#
# 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
# limitations under the License.
#

from typing import Optional, Tuple

import torch
from vllm.model_executor.layers.rotary_embedding import (
    DeepseekScalingRotaryEmbedding, MRotaryEmbedding, RotaryEmbedding)


def rope_forward_oot(
    self,
    positions: torch.Tensor,
    query: torch.Tensor,
    key: torch.Tensor,
    offsets: Optional[torch.Tensor] = None,
) -> Tuple[torch.Tensor, torch.Tensor]:
    import torch_npu

    if self.cos_sin_cache.device != query.device:
        self.cos_sin_cache = self.cos_sin_cache.to(query.device)
    if self.cos_sin_cache.dtype != query.dtype:
        self.cos_sin_cache = self.cos_sin_cache.to(query.dtype)
    if offsets is not None or self.cos_sin_cache.shape[-1] != self.head_size:
        return self.forward_native(positions, query, key, offsets)
    else:
        # TODO: Remove the contiguous in the future.
        query = query.contiguous()
        key = key.contiguous()
        torch_npu._npu_rotary_embedding(
            positions,
            query,
            key,
            self.head_size,
            self.cos_sin_cache,
            self.is_neox_style,
        )
    return query, key


def rope_deepseek_forward_oot(
    self,
    positions: torch.Tensor,
    query: torch.Tensor,
    key: torch.Tensor,
    offsets: Optional[torch.Tensor] = None,
) -> Tuple[torch.Tensor, torch.Tensor]:
    import torch_npu

    if self.cos_sin_cache.device != query.device:
        self.cos_sin_cache = self.cos_sin_cache.to(query.device)
    if self.cos_sin_cache.dtype != query.dtype:
        self.cos_sin_cache = self.cos_sin_cache.to(query.dtype)
    if offsets is not None:
        raise NotImplementedError(
            "Batched rotary embedding is currently not supported on NPU.")
    else:
        # TODO: Remove the contiguous in the future.
        ori_query_shape, ori_key_shape = query.shape, key.shape
        query = query.contiguous().view(query.shape[0], -1)
        key = key.contiguous().view(query.shape[0], -1)
        torch_npu._npu_rotary_embedding(
            positions,
            query,
            key,
            self.head_size,
            self.cos_sin_cache,
            self.is_neox_style,
        )
        query = query.view(ori_query_shape)
        key = key.view(ori_key_shape)

    return query, key


def mrope_forward(
    self,
    positions: torch.Tensor,
    query: torch.Tensor,
    key: torch.Tensor,
) -> Tuple[torch.Tensor, torch.Tensor]:
    import torch_npu
    mrope_section = [0, 0, 0] if positions.ndim == 1 else self.mrope_section
    query, key = torch_npu.npu_mrope(positions,
                                     query.contiguous(),
                                     key.contiguous(),
                                     self.cos_sin_cache.contiguous(),
                                     self.head_size,
                                     mrope_section=mrope_section,
                                     rotary_mode='half')
    return query, key


RotaryEmbedding.forward_oot = rope_forward_oot
MRotaryEmbedding.forward = mrope_forward
DeepseekScalingRotaryEmbedding.forward = rope_deepseek_forward_oot
