import math
import os
from collections import OrderedDict
from typing import Optional
import logging

import numpy as np
from torch import Tensor
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchmetrics.functional import(
    scale_invariant_signal_noise_ratio as si_snr,
    signal_noise_ratio as snr,
    signal_distortion_ratio as sdr,
    scale_invariant_signal_distortion_ratio as si_sdr)

from msclap import CLAP

from speechbrain.lobes.models.transformer.Transformer import PositionalEncoding

os.environ.setdefault("TOKENIZERS_PARALLELISM", "false")

def mod_pad(x, chunk_size, pad):
    # Mod pad the input to perform integer number of
    # inferences
    mod = 0
    if (x.shape[-1] % chunk_size) != 0:
        mod = chunk_size - (x.shape[-1] % chunk_size)

    x = F.pad(x, (0, mod))
    x = F.pad(x, pad)

    return x, mod

class LayerNormPermuted(nn.LayerNorm):
    def __init__(self, *args, **kwargs):
        super(LayerNormPermuted, self).__init__(*args, **kwargs)

    def forward(self, x):
        """
        Args:
            x: [B, C, T]
        """
        x = x.permute(0, 2, 1) # [B, T, C]
        x = super().forward(x)
        x = x.permute(0, 2, 1) # [B, C, T]
        return x

class DepthwiseSeparableConv(nn.Module):
    """
    Depthwise separable convolutions
    """
    def __init__(self, in_channels, out_channels, kernel_size, stride,
                 padding, dilation):
        super(DepthwiseSeparableConv, self).__init__()

        self.layers = nn.Sequential(
            nn.Conv1d(in_channels, in_channels, kernel_size, stride,
                      padding, groups=in_channels, dilation=dilation),
            LayerNormPermuted(in_channels),
            nn.ReLU(),
            nn.Conv1d(in_channels, out_channels, kernel_size=1, stride=1,
                      padding=0),
            LayerNormPermuted(out_channels),
            nn.ReLU(),
        )

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

class DilatedCausalConvEncoder(nn.Module):
    """
    A dilated causal convolution based encoder for encoding
    time domain audio input into latent space.
    """
    def __init__(self, channels, num_layers, kernel_size=3):
        super(DilatedCausalConvEncoder, self).__init__()
        self.channels = channels
        self.num_layers = num_layers
        self.kernel_size = kernel_size

        # Compute buffer lengths for each layer
        # buf_length[i] = (kernel_size - 1) * dilation[i]
        self.buf_lengths = [(kernel_size - 1) * 2**i
                            for i in range(num_layers)]

        # Compute buffer start indices for each layer
        self.buf_indices = [0]
        for i in range(num_layers - 1):
            self.buf_indices.append(
                self.buf_indices[-1] + self.buf_lengths[i])

        # Dilated causal conv layers aggregate previous context to obtain
        # contexful encoded input.
        _dcc_layers = OrderedDict()
        for i in range(num_layers):
            dcc_layer = DepthwiseSeparableConv(
                channels, channels, kernel_size=3, stride=1,
                padding=0, dilation=2**i)
            _dcc_layers.update({'dcc_%d' % i: dcc_layer})
        self.dcc_layers = nn.Sequential(_dcc_layers)

    def init_ctx_buf(self, batch_size, device):
        """
        Returns an initialized context buffer for a given batch size.
        """
        return torch.zeros(
            (batch_size, self.channels,
                 (self.kernel_size - 1) * (2**self.num_layers - 1)),
            device=device)

    def forward(self, x, ctx_buf):
        """
        Encodes input audio `x` into latent space, and aggregates
        contextual information in `ctx_buf`. Also generates new context
        buffer with updated context.
        Args:
            x: [B, in_channels, T]
                Input multi-channel audio.
            ctx_buf: {[B, channels, self.buf_length[0]], ...}
                A list of tensors holding context for each dilation
                causal conv layer. (len(ctx_buf) == self.num_layers)
        Returns:
            ctx_buf: {[B, channels, self.buf_length[0]], ...}
                Updated context buffer with output as the
                last element.
        """
        T = x.shape[-1] # Sequence length

        #print(f"x.shape: {x.shape}")
        #print(f"ctx_buf.shape: {ctx_buf.shape}")

        for i in range(self.num_layers):
            buf_start_idx = self.buf_indices[i]
            buf_end_idx = self.buf_indices[i] + self.buf_lengths[i]

