"""Projector that maps hidden states from the LLM component to multimodal logits."""

import torch
from torch import nn

from dataclasses import dataclass
from typing import Optional, Tuple

from .common import HiggsAudioPreTrainedModel
from .configuration_higgs_audio import HiggsAudioConfig


@dataclass
class HiggsAudioDecoderLayerOutput:
    logits: torch.FloatTensor
    audio_logits: torch.FloatTensor
    attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
    past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None


class HiggsAudioDecoderProjector(HiggsAudioPreTrainedModel):
    """Projection layers that map hidden states from the LLM component to audio / text logits.

    We support two type of audio head:
    - Basic Audio Head:
        Directly map the hidden states to audio logits for all the codebooks.
    """

    def __init__(self, config: HiggsAudioConfig, layer_idx: Optional[int] = None):
        super().__init__(config)
        self.text_lm_head = nn.Linear(config.text_config.hidden_size, config.text_config.vocab_size, bias=False)
        self.audio_lm_head = nn.Linear(
            config.text_config.hidden_size,
            config.audio_num_codebooks * (config.audio_codebook_size + 2),
            bias=False,
        )

        # Initialize weights and apply final processing
        self.post_init()

    def forward(
        self,
        hidden_states,
        audio_out_mask,
        label_audio_ids=None,
        attention_mask=None,
        position_ids=None,
        past_key_values=None,
        use_cache=None,
        output_attentions=None,
        output_hidden_states=None,
        output_audio_hidden_states=False,
        cache_position=None,
    ):
        """
        Args:
            hidden_states (`torch.Tensor` of shape `(batch_size, seq_len, hidden_size)`):
                Hidden states from the LLM component
            audio_out_mask (`torch.Tensor` of shape `(batch_size, seq_len)`):
                Mask for identifying the audio out tokens.
            label_audio_ids (`torch.Tensor` of shape `(num_codebooks, num_audio_out_tokens)`):
                Label tokens for the audio-out part. This is used for calculating the logits if RQ-Transformer is used.
            attention_mask (`torch.Tensor` of shape `(batch_size, seq_len)`):
                Mask to avoid performing attention on padding token indices
            position_ids (`torch.Tensor` of shape `(batch_size, seq_len)`):
                Position ids for the input tokens

        Returns:
            logits (`torch.Tensor` of shape `(batch_size, seq_len, vocab_size)`):
                Logits for text tokens
            audio_logits (`torch.Tensor` of shape `(num_audio_out_tokens, audio_num_codebooks * audio_codebook_size)`):
                Logits for audio tokens. We ensure `num_text_tokens + num_audio_tokens == batch_size * seq_len`
        """
        logits = self.text_lm_head(hidden_states)

        all_hidden_states = () if output_hidden_states else None
        all_self_attns = () if output_attentions else None
        next_decoder_cache = None

        # TODO(sxjscience) Need to check if DeepSpeed Zero3 supports zero-shape input.
        if self.config.audio_decoder_proj_num_layers > 0:
            # create position embeddings to be shared across the decoder layers
            position_embeddings = self.rotary_emb(hidden_states, position_ids)
            for decoder_layer in self.transformer_layers:
                if output_hidden_states:
                    all_hidden_states += (hidden_states,)

                if self.gradient_checkpointing and self.training:
                    layer_outputs = self._gradient_checkpointing_func(
                        decoder_layer.__call__,
                        hidden_states,
                        attention_mask,
                        position_ids,
                        past_key_values,
                        output_attentions,
                        use_cache,
                        cache_position,
                        position_embeddings,
                    )
                else:
                    layer_outputs = decoder_layer(
                        hidden_states,
                        attention_mask=attention_mask,
                        position_ids=position_ids,
                        past_key_value=past_key_values,
                        output_attentions=output_attentions,
                        use_cache=use_cache,
                        cache_position=cache_position,
                        position_embeddings=position_embeddings,
                    )
                hidden_states = layer_outputs[0]
            hidden_states = self.norm(hidden_states)

            if output_hidden_states:
                all_hidden_states += (hidden_states,)

            if output_attentions:
                all_self_attns += (layer_outputs[1],)

            if use_cache:
                next_decoder_cache = layer_outputs[2 if output_attentions else 1]

        next_cache = next_decoder_cache if use_cache else None

        audio_logits = self.audio_lm_head(hidden_states[audio_out_mask])

        if output_audio_hidden_states:
            audio_hidden_states = hidden_states[audio_out_mask]
        else:
            audio_hidden_states = None

        return (
            logits,
            audio_logits,
            all_self_attns,
            all_hidden_states,
            audio_hidden_states,
            next_cache,
        )