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


class ConformerEncoder(nn.Module):
    def __init__(self, idim, n_layers, n_head, d_model,
                 residual_dropout=0.1, dropout_rate=0.1, kernel_size=33,
                 pe_maxlen=5000):
        super().__init__()
        self.odim = d_model

        self.input_preprocessor = Conv2dSubsampling(idim, d_model)
        self.positional_encoding = RelPositionalEncoding(d_model)
        self.dropout = nn.Dropout(residual_dropout)

        self.layer_stack = nn.ModuleList()
        for l in range(n_layers):
            block = RelPosEmbConformerBlock(d_model, n_head,
                        residual_dropout,
                        dropout_rate, kernel_size)
            self.layer_stack.append(block)

    def forward(self, padded_input, input_lengths, pad=True):
        if pad:
            padded_input = F.pad(padded_input,
                (0, 0, 0, self.input_preprocessor.context - 1), 'constant', 0.0)
        src_mask = self.padding_position_is_0(padded_input, input_lengths)

        embed_output, input_lengths, src_mask = self.input_preprocessor(padded_input, src_mask)
        enc_output = self.dropout(embed_output)

        pos_emb = self.dropout(self.positional_encoding(embed_output))

        enc_outputs = []
        for enc_layer in self.layer_stack:
            enc_output = enc_layer(enc_output, pos_emb, slf_attn_mask=src_mask,
                                   pad_mask=src_mask)
            enc_outputs.append(enc_output)

        return enc_output, input_lengths, src_mask

    def padding_position_is_0(self, padded_input, input_lengths):
        N, T = padded_input.size()[:2]
        mask = torch.ones((N, T)).to(padded_input.device)
        for i in range(N):
            mask[i, input_lengths[i]:] = 0
        mask = mask.unsqueeze(dim=1)
        return mask.to(torch.uint8)


class RelPosEmbConformerBlock(nn.Module):
    def __init__(self, d_model, n_head,
                 residual_dropout=0.1,
                 dropout_rate=0.1, kernel_size=33):
        super().__init__()
        self.ffn1 = ConformerFeedForward(d_model, dropout_rate)
        self.mhsa = RelPosMultiHeadAttention(n_head, d_model,
                                             residual_dropout)
        self.conv = ConformerConvolution(d_model, kernel_size,
                                         dropout_rate)
        self.ffn2 = ConformerFeedForward(d_model, dropout_rate)
        self.layer_norm = nn.LayerNorm(d_model)

    def forward(self, x, pos_emb, slf_attn_mask=None, pad_mask=None):
        out = 0.5 * x + 0.5 * self.ffn1(x)
        out = self.mhsa(out, out, out, pos_emb, mask=slf_attn_mask)[0]
        out = self.conv(out, pad_mask)
        out = 0.5 * out + 0.5 * self.ffn2(out)
        out = self.layer_norm(out)
        return out


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


class Conv2dSubsampling(nn.Module):
    def __init__(self, idim, d_model, out_channels=32):
        super().__init__()
        self.conv = nn.Sequential(
            nn.Conv2d(1, out_channels, 3, 2),
            nn.ReLU(),
            nn.Conv2d(out_channels, out_channels, 3, 2),
            nn.ReLU(),
        )
        subsample_idim = ((idim - 1) // 2 - 1) // 2
        self.out = nn.Linear(out_channels * subsample_idim, d_model)

        self.subsampling = 4
        left_context = right_context = 3  # both exclude currect frame
        self.context = left_context + 1 + right_context  # 7

    def forward(self, x, x_mask):
        x = x.unsqueeze(1)
        x = self.conv(x)
        N, C, T, D = x.size()
        x = self.out(x.transpose(1, 2).contiguous().view(N, T, C * D))
        mask = x_mask[:, :, :-2:2][:, :, :-2:2]
        input_lengths = mask[:, -1, :].sum(dim=-1)
        return x, input_lengths, mask


class RelPositionalEncoding(torch.nn.Module):
    def __init__(self, d_model, max_len=5000):
        super().__init__()
        pe_positive = torch.zeros(max_len, d_model, requires_grad=False)
        pe_negative = torch.zeros(max_len, d_model, requires_grad=False)
        position = torch.arange(0, max_len).unsqueeze(1).float()
        div_term = torch.exp(torch.arange(0, d_model, 2).float() *
                             -(torch.log(torch.tensor(10000.0)).item()/d_model))
        pe_positive[:, 0::2] = torch.sin(position * div_term)
        pe_positive[:, 1::2] = torch.cos(position * div_term)
        pe_negative[:, 0::2] = torch.sin(-1 * position * div_term)
        pe_negative[:, 1::2] = torch.cos(-1 * position * div_term)

