forward

class BertLMHeadModel(BertPreTrainedModel):

    _keys_to_ignore_on_load_unexpected = [r"pooler"]
    _keys_to_ignore_on_load_missing = [r"position_ids", r"predictions.decoder.bias"]

    def __init__(self, config):
        super().__init__(config)

        self.bert = BertModel(config, add_pooling_layer=False)
        self.cls = BertOnlyMLMHead(config)

        self.init_weights()

    def get_output_embeddings(self):
        return self.cls.predictions.decoder

    def set_output_embeddings(self, new_embeddings):
        self.cls.predictions.decoder = new_embeddings

    def forward(
        self,
        input_ids=None,
        attention_mask=None,
        position_ids=None,
        head_mask=None,
        inputs_embeds=None,
        encoder_hidden_states=None,
        encoder_attention_mask=None,
        labels=None,
        past_key_values=None,
        use_cache=None,
        output_attentions=None,
        output_hidden_states=None,
        return_dict=None,
        return_logits=False,
        is_decoder=True,
        reduction="mean",
        mode="multimodal",
        soft_labels=None,
        alpha=0,
    ):
        r"""
        encoder_hidden_states  (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):
            Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
            the model is configured as a decoder.
        encoder_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
            Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
            the cross-attention if the model is configured as a decoder. Mask values selected in ``[0, 1]``:
            - 1 for tokens that are **not masked**,
            - 0 for tokens that are **masked**.
        labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
            Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in
            ``[-100, 0, ..., config.vocab_size]`` (see ``input_ids`` docstring) Tokens with indices set to ``-100`` are
            ignored (masked), the loss is only computed for the tokens with labels n ``[0, ..., config.vocab_size]``
        past_key_values (:obj:`tuple(tuple(torch.FloatTensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
            Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
            If :obj:`past_key_values` are used, the user can optionally input only the last :obj:`decoder_input_ids`
            (those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)`
            instead of all :obj:`decoder_input_ids` of shape :obj:`(batch_size, sequence_length)`.
        use_cache (:obj:`bool`, `optional`):
            If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up
            decoding (see :obj:`past_key_values`).
        Returns:
        Example::
            >>> from transformers import BertTokenizer, BertLMHeadModel, BertConfig
            >>> import torch
            >>> tokenizer = BertTokenizer.from_pretrained('bert-base-cased')
            >>> config = BertConfig.from_pretrained("bert-base-cased")
            >>> model = BertLMHeadModel.from_pretrained('bert-base-cased', config=config)
            >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
            >>> outputs = model(**inputs)
            >>> prediction_logits = outputs.logits
        """
        return_dict = (
            return_dict if return_dict is not None else self.config.use_return_dict
        )
        if labels is not None:
            use_cache = False

        outputs = self.bert(
            input_ids,
            attention_mask=attention_mask,
            position_ids=position_ids,
            head_mask=head_mask,
            inputs_embeds=inputs_embeds,
            encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=encoder_attention_mask,
            past_key_values=past_key_values,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            is_decoder=is_decoder,
            mode=mode,
        )

        sequence_output = outputs[0]
        prediction_scores = self.cls(sequence_output)

        if return_logits:
            return prediction_scores[:, :-1, :].contiguous()

        lm_loss = None
        if labels is not None:
            # we are doing next-token prediction; shift prediction scores and input ids by one
            shifted_prediction_scores = prediction_scores[:, :-1, :].contiguous()
            labels = labels[:, 1:].contiguous()
            loss_fct = CrossEntropyLoss(reduction=reduction, label_smoothing=0.1)
            lm_loss = loss_fct(
                shifted_prediction_scores.view(-1, self.config.vocab_size),
                labels.view(-1),
            )
            if reduction == "none":
                lm_loss = lm_loss.view(prediction_scores.size(0), -1).sum(1)

        if soft_labels is not None:
            loss_distill = -torch.sum(
                F.log_softmax(shifted_prediction_scores, dim=-1) * soft_labels, dim=-1
            )
            loss_distill = (loss_distill * (labels != -100)).sum(1)
            lm_loss = (1 - alpha) * lm_loss + alpha * loss_distill

        if not return_dict:
            output = (prediction_scores,) + outputs[2:]
            return ((lm_loss,) + output) if lm_loss is not None else output

        return CausalLMOutputWithCrossAttentions(
            loss=lm_loss,
            logits=prediction_scores,
            past_key_values=outputs.past_key_values,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
            cross_attentions=outputs.cross_attentions,
        )

    def prepare_inputs_for_generation(
        self, input_ids, past=None, attention_mask=None, **model_kwargs
    ):
        input_shape = input_ids.shape
        # if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly
        if attention_mask is None:
            attention_mask = input_ids.new_ones(input_shape)

        # cut decoder_input_ids if past is used
        if past is not None:
            input_ids = input_ids[:, -1:]

        return {
            "input_ids": input_ids,
            "attention_mask": attention_mask,
            "past_key_values": past,
            "encoder_hidden_states": model_kwargs.get("encoder_hidden_states", None),
            "encoder_attention_mask": model_kwargs.get("encoder_attention_mask", None),
            "is_decoder": True,


            self.key = nn.Linear(config.encoder_width, self.all_head_size)
            self.value = nn.Linear(config.encoder_width, self.all_head_size)
        else:
            self.key = nn.Linear(config.hidden_size, self.all_head_size)
            self.value = nn.Linear(config.hidden_size, self.all_head_size)

        self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
        self.position_embedding_type = getattr(
            config, "position_embedding_type", "absolute"
        )
        if (
            self.position_embedding_type == "relative_key"
            or self.position_embedding_type == "relative_key_query"
        ):
            self.max_position_embeddings = config.max_position_embeddings
            self.distance_embedding = nn.Embedding(
                2 * config.max_position_embeddings - 1, self.attention_head_size
            )
        self.save_attention = False

    def save_attn_gradients(self, attn_gradients):
        self.attn_gradients = attn_gradients

    def get_attn_gradients(self):
        return self.attn_gradients

    def save_attention_map(self, attention_map):
        self.attention_map = attention_map

    def get_attention_map(self):
        return self.attention_map

    def transpose_for_scores(self, x):
        new_x_shape = x.size()[:-1] + (
            self.num_attention_heads,
            self.attention_head_size,
        )
        x = x.view(*new_x_shape)
        return x.permute(0, 2, 1, 3)

    def forward(
        self,


            self.key = nn.Linear(config.hidden_size, self.all_head_size)
            self.value = nn.Linear(config.hidden_size, self.all_head_size)