            # DCC input: concatenation of current output and context
            dcc_in = torch.cat(
                (ctx_buf[..., buf_start_idx:buf_end_idx], x), dim=-1)

            # Add breakpoint at specific layer (e.g., layer 5)
            #if i == 0:
            #    print(f"\n[Breakpoint at layer {i}]")
            #    print(f"x.shape: {x.shape}")
            #    print(f"Buffer slice: [{buf_start_idx}:{buf_end_idx}]")
            #    print(f"dcc_in.shape: {dcc_in.shape}")
            #    breakpoint()  # Interactive debugging here

            # Push current output to the context buffer
            ctx_buf[..., buf_start_idx:buf_end_idx] = \
                dcc_in[..., -self.buf_lengths[i]:]

            # Residual connection
            x = x + self.dcc_layers[i](dcc_in)

        return x, ctx_buf

class CausalTransformerDecoderLayer(torch.nn.TransformerDecoderLayer):
    """
    Adapted from:
    "https://github.com/alexmt-scale/causal-transformer-decoder/blob/"
    "0caf6ad71c46488f76d89845b0123d2550ef792f/"
    "causal_transformer_decoder/model.py#L77"
    """
    def forward(
        self,
        tgt: Tensor,
        memory: Optional[Tensor] = None,
        chunk_size: int = 1
    ) -> Tensor:
        tgt_last_tok = tgt[:, -chunk_size:, :]

        # self attention part
        tmp_tgt, sa_map = self.self_attn(
            tgt_last_tok,
            tgt,
            tgt,
            attn_mask=None,  # not needed because we only care about the last token
            key_padding_mask=None,
        )
        tgt_last_tok = tgt_last_tok + self.dropout1(tmp_tgt)
        tgt_last_tok = self.norm1(tgt_last_tok)

        # encoder-decoder attention
        ca_map = None
        if memory is not None:
            tmp_tgt, ca_map = self.multihead_attn(
                tgt_last_tok,
                memory,
                memory,
                attn_mask=None, # Attend to the entire chunk
                key_padding_mask=None,
            )
            tgt_last_tok = tgt_last_tok + self.dropout2(tmp_tgt)
            tgt_last_tok = self.norm2(tgt_last_tok)

        # final feed-forward network
        tmp_tgt = self.linear2(
            self.dropout(self.activation(self.linear1(tgt_last_tok)))
        )
        tgt_last_tok = tgt_last_tok + self.dropout3(tmp_tgt)
        tgt_last_tok = self.norm3(tgt_last_tok)
        return tgt_last_tok, sa_map, ca_map

class CausalTransformerDecoder(nn.Module):
    """
    A casual transformer decoder which decodes input vectors using
    precisely `ctx_len` past vectors in the sequence, and using no future
    vectors at all.
    """
    def __init__(self, model_dim, ctx_len, chunk_size, num_layers,
                 nhead, use_pos_enc, ff_dim, conditioning='conv'):
        super(CausalTransformerDecoder, self).__init__()
        self.num_layers = num_layers
        self.model_dim = model_dim
        self.ctx_len = ctx_len
        self.chunk_size = chunk_size
        self.nhead = nhead
        self.use_pos_enc = use_pos_enc
        self.unfold = nn.Unfold(kernel_size=(ctx_len + chunk_size, 1), stride=chunk_size)
        self.pos_enc_tgt = PositionalEncoding(model_dim, max_len=1000)
        self.pos_enc_mem = PositionalEncoding(model_dim, max_len=100)
        self.tf_dec_layers = nn.ModuleList([CausalTransformerDecoderLayer(
            d_model=model_dim, nhead=nhead, dim_feedforward=ff_dim,
            batch_first=True) for _ in range(num_layers)])
        self.conditioning = conditioning

        if conditioning == 'film':
            self.film = nn.Sequential(
                nn.Linear(model_dim, 2 * model_dim),
                nn.ReLU())

    def init_ctx_buf(self, batch_size, device):
        return torch.zeros(
            (batch_size, self.num_layers + 1, self.ctx_len, self.model_dim),
            device=device)

    def _causal_unfold(self, x):
        """
        Unfolds the sequence into a batch of sequences
        prepended with `ctx_len` previous values.