        pe_positive = torch.flip(pe_positive, [0]).unsqueeze(0)
        pe_negative = pe_negative[1:].unsqueeze(0)
        pe = torch.cat([pe_positive, pe_negative], dim=1)
        self.register_buffer('pe', pe)

    def forward(self, x):
        # Tmax = 2 * max_len - 1
        Tmax, T = self.pe.size(1), x.size(1)
        pos_emb = self.pe[:, Tmax // 2 - T + 1 : Tmax // 2 + T].clone().detach()
        return pos_emb


class ConformerFeedForward(nn.Module):
    def __init__(self, d_model, dropout_rate=0.1):
        super().__init__()
        pre_layer_norm = nn.LayerNorm(d_model)
        linear_expand = nn.Linear(d_model, d_model*4)
        nonlinear = Swish()
        dropout_pre = nn.Dropout(dropout_rate)
        linear_project = nn.Linear(d_model*4, d_model)
        dropout_post = nn.Dropout(dropout_rate)
        self.net = nn.Sequential(pre_layer_norm,
                                 linear_expand,
                                 nonlinear,
                                 dropout_pre,
                                 linear_project,
                                 dropout_post)

    def forward(self, x):
        residual = x
        output = self.net(x)
        output = output + residual
        return output


class ConformerConvolution(nn.Module):
    def __init__(self, d_model, kernel_size=33, dropout_rate=0.1):
        super().__init__()
        assert kernel_size % 2 == 1
        self.pre_layer_norm = nn.LayerNorm(d_model)
        self.pointwise_conv1 = nn.Conv1d(d_model, d_model*4, kernel_size=1, bias=False)
        self.glu = F.glu
        self.padding = (kernel_size - 1) // 2
        self.depthwise_conv = nn.Conv1d(d_model*2, d_model*2,
                                        kernel_size, stride=1,
                                        padding=self.padding,
                                        groups=d_model*2, bias=False)
        self.batch_norm = nn.LayerNorm(d_model*2)
        self.swish = Swish()
        self.pointwise_conv2 = nn.Conv1d(d_model*2, d_model, kernel_size=1, bias=False)
        self.dropout = nn.Dropout(dropout_rate)

    def forward(self, x, mask=None):
        residual = x
        out = self.pre_layer_norm(x)
        out = out.transpose(1, 2)
        if mask is not None:
            out.masked_fill_(mask.ne(1), 0.0)
        out = self.pointwise_conv1(out)
        out = F.glu(out, dim=1)
        out = self.depthwise_conv(out)

        out = out.transpose(1, 2)
        out = self.swish(self.batch_norm(out))
        out = out.transpose(1, 2)

        out = self.dropout(self.pointwise_conv2(out))
        if mask is not None:
            out.masked_fill_(mask.ne(1), 0.0)
        out = out.transpose(1, 2)
        return out + residual


class EncoderMultiHeadAttention(nn.Module):
    def __init__(self, n_head, d_model,
                 residual_dropout=0.1):
        super().__init__()
        assert d_model % n_head == 0
        self.n_head = n_head
        self.d_k = d_model // n_head
        self.d_v = self.d_k

        self.w_qs = nn.Linear(d_model, n_head * self.d_k, bias=False)
        self.w_ks = nn.Linear(d_model, n_head * self.d_k, bias=False)
        self.w_vs = nn.Linear(d_model, n_head * self.d_v, bias=False)

        self.layer_norm_q = nn.LayerNorm(d_model)
        self.layer_norm_k = nn.LayerNorm(d_model)
        self.layer_norm_v = nn.LayerNorm(d_model)

        self.attention = ScaledDotProductAttention(temperature=self.d_k ** 0.5)
        self.fc = nn.Linear(n_head * self.d_v, d_model, bias=False)
        self.dropout = nn.Dropout(residual_dropout)

    def forward(self, q, k, v, mask=None):
        sz_b, len_q = q.size(0), q.size(1)

