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import math
import torch
import torch.nn as nn
from torch.nn.modules.utils import consume_prefix_in_state_dict_if_present

class CustomLSTM(nn.Module):
    def __init__(self, input_sz, hidden_sz):
        super().__init__()
        self.input_sz = input_sz
        self.hidden_size = hidden_sz
        self.W = nn.Parameter(torch.Tensor(input_sz, hidden_sz * 4))
        self.U = nn.Parameter(torch.Tensor(hidden_sz, hidden_sz * 4))
        self.bias = nn.Parameter(torch.Tensor(hidden_sz * 4))
        self.init_weights()

    def init_weights(self):
        stdv = 1.0 / math.sqrt(self.hidden_size)
        for weight in self.parameters():
            weight.data.uniform_(-stdv, stdv)

    def forward(self, x,
                init_states=None):
        """Assumes x is of shape (batch, sequence, feature)"""
        #print(type(x))
        #print(x.shape)
        bs, seq_sz, _ = x.size()
        hidden_seq = []
        if init_states is None:
            h_t, c_t = (torch.zeros(bs, self.hidden_size).to(x.device),
                        torch.zeros(bs, self.hidden_size).to(x.device))
        else:
            h_t, c_t = init_states

        HS = self.hidden_size
        for t in range(seq_sz):
            x_t = x[:, t, :]
            # batch the computations into a single matrix multiplication
            gates = x_t @ self.W + h_t @ self.U + self.bias
            i_t, f_t, g_t, o_t = (
                torch.sigmoid(gates[:, :HS]), # input
                torch.sigmoid(gates[:, HS:HS*2]), # forget
                torch.tanh(gates[:, HS*2:HS*3]),
                torch.sigmoid(gates[:, HS*3:]), # output
            )
            c_t = f_t * c_t + i_t * g_t
            h_t = o_t * torch.tanh(c_t)
            hidden_seq.append(h_t.unsqueeze(0))
        hidden_seq = torch.cat(hidden_seq, dim=0)
        # reshape from shape (sequence, batch, feature) to (batch, sequence, feature)
        hidden_seq = hidden_seq.transpose(0, 1).contiguous()
        return hidden_seq, (h_t, c_t)

hparams = {
        'n_mel_channels': 128,  # From LogMelSpectrogram
        'postnet_embedding_dim': 512,  # Common choice, adjust as needed
        'postnet_kernel_size': 5,  # Common choice, adjust as needed
        'postnet_n_convolutions': 5,  # Typical number of Postnet convolutions
    }

class ConvNorm(torch.nn.Module):
    def __init__(self, in_channels, out_channels, kernel_size=1, stride=1,
                 padding=None, dilation=1, bias=True, w_init_gain='linear'):
        super(ConvNorm, self).__init__()
        if padding is None:
            assert(kernel_size % 2 == 1)
            padding = int(dilation * (kernel_size - 1) / 2)

        self.conv = torch.nn.Conv1d(in_channels, out_channels,
                                    kernel_size=kernel_size, stride=stride,
                                    padding=padding, dilation=dilation,
                                    bias=bias)

        torch.nn.init.xavier_uniform_(
            self.conv.weight, gain=torch.nn.init.calculate_gain(w_init_gain))

    def forward(self, signal):
        conv_signal = self.conv(signal)
        return conv_signal

URLS = {
    "hubert-discrete": "https://github.com/bshall/acoustic-model/releases/download/v0.1/hubert-discrete-d49e1c77.pt",
    "hubert-soft": "https://github.com/bshall/acoustic-model/releases/download/v0.1/hubert-soft-0321fd7e.pt",
}


class AcousticModel(nn.Module):
    def __init__(self, discrete: bool = False, upsample: bool = True, use_custom_lstm=False):
        super().__init__()
        # self.spk_projection = nn.Linear(512+512, 512)
        self.encoder = Encoder(discrete, upsample)
        self.decoder = Decoder(use_custom_lstm=use_custom_lstm)
        self.postnet = Postnet(hparams)  # Add this line. Ensure hparams is defined or pass explicit parameters

    def forward(self, x: torch.Tensor, spk_embs, mels: torch.Tensor) -> torch.Tensor:
        x = self.encoder(x)
        exp_spk_embs = spk_embs.unsqueeze(1).expand(-1, x.size(1), -1)
        concat_x = torch.cat([x, exp_spk_embs], dim=-1)
        # x = self.spk_projection(concat_x)
        output = self.decoder(concat_x, mels)
        postnet_output = self.postnet(output) + output
        return postnet_output

