#
# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
# This file is a part of the vllm-ascend project.
#
# 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
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import torch
from vllm.model_executor.models import bert

# aclgraph does not support shift operator for now
# TODO: revert me when aclgraph supports shift operator
TOKEN_TYPE_MULTIPLIER = 1 << 30
TOKEN_MASK = TOKEN_TYPE_MULTIPLIER - 1


def _encode_token_type_ids(input_ids: torch.Tensor,
                           token_type_ids: torch.Tensor) -> None:
    # input_ids can be padded to the right
    input_ids[:token_type_ids.shape[0]].bitwise_or_(token_type_ids *
                                                    TOKEN_TYPE_MULTIPLIER)


def _decode_token_type_ids(input_ids: torch.Tensor) -> torch.Tensor:

    token_type_ids = input_ids // TOKEN_TYPE_MULTIPLIER

    input_ids.bitwise_and_(TOKEN_MASK)

    return token_type_ids


bert._encode_token_type_ids = _encode_token_type_ids
bert._decode_token_type_ids = _decode_token_type_ids