        self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
        self.position_embedding_type = getattr(
            config, "position_embedding_type", "absolute"
        )
        if (
            self.position_embedding_type == "relative_key"
            or self.position_embedding_type == "relative_key_query"
        ):
            self.max_position_embeddings = config.max_position_embeddings
            self.distance_embedding = nn.Embedding(
                2 * config.max_position_embeddings - 1, self.attention_head_size
            )
        self.save_attention = False

    def save_attn_gradients(self, attn_gradients):
        self.attn_gradients = attn_gradients

    def get_attn_gradients(self):
        return self.attn_gradients

    def save_attention_map(self, attention_map):
        self.attention_map = attention_map

    def get_attention_map(self):
        return self.attention_map

    def transpose_for_scores(self, x):
        new_x_shape = x.size()[:-1] + (
            self.num_attention_heads,
            self.attention_head_size,
        )
        x = x.view(*new_x_shape)
        return x.permute(0, 2, 1, 3)

    def forward(
        self,
        hidden_states,
        attention_mask=None,
        head_mask=None,


        self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
        self.position_embedding_type = getattr(
            config, "position_embedding_type", "absolute"
        )
        if (
            self.position_embedding_type == "relative_key"
            or self.position_embedding_type == "relative_key_query"
        ):
            self.max_position_embeddings = config.max_position_embeddings
            self.distance_embedding = nn.Embedding(
                2 * config.max_position_embeddings - 1, self.attention_head_size
            )
        self.save_attention = False

    def save_attn_gradients(self, attn_gradients):
        self.attn_gradients = attn_gradients

    def get_attn_gradients(self):
        return self.attn_gradients

    def save_attention_map(self, attention_map):
        self.attention_map = attention_map

    def get_attention_map(self):
        return self.attention_map

    def transpose_for_scores(self, x):
        new_x_shape = x.size()[:-1] + (
            self.num_attention_heads,
            self.attention_head_size,
        )
        x = x.view(*new_x_shape)
        return x.permute(0, 2, 1, 3)

    def forward(
        self,
        hidden_states,
        attention_mask=None,
        head_mask=None,
        encoder_hidden_states=None,
        encoder_attention_mask=None,
        past_key_value=None,


        )
        if (
            self.position_embedding_type == "relative_key"
            or self.position_embedding_type == "relative_key_query"
        ):
            self.max_position_embeddings = config.max_position_embeddings
            self.distance_embedding = nn.Embedding(
                2 * config.max_position_embeddings - 1, self.attention_head_size
            )
        self.save_attention = False

    def save_attn_gradients(self, attn_gradients):
        self.attn_gradients = attn_gradients

    def get_attn_gradients(self):
        return self.attn_gradients

    def save_attention_map(self, attention_map):
        self.attention_map = attention_map

    def get_attention_map(self):
        return self.attention_map

    def transpose_for_scores(self, x):
        new_x_shape = x.size()[:-1] + (
            self.num_attention_heads,
            self.attention_head_size,
        )
        x = x.view(*new_x_shape)
        return x.permute(0, 2, 1, 3)

    def forward(
        self,
        hidden_states,
        attention_mask=None,
        head_mask=None,
        encoder_hidden_states=None,
        encoder_attention_mask=None,
        past_key_value=None,
        output_attentions=False,
    ):
        mixed_query_layer = self.query(hidden_states)


            or self.position_embedding_type == "relative_key_query"
        ):
            self.max_position_embeddings = config.max_position_embeddings
            self.distance_embedding = nn.Embedding(
                2 * config.max_position_embeddings - 1, self.attention_head_size
            )
        self.save_attention = False

    def save_attn_gradients(self, attn_gradients):
        self.attn_gradients = attn_gradients

    def get_attn_gradients(self):
        return self.attn_gradients

    def save_attention_map(self, attention_map):
        self.attention_map = attention_map

    def get_attention_map(self):
        return self.attention_map

    def transpose_for_scores(self, x):
        new_x_shape = x.size()[:-1] + (
            self.num_attention_heads,
            self.attention_head_size,
        )
        x = x.view(*new_x_shape)
        return x.permute(0, 2, 1, 3)

    def forward(
        self,
        hidden_states,
        attention_mask=None,
        head_mask=None,
        encoder_hidden_states=None,
        encoder_attention_mask=None,
        past_key_value=None,
        output_attentions=False,
    ):
        mixed_query_layer = self.query(hidden_states)

        # If this is instantiated as a cross-attention module, the keys
        # and values come from an encoder; the attention mask needs to be
        # such that the encoder's padding tokens are not attended to.
        is_cross_attention = encoder_hidden_states is not None

        if is_cross_attention:
            key_layer = self.transpose_for_scores(self.key(encoder_hidden_states))


    def __init__(self, config):
        super().__init__()
        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)

    def forward(self, hidden_states, input_tensor):
        hidden_states = self.dense(hidden_states)
        hidden_states = self.dropout(hidden_states)
        hidden_states = self.LayerNorm(hidden_states + input_tensor)
        return hidden_states

class BertAttention(nn.Module):
    def __init__(self, config, is_cross_attention=False):
        super().__init__()
        self.self = BertSelfAttention(config, is_cross_attention)
        self.output = BertSelfOutput(config)
        self.pruned_heads = set()

    def prune_heads(self, heads):
        if len(heads) == 0:
            return
        heads, index = find_pruneable_heads_and_indices(
            heads,
            self.self.num_attention_heads,
            self.self.attention_head_size,
            self.pruned_heads,
        )

        # Prune linear layers
        self.self.query = prune_linear_layer(self.self.query, index)
        self.self.key = prune_linear_layer(self.self.key, index)
        self.self.value = prune_linear_layer(self.self.value, index)
        self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)

        # Update hyper params and store pruned heads
        self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
        self.self.all_head_size = (
            self.self.attention_head_size * self.self.num_attention_heads
        )
        self.pruned_heads = self.pruned_heads.union(heads)

    def forward(
        self,
        hidden_states,
        attention_mask=None,
        head_mask=None,
        encoder_hidden_states=None,
        encoder_attention_mask=None,
        past_key_value=None,
        output_attentions=False,
    ):
        self_outputs = self.self(
            hidden_states,
            attention_mask,
            head_mask,
            encoder_hidden_states,
            encoder_attention_mask,
            past_key_value,
            output_attentions,
        )


                    encoder_hidden_states,
                    encoder_attention_mask,
                    output_attentions=output_attentions,
                )
                attention_output = cross_attention_outputs[0]
                outputs = (
                    outputs + cross_attention_outputs[1:-1]
                )  # add cross attentions if we output attention weights
        layer_output = apply_chunking_to_forward(
            self.feed_forward_chunk,
            self.chunk_size_feed_forward,
            self.seq_len_dim,
            attention_output,
        )
        outputs = (layer_output,) + outputs

        outputs = outputs + (present_key_value,)