    #def forward(self, x: torch.Tensor, mels: torch.Tensor) -> torch.Tensor:
    #    x = self.encoder(x)
    #    return self.decoder(x, mels)

    def forward_test(self, x, spk_embs, mels):
      print('x shape', x.shape)
      print('se shape', spk_embs.shape)
      print('mels shape', mels.shape)
      x = self.encoder(x)
      print('x_enc shape', x.shape)
      return

    @torch.inference_mode()
    def generate(self, x: torch.Tensor, spk_embs) -> torch.Tensor:
        x = self.encoder(x)
        exp_spk_embs = spk_embs.unsqueeze(1).expand(-1, x.size(1), -1)
        concat_x = torch.cat([x, exp_spk_embs], dim=-1)
        # x = self.spk_projection(concat_x)
        mels = self.decoder.generate(concat_x)
        postnet_mels = self.postnet(mels) + mels
        return postnet_mels


class Encoder(nn.Module):
    def __init__(self, discrete: bool = False, upsample: bool = True):
        super().__init__()
        self.embedding = nn.Embedding(100 + 1, 256) if discrete else None
        self.prenet = PreNet(256, 256, 256)
        self.convs = nn.Sequential(
            nn.Conv1d(256, 512, 5, 1, 2),
            nn.ReLU(),
            nn.Dropout(0.3),
            nn.InstanceNorm1d(512),
            nn.ConvTranspose1d(512, 512, 4, 2, 1) if upsample else nn.Identity(),
            nn.Dropout(0.3),
            nn.Conv1d(512, 512, 5, 1, 2),
            nn.ReLU(),
            nn.Dropout(0.3),
            nn.InstanceNorm1d(512),
            nn.Conv1d(512, 512, 5, 1, 2),
            nn.ReLU(),
            nn.Dropout(0.3),
            nn.InstanceNorm1d(512),
        )

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        if self.embedding is not None:
            x = self.embedding(x)
        x = self.prenet(x)
        x = self.convs(x.transpose(1, 2))
        return x.transpose(1, 2)


class Decoder(nn.Module):
    def __init__(self, use_custom_lstm=False):
        super().__init__()
        self.use_custom_lstm = use_custom_lstm
        self.prenet = PreNet(128, 256, 256)
        if use_custom_lstm:
          self.lstm1 = CustomLSTM(1024 + 256, 1024)
          self.lstm2 = CustomLSTM(1024, 1024)
          self.lstm3 = CustomLSTM(1024, 1024)
        else:
          self.lstm1 = nn.LSTM(1024 + 256, 1024)
          self.lstm2 = nn.LSTM(1024, 1024)
          self.lstm3 = nn.LSTM(1024, 1024)
        self.proj = nn.Linear(1024, 128, bias=False)
        self.dropout = nn.Dropout(0.3)

    def forward(self, x: torch.Tensor, mels: torch.Tensor) -> torch.Tensor:
        mels = self.prenet(mels)
        x, _ = self.lstm1(torch.cat((x, mels), dim=-1))
        x = self.dropout(x)
        res = x
        x, _ = self.lstm2(x)
        x = self.dropout(x)
        x = res + x
        res = x
        x, _ = self.lstm3(x)
        x = self.dropout(x)
        x = res + x
        return self.proj(x)

