# Copyright (c) 2025 Bytedance Ltd. and/or its affiliates. All rights reserved.
# Copyright (c) 2024 Black Forest Labs and The XLabs-AI Team. All rights reserved.

# 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.

import torch
from einops import rearrange
from torch import Tensor


def attention(q: Tensor, k: Tensor, v: Tensor, pe: Tensor, attn_mask: Tensor = None) -> Tensor:
    q, k = apply_rope(q, k, pe)
    if attn_mask is None:
        x = torch.nn.functional.scaled_dot_product_attention(q, k, v)
    else:
        x = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask = attn_mask)
    x = rearrange(x, "B H L D -> B L (H D)")

    return x

def attention2(q: Tensor, k: Tensor, v: Tensor, pe_q: Tensor, pe_k: Tensor) -> Tensor:
    q = apply_rope2(q, pe_q)
    k = apply_rope2(k, pe_k)

    x = torch.nn.functional.scaled_dot_product_attention(q, k, v)
    x = rearrange(x, "B H L D -> B L (H D)")

    return x

def rope(pos: Tensor, dim: int, theta: int) -> Tensor:
    assert dim % 2 == 0
    scale = torch.arange(0, dim, 2, dtype=torch.float64, device=pos.device) / dim
    omega = 1.0 / (theta**scale)
    out = torch.einsum("...n,d->...nd", pos, omega)
    out = torch.stack([torch.cos(out), -torch.sin(out), torch.sin(out), torch.cos(out)], dim=-1)
    out = rearrange(out, "b n d (i j) -> b n d i j", i=2, j=2)
    return out.float()


def apply_rope(xq: Tensor, xk: Tensor, freqs_cis: Tensor) -> tuple[Tensor, Tensor]:
    xq_ = xq.float().reshape(*xq.shape[:-1], -1, 1, 2)
    xk_ = xk.float().reshape(*xk.shape[:-1], -1, 1, 2)
    xq_out = freqs_cis[..., 0] * xq_[..., 0] + freqs_cis[..., 1] * xq_[..., 1]
    xk_out = freqs_cis[..., 0] * xk_[..., 0] + freqs_cis[..., 1] * xk_[..., 1]
    return xq_out.reshape(*xq.shape).type_as(xq), xk_out.reshape(*xk.shape).type_as(xk)

def apply_rope2(xq: Tensor, freqs_cis: Tensor) -> tuple[Tensor, Tensor]:
    xq_ = xq.float().reshape(*xq.shape[:-1], -1, 1, 2)
    xq_out = freqs_cis[..., 0] * xq_[..., 0] + freqs_cis[..., 1] * xq_[..., 1]
    return xq_out.reshape(*xq.shape).type_as(xq)