        return outputs

    def feed_forward_chunk(self, attention_output):
        intermediate_output = self.intermediate(attention_output)
        layer_output = self.output(intermediate_output, attention_output)
        return layer_output

class BertEncoder(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.config = config
        self.layer = nn.ModuleList(
            [BertLayer(config, i) for i in range(config.num_hidden_layers)]
        )
        self.gradient_checkpointing = False

    def forward(
        self,
        hidden_states,
        attention_mask=None,
        head_mask=None,
        encoder_hidden_states=None,
        encoder_attention_mask=None,
        past_key_values=None,
        use_cache=None,


class BertOnlyMLMHead(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.predictions = BertLMPredictionHead(config)

    def forward(self, sequence_output):
        prediction_scores = self.predictions(sequence_output)
        return prediction_scores

class BertPreTrainedModel(PreTrainedModel):
    """
    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
    models.
    """

    config_class = BertConfig
    base_model_prefix = "bert"
    _keys_to_ignore_on_load_missing = [r"position_ids"]

    def _init_weights(self, module):
        """Initialize the weights"""
        if isinstance(module, (nn.Linear, nn.Embedding)):
            # Slightly different from the TF version which uses truncated_normal for initialization
            # cf https://github.com/pytorch/pytorch/pull/5617
            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
        elif isinstance(module, nn.LayerNorm):
            module.bias.data.zero_()
            module.weight.data.fill_(1.0)
        if isinstance(module, nn.Linear) and module.bias is not None:
            module.bias.data.zero_()

class BertModel(BertPreTrainedModel):
    """
    The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of
    cross-attention is added between the self-attention layers, following the architecture described in `Attention is
    all you need <https://arxiv.org/abs/1706.03762>`__ by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit,
    Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin.
    argument and :obj:`add_cross_attention` set to :obj:`True`; an :obj:`encoder_hidden_states` is then expected as an
    input to the forward pass.
    """

    def __init__(self, config, add_pooling_layer=True):
        super().__init__(config)
        self.config = config

        self.embeddings = BertEmbeddings(config)

        self.encoder = BertEncoder(config)


    The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of
    cross-attention is added between the self-attention layers, following the architecture described in `Attention is
    all you need <https://arxiv.org/abs/1706.03762>`__ by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit,
    Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin.
    argument and :obj:`add_cross_attention` set to :obj:`True`; an :obj:`encoder_hidden_states` is then expected as an
    input to the forward pass.
    """

    def __init__(self, config, add_pooling_layer=True):
        super().__init__(config)
        self.config = config

        self.embeddings = BertEmbeddings(config)

        self.encoder = BertEncoder(config)

        self.pooler = BertPooler(config) if add_pooling_layer else None

        self.init_weights()

    def get_input_embeddings(self):
        return self.embeddings.word_embeddings

    def set_input_embeddings(self, value):
        self.embeddings.word_embeddings = value

    def _prune_heads(self, heads_to_prune):
        """
        Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
        class PreTrainedModel
        """
        for layer, heads in heads_to_prune.items():
            self.encoder.layer[layer].attention.prune_heads(heads)

    def get_extended_attention_mask(
        self,
        attention_mask: Tensor,
        input_shape: Tuple[int],
        device: device,
        is_decoder: bool,
    ) -> Tensor:
        """


    Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin.
    argument and :obj:`add_cross_attention` set to :obj:`True`; an :obj:`encoder_hidden_states` is then expected as an
    input to the forward pass.
    """

    def __init__(self, config, add_pooling_layer=True):
        super().__init__(config)
        self.config = config

        self.embeddings = BertEmbeddings(config)

        self.encoder = BertEncoder(config)

        self.pooler = BertPooler(config) if add_pooling_layer else None

        self.init_weights()

    def get_input_embeddings(self):
        return self.embeddings.word_embeddings

    def set_input_embeddings(self, value):
        self.embeddings.word_embeddings = value

    def _prune_heads(self, heads_to_prune):
        """
        Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
        class PreTrainedModel
        """
        for layer, heads in heads_to_prune.items():
            self.encoder.layer[layer].attention.prune_heads(heads)

    def get_extended_attention_mask(
        self,
        attention_mask: Tensor,
        input_shape: Tuple[int],
        device: device,
        is_decoder: bool,
    ) -> Tensor:
        """
        Makes broadcastable attention and causal masks so that future and masked tokens are ignored.

        Arguments:


    """

    def __init__(self, config, add_pooling_layer=True):
        super().__init__(config)
        self.config = config

        self.embeddings = BertEmbeddings(config)

        self.encoder = BertEncoder(config)

        self.pooler = BertPooler(config) if add_pooling_layer else None

        self.init_weights()

    def get_input_embeddings(self):
        return self.embeddings.word_embeddings

    def set_input_embeddings(self, value):
        self.embeddings.word_embeddings = value

    def _prune_heads(self, heads_to_prune):
        """
        Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
        class PreTrainedModel
        """
        for layer, heads in heads_to_prune.items():
            self.encoder.layer[layer].attention.prune_heads(heads)

    def get_extended_attention_mask(
        self,
        attention_mask: Tensor,
        input_shape: Tuple[int],
        device: device,
        is_decoder: bool,
    ) -> Tensor:
        """
        Makes broadcastable attention and causal masks so that future and masked tokens are ignored.

        Arguments:
            attention_mask (:obj:`torch.Tensor`):
                Mask with ones indicating tokens to attend to, zeros for tokens to ignore.
            input_shape (:obj:`Tuple[int]`):
                The shape of the input to the model.
            device: (:obj:`torch.device`):
                The device of the input to the model.

        Returns:


        self.encoder = BertEncoder(config)

        self.pooler = BertPooler(config) if add_pooling_layer else None

        self.init_weights()

    def get_input_embeddings(self):
        return self.embeddings.word_embeddings

    def set_input_embeddings(self, value):
        self.embeddings.word_embeddings = value

    def _prune_heads(self, heads_to_prune):
        """
        Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
        class PreTrainedModel
        """
        for layer, heads in heads_to_prune.items():
            self.encoder.layer[layer].attention.prune_heads(heads)

    def get_extended_attention_mask(
        self,
        attention_mask: Tensor,
        input_shape: Tuple[int],
        device: device,
        is_decoder: bool,
    ) -> Tensor:
        """
        Makes broadcastable attention and causal masks so that future and masked tokens are ignored.

        Arguments:
            attention_mask (:obj:`torch.Tensor`):
                Mask with ones indicating tokens to attend to, zeros for tokens to ignore.
            input_shape (:obj:`Tuple[int]`):
                The shape of the input to the model.
            device: (:obj:`torch.device`):
                The device of the input to the model.

