diff --git "a/VitsModelSplit/vits_model2.py" "b/VitsModelSplit/vits_model2.py"
new file mode 100644--- /dev/null
+++ "b/VitsModelSplit/vits_model2.py"
@@ -0,0 +1,2453 @@
+
+import numpy as np
+import torch
+from torch import nn
+import math
+from typing import Any, Callable, Optional, Tuple, Union
+from torch.cuda.amp import autocast, GradScaler
+
+from .vits_config import VitsConfig,VitsPreTrainedModel
+from .flow import VitsResidualCouplingBlock
+from .duration_predictor import VitsDurationPredictor, VitsStochasticDurationPredictor
+from .encoder import VitsTextEncoder
+from .decoder import VitsHifiGan
+from .posterior_encoder import VitsPosteriorEncoder
+from .discriminator import VitsDiscriminator
+from .vits_output import VitsModelOutput, VitsTrainingOutput
+from  .dataset_features_collector import FeaturesCollectionDataset
+from .feature_extraction import VitsFeatureExtractor
+
+import os
+import sys
+from typing import Optional
+import tempfile
+from torch.cuda.amp import autocast, GradScaler
+
+from IPython.display import clear_output
+from transformers import set_seed
+import wandb
+import logging
+import copy
+Lst=['input_ids',
+ 'attention_mask',
+ 'waveform',
+ 'labels',
+ 'labels_attention_mask',
+ 'mel_scaled_input_features']
+
+def covert_cuda_batch(d):
+  #return d
+  for key in Lst:
+      d[key]=d[key].cuda(non_blocking=True)
+  # for key in d['text_encoder_output']:
+  #   d['text_encoder_output'][key]=d['text_encoder_output'][key].cuda(non_blocking=True)
+  for key in d['posterior_encode_output']:
+    d['posterior_encode_output'][key]=d['posterior_encode_output'][key].cuda(non_blocking=True)
+
+  return d
+def generator_loss(disc_outputs):
+    total_loss = 0
+    gen_losses = []
+    for disc_output in disc_outputs:
+        disc_output = disc_output
+        loss = torch.mean((1 - disc_output) ** 2)
+        gen_losses.append(loss)
+        total_loss += loss
+
+    return total_loss, gen_losses
+
+def discriminator_loss(disc_real_outputs, disc_generated_outputs):
+    loss = 0
+    real_losses = 0
+    generated_losses = 0
+    for disc_real, disc_generated in zip(disc_real_outputs, disc_generated_outputs):
+        real_loss = torch.mean((1 - disc_real) ** 2)
+        generated_loss = torch.mean(disc_generated**2)
+        loss += real_loss + generated_loss
+        real_losses += real_loss
+        generated_losses += generated_loss
+
+    return loss, real_losses, generated_losses
+
+def feature_loss(feature_maps_real, feature_maps_generated):
+    loss = 0
+    for feature_map_real, feature_map_generated in zip(feature_maps_real, feature_maps_generated):
+        for real, generated in zip(feature_map_real, feature_map_generated):
+            real = real.detach()
+            loss += torch.mean(torch.abs(real - generated))
+
+    return loss * 2
+def kl_loss(z_p, logs_q, m_p, logs_p, z_mask):
+  """
+  z_p, logs_q: [b, h, t_t]
+  m_p, logs_p: [b, h, t_t]
+  """
+  z_p = z_p.float()
+  logs_q = logs_q.float()
+  m_p = m_p.float()
+  logs_p = logs_p.float()
+  z_mask = z_mask.float()
+
+  kl = logs_p - logs_q - 0.5
+  kl += 0.5 * ((z_p - m_p)**2) * torch.exp(-2. * logs_p)
+  kl = torch.sum(kl * z_mask)
+  l = kl / torch.sum(z_mask)
+  return l
+#.............................................
+# def kl_loss(prior_latents, posterior_log_variance, prior_means, prior_log_variance, labels_mask):
+
+
+#   kl = prior_log_variance - posterior_log_variance - 0.5
+#   kl += 0.5 * ((prior_latents - prior_means) ** 2) * torch.exp(-2.0 * prior_log_variance)
+#   kl = torch.sum(kl * labels_mask)
+#   loss = kl / torch.sum(labels_mask)
+#   return loss
+
+def get_state_grad_loss(k1=True,
+                             mel=True,
+                             duration=True,
+                             generator=True,
+                             discriminator=True):
+                             return {'k1':k1,'mel':mel,'duration':duration,'generator':generator,'discriminator':discriminator}
+
+
+def clip_grad_value_(parameters, clip_value, norm_type=2):
+  if isinstance(parameters, torch.Tensor):
+    parameters = [parameters]
+  parameters = list(filter(lambda p: p.grad is not None, parameters))
+  norm_type = float(norm_type)
+  if clip_value is not None:
+    clip_value = float(clip_value)
+
+  total_norm = 0
+  for p in parameters:
+    param_norm = p.grad.data.norm(norm_type)
+    total_norm += param_norm.item() ** norm_type
+    if clip_value is not None:
+      p.grad.data.clamp_(min=-clip_value, max=clip_value)
+  total_norm = total_norm ** (1. / norm_type)
+  return total_norm
+
+
+class VitsModel(VitsPreTrainedModel):
+
+    def __init__(self, config: VitsConfig):
+        super().__init__(config)
+
+        self.config = config
+        self.text_encoder = VitsTextEncoder(config)
+        self.flow = VitsResidualCouplingBlock(config)
+        self.decoder = VitsHifiGan(config)
+
+
+
+        if config.use_stochastic_duration_prediction:
+            self.duration_predictor = VitsStochasticDurationPredictor(config)
+        else:
+            self.duration_predictor = VitsDurationPredictor(config)
+
+        if config.num_speakers > 1:
+            self.embed_speaker = nn.Embedding(config.num_speakers, config.speaker_embedding_size)
+
+        # This is used only for training.
+        self.posterior_encoder = VitsPosteriorEncoder(config)
+        self.discriminator = VitsDiscriminator(config)
+
+        # These parameters control the synthesised speech properties
+        self.speaking_rate = config.speaking_rate
+        self.noise_scale = config.noise_scale
+        self.noise_scale_duration = config.noise_scale_duration
+        self.segment_size = self.config.segment_size // self.config.hop_length
+
+        # Initialize weights and apply final processing
+        self.post_init()
+        self.monotonic_alignment_function=self.monotonic_align_max_path
+
+
+
+    #....................................
+    def setMfA(self,fn):
+      self.monotonic_alignment_function=fn
+
+
+
+    def monotonic_align_max_path(self,log_likelihoods, mask):
+        # used for training - awfully slow
+        # an alternative is proposed in examples/pytorch/text-to-speech/run_vits_finetuning.py
+        path = torch.zeros_like(log_likelihoods)
+
+        text_length_maxs = mask.sum(1)[:, 0]
+        latent_length_maxs = mask.sum(2)[:, 0]
+
+        indexes = latent_length_maxs - 1
+
+        max_neg_val = -1e9
+
+        for batch_id in range(len(path)):
+            index = int(indexes[batch_id].item())
+            text_length_max = int(text_length_maxs[batch_id].item())
+            latent_length_max = int(latent_length_maxs[batch_id].item())
+
+            for y in range(text_length_max):
+                for x in range(max(0, latent_length_max + y - text_length_max), min(latent_length_max, y + 1)):
+                    if x == y:
+                        v_cur = max_neg_val
+                    else:
+                        v_cur = log_likelihoods[batch_id, y - 1, x]
+                    if x == 0:
+                        if y == 0:
+                            v_prev = 0.0
+                        else:
+                            v_prev = max_neg_val
+                    else:
+                        v_prev = log_likelihoods[batch_id, y - 1, x - 1]
+                    log_likelihoods[batch_id, y, x] += max(v_prev, v_cur)
+
+            for y in range(text_length_max - 1, -1, -1):
+                path[batch_id, y, index] = 1
+                if index != 0 and (
+                    index == y or log_likelihoods[batch_id, y - 1, index] < log_likelihoods[batch_id, y - 1, index - 1]
+                ):
+                    index = index - 1
+        return path
+
+    #....................................
+
+    def slice_segments(self,hidden_states, ids_str, segment_size=4):
+
+        batch_size, channels, _ = hidden_states.shape
+        # 1d tensor containing the indices to keep
+        indices = torch.arange(segment_size).to(ids_str.device)
+        # extend the indices to match the shape of hidden_states
+        indices = indices.view(1, 1, -1).expand(batch_size, channels, -1)
+        # offset indices with ids_str
+        indices = indices + ids_str.view(-1, 1, 1)
+        # gather indices
+        output = torch.gather(hidden_states, dim=2, index=indices)
+
+        return output
+
+
+    #....................................
+
+
+    def rand_slice_segments(self,hidden_states, sample_lengths=None, segment_size=4):
+
+        batch_size, _, seq_len = hidden_states.size()
+        if sample_lengths is None:
+            sample_lengths = seq_len
+        ids_str_max = sample_lengths - segment_size + 1
+        ids_str = (torch.rand([batch_size]).to(device=hidden_states.device) * ids_str_max).to(dtype=torch.long)
+        ret = self.slice_segments(hidden_states, ids_str, segment_size)
+
+        return ret, ids_str
+
+    #....................................
+
+    def resize_speaker_embeddings(
+        self,
+        new_num_speakers: int,
+        speaker_embedding_size: Optional[int] = None,
+        pad_to_multiple_of: Optional[int] = 2,
+    ):
+        if pad_to_multiple_of is not None:
+            new_num_speakers = ((new_num_speakers + pad_to_multiple_of - 1) // pad_to_multiple_of) * pad_to_multiple_of
+
+        # first, take care of embed_speaker
+        if self.config.num_speakers <= 1:
+            if speaker_embedding_size is None:
+                raise ValueError(
+                    "The current model had no previous speaker embedding, but `speaker_embedding_size` is not specified. Pass `speaker_embedding_size` to this method."
+                )
+            # create new embedding layer
+            new_embeddings = nn.Embedding(
+                new_num_speakers,
+                speaker_embedding_size,
+                device=self.device,
+            )
+            # initialize all new embeddings
+            self._init_weights(new_embeddings)
+        else:
+            new_embeddings = self._get_resized_embeddings(self.embed_speaker, new_num_speakers)
+
+        self.embed_speaker = new_embeddings
+
+        # then take care of sub-models
+        self.flow.resize_speaker_embeddings(speaker_embedding_size)
+        for flow in self.flow.flows:
+            self._init_weights(flow.wavenet.cond_layer)
+
+        self.decoder.resize_speaker_embedding(speaker_embedding_size)
+        self._init_weights(self.decoder.cond)
+
+        self.duration_predictor.resize_speaker_embeddings(speaker_embedding_size)
+        self._init_weights(self.duration_predictor.cond)
+
+        self.posterior_encoder.resize_speaker_embeddings(speaker_embedding_size)
+        self._init_weights(self.posterior_encoder.wavenet.cond_layer)
+
+        self.config.num_speakers = new_num_speakers
+        self.config.speaker_embedding_size = speaker_embedding_size
+
+    #....................................
+
+    def get_input_embeddings(self):
+        return self.text_encoder.get_input_embeddings()
+
+    #....................................
+
+    def set_input_embeddings(self, value):
+        self.text_encoder.set_input_embeddings(value)
+
+    #....................................
+
+    def apply_weight_norm(self):
+        self.decoder.apply_weight_norm()
+        self.flow.apply_weight_norm()
+        self.posterior_encoder.apply_weight_norm()
+
+    #....................................
+
+    def remove_weight_norm(self):
+        self.decoder.remove_weight_norm()
+        self.flow.remove_weight_norm()
+        self.posterior_encoder.remove_weight_norm()
+
+    #....................................
+
+    def discriminate(self, hidden_states):
+        return self.discriminator(hidden_states)
+
+    #....................................
+
+    def get_encoder(self):
+        return self.text_encoder
+
+    #....................................
