import torch
from torch import nn
from transformers.trainer_pt_utils import LabelSmoother
from typing import List, Tuple # Added for type hints
from torchmetrics.classification import MulticlassAccuracy
IGNORE_TOKEN_ID = LabelSmoother.ignore_index


class EncoderProjector(nn.Module):
    """
    The encoder projector module. It is used to project the encoder outputs to the same dimension as the language model.
    Modified from https://github.com/X-LANCE/SLAM-LLM/blob/main/src/slam_llm/models/projector.py.
    Args:
        encoder_dim (:obj:`int`): The dimension of the encoder outputs.
        llm_dim (:obj:`int`): The dimension of the language model.
        downsample_rate (:obj:`int`, `optional`, defaults to 5): The downsample rate to use.
    """

    def __init__(self, encoder_dim, llm_dim, downsample_rate=5):
        super().__init__()
        self.downsample_rate = downsample_rate
        self.linear1 = nn.Linear(encoder_dim * self.downsample_rate, llm_dim)
        self.relu = nn.ReLU()
        self.linear2 = nn.Linear(llm_dim, llm_dim)

    def forward(self, x):

        batch_size, seq_len, feat_dim = x.size()
        num_frames_to_discard = seq_len % self.downsample_rate
        if num_frames_to_discard > 0:
            x = x[:, :-num_frames_to_discard, :]
        seq_len = x.size(1)

        x = x.contiguous()
        x = x.view(
            batch_size, seq_len // self.downsample_rate, feat_dim * self.downsample_rate
        )

        x = self.linear1(x)
        x = self.relu(x)
        x = self.linear2(x)
        return x


class SPEECH_LLM(nn.Module):
    """
    The Speech-to-Text model. It consists of an encoder, a language model and an encoder projector.
    The encoder is used to extract speech features from the input speech signal.
    The encoder projector is used to project the encoder outputs to the same dimension as the language model.
    The language model is used to generate the text from the speech features.
    Args:
        encoder (:obj:`nn.Module`): The encoder module.
        llm (:obj:`nn.Module`): The language model module.
        encoder_projector (:obj:`nn.Module`): The encoder projector module.
    """

    def __init__(
        self,
        encoder: nn.Module,
        llm: nn.Module,
        encoder_projector: nn.Module,
        codec_lm: nn.Module = None,
        codec_lm_padding_side: str = "left",
    ):
        super().__init__()
        self.encoder = encoder
        self.llm = llm
        self.encoder_projector = encoder_projector
        self.codec_lm = codec_lm
        if self.codec_lm:
            self.speech_token_projector = nn.Linear(
                self.llm.config.hidden_size, self.codec_lm.config.hidden_size
            )
            self.codec_lm_head = nn.Linear(
                self.codec_lm.config.hidden_size, self.codec_lm.config.vocab_size
            )
            # to torch.float16
            self.speech_token_projector = self.speech_token_projector.to(
                dtype=torch.float16
            )
            self.codec_lm_head = self.codec_lm_head.to(dtype=torch.float16)
            self.loss_fct = torch.nn.CrossEntropyLoss()
            self.codec_lm_padding_side = codec_lm_padding_side

            self.audio_accuracy_metric = MulticlassAccuracy(
                self.codec_lm.vocab_size,
                top_k=10,
                average="micro",
                multidim_average="global",
                ignore_index=IGNORE_TOKEN_ID,
            )
    def _merge_input_ids_with_speech_features(
        self, speech_features, inputs_embeds, input_ids, attention_mask, labels=None
    ):
        """
        Merge the speech features with the input_ids and attention_mask. This is done by replacing the speech tokens
        with the speech features and padding the input_ids to the maximum length of the speech features.
        Modified from https://github.com/huggingface/transformers/blob/main/src/transformers/models/llava/modeling_llava.py#L277.
        Args:
            speech_features (:obj:`torch.Tensor`): The speech features to merge with the input_ids.
            inputs_embeds (:obj:`torch.Tensor`): The embeddings of the input_ids.
            input_ids (:obj:`torch.Tensor`): The input ids to merge.
            attention_mask (:obj:`torch.Tensor`): The attention mask to merge.
            labels (:obj:`torch.Tensor`, `optional`): The labels to merge.
        Returns:
            :obj:`Tuple(torch.Tensor)`: The merged embeddings, attention mask, labels and position ids.
        """
        num_speechs, speech_len, embed_dim = speech_features.shape
        batch_size, sequence_length = input_ids.shape
        left_padding = not torch.sum(
            input_ids[:, -1] == torch.tensor(self.llm.config.pad_token_id)
        )
        # 1. Create a mask to know where special speech tokens are
        special_speech_token_mask = input_ids == self.llm.config.default_speech_token_id
        num_special_speech_tokens = torch.sum(special_speech_token_mask, dim=-1)
        # Compute the maximum embed dimension
        max_embed_dim = (
            num_special_speech_tokens.max() * (speech_len - 1)
        ) + sequence_length
        batch_indices, non_speech_indices = torch.where(
            input_ids != self.llm.config.default_speech_token_id
        )

