"""
ein notation:
b - batch
n - sequence
nt - text sequence
nw - raw wave length
d - dimension
"""

from __future__ import annotations
from typing import Optional
import math

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

from einops import rearrange
from x_transformers.x_transformers import apply_rotary_pos_emb


# raw wav to mel spec

class MelSpec(nn.Module):
    def __init__(
        self,
        filter_length = 1024,
        hop_length = 256,
        win_length = 1024,
        n_mel_channels = 100,
        target_sample_rate = 24_000,
        normalize = False,
        power = 1,
        norm = None,
        center = True,
    ):
        super().__init__()
        self.n_mel_channels = n_mel_channels

        self.mel_stft = torchaudio.transforms.MelSpectrogram(
            sample_rate = target_sample_rate,
            n_fft = filter_length,
            win_length = win_length,
            hop_length = hop_length,
            n_mels = n_mel_channels,
            power = power,
            center = center,
            normalized = normalize,
            norm = norm,
        )

        self.register_buffer('dummy', torch.tensor(0), persistent = False)

    def forward(self, inp):
        if len(inp.shape) == 3:
            inp = rearrange(inp, 'b 1 nw -> b nw')

        assert len(inp.shape) == 2

        if self.dummy.device != inp.device:
            self.to(inp.device)

        mel = self.mel_stft(inp)
        mel = mel.clamp(min = 1e-5).log()
        return mel
    

# sinusoidal position embedding

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

    def forward(self, x, scale=1000):
        device = x.device
        half_dim = self.dim // 2
        emb = math.log(10000) / (half_dim - 1)
        emb = torch.exp(torch.arange(half_dim, device=device).float() * -emb)
        emb = scale * x.unsqueeze(1) * emb.unsqueeze(0)
        emb = torch.cat((emb.sin(), emb.cos()), dim=-1)
        return emb


# convolutional position embedding

class ConvPositionEmbedding(nn.Module):
    def __init__(self, dim, kernel_size = 31, groups = 16):
        super().__init__()
        assert kernel_size % 2 != 0
        self.conv1d = nn.Sequential(
            nn.Conv1d(dim, dim, kernel_size, groups = groups, padding = kernel_size // 2),
            nn.Mish(),
            nn.Conv1d(dim, dim, kernel_size, groups = groups, padding = kernel_size // 2),
            nn.Mish(),
        )

    def forward(self, x: float['b n d'], mask: bool['b n'] | None  = None):
        if mask is not None:
            mask = mask[..., None]
            x = x.masked_fill(~mask, 0.)

        x = rearrange(x, 'b n d -> b d n')
        x = self.conv1d(x)
        out = rearrange(x, 'b d n -> b n d')

        if mask is not None:
            out = out.masked_fill(~mask, 0.)

        return out


# rotary positional embedding related

def precompute_freqs_cis(dim: int, end: int, theta: float = 10000.0, theta_rescale_factor=1.):
    # proposed by reddit user bloc97, to rescale rotary embeddings to longer sequence length without fine-tuning
    # has some connection to NTK literature
    # https://www.reddit.com/r/LocalLLaMA/comments/14lz7j5/ntkaware_scaled_rope_allows_llama_models_to_have/
    # https://github.com/lucidrains/rotary-embedding-torch/blob/main/rotary_embedding_torch/rotary_embedding_torch.py
    theta *= theta_rescale_factor ** (dim / (dim - 2))
    freqs = 1.0 / (theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim))
    t = torch.arange(end, device=freqs.device)  # type: ignore
    freqs = torch.outer(t, freqs).float()  # type: ignore
    freqs_cos = torch.cos(freqs)  # real part
    freqs_sin = torch.sin(freqs)  # imaginary part
    return torch.cat([freqs_cos, freqs_sin], dim=-1)

def get_pos_embed_indices(start, length, max_pos, scale=1.):
    # length = length if isinstance(length, int) else length.max()
    scale = scale * torch.ones_like(start, dtype=torch.float32)  # in case scale is a scalar
    pos = start.unsqueeze(1) + (
            torch.arange(length, device=start.device, dtype=torch.float32).unsqueeze(0) *
            scale.unsqueeze(1)).long()
    # avoid extra long error.
    pos = torch.where(pos < max_pos, pos, max_pos - 1)
    return pos


# Global Response Normalization layer (Instance Normalization ?)

class GRN(nn.Module):
    def __init__(self, dim):
        super().__init__()
        self.gamma = nn.Parameter(torch.zeros(1, 1, dim))
        self.beta = nn.Parameter(torch.zeros(1, 1, dim))

    def forward(self, x):
        Gx = torch.norm(x, p=2, dim=1, keepdim=True)
        Nx = Gx / (Gx.mean(dim=-1, keepdim=True) + 1e-6)
        return self.gamma * (x * Nx) + self.beta + x