+
+    def _inference_forward(
+        self,
+        input_ids: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        speaker_embeddings: Optional[torch.Tensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        padding_mask: Optional[torch.Tensor] = None,
+    ):
+        text_encoder_output = self.text_encoder(
+            input_ids=input_ids,
+            padding_mask=padding_mask,
+            attention_mask=attention_mask,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        hidden_states = text_encoder_output[0] if not return_dict else text_encoder_output.last_hidden_state
+        hidden_states = hidden_states.transpose(1, 2)
+        input_padding_mask = padding_mask.transpose(1, 2)
+
+        prior_means = text_encoder_output[1] if not return_dict else text_encoder_output.prior_means
+        prior_log_variances = text_encoder_output[2] if not return_dict else text_encoder_output.prior_log_variances
+
+        if self.config.use_stochastic_duration_prediction:
+            log_duration = self.duration_predictor(
+                hidden_states,
+                input_padding_mask,
+                speaker_embeddings,
+                reverse=True,
+                noise_scale=self.noise_scale_duration,
+            )
+        else:
+            log_duration = self.duration_predictor(hidden_states, input_padding_mask, speaker_embeddings)
+
+        length_scale = 1.0 / self.speaking_rate
+        duration = torch.ceil(torch.exp(log_duration) * input_padding_mask * length_scale)
+        predicted_lengths = torch.clamp_min(torch.sum(duration, [1, 2]), 1).long()
+
+
+        # Create a padding mask for the output lengths of shape (batch, 1, max_output_length)
+        indices = torch.arange(predicted_lengths.max(), dtype=predicted_lengths.dtype, device=predicted_lengths.device)
+        output_padding_mask = indices.unsqueeze(0) < predicted_lengths.unsqueeze(1)
+        output_padding_mask = output_padding_mask.unsqueeze(1).to(input_padding_mask.dtype)
+
+        # Reconstruct an attention tensor of shape (batch, 1, out_length, in_length)
+        attn_mask = torch.unsqueeze(input_padding_mask, 2) * torch.unsqueeze(output_padding_mask, -1)
+        batch_size, _, output_length, input_length = attn_mask.shape
+        cum_duration = torch.cumsum(duration, -1).view(batch_size * input_length, 1)
+        indices = torch.arange(output_length, dtype=duration.dtype, device=duration.device)
+        valid_indices = indices.unsqueeze(0) < cum_duration
+        valid_indices = valid_indices.to(attn_mask.dtype).view(batch_size, input_length, output_length)
+        padded_indices = valid_indices - nn.functional.pad(valid_indices, [0, 0, 1, 0, 0, 0])[:, :-1]
+        attn = padded_indices.unsqueeze(1).transpose(2, 3) * attn_mask
+
+        # Expand prior distribution
+        prior_means = torch.matmul(attn.squeeze(1), prior_means).transpose(1, 2)
+        prior_log_variances = torch.matmul(attn.squeeze(1), prior_log_variances).transpose(1, 2)
+
+        prior_latents = prior_means + torch.randn_like(prior_means) * torch.exp(prior_log_variances) * self.noise_scale
+        latents = self.flow(prior_latents, output_padding_mask, speaker_embeddings, reverse=True)
+
+        spectrogram = latents * output_padding_mask
+        waveform = self.decoder(spectrogram, speaker_embeddings)
+        waveform = waveform.squeeze(1)
+        sequence_lengths = predicted_lengths * np.prod(self.config.upsample_rates)
+
+        if not return_dict:
+            outputs = (waveform, sequence_lengths, spectrogram) + text_encoder_output[3:]
+            return outputs
+
+        return VitsModelOutput(
+            waveform=waveform,
+            sequence_lengths=sequence_lengths,
+            spectrogram=spectrogram,
+            hidden_states=text_encoder_output.hidden_states,
+            attentions=text_encoder_output.attentions,
+        )
+
+    #....................................
+
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        speaker_id: Optional[int] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        labels: Optional[torch.FloatTensor] = None,
+        labels_attention_mask: Optional[torch.Tensor] = None,
+        monotonic_alignment_function: Optional[Callable] = None,
+    ) -> Union[Tuple[Any], VitsModelOutput]:
+
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        monotonic_alignment_function = (
+            self.monotonic_align_max_path if monotonic_alignment_function is None else monotonic_alignment_function
+        )
+
+        if attention_mask is not None:
+            input_padding_mask = attention_mask.unsqueeze(-1).float()
+        else:
+            input_padding_mask = torch.ones_like(input_ids).unsqueeze(-1).float()
+
+        if self.config.num_speakers > 1 and speaker_id is not None:
+            if isinstance(speaker_id, int):
+                speaker_id = torch.full(size=(1,), fill_value=speaker_id, device=self.device)
+            elif isinstance(speaker_id, (list, tuple, np.ndarray)):
+                speaker_id = torch.tensor(speaker_id, device=self.device)
+
+            if not ((0 <= speaker_id).all() and (speaker_id < self.config.num_speakers).all()).item():
+                raise ValueError(f"Set `speaker_id` in the range 0-{self.config.num_speakers - 1}.")
+            if not (len(speaker_id) == 1 or len(speaker_id == len(input_ids))):
+                raise ValueError(
+                    f"You passed {len(speaker_id)} `speaker_id` but you should either pass one speaker id or `batch_size` `speaker_id`."
+                )
+
+            speaker_embeddings = self.embed_speaker(speaker_id).unsqueeze(-1)
+        else:
+            speaker_embeddings = None
+
+        # if inference, return inference forward of VitsModel
+        if labels is None:
+            return self._inference_forward(
+                input_ids,
+                attention_mask,
+                speaker_embeddings,
+                output_attentions,
+                output_hidden_states,
+                return_dict,
+                input_padding_mask,
+            )
+
+        if labels_attention_mask is not None:
+            labels_padding_mask = labels_attention_mask.unsqueeze(1).float()
+        else:
+            labels_attention_mask = torch.ones((labels.shape[0], labels.shape[2])).float().to(self.device)
+            labels_padding_mask = labels_attention_mask.unsqueeze(1)
+
+        text_encoder_output = self.text_encoder(
+            input_ids=input_ids,
+            padding_mask=input_padding_mask,
+            attention_mask=attention_mask,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        hidden_states = text_encoder_output[0] if not return_dict else text_encoder_output.last_hidden_state
+        hidden_states = hidden_states.transpose(1, 2)
+        input_padding_mask = input_padding_mask.transpose(1, 2)
+        prior_means = text_encoder_output[1] if not return_dict else text_encoder_output.prior_means
+        prior_log_variances = text_encoder_output[2] if not return_dict else text_encoder_output.prior_log_variances
+
+        latents, posterior_means, posterior_log_variances = self.posterior_encoder(
+            labels, labels_padding_mask, speaker_embeddings
+        )
+        prior_latents = self.flow(latents, labels_padding_mask, speaker_embeddings, reverse=False)
+
+        prior_means, prior_log_variances = prior_means.transpose(1, 2), prior_log_variances.transpose(1, 2)
+        with torch.no_grad():
+            # negative cross-entropy
+
+            # [batch_size, d, latent_length]
+            prior_variances = torch.exp(-2 * prior_log_variances)
+            # [batch_size, 1, latent_length]
+            neg_cent1 = torch.sum(-0.5 * math.log(2 * math.pi) - prior_log_variances, [1], keepdim=True)
+            # [batch_size, text_length, d] x [batch_size, d, latent_length] = [batch_size, text_length, latent_length]
+            neg_cent2 = torch.matmul(-0.5 * (prior_latents**2).transpose(1, 2), prior_variances)
+            # [batch_size, text_length, d] x [batch_size, d, latent_length] = [batch_size, text_length, latent_length]
+            neg_cent3 = torch.matmul(prior_latents.transpose(1, 2), (prior_means * prior_variances))
+            # [batch_size, 1, latent_length]
+            neg_cent4 = torch.sum(-0.5 * (prior_means**2) * prior_variances, [1], keepdim=True)
+
+            # [batch_size, text_length, latent_length]
+            neg_cent = neg_cent1 + neg_cent2 + neg_cent3 + neg_cent4
+
+            attn_mask = torch.unsqueeze(input_padding_mask, 2) * torch.unsqueeze(labels_padding_mask, -1)
+
+            attn = monotonic_alignment_function(neg_cent, attn_mask.squeeze(1)).unsqueeze(1).detach()
+
+        durations = attn.sum(2)
+
+        if self.config.use_stochastic_duration_prediction:
+            log_duration = self.duration_predictor(
+                hidden_states, input_padding_mask, speaker_embeddings, durations=durations, reverse=False
+            )
+            log_duration = log_duration / torch.sum(input_padding_mask)
+        else:
+            log_duration_padded = torch.log(durations + 1e-6) * input_padding_mask
+            log_duration = self.duration_predictor(hidden_states, input_padding_mask, speaker_embeddings)
+            log_duration = torch.sum((log_duration - log_duration_padded) ** 2, [1, 2]) / torch.sum(input_padding_mask)
+
+        # expand priors
+        prior_means = torch.matmul(attn.squeeze(1), prior_means.transpose(1, 2)).transpose(1, 2)
+        prior_log_variances = torch.matmul(attn.squeeze(1), prior_log_variances.transpose(1, 2)).transpose(1, 2)
+
+        label_lengths = labels_attention_mask.sum(dim=1)
+        latents_slice, ids_slice = self.rand_slice_segments(latents, label_lengths, segment_size=self.segment_size)
+
+        waveform = self.decoder(latents_slice, speaker_embeddings)
+
+        if not return_dict:
+            outputs = (
+                waveform,
+                log_duration,
+                attn,
+                ids_slice,
+                input_padding_mask,
+                labels_padding_mask,
+                latents,
+                prior_latents,
+                prior_means,
+                prior_log_variances,
+                posterior_means,
+                posterior_log_variances,
+            )
+            return outputs
+
+        return VitsTrainingOutput(
+            waveform=waveform,
+            log_duration=log_duration,
+            attn=attn,
+            ids_slice=ids_slice,
+            input_padding_mask=input_padding_mask,
+            labels_padding_mask=labels_padding_mask,
+            latents=latents,
+            prior_latents=prior_latents,
+            prior_means=prior_means,
+            prior_log_variances=prior_log_variances,
+            posterior_means=posterior_means,
+            posterior_log_variances=posterior_log_variances,
+        )
+    def slice_segments(self,hidden_states, ids_str, segment_size=4):
+
+        batch_size, channels, _ = hidden_states.shape
+        # 1d tensor containing the indices to keep
+        indices = torch.arange(segment_size).to(ids_str.device)
+        # extend the indices to match the shape of hidden_states
+        indices = indices.view(1, 1, -1).expand(batch_size, channels, -1)
+        # offset indices with ids_str
+        indices = indices + ids_str.view(-1, 1, 1)
+        # gather indices
+        output = torch.gather(hidden_states, dim=2, index=indices)
+
+        return output
+
+    #....................................
+
+    def rand_slice_segments(self,hidden_states, sample_lengths=None, segment_size=4):
+        batch_size, _, seq_len = hidden_states.size()
+        if sample_lengths is None:
+            sample_lengths = seq_len
+        ids_str_max = sample_lengths - segment_size + 1
+        ids_str = (torch.rand([batch_size]).to(device=hidden_states.device) * ids_str_max).to(dtype=torch.long)
+        ret = self.slice_segments(hidden_states, ids_str, segment_size)
+
+        return ret, ids_str
+
+    def forward_k(
+        self,
+        input_ids: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        speaker_id: Optional[int] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        labels: Optional[torch.FloatTensor] = None,
+        labels_attention_mask: Optional[torch.Tensor] = None,
+        text_encoder_output=None,
+        posterior_encode_output=None,
+        monotonic_alignment_function: Optional[Callable] = None,
+    ) -> Union[Tuple[Any], VitsModelOutput]:
+
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        monotonic_alignment_function = (
+            self.monotonic_align_max_path if monotonic_alignment_function is None else monotonic_alignment_function
+        )
+
+        if attention_mask is not None:
+            input_padding_mask = attention_mask.unsqueeze(-1).float()
+        else:
+            input_padding_mask = torch.ones_like(input_ids).unsqueeze(-1).float()
+
+        if self.config.num_speakers > 1 and speaker_id is not None:
+            if isinstance(speaker_id, int):
+                speaker_id = torch.full(size=(1,), fill_value=speaker_id, device=self.device)
+            elif isinstance(speaker_id, (list, tuple, np.ndarray)):
+                speaker_id = torch.tensor(speaker_id, device=self.device)
+
+            if not ((0 <= speaker_id).all() and (speaker_id < self.config.num_speakers).all()).item():
+                raise ValueError(f"Set `speaker_id` in the range 0-{self.config.num_speakers - 1}.")