    @torch.inference_mode()
    def generate(self, xs: torch.Tensor) -> torch.Tensor:
        m = torch.zeros(xs.size(0), 128, device=xs.device)
        if self.use_custom_lstm:
          h1 = torch.zeros(xs.size(0), 1024, device=xs.device)
          c1 = torch.zeros(xs.size(0), 1024, device=xs.device)
          h2 = torch.zeros(xs.size(0), 1024, device=xs.device)
          c2 = torch.zeros(xs.size(0), 1024, device=xs.device)
          h3 = torch.zeros(xs.size(0), 1024, device=xs.device)
          c3 = torch.zeros(xs.size(0), 1024, device=xs.device)
        else:
          h1 = torch.zeros(1, xs.size(0), 1024, device=xs.device)
          c1 = torch.zeros(1, xs.size(0), 1024, device=xs.device)
          h2 = torch.zeros(1, xs.size(0), 1024, device=xs.device)
          c2 = torch.zeros(1, xs.size(0), 1024, device=xs.device)
          h3 = torch.zeros(1, xs.size(0), 1024, device=xs.device)
          c3 = torch.zeros(1, xs.size(0), 1024, device=xs.device)


        mel = []
        for x in torch.unbind(xs, dim=1):
            m = self.prenet(m)
            x = torch.cat((x, m), dim=1).unsqueeze(1)
            x1, (h1, c1) = self.lstm1(x, (h1, c1))
            x2, (h2, c2) = self.lstm2(x1, (h2, c2))
            x = x1 + x2
            x3, (h3, c3) = self.lstm3(x, (h3, c3))
            x = x + x3
            m = self.proj(x).squeeze(1)
            mel.append(m)
        return torch.stack(mel, dim=1)


class PreNet(nn.Module):
    def __init__(
        self,
        input_size: int,
        hidden_size: int,
        output_size: int,
        dropout: float = 0.5,
    ):
        super().__init__()
        self.net = nn.Sequential(
            nn.Linear(input_size, hidden_size),
            nn.ReLU(),
            nn.Dropout(dropout),
            nn.Linear(hidden_size, output_size),
            nn.ReLU(),
            nn.Dropout(dropout),
        )

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        return self.net(x)


def _acoustic(
    name: str,
    discrete: bool,
    upsample: bool,
    pretrained: bool = True,
    progress: bool = True,
) -> AcousticModel:
    acoustic = AcousticModel(discrete, upsample)
    if pretrained:
        checkpoint = torch.hub.load_state_dict_from_url(URLS[name], progress=progress)
        consume_prefix_in_state_dict_if_present(checkpoint["acoustic-model"], "module.")
        acoustic.load_state_dict(checkpoint["acoustic-model"])
        acoustic.eval()
    return acoustic


def hubert_discrete(
    pretrained: bool = True,
    progress: bool = True,
) -> AcousticModel:
    r"""HuBERT-Discrete acoustic model from `"A Comparison of Discrete and Soft Speech Units for Improved Voice Conversion"`.
    Args:
        pretrained (bool): load pretrained weights into the model
        progress (bool): show progress bar when downloading model
    """
    return _acoustic(
        "hubert-discrete",
        discrete=True,
        upsample=True,
        pretrained=pretrained,
        progress=progress,
    )


def hubert_soft(
    pretrained: bool = True,
    progress: bool = True,
) -> AcousticModel:
    r"""HuBERT-Soft acoustic model from `"A Comparison of Discrete and Soft Speech Units for Improved Voice Conversion"`.
    Args:
        pretrained (bool): load pretrained weights into the model
        progress (bool): show progress bar when downloading model
    """
    return _acoustic(
        "hubert-soft",
        discrete=False,
        upsample=True,
        pretrained=pretrained,
        progress=progress,
    )

class Postnet(nn.Module):
    def __init__(self, hparams):
        super(Postnet, self).__init__()
        self.convolutions = nn.ModuleList()