        residual = q
        q, k, v = self.forward_qkv(q, k, v)

        output, attn = self.attention(q, k, v, mask=mask)

        output = self.forward_output(output, residual, sz_b, len_q)
        return output, attn

    def forward_qkv(self, q, k, v):
        d_k, d_v, n_head = self.d_k, self.d_v, self.n_head
        sz_b, len_q, len_k, len_v = q.size(0), q.size(1), k.size(1), v.size(1)

        q = self.layer_norm_q(q)
        k = self.layer_norm_k(k)
        v = self.layer_norm_v(v)

        q = self.w_qs(q).view(sz_b, len_q, n_head, d_k)
        k = self.w_ks(k).view(sz_b, len_k, n_head, d_k)
        v = self.w_vs(v).view(sz_b, len_v, n_head, d_v)
        q = q.transpose(1, 2)
        k = k.transpose(1, 2)
        v = v.transpose(1, 2)
        return q, k, v

    def forward_output(self, output, residual, sz_b, len_q):
        output = output.transpose(1, 2).contiguous().view(sz_b, len_q, -1)
        fc_out = self.fc(output)
        output = self.dropout(fc_out)
        output = output + residual
        return output


class ScaledDotProductAttention(nn.Module):
    def __init__(self, temperature):
        super().__init__()
        self.temperature = temperature
        self.dropout = nn.Dropout(0.0)
        self.INF = float('inf')

    def forward(self, q, k, v, mask=None):
        attn = torch.matmul(q, k.transpose(2, 3)) / self.temperature
        output, attn = self.forward_attention(attn, v, mask)
        return output, attn

    def forward_attention(self, attn, v, mask=None):
        if mask is not None:
            mask = mask.unsqueeze(1)
            mask = mask.eq(0)
            attn = attn.masked_fill(mask, -self.INF)
            attn = torch.softmax(attn, dim=-1).masked_fill(mask, 0.0)
        else:
            attn = torch.softmax(attn, dim=-1)

        d_attn = self.dropout(attn)
        output = torch.matmul(d_attn, v)

        return output, attn


class RelPosMultiHeadAttention(EncoderMultiHeadAttention):
    def __init__(self, n_head, d_model,
                 residual_dropout=0.1):
        super().__init__(n_head, d_model,
                         residual_dropout)
        d_k = d_model // n_head
        self.scale = 1.0 / (d_k ** 0.5)
        self.linear_pos = nn.Linear(d_model, n_head * d_k, bias=False)
        self.pos_bias_u = nn.Parameter(torch.FloatTensor(n_head, d_k))
        self.pos_bias_v = nn.Parameter(torch.FloatTensor(n_head, d_k))
        torch.nn.init.xavier_uniform_(self.pos_bias_u)
        torch.nn.init.xavier_uniform_(self.pos_bias_v)

    def _rel_shift(self, x):
        N, H, T1, T2 = x.size()
        zero_pad = torch.zeros((N, H, T1, 1), device=x.device, dtype=x.dtype)
        x_padded = torch.cat([zero_pad, x], dim=-1)

        x_padded = x_padded.view(N, H, T2 + 1, T1)
        x = x_padded[:, :, 1:].view_as(x)
        x = x[:, :, :, : x.size(-1) // 2 + 1]
        return x

    def forward(self, q, k, v, pos_emb, mask=None):
        sz_b, len_q = q.size(0), q.size(1)

        residual = q
        q, k, v = self.forward_qkv(q, k, v)

        q = q.transpose(1, 2)
        n_batch_pos = pos_emb.size(0)
        p = self.linear_pos(pos_emb).view(n_batch_pos, -1, self.n_head, self.d_k)
        p = p.transpose(1, 2)

        q_with_bias_u = (q + self.pos_bias_u).transpose(1, 2)
        q_with_bias_v = (q + self.pos_bias_v).transpose(1, 2)

        matrix_ac = torch.matmul(q_with_bias_u, k.transpose(-2, -1))

        matrix_bd = torch.matmul(q_with_bias_v, p.transpose(-2, -1))
        matrix_bd = self._rel_shift(matrix_bd)

        attn_scores = matrix_ac + matrix_bd
        attn_scores.mul_(self.scale)

        output, attn = self.attention.forward_attention(attn_scores, v, mask=mask)

        output = self.forward_output(output, residual, sz_b, len_q)
        return output, attn