# -*- encoding: utf-8 -*-
# File: audio.py
# Description: None


from typing import Iterable, List, Optional

import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch import Tensor


class LayerNorm(nn.LayerNorm):
    def forward(self, x: Tensor) -> Tensor:
        return super().forward(x).type(x.dtype)


class Linear(nn.Linear):
    def forward(self, x: Tensor) -> Tensor:
        return F.linear(
            x,
            self.weight.to(x.dtype),
            None if self.bias is None else self.bias.to(x.dtype),
        )


class Conv1d(nn.Conv1d):
    def _conv_forward(self, x: Tensor, weight: Tensor, bias: Optional[Tensor]) -> Tensor:
        return super()._conv_forward(x, weight.to(x.dtype), None if bias is None else bias.to(x.dtype))


def sinusoids(length, channels, max_timescale=10000):
    """Returns sinusoids for positional embedding"""
    assert channels % 2 == 0
    log_timescale_increment = np.log(max_timescale) / (channels // 2 - 1)
    inv_timescales = torch.exp(-log_timescale_increment * torch.arange(channels // 2))
    scaled_time = torch.arange(length)[:, np.newaxis] * inv_timescales[np.newaxis, :]
    return torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], dim=1)


class MultiHeadAttention(nn.Module):
    def __init__(self, n_state: int, n_head: int):
        super().__init__()
        self.n_head = n_head
        self.query = Linear(n_state, n_state)
        self.key = Linear(n_state, n_state, bias=False)
        self.value = Linear(n_state, n_state)
        self.out = Linear(n_state, n_state)

    def forward(
        self,
        x: Tensor,
        xa: Optional[Tensor] = None,
        mask: Optional[Tensor] = None,
        kv_cache: Optional[dict] = None,
    ):
        q = self.query(x)

        if kv_cache is None or xa is None or self.key not in kv_cache:
            # hooks, if installed (i.e. kv_cache is not None), will prepend the cached kv tensors;
            # otherwise, perform key/value projections for self- or cross-attention as usual.
            k = self.key(x if xa is None else xa)
            v = self.value(x if xa is None else xa)
        else:
            # for cross-attention, calculate keys and values once and reuse in subsequent calls.
            k = kv_cache[self.key]
            v = kv_cache[self.value]

        wv, qk = self.qkv_attention(q, k, v, mask)
        return self.out(wv), qk

    def qkv_attention(self, q: Tensor, k: Tensor, v: Tensor, mask: Optional[Tensor] = None):
        n_batch, n_ctx, n_state = q.shape
        scale = (n_state // self.n_head) ** -0.25
        q = q.view(*q.shape[:2], self.n_head, -1).permute(0, 2, 1, 3) * scale
        k = k.view(*k.shape[:2], self.n_head, -1).permute(0, 2, 3, 1) * scale
        v = v.view(*v.shape[:2], self.n_head, -1).permute(0, 2, 1, 3)

        qk = q @ k
        if mask is not None:
            qk += mask

        w = F.softmax(qk, dim=-1).to(q.dtype)
        return (w @ v).permute(0, 2, 1, 3).flatten(start_dim=2), qk.detach()


class ResidualAttentionBlock(nn.Module):
    def __init__(self, n_state: int, n_head: int, cross_attention: bool = False):
        super().__init__()

        self.attn = MultiHeadAttention(n_state, n_head)
        self.attn_ln = LayerNorm(n_state)

        self.cross_attn = MultiHeadAttention(n_state, n_head) if cross_attention else None
        self.cross_attn_ln = LayerNorm(n_state) if cross_attention else None

        n_mlp = n_state * 4
        self.mlp = nn.Sequential(Linear(n_state, n_mlp), nn.GELU(), Linear(n_mlp, n_state))
        self.mlp_ln = LayerNorm(n_state)

    def forward(
        self,
        x: Tensor,
        xa: Optional[Tensor] = None,
        mask: Optional[Tensor] = None,
        kv_cache: Optional[dict] = None,
    ):
        x = x + self.attn(self.attn_ln(x), mask=mask, kv_cache=kv_cache)[0]
        if self.cross_attn:
            x = x + self.cross_attn(self.cross_attn_ln(x), xa, kv_cache=kv_cache)[0]
        x = x + self.mlp(self.mlp_ln(x))
        return x