        self.convolutions.append(
            nn.Sequential(
                ConvNorm(in_channels=hparams['n_mel_channels'],  # Adjusted input channels
                         out_channels=hparams['postnet_embedding_dim'],  # Output channels remain the same
                         kernel_size=hparams['postnet_kernel_size'], stride=1,
                         padding=int((hparams['postnet_kernel_size'] - 1) / 2),  # Dynamic padding
                         dilation=1, bias=True, w_init_gain='tanh'),
                nn.BatchNorm1d(hparams['postnet_embedding_dim'])
            )
        )

        for i in range(1, hparams['postnet_n_convolutions'] - 1):
            self.convolutions.append(
                nn.Sequential(
                    ConvNorm(hparams['postnet_embedding_dim'],
                             hparams['postnet_embedding_dim'],
                             kernel_size=hparams['postnet_kernel_size'], stride=1,
                             padding=int((hparams['postnet_kernel_size'] - 1) / 2),  # Dynamic padding
                             dilation=1, w_init_gain='tanh'),
                    nn.BatchNorm1d(hparams['postnet_embedding_dim'])
                )
            )

        self.convolutions.append(
            nn.Sequential(
                ConvNorm(hparams['postnet_embedding_dim'], hparams['n_mel_channels'],
                         kernel_size=hparams['postnet_kernel_size'], stride=1,
                         padding=int((hparams['postnet_kernel_size'] - 1) / 2),  # Dynamic padding
                         dilation=1, w_init_gain='linear'),
                nn.BatchNorm1d(hparams['n_mel_channels'])
            )
        )

    def forward(self, x):
        #print(f"Input shape to Postnet: {x.shape}")
        x = x.transpose(1, 2)
        for i, conv in enumerate(self.convolutions[:-1]):
            x = conv(x)
            #print(f"Shape after Convolution {i+1}: {x.shape}")
            x = torch.tanh(x)
            x = F.dropout(x, 0.5, self.training)

        # Last layer
        x = self.convolutions[-1](x)
        #print(f"Shape after last Convolution: {x.shape}")
        x = F.dropout(x, 0.5, self.training)
        x = x.transpose(1, 2)

        return x

	import math
	import torch
	import torch.nn as nn
	from torch.nn.modules.utils import consume_prefix_in_state_dict_if_present

	class CustomLSTM(nn.Module):
	def __init__(self, input_sz, hidden_sz):
	super().__init__()
	self.input_sz = input_sz
	self.hidden_size = hidden_sz
	self.W = nn.Parameter(torch.Tensor(input_sz, hidden_sz * 4))
	self.U = nn.Parameter(torch.Tensor(hidden_sz, hidden_sz * 4))
	self.bias = nn.Parameter(torch.Tensor(hidden_sz * 4))
	self.init_weights()

	def init_weights(self):
	stdv = 1.0 / math.sqrt(self.hidden_size)
	for weight in self.parameters():
	weight.data.uniform_(-stdv, stdv)

	def forward(self, x,
	init_states=None):
	"""Assumes x is of shape (batch, sequence, feature)"""
	#print(type(x))
	#print(x.shape)
	bs, seq_sz, _ = x.size()
	hidden_seq = []
	if init_states is None:
	h_t, c_t = (torch.zeros(bs, self.hidden_size).to(x.device),
	torch.zeros(bs, self.hidden_size).to(x.device))
	else:
	h_t, c_t = init_states

	HS = self.hidden_size
	for t in range(seq_sz):
	x_t = x[:, t, :]
	# batch the computations into a single matrix multiplication
	gates = x_t @ self.W + h_t @ self.U + self.bias
	i_t, f_t, g_t, o_t = (
	torch.sigmoid(gates[:, :HS]), # input
	torch.sigmoid(gates[:, HS:HS*2]), # forget
	torch.tanh(gates[:, HS2:HS3]),
	torch.sigmoid(gates[:, HS*3:]), # output
	)
	c_t = f_t * c_t + i_t * g_t
	h_t = o_t * torch.tanh(c_t)
	hidden_seq.append(h_t.unsqueeze(0))
	hidden_seq = torch.cat(hidden_seq, dim=0)
	# reshape from shape (sequence, batch, feature) to (batch, sequence, feature)
	hidden_seq = hidden_seq.transpose(0, 1).contiguous()
	return hidden_seq, (h_t, c_t)