        # 2. Compute the positions where text should be written
        # Calculate new positions for text tokens in merged speech-text sequence.
        # `special_speech_token_mask` identifies speech tokens. Each speech token will be replaced by `nb_text_tokens_per_speechs - 1` text tokens.
        # `torch.cumsum` computes how each speech token shifts subsequent text token positions.
        # - 1 to adjust for zero-based indexing, as `cumsum` inherently increases indices by one.
        new_token_positions = (
            torch.cumsum((special_speech_token_mask * (speech_len - 1) + 1), -1) - 1
        )
        nb_speech_pad = max_embed_dim - 1 - new_token_positions[:, -1]
        if left_padding:
            new_token_positions += nb_speech_pad[:, None]  # offset for left padding
        text_to_overwrite = new_token_positions[batch_indices, non_speech_indices]

        # 3. Create the full embedding, already padded to the maximum position
        final_embedding = torch.zeros(
            batch_size,
            max_embed_dim,
            embed_dim,
            dtype=inputs_embeds.dtype,
            device=inputs_embeds.device,
        )
        final_attention_mask = torch.zeros(
            batch_size,
            max_embed_dim,
            dtype=attention_mask.dtype,
            device=inputs_embeds.device,
        )
        if labels is not None:
            final_labels = torch.full(
                (batch_size, max_embed_dim),
                IGNORE_TOKEN_ID,
                dtype=input_ids.dtype,
                device=input_ids.device,
            )
        # In case the Vision model or the Language model has been offloaded to CPU, we need to manually
        # set the corresponding tensors into their correct target device.
        target_device = inputs_embeds.device
        batch_indices, non_speech_indices, text_to_overwrite = (
            batch_indices.to(target_device),
            non_speech_indices.to(target_device),
            text_to_overwrite.to(target_device),
        )
        attention_mask = attention_mask.to(target_device)

        # 4. Fill the embeddings based on the mask. If we have ["hey" "<speech>", "how", "are"]
        # we need to index copy on [0, 577, 578, 579] for the text and [1:576] for the speech features
        final_embedding[batch_indices, text_to_overwrite] = inputs_embeds[
            batch_indices, non_speech_indices
        ]
        final_attention_mask[batch_indices, text_to_overwrite] = attention_mask[
            batch_indices, non_speech_indices
        ]
        if labels is not None:
            final_labels[batch_indices, text_to_overwrite] = labels[
                batch_indices, non_speech_indices
            ]

        # 5. Fill the embeddings corresponding to the speechs. Anything that is not `text_positions` needs filling (#29835)
        speech_to_overwrite = torch.full(
            (batch_size, max_embed_dim),
            True,
            dtype=torch.bool,
            device=inputs_embeds.device,
        )
        speech_to_overwrite[batch_indices, text_to_overwrite] = False
        speech_to_overwrite &= speech_to_overwrite.cumsum(-1) - 1 >= nb_speech_pad[
            :, None
        ].to(target_device)

        if speech_to_overwrite.sum() != speech_features.shape[:-1].numel():
            raise ValueError(
                f"The input provided to the model are wrong. The number of speech tokens is {torch.sum(special_speech_token_mask)} while"
                f" the number of speech given to the model is {num_speechs}. This prevents correct indexing and breaks batch generation."
            )

        final_embedding[speech_to_overwrite] = (
            speech_features.contiguous().reshape(-1, embed_dim).to(target_device)
        )
        final_attention_mask |= speech_to_overwrite
        position_ids = (final_attention_mask.cumsum(-1) - 1).masked_fill_(
            (final_attention_mask == 0), 1
        )