        Returns:
            :obj:`torch.Tensor` The extended attention mask, with a the same dtype as :obj:`attention_mask.dtype`.
        """
        # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
        # ourselves in which case we just need to make it broadcastable to all heads.
        if attention_mask.dim() == 3:
            extended_attention_mask = attention_mask[:, None, :, :]
        elif attention_mask.dim() == 2:
            # Provided a padding mask of dimensions [batch_size, seq_length]
            # - if the model is a decoder, apply a causal mask in addition to the padding mask
            # - if the model is an encoder, make the mask broadcastable to [batch_size, num_heads, seq_length, seq_length]
            if is_decoder:
                batch_size, seq_length = input_shape

                seq_ids = torch.arange(seq_length, device=device)
                causal_mask = (
                    seq_ids[None, None, :].repeat(batch_size, seq_length, 1)
                    <= seq_ids[None, :, None]
                )
                # in case past_key_values are used we need to add a prefix ones mask to the causal mask
                # causal and attention masks must have same type with pytorch version < 1.3
                causal_mask = causal_mask.to(attention_mask.dtype)

                if causal_mask.shape[1] < attention_mask.shape[1]:
                    prefix_seq_len = attention_mask.shape[1] - causal_mask.shape[1]
                    causal_mask = torch.cat(
                        [
                            torch.ones(
                                (batch_size, seq_length, prefix_seq_len),
                                device=device,
                                dtype=causal_mask.dtype,
                            ),
                            causal_mask,
                        ],
                        axis=-1,
                    )

                extended_attention_mask = (
                    causal_mask[:, None, :, :] * attention_mask[:, None, None, :]
                )
            else:
                extended_attention_mask = attention_mask[:, None, None, :]
        else:
            raise ValueError(
                "Wrong shape for input_ids (shape {}) or attention_mask (shape {})".format(
                    input_shape, attention_mask.shape
                )
            )

        # Since attention_mask is 1.0 for positions we want to attend and 0.0 for
        # masked positions, this operation will create a tensor which is 0.0 for
        # positions we want to attend and -10000.0 for masked positions.
        # Since we are adding it to the raw scores before the softmax, this is
        # effectively the same as removing these entirely.
        extended_attention_mask = extended_attention_mask.to(
            dtype=self.dtype
        )  # fp16 compatibility
        extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0
        return extended_attention_mask

    def forward(
        self,
        input_ids=None,
        attention_mask=None,
        token_type_ids=None,
        position_ids=None,
        head_mask=None,
        inputs_embeds=None,
        encoder_embeds=None,
        encoder_hidden_states=None,
        encoder_attention_mask=None,
        past_key_values=None,
        use_cache=None,
        output_attentions=None,
        output_hidden_states=None,
        return_dict=None,
        is_decoder=False,
        mode="multimodal",
    ):


            device=input_ids.device,
        )
        input_ids = torch.cat([input_ids, dummy_token], dim=1)

        return {"input_ids": input_ids, "attention_mask": attention_mask}

class BertLMHeadModel(BertPreTrainedModel):

    _keys_to_ignore_on_load_unexpected = [r"pooler"]
    _keys_to_ignore_on_load_missing = [r"position_ids", r"predictions.decoder.bias"]

    def __init__(self, config):
        super().__init__(config)

        self.bert = BertModel(config, add_pooling_layer=False)
        self.cls = BertOnlyMLMHead(config)

        self.init_weights()

    def get_output_embeddings(self):
        return self.cls.predictions.decoder

    def set_output_embeddings(self, new_embeddings):
        self.cls.predictions.decoder = new_embeddings

    def forward(
        self,
        input_ids=None,
        attention_mask=None,
        position_ids=None,
        head_mask=None,
        inputs_embeds=None,
        encoder_hidden_states=None,
        encoder_attention_mask=None,
        labels=None,
        past_key_values=None,
        use_cache=None,
        output_attentions=None,
        output_hidden_states=None,
        return_dict=None,
        return_logits=False,


        return {"input_ids": input_ids, "attention_mask": attention_mask}

class BertLMHeadModel(BertPreTrainedModel):

    _keys_to_ignore_on_load_unexpected = [r"pooler"]
    _keys_to_ignore_on_load_missing = [r"position_ids", r"predictions.decoder.bias"]

    def __init__(self, config):
        super().__init__(config)

        self.bert = BertModel(config, add_pooling_layer=False)
        self.cls = BertOnlyMLMHead(config)

        self.init_weights()

    def get_output_embeddings(self):
        return self.cls.predictions.decoder

    def set_output_embeddings(self, new_embeddings):
        self.cls.predictions.decoder = new_embeddings

    def forward(
        self,
        input_ids=None,
        attention_mask=None,
        position_ids=None,
        head_mask=None,
        inputs_embeds=None,
        encoder_hidden_states=None,
        encoder_attention_mask=None,
        labels=None,
        past_key_values=None,
        use_cache=None,
        output_attentions=None,
        output_hidden_states=None,
        return_dict=None,
        return_logits=False,
        is_decoder=True,
        reduction="mean",
        mode="multimodal",


        if soft_labels is not None:
            loss_distill = -torch.sum(
                F.log_softmax(shifted_prediction_scores, dim=-1) * soft_labels, dim=-1
            )
            loss_distill = (loss_distill * (labels != -100)).sum(1)
            lm_loss = (1 - alpha) * lm_loss + alpha * loss_distill

        if not return_dict:
            output = (prediction_scores,) + outputs[2:]
            return ((lm_loss,) + output) if lm_loss is not None else output

        return CausalLMOutputWithCrossAttentions(
            loss=lm_loss,
            logits=prediction_scores,
            past_key_values=outputs.past_key_values,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
            cross_attentions=outputs.cross_attentions,
        )

    def prepare_inputs_for_generation(
        self, input_ids, past=None, attention_mask=None, **model_kwargs
    ):
        input_shape = input_ids.shape
        # if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly
        if attention_mask is None:
            attention_mask = input_ids.new_ones(input_shape)

        # cut decoder_input_ids if past is used
        if past is not None:
            input_ids = input_ids[:, -1:]

        return {
            "input_ids": input_ids,
            "attention_mask": attention_mask,
            "past_key_values": past,
            "encoder_hidden_states": model_kwargs.get("encoder_hidden_states", None),
            "encoder_attention_mask": model_kwargs.get("encoder_attention_mask", None),
            "is_decoder": True,
        }

    def _reorder_cache(self, past, beam_idx):
        reordered_past = ()
        for layer_past in past:
            reordered_past += (
                tuple(
                    past_state.index_select(0, beam_idx) for past_state in layer_past
                ),
            )
        return reordered_past

class XBertLMHeadDecoder(BertLMHeadModel):
    """
    This class decouples the decoder forward logic from the VL model.
    In this way, different VL models can share this decoder as long as
    they feed encoder_embeds as required.
    """