# ConvNeXt-V2 Block https://github.com/facebookresearch/ConvNeXt-V2/blob/main/models/convnextv2.py
# ref: https://github.com/bfs18/e2_tts/blob/main/rfwave/modules.py#L108

class ConvNeXtV2Block(nn.Module):
    def __init__(
        self,
        dim: int,
        intermediate_dim: int,
        dilation: int = 1,
    ):
        super().__init__()
        padding = (dilation * (7 - 1)) // 2
        self.dwconv = nn.Conv1d(dim, dim, kernel_size=7, padding=padding, groups=dim, dilation=dilation)  # depthwise conv
        self.norm = nn.LayerNorm(dim, eps=1e-6)
        self.pwconv1 = nn.Linear(dim, intermediate_dim)  # pointwise/1x1 convs, implemented with linear layers
        self.act = nn.GELU()
        self.grn = GRN(intermediate_dim)
        self.pwconv2 = nn.Linear(intermediate_dim, dim)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        residual = x
        x = x.transpose(1, 2)  # b n d -> b d n
        x = self.dwconv(x)
        x = x.transpose(1, 2)  # b d n -> b n d
        x = self.norm(x)
        x = self.pwconv1(x)
        x = self.act(x)
        x = self.grn(x)
        x = self.pwconv2(x)
        return residual + x


# AdaLayerNormZero
# return with modulated x for attn input, and params for later mlp modulation

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

        self.silu = nn.SiLU()
        self.linear = nn.Linear(dim, dim * 6)

        self.norm = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)

    def forward(self, x, emb = None):
        emb = self.linear(self.silu(emb))
        shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = torch.chunk(emb, 6, dim=1)

        x = self.norm(x) * (1 + scale_msa[:, None]) + shift_msa[:, None]
        return x, gate_msa, shift_mlp, scale_mlp, gate_mlp


# AdaLayerNormZero for final layer
# return only with modulated x for attn input, cuz no more mlp modulation

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

        self.silu = nn.SiLU()
        self.linear = nn.Linear(dim, dim * 2)

        self.norm = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)

    def forward(self, x, emb):
        emb = self.linear(self.silu(emb))
        scale, shift = torch.chunk(emb, 2, dim=1)

        x = self.norm(x) * (1 + scale)[:, None, :] + shift[:, None, :]
        return x


# FeedForward

class FeedForward(nn.Module):
    def __init__(self, dim, dim_out = None, mult = 4, dropout = 0., approximate: str = 'none'):
        super().__init__()
        inner_dim = int(dim * mult)
        dim_out = dim_out if dim_out is not None else dim

        activation = nn.GELU(approximate=approximate)
        project_in = nn.Sequential(
            nn.Linear(dim, inner_dim),
            activation
        )
        self.ff = nn.Sequential(
            project_in,
            nn.Dropout(dropout),
            nn.Linear(inner_dim, dim_out)
        )

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


# Attention with possible joint part
# modified from diffusers/src/diffusers/models/attention_processor.py

class Attention(nn.Module):
    def __init__(
        self,
        processor: JointAttnProcessor | AttnProcessor,
        dim: int,
        heads: int = 8,
        dim_head: int = 64,
        dropout: float = 0.0,
        context_dim: Optional[int] = None, # if not None -> joint attention
        context_pre_only = None,
    ):
        super().__init__()

        if not hasattr(F, "scaled_dot_product_attention"):
            raise ImportError("Attention equires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")

        self.processor = processor

        self.dim = dim
        self.heads = heads
        self.inner_dim = dim_head * heads
        self.dropout = dropout

        self.context_dim = context_dim
        self.context_pre_only = context_pre_only

        self.to_q = nn.Linear(dim, self.inner_dim)
        self.to_k = nn.Linear(dim, self.inner_dim)
        self.to_v = nn.Linear(dim, self.inner_dim)

        if self.context_dim is not None:
            self.to_k_c = nn.Linear(context_dim, self.inner_dim)
            self.to_v_c = nn.Linear(context_dim, self.inner_dim)
            if self.context_pre_only is not None:
                self.to_q_c = nn.Linear(context_dim, self.inner_dim)

        self.to_out = nn.ModuleList([])
        self.to_out.append(nn.Linear(self.inner_dim, dim))
        self.to_out.append(nn.Dropout(dropout))

        if self.context_pre_only is not None and not self.context_pre_only:
            self.to_out_c = nn.Linear(self.inner_dim, dim)

    def forward(
        self,
        x: float['b n d'], # noised input x
        c: float['b n d'] = None,  # context c
        mask: bool['b n'] | None = None,
        rope = None,  # rotary position embedding for x
        c_rope = None,  # rotary position embedding for c
    ) -> torch.Tensor:
        if c is not None:
            return self.processor(self, x, c = c, mask = mask, rope = rope, c_rope = c_rope)
        else:
            return self.processor(self, x, mask = mask, rope = rope)


