"""Custom normalization layers."""
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from aphrodite.modeling._custom_op import CustomOp


class LayerNorm(nn.LayerNorm):

    def __init__(
        self,
        hidden_size: int,
        eps: float = 1e-6,
    ) -> None:
        super().__init__(hidden_size, eps=eps)

    def forward(
        self,
        x: torch.Tensor,
        residual: Optional[torch.Tensor] = None,
    ) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
        """normalization."""
        if residual is not None:
            x = x + residual
            residual = x
        x = super().forward(x)
        if residual is None:
            return x
        else:
            return x, residual


class RMSNorm(CustomOp):
    """Root mean square normalization.

    Computes x -> w * x / sqrt(E[x^2] + eps) where w is the learned weight.
    Refer to https://arxiv.org/abs/1910.07467
    """

    def __init__(
        self,
        hidden_size: int,
        eps: float = 1e-6,
    ) -> None:
        super().__init__()
        self.weight = nn.Parameter(torch.ones(hidden_size))
        self.variance_epsilon = eps

    def forward_native(
        self,
        x: torch.Tensor,
        residual: Optional[torch.Tensor] = None,
    ) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
        """PyTorch-native implementation equivalent to forward()."""
        orig_dtype = x.dtype
        x = x.to(torch.float32)
        if residual is not None:
            x = x + residual.to(torch.float32)
            residual = x.to(orig_dtype)

        variance = x.pow(2).mean(dim=-1, keepdim=True)
        x = x * torch.rsqrt(variance + self.variance_epsilon)
        x = x.to(orig_dtype) * self.weight
        if residual is None:
            return x
        else:
            return x, residual

    def forward_cuda(
        self,
        x: torch.Tensor,
        residual: Optional[torch.Tensor] = None,
    ) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
        from aphrodite import _custom_ops as ops
        if residual is not None:
            ops.fused_add_rms_norm(
                x,
                residual,
                self.weight.data,
                self.variance_epsilon,
            )
            return x, residual
        out = torch.empty_like(x)
        ops.rms_norm(
            out,
            x,
            self.weight.data,
            self.variance_epsilon,
        )
        return out

    def forward_xpu(
        self,
        x: torch.Tensor,
        residual: Optional[torch.Tensor] = None,
    ) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
        from aphrodite._ipex_ops import ipex_ops as ops

        if residual is not None:
            ops.fused_add_rms_norm(
                x,
                residual,
                self.weight.data,
                self.variance_epsilon,
            )
            return x, residual
        out = torch.empty_like(x)
        ops.rms_norm(
            out,
            x,
            self.weight.data,
            self.variance_epsilon,
        )
        return out

    def extra_repr(self) -> str:
        s = f"hidden_size={self.weight.data.size(0)}"
        s += f", eps={self.variance_epsilon}"
        return s


class GemmaRMSNorm(CustomOp):
    """RMS normalization for Gemma.
    Two differences from the above RMSNorm:
        1. x * (1 + w) instead of x * w.
        2. (x * w).to(orig_dtype) instead of x.to(orig_dtype) * w.
    """

    def __init__(
        self,
        hidden_size: int,
        eps: float = 1e-6,
    ) -> None:
        super().__init__()
        self.weight = nn.Parameter(torch.zeros(hidden_size))
        self.variance_epsilon = eps

    def forward_native(
        self,
        x: torch.Tensor,
        residual: Optional[torch.Tensor] = None,
    ) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
        """PyTorch-native implementation equivalent to forward()."""
        orig_dtype = x.dtype
        if residual is not None:
            x = x + residual
            residual = x

        x = x.float()
        variance = x.pow(2).mean(dim=-1, keepdim=True)
        x = x * torch.rsqrt(variance + self.variance_epsilon)
        # Llama does x.to(float16) * w whilst Gemma is (x * w).to(float16)
        # See https://github.com/huggingface/transformers/pull/29402
        x = x * (1.0 + self.weight.float())
        x = x.to(orig_dtype)
        return x if residual is None else (x, residual)

    def forward_cuda(
        self,
        x: torch.Tensor,
        residual: Optional[torch.Tensor] = None,
    ) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
        # TODO: Implement an optimized kernel for GemmaRMSNorm.
        return self.forward_native(x, residual)