    @classmethod


        self, input_ids, past=None, attention_mask=None, **model_kwargs
    ):
        input_shape = input_ids.shape
        # if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly
        if attention_mask is None:
            attention_mask = input_ids.new_ones(input_shape)

        # cut decoder_input_ids if past is used
        if past is not None:
            input_ids = input_ids[:, -1:]

        return {
            "input_ids": input_ids,
            "attention_mask": attention_mask,
            "past_key_values": past,
            "encoder_hidden_states": model_kwargs.get("encoder_hidden_states", None),
            "encoder_attention_mask": model_kwargs.get("encoder_attention_mask", None),
            "is_decoder": True,
        }

    def _reorder_cache(self, past, beam_idx):
        reordered_past = ()
        for layer_past in past:
            reordered_past += (
                tuple(
                    past_state.index_select(0, beam_idx) for past_state in layer_past
                ),
            )
        return reordered_past

class XBertLMHeadDecoder(BertLMHeadModel):
    """
    This class decouples the decoder forward logic from the VL model.
    In this way, different VL models can share this decoder as long as
    they feed encoder_embeds as required.
    """

    @classmethod
    def from_config(cls, cfg, from_pretrained=False):

        med_config_path = get_abs_path(cfg.get("med_config_path"))
        med_config = BertConfig.from_json_file(med_config_path)

        if from_pretrained:
            return cls.from_pretrained("bert-base-uncased", config=med_config)
        else:
            return cls(config=med_config)


                **model_kwargs
            )
        else:
            # beam search
            outputs = self.generate(
                input_ids=tokenized_prompt.input_ids,
                max_length=max_length,
                min_length=min_length,
                num_beams=num_beams,
                eos_token_id=sep_token_id,
                pad_token_id=pad_token_id,
                repetition_penalty=repetition_penalty,
                **model_kwargs
            )

        return outputs

class XBertEncoder(BertModel, BaseEncoder):
    @classmethod
    def from_config(cls, cfg, from_pretrained=False):

        med_config_path = get_abs_path(cfg.get("med_config_path"))
        med_config = BertConfig.from_json_file(med_config_path)

        if from_pretrained:
            return cls.from_pretrained(
                "bert-base-uncased", config=med_config, add_pooling_layer=False
            )
        else:
            return cls(config=med_config, add_pooling_layer=False)

    def forward_automask(self, tokenized_text, visual_embeds, **kwargs):
        image_atts = torch.ones(visual_embeds.size()[:-1], dtype=torch.long).to(
            self.device
        )

        text = tokenized_text
        text_output = super().forward(
            text.input_ids,
            attention_mask=text.attention_mask,
            encoder_hidden_states=visual_embeds,
            encoder_attention_mask=image_atts,
            return_dict=True,
        )

        return text_output

    def forward_text(self, tokenized_text, **kwargs):
        text = tokenized_text
        token_type_ids = kwargs.get("token_type_ids", None)


class XBertLMHeadDecoder(BertLMHeadModel):
    """
    This class decouples the decoder forward logic from the VL model.
    In this way, different VL models can share this decoder as long as
    they feed encoder_embeds as required.
    """

    @classmethod
    def from_config(cls, cfg, from_pretrained=False):

        med_config_path = get_abs_path(cfg.get("med_config_path"))
        med_config = BertConfig.from_json_file(med_config_path)

        if from_pretrained:
            return cls.from_pretrained("bert-base-uncased", config=med_config)
        else:
            return cls(config=med_config)

    def generate_from_encoder(
        self,
        tokenized_prompt,
        visual_embeds,
        sep_token_id,
        pad_token_id,
        use_nucleus_sampling=False,
        num_beams=3,
        max_length=30,
        min_length=10,
        top_p=0.9,
        repetition_penalty=1.0,
        **kwargs
    ):

        if not use_nucleus_sampling:
            num_beams = num_beams
            visual_embeds = visual_embeds.repeat_interleave(num_beams, dim=0)

        image_atts = torch.ones(visual_embeds.size()[:-1], dtype=torch.long).to(
            self.device
        )

        model_kwargs = {
            "encoder_hidden_states": visual_embeds,
            "encoder_attention_mask": image_atts,
        }

        if use_nucleus_sampling:
            # nucleus sampling
            outputs = self.generate(
                input_ids=tokenized_prompt.input_ids,
                max_length=max_length,
                min_length=min_length,
                do_sample=True,
                top_p=top_p,
                num_return_sequences=1,
                eos_token_id=sep_token_id,
                pad_token_id=pad_token_id,
                repetition_penalty=1.1,
                **model_kwargs
            )
        else:
            # beam search
            outputs = self.generate(
                input_ids=tokenized_prompt.input_ids,
                max_length=max_length,
                min_length=min_length,
                num_beams=num_beams,
                eos_token_id=sep_token_id,
                pad_token_id=pad_token_id,
                repetition_penalty=repetition_penalty,
                **model_kwargs
            )

        return outputs

class XBertEncoder(BertModel, BaseEncoder):
    @classmethod
    def from_config(cls, cfg, from_pretrained=False):

        med_config_path = get_abs_path(cfg.get("med_config_path"))
        med_config = BertConfig.from_json_file(med_config_path)

        if from_pretrained:
            return cls.from_pretrained(
                "bert-base-uncased", config=med_config, add_pooling_layer=False
            )
        else:
            return cls(config=med_config, add_pooling_layer=False)

    def forward_automask(self, tokenized_text, visual_embeds, **kwargs):
        image_atts = torch.ones(visual_embeds.size()[:-1], dtype=torch.long).to(
            self.device
        )


                **model_kwargs
            )

        return outputs

class XBertEncoder(BertModel, BaseEncoder):
    @classmethod
    def from_config(cls, cfg, from_pretrained=False):

        med_config_path = get_abs_path(cfg.get("med_config_path"))
        med_config = BertConfig.from_json_file(med_config_path)

        if from_pretrained:
            return cls.from_pretrained(
                "bert-base-uncased", config=med_config, add_pooling_layer=False
            )
        else:
            return cls(config=med_config, add_pooling_layer=False)

    def forward_automask(self, tokenized_text, visual_embeds, **kwargs):
        image_atts = torch.ones(visual_embeds.size()[:-1], dtype=torch.long).to(
            self.device
        )

        text = tokenized_text
        text_output = super().forward(
            text.input_ids,
            attention_mask=text.attention_mask,
            encoder_hidden_states=visual_embeds,
            encoder_attention_mask=image_atts,
            return_dict=True,
        )

        return text_output

    def forward_text(self, tokenized_text, **kwargs):
        text = tokenized_text
        token_type_ids = kwargs.get("token_type_ids", None)