        Args:
            x: [B, ctx_len + L, C]
            ctx_len: int
        Returns:
            [B * L, ctx_len + 1, C]
        """
        B, T, C = x.shape
        x = x.permute(0, 2, 1) # [B, C, ctx_len + L]
        x = self.unfold(x.unsqueeze(-1)) # [B, C * (ctx_len + chunk_size), -1]
        x = x.permute(0, 2, 1)
        x = x.reshape(B, -1, C, self.ctx_len + self.chunk_size)
        x = x.reshape(-1, C, self.ctx_len + self.chunk_size)
        x = x.permute(0, 2, 1)
        return x

    def forward(self, input, embedding, ctx_buf, K=4000):
        """
        Args:
            input: [B, model_dim, T]
            embedding: [B, NE, model_dim, embed_len]
            ctx_buf: [B, num_layers, ctx_len, model_dim]
            K: int
                Number of batches to process at once to avoid OOM.
        Returns:
            output: [B, model_dim, T]
            ctx_buf: [B, num_layers, ctx_len, model_dim]
        """

        # Mod pad the input so the sequence length is a multiple
        # of chunk_size.
        input, mod = mod_pad(input, self.chunk_size, (0, 0))

        # Init
        B, C, T = input.shape
        output = input.permute(0, 2, 1).contiguous()
        mem = None

        if self.conditioning == 'conv':
            # Convolutional/mutltiplicative conditioning
            input = input.view(1, B * C, T)
            input = F.pad(
                input, (embedding.shape[-1] - 1, 0)) # [1, B * C, T + embed_len - 1]
            emb_filter = torch.mean(embedding, dim=1).reshape(B * C, 1, -1)
            output = F.conv1d(input, emb_filter, groups=B * C)
            output = output.view(B, C, T)
            output = output.permute(0, 2, 1)
        elif self.conditioning == 'attn':
            # Use cross attn for conditioning
            mem = embedding.permute(0, 1, 3, 2) # [B, NE, embed_len, C]
            if self.use_pos_enc:
                mem = mem.view(-1, mem.shape[-2], mem.shape[-1])
                mem = mem + self.pos_enc_mem(mem)
                mem = mem.view(B, -1, mem.shape[-2], mem.shape[-1])
            mem = mem.reshape(B, -1, mem.shape[-1]) # [B, NE * embed_len, C]
            mem = mem.unsqueeze(1).repeat(
                1, (T // self.chunk_size), 1, 1
            ) # [B, T // chunk_size, NE * embed_len, C]
            mem = mem.reshape(
                -1, mem.shape[-2], mem.shape[-1]
            ) # [B * (T // chunk_size), NE * embed_len, C]
        elif self.conditioning == 'film':
            # Use FILM for conditioning
            emb_filter = torch.mean(embedding, dim=(1, 3)) # [B, C]
            emb_filter = self.film(emb_filter) # [B, 2 * C]
            gamma, beta = emb_filter.chunk(2, dim=-1)
            output = output * gamma.unsqueeze(1) + beta.unsqueeze(1)
        else:
            emb_filter = torch.mean(embedding, dim=(1, 3)) # [B, C]
            output = output * emb_filter.unsqueeze(1) # [B, T, C]

        for i, layer in enumerate(self.tf_dec_layers):
            # Prepend the context to the input and update the context
            # [B, ctx_len + T, C]
            tgt = torch.cat([ctx_buf[:, i, :, :], output], dim=1)
            ctx_buf[:, i, :, :] = tgt[:, -self.ctx_len:, :]

            # Unfold the sequence into a batch of sequences prepended
            # with `ctx_len` previous values.
            # [B * (T // chunk_size), ctx_len + chunk_size, C]
            tgt = self._causal_unfold(tgt)

            # Positional encoding
            if i == 0 and self.use_pos_enc:
                tgt = tgt + self.pos_enc_tgt(tgt)

            _tgt = torch.zeros_like(tgt)[:, :self.chunk_size, :]
            for k in range(int(math.ceil(tgt.shape[0] / K))):
                s, e = k * K, (k + 1) * K
                _mem = None if mem is None else mem[s:e]
                _tgt[s:e], _, _ = layer(tgt[s:e], _mem, self.chunk_size)

            output = _tgt.reshape(B, T, C)

        # Remove the mod padding
        output = output.permute(0, 2, 1)
        if mod != 0:
            output = output[:, :, :-mod]

        return output, ctx_buf

class MaskNet(nn.Module):
    def __init__(self, model_dim, num_enc_layers, dec_buf_len,
                 dec_chunk_size, num_dec_layers, use_pos_enc, conditioning):
        super(MaskNet, self).__init__()