+            if not (len(speaker_id) == 1 or len(speaker_id == len(input_ids))):
+                raise ValueError(
+                    f"You passed {len(speaker_id)} `speaker_id` but you should either pass one speaker id or `batch_size` `speaker_id`."
+                )
+
+            speaker_embeddings = self.embed_speaker(speaker_id).unsqueeze(-1)
+        else:
+            speaker_embeddings = None
+
+        # if inference, return inference forward of VitsModel
+        if labels is None:
+            return self._inference_forward(
+                input_ids,
+                attention_mask,
+                speaker_embeddings,
+                output_attentions,
+                output_hidden_states,
+                return_dict,
+                input_padding_mask,
+            )
+
+        if labels_attention_mask is not None:
+            labels_padding_mask = labels_attention_mask.unsqueeze(1).float()
+        else:
+            labels_attention_mask = torch.ones((labels.shape[0], labels.shape[2])).float().to(self.device)
+            labels_padding_mask = labels_attention_mask.unsqueeze(1)
+        if text_encoder_output is None:
+            text_encoder_output = self.text_encoder(
+                input_ids=input_ids,
+                padding_mask=input_padding_mask,
+                attention_mask=attention_mask,
+                output_attentions=output_attentions,
+                output_hidden_states=output_hidden_states,
+                return_dict=return_dict,
+            )
+        hidden_states = text_encoder_output[0] if not return_dict else text_encoder_output.last_hidden_state
+        hidden_states = hidden_states.transpose(1, 2)
+        input_padding_mask = input_padding_mask.transpose(1, 2)
+        prior_means = text_encoder_output[1] if not return_dict else text_encoder_output.prior_means
+        prior_log_variances = text_encoder_output[2] if not return_dict else text_encoder_output.prior_log_variances
+        if posterior_encode_output is None:
+            latents, posterior_means, posterior_log_variances = self.posterior_encoder(
+                labels, labels_padding_mask, speaker_embeddings
+            )
+        else:
+          latents=posterior_encode_output['posterior_latents']
+          posterior_means=posterior_encode_output['posterior_means']
+          posterior_log_variances=posterior_encode_output['posterior_log_variances']
+
+        prior_latents = self.flow(latents, labels_padding_mask, speaker_embeddings, reverse=False)
+
+        prior_means, prior_log_variances = prior_means.transpose(1, 2), prior_log_variances.transpose(1, 2)
+        with torch.no_grad():
+            # negative cross-entropy
+
+            # [batch_size, d, latent_length]
+            prior_variances = torch.exp(-2 * prior_log_variances)
+            # [batch_size, 1, latent_length]
+            neg_cent1 = torch.sum(-0.5 * math.log(2 * math.pi) - prior_log_variances, [1], keepdim=True)
+            # [batch_size, text_length, d] x [batch_size, d, latent_length] = [batch_size, text_length, latent_length]
+            neg_cent2 = torch.matmul(-0.5 * (prior_latents**2).transpose(1, 2), prior_variances)
+            # [batch_size, text_length, d] x [batch_size, d, latent_length] = [batch_size, text_length, latent_length]
+            neg_cent3 = torch.matmul(prior_latents.transpose(1, 2), (prior_means * prior_variances))
+            # [batch_size, 1, latent_length]
+            neg_cent4 = torch.sum(-0.5 * (prior_means**2) * prior_variances, [1], keepdim=True)
+
+            # [batch_size, text_length, latent_length]
+            neg_cent = neg_cent1 + neg_cent2 + neg_cent3 + neg_cent4
+
+            attn_mask = torch.unsqueeze(input_padding_mask, 2) * torch.unsqueeze(labels_padding_mask, -1)
+
+            attn = monotonic_alignment_function(neg_cent, attn_mask.squeeze(1)).unsqueeze(1).detach()
+
+        durations = attn.sum(2)
+
+        if self.config.use_stochastic_duration_prediction:
+            log_duration = self.duration_predictor(
+                hidden_states, input_padding_mask, speaker_embeddings, durations=durations, reverse=False
+            )
+            log_duration = log_duration / torch.sum(input_padding_mask)
+        else:
+            log_duration_padded = torch.log(durations + 1e-6) * input_padding_mask
+            log_duration = self.duration_predictor(hidden_states, input_padding_mask, speaker_embeddings)
+            log_duration = torch.sum((log_duration - log_duration_padded) ** 2, [1, 2]) / torch.sum(input_padding_mask)
+
+        # expand priors
+        prior_means = torch.matmul(attn.squeeze(1), prior_means.transpose(1, 2)).transpose(1, 2)
+        prior_log_variances = torch.matmul(attn.squeeze(1), prior_log_variances.transpose(1, 2)).transpose(1, 2)
+
+        label_lengths = labels_attention_mask.sum(dim=1)
+        latents_slice, ids_slice = self.rand_slice_segments(latents, label_lengths, segment_size=self.segment_size)
+
+        waveform = self.decoder(latents_slice, speaker_embeddings)
+
+        if not return_dict:
+            outputs = (
+                waveform,
+                log_duration,
+                attn,
+                ids_slice,
+                input_padding_mask,
+                labels_padding_mask,
+                latents,
+                prior_latents,
+                prior_means,
+                prior_log_variances,
+                posterior_means,
+                posterior_log_variances,
+            )
+            return outputs
+
+        return VitsTrainingOutput(
+            waveform=waveform,
+            log_duration=log_duration,
+            attn=attn,
+            ids_slice=ids_slice,
+            input_padding_mask=input_padding_mask,
+            labels_padding_mask=labels_padding_mask,
+            latents=latents,
+            prior_latents=prior_latents,
+            prior_means=prior_means,
+            prior_log_variances=prior_log_variances,
+            posterior_means=posterior_means,
+            posterior_log_variances=posterior_log_variances,
+        )
+
+    def trainer(self,
+              train_dataset_dir = None,
+              eval_dataset_dir = None,
+              full_generation_dir = None,
+              feature_extractor = VitsFeatureExtractor(),
+              training_args = None,
+              full_generation_sample_index= 0,
+              project_name = "Posterior_Decoder_Finetuning",
+              wandbKey = "782b6a6e82bbb5a5348de0d3c7d40d1e76351e79",
+              is_used_text_encoder=True,
+              is_used_posterior_encode=True,
+              dict_state_grad_loss=None,
+              nk=1,
+              path_save_model='./',
+              maf=None
+
+
+              ):
+
+
+        os.makedirs(training_args.output_dir,exist_ok=True)
+        logger = logging.getLogger(f"{__name__} Training")
+        log_level = training_args.get_process_log_level()
+        logger.setLevel(log_level)
+
+        wandb.login(key= wandbKey)
+        wandb.init(project= project_name,config = training_args.to_dict())
+        if dict_state_grad_loss is None:
+            dict_state_grad_loss=get_state_grad_loss()
+
+
+        set_seed(training_args.seed)
+        # Apply Weight Norm Decoder
+        # self.apply_weight_norm()
+        # Save Config
+        self.config.save_pretrained(training_args.output_dir)
+
+        train_dataset = FeaturesCollectionDataset(dataset_dir = train_dataset_dir,
+                                                  device = self.device
+                                                  )
+
+        eval_dataset = None
+        if training_args.do_eval:
+            eval_dataset = FeaturesCollectionDataset(dataset_dir = eval_dataset_dir,
+                                                     device = self.device
+                                                     )
+
+        full_generation_dataset = FeaturesCollectionDataset(dataset_dir = full_generation_dir,
+                                                            device = self.device
+                                                            )
+        self.full_generation_sample = full_generation_dataset[full_generation_sample_index]
+
+        # init optimizer, lr_scheduler
+
+        optimizer = torch.optim.AdamW(
+            self.parameters(),
+            training_args.learning_rate,
+            betas=[training_args.adam_beta1, training_args.adam_beta2],
+            eps=training_args.adam_epsilon,
+        )
+
+         # hack to be able to train on multiple device
+
+
+        # disc_optimizer = torch.optim.AdamW(
+        #         self.discriminator.parameters(),
+        #         training_args.learning_rate,
+        #         betas=[training_args.adam_beta1, training_args.adam_beta2],
+        #         eps=training_args.adam_epsilon,
+        #     )
+        lr_scheduler = torch.optim.lr_scheduler.ExponentialLR(
+            optimizer, gamma=training_args.lr_decay, last_epoch=-1
+            )
+        # disc_lr_scheduler = torch.optim.lr_scheduler.ExponentialLR(
+        #                disc_optimizer, gamma=training_args.lr_decay, last_epoch=-1)
+
+
+        logger.info("***** Running training *****")
+        logger.info(f"  Num Epochs = {training_args.num_train_epochs}")
+
+
+        #.......................loop training............................