        # 6. Mask out the embedding at padding positions, as we later use the past_key_value value to determine the non-attended tokens.
        batch_indices, pad_indices = torch.where(
            input_ids == self.llm.config.pad_token_id
        )
        indices_to_mask = new_token_positions[batch_indices, pad_indices]

        final_embedding[batch_indices, indices_to_mask] = 0

        if labels is None:
            final_labels = None

        return final_embedding, final_attention_mask, final_labels, position_ids

    def forward(
        self,
        fbank: torch.Tensor = None,
        input_ids: torch.LongTensor = None,
        attention_mask: torch.Tensor = None,
        labels: torch.LongTensor = None,
    ):
        encoder_outs = self.encoder(fbank)

        speech_features = self.encoder_projector(encoder_outs)

        inputs_embeds = self.llm.get_input_embeddings()(input_ids)

        (
            inputs_embeds,
            attention_mask,
            labels,
            _,
        ) = self._merge_input_ids_with_speech_features(
            speech_features, inputs_embeds, input_ids, attention_mask, labels
        )

        model_outputs = self.llm(
            inputs_embeds=inputs_embeds, attention_mask=attention_mask, labels=labels
        )

        with torch.no_grad():
            preds = torch.argmax(model_outputs.logits, -1)
            acc = compute_accuracy(
                preds.detach()[:, :-1],
                labels.detach()[:, 1:],
                ignore_label=IGNORE_TOKEN_ID,
            )
        return model_outputs.loss, acc

    def forward_with_speech_output(
        self,
        fbank: torch.Tensor = None,
        input_ids: torch.LongTensor = None,
        attention_mask: torch.Tensor = None,
        labels: torch.LongTensor = None,
        speech_codec_ids: torch.LongTensor = None,
    ):
        encoder_outs = self.encoder(fbank)

        speech_features = self.encoder_projector(encoder_outs)

        inputs_embeds = self.llm.get_input_embeddings()(input_ids)

        (
            inputs_embeds,
            attention_mask,
            labels,
            _,
        ) = self._merge_input_ids_with_speech_features(
            speech_features, inputs_embeds, input_ids, attention_mask, labels
        )

        # get the label start_index in inputs_embeds from labels
        text_label_start_index_list = []
        for i in range(labels.shape[0]):
            text_label_start_index = torch.where(labels[i] != IGNORE_TOKEN_ID)[0][0]
            text_label_start_index_list.append(text_label_start_index)

        model_outputs = self.llm(
            inputs_embeds=inputs_embeds, attention_mask=attention_mask, labels=labels, output_hidden_states=True
        )
        text_loss = model_outputs.loss

        # prepare codec lm inputs
        audio_codes_lens = torch.tensor(
            [len(x) for x in speech_codec_ids], dtype=torch.int64, device=input_ids.device
        )
        # print(audio_codes_lens, "audio_codes_lens")
        max_len_speech_codec = max(audio_codes_lens)
        delay_step = 2
        audio_codes = torch.full(
            (inputs_embeds.shape[0], max_len_speech_codec + inputs_embeds.shape[1] + 1),
            self.codec_lm.config.pad_token_id,
            dtype=torch.int64,
            device=input_ids.device
        )
        audio_labels = audio_codes.clone()
        total_len = audio_codes.shape[1]

        for i, speech_codec in enumerate(speech_codec_ids):
            text_label_start_index = text_label_start_index_list[i]
            speech_codec = torch.tensor(
                speech_codec, dtype=torch.int64, device=input_ids.device
            )
            speech_codec_len = len(speech_codec)

            # Calculate lengths of non-padding content
            codes_len = text_label_start_index + delay_step + 1 + speech_codec_len
            # Actual label content length (speech codec tokens + eos token)
            labels_actual_content_len = speech_codec_len + 1

            if self.codec_lm_padding_side == "right":
                # Fill audio_codes (right padding)
                codes_end_idx = text_label_start_index + delay_step + 1 + speech_codec_len
                audio_codes[i, :text_label_start_index + delay_step + 1] = self.codec_lm.config.bos_token_id # mask token_id
                audio_codes[i, text_label_start_index + delay_step + 1 : codes_end_idx] = speech_codec

                # Fill audio_labels (right padding)
                labels_start_idx = text_label_start_index + delay_step
                labels_speech_end_idx = labels_start_idx + speech_codec_len
                audio_labels[i, labels_start_idx : labels_speech_end_idx] = speech_codec
                audio_labels[i, labels_speech_end_idx] = self.codec_lm.config.eos_token_id

            elif self.codec_lm_padding_side == "left":
                # Calculate start indices for left padding (shifting content to the right)
                codes_start_idx = total_len - codes_len
                labels_start_idx = total_len - labels_actual_content_len # Start index for the actual label content