        # Encoder based on dilated causal convolutions.
        self.encoder = DilatedCausalConvEncoder(channels=model_dim,
                                                num_layers=num_enc_layers)

        # Transformer decoder that operates on chunks of size
        # buffer size.
        self.decoder = CausalTransformerDecoder(
            model_dim=model_dim, ctx_len=dec_buf_len, chunk_size=dec_chunk_size,
            num_layers=num_dec_layers, nhead=8, use_pos_enc=use_pos_enc,
            ff_dim=2 * model_dim, conditioning=conditioning)

    def forward(self, x, l, enc_buf, dec_buf):
        """
        Generates a mask based on encoded input `e` and the one-hot
        label `label`.

        Args:
            x: [B, C, T]
                Input audio sequence
            l: [B, C]
                Label embedding
            ctx_buf: {[B, C, <receptive field of the layer>], ...}
                List of context buffers maintained by DCC encoder
        """
        # Enocder the label integrated input
        e, enc_buf = self.encoder(x, enc_buf)

        # Decoder conditioned on embedding
        m, dec_buf = self.decoder(input=e, embedding=l, ctx_buf=dec_buf)

        return m, enc_buf, dec_buf

class Net(nn.Module):
    _clap_models = {}
    _warned_cuda_fallback = False

    def __init__(self, label_len, L=8,
                 model_dim=512, num_enc_layers=10,
                 dec_buf_len=100, num_dec_layers=2,
                 dec_chunk_size=72, out_buf_len=2,
                 use_pos_enc=True, conditioning="mult", lookahead=True):
        super(Net, self).__init__()
        self.L = L
        self.out_buf_len = out_buf_len
        self.model_dim = model_dim
        self.lookahead = lookahead

        # Input conv to convert input audio to a latent representation
        kernel_size = 3 * L if lookahead else L
        self.in_conv = nn.Sequential(
            nn.Conv1d(in_channels=1,
                      out_channels=model_dim, kernel_size=kernel_size, stride=L,
                      padding=0, bias=False),
            nn.ReLU())

        # Label embedding layer
        self.label_embedding = nn.Sequential(
            nn.Linear(label_len, 512),
            nn.LayerNorm(512),
            nn.ReLU(),
            nn.Linear(512, model_dim),
            nn.LayerNorm(model_dim),
            nn.ReLU())

        self._label_cache = {}

        # Mask generator
        self.mask_gen = MaskNet(
            model_dim=model_dim, num_enc_layers=num_enc_layers,
            dec_buf_len=dec_buf_len,
            dec_chunk_size=dec_chunk_size, num_dec_layers=num_dec_layers,
            use_pos_enc=use_pos_enc, conditioning=conditioning)

        # Output conv layer
        self.out_conv = nn.Sequential(
            nn.ConvTranspose1d(
                in_channels=model_dim, out_channels=1,
                kernel_size=(out_buf_len + 1) * L,
                stride=L,
                padding=out_buf_len * L, bias=False),
            nn.Tanh())

    def init_buffers(self, batch_size, device):
        enc_buf = self.mask_gen.encoder.init_ctx_buf(batch_size, device)
        dec_buf = self.mask_gen.decoder.init_ctx_buf(batch_size, device)
        out_buf = torch.zeros(batch_size, self.model_dim, self.out_buf_len,
                              device=device)
        return enc_buf, dec_buf, out_buf

    def _encode_label_batch(self, label_batch, device):
        """Convert batches of label text into CLAP embeddings."""
        embeddings = []

        text_encoder = self._get_text_encoder(device)

        for label_group in label_batch:
            if isinstance(label_group, str):
                label_group = [label_group]
            elif label_group is None:
                raise ValueError("Received `None` for label data")

            label_group = [str(label) for label in label_group if label is not None]
            if not label_group:
                raise ValueError("Expected at least one label per sample")

            missing_labels = [label for label in label_group
                              if label not in self._label_cache]

            if missing_labels:
                with torch.no_grad():
                    new_embeddings = text_encoder.get_text_embeddings(missing_labels)

                if not isinstance(new_embeddings, torch.Tensor):
                    new_embeddings = torch.from_numpy(np.asarray(new_embeddings))

                if new_embeddings.ndim == 1:
                    new_embeddings = new_embeddings.unsqueeze(0)

                for label_text, embedding in zip(missing_labels, new_embeddings):
                    self._label_cache[label_text] = (
                        embedding.detach().cpu().to(torch.float32))