	hparams = {
	'n_mel_channels': 128, # From LogMelSpectrogram
	'postnet_embedding_dim': 512, # Common choice, adjust as needed
	'postnet_kernel_size': 5, # Common choice, adjust as needed
	'postnet_n_convolutions': 5, # Typical number of Postnet convolutions
	}

	class ConvNorm(torch.nn.Module):
	def __init__(self, in_channels, out_channels, kernel_size=1, stride=1,
	padding=None, dilation=1, bias=True, w_init_gain='linear'):
	super(ConvNorm, self).__init__()
	if padding is None:
	assert(kernel_size % 2 == 1)
	padding = int(dilation * (kernel_size - 1) / 2)

	self.conv = torch.nn.Conv1d(in_channels, out_channels,
	kernel_size=kernel_size, stride=stride,
	padding=padding, dilation=dilation,
	bias=bias)

	torch.nn.init.xavier_uniform_(
	self.conv.weight, gain=torch.nn.init.calculate_gain(w_init_gain))

	def forward(self, signal):
	conv_signal = self.conv(signal)
	return conv_signal

	URLS = {
	"hubert-discrete": "https://github.com/bshall/acoustic-model/releases/download/v0.1/hubert-discrete-d49e1c77.pt",
	"hubert-soft": "https://github.com/bshall/acoustic-model/releases/download/v0.1/hubert-soft-0321fd7e.pt",
	}


	class AcousticModel(nn.Module):
	def __init__(self, discrete: bool = False, upsample: bool = True, use_custom_lstm=False):
	super().__init__()
	# self.spk_projection = nn.Linear(512+512, 512)
	self.encoder = Encoder(discrete, upsample)
	self.decoder = Decoder(use_custom_lstm=use_custom_lstm)
	self.postnet = Postnet(hparams) # Add this line. Ensure hparams is defined or pass explicit parameters

	def forward(self, x: torch.Tensor, spk_embs, mels: torch.Tensor) -> torch.Tensor:
	x = self.encoder(x)
	exp_spk_embs = spk_embs.unsqueeze(1).expand(-1, x.size(1), -1)
	concat_x = torch.cat([x, exp_spk_embs], dim=-1)
	# x = self.spk_projection(concat_x)
	output = self.decoder(concat_x, mels)
	postnet_output = self.postnet(output) + output
	return postnet_output

	#def forward(self, x: torch.Tensor, mels: torch.Tensor) -> torch.Tensor:
	# x = self.encoder(x)
	# return self.decoder(x, mels)

	def forward_test(self, x, spk_embs, mels):
	print('x shape', x.shape)
	print('se shape', spk_embs.shape)
	print('mels shape', mels.shape)
	x = self.encoder(x)
	print('x_enc shape', x.shape)
	return

	@torch.inference_mode()
	def generate(self, x: torch.Tensor, spk_embs) -> torch.Tensor:
	x = self.encoder(x)
	exp_spk_embs = spk_embs.unsqueeze(1).expand(-1, x.size(1), -1)
	concat_x = torch.cat([x, exp_spk_embs], dim=-1)
	# x = self.spk_projection(concat_x)
	mels = self.decoder.generate(concat_x)
	postnet_mels = self.postnet(mels) + mels
	return postnet_mels


	class Encoder(nn.Module):
	def __init__(self, discrete: bool = False, upsample: bool = True):
	super().__init__()
	self.embedding = nn.Embedding(100 + 1, 256) if discrete else None
	self.prenet = PreNet(256, 256, 256)
	self.convs = nn.Sequential(
	nn.Conv1d(256, 512, 5, 1, 2),
	nn.ReLU(),
	nn.Dropout(0.3),
	nn.InstanceNorm1d(512),
	nn.ConvTranspose1d(512, 512, 4, 2, 1) if upsample else nn.Identity(),
	nn.Dropout(0.3),
	nn.Conv1d(512, 512, 5, 1, 2),
	nn.ReLU(),
	nn.Dropout(0.3),
	nn.InstanceNorm1d(512),
	nn.Conv1d(512, 512, 5, 1, 2),
	nn.ReLU(),
	nn.Dropout(0.3),
	nn.InstanceNorm1d(512),
	)