                # Fill audio_codes (left padding)
                codes_speech_start_idx = codes_start_idx + text_label_start_index + delay_step + 1
                audio_codes[i, codes_start_idx : codes_speech_start_idx] = self.codec_lm.config.bos_token_id # mask token_id
                audio_codes[i, codes_speech_start_idx : total_len] = speech_codec

                # Fill audio_labels (left padding)
                labels_speech_end_idx = labels_start_idx + speech_codec_len
                # Note: The beginning part remains pad_token_id
                audio_labels[i, labels_start_idx : labels_speech_end_idx] = speech_codec
                audio_labels[i, labels_speech_end_idx] = self.codec_lm.config.eos_token_id
            else:
                 raise ValueError(f"Unsupported padding side: {self.codec_lm_padding_side}")

        audio_attention_mask = audio_codes.ne(self.codec_lm.config.pad_token_id) # TODO: do we need to change bos tokens to pad token or mask token?
        audio_embeddings = self.codec_lm.get_input_embeddings()(audio_codes)
       
        # input_ids: seq_len T1, audio_codec seq_len T2
        text_last_hidden_outputs = model_outputs.hidden_states[-1]
        text_input_embeds = inputs_embeds + text_last_hidden_outputs
        text_input_embeds = self.speech_token_projector(text_input_embeds)

        T_merged = text_input_embeds.shape[1]
        T_audio = audio_embeddings.shape[1]

        if self.codec_lm_padding_side == "right":
            # Add to the beginning for right padding
            audio_embeddings[:, :T_merged] += text_input_embeds
        elif self.codec_lm_padding_side == "left":
            # Need to add to the shifted position for left padding
            # Calculate the length of the non-padded sequence for each item
            seq_lens = audio_attention_mask.sum(dim=1) # Shape (B)
            for i in range(audio_embeddings.shape[0]):
                item_len = seq_lens[i].item() # Get the non-padded length for item i
                start_idx_content = T_audio - item_len # Start index of the content for item i
                end_idx_target = start_idx_content + T_merged # End index of the target slice within the content
                # Add the text_input_embeds to the calculated slice
                if end_idx_target > T_audio:
                    # If the text input is longer than the audio input, we need to pad the audio input
                    cut_off_len = T_audio - start_idx_content
                    audio_embeddings[i, start_idx_content:end_idx_target] = text_input_embeds[i, :cut_off_len]
                else:
                    audio_embeddings[i, start_idx_content:end_idx_target] += text_input_embeds[i]
        else:
             raise ValueError(f"Unsupported padding side: {self.codec_lm_padding_side}")

        speech_outputs = self.codec_lm(
            attention_mask=audio_attention_mask,
            inputs_embeds=audio_embeddings,
            return_dict=True,
            output_hidden_states=True,
        )
        last_hidden_state = speech_outputs.hidden_states[-1].clone()

        audio_logits = self.codec_lm_head(last_hidden_state) # shape, B, T, vocab_size
        audio_logits = audio_logits.contiguous().view(-1, self.codec_lm.config.vocab_size)
        audio_labels = audio_labels.contiguous().view(-1)
        audio_labels = audio_labels.masked_fill(
            audio_labels == self.codec_lm.config.pad_token_id, IGNORE_TOKEN_ID
        )
        codec_loss = self.loss_fct(audio_logits, audio_labels)
        audio_preds = torch.argmax(audio_logits, -1)

        
        with torch.no_grad():
            preds = torch.argmax(model_outputs.logits, -1)
            acc = compute_accuracy(
                preds.detach()[:, :-1],
                labels.detach()[:, 1:],
                ignore_label=IGNORE_TOKEN_ID,
            )
            audio_acc = compute_accuracy(
                audio_preds.detach(),
                audio_labels.detach(),
                ignore_label=IGNORE_TOKEN_ID,
            )
            audio_topk_acc = self.audio_accuracy_metric(
                audio_logits.detach(),
                audio_labels.detach()).item()


        return text_loss, acc, codec_loss, audio_acc, audio_topk_acc
    
    def decode(
        self,
        fbank: torch.Tensor = None,
        input_ids: torch.LongTensor = None,
        attention_mask: torch.Tensor = None,
        **kwargs,
    ):