+
+        global_step = 0
+
+        for epoch in range(training_args.num_train_epochs):
+            train_losses_sum = 0
+            lr_scheduler.step()
+          #  disc_lr_scheduler.step()
+            print(f"  Num Epochs = {epoch}")
+            if epoch%nk==0:
+              print('Save checkpoints Model :',int(epoch/nk))
+              self.save_pretrained(path_save_model)
+
+
+
+
+            for step, batch in enumerate(train_dataset):
+
+                # forward through model
+                # outputs = self.forward(
+                #     labels=batch["labels"],
+                #     labels_attention_mask=batch["labels_attention_mask"],
+                #     speaker_id=batch["speaker_id"]
+                #     )
+                #if step==10:break
+
+                model_outputs = self.forward_k(
+                    input_ids=batch["input_ids"],
+                    attention_mask=batch["attention_mask"],
+                    labels=batch["labels"],
+                    labels_attention_mask=batch["labels_attention_mask"],
+                    speaker_id=batch["speaker_id"],
+                    text_encoder_output =None  if is_used_text_encoder else batch['text_encoder_output'],
+                    posterior_encode_output=None  if is_used_posterior_encode else batch['posterior_encode_output'],
+                    return_dict=True,
+                    monotonic_alignment_function=maf,
+                )
+
+                mel_scaled_labels = batch["mel_scaled_input_features"]
+                mel_scaled_target = self.slice_segments(mel_scaled_labels, model_outputs.ids_slice,self.segment_size)
+                mel_scaled_generation = feature_extractor._torch_extract_fbank_features(model_outputs.waveform.squeeze(1))[1]
+
+                target_waveform = batch["waveform"].transpose(1, 2)
+                target_waveform = self.slice_segments(
+                                    target_waveform,
+                                    model_outputs.ids_slice * feature_extractor.hop_length,
+                                    self.config.segment_size
+                                )
+                optimizer.zero_grad()
+
+                displayloss={}
+                # backpropagate
+                if dict_state_grad_loss['k1']:
+                    loss_kl = kl_loss(
+                        model_outputs.prior_latents,
+                        model_outputs.posterior_log_variances,
+                        model_outputs.prior_means,
+                        model_outputs.prior_log_variances,
+                        model_outputs.labels_padding_mask,
+                    )
+                    loss_kl=loss_kl*training_args.weight_kl
+                    displayloss['loss_kl']=loss_kl.detach().item()
+                    #if displayloss['loss_kl']>=0:
+                      #  loss_kl.backward()
+
+                if dict_state_grad_loss['mel']:
+                    loss_mel = torch.nn.functional.l1_loss(mel_scaled_target, mel_scaled_generation)
+                    displayloss['loss_mel'] = loss_mel.detach().item()
+                    train_losses_sum = train_losses_sum + displayloss['loss_mel']
+                   # if displayloss['loss_mel']>=0:
+                      #  loss_mel.backward()
+
+                if dict_state_grad_loss['duration']:
+                    loss_duration=torch.sum(model_outputs.log_duration)*training_args.weight_duration
+                    displayloss['loss_duration'] = loss_duration.detach().item()
+                  #  if displayloss['loss_duration']>=0:
+                     #   loss_duration.backward()
+
+                discriminator_target, fmaps_target = self.discriminator(target_waveform)
+                discriminator_candidate, fmaps_candidate = self.discriminator(model_outputs.waveform.detach())
+                if dict_state_grad_loss['discriminator']:
+
+                    loss_disc, loss_real_disc, loss_fake_disc = discriminator_loss(
+                        discriminator_target, discriminator_candidate
+                    )
+
+                    dk={"step_loss_disc": loss_disc.detach().item(),
+                        "step_loss_real_disc": loss_real_disc.detach().item(),
+                        "step_loss_fake_disc": loss_fake_disc.detach().item()}
+                    displayloss['dict_loss_discriminator']=dk
+                    loss_dd = loss_disc# + loss_real_disc + loss_fake_disc
+
+                    loss_dd.backward()
+                discriminator_target, fmaps_target = self.discriminator(target_waveform)
+
+                discriminator_candidate, fmaps_candidate = self.discriminator(model_outputs.waveform.detach())
+
+                if dict_state_grad_loss['generator']:
+                    loss_fmaps = feature_loss(fmaps_target, fmaps_candidate)
+                    loss_gen, losses_gen = generator_loss(discriminator_candidate)
+                    loss_gen=loss_gen * training_args.weight_gen
+                    displayloss['loss_gen'] = loss_gen.detach().item()
+                 #   loss_gen.backward(retain_graph=True)
+                    loss_fmaps=loss_fmaps * training_args.weight_fmaps
+                    displayloss['loss_fmaps'] = loss_fmaps.detach().item()
+                 #   loss_fmaps.backward(retain_graph=True)
+                    total_generator_loss = (
+                        loss_duration
+                        + loss_mel
+                        + loss_kl
+                        + loss_fmaps
+                        + loss_gen
+                    )
+                    total_generator_loss.backward()
+
+
+
+
+
+                optimizer.step()
+
+
+
+
+                print(f"TRAINIG - batch {step}, waveform {(batch['waveform'].shape)}, lr {lr_scheduler.get_last_lr()[0]}... ")
+                print(f"display loss function  enable  :{displayloss}")
+
+                global_step +=1
+
+                # validation
+
+                do_eval = training_args.do_eval and (global_step % training_args.eval_steps == 0)
+                if do_eval:
+                    logger.info("Running validation... ")
+                    eval_losses_sum = 0
+                    cc=0;
+                    for step, batch in enumerate(eval_dataset):
+                        break
+                        if cc>2: break
+                        cc+=1
+                        with torch.no_grad():
+                            model_outputs = self.forward(
+                                input_ids=batch["input_ids"],
+                            attention_mask=batch["attention_mask"],
+                            labels=batch["labels"],
+                            labels_attention_mask=batch["labels_attention_mask"],
+                            speaker_id=batch["speaker_id"],
+
+
+                            return_dict=True,
+                            monotonic_alignment_function=None,
+                                )
+
+                        mel_scaled_labels = batch["mel_scaled_input_features"]
+                        mel_scaled_target = self.slice_segments(mel_scaled_labels, model_outputs.ids_slice,self.segment_size)
+                        mel_scaled_generation = feature_extractor._torch_extract_fbank_features(model_outputs.waveform.squeeze(1))[1]
+                        loss = loss_mel.detach().item()
+                        eval_losses_sum +=loss
+
+                        loss_mel = torch.nn.functional.l1_loss(mel_scaled_target, mel_scaled_generation)
+                        print(f"VALIDATION - batch {step}, waveform {(batch['waveform'].shape)}, step_loss_mel {loss} ... ")
+
+
+
+                    with torch.no_grad():
+                        full_generation_sample = self.full_generation_sample
+                        full_generation =self.forward(
+                          input_ids =full_generation_sample["input_ids"],
+                          attention_mask=full_generation_sample["attention_mask"],
+                          speaker_id=full_generation_sample["speaker_id"]
+                          )
+
+                        full_generation_waveform = full_generation.waveform.cpu().numpy()
+
+                    wandb.log({
+                    "eval_losses": eval_losses_sum,
+                    "full generations samples": [
+                        wandb.Audio(w.reshape(-1), caption=f"Full generation sample {epoch}", sample_rate=16000)
+                        for w in full_generation_waveform],})
+
+            wandb.log({"train_losses":train_losses_sum})
+
+        # add weight norms
+        # self.remove_weight_norm()
+
+        try:
+          torch.save(self.posterior_encoder.state_dict(), os.path.join(training_args.output_dir,"posterior_encoder.pt"))
+          torch.save(self.decoder.state_dict(), os.path.join(training_args.output_dir,"decoder.pt"))
+        except:pass
+
+
+        logger.info("Running final full generations samples... ")
+
+
+        with torch.no_grad():
+
+            full_generation_sample = self.full_generation_sample
+            full_generation = self.forward(
+                    input_ids=full_generation_sample["labels"],
+                    attention_mask=full_generation_sample["labels_attention_mask"],
+                    speaker_id=full_generation_sample["speaker_id"]
+                    )
+
+            full_generation_waveform = full_generation.waveform.cpu().numpy()
+
+        wandb.log({"eval_losses": eval_losses_sum,
+                "full generations samples": [
+                    wandb.Audio(w.reshape(-1), caption=f"Full generation sample {epoch}",
+                                sample_rate=16000) for w in full_generation_waveform],
+                })
+
+
+        logger.info("***** Training / Inference Done *****")
+
+    #....................................
+
+
+    def trainer_to_cuda(self,
+              train_dataset_dir = None,
+              eval_dataset_dir = None,
+              full_generation_dir = None,
+              feature_extractor = VitsFeatureExtractor(),
+              training_args = None,
+              full_generation_sample_index= 0,
+              project_name = "Posterior_Decoder_Finetuning",
+              wandbKey = "782b6a6e82bbb5a5348de0d3c7d40d1e76351e79",
+              is_used_text_encoder=True,
+              is_used_posterior_encode=True,
+              dict_state_grad_loss=None,
+              nk=1,
+              path_save_model='./',
+                        maf=None
+
+
+              ):
+
+
+        os.makedirs(training_args.output_dir,exist_ok=True)
+        logger = logging.getLogger(f"{__name__} Training")
+        log_level = training_args.get_process_log_level()
+        logger.setLevel(log_level)
+
+        wandb.login(key= wandbKey)
+        wandb.init(project= project_name,config = training_args.to_dict())
+        if dict_state_grad_loss is None:
+            dict_state_grad_loss=get_state_grad_loss()
+
+
+        set_seed(training_args.seed)
+        scaler = GradScaler(enabled=training_args.fp16)
+
+        # Apply Weight Norm Decoder
+        # self.apply_weight_norm()
+        # Save Config
+        self.config.save_pretrained(training_args.output_dir)
+
+        train_dataset = FeaturesCollectionDataset(dataset_dir = train_dataset_dir,
+                                                  device = self.device
+                                                  )
+
+        eval_dataset = None
+        if training_args.do_eval:
+            eval_dataset = FeaturesCollectionDataset(dataset_dir = eval_dataset_dir,
+                                                     device = self.device
+                                                     )
+
+        full_generation_dataset = FeaturesCollectionDataset(dataset_dir = full_generation_dir,
+                                                            device = self.device
+                                                            )
+        self.full_generation_sample = full_generation_dataset[full_generation_sample_index]
+
+        # init optimizer, lr_scheduler
+        discriminator=self.discriminator
+        self.discriminator=None
+
+        optimizer = torch.optim.AdamW(
+            self.parameters(),
+            training_args.learning_rate,
+            betas=[training_args.adam_beta1, training_args.adam_beta2],
+            eps=training_args.adam_epsilon,
+        )
+
+         # hack to be able to train on multiple device
+
+
+        disc_optimizer = torch.optim.AdamW(
+                discriminator.parameters(),
+                training_args.d_learning_rate,
+                betas=[training_args.d_adam_beta1, training_args.d_adam_beta2],
+                eps=training_args.adam_epsilon,
+            )
+        lr_scheduler = torch.optim.lr_scheduler.ExponentialLR(
+            optimizer, gamma=training_args.lr_decay, last_epoch=-1
+            )
+        disc_lr_scheduler = torch.optim.lr_scheduler.ExponentialLR(
+                       disc_optimizer, gamma=training_args.lr_decay, last_epoch=-1)
+
+
+        logger.info("***** Running training *****")
+        logger.info(f"  Num Epochs = {training_args.num_train_epochs}")
+
+
+        #.......................loop training............................