# Attention processor

class AttnProcessor:
    def __init__(self):
        pass

    def __call__(
        self,
        attn: Attention,
        x: float['b n d'], # noised input x
        mask: bool['b n'] | None = None,
        rope = None,  # rotary position embedding
    ) -> torch.FloatTensor:

        batch_size = x.shape[0]

        # `sample` projections.
        query = attn.to_q(x)
        key = attn.to_k(x)
        value = attn.to_v(x)

        # apply rotary position embedding
        if rope is not None:
            freqs, xpos_scale = rope
            q_xpos_scale, k_xpos_scale = (xpos_scale, xpos_scale ** -1.) if xpos_scale is not None else (1., 1.)

            query = apply_rotary_pos_emb(query, freqs, q_xpos_scale)
            key = apply_rotary_pos_emb(key, freqs, k_xpos_scale)

        # attention
        inner_dim = key.shape[-1]
        head_dim = inner_dim // attn.heads
        query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
        key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
        value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)

        # mask. e.g. inference got a batch with different target durations, mask out the padding
        if mask is not None:
            attn_mask = mask
            attn_mask = rearrange(attn_mask, 'b n -> b 1 1 n')
            attn_mask = attn_mask.expand(batch_size, attn.heads, query.shape[-2], key.shape[-2])
        else:
            attn_mask = None

        x = F.scaled_dot_product_attention(query, key, value, attn_mask=attn_mask, dropout_p=0.0, is_causal=False)
        x = x.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
        x = x.to(query.dtype)

        # linear proj
        x = attn.to_out[0](x)
        # dropout
        x = attn.to_out[1](x)

        if mask is not None:
            mask = rearrange(mask, 'b n -> b n 1')
            x = x.masked_fill(~mask, 0.)

        return x
    

# Joint Attention processor for MM-DiT
# modified from diffusers/src/diffusers/models/attention_processor.py

class JointAttnProcessor:
    def __init__(self):
        pass

    def __call__(
        self,
        attn: Attention,
        x: float['b n d'], # noised input x
        c: float['b nt d'] = None,  # context c, here text
        mask: bool['b n'] | None = None,
        rope = None,  # rotary position embedding for x
        c_rope = None,  # rotary position embedding for c
    ) -> torch.FloatTensor:
        residual = x

        batch_size = c.shape[0]

        # `sample` projections.
        query = attn.to_q(x)
        key = attn.to_k(x)
        value = attn.to_v(x)

        # `context` projections.
        c_query = attn.to_q_c(c)
        c_key = attn.to_k_c(c)
        c_value = attn.to_v_c(c)

        # apply rope for context and noised input independently
        if rope is not None:
            freqs, xpos_scale = rope
            q_xpos_scale, k_xpos_scale = (xpos_scale, xpos_scale ** -1.) if xpos_scale is not None else (1., 1.)
            query = apply_rotary_pos_emb(query, freqs, q_xpos_scale)
            key = apply_rotary_pos_emb(key, freqs, k_xpos_scale)
        if c_rope is not None:
            freqs, xpos_scale = c_rope
            q_xpos_scale, k_xpos_scale = (xpos_scale, xpos_scale ** -1.) if xpos_scale is not None else (1., 1.)
            c_query = apply_rotary_pos_emb(c_query, freqs, q_xpos_scale)
            c_key = apply_rotary_pos_emb(c_key, freqs, k_xpos_scale)

        # attention
        query = torch.cat([query, c_query], dim=1)
        key = torch.cat([key, c_key], dim=1)
        value = torch.cat([value, c_value], dim=1)

        inner_dim = key.shape[-1]
        head_dim = inner_dim // attn.heads
        query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
        key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
        value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)

        # mask. e.g. inference got a batch with different target durations, mask out the padding
        if mask is not None:
            attn_mask = F.pad(mask, (0, c.shape[1]), value = True)  # no mask for c (text)
            attn_mask = rearrange(attn_mask, 'b n -> b 1 1 n')
            attn_mask = attn_mask.expand(batch_size, attn.heads, query.shape[-2], key.shape[-2])
        else:
            attn_mask = None

        x = F.scaled_dot_product_attention(query, key, value, attn_mask=attn_mask, dropout_p=0.0, is_causal=False)
        x = x.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
        x = x.to(query.dtype)

        # Split the attention outputs.
        x, c = (
            x[:, :residual.shape[1]],
            x[:, residual.shape[1]:],
        )

