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

import torch

from vllm.v1.request import Request, RequestStatus


def remove_all(lst: list, items_to_remove: set) -> list:
    """Remove all items from a list that are in the items_to_remove set.

    This method optimizes for the common case of removing a single item,
    falling back to list comprehension for multiple items.

    Args:
        lst: The list to remove items from
        items_to_remove: Set of items to remove

    Returns:
        Either the modified original list (for single item removal) or
        a new list (for multiple item removal). Callers should use the
        returned value.

    Note:
        For single item removal, this modifies the original list in-place
        and returns it. For multiple items, it creates and returns a new list.
    """
    if not items_to_remove:
        return lst

    if len(items_to_remove) == 1:
        # Fast path for single item removal (most common case)
        item = next(iter(items_to_remove))
        with contextlib.suppress(ValueError):
            lst.remove(item)
        return lst
    # For multiple items, use list comprehension
    return [item for item in lst if item not in items_to_remove]


def check_stop(
    request: Request, max_model_len: int, pooler_output: torch.Tensor | None = None
) -> bool:
    if request.pooling_params:
        if pooler_output is not None:
            request.status = RequestStatus.FINISHED_STOPPED
            return True
        return False

    sampling_params = request.sampling_params
    assert sampling_params is not None

    if request.num_output_tokens < sampling_params.min_tokens:
        return False

    last_token_id = request.output_token_ids[-1]
    if not sampling_params.ignore_eos and last_token_id == request.eos_token_id:
        request.status = RequestStatus.FINISHED_STOPPED
        return True

    if last_token_id in (sampling_params.stop_token_ids or ()):
        request.status = RequestStatus.FINISHED_STOPPED
        request.stop_reason = last_token_id
        return True
    if (
        request.num_tokens >= max_model_len
        or request.num_output_tokens >= request.max_tokens
    ):
        request.status = RequestStatus.FINISHED_LENGTH_CAPPED
        return True
    return False