+
+        global_step = 0
+
+        for epoch in range(training_args.num_train_epochs):
+            train_losses_sum = 0
+            lr_scheduler.step()
+
+            disc_lr_scheduler.step()
+            print(f"  Num Epochs = {epoch}")
+            if (epoch+1)%nk==0:
+              clear_output()
+              print('Save checkpoints Model :',int(epoch/nk))
+              self.discriminator=discriminator
+
+              self.save_pretrained(path_save_model)
+              self.discriminator=None
+
+
+
+
+            for step, batch in enumerate(train_dataset):
+
+                # forward through model
+                # outputs = self.forward(
+                #     labels=batch["labels"],
+                #     labels_attention_mask=batch["labels_attention_mask"],
+                #     speaker_id=batch["speaker_id"]
+                #     )
+                #if step==10:break
+                batch=covert_cuda_batch(batch)
+
+                with autocast(enabled=training_args.fp16):
+
+
+                  model_outputs = self.forward_k(
+                      input_ids=batch["input_ids"],
+                      attention_mask=batch["attention_mask"],
+                      labels=batch["labels"],
+                      labels_attention_mask=batch["labels_attention_mask"],
+                      speaker_id=batch["speaker_id"],
+                      text_encoder_output =None  if is_used_text_encoder else batch['text_encoder_output'],
+                      posterior_encode_output=None  if is_used_posterior_encode else batch['posterior_encode_output'],
+                      return_dict=True,
+                      monotonic_alignment_function= maf,
+                  )
+
+                  mel_scaled_labels = batch["mel_scaled_input_features"]
+                  mel_scaled_target = self.slice_segments(mel_scaled_labels, model_outputs.ids_slice,self.segment_size)
+                  mel_scaled_generation = feature_extractor._torch_extract_fbank_features(model_outputs.waveform.squeeze(1))[1]
+
+                  target_waveform = batch["waveform"].transpose(1, 2)
+                  target_waveform = self.slice_segments(
+                                      target_waveform,
+                                      model_outputs.ids_slice * feature_extractor.hop_length,
+                                      self.config.segment_size
+                                  )
+
+                  discriminator_target, fmaps_target = discriminator(target_waveform)
+                  discriminator_candidate, fmaps_candidate = discriminator(model_outputs.waveform.detach())
+                  #with autocast(enabled=False):
+                  if dict_state_grad_loss['discriminator']:
+
+
+                        loss_disc, loss_real_disc, loss_fake_disc = discriminator_loss(
+                            discriminator_target, discriminator_candidate
+                        )
+
+                        dk={"step_loss_disc": loss_disc.detach().item(),
+                            "step_loss_real_disc": loss_real_disc.detach().item(),
+                            "step_loss_fake_disc": loss_fake_disc.detach().item()}
+                        displayloss['dict_loss_discriminator']=dk
+                        loss_dd = loss_disc# + loss_real_disc + loss_fake_disc
+
+                  #  loss_dd.backward()
+
+                disc_optimizer.zero_grad()
+                scaler.scale(loss_dd).backward()
+                scaler.unscale_(disc_optimizer )
+                grad_norm_d = clip_grad_value_(discriminator.parameters(), None)
+                scaler.step(disc_optimizer)
+
+
+                with autocast(enabled=training_args.fp16):
+
+                  displayloss={}
+                  # backpropagate
+                  if dict_state_grad_loss['k1']:
+                      loss_kl = kl_loss(
+                          model_outputs.prior_latents,
+                          model_outputs.posterior_log_variances,
+                          model_outputs.prior_means,
+                          model_outputs.prior_log_variances,
+                          model_outputs.labels_padding_mask,
+                      )
+                      loss_kl=loss_kl*training_args.weight_kl
+                      displayloss['loss_kl']=loss_kl.detach().item()
+                      #if displayloss['loss_kl']>=0:
+                        #  loss_kl.backward()
+
+                  if dict_state_grad_loss['mel']:
+                      loss_mel = torch.nn.functional.l1_loss(mel_scaled_target, mel_scaled_generation)
+                      displayloss['loss_mel'] = loss_mel.detach().item()
+                      train_losses_sum = train_losses_sum + displayloss['loss_mel']
+                    # if displayloss['loss_mel']>=0:
+                        #  loss_mel.backward()
+
+                  if dict_state_grad_loss['duration']:
+                      loss_duration=torch.sum(model_outputs.log_duration)*training_args.weight_duration
+                      displayloss['loss_duration'] = loss_duration.detach().item()
+                    #  if displayloss['loss_duration']>=0:
+                      #   loss_duration.backward()
+
+
+
+
+                  discriminator_target, fmaps_target = discriminator(target_waveform)
+
+                  discriminator_candidate, fmaps_candidate = discriminator(model_outputs.waveform.detach())
+
+                  if dict_state_grad_loss['generator']:
+                      loss_fmaps = feature_loss(fmaps_target, fmaps_candidate)
+                      loss_gen, losses_gen = generator_loss(discriminator_candidate)
+                      loss_gen=loss_gen * training_args.weight_gen
+                      displayloss['loss_gen'] = loss_gen.detach().item()
+                  #   loss_gen.backward(retain_graph=True)
+                      loss_fmaps=loss_fmaps * training_args.weight_fmaps
+                      displayloss['loss_fmaps'] = loss_fmaps.detach().item()
+                  #   loss_fmaps.backward(retain_graph=True)
+                      total_generator_loss = (
+                          loss_duration
+                          + loss_mel
+                          + loss_kl
+                          + loss_fmaps
+                          + loss_gen
+                      )
+                     # total_generator_loss.backward()
+                optimizer.zero_grad()
+                scaler.scale(total_generator_loss).backward()
+                scaler.unscale_(optimizer)
+                grad_norm_g = clip_grad_value_(self.parameters(), None)
+                scaler.step(optimizer)
+                scaler.update()
+
+
+
+
+
+
+                # optimizer.step()
+
+
+
+
+                print(f"TRAINIG - batch {step}, waveform {(batch['waveform'].shape)}, lr {lr_scheduler.get_last_lr()[0]}... ")
+                print(f"display loss function  enable  :{displayloss}")
+
+                global_step +=1
+
+                # validation
+
+                do_eval = training_args.do_eval and (global_step % training_args.eval_steps == 0)
+                if do_eval:
+                    logger.info("Running validation... ")
+                    eval_losses_sum = 0
+                    cc=0;
+                    for step, batch in enumerate(eval_dataset):
+                        break
+                        if cc>2: break
+                        cc+=1
+                        with torch.no_grad():
+                            model_outputs = self.forward(
+                                input_ids=batch["input_ids"],
+                            attention_mask=batch["attention_mask"],
+                            labels=batch["labels"],
+                            labels_attention_mask=batch["labels_attention_mask"],
+                            speaker_id=batch["speaker_id"],
+
+
+                            return_dict=True,
+                            monotonic_alignment_function=None,
+                                )
+
+                        mel_scaled_labels = batch["mel_scaled_input_features"]
+                        mel_scaled_target = self.slice_segments(mel_scaled_labels, model_outputs.ids_slice,self.segment_size)
+                        mel_scaled_generation = feature_extractor._torch_extract_fbank_features(model_outputs.waveform.squeeze(1))[1]
+                        loss = loss_mel.detach().item()
+                        eval_losses_sum +=loss
+
+                        loss_mel = torch.nn.functional.l1_loss(mel_scaled_target, mel_scaled_generation)
+                        print(f"VALIDATION - batch {step}, waveform {(batch['waveform'].shape)}, step_loss_mel {loss} ... ")
+
+
+
+                    with torch.no_grad():
+                        full_generation_sample = self.full_generation_sample
+                        full_generation =self.forward(
+                          input_ids =full_generation_sample["input_ids"],
+                          attention_mask=full_generation_sample["attention_mask"],
+                          speaker_id=full_generation_sample["speaker_id"]
+                          )
+
+                        full_generation_waveform = full_generation.waveform.cpu().numpy()
+
+                    wandb.log({
+                    "eval_losses": eval_losses_sum,
+                    "full generations samples": [
+                        wandb.Audio(w.reshape(-1), caption=f"Full generation sample {epoch}", sample_rate=16000)
+                        for w in full_generation_waveform],})
+
+            wandb.log({"train_losses":train_losses_sum})
+
+        # add weight norms
+        # self.remove_weight_norm()
+
+        try:
+          torch.save(self.posterior_encoder.state_dict(), os.path.join(training_args.output_dir,"posterior_encoder.pt"))
+          torch.save(self.decoder.state_dict(), os.path.join(training_args.output_dir,"decoder.pt"))
+        except:pass
+
+
+        logger.info("Running final full generations samples... ")
+
+
+        with torch.no_grad():
+
+            full_generation_sample = self.full_generation_sample
+            full_generation = self.forward(
+                    input_ids=full_generation_sample["labels"],
+                    attention_mask=full_generation_sample["labels_attention_mask"],
+                    speaker_id=full_generation_sample["speaker_id"]
+                    )
+
+            full_generation_waveform = full_generation.waveform.cpu().numpy()
+
+        wandb.log({"eval_losses": eval_losses_sum,
+                "full generations samples": [
+                    wandb.Audio(w.reshape(-1), caption=f"Full generation sample {epoch}",
+                                sample_rate=16000) for w in full_generation_waveform],
+                })
+
+
+        logger.info("***** Training / Inference Done *****")
+
+    #....................................
+    def trainer_to_cuda(self,
+              train_dataset_dir = None,
+              eval_dataset_dir = None,
+              full_generation_dir = None,
+              feature_extractor = VitsFeatureExtractor(),
+              training_args = None,
+              full_generation_sample_index= 0,
+              project_name = "Posterior_Decoder_Finetuning",
+              wandbKey = "782b6a6e82bbb5a5348de0d3c7d40d1e76351e79",
+              is_used_text_encoder=True,
+              is_used_posterior_encode=True,
+              dict_state_grad_loss=None,
+              nk=1,
+              path_save_model='./',
+               maf=None
+
+
+              ):
+
+
+        os.makedirs(training_args.output_dir,exist_ok=True)
+        logger = logging.getLogger(f"{__name__} Training")
+        log_level = training_args.get_process_log_level()
+        logger.setLevel(log_level)
+
+        wandb.login(key= wandbKey)
+        wandb.init(project= project_name,config = training_args.to_dict())
+        if dict_state_grad_loss is None:
+            dict_state_grad_loss=get_state_grad_loss()
+
+
+        set_seed(training_args.seed)
+        scaler = GradScaler(enabled=training_args.fp16)
+
+        # Apply Weight Norm Decoder
+        # self.apply_weight_norm()
+        # Save Config
+        self.config.save_pretrained(training_args.output_dir)
+
+        train_dataset = FeaturesCollectionDataset(dataset_dir = train_dataset_dir,
+                                                  device = self.device
+                                                  )
+
+        eval_dataset = None
+        if training_args.do_eval:
+            eval_dataset = FeaturesCollectionDataset(dataset_dir = eval_dataset_dir,
+                                                     device = self.device
+                                                     )
+
+        full_generation_dataset = FeaturesCollectionDataset(dataset_dir = full_generation_dir,
+                                                            device = self.device
+                                                            )
+        self.full_generation_sample = full_generation_dataset[full_generation_sample_index]
+
+        # init optimizer, lr_scheduler
+
+        optimizer = torch.optim.AdamW(
+            self.parameters(),
+            training_args.learning_rate,
+            betas=[training_args.adam_beta1, training_args.adam_beta2],
+            eps=training_args.adam_epsilon,
+        )
+
+         # hack to be able to train on multiple device
+
+
+        # disc_optimizer = torch.optim.AdamW(
+        #         self.discriminator.parameters(),
+        #         training_args.d_learning_rate,
+        #         betas=[training_args.d_adam_beta1, training_args.d_adam_beta2],
+        #         eps=training_args.adam_epsilon,
+        #     )
+        lr_scheduler = torch.optim.lr_scheduler.ExponentialLR(
+            optimizer, gamma=training_args.lr_decay, last_epoch=-1
+            )
+        # disc_lr_scheduler = torch.optim.lr_scheduler.ExponentialLR(
+        #                disc_optimizer, gamma=training_args.lr_decay, last_epoch=-1)
+
+
+        logger.info("***** Running training *****")
+        logger.info(f"  Num Epochs = {training_args.num_train_epochs}")
+
+
+        #.......................loop training............................