        # linear proj
        x = attn.to_out[0](x)
        # dropout
        x = attn.to_out[1](x)
        if not attn.context_pre_only:
            c = attn.to_out_c(c)

        if mask is not None:
            mask = rearrange(mask, 'b n -> b n 1')
            x = x.masked_fill(~mask, 0.)
            # c = c.masked_fill(~mask, 0.)  # no mask for c (text)

        return x, c


# DiT Block

class DiTBlock(nn.Module):

    def __init__(self, dim, heads, dim_head, ff_mult = 4, dropout = 0.1):
        super().__init__()
        
        self.attn_norm = AdaLayerNormZero(dim)
        self.attn = Attention(
            processor = AttnProcessor(),
            dim = dim,
            heads = heads,
            dim_head = dim_head,
            dropout = dropout,
            )
        
        self.ff_norm = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)
        self.ff = FeedForward(dim = dim, mult = ff_mult, dropout = dropout, approximate = "tanh")

    def forward(self, x, t, mask = None, rope = None): # x: noised input, t: time embedding
        # pre-norm & modulation for attention input
        norm, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.attn_norm(x, emb=t)

        # attention
        attn_output = self.attn(x=norm, mask=mask, rope=rope)

        # process attention output for input x
        x = x + gate_msa.unsqueeze(1) * attn_output
        
        norm = self.ff_norm(x) * (1 + scale_mlp[:, None]) + shift_mlp[:, None]
        ff_output = self.ff(norm)
        x = x + gate_mlp.unsqueeze(1) * ff_output

        return x


# MMDiT Block https://arxiv.org/abs/2403.03206

class MMDiTBlock(nn.Module):
    r""" 
    modified from diffusers/src/diffusers/models/attention.py

    notes.
    _c: context related. text, cond, etc. (left part in sd3 fig2.b)
    _x: noised input related. (right part)
    context_pre_only: last layer only do prenorm + modulation cuz no more ffn
    """

    def __init__(self, dim, heads, dim_head, ff_mult = 4, dropout = 0.1, context_pre_only = False):
        super().__init__()

        self.context_pre_only = context_pre_only
        
        self.attn_norm_c = AdaLayerNormZero_Final(dim) if context_pre_only else AdaLayerNormZero(dim)
        self.attn_norm_x = AdaLayerNormZero(dim)
        self.attn = Attention(
            processor = JointAttnProcessor(),
            dim = dim,
            heads = heads,
            dim_head = dim_head,
            dropout = dropout,
            context_dim = dim,
            context_pre_only = context_pre_only,
            )

        if not context_pre_only:
            self.ff_norm_c = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)
            self.ff_c = FeedForward(dim = dim, mult = ff_mult, dropout = dropout, approximate = "tanh")
        else:
            self.ff_norm_c = None
            self.ff_c = None
        self.ff_norm_x = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)
        self.ff_x = FeedForward(dim = dim, mult = ff_mult, dropout = dropout, approximate = "tanh")

    def forward(self, x, c, t, mask = None, rope = None, c_rope = None): # x: noised input, c: context, t: time embedding
        # pre-norm & modulation for attention input
        if self.context_pre_only:
            norm_c = self.attn_norm_c(c, t)
        else:
            norm_c, c_gate_msa, c_shift_mlp, c_scale_mlp, c_gate_mlp = self.attn_norm_c(c, emb=t)
        norm_x, x_gate_msa, x_shift_mlp, x_scale_mlp, x_gate_mlp = self.attn_norm_x(x, emb=t)

        # attention
        x_attn_output, c_attn_output = self.attn(x=norm_x, c=norm_c, mask=mask, rope=rope, c_rope=c_rope)

        # process attention output for context c
        if self.context_pre_only:
            c = None
        else: # if not last layer
            c = c + c_gate_msa.unsqueeze(1) * c_attn_output

            norm_c = self.ff_norm_c(c) * (1 + c_scale_mlp[:, None]) + c_shift_mlp[:, None]
            c_ff_output = self.ff_c(norm_c)
            c = c + c_gate_mlp.unsqueeze(1) * c_ff_output

        # process attention output for input x
        x = x + x_gate_msa.unsqueeze(1) * x_attn_output
        
        norm_x = self.ff_norm_x(x) * (1 + x_scale_mlp[:, None]) + x_shift_mlp[:, None]
        x_ff_output = self.ff_x(norm_x)
        x = x + x_gate_mlp.unsqueeze(1) * x_ff_output

        return c, x


# time step conditioning embedding

class TimestepEmbedding(nn.Module):
    def __init__(self, dim, freq_embed_dim=256):
        super().__init__()
        self.time_embed = SinusPositionEmbedding(freq_embed_dim)
        self.time_mlp = nn.Sequential(
            nn.Linear(freq_embed_dim, dim),
            nn.SiLU(),
            nn.Linear(dim, dim)
        )

    def forward(self, timestep: float['b']):
        time_hidden = self.time_embed(timestep)
        time = self.time_mlp(time_hidden)  # b d
        return time