        encoder_outs = self.encoder(fbank)
        speech_features = self.encoder_projector(encoder_outs)
        speech_features = speech_features.to(torch.float16)
        inputs_embeds = self.llm.get_input_embeddings()(input_ids)
        (
            inputs_embeds,
            attention_mask,
            _,
            _,
        ) = self._merge_input_ids_with_speech_features(
            speech_features, inputs_embeds, input_ids, attention_mask
        )
        generated_ids = self.llm.generate(
            inputs_embeds=inputs_embeds,
            attention_mask=attention_mask,
            max_new_tokens=kwargs.get("max_new_tokens", 1024),
            num_beams=kwargs.get("num_beams", 1),
            do_sample=kwargs.get("do_sample", True),
            min_length=kwargs.get("min_length", 1),
            top_p=kwargs.get("top_p", 0.5),
            top_k=kwargs.get("top_k", 20),
            repetition_penalty=kwargs.get("repetition_penalty", 1.1),
            temperature=kwargs.get("temperature", 0.7),
            bos_token_id=self.llm.config.bos_token_id,
            eos_token_id=self.llm.config.eos_token_id,
            pad_token_id=self.llm.config.pad_token_id,
        )

        # generated_ids = self.llm.generate(
        #     inputs_embeds=inputs_embeds,
        #     max_new_tokens=kwargs.get("max_new_tokens", 200),
        #     num_beams=kwargs.get("num_beams", 1),
        #     do_sample=kwargs.get("do_sample", False),
        #     min_length=kwargs.get("min_length", 1),
        #     top_p=kwargs.get("top_p", 1.0),
        #     repetition_penalty=kwargs.get("repetition_penalty", 1.0),
        #     temperature=kwargs.get("temperature", 1.0),
        #     length_penalty=kwargs.get("length_penalty", 1.0),
        #     bos_token_id=self.llm.config.bos_token_id,
        #     eos_token_id=self.llm.config.eos_token_id,
        #     pad_token_id=self.llm.config.pad_token_id,
        # )
        return generated_ids

    def decode_with_speech_output(
        self,
        fbank: torch.Tensor = None,
        input_ids: torch.LongTensor = None, # Prompt input_ids
        attention_mask: torch.Tensor = None, # Prompt attention_mask
        max_text_new_tokens: int = 1024,
        max_speech_new_tokens: int = 1024, # Max length for speech tokens
        llm_kwargs: dict = None, # Kwargs for text LLM generate
        codec_lm_kwargs: dict = None # Kwargs for codec LM (e.g., temperature for sampling) - NOT IMPLEMENTED YET
    ) -> Tuple[torch.LongTensor, List[List[int]]]:
        """
        Generates text and corresponding speech tokens using the revised logic.

        Args:
            fbank: Input audio features.
            input_ids: Input token IDs for the text prompt.
            attention_mask: Attention mask for the text prompt.
            max_text_new_tokens: Max new tokens for text generation.
            max_speech_new_tokens: Max new tokens for speech generation.
            llm_kwargs: Additional arguments for self.llm.generate.
            codec_lm_kwargs: Additional arguments for self.codec_lm.generate.

        Returns:
            Tuple[torch.LongTensor, List[List[int]]]:
                - generated_text_ids: Tensor of generated text token IDs (including prompt).
                - generated_speech_tokens: List of lists, where each inner list contains
                                           the generated speech codec tokens for a batch item.
        """
        assert fbank.shape[0] == 1, "Batch size must be 1 for speech generation."
        if not self.codec_lm or not self.speech_token_projector or not self.codec_lm_head:
            raise ValueError("codec_lm and associated layers must be initialized to generate speech output.")

        device = next(self.parameters()).device # Use model's device
        batch_size = fbank.shape[0]

        # --- 1. Prepare Prompt Embeddings ---
        encoder_outs = self.encoder(fbank)
        speech_features = self.encoder_projector(encoder_outs)
        speech_features = speech_features.to(self.llm.dtype) # Ensure matching dtype

        prompt_embeds = self.llm.get_input_embeddings()(input_ids)

        # Merge speech features with prompt embeddings
        (
            merged_prompt_inputs_embeds,
            merged_prompt_attention_mask,
            _,
            _,
        ) = self._merge_input_ids_with_speech_features(
            speech_features, prompt_embeds, input_ids, attention_mask
        )

