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#
# 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 operator
from functools import reduce

import paddle
import paddle.nn.functional as F


class IndexFirstAxis(paddle.autograd.PyLayer):

    @staticmethod
    def forward(ctx, input, indices):
        from einops import rearrange, repeat

        ctx.save_for_backward(indices)
        assert input.ndim >= 2
        ctx.first_axis_dim, other_shape = input.shape[0], input.shape[1:]
        second_dim = reduce(operator.mul, other_shape, 1)
        return paddle.take_along_axis(
            arr=rearrange(input, "b ... -> b (...)"),
            axis=0,
            indices=repeat(indices, "z -> z d", d=second_dim),
        ).reshape([-1, *other_shape])

    @staticmethod
    def backward(ctx, grad_output):
        """Class Attribute: torch.autograd.function.FunctionCtx.saved_tensors, can not convert, please check whether it is torch.Tensor.*/torch.autograd.function.FunctionCtx.*/torch.distributions.Distribution.* and convert manually"""
        from einops import rearrange, repeat

        (indices,) = ctx.saved_tensor()
        assert grad_output.ndim >= 2
        other_shape = grad_output.shape[1:]
        grad_output = rearrange(grad_output, "b ... -> b (...)")
        grad_input = paddle.zeros(
            shape=[ctx.first_axis_dim, tuple(grad_output.shape)[1]],
            dtype=grad_output.dtype,
        )

        grad_input.put_along_axis_(
            axis=0,
            indices=repeat(indices, "z -> z d", d=tuple(grad_output.shape)[1]),
            values=grad_output,
        )
        return grad_input.reshape([ctx.first_axis_dim, *other_shape]), None


index_first_axis = IndexFirstAxis.apply


class IndexPutFirstAxis(paddle.autograd.PyLayer):

    @staticmethod
    def forward(ctx, values, indices, first_axis_dim):
        ctx.save_for_backward(indices)
        assert indices.ndim == 1
        assert values.ndim >= 2
        output = paddle.zeros(
            shape=[first_axis_dim, *tuple(values.shape)[1:]], dtype=values.dtype
        )
        output[indices] = values
        return output

    @staticmethod
    def backward(ctx, grad_output):
        """Class Attribute: torch.autograd.function.FunctionCtx.saved_tensors, can not convert, please check whether it is torch.Tensor.*/torch.autograd.function.FunctionCtx.*/torch.distributions.Distribution.* and convert manually"""
        (indices,) = ctx.saved_tensor()
        grad_values = grad_output[indices]
        return grad_values, None


index_put_first_axis = IndexPutFirstAxis.apply


def unpad_input(hidden_states, attention_mask):
    """
    Arguments:
        hidden_states: (batch, seqlen, ...)
        attention_mask: (batch, seqlen), bool / int, 1 means valid and 0 means not valid.
    Return:
        hidden_states: (total_nnz, ...), where total_nnz = number of tokens in selected in attention_mask.
        indices: (total_nnz), the indices of non-masked tokens from the flattened input sequence.
        cu_seqlens: (batch + 1), the cumulative sequence lengths, used to index into hidden_states.
        max_seqlen_in_batch: int
    """
    from einops import rearrange

    seqlens_in_batch = paddle.sum(attention_mask, axis=-1, dtype="int32")
    indices = paddle.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
    max_seqlen_in_batch = paddle.max(seqlens_in_batch).item()
    cu_seqlens = F.pad(paddle.cumsum(seqlens_in_batch, axis=0), [1, 0])

    return (
        index_first_axis(rearrange(hidden_states, "b s ... -> (b s) ..."), indices),
        indices,
        cu_seqlens,
        max_seqlen_in_batch,
    )


def pad_input(hidden_states, indices, batch, seqlen):
    """
    Arguments:
        hidden_states: (total_nnz, ...), where total_nnz = number of tokens in selected in attention_mask.
        indices: (total_nnz), the indices that represent the non-masked tokens of the original padded input sequence.
        batch: int, batch size for the padded sequence.
        seqlen: int, maximum sequence length for the padded sequence.
    Return:
        hidden_states: (batch, seqlen, ...)
    """
    from einops import rearrange

    output = index_put_first_axis(hidden_states, indices, batch * seqlen)
    return rearrange(output, "(b s) ... -> b s ...", b=batch)