        text_output = super().forward(
            text.input_ids,
            attention_mask=text.attention_mask,
            token_type_ids=token_type_ids,
            return_dict=True,
            mode="text",
        )

        return text_output


            )
        else:
            return cls(config=med_config, add_pooling_layer=False)

    def forward_automask(self, tokenized_text, visual_embeds, **kwargs):
        image_atts = torch.ones(visual_embeds.size()[:-1], dtype=torch.long).to(
            self.device
        )

        text = tokenized_text
        text_output = super().forward(
            text.input_ids,
            attention_mask=text.attention_mask,
            encoder_hidden_states=visual_embeds,
            encoder_attention_mask=image_atts,
            return_dict=True,
        )

        return text_output

    def forward_text(self, tokenized_text, **kwargs):
        text = tokenized_text
        token_type_ids = kwargs.get("token_type_ids", None)

        text_output = super().forward(
            text.input_ids,
            attention_mask=text.attention_mask,
            token_type_ids=token_type_ids,
            return_dict=True,
            mode="text",
        )

        return text_output


        # compatibility for ALBEF and BLIP
        # for i in range(self.config.num_hidden_layers):
        for i in range(start_layer, output_layer):
            layer_module = self.layer[i]
            if output_hidden_states:
                all_hidden_states = all_hidden_states + (hidden_states,)

            layer_head_mask = head_mask[i] if head_mask is not None else None
            past_key_value = past_key_values[i] if past_key_values is not None else None

            # TODO pay attention to this.
            if self.gradient_checkpointing and self.training:

                if use_cache:
                    logger.warn(
                        "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
                    )
                    use_cache = False

                def create_custom_forward(module):
                    def custom_forward(*inputs):
                        return module(*inputs, past_key_value, output_attentions)

                    return custom_forward

                layer_outputs = torch.utils.checkpoint.checkpoint(
                    create_custom_forward(layer_module),
                    hidden_states,
                    attention_mask,
                    layer_head_mask,
                    encoder_hidden_states,
                    encoder_attention_mask,
                    mode=mode,
                )
            else:
                layer_outputs = layer_module(
                    hidden_states,
                    attention_mask,
                    layer_head_mask,
                    encoder_hidden_states,
                    encoder_attention_mask,
                    past_key_value,
                    output_attentions,
                    mode=mode,


        # compatibility for ALBEF and BLIP
        # for i in range(self.config.num_hidden_layers):
        for i in range(start_layer, output_layer):
            layer_module = self.layer[i]
            if output_hidden_states:
                all_hidden_states = all_hidden_states + (hidden_states,)

            layer_head_mask = head_mask[i] if head_mask is not None else None
            past_key_value = past_key_values[i] if past_key_values is not None else None

            # TODO pay attention to this.
            if self.gradient_checkpointing and self.training:

                if use_cache:
                    logger.warn(
                        "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
                    )
                    use_cache = False

                def create_custom_forward(module):
                    def custom_forward(*inputs):
                        return module(*inputs, past_key_value, output_attentions)

                    return custom_forward

                layer_outputs = torch.utils.checkpoint.checkpoint(
                    create_custom_forward(layer_module),
                    hidden_states,
                    attention_mask,
                    layer_head_mask,
                    encoder_hidden_states,
                    encoder_attention_mask,
                    mode=mode,
                )
            else:
                layer_outputs = layer_module(
                    hidden_states,
                    attention_mask,
                    layer_head_mask,
                    encoder_hidden_states,
                    encoder_attention_mask,
                    past_key_value,


"""
 Copyright (c) 2023, salesforce.com, inc.
 All rights reserved.
 SPDX-License-Identifier: BSD-3-Clause
 For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause
"""

from lavis.models.base_model import BaseEncoder
from lavis.models.beats.BEATs import BEATs, BEATsConfig
import torch
from lavis.common.utils import is_url
from lavis.common.dist_utils import download_cached_file
import os

# ckp_path =  "https://valle.blob.core.windows.net/share/BEATs/BEATs_iter3_plus_AS2M.pt?sv=2020-08-04&st=2023-03-01T07%3A51%3A05Z&se=2033-03-02T07%3A51%3A00Z&sr=c&sp=rl&sig=QJXmSJG9DbMKf48UDIU1MfzIro8HQOf3sqlNXiflY1I%3D"

ckp_path = '/nas-hdd/shoubin/pretrained_model/BEATs_iter3_plus_AS2M.pt'

class BeatsEncoder(BaseEncoder):
    def __init__(self, checkpoint_path=ckp_path):
        super().__init__()

        # load the pre-trained checkpoints
        if is_url(checkpoint_path):
            cached_file = download_cached_file(
                checkpoint_path, check_hash=False, progress=True
            )
            checkpoint = torch.load(cached_file)
        elif os.path.isfile(checkpoint_path):
            print('loading checkpoint for BEATs Encoder')
            checkpoint = torch.load(checkpoint_path)

        cfg = BEATsConfig(checkpoint['cfg'])
        self.num_features = cfg.encoder_embed_dim
        self.model = BEATs(cfg)
        self.model.load_state_dict(checkpoint['model'])
        self.model.eval()

    @classmethod
    def from_config(cls, cfg):
        checkpoint_path = cfg.get("checkpoint_path", ckp_path)
        return cls(checkpoint_path)

    def forward(self, x):
        with torch.no_grad():
            return self.model.extract_features(x.squeeze(1))[0]


    def __init__(self, checkpoint_path=ckp_path):
        super().__init__()

        # load the pre-trained checkpoints
        if is_url(checkpoint_path):
            cached_file = download_cached_file(
                checkpoint_path, check_hash=False, progress=True
            )
            checkpoint = torch.load(cached_file)
        elif os.path.isfile(checkpoint_path):
            print('loading checkpoint for BEATs Encoder')
            checkpoint = torch.load(checkpoint_path)

        cfg = BEATsConfig(checkpoint['cfg'])
        self.num_features = cfg.encoder_embed_dim
        self.model = BEATs(cfg)
        self.model.load_state_dict(checkpoint['model'])
        self.model.eval()

    @classmethod
    def from_config(cls, cfg):
        checkpoint_path = cfg.get("checkpoint_path", ckp_path)
        return cls(checkpoint_path)

    def forward(self, x):
        with torch.no_grad():
            return self.model.extract_features(x.squeeze(1))[0]


        if is_url(checkpoint_path):
            cached_file = download_cached_file(
                checkpoint_path, check_hash=False, progress=True
            )
            checkpoint = torch.load(cached_file)
        elif os.path.isfile(checkpoint_path):
            print('loading checkpoint for BEATs Encoder')
            checkpoint = torch.load(checkpoint_path)

        cfg = BEATsConfig(checkpoint['cfg'])
        self.num_features = cfg.encoder_embed_dim
        self.model = BEATs(cfg)
        self.model.load_state_dict(checkpoint['model'])
        self.model.eval()