        # --- 2. Generate Text using LLM ---
        # Use merged embeds/mask as input to generate
        # Ensure kwargs passed are suitable for llm.generate
        # Note: Using default generation params from `decode` if not provided in kwargs
        final_llm_kwargs = {
            "bos_token_id": self.llm.config.bos_token_id,
            "eos_token_id": self.llm.config.eos_token_id,
            "pad_token_id": self.llm.config.pad_token_id,
            "num_beams": 1,
            "do_sample": True, # Typically false for S2ST/S2TT tasks unless exploration needed
            "top_p": 0.5,
            "top_k": 20,
            "repetition_penalty": 1.1,
            "temperature": 0.7,
            **(llm_kwargs or {}) # User-provided kwargs override defaults
        }

        text_outputs = self.llm.generate(
            inputs_embeds=merged_prompt_inputs_embeds,
            attention_mask=merged_prompt_attention_mask,
            max_new_tokens=max_text_new_tokens,
            return_dict_in_generate=True,
            output_hidden_states=True,
            **final_llm_kwargs
        )

        generated_text_ids = text_outputs.sequences # [B, S_full]
        thinker_token_embeds = [
            token_hidden_states[0].to(self.llm.device) for token_hidden_states in text_outputs.hidden_states
        ]
        thinker_hidden_states = [
            token_hidden_states[-1].to(self.llm.device) for token_hidden_states in text_outputs.hidden_states
        ]
        thinker_reply_part = torch.cat(thinker_hidden_states[1:], dim=1) + torch.cat(thinker_token_embeds[1:], dim=1)
        thinker_prompt_part = thinker_hidden_states[0] + thinker_token_embeds[0]

        thinker_prompt_part = self.speech_token_projector(thinker_prompt_part) # [B, S_full, D_codec]
        thinker_reply_part = self.speech_token_projector(thinker_reply_part) # [B, S_full, D_codec]
        
        
        delay_step = 2
        thinker_prompt_part_seq_len = thinker_prompt_part.shape[1]
        talker_input_ids = torch.full(
            (batch_size, thinker_prompt_part_seq_len + delay_step), self.codec_lm.config.bos_token_id, dtype=torch.long, device=self.llm.device
        )
        talker_inputs_embeds = self.codec_lm.get_input_embeddings()(talker_input_ids) # [B, S_full, D_codec]
        thinker_input_embeds = torch.cat(
            [
                thinker_prompt_part,
                thinker_reply_part[:, :delay_step, :],
            ],
            dim=1,
        )
        talker_inputs_embeds += thinker_input_embeds
        thinker_reply_part = thinker_reply_part[:, delay_step:, :] # [B, S_full, D_codec]


        past_key_values = None
        # generated_speech_tokens_list = [[] for _ in range(batch_size)]
        # unfinished_sequences = torch.ones(batch_size, dtype=torch.long, device=device)
        generated_speech_tokens_list = []
        next_token_ids = None
        # text_context_len = projected_text_embeds.shape[1] # S_full
        for t in range(max_speech_new_tokens):
            # Get embedding for the *current* input token ID (initially BOS, then generated tokens)
            # current_speech_embeds = self.codec_lm.get_input_embeddings()(current_speech_input_ids) # [B, 1, D_codec]
            if next_token_ids is not None:
                talker_inputs_embeds = self.codec_lm.get_input_embeddings()(next_token_ids) # [B, 1, D_codec]
                if thinker_reply_part.shape[1] > 0:
                    talker_inputs_embeds += thinker_reply_part[:, :1, :]
                    thinker_reply_part = thinker_reply_part[:, 1:, :] # Remove the first token for next step
            # # Add the projected text embedding corresponding to the current timestep `t`
            # if t < text_context_len:
            #     # Text context from the full generated text sequence
            #     current_text_context_embed = projected_text_embeds[:, t:t+1, :] # [B, 1, D_codec]
            #     inputs_embeds = current_speech_embeds + current_text_context_embed
            # else:
            #     # No more text context to add
            #     inputs_embeds = current_speech_embeds
                
