# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project

import enum
import time
from collections.abc import Mapping
from typing import Any, Literal

import msgspec
import numpy as np
import torch
from typing_extensions import deprecated

from vllm.lora.request import LoRARequest
from vllm.multimodal.inputs import MultiModalFeatureSpec
from vllm.pooling_params import PoolingParams
from vllm.sampling_params import SamplingParams
from vllm.v1.metrics.stats import SchedulerStats
from vllm.v1.outputs import LogprobsLists, LogprobsTensors
from vllm.v1.serial_utils import UtilityResult

# Type for pause_generation mode parameter.
# - "abort": Abort all in-flight requests immediately (default).
# - "wait": Wait for in-flight requests to complete before pausing.
# - "keep": Freeze requests in queue; they resume on resume_generation().
PauseMode = Literal["abort", "wait", "keep"]

# These are possible values of RequestOutput.finish_reason,
# so form part of the external API.
FINISH_REASON_STRINGS = ("stop", "length", "abort", "error")


class FinishReason(enum.IntEnum):
    """
    Reason a request finished - stop, length, abort, or error.

    Int rather than Str for more compact serialization.

    stop - a stop string was emitted
    length - max_tokens was consumed, or max_model_len was reached
    abort - aborted by client
    error - retryable request-level internal error (e.g., KV load failure).
            Invariant: always converted to 500 Internal Server Error.

    """

    STOP = 0
    LENGTH = 1
    ABORT = 2
    ERROR = 3

    def __str__(self):
        return FINISH_REASON_STRINGS[self.value]


class EngineCoreRequest(
    msgspec.Struct,
    array_like=True,  # type: ignore[call-arg]
    omit_defaults=True,  # type: ignore[call-arg]
    gc=False,
):  # type: ignore[call-arg]
    request_id: str
    prompt_token_ids: list[int] | None
    mm_features: list[MultiModalFeatureSpec] | None
    sampling_params: SamplingParams | None
    pooling_params: PoolingParams | None
    arrival_time: float
    lora_request: LoRARequest | None
    cache_salt: str | None
    data_parallel_rank: int | None
    prompt_embeds: torch.Tensor | None = None

    # Index of the client, used to ensure outputs are sent back to the same
    # client for this request when scaling out the front-end.
    client_index: int = 0

    # Used in DP case to indicate which wave of requests this is expected to
    # belong to, to cover a race condition where the request is sent before
    # a wave finished notification is received.
    current_wave: int = 0
    priority: int = 0

    trace_headers: Mapping[str, str] | None = None
    resumable: bool = False

    # The user-provided request ID. This field is set internally,
    # copied from the provided request_id that's originally assigned
    # to the request_id field, see InputProcessor.assign_request_id().
    # Used in outputs and to support abort(req_id, internal=False).
    external_req_id: str | None = None

    reasoning_ended: bool | None = None

    @property
    def params(self) -> SamplingParams | PoolingParams:
        """Return the processed params (sampling or pooling)."""
        if self.sampling_params is not None:
            return self.sampling_params
        assert self.pooling_params is not None
        return self.pooling_params

    @property
    @deprecated(
        "EngineCoreRequest.eos_token_id will be removed in v0.18. "
        "Please use EngineCoreRequest.sampling_params.eos_token_id instead."
    )
    def eos_token_id(self) -> int | None:
        if self.sampling_params is None:
            return None

        return self.sampling_params.eos_token_id


class EngineCoreEventType(enum.IntEnum):
    """The type of engine core request event."""

    QUEUED = 1
    SCHEDULED = 2
    PREEMPTED = 3


class EngineCoreEvent(msgspec.Struct):
    """A timestamped engine core event associated with a request.

    The timestamp is a monotonic timestamps and is used for by the engine
    frontend to calculate intervals between engine core events. These
    timestamps should not be compared with timestamps from other processes.
    """

    type: EngineCoreEventType
    timestamp: float

    @classmethod
    def new_event(
        cls, event_type: EngineCoreEventType, timestamp: float | None = None
    ) -> "EngineCoreEvent":
        timestamp = time.monotonic() if timestamp is None else timestamp
        return cls(event_type, timestamp)


class EngineCoreOutput(
    msgspec.Struct,
    array_like=True,  # type: ignore[call-arg]
    omit_defaults=True,  # type: ignore[call-arg]
    gc=False,
):  # type: ignore[call-arg]
    request_id: str
    new_token_ids: list[int]

    new_logprobs: LogprobsLists | None = None
    new_prompt_logprobs_tensors: LogprobsTensors | None = None

    pooling_output: torch.Tensor | None = None

    finish_reason: FinishReason | None = None
    stop_reason: int | str | None = None
    events: list[EngineCoreEvent] | None = None
    kv_transfer_params: dict[str, Any] | None = None

    trace_headers: Mapping[str, str] | None = None
    # The number of tokens with prefix cache hits (local + external).
    num_cached_tokens: int = 0
    # The number of tokens computed remotely (original count from connector).
    num_external_computed_tokens: int = 0
    routed_experts: np.ndarray | None = None
    # The number of NaNs in logits.
    # A value greater than 0 indicates that the output is corrupted.
    num_nans_in_logits: int = 0

    @property
    def finished(self) -> bool:
        return self.finish_reason is not None


class UtilityOutput(
    msgspec.Struct,
    array_like=True,  # type: ignore[call-arg]
    gc=False,
):  # type: ignore[call-arg]
    call_id: int

    # Non-None implies the call failed, result should be None.
    failure_message: str | None = None
    result: UtilityResult | None = None


class EngineCoreOutputs(
    msgspec.Struct,
    array_like=True,  # type: ignore[call-arg]
    omit_defaults=True,  # type: ignore[call-arg]
    gc=False,
):  # type: ignore[call-arg]
    # NOTE(Nick): We could consider ways to make this more compact,
    # e.g. columnwise layout

    engine_index: int = 0

    # [num_reqs]
    outputs: list[EngineCoreOutput] = []
    scheduler_stats: SchedulerStats | None = None
    timestamp: float = 0.0

    utility_output: UtilityOutput | None = None
    finished_requests: set[str] | None = None

    # In DP case, used to signal that the current wave of requests
    # has finished and the engines are paused.
    wave_complete: int | None = None
    # In DP case, used to signal that a request was received for an
    # "old" wave, so the next wave needs to be started in other engines.
    start_wave: int | None = None

    def __post_init__(self):
        if self.timestamp == 0.0:
            self.timestamp = time.monotonic()


class EngineCoreRequestType(enum.Enum):
    """
    Request types defined as hex byte strings, so it can be sent over sockets
    without separate encoding step.
    """

    ADD = b"\x00"
    ABORT = b"\x01"
    START_DP_WAVE = b"\x02"
    UTILITY = b"\x03"
    # Sentinel used within EngineCoreProc.
    EXECUTOR_FAILED = b"\x04"


class ReconfigureDistributedRequest(msgspec.Struct):
    new_data_parallel_size: int
    new_data_parallel_rank: int
    new_data_parallel_rank_local: int
    new_data_parallel_master_ip: str
    new_data_parallel_master_port: int


class ReconfigureRankType(enum.IntEnum):
    """
    Rank type for reconfiguring distributed request.
    """

    KEEP_CURRENT_RANK = -1
    SHUTDOWN_CURRENT_RANK = -2