    @classmethod
    def from_config(cls, cfg):
        checkpoint_path = cfg.get("checkpoint_path", ckp_path)
        return cls(checkpoint_path)

    def forward(self, x):
        with torch.no_grad():
            return self.model.extract_features(x.squeeze(1))[0]


lavis.models.beats.backbone

# --------------------------------------------------------
# BEATs: Audio Pre-Training with Acoustic Tokenizers (https://arxiv.org/abs/2212.09058)
# Github source: https://github.com/microsoft/unilm/tree/master/beats
# Copyright (c) 2022 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Based on fairseq code bases
# https://github.com/pytorch/fairseq
# --------------------------------------------------------

import math
import numpy as np
from typing import Dict, Optional, Tuple
import torch
from torch import Tensor, nn
import torch.nn.functional as F
from torch.nn import LayerNorm, Parameter
from lavis.models.beats.modules import (
    GradMultiply,
    SamePad,
    get_activation_fn,
    GLU_Linear,
    quant_noise,
)

class TransformerEncoder(nn.Module):
    def __init__(self, args):
        super().__init__()

        self.dropout = args.dropout
        self.embedding_dim = args.encoder_embed_dim

        self.pos_conv = nn.Conv1d(
            self.embedding_dim,
            self.embedding_dim,
            kernel_size=args.conv_pos,
            padding=args.conv_pos // 2,
            groups=args.conv_pos_groups,
        )
        dropout = 0
        std = math.sqrt((4 * (1.0 - dropout)) / (args.conv_pos * self.embedding_dim))
        nn.init.normal_(self.pos_conv.weight, mean=0, std=std)
        nn.init.constant_(self.pos_conv.bias, 0)

        self.pos_conv = nn.utils.weight_norm(self.pos_conv, name="weight", dim=2)
        self.pos_conv = nn.Sequential(self.pos_conv, SamePad(args.conv_pos), nn.GELU())

        if hasattr(args, "relative_position_embedding"):
            self.relative_position_embedding = args.relative_position_embedding
            self.num_buckets = args.num_buckets
            self.max_distance = args.max_distance
        else:
            self.relative_position_embedding = False
            self.num_buckets = 0
            self.max_distance = 0

        self.layers = nn.ModuleList(
            [
                TransformerSentenceEncoderLayer(
                    embedding_dim=self.embedding_dim,
                    ffn_embedding_dim=args.encoder_ffn_embed_dim,
                    num_attention_heads=args.encoder_attention_heads,
                    dropout=self.dropout,
                    attention_dropout=args.attention_dropout,
                    activation_dropout=args.activation_dropout,
                    activation_fn=args.activation_fn,
                    layer_norm_first=args.layer_norm_first,
                    deep_norm=args.deep_norm,
                    has_relative_attention_bias=self.relative_position_embedding,
                    num_buckets=self.num_buckets,
                    max_distance=self.max_distance,
                    gru_rel_pos=args.gru_rel_pos,
                    encoder_layers=args.encoder_layers,
                )
                for i in range(args.encoder_layers)
            ]
        )
        if self.relative_position_embedding:
            for i in range(1, args.encoder_layers):
                del self.layers[i].self_attn.relative_attention_bias
                self.layers[i].self_attn.relative_attention_bias = self.layers[0].self_attn.relative_attention_bias

        self.layer_norm_first = args.layer_norm_first
        self.layer_norm = LayerNorm(self.embedding_dim)
        self.layerdrop = args.encoder_layerdrop

        self.apply(init_bert_params)

        if args.deep_norm:
            deep_norm_beta = math.pow(8 * args.encoder_layers, -1 / 4)
            for i in range(args.encoder_layers):
                nn.init.xavier_normal_(self.layers[i].self_attn.k_proj.weight, gain=1)
                nn.init.xavier_normal_(self.layers[i].self_attn.v_proj.weight, gain=deep_norm_beta)
                nn.init.xavier_normal_(self.layers[i].self_attn.q_proj.weight, gain=1)
                nn.init.xavier_normal_(self.layers[i].self_attn.out_proj.weight, gain=deep_norm_beta)
                nn.init.xavier_normal_(self.layers[i].fc1.weight, gain=deep_norm_beta)
                nn.init.xavier_normal_(self.layers[i].fc2.weight, gain=deep_norm_beta)

        self.layer_wise_gradient_decay_ratio = getattr(args, "layer_wise_gradient_decay_ratio", 1)

    def forward(self, x, padding_mask=None, layer=None):
        x, layer_results = self.extract_features(x, padding_mask, layer)

        if self.layer_norm_first and layer is None:
            x = self.layer_norm(x)

        return x, layer_results

    def extract_features(self, x, padding_mask=None, tgt_layer=None):

        if padding_mask is not None:
            x[padding_mask] = 0

        x_conv = self.pos_conv(x.transpose(1, 2))
        x_conv = x_conv.transpose(1, 2)
        x = x + x_conv

        if not self.layer_norm_first:
            x = self.layer_norm(x)

        x = F.dropout(x, p=self.dropout, training=self.training)

        # B x T x C -> T x B x C
        x = x.transpose(0, 1)

        layer_results = []
        z = None
        if tgt_layer is not None:
            layer_results.append((x, z))
        r = None
        pos_bias = None
        for i, layer in enumerate(self.layers):
            if self.layer_wise_gradient_decay_ratio != 1.0:
                x = GradMultiply.apply(x, self.layer_wise_gradient_decay_ratio)
            dropout_probability = np.random.random()
            if not self.training or (dropout_probability > self.layerdrop):
                x, z, pos_bias = layer(x, self_attn_padding_mask=padding_mask, need_weights=False, pos_bias=pos_bias)
            if tgt_layer is not None:
                layer_results.append((x, z))
            if i == tgt_layer:
                r = x
                break

        if r is not None:
            x = r

        # T x B x C -> B x T x C
        x = x.transpose(0, 1)

        return x, layer_results

class TransformerSentenceEncoderLayer(nn.Module):
    def __init__(
            self,
            embedding_dim: float = 768,
            ffn_embedding_dim: float = 3072,
            num_attention_heads: float = 8,
            dropout: float = 0.1,
            attention_dropout: float = 0.1,
            activation_dropout: float = 0.1,
            activation_fn: str = "relu",
            layer_norm_first: bool = False,
            deep_norm: bool = False,
            has_relative_attention_bias: bool = False,
            num_buckets: int = 0,
            max_distance: int = 0,
            rescale_init: bool = False,
            gru_rel_pos: bool = False,
            encoder_layers: int = 0,
    ) -> None:

        super().__init__()
        self.embedding_dim = embedding_dim
        self.dropout = dropout
        self.activation_dropout = activation_dropout

        self.activation_name = activation_fn
        self.activation_fn = get_activation_fn(activation_fn)
        self.self_attn = MultiheadAttention(
            self.embedding_dim,
            num_attention_heads,
            dropout=attention_dropout,
            self_attention=True,
            has_relative_attention_bias=has_relative_attention_bias,
            num_buckets=num_buckets,
            max_distance=max_distance,
            rescale_init=rescale_init,
            gru_rel_pos=gru_rel_pos,
        )

        self.dropout1 = nn.Dropout(dropout)
        self.dropout2 = nn.Dropout(self.activation_dropout)
        self.dropout3 = nn.Dropout(dropout)

        self.layer_norm_first = layer_norm_first

        self.self_attn_layer_norm = LayerNorm(self.embedding_dim)

        if self.activation_name == "glu":
            self.fc1 = GLU_Linear(self.embedding_dim, ffn_embedding_dim, "swish")
        else:
            self.fc1 = nn.Linear(self.embedding_dim, ffn_embedding_dim)
        self.fc2 = nn.Linear(ffn_embedding_dim, self.embedding_dim)

        self.final_layer_norm = LayerNorm(self.embedding_dim)

        self.deep_norm = deep_norm
        if self.deep_norm:
            self.deep_norm_alpha = math.pow(2 * encoder_layers, 1 / 4)
        else:
            self.deep_norm_alpha = 1

    def forward(
            self,
            x: torch.Tensor,
            self_attn_mask: torch.Tensor = None,
            self_attn_padding_mask: torch.Tensor = None,
            need_weights: bool = False,
            pos_bias=None
    ):
        residual = x

        if self.layer_norm_first:
            x = self.self_attn_layer_norm(x)
            x, attn, pos_bias = self.self_attn(
                query=x,
                key=x,
                value=x,
                key_padding_mask=self_attn_padding_mask,
                need_weights=False,
                attn_mask=self_attn_mask,
                position_bias=pos_bias
            )
            x = self.dropout1(x)
            x = residual + x

            residual = x
            x = self.final_layer_norm(x)
            if self.activation_name == "glu":
                x = self.fc1(x)
            else:
                x = self.activation_fn(self.fc1(x))
            x = self.dropout2(x)
            x = self.fc2(x)
            x = self.dropout3(x)
            x = residual + x
        else:
            x, attn, pos_bias = self.self_attn(
                query=x,
                key=x,
                value=x,
                key_padding_mask=self_attn_padding_mask,
                need_weights=need_weights,
                attn_mask=self_attn_mask,
                position_bias=pos_bias
            )

            x = self.dropout1(x)
            x = residual * self.deep_norm_alpha + x

            x = self.self_attn_layer_norm(x)

            residual = x
            if self.activation_name == "glu":
                x = self.fc1(x)
            else:
                x = self.activation_fn(self.fc1(x))
            x = self.dropout2(x)
            x = self.fc2(x)
            x = self.dropout3(x)
            x = residual * self.deep_norm_alpha + x
            x = self.final_layer_norm(x)

        return x, attn, pos_bias

class MultiheadAttention(nn.Module):
    """Multi-headed attention.

    See "Attention Is All You Need" for more details.
    """

    def __init__(
            self,
            embed_dim,
            num_heads,
            kdim=None,
            vdim=None,
            dropout=0.0,
            bias=True,
            add_bias_kv=False,
            add_zero_attn=False,
            self_attention=False,
            encoder_decoder_attention=False,
            q_noise=0.0,
            qn_block_size=8,
            has_relative_attention_bias=False,
            num_buckets=32,
            max_distance=128,
            gru_rel_pos=False,
            rescale_init=False,
    ):
        super().__init__()
        self.embed_dim = embed_dim
        self.kdim = kdim if kdim is not None else embed_dim
        self.vdim = vdim if vdim is not None else embed_dim
        self.qkv_same_dim = self.kdim == embed_dim and self.vdim == embed_dim

        self.num_heads = num_heads
        self.dropout_module = nn.Dropout(dropout)

        self.has_relative_attention_bias = has_relative_attention_bias
        self.num_buckets = num_buckets
        self.max_distance = max_distance
        if self.has_relative_attention_bias:
            self.relative_attention_bias = nn.Embedding(num_buckets, num_heads)

        self.head_dim = embed_dim // num_heads
        self.q_head_dim = self.head_dim
        self.k_head_dim = self.head_dim
        assert (
                self.head_dim * num_heads == self.embed_dim
        ), "embed_dim must be divisible by num_heads"
        self.scaling = self.head_dim ** -0.5

        self.self_attention = self_attention
        self.encoder_decoder_attention = encoder_decoder_attention

        assert not self.self_attention or self.qkv_same_dim, (
            "Self-attention requires query, key and " "value to be of the same size"
        )

        k_bias = True
        if rescale_init:
            k_bias = False

        k_embed_dim = embed_dim
        q_embed_dim = embed_dim

        self.k_proj = quant_noise(
            nn.Linear(self.kdim, k_embed_dim, bias=k_bias), q_noise, qn_block_size
        )
        self.v_proj = quant_noise(
            nn.Linear(self.vdim, embed_dim, bias=bias), q_noise, qn_block_size
        )
        self.q_proj = quant_noise(
            nn.Linear(embed_dim, q_embed_dim, bias=bias), q_noise, qn_block_size
        )

        self.out_proj = quant_noise(
            nn.Linear(embed_dim, embed_dim, bias=bias), q_noise, qn_block_size
        )

        if add_bias_kv:
            self.bias_k = Parameter(torch.Tensor(1, 1, embed_dim))
            self.bias_v = Parameter(torch.Tensor(1, 1, embed_dim))
        else:
            self.bias_k = self.bias_v = None

        self.add_zero_attn = add_zero_attn

        self.gru_rel_pos = gru_rel_pos
        if self.gru_rel_pos:
            self.grep_linear = nn.Linear(self.q_head_dim, 8)
            self.grep_a = nn.Parameter(torch.ones(1, num_heads, 1, 1))

        self.reset_parameters()

    def reset_parameters(self):
        if self.qkv_same_dim:
            # Empirically observed the convergence to be much better with
            # the scaled initialization
            nn.init.xavier_uniform_(self.k_proj.weight, gain=1 / math.sqrt(2))
            nn.init.xavier_uniform_(self.v_proj.weight, gain=1 / math.sqrt(2))
            nn.init.xavier_uniform_(self.q_proj.weight, gain=1 / math.sqrt(2))
        else:
            nn.init.xavier_uniform_(self