            # Forward pass through codec LM for one step
            # We provide inputs_embeds directly, bypassing prepare_inputs_for_generation
            codec_outputs = self.codec_lm(
                inputs_embeds=talker_inputs_embeds, # Combined embedding for this step
                past_key_values=past_key_values,
                use_cache=True,
                return_dict=True,
                output_hidden_states=True,
                # No attention mask needed here when using past_key_values and single token input
            )
            last_token_hidden_state = codec_outputs.hidden_states[-1][:, -1, :] # [B, D_codec]
            # Get logits for the *last* token generated in this step
            next_token_logits = self.codec_lm_head(last_token_hidden_state) # Use -1 index
            # suppress tokens between 4096:len(vocab)-3
            next_token_logits[:, 4096:-3] = -float("Inf")
            next_token_ids = topk_sampling(
                next_token_logits,
            )
            print(next_token_ids, "next_token_ids", t, next_token_ids.shape)
            if next_token_ids[0, 0] == self.codec_lm.config.eos_token_id:
                break
            # current_speech_input_ids = next_token_ids # Use the newly generated token ID as input for next step
            past_key_values = codec_outputs.past_key_values # Update KV cache
            generated_speech_tokens_list.append(next_token_ids.squeeze(1).cpu().tolist()[0])
        # --- 6. Return Results ---
        return generated_text_ids, generated_speech_tokens_list

def compute_accuracy(pad_outputs, pad_targets, ignore_label):
    """Calculate accuracy.
    Copied from https://github.com/X-LANCE/SLAM-LLM/blob/main/src/slam_llm/utils/metric.py
    Args:
        pad_outputs (LongTensor): Prediction tensors (B, Lmax).
        pad_targets (LongTensor): Target label tensors (B, Lmax).
        ignore_label (int): Ignore label id.

    Returns:
        float: Accuracy value (0.0 - 1.0).

    """
    mask = pad_targets != ignore_label
    numerator = torch.sum(
        pad_outputs.masked_select(mask) == pad_targets.masked_select(mask)
    )
    denominator = torch.sum(mask)
    return numerator.float() / denominator.float()


def topk_sampling(
    logits,
    top_k=50,
    top_p=0.95,
    temperature=0.8,
):
    if temperature != 1.0:
        logits = logits / temperature
    # Top-p/top-k filtering
    logits_filtered = top_k_top_p_filtering(
        logits.clone(), top_k=top_k, top_p=top_p, min_tokens_to_keep=2
    )
    # Sample
    probs = torch.nn.functional.softmax(logits_filtered, dim=-1)
    tokens = torch.multinomial(probs, num_samples=1)

    return tokens


# https://github.com/microsoft/unilm/blob/master/xtune/src/transformers/modeling_utils.py
def top_k_top_p_filtering(
    logits, top_k=20, top_p=0.5, filter_value=-float("Inf"), min_tokens_to_keep=1
):
    """Filter a distribution of logits using top-k and/or nucleus (top-p) filtering
    Args:
        logits: logits distribution shape (batch size, vocabulary size)
        if top_k > 0: keep only top k tokens with highest probability (top-k filtering).
        if top_p < 1.0: keep the top tokens with cumulative probability >= top_p (nucleus filtering).
            Nucleus filtering is described in Holtzman et al. (http://arxiv.org/abs/1904.09751)
        Make sure we keep at least min_tokens_to_keep per batch example in the output
    From: https://gist.github.com/thomwolf/1a5a29f6962089e871b94cbd09daf317
    """
    if top_k > 0:
        top_k = min(max(top_k, min_tokens_to_keep), logits.size(-1))  # Safety check
        # Remove all tokens with a probability less than the last token of the top-k
        indices_to_remove = logits < torch.topk(logits, top_k)[0][..., -1, None]
        logits[indices_to_remove] = filter_value

    if top_p < 1.0:
        sorted_logits, sorted_indices = torch.sort(logits, descending=True)
        cumulative_probs = torch.cumsum(
            torch.nn.functional.softmax(sorted_logits, dim=-1), dim=-1
        )

        # Remove tokens with cumulative probability above the threshold (token with 0 are kept)
        sorted_indices_to_remove = cumulative_probs > top_p
        if min_tokens_to_keep > 1:
            # Keep at least min_tokens_to_keep (set to min_tokens_to_keep-1 because we add the first one below)
            sorted_indices_to_remove[..., :min_tokens_to_keep] = 0
        # Shift the indices to the right to keep also the first token above the threshold
        sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone()
        sorted_indices_to_remove[..., 0] = 0

        # scatter sorted tensors to original indexing
        indices_to_remove = sorted_indices_to_remove.scatter(
            1, sorted_indices, sorted_indices_to_remove
        )
        logits[indices_to_remove] = filter_value
    return logits