# SPDX-FileCopyrightText: Copyright (c) 2022-2024 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 ....functional import allgather, concat, conv2d, slice, stack, unsqueeze
from ....layers import Conv2d
from ....mapping import Mapping
from ....module import Module


class DistriConv2dPP(Module):

    def __init__(self,
                 conv: Conv2d,
                 mapping: Mapping = Mapping(),
                 is_first_layer: bool = False):
        super().__init__()
        self.mapping = mapping
        self.conv = conv
        self.is_first_layer = is_first_layer

    def sliced_forward(self, x):
        mapping = self.mapping
        b, c, h, w = x.shape
        assert h % mapping.tp_size == 0

        stride = self.conv.stride[0]
        padding = self.conv.padding[0]

        output_h = x.shape[2] // stride // mapping.tp_size
        idx = mapping.tp_rank
        h_begin = output_h * idx * stride - padding
        h_end = output_h * (idx + 1) * stride + padding
        pre_padding = [0, padding]
        post_padding = [0, padding]
        if h_begin < 0:
            h_begin = 0
            pre_padding[0] = padding
        if h_end > h:
            h_end = h
            post_padding[0] = padding
        sliced_input = slice(x, [0, 0, h_begin, 0], [b, c, h_end - h_begin, w])
        return conv2d(sliced_input,
                      self.conv.weight.value,
                      None if self.conv.bias is None else self.conv.bias.value,
                      stride=self.conv.stride,
                      padding=(0, 0),
                      pre_padding=tuple(pre_padding),
                      post_padding=tuple(post_padding))

    def forward(self, x, *args, **kwargs):
        mapping = self.mapping
        if self.is_first_layer:
            full_x = x
            output = self.sliced_forward(full_x)
        else:
            boundary_size = self.conv.padding[0]

            def create_padded_x(x, boundaries):
                preH = 0
                postH = 0
                if mapping.tp_rank == 0:
                    b = boundaries.select(0, mapping.tp_rank + 1).select(0, 0)
                    padded_x = concat([x, b], dim=2)
                    preH = boundary_size
                elif mapping.tp_rank == mapping.tp_size - 1:
                    b = boundaries.select(0, mapping.tp_rank - 1).select(0, 1)
                    padded_x = concat([b, x], dim=2)
                    postH = boundary_size
                else:
                    b0 = boundaries.select(0, mapping.tp_rank - 1).select(0, 1)
                    b1 = boundaries.select(0, mapping.tp_rank + 1).select(0, 0)
                    padded_x = concat(
                        [
                            b0,
                            x,
                            b1,
                        ],
                        dim=2,
                    )
                return padded_x, preH, postH

            n, c, h, w = x.shape
            b0 = slice(x, [0, 0, 0, 0], [n, c, boundary_size, w])
            b1 = slice(x, [0, 0, h - boundary_size, 0],
                       [n, c, boundary_size, w])
            boundary = stack([b0, b1], dim=0)

            boundaries = allgather(unsqueeze(boundary, 0),
                                   group=mapping.tp_group)
            padded_x, preH, postH = create_padded_x(x, boundaries)
            output = conv2d(padded_x,
                            self.conv.weight.value,
                            self.conv.bias.value,
                            stride=self.conv.stride,
                            pre_padding=(preH, self.conv.padding[1]),
                            post_padding=(postH, self.conv.padding[1]))
        return output