.append([])

                if req.return_hidden_states:
                    if output_hidden_states is None:
                        output_hidden_states = []
                    output_hidden_states.append(req.hidden_states)

            if (
                req.finished()
                and self.tp_rank == 0
                and self.server_args.enable_request_time_stats_logging
            ):
                req.log_time_stats()

        # Send to detokenizer
        if rids:
            if self.model_config.is_multimodal_gen:
                return

            self.send_to_detokenizer.send_pyobj(
                BatchTokenIDOutput(
                    finished_reasons,
                    decoded_texts,
                    decode_ids_list,
                    read_offsets,
                    output_ids,
                    skip_special_tokens,
                    spaces_between_special_tokens,
                    no_stop_trim,
                    prompt_tokens,
                    completion_tokens,
                    cached_tokens,
                    spec_verify_ct,
                    spec_accepted_tokens,
                    input_token_logprobs_val,
                    input_token_logprobs_idx,
                    output_token_logprobs_val,
                    output_token_logprobs_idx,
                    input_top_logprobs_val,
                    input_top_logprobs_idx,
                    output_top_logprobs_val,
                    output_top_logprobs_idx,
                    input_token_ids_logprobs_val,
                    input_token_ids_logprobs_idx,
                    output_token_ids_logprobs_val,
                    output_token_ids_logprobs_idx,
                    output_token_entropy_val=None,
                    output_hidden_states=output_hidden_states,
                    rids=rids,
                    placeholder_tokens_idx=None,
                    placeholder_tokens_val=None,
                )
            )

    def stream_output_embedding(self: Scheduler, reqs: List[Req]):
        rids = []
        finished_reasons: List[BaseFinishReason] = []

        embeddings = []
        prompt_tokens = []
        cached_tokens = []
        for req in reqs:
            if req.finished():
                rids.append(req.rid)
                finished_reasons.append(req.finished_reason.to_json())
                embeddings.append(req.embedding)
                prompt_tokens.append(len(req.origin_input_ids))
                cached_tokens.append(req.cached_tokens)
        self.send_to_detokenizer.send_pyobj(
            BatchEmbeddingOutput(
                finished_reasons,
                embeddings,
                prompt_tokens,
                cached_tokens,
                rids=rids,
                placeholder_tokens_idx=None,
                placeholder_tokens_val=None,
            )
        )