class AudioEncoder(nn.Module):
    def __init__(
        self,
        n_mels: int,
        n_ctx: int,
        n_state: int,
        n_head: int,
        n_layer: int,
        output_dim: int = 512,
        avg_pool: bool = True,
        add_audio_bos_eos_token: bool = True,
        **kwargs,
    ):
        super().__init__()
        self.conv1 = Conv1d(n_mels, n_state, kernel_size=3, padding=1)
        self.conv2 = Conv1d(n_state, n_state, kernel_size=3, stride=2, padding=1)
        self.register_buffer("positional_embedding", sinusoids(n_ctx, n_state))

        self.blocks: Iterable[ResidualAttentionBlock] = nn.ModuleList(
            [ResidualAttentionBlock(n_state, n_head) for _ in range(n_layer)]
        )
        self.ln_post = LayerNorm(n_state)

        if avg_pool:
            self.avg_pooler = nn.AvgPool1d(2, stride=2)
        else:
            self.avg_pooler = None
        self.proj = nn.Linear(n_state, output_dim)
        if add_audio_bos_eos_token:
            self.audio_bos_eos_token = nn.Embedding(2, output_dim)
        else:
            self.audio_bos_eos_token = None
        self.output_dim = output_dim
        self.n_head = n_head

    def forward(self, x: Tensor, padding_mask: Tensor = None, audio_lengths: Tensor = None):
        """
        x : torch.Tensor, shape = (batch_size, n_mels, n_ctx)
            the mel spectrogram of the audio
        """
        x = x.to(dtype=self.conv1.weight.dtype, device=self.conv1.weight.device)
        if audio_lengths is not None:
            input_mel_len = audio_lengths[:, 0] * 2
            max_mel_len_in_batch = input_mel_len.max()
            x = x[:, :, :max_mel_len_in_batch]
        x = F.gelu(self.conv1(x))
        x = F.gelu(self.conv2(x))
        x = x.permute(0, 2, 1)  # B, L, D
        bsz = x.size(0)
        src_len = x.size(1)

        self.input_positional_embedding = self.positional_embedding[:src_len]
        assert (
            x.shape[1:] == self.input_positional_embedding.shape
        ), f"incorrect audio shape: {x.shape[1:], self.input_positional_embedding.shape}"
        x = (x + self.input_positional_embedding).to(x.dtype)
        if padding_mask is not None:
            padding_mask = padding_mask.to(dtype=self.conv1.weight.dtype, device=self.conv1.weight.device)
            batch_src_len = padding_mask.size(1)
            x = x[:, :batch_src_len, :]
            padding_mask = padding_mask.view(bsz, -1, batch_src_len)
            padding_mask_ = padding_mask.all(1)
            x[padding_mask_] = 0
            key_padding_mask = (
                padding_mask_.view(bsz, 1, 1, batch_src_len)
                .expand(-1, self.n_head, -1, -1)
                .reshape(bsz, self.n_head, 1, batch_src_len)
            )
            new_padding_mask = torch.zeros_like(key_padding_mask, dtype=x.dtype)
            padding_mask = new_padding_mask.masked_fill(key_padding_mask, float("-inf"))

        for block in self.blocks:
            x = block(x, mask=padding_mask)

        if self.avg_pooler:
            x = x.permute(0, 2, 1)
            x = self.avg_pooler(x)
            x = x.permute(0, 2, 1)

        x = self.ln_post(x)
        x = self.proj(x)

        if self.audio_bos_eos_token is not None:
            bos = self.audio_bos_eos_token.weight[0][None, :]
            eos = self.audio_bos_eos_token.weight[1][None, :]
        else:
            bos, eos = None, None
        return x, bos, eos

    def encode(
        self,
        input_audios: Tensor,
        input_audio_lengths: Tensor,
        audio_span_tokens: List,
    ):
        real_input_audio_lens = input_audio_lengths[:, 0].tolist()
        max_len_in_batch = max(real_input_audio_lens)
        padding_mask = torch.ones([input_audios.size(0), max_len_in_batch]).to(
            dtype=self.conv1.weight.dtype, device=self.conv1.weight.device
        )
        for index in range(len(input_audios)):
            padding_mask[index, : input_audio_lengths[index][0].item()] = 0
        x, bos, eos = self(input_audios, padding_mask, input_audio_lengths)
        output_audios = []
        for i in range(len(audio_span_tokens)):
            audio_span = audio_span_tokens[i]
            audio = x[i][: audio_span - 2]
            if bos is not None:
                audio = torch.concat([bos, audio, eos])
            assert len(audio) == audio_span
            output_audios.append(audio)
        return output_audios