# Source code for tensorrt_llm.layers.conv

    # SPDX-FileCopyrightText: Copyright (c) 2022-2023 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.
    from typing import Tuple

    from ..functional import conv2d, conv_transpose2d
    from ..module import Module
    from ..parameter import Parameter

    class Conv2d(Module):

        def __init__(
                self,
                in_channels: int,
                out_channels: int,
                kernel_size: Tuple[int, int],
                stride: Tuple[int, int] = (1, 1),
                padding: Tuple[int, int] = (0, 0),
                dilation: Tuple[int, int] = (1, 1),
                groups: int = 1,
                bias: bool = True,
                padding_mode: str = 'zeros',  # TODO: refine this type
                dtype=None) -> None:
            super().__init__()
            if groups <= 0:
                raise ValueError('groups must be a positive integer')
            if in_channels % groups != 0:
                raise ValueError('in_channels must be divisible by groups')
            if out_channels % groups != 0:
                raise ValueError('out_channels must be divisible by groups')

            self.in_channels = in_channels
            self.out_channels = out_channels
            self.kernel_size = kernel_size
            self.stride = stride
            self.padding = padding
            self.dilation = dilation
            self.groups = groups
            self.padding_mode = padding_mode

            self.weight = Parameter(shape=(out_channels, in_channels // groups,
                                           *kernel_size),
                                    dtype=dtype)
            if bias:
                self.bias = Parameter(shape=(out_channels, ), dtype=dtype)
            else:
                self.register_parameter('bias', None)

        def forward(self, input):
            return conv2d(input, self.weight.value,
                          None if self.bias is None else self.bias.value,
                          self.stride, self.padding, self.dilation, self.groups)

    class ConvTranspose2d(Module):

        def __init__(
                self,
                in_channels: int,
                out_channels: int,
                kernel_size: Tuple[int, int],
                stride: Tuple[int, int] = (1, 1),
                padding: Tuple[int, int] = (0, 0),
                output_padding: Tuple[int, int] = (0, 0),
                dilation: Tuple[int, int] = (1, 1),
                groups: int = 1,
                bias: bool = True,
                padding_mode: str = 'zeros',  # TODO: refine this type
                dtype=None) -> None:
            super().__init__()
            if groups <= 0:
                raise ValueError('groups must be a positive integer')
            if in_channels % groups != 0:
                raise ValueError('in_channels must be divisible by groups')
            if out_channels % groups != 0:
                raise ValueError('out_channels must be divisible by groups')

            self.in_channels = in_channels
            self.out_channels = out_channels
            self.kernel_size = kernel_size
            self.stride = stride
            self.padding = padding
            self.output_padding = output_padding
            self.dilation = dilation
            self.groups = groups
            self.padding_mode = padding_mode

            self.weight = Parameter(shape=(in_channels, out_channels // groups,
                                           *kernel_size),
                                    dtype=dtype)

            if bias:
                self.bias = Parameter(shape=(out_channels, ), dtype=dtype)
            else:
                self.register_parameter('bias', None)

        def _output_padding(self,
                            input,
                            output_size,
                            stride,
                            padding,
                            kernel_size,
                            num_spatial_dims: int,
                            dilation=None):
            if output_size is None:
                ret = self.output_padding
            else:
                has_batch_dim = input.dim() == num_spatial_dims + 2
                num_non_spatial_dims = 2 if has_batch_dim else 1
                if len(output_size) == num_non_spatial_dims + num_spatial_dims:
                    output_size = output_size[num_non_spatial_dims:]
                if len(output_size) != num_spatial_dims:
                    raise ValueError(
                        "ConvTranspose{}D: for {}D input, output_size must have {} or {} elements (got {})"
                        .format(num_spatial_dims, input.dim(), num_spatial_dims,
                                num_non_spatial_dims + num_spatial_dims,
                                len(output_size)))

                min_sizes = []
                max_sizes = []
                for d in range(num_spatial_dims):
                    dim_size = (
                        (input.size(d + num_non_spatial_dims) - 1) * stride[d] -
                        2 * padding[d] +
                        (dilation[d] if dilation is not None else 1) *
                        (kernel_size[d] - 1) + 1)
                    min_sizes.append(dim_size)
                    max_sizes.append(min_sizes[d] + stride[d] - 1)

                for i in range(len(output_size)):
                    size = output_size[i]
                    min_size = min_sizes[i]
                    max_size = max_sizes[i]
                    if size < min_size or size > max_size:
                        raise ValueError((
                            "requested an output size of {}, but valid sizes range "
                            "from {} to {} (for an input of {})").format(
                                output_size, min_sizes, max_sizes,
                                input.size()[2:]))

                res = []
                for d in range(num_spatial_dims):
                    res.append(output_size[d] - min_sizes[d])

                ret = res
            return ret

        def forward(self, input, output_size=None):
            num_spatial_dims = 2
            output_padding = self._output_padding(input, output_size, self.stride,
                                                  self.padding, self.kernel_size,
                                                  num_spatial_dims, self.dilation)

            return conv_transpose2d(input, self.weight.value,
                                    None if self.bias is None else self.bias.value,
                                    self.stride, self.padding, output_padding,
                                    self.dilation, self.groups)