+
+        global_step = 0
+
+        for epoch in range(training_args.num_train_epochs):
+            train_losses_sum = 0
+            lr_scheduler.step()
+
+            # disc_lr_scheduler.step()
+            print(f"  Num Epochs = {epoch}")
+            if (epoch+1)%nk==0:
+             clear_output()
+             print('Save checkpoints Model :',int(epoch/nk))
+             self.save_pretrained(path_save_model)
+
+            for step, batch in enumerate(train_dataset):
+
+                # forward through model
+                # outputs = self.forward(
+                #     labels=batch["labels"],
+                #     labels_attention_mask=batch["labels_attention_mask"],
+                #     speaker_id=batch["speaker_id"]
+                #     )
+                #if step==10:break
+                batch=covert_cuda_batch(batch)
+                displayloss={}
+
+
+                with autocast(enabled=training_args.fp16):
+
+
+                  model_outputs = self.forward_k(
+                      input_ids=batch["input_ids"],
+                      attention_mask=batch["attention_mask"],
+                      labels=batch["labels"],
+                      labels_attention_mask=batch["labels_attention_mask"],
+                      speaker_id=batch["speaker_id"],
+                      text_encoder_output =None  if is_used_text_encoder else batch['text_encoder_output'],
+                      posterior_encode_output=None  if is_used_posterior_encode else batch['posterior_encode_output'],
+                      return_dict=True,
+                      monotonic_alignment_function=maf,
+                  )
+
+                  mel_scaled_labels = batch["mel_scaled_input_features"]
+                  mel_scaled_target = self.slice_segments(mel_scaled_labels, model_outputs.ids_slice,self.segment_size)
+                  mel_scaled_generation = feature_extractor._torch_extract_fbank_features(model_outputs.waveform.squeeze(1))[1]
+
+                  target_waveform = batch["waveform"].transpose(1, 2)
+                  target_waveform = self.slice_segments(
+                                      target_waveform,
+                                      model_outputs.ids_slice * feature_extractor.hop_length,
+                                      self.config.segment_size
+                                  )
+
+                  discriminator_target, fmaps_target = self.discriminator(target_waveform)
+                  discriminator_candidate, fmaps_candidate = self.discriminator(model_outputs.waveform.detach())
+                  with autocast(enabled=False):
+                     if dict_state_grad_loss['discriminator']:
+                      #  disc_optimizer.zero_grad()
+
+                        loss_disc, loss_real_disc, loss_fake_disc = discriminator_loss(
+                            discriminator_target, discriminator_candidate
+                        )
+
+                        dk={"step_loss_disc": loss_disc.detach().item(),
+                            "step_loss_real_disc": loss_real_disc.detach().item(),
+                            "step_loss_fake_disc": loss_fake_disc.detach().item()}
+                        displayloss['dict_loss_discriminator']=dk
+                        loss_dd = loss_disc# + loss_real_disc + loss_fake_disc
+
+                  #  loss_dd.backward()
+                optimizer.zero_grad()
+                # disc_optimizer.zero_grad()
+                scaler.scale(loss_dd).backward()
+                # scaler.unscale_(disc_optimizer)
+                #grad_norm_d = clip_grad_value_(self.discriminator.parameters(), None)
+                # scaler.step(disc_optimizer)
+
+
+                with autocast(enabled=training_args.fp16):
+
+
+
+
+                  # backpropagate
+
+
+
+
+                  discriminator_target, fmaps_target = self.discriminator(target_waveform)
+
+                  discriminator_candidate, fmaps_candidate = self.discriminator(model_outputs.waveform.detach())
+                  with autocast(enabled=False):
+                    if dict_state_grad_loss['k1']:
+                      loss_kl = kl_loss(
+                          model_outputs.prior_latents,
+                          model_outputs.posterior_log_variances,
+                          model_outputs.prior_means,
+                          model_outputs.prior_log_variances,
+                          model_outputs.labels_padding_mask,
+                      )
+                      loss_kl=loss_kl*training_args.weight_kl
+                      displayloss['loss_kl']=loss_kl.detach().item()
+                      #if displayloss['loss_kl']>=0:
+                        #  loss_kl.backward()
+
+                    if dict_state_grad_loss['mel']:
+                          loss_mel = torch.nn.functional.l1_loss(mel_scaled_target, mel_scaled_generation)
+                          displayloss['loss_mel'] = loss_mel.detach().item()
+                          train_losses_sum = train_losses_sum + displayloss['loss_mel']
+                        # if displayloss['loss_mel']>=0:
+                            #  loss_mel.backward()
+
+                    if dict_state_grad_loss['duration']:
+                          loss_duration=torch.sum(model_outputs.log_duration)*training_args.weight_duration
+                          displayloss['loss_duration'] = loss_duration.detach().item()
+                        #  if displayloss['loss_duration']>=0:
+                          #   loss_duration.backward()
+
+                    if dict_state_grad_loss['generator']:
+                        loss_fmaps = feature_loss(fmaps_target, fmaps_candidate)
+                        loss_gen, losses_gen = generator_loss(discriminator_candidate)
+                        loss_gen=loss_gen * training_args.weight_gen
+                        displayloss['loss_gen'] = loss_gen.detach().item()
+                    #   loss_gen.backward(retain_graph=True)
+                        loss_fmaps=loss_fmaps * training_args.weight_fmaps
+                        displayloss['loss_fmaps'] = loss_fmaps.detach().item()
+                    #   loss_fmaps.backward(retain_graph=True)
+                        total_generator_loss = (
+                            loss_duration
+                            + loss_mel
+                            + loss_kl
+                            + loss_fmaps
+                            + loss_gen
+                        )
+                     # total_generator_loss.backward()
+                scaler.scale(total_generator_loss).backward()
+                scaler.unscale_(optimizer)
+                grad_norm_g = clip_grad_value_(self.parameters(), None)
+                scaler.step(optimizer)
+                scaler.update()
+
+
+
+
+
+
+                # optimizer.step()
+
+
+
+
+                print(f"TRAINIG - batch {step},Grad G{grad_norm_g}, waveform {(batch['waveform'].shape)}, lr {lr_scheduler.get_last_lr()[0]}... ")
+                print(f"display loss function  enable  :{displayloss}")
+
+                global_step +=1
+
+                # validation
+
+                do_eval = training_args.do_eval and (global_step % training_args.eval_steps == 0)
+                if do_eval:
+                    logger.info("Running validation... ")
+                    eval_losses_sum = 0
+                    cc=0;
+                    for step, batch in enumerate(eval_dataset):
+                        break
+                        if cc>2: break
+                        cc+=1
+                        with torch.no_grad():
+                            model_outputs = self.forward(
+                                input_ids=batch["input_ids"],
+                            attention_mask=batch["attention_mask"],
+                            labels=batch["labels"],
+                            labels_attention_mask=batch["labels_attention_mask"],
+                            speaker_id=batch["speaker_id"],
+
+
+                            return_dict=True,
+                            monotonic_alignment_function=None,
+                                )
+
+                        mel_scaled_labels = batch["mel_scaled_input_features"]
+                        mel_scaled_target = self.slice_segments(mel_scaled_labels, model_outputs.ids_slice,self.segment_size)
+                        mel_scaled_generation = feature_extractor._torch_extract_fbank_features(model_outputs.waveform.squeeze(1))[1]
+                        loss = loss_mel.detach().item()
+                        eval_losses_sum +=loss
+
+                        loss_mel = torch.nn.functional.l1_loss(mel_scaled_target, mel_scaled_generation)
+                        print(f"VALIDATION - batch {step}, waveform {(batch['waveform'].shape)}, step_loss_mel {loss} ... ")
+
+
+
+                    with torch.no_grad():
+                        full_generation_sample = self.full_generation_sample
+                        full_generation =self.forward(
+                          input_ids =full_generation_sample["input_ids"],
+                          attention_mask=full_generation_sample["attention_mask"],
+                          speaker_id=full_generation_sample["speaker_id"]
+                          )
+
+                        full_generation_waveform = full_generation.waveform.cpu().numpy()
+
+                    wandb.log({
+                    "eval_losses": eval_losses_sum,
+                    "full generations samples": [
+                        wandb.Audio(w.reshape(-1), caption=f"Full generation sample {epoch}", sample_rate=16000)
+                        for w in full_generation_waveform],})
+
+            wandb.log({"train_losses":train_losses_sum})
+
+        # add weight norms
+        # self.remove_weight_norm()
+
+        try:
+          torch.save(self.posterior_encoder.state_dict(), os.path.join(training_args.output_dir,"posterior_encoder.pt"))
+          torch.save(self.decoder.state_dict(), os.path.join(training_args.output_dir,"decoder.pt"))
+        except:pass
+
+
+        logger.info("Running final full generations samples... ")
+
+
+        with torch.no_grad():
+
+            full_generation_sample = self.full_generation_sample
+            full_generation = self.forward(
+                    input_ids=full_generation_sample["labels"],
+                    attention_mask=full_generation_sample["labels_attention_mask"],
+                    speaker_id=full_generation_sample["speaker_id"]
+                    )
+
+            full_generation_waveform = full_generation.waveform.cpu().numpy()
+
+        wandb.log({"eval_losses": eval_losses_sum,
+                "full generations samples": [
+                    wandb.Audio(w.reshape(-1), caption=f"Full generation sample {epoch}",
+                                sample_rate=16000) for w in full_generation_waveform],
+                })
+
+
+        logger.info("***** Training / Inference Done *****")
+
+    #....................................
+
+    def trainer_to(self,
+              train_dataset_dir = None,
+              eval_dataset_dir = None,
+              full_generation_dir = None,
+              feature_extractor = VitsFeatureExtractor(),
+              training_args = None,
+              full_generation_sample_index= 0,
+              project_name = "Posterior_Decoder_Finetuning",
+              wandbKey = "782b6a6e82bbb5a5348de0d3c7d40d1e76351e79",
+              is_used_text_encoder=True,
+              is_used_posterior_encode=True,
+              dict_state_grad_loss=None,
+              nk=1,
+              path_save_model='./',
+               maf=None
+
+
+              ):
+
+
+        os.makedirs(training_args.output_dir,exist_ok=True)
+        logger = logging.getLogger(f"{__name__} Training")
+        log_level = training_args.get_process_log_level()
+        logger.setLevel(log_level)
+
+        wandb.login(key= wandbKey)
+        wandb.init(project= project_name,config = training_args.to_dict())
+        if dict_state_grad_loss is None:
+            dict_state_grad_loss=get_state_grad_loss()
+
+
+        set_seed(training_args.seed)
+        # Apply Weight Norm Decoder
+        # self.apply_weight_norm()
+        # Save Config
+        self.config.save_pretrained(training_args.output_dir)
+
+        train_dataset = FeaturesCollectionDataset(dataset_dir = train_dataset_dir,
+                                                  device = self.device
+                                                  )
+
+        eval_dataset = None
+        if training_args.do_eval:
+            eval_dataset = FeaturesCollectionDataset(dataset_dir = eval_dataset_dir,
+                                                     device = self.device
+                                                     )
+
+        full_generation_dataset = FeaturesCollectionDataset(dataset_dir = full_generation_dir,
+                                                            device = self.device
+                                                            )
+        self.full_generation_sample = full_generation_dataset[full_generation_sample_index]
+
+        # init optimizer, lr_scheduler
+
+        optimizer = torch.optim.AdamW(
+            self.parameters(),
+            training_args.learning_rate,
+            betas=[training_args.adam_beta1, training_args.adam_beta2],
+            eps=training_args.adam_epsilon,
+        )
+
+         # hack to be able to train on multiple device
+
+
+        disc_optimizer = torch.optim.AdamW(
+                self.discriminator.parameters(),
+                training_args.d_learning_rate,
+                betas=[training_args.d_adam_beta1, training_args.d_adam_beta2],
+                eps=training_args.adam_epsilon,
+            )
+        lr_scheduler = torch.optim.lr_scheduler.ExponentialLR(
+            optimizer, gamma=training_args.lr_decay, last_epoch=-1
+            )
+        disc_lr_scheduler = torch.optim.lr_scheduler.ExponentialLR(
+                       disc_optimizer, gamma=training_args.lr_decay, last_epoch=-1)
+
+
+        logger.info("***** Running training *****")
+        logger.info(f"  Num Epochs = {training_args.num_train_epochs}")
+
+
+        #.......................loop training............................