            cached = [self._label_cache[label] for label in label_group]
            stacked = torch.stack(cached)
            embeddings.append(stacked.mean(dim=0))

        batch_embeddings = torch.stack(embeddings)
        return batch_embeddings.to(device)

    def _get_text_encoder(self, device):
        use_cuda = (device.type == 'cuda')
        if use_cuda and not torch.cuda.is_available():
            if not Net._warned_cuda_fallback:
                logging.warning(
                    "CLAP text encoder requested on CUDA device but CUDA is unavailable; using CPU encoder instead")
                Net._warned_cuda_fallback = True
            use_cuda = False

        if use_cuda not in Net._clap_models:
            Net._clap_models[use_cuda] = CLAP(version="2023", use_cuda=use_cuda)

        return Net._clap_models[use_cuda]
    
    def predict(self, x, label, enc_buf, dec_buf, out_buf, pad=True):
        mod = 0
        if pad:
            pad_size = (self.L, self.L) if self.lookahead else (0, 0)
            x, mod = mod_pad(x, chunk_size=self.L, pad=pad_size)

        # Generate latent space representation of the input
        x = self.in_conv(x)

        # Generate label embedding
        l = self.label_embedding(label) # [B, label_len] --> [B, channels]
        l = l.unsqueeze(1).unsqueeze(-1) # [B, 1, channels, 1]

        # Generate mask corresponding to the label
        m, enc_buf, dec_buf = self.mask_gen(x, l, enc_buf, dec_buf)

        # Apply mask and decode
        x = x * m
        x = torch.cat((out_buf, x), dim=-1)
        out_buf = x[..., -self.out_buf_len:]
        x = self.out_conv(x)

        # Remove mod padding, if present.
        if mod != 0:
            x = x[:, :, :-mod]
        
        return x, enc_buf, dec_buf, out_buf

    def forward(self, inputs, init_enc_buf=None, init_dec_buf=None,
                init_out_buf=None, pad=True):
        """
        Extracts the audio corresponding to the `label` in the given
        `mixture`. Generates `chunk_size` samples per iteration.
        Args:
            mixed: [B, n_mics, T]
                input audio mixture
            label: [B, num_labels]
                one hot label
        Returns:
            out: [B, n_spk, T]
                extracted audio with sounds corresponding to the `label`
        """
        x, label = inputs['mixture'], inputs['label_vector']
        label = self._encode_label_batch(label, x.device)

        if init_enc_buf is None or init_dec_buf is None or init_out_buf is None:
            assert init_enc_buf is None and \
                   init_dec_buf is None and \
                   init_out_buf is None, \
                "Both buffers have to initialized, or " \
                "both of them have to be None."
            enc_buf, dec_buf, out_buf = self.init_buffers(
                x.shape[0], x.device)
        else:
            enc_buf, dec_buf, out_buf = \
                init_enc_buf, init_dec_buf, init_out_buf

        x, enc_buf, dec_buf, out_buf = self.predict(
            x, label, enc_buf, dec_buf, out_buf)

        if init_enc_buf is None:
            return x
        else:
            return x, enc_buf, dec_buf, out_buf

# Define optimizer, loss and metrics

def optimizer(model, data_parallel=False, **kwargs):
    # Trainable parameters
    params = [p for p in model.parameters() if p.requires_grad]
    return optim.Adam(params, **kwargs)

def loss(pred, tgt):
    return -si_snr(pred, tgt).mean()

def metrics(mixed, output, gt):
    """ Function to compute metrics """
    metrics = {}

    def metric_i(metric, src, pred, tgt):
        _vals = []
        for s, t, p in zip(src, tgt, pred):
            _vals.append((metric(p, t) - metric(s, t)).cpu().item())
        return _vals

    for m_fn in [snr, si_snr]:
        metrics[m_fn.__name__] = metric_i(m_fn,
                                          mixed[:, :gt.shape[1], :],
                                          output,
                                          gt)

    return metrics

if __name__ == '__main__':
    net = CausalTransformerDecoder(
        model_dim=8, ctx_len=4, chunk_size=4, num_layers=2, nhead=4, conditioning='attn',
        use_pos_enc=True, ff_dim=16
    )
    x = torch.randn(2, 8, 16)
    e = torch.randn(2, 2, 8, 2)
    buf = torch.rand(2, 2, 4, 8)
    out = net(x, e, buf)
    print(out[0].shape, out[1].shape)