#
# SPDX-FileCopyrightText: Copyright (c) 2024-2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# 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 triton
import triton.language as tl

@triton.jit
def add_kernel(x_ptr, y_ptr, n_elements, BLOCK_SIZE: tl.constexpr):
    pid = tl.program_id(0)
    offsets = pid * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE)
    mask = offsets < n_elements
    x = tl.load(x_ptr + offsets, mask=mask)
    tl.store(y_ptr + offsets, x + 1, mask=mask)


@triton.jit
def circ_pad_kernel(
    # input tensor
    X,
    # extra scalar args in between input and output tensors
    # for kernel signature to be compatible with AOT plugin impl
    all_pads_0,
    all_pads_2,
    all_pads_4,
    all_pads_6,
    orig_dims_0,
    orig_dims_1,
    orig_dims_2,
    orig_dims_3,
    Y_shape_1,
    Y_shape_2,
    Y_shape_3,
    X_len,
    Y_len,
    # output tensor
    Y,
    BLOCK_SIZE: tl.constexpr,
):
    pid = tl.program_id(0)
    i = pid * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE)

    mask_y = i < Y_len

    i3 = i % Y_shape_3
    i2 = (i // Y_shape_3) % Y_shape_2
    i1 = (i // Y_shape_3 // Y_shape_2) % Y_shape_1
    i0 = i // Y_shape_3 // Y_shape_2 // Y_shape_1

    j0 = (i0 - all_pads_0 + orig_dims_0) % orig_dims_0
    j1 = (i1 - all_pads_2 + orig_dims_1) % orig_dims_1
    j2 = (i2 - all_pads_4 + orig_dims_2) % orig_dims_2
    j3 = (i3 - all_pads_6 + orig_dims_3) % orig_dims_3

    load_idx = (
        orig_dims_3 * orig_dims_2 * orig_dims_1 * j0
        + orig_dims_3 * orig_dims_2 * j1
        + orig_dims_3 * j2
        + j3
    )
    mask_x = load_idx < X_len

    x = tl.load(X + load_idx, mask=mask_x)

    tl.store(Y + i, x, mask=mask_y)