from typing import Optional, Tuple, Union
import torch
from torch import nn
from transformers.modeling_outputs import BaseModelOutput
from transformers import Wav2Vec2BertModel, Wav2Vec2BertConfig, MllamaPreTrainedModel
from transformers.models.wav2vec2_bert.modeling_wav2vec2_bert import Wav2Vec2BertAdapterLayer
from .configuration_llama3 import Llama3Config

class AudioAdapter(nn.Module):
    def __init__(self, config: Wav2Vec2BertConfig):
        super().__init__()
        # feature dim might need to be down-projected
        if config.output_hidden_size != config.hidden_size:
            self.proj = nn.Linear(config.hidden_size, config.output_hidden_size)
        else:
            self.proj = None
        self.layers = nn.ModuleList(Wav2Vec2BertAdapterLayer(config) for _ in range(config.num_adapter_layers))
        self.layerdrop = config.layerdrop

        self.kernel_size = config.adapter_kernel_size
        self.stride = config.adapter_stride

    def _compute_sub_sample_lengths_from_attention_mask(self, seq_lens):
        if seq_lens is None:
            return seq_lens
        pad = self.stride // 2
        seq_lens = ((seq_lens + 2 * pad - self.kernel_size) / self.stride) + 1
        return seq_lens.floor()

    def forward(self, hidden_states, attention_mask=None):
        # down project hidden_states if necessary
        if self.proj is not None:
            hidden_states = self.proj(hidden_states)

        sub_sampled_lengths = None
        if attention_mask is not None:
            sub_sampled_lengths = (attention_mask.size(1) - (1 - attention_mask.int()).sum(1)).to(hidden_states.device)

        for layer in self.layers:
            layerdrop_prob = torch.rand([])
            sub_sampled_lengths = self._compute_sub_sample_lengths_from_attention_mask(sub_sampled_lengths)
            if not self.training or (layerdrop_prob > self.layerdrop):
                hidden_states = layer(
                    hidden_states, attention_mask=attention_mask, sub_sampled_lengths=sub_sampled_lengths
                )

        return hidden_states

        
class Llama3Embedding(MllamaPreTrainedModel):
    config_class = Llama3Config
    base_model_prefix = "audio_model"
    def __init__(self, config: Llama3Config):
        super().__init__(config)
        assert config.audio_config.output_hidden_size == config.text_config.hidden_size
        self.text_embeddings = nn.Embedding(config.text_config.vocab_size, config.text_config.hidden_size, config.text_config.pad_token_id)
        config.audio_config.add_adapter = False
        self.audio_encoder = Wav2Vec2BertModel(config.audio_config)
        self.audio_adapter = AudioAdapter(config.audio_config)
        self.start_of_audio = nn.Parameter(data=torch.zeros((1, config.audio_config.output_hidden_size)), requires_grad=True)
        self.end_of_audio = nn.Parameter(data=torch.zeros((1, config.audio_config.output_hidden_size)), requires_grad=True)
        self.text_config = config.text_config
            
    def forward(
        self,
        input_ids: torch.LongTensor = None,
        audio_features: Optional[torch.Tensor] = None,
    ) -> Union[BaseModelOutput, Tuple[torch.Tensor, ...]]:
        input_embeddings = self.text_embeddings(torch.clamp(input_ids, min=0))
        if audio_features is None:
            return input_embeddings
        bs, max_num_img, l, d = audio_features.shape
        audio_embeddings = self.audio_encoder(input_features=audio_features.view((bs*max_num_img, l, d)))['last_hidden_state']
        audio_embeddings = self.audio_adapter(audio_embeddings)
        audio_embeddings = audio_embeddings.view((bs, max_num_img, -1, self.start_of_audio.shape[-1]))        

        for i in range(bs):
            for j in range(max_num_img):
                audio_id = -1 - j
                if torch.any(input_ids[i] == audio_id):
                    positions = torch.nonzero(input_ids[i] == audio_id, as_tuple=True)
                    seq_len = input_embeddings[i][positions].shape[0] - 2
                    input_embeddings[i] = input_embeddings[i].index_put(positions, torch.concat([self.start_of_audio, audio_embeddings[i, j, :, :], self.end_of_audio]), accumulate=False)
        return input_embeddings