+
+        global_step = 0
+
+        for epoch in range(training_args.num_train_epochs):
+            train_losses_sum = 0
+            lr_scheduler.step()
+
+            disc_lr_scheduler.step()
+            print(f"  Num Epochs = {epoch}")
+            if epoch%nk==0:
+              clear_output()
+              print('')
+              print('Save checkpoints Model :',int(epoch/nk))
+              self.save_pretrained(path_save_model)
+
+
+
+
+            for step, batch in enumerate(train_dataset):
+
+                # forward through model
+                # outputs = self.forward(
+                #     labels=batch["labels"],
+                #     labels_attention_mask=batch["labels_attention_mask"],
+                #     speaker_id=batch["speaker_id"]
+                #     )
+                #if step==10:break
+                batch=covert_cuda_batch(batch)
+
+                waveform,ids_slice,log_duration,prior_latents,posterior_log_variances,prior_means,prior_log_variances,labels_padding_mask=self.forward_train(
+                    input_ids=batch["input_ids"],
+                    attention_mask=batch["attention_mask"],
+                    labels=batch["labels"],
+                    labels_attention_mask=batch["labels_attention_mask"],
+                    speaker_id=batch["speaker_id"],
+                    text_encoder_output =None , #if is_used_text_encoder else batch['text_encoder_output'],
+                    posterior_encode_output=batch['posterior_encode_output'] ,#  if is_used_posterior_encode else ,
+                    return_dict=True,
+                    monotonic_alignment_function= maf,
+                )
+
+                mel_scaled_labels = batch["mel_scaled_input_features"]
+                mel_scaled_target = self.slice_segments(mel_scaled_labels, ids_slice,self.segment_size)
+                mel_scaled_generation = feature_extractor._torch_extract_fbank_features(waveform.squeeze(1))[1]
+
+                target_waveform = batch["waveform"].transpose(1, 2)
+                target_waveform = self.slice_segments(
+                                    target_waveform,
+                                    ids_slice * feature_extractor.hop_length,
+                                    self.config.segment_size
+                                )
+
+
+
+                displayloss={}
+                # backpropagate
+                #if dict_state_grad_loss['k1']:
+                loss_kl = kl_loss(
+                    prior_latents,
+                    posterior_log_variances,
+                    prior_means,
+                   prior_log_variances,
+                   labels_padding_mask,
+                )
+                loss_kl=loss_kl*training_args.weight_kl
+                displayloss['loss_kl']=loss_kl.detach().item()
+                    #if displayloss['loss_kl']>=0:
+                      #  loss_kl.backward()
+
+              #  if dict_state_grad_loss['mel']:
+                loss_mel = torch.nn.functional.l1_loss(mel_scaled_target, mel_scaled_generation)
+                displayloss['loss_mel'] = loss_mel.detach().item()
+                train_losses_sum = train_losses_sum + displayloss['loss_mel']
+                # if displayloss['loss_mel']>=0:
+                      #  loss_mel.backward()
+
+                #if dict_state_grad_loss['duration']:
+                loss_duration=torch.sum(log_duration)*training_args.weight_duration
+                displayloss['loss_duration'] = loss_duration.detach().item()
+              #  if displayloss['loss_duration']>=0:
+                     #   loss_duration.backward()
+
+                discriminator_target, fmaps_target = self.discriminator(target_waveform)
+                discriminator_candidate, fmaps_candidate = self.discriminator(waveform.detach())
+                #if dict_state_grad_loss['discriminator']:
+                disc_optimizer.zero_grad()
+
+                loss_disc, loss_real_disc, loss_fake_disc = discriminator_loss(
+                    discriminator_target, discriminator_candidate
+                )
+
+                dk={"step_loss_disc": loss_disc.detach().item(),
+                    "step_loss_real_disc": loss_real_disc.detach().item(),
+                    "step_loss_fake_disc": loss_fake_disc.detach().item()}
+                displayloss['dict_loss_discriminator']=dk
+                loss_dd = loss_disc# + loss_real_disc + loss_fake_disc
+
+                loss_dd.backward()
+                disc_optimizer.step()
+
+
+                discriminator_target, fmaps_target = self.discriminator(target_waveform)
+
+                discriminator_candidate, fmaps_candidate = self.discriminator(waveform.detach())
+                optimizer.zero_grad()
+             #   if dict_state_grad_loss['generator']:
+                loss_fmaps = feature_loss(fmaps_target, fmaps_candidate)
+                loss_gen, losses_gen = generator_loss(discriminator_candidate)
+                loss_gen=loss_gen * training_args.weight_gen
+                displayloss['loss_gen'] = loss_gen.detach().item()
+              #   loss_gen.backward(retain_graph=True)
+                loss_fmaps=loss_fmaps * training_args.weight_fmaps
+                displayloss['loss_fmaps'] = loss_fmaps.detach().item()
+              #   loss_fmaps.backward(retain_graph=True)
+                total_generator_loss = (
+                    loss_duration
+                    + loss_mel
+                    + loss_kl
+                    + loss_fmaps
+                    + loss_gen
+                )
+                total_generator_loss.backward()
+
+
+
+
+
+                optimizer.step()
+
+
+
+
+                print(f"TRAINIG - batch {step}, waveform {(batch['waveform'].shape)}, lr {lr_scheduler.get_last_lr()[0]}... ")
+                print(f"display loss function  enable  :{displayloss}")
+
+                global_step +=1
+
+                # validation
+
+                do_eval = training_args.do_eval and (global_step % training_args.eval_steps == 0)
+                if do_eval:
+                    logger.info("Running validation... ")
+                    eval_losses_sum = 0
+                    cc=0;
+                    for step, batch in enumerate(eval_dataset):
+                        break
+                        if cc>2: break
+                        cc+=1
+                        with torch.no_grad():
+                            model_outputs = self.forward(
+                                input_ids=batch["input_ids"],
+                            attention_mask=batch["attention_mask"],
+                            labels=batch["labels"],
+                            labels_attention_mask=batch["labels_attention_mask"],
+                            speaker_id=batch["speaker_id"],
+
+
+                            return_dict=True,
+                            monotonic_alignment_function=None,
+                                )
+
+                        mel_scaled_labels = batch["mel_scaled_input_features"]
+                        mel_scaled_target = self.slice_segments(mel_scaled_labels, model_outputs.ids_slice,self.segment_size)
+                        mel_scaled_generation = feature_extractor._torch_extract_fbank_features(model_outputs.waveform.squeeze(1))[1]
+                        loss = loss_mel.detach().item()
+                        eval_losses_sum +=loss
+
+                        loss_mel = torch.nn.functional.l1_loss(mel_scaled_target, mel_scaled_generation)
+                        print(f"VALIDATION - batch {step}, waveform {(batch['waveform'].shape)}, step_loss_mel {loss} ... ")
+
+
+
+                    with torch.no_grad():
+                        full_generation_sample = self.full_generation_sample
+                        full_generation =self.forward(
+                          input_ids =full_generation_sample["input_ids"],
+                          attention_mask=full_generation_sample["attention_mask"],
+                          speaker_id=full_generation_sample["speaker_id"]
+                          )
+
+                        full_generation_waveform = full_generation.waveform.cpu().numpy()
+
+                    wandb.log({
+                    "eval_losses": eval_losses_sum,
+                    "full generations samples": [
+                        wandb.Audio(w.reshape(-1), caption=f"Full generation sample {epoch}", sample_rate=16000)
+                        for w in full_generation_waveform],})
+
+            wandb.log({"train_losses":train_losses_sum})
+
+        # add weight norms
+        # self.remove_weight_norm()
+
+        try:
+          torch.save(self.posterior_encoder.state_dict(), os.path.join(training_args.output_dir,"posterior_encoder.pt"))
+          torch.save(self.decoder.state_dict(), os.path.join(training_args.output_dir,"decoder.pt"))
+        except:pass
+
+
+        logger.info("Running final full generations samples... ")
+
+
+        with torch.no_grad():
+
+            full_generation_sample = self.full_generation_sample
+            full_generation = self.forward(
+                    input_ids=full_generation_sample["labels"],
+                    attention_mask=full_generation_sample["labels_attention_mask"],
+                    speaker_id=full_generation_sample["speaker_id"]
+                    )
+
+            full_generation_waveform = full_generation.waveform.cpu().numpy()
+
+        wandb.log({"eval_losses": eval_losses_sum,
+                "full generations samples": [
+                    wandb.Audio(w.reshape(-1), caption=f"Full generation sample {epoch}",
+                                sample_rate=16000) for w in full_generation_waveform],
+                })
+
+
+        logger.info("***** Training / Inference Done *****")
+
+    #....................................
+
+
+
+
+    def trainer_to_cuda1(self,
+              train_dataset_dir = None,
+              eval_dataset_dir = None,
+              full_generation_dir = None,
+              feature_extractor = VitsFeatureExtractor(),
+              training_args = None,
+              full_generation_sample_index= 0,
+              project_name = "Posterior_Decoder_Finetuning",
+              wandbKey = "782b6a6e82bbb5a5348de0d3c7d40d1e76351e79",
+              is_used_text_encoder=True,
+              is_used_posterior_encode=True,
+              dict_state_grad_loss=None,
+              nk=1,
+              path_save_model='./',
+                        maf=None
+
+
+              ):
+
+
+        os.makedirs(training_args.output_dir,exist_ok=True)
+        logger = logging.getLogger(f"{__name__} Training")
+        log_level = training_args.get_process_log_level()
+        logger.setLevel(log_level)
+
+        wandb.login(key= wandbKey)
+        wandb.init(project= project_name,config = training_args.to_dict())
+        if dict_state_grad_loss is None:
+            dict_state_grad_loss=get_state_grad_loss()
+
+
+        set_seed(training_args.seed)
+        scaler = GradScaler(enabled=training_args.fp16)
+
+        # Apply Weight Norm Decoder
+        # self.apply_weight_norm()
+        # Save Config
+        self.config.save_pretrained(training_args.output_dir)
+
+        train_dataset = FeaturesCollectionDataset(dataset_dir = train_dataset_dir,
+                                                  device = self.device
+                                                  )
+
+        eval_dataset = None
+        if training_args.do_eval:
+            eval_dataset = FeaturesCollectionDataset(dataset_dir = eval_dataset_dir,
+                                                     device = self.device
+                                                     )
+
+        full_generation_dataset = FeaturesCollectionDataset(dataset_dir = full_generation_dir,
+                                                            device = self.device
+                                                            )
+        self.full_generation_sample = full_generation_dataset[full_generation_sample_index]
+
+        # init optimizer, lr_scheduler
+        discriminator=self.discriminator
+        self.discriminator=None
+
+        optimizer = torch.optim.AdamW(
+            self.parameters(),
+            training_args.learning_rate,
+            betas=[training_args.adam_beta1, training_args.adam_beta2],
+            eps=training_args.adam_epsilon,
+        )
+
+         # hack to be able to train on multiple device
+
+
+        disc_optimizer = torch.optim.AdamW(
+                discriminator.parameters(),
+                training_args.d_learning_rate,
+                betas=[training_args.d_adam_beta1, training_args.d_adam_beta2],
+                eps=training_args.adam_epsilon,
+            )
+        lr_scheduler = torch.optim.lr_scheduler.ExponentialLR(
+            optimizer, gamma=training_args.lr_decay, last_epoch=-1
+            )
+        disc_lr_scheduler = torch.optim.lr_scheduler.ExponentialLR(
+                       disc_optimizer, gamma=training_args.lr_decay, last_epoch=-1)
+
+
+        logger.info("***** Running training *****")
+        logger.info(f"  Num Epochs = {training_args.num_train_epochs}")
+
+
+        #.......................loop training............................
