# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: MIT
#
# Permission is hereby granted, free of charge, to any person obtaining a
# copy of this software and associated documentation files (the "Software"),
# to deal in the Software without restriction, including without limitation
# the rights to use, copy, modify, merge, publish, distribute, sublicense,
# and/or sell copies of the Software, and to permit persons to whom the
# Software is furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL
# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
# DEALINGS IN THE SOFTWARE.

# Copyright (c) Kyutai, all rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.

import typing as tp

from einops import rearrange
import torch
from torch import nn

from .conv import StreamingConv1d, StreamingConvTranspose1d


class ConvDownsample1d(nn.Module):
    """
    Downsampling by some integer amount `stride` using convolutions
    with a kernel size of twice the stride.
    If `causal` is True, the output uses a causal convolution.
    """

    def __init__(
        self,
        stride: int,
        dimension: tp.Optional[int] = None,
        causal: bool = False,
        learnt: bool = False,
        channel_wise: bool = False,
    ):
        super().__init__()
        self.learnt = learnt
        self.channel_wise = channel_wise
        groups = 1
        if learnt:
            assert dimension is not None, "Dimension required for learnt convolutions."
            in_channels = dimension
            out_channels = dimension
            if channel_wise:
                groups = dimension
        else:
            in_channels = 1
            out_channels = 1

        self.conv = StreamingConv1d(
            in_channels,
            out_channels,
            kernel_size=2 * stride,
            stride=stride,
            causal=causal,
            groups=groups,
            bias=False,
            pad_mode="replicate",
        )
        if not learnt:
            actual_conv = self.conv.conv.conv
            actual_conv.weight.requires_grad_(False)
            actual_conv.weight.data.fill_(1.0 / (2 * stride))

    def forward(self, x: torch.Tensor):
        batch_size = len(x)
        if not self.learnt:
            x = rearrange(x, "b c t -> (b c) () t")
        y = self.conv(x)
        if not self.learnt:
            y = rearrange(y, "(b c) () t -> b c t", b=batch_size)
        return y


class ConvTrUpsample1d(nn.Module):
    """
    Upsample by some integer amount `stride` using transposed convolutions.
    """

    def __init__(
        self,
        stride: int,
        dimension: tp.Optional[int] = None,
        causal: bool = False,
        learnt: bool = False,
        channel_wise: bool = False,
    ):
        super().__init__()
        self.learnt = learnt
        self.channel_wise = channel_wise
        groups = 1
        if learnt:
            assert dimension is not None, "Dimension required for learnt convolutions."
            in_channels = dimension
            out_channels = dimension
            if channel_wise:
                groups = dimension
        else:
            in_channels = 1
            out_channels = 1

        self.convtr = StreamingConvTranspose1d(
            in_channels,
            out_channels,
            kernel_size=2 * stride,
            stride=stride,
            causal=causal,
            groups=groups,
            bias=False,
        )
        if not learnt:
            actual_convtr = self.convtr.convtr.convtr
            actual_convtr.weight.requires_grad_(False)
            actual_convtr.weight.data.fill_(1.0)

    def forward(self, x: torch.Tensor):
        batch_size = len(x)
        if not self.learnt:
            x = rearrange(x, "b c t -> (b c) () t")
        y = self.convtr(x)
        if not self.learnt:
            x_for_normalization = torch.ones_like(x[:1])
            normalization = self.convtr(x_for_normalization)
            y = y / normalization
            y = rearrange(y, "(b c) () t -> b c t", b=batch_size)
        return y