import torch
from torch import nn
import torch.nn.functional as F

from einops.layers.torch import Rearrange
from ring_attention_pytorch import RingAttention

# helper functions


def exists(val):
    return val is not None


def default(val, d):
    return val if exists(val) else d


def calc_same_padding(kernel_size):
    pad = kernel_size // 2
    return (pad, pad - (kernel_size + 1) % 2)


# helper classes


class Swish(nn.Module):
    def forward(self, x):
        return x * x.sigmoid()


class GLU(nn.Module):
    def __init__(self, dim):
        super().__init__()
        self.dim = dim

    def forward(self, x):
        out, gate = x.chunk(2, dim=self.dim)
        return out * gate.sigmoid()


class DepthWiseConv1d(nn.Module):
    def __init__(self, chan_in, chan_out, kernel_size, padding):
        super().__init__()
        self.padding = padding
        self.conv = nn.Conv1d(chan_in, chan_out, kernel_size, groups=chan_in)

    def forward(self, x):
        x = F.pad(x, self.padding)
        return self.conv(x)


# attention, feedforward, and conv module


class Scale(nn.Module):
    def __init__(self, scale, fn):
        super().__init__()
        self.fn = fn
        self.scale = scale

    def forward(self, x, **kwargs):
        return self.fn(x, **kwargs) * self.scale


class PreNorm(nn.Module):
    def __init__(self, dim, fn):
        super().__init__()
        self.fn = fn
        self.norm = nn.LayerNorm(dim)

    def forward(self, x, **kwargs):

        x = self.norm(x.to(x.device))

        out = self.fn(x.to(x.device), **kwargs)
   
        return out


class FeedForward(nn.Module):
    def __init__(self, dim, mult=4, dropout=0.0):
        super().__init__()
        self.net = nn.Sequential(
            nn.Linear(dim, dim * mult),
            Swish(),
            nn.Dropout(dropout),
            nn.Linear(dim * mult, dim),
            nn.Dropout(dropout),
        )

    def forward(self, x):
        return self.net(x)


class ConformerConvModule(nn.Module):
    def __init__(
        self, dim, causal=False, expansion_factor=2, kernel_size=31, dropout=0.0
    ):
        super().__init__()

        inner_dim = dim * expansion_factor
        padding = calc_same_padding(kernel_size) if not causal else (kernel_size - 1, 0)

        self.net = nn.Sequential(
            nn.LayerNorm(dim),
            Rearrange("b n c -> b c n"),
            nn.Conv1d(dim, inner_dim * 2, 1),
            GLU(dim=1),
            DepthWiseConv1d(
                inner_dim, inner_dim, kernel_size=kernel_size, padding=padding
            ),
            nn.BatchNorm1d(inner_dim) if not causal else nn.Identity(),
            Swish(),
            nn.Conv1d(inner_dim, dim, 1),
            Rearrange("b c n -> b n c"),
            nn.Dropout(dropout),
        )

    def forward(self, x):
        return self.net(x)


# Conformer Block


class ConformerBlock(nn.Module):
    def __init__(
        self,
        *,
        dim,
        dim_head=64,
        heads=8,
        ff_mult=4,
        conv_expansion_factor=2,
        conv_kernel_size=31,
        attn_dropout=0.0,
        ff_dropout=0.0,
        conv_dropout=0.0,
        conv_causal=False
    ):
        super().__init__()
        self.ff1 = FeedForward(dim=dim, mult=ff_mult, dropout=ff_dropout)
        self.attn = RingAttention(
            dim=dim,
            dim_head=dim_head,
            heads=heads,
            causal=True,
            auto_shard_seq=False, # doesn't work on multi-gpu setup for some reason
            ring_attn=True,
            ring_seq_size=512,
        )
        self.self_attn_dropout = torch.nn.Dropout(attn_dropout)
        self.conv = ConformerConvModule(
            dim=dim,
            causal=conv_causal,
            expansion_factor=conv_expansion_factor,
            kernel_size=conv_kernel_size,
            dropout=conv_dropout,
        )
        self.ff2 = FeedForward(dim=dim, mult=ff_mult, dropout=ff_dropout)

        self.attn = PreNorm(dim, self.attn)
        self.ff1 = Scale(0.5, PreNorm(dim, self.ff1))
        self.ff2 = Scale(0.5, PreNorm(dim, self.ff2))

        self.post_norm = nn.LayerNorm(dim)


    def forward(self, x, mask=None):
        x_ff1 = self.ff1(x) + x
        
        x = self.attn(x, mask=mask)
        x = self.self_attn_dropout(x)
        x = x + x_ff1
        x = self.conv(x) + x
        x = self.ff2(x) + x
        x = self.post_norm(x)
        return x



# Conformer


class Conformer(nn.Module):
    def __init__(
        self,

        dim,
        *,
        depth,
        dim_head=64,
        heads=8,
        ff_mult=4,
        conv_expansion_factor=2,
        conv_kernel_size=31,
        attn_dropout=0.0,
        ff_dropout=0.0,
        conv_dropout=0.0,
        conv_causal=False
    ):
        super().__init__()
        self.dim = dim

        self.layers = nn.ModuleList([])

        for _ in range(depth):
            self.layers.append(
                ConformerBlock(
                    dim=dim,
                    dim_head=dim_head,
                    heads=heads,
                    ff_mult=ff_mult,
                    conv_expansion_factor=conv_expansion_factor,
                    conv_kernel_size=conv_kernel_size,
                    conv_causal=conv_causal,
                )
            )


    def forward(self, x):

        for block in self.layers:
            
            x = block(x)

        return x