+
+        global_step = 0
+
+        for epoch in range(training_args.num_train_epochs):
+            train_losses_sum = 0
+            lr_scheduler.step()
+
+            disc_lr_scheduler.step()
+            print(f"  Num Epochs = {epoch}")
+            if epoch%nk==0:
+              clear_output()
+              print('Save checkpoints Model :',int(epoch/nk))
+              self.discriminator=discriminator
+
+              self.save_pretrained(path_save_model)
+              self.discriminator=None
+
+
+
+
+            for step, batch in enumerate(train_dataset):
+
+                # forward through model
+                # outputs = self.forward(
+                #     labels=batch["labels"],
+                #     labels_attention_mask=batch["labels_attention_mask"],
+                #     speaker_id=batch["speaker_id"]
+                #     )
+                #if step==10:break
+                batch=covert_cuda_batch(batch)
+                displayloss={}
+
+                with autocast(enabled=training_args.fp16):
+
+
+                  model_outputs = self.forward_k(
+                      input_ids=batch["input_ids"],
+                      attention_mask=batch["attention_mask"],
+                      labels=batch["labels"],
+                      labels_attention_mask=batch["labels_attention_mask"],
+                      speaker_id=batch["speaker_id"],
+                      text_encoder_output =None  if is_used_text_encoder else batch['text_encoder_output'],
+                      posterior_encode_output=None  if is_used_posterior_encode else batch['posterior_encode_output'],
+                      return_dict=True,
+                      monotonic_alignment_function= maf,
+                  )
+
+                  mel_scaled_labels = batch["mel_scaled_input_features"]
+                  mel_scaled_target = self.slice_segments(mel_scaled_labels, model_outputs.ids_slice,self.segment_size)
+                  mel_scaled_generation = feature_extractor._torch_extract_fbank_features(model_outputs.waveform.squeeze(1))[1]
+
+                  target_waveform = batch["waveform"].transpose(1, 2)
+                  target_waveform = self.slice_segments(
+                                      target_waveform,
+                                      model_outputs.ids_slice * feature_extractor.hop_length,
+                                      self.config.segment_size
+                                  )
+
+                  discriminator_target, fmaps_target = discriminator(target_waveform)
+                  discriminator_candidate, fmaps_candidate = discriminator(model_outputs.waveform.detach())
+                  #with autocast(enabled=False):
+                  if dict_state_grad_loss['discriminator']:
+
+
+                        loss_disc, loss_real_disc, loss_fake_disc = discriminator_loss(
+                            discriminator_target, discriminator_candidate
+                        )
+
+                        dk={"step_loss_disc": loss_disc.detach().item(),
+                            "step_loss_real_disc": loss_real_disc.detach().item(),
+                            "step_loss_fake_disc": loss_fake_disc.detach().item()}
+                        displayloss['dict_loss_discriminator']=dk
+                        loss_dd = loss_disc# + loss_real_disc + loss_fake_disc
+
+                disc_optimizer.zero_grad()
+                loss_dd.backward()
+
+
+                # scaler.scale(loss_dd).backward()
+                # scaler.unscale_(disc_optimizer )
+                grad_norm_d = clip_grad_value_(discriminator.parameters(), None)
+                disc_optimizer.step()
+
+
+                with autocast(enabled=training_args.fp16):
+
+
+                  # backpropagate
+                  if dict_state_grad_loss['k1']:
+                      loss_kl = kl_loss(
+                          model_outputs.prior_latents,
+                          model_outputs.posterior_log_variances,
+                          model_outputs.prior_means,
+                          model_outputs.prior_log_variances,
+                          model_outputs.labels_padding_mask,
+                      )
+                      loss_kl=loss_kl*training_args.weight_kl
+                      displayloss['loss_kl']=loss_kl.detach().item()
+                      #if displayloss['loss_kl']>=0:
+                        #  loss_kl.backward()
+
+                  if dict_state_grad_loss['mel']:
+                      loss_mel = torch.nn.functional.l1_loss(mel_scaled_target, mel_scaled_generation)
+                      displayloss['loss_mel'] = loss_mel.detach().item()
+                      train_losses_sum = train_losses_sum + displayloss['loss_mel']
+                    # if displayloss['loss_mel']>=0:
+                        #  loss_mel.backward()
+
+                  if dict_state_grad_loss['duration']:
+                      loss_duration=torch.sum(model_outputs.log_duration)*training_args.weight_duration
+                      displayloss['loss_duration'] = loss_duration.detach().item()
+                    #  if displayloss['loss_duration']>=0:
+                      #   loss_duration.backward()
+
+
+
+
+                  discriminator_target, fmaps_target = discriminator(target_waveform)
+
+                  discriminator_candidate, fmaps_candidate = discriminator(model_outputs.waveform.detach())
+
+                  if dict_state_grad_loss['generator']:
+                      loss_fmaps = feature_loss(fmaps_target, fmaps_candidate)
+                      loss_gen, losses_gen = generator_loss(discriminator_candidate)
+                      loss_gen=loss_gen * training_args.weight_gen
+                      displayloss['loss_gen'] = loss_gen.detach().item()
+                  #   loss_gen.backward(retain_graph=True)
+                      loss_fmaps=loss_fmaps * training_args.weight_fmaps
+                      displayloss['loss_fmaps'] = loss_fmaps.detach().item()
+                  #   loss_fmaps.backward(retain_graph=True)
+                      total_generator_loss = (
+                          loss_duration
+                          + loss_mel
+                          + loss_kl
+                          + loss_fmaps
+                          + loss_gen
+                      )
+
+                optimizer.zero_grad()
+                total_generator_loss.backward()
+                # scaler.scale(total_generator_loss).backward()
+                # scaler.unscale_(optimizer)
+                grad_norm_g = clip_grad_value_(self.parameters(), None)
+                optimizer.step()
+                # scaler.update()
+
+
+
+
+
+
+                # optimizer.step()
+
+
+
+
+                print(f"TRAINIG - batch {step}, waveform {(batch['waveform'].shape)}, lr {lr_scheduler.get_last_lr()[0]}... ")
+                print(f"display loss function  enable  :{displayloss}")
+
+                global_step +=1
+
+                # validation
+
+                do_eval = training_args.do_eval and (global_step % training_args.eval_steps == 0)
+                if do_eval:
+                    logger.info("Running validation... ")
+                    eval_losses_sum = 0
+                    cc=0;
+                    for step, batch in enumerate(eval_dataset):
+                        break
+                        if cc>2: break
+                        cc+=1
+                        with torch.no_grad():
+                            model_outputs = self.forward(
+                                input_ids=batch["input_ids"],
+                            attention_mask=batch["attention_mask"],
+                            labels=batch["labels"],
+                            labels_attention_mask=batch["labels_attention_mask"],
+                            speaker_id=batch["speaker_id"],
+
+
+                            return_dict=True,
+                            monotonic_alignment_function=None,
+                                )
+
+                        mel_scaled_labels = batch["mel_scaled_input_features"]
+                        mel_scaled_target = self.slice_segments(mel_scaled_labels, model_outputs.ids_slice,self.segment_size)
+                        mel_scaled_generation = feature_extractor._torch_extract_fbank_features(model_outputs.waveform.squeeze(1))[1]
+                        loss = loss_mel.detach().item()
+                        eval_losses_sum +=loss
+
+                        loss_mel = torch.nn.functional.l1_loss(mel_scaled_target, mel_scaled_generation)
+                        print(f"VALIDATION - batch {step}, waveform {(batch['waveform'].shape)}, step_loss_mel {loss} ... ")
+
+
+
+                    with torch.no_grad():
+                        full_generation_sample = self.full_generation_sample
+                        full_generation =self.forward(
+                          input_ids =full_generation_sample["input_ids"],
+                          attention_mask=full_generation_sample["attention_mask"],
+                          speaker_id=full_generation_sample["speaker_id"]
+                          )
+
+                        full_generation_waveform = full_generation.waveform.cpu().numpy()
+
+                    wandb.log({
+                    "eval_losses": eval_losses_sum,
+                    "full generations samples": [
+                        wandb.Audio(w.reshape(-1), caption=f"Full generation sample {epoch}", sample_rate=16000)
+                        for w in full_generation_waveform],})
+
+            wandb.log({"train_losses":train_losses_sum})
+
+        # add weight norms
+        # self.remove_weight_norm()
+
+        try:
+          torch.save(self.posterior_encoder.state_dict(), os.path.join(training_args.output_dir,"posterior_encoder.pt"))
+          torch.save(self.decoder.state_dict(), os.path.join(training_args.output_dir,"decoder.pt"))
+        except:pass
+
+
+        logger.info("Running final full generations samples... ")
+
+
+        with torch.no_grad():
+
+            full_generation_sample = self.full_generation_sample
+            full_generation = self.forward(
+                    input_ids=full_generation_sample["labels"],
+                    attention_mask=full_generation_sample["labels_attention_mask"],
+                    speaker_id=full_generation_sample["speaker_id"]
+                    )
+
+            full_generation_waveform = full_generation.waveform.cpu().numpy()
+
+        wandb.log({"eval_losses": eval_losses_sum,
+                "full generations samples": [
+                    wandb.Audio(w.reshape(-1), caption=f"Full generation sample {epoch}",
+                                sample_rate=16000) for w in full_generation_waveform],
+                })
+
+
+        logger.info("***** Training / Inference Done *****")
+
+    def forward_train(
+        self,
+        input_ids: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        speaker_id: Optional[int] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        labels: Optional[torch.FloatTensor] = None,
+        labels_attention_mask: Optional[torch.Tensor] = None,
+        text_encoder_output=None,
+        posterior_encode_output=None,
+        monotonic_alignment_function: Optional[Callable] = None,
+        speaker_embeddings=None
+    ) -> Union[Tuple[Any], VitsModelOutput]:
+
+        #output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states# if output_hidden_states is not None else self.config.output_hidden_states
+        )
+       # return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+
+       # if attention_mask is not None:
+        input_padding_mask = attention_mask.unsqueeze(-1).float()
+        #else:
+         #   input_padding_mask = torch.ones_like(input_ids).unsqueeze(-1).float()
+
+        # speaker_embeddings=None
+       # if labels_attention_mask is not None:
+        labels_padding_mask = labels_attention_mask.unsqueeze(1).float()
+        # else:
+        #     labels_attention_mask = torch.ones((labels.shape[0], labels.shape[2])).float().to(self.device)
+        #     labels_padding_mask = labels_attention_mask.unsqueeze(1)
+        if text_encoder_output is None:
+          text_encoder_output = self.text_encoder(
+                  input_ids=input_ids,
+                  padding_mask=input_padding_mask,
+                  attention_mask=attention_mask,
+                  output_attentions=output_attentions,
+                  output_hidden_states=output_hidden_states,
+                  return_dict=return_dict,
+              )
+        #hidden_states = text_encoder_output[0] #if not return_dict else text_encoder_output.last_hidden_state
+        hidden_states = text_encoder_output[0].transpose(1, 2)
+        input_padding_mask = input_padding_mask.transpose(1, 2)
+        prior_means = text_encoder_output[1].transpose(1, 2) #if not return_dict else text_encoder_output.prior_means
+        prior_log_variances = text_encoder_output[2].transpose(1, 2) #if not return_dict else text_encoder_output.prior_log_variances
+
+        if posterior_encode_output is None:
+             latents, posterior_means, posterior_log_variances = self.posterior_encoder(
+                labels, labels_padding_mask, speaker_embeddings
+            )
+        else:
+             latents=posterior_encode_output['posterior_latents']
+             posterior_means=posterior_encode_output['posterior_means']
+             posterior_log_variances=posterior_encode_output['posterior_log_variances']
+
+        prior_latents = self.flow(latents, labels_padding_mask, speaker_embeddings, reverse=False)
+
+        # prior_means, prior_log_variances = prior_means.transpose(1, 2), prior_log_variances.transpose(1, 2)
+        with torch.no_grad():
+            # negative cross-entropy
+
+            # [batch_size, d, latent_length]
+            prior_variances = torch.exp(-2 * prior_log_variances)
+            # [batch_size, 1, latent_length]
+            neg_cent1 = torch.sum(-0.5 * math.log(2 * math.pi) - prior_log_variances, [1], keepdim=True)
+            # [batch_size, text_length, d] x [batch_size, d, latent_length] = [batch_size, text_length, latent_length]
+            neg_cent2 = torch.matmul(-0.5 * (prior_latents**2).transpose(1, 2), prior_variances)
+            # [batch_size, text_length, d] x [batch_size, d, latent_length] = [batch_size, text_length, latent_length]
+            neg_cent3 = torch.matmul(prior_latents.transpose(1, 2), (prior_means * prior_variances))
+            # [batch_size, 1, latent_length]
+            neg_cent4 = torch.sum(-0.5 * (prior_means**2) * prior_variances, [1], keepdim=True)
+
+            # [batch_size, text_length, latent_length]
+            neg_cent = neg_cent1 + neg_cent2 + neg_cent3 + neg_cent4
+
+            attn_mask = torch.unsqueeze(input_padding_mask, 2) * torch.unsqueeze(labels_padding_mask, -1)
+
+            attn = monotonic_alignment_function(neg_cent, attn_mask.squeeze(1)).unsqueeze(1).detach()
+
+        durations = attn.sum(2)
+
+        #if self.config.use_stochastic_duration_prediction:
+        log_duration = self.duration_predictor(
+                hidden_states, input_padding_mask, speaker_embeddings, durations=durations, reverse=False
+            )
+        log_duration = log_duration / torch.sum(input_padding_mask)
+        # else:
+        #     log_duration_padded = torch.log(durations + 1e-6) * input_padding_mask
+        #     log_duration = self.duration_predictor(hidden_states, input_padding_mask, speaker_embeddings)
+        #     log_duration = torch.sum((log_duration - log_duration_padded) ** 2, [1, 2]) / torch.sum(input_padding_mask)
+
+        # expand priors
+        prior_means = torch.matmul(attn.squeeze(1), prior_means.transpose(1, 2)).transpose(1, 2)
+        prior_log_variances = torch.matmul(attn.squeeze(1), prior_log_variances.transpose(1, 2)).transpose(1, 2)
+
+        label_lengths = labels_attention_mask.sum(dim=1)
+        latents_slice, ids_slice = self.rand_slice_segments(latents, label_lengths, segment_size=self.segment_size)
+        waveform = self.decoder(latents_slice, speaker_embeddings)
+        return waveform,ids_slice,log_duration,prior_latents,posterior_log_variances,prior_means,prior_log_variances,labels_padding_mask