	def forward(self, x: torch.Tensor) -> torch.Tensor:
	if self.embedding is not None:
	x = self.embedding(x)
	x = self.prenet(x)
	x = self.convs(x.transpose(1, 2))
	return x.transpose(1, 2)


	class Decoder(nn.Module):
	def __init__(self, use_custom_lstm=False):
	super().__init__()
	self.use_custom_lstm = use_custom_lstm
	self.prenet = PreNet(128, 256, 256)
	if use_custom_lstm:
	self.lstm1 = CustomLSTM(1024 + 256, 1024)
	self.lstm2 = CustomLSTM(1024, 1024)
	self.lstm3 = CustomLSTM(1024, 1024)
	else:
	self.lstm1 = nn.LSTM(1024 + 256, 1024)
	self.lstm2 = nn.LSTM(1024, 1024)
	self.lstm3 = nn.LSTM(1024, 1024)
	self.proj = nn.Linear(1024, 128, bias=False)
	self.dropout = nn.Dropout(0.3)

	def forward(self, x: torch.Tensor, mels: torch.Tensor) -> torch.Tensor:
	mels = self.prenet(mels)
	x, _ = self.lstm1(torch.cat((x, mels), dim=-1))
	x = self.dropout(x)
	res = x
	x, _ = self.lstm2(x)
	x = self.dropout(x)
	x = res + x
	res = x
	x, _ = self.lstm3(x)
	x = self.dropout(x)
	x = res + x
	return self.proj(x)

	@torch.inference_mode()
	def generate(self, xs: torch.Tensor) -> torch.Tensor:
	m = torch.zeros(xs.size(0), 128, device=xs.device)
	if self.use_custom_lstm:
	h1 = torch.zeros(xs.size(0), 1024, device=xs.device)
	c1 = torch.zeros(xs.size(0), 1024, device=xs.device)
	h2 = torch.zeros(xs.size(0), 1024, device=xs.device)
	c2 = torch.zeros(xs.size(0), 1024, device=xs.device)
	h3 = torch.zeros(xs.size(0), 1024, device=xs.device)
	c3 = torch.zeros(xs.size(0), 1024, device=xs.device)
	else:
	h1 = torch.zeros(1, xs.size(0), 1024, device=xs.device)
	c1 = torch.zeros(1, xs.size(0), 1024, device=xs.device)
	h2 = torch.zeros(1, xs.size(0), 1024, device=xs.device)
	c2 = torch.zeros(1, xs.size(0), 1024, device=xs.device)
	h3 = torch.zeros(1, xs.size(0), 1024, device=xs.device)
	c3 = torch.zeros(1, xs.size(0), 1024, device=xs.device)


	mel = []
	for x in torch.unbind(xs, dim=1):
	m = self.prenet(m)
	x = torch.cat((x, m), dim=1).unsqueeze(1)
	x1, (h1, c1) = self.lstm1(x, (h1, c1))
	x2, (h2, c2) = self.lstm2(x1, (h2, c2))
	x = x1 + x2
	x3, (h3, c3) = self.lstm3(x, (h3, c3))
	x = x + x3
	m = self.proj(x).squeeze(1)
	mel.append(m)
	return torch.stack(mel, dim=1)


	class PreNet(nn.Module):
	def __init__(
	self,
	input_size: int,
	hidden_size: int,
	output_size: int,
	dropout: float = 0.5,
	):
	super().__init__()
	self.net = nn.Sequential(
	nn.Linear(input_size, hidden_size),
	nn.ReLU(),
	nn.Dropout(dropout),
	nn.Linear(hidden_size, output_size),
	nn.ReLU(),
	nn.Dropout(dropout),
	)

	def forward(self, x: torch.Tensor) -> torch.Tensor:
	return self.net(x)


	def _acoustic(
	name: str,
	discrete: bool,
	upsample: bool,
	pretrained: bool = True,
	progress: bool = True,
	) -> AcousticModel:
	acoustic = AcousticModel(discrete, upsample)
	if pretrained:
	checkpoint = torch.hub.load_state_dict_from_url(URLS[name], progress=progress)
	consume_prefix_in_state_dict_if_present(checkpoint["acoustic-model"], "module.")
	acoustic.load_state_dict(checkpoint["acoustic-model"])
	acoustic.eval()
	return acoustic


	def hubert_discrete(
	pretrained: bool = True,
	progress: bool = True,
	) -> AcousticModel:
	r"""HuBERT-Discrete acoustic model from `"A Comparison of Discrete and Soft Speech Units for Improved Voice Conversion"`.
	Args:
	pretrained (bool): load pretrained weights into the model
	progress (bool): show progress bar when downloading model
	"""
	return _acoustic(
	"hubert-discrete",
	discrete=True,
	upsample=True,
	pretrained=pretrained,
	progress=progress,
	)


	def hubert_soft(
	pretrained: bool = True,
	progress: bool = True,
	) -> AcousticModel:
	r"""HuBERT-Soft acoustic model from `"A Comparison of Discrete and Soft Speech Units for Improved Voice Conversion"`.
	Args:
	pretrained (bool): load pretrained weights into the model
	progress (bool): show progress bar when downloading model
	"""
	return _acoustic(
	"hubert-soft",
	discrete=False,
	upsample=True,
	pretrained=pretrained,
	progress=progress,
	)

	class Postnet(nn.Module):
	def __init__(self, hparams):
	super(Postnet, self).__init__()
	self.convolutions = nn.ModuleList()

	self.convolutions.append(
	nn.Sequential(
	ConvNorm(in_channels=hparams['n_mel_channels'], # Adjusted input channels
	out_channels=hparams['postnet_embedding_dim'], # Output channels remain the same
	kernel_size=hparams['postnet_kernel_size'], stride=1,
	padding=int((hparams['postnet_kernel_size'] - 1) / 2), # Dynamic padding
	dilation=1, bias=True, w_init_gain='tanh'),
	nn.BatchNorm1d(hparams['postnet_embedding_dim'])
	)
	)

	for i in range(1, hparams['postnet_n_convolutions'] - 1):
	self.convolutions.append(
	nn.Sequential(
	ConvNorm(hparams['postnet_embedding_dim'],
	hparams['postnet_embedding_dim'],
	kernel_size=hparams['postnet_kernel_size'], stride=1,
	padding=int((hparams['postnet_kernel_size'] - 1) / 2), # Dynamic padding
	dilation=1, w_init_gain='tanh'),
	nn.BatchNorm1d(hparams['postnet_embedding_dim'])
	)
	)

	self.convolutions.append(
	nn.Sequential(
	ConvNorm(hparams['postnet_embedding_dim'], hparams['n_mel_channels'],
	kernel_size=hparams['postnet_kernel_size'], stride=1,
	padding=int((hparams['postnet_kernel_size'] - 1) / 2), # Dynamic padding
	dilation=1, w_init_gain='linear'),
	nn.BatchNorm1d(hparams['n_mel_channels'])
	)
	)

	def forward(self, x):
	#print(f"Input shape to Postnet: {x.shape}")
	x = x.transpose(1, 2)
	for i, conv in enumerate(self.convolutions[:-1]):
	x = conv(x)
	#print(f"Shape after Convolution {i+1}: {x.shape}")
	x = torch.tanh(x)
	x = F.dropout(x, 0.5, self.training)

	# Last layer
	x = self.convolutions[-1](x)
	#print(f"Shape after last Convolution: {x.shape}")
	x = F.dropout(x, 0.5, self.training)
	x = x.transpose(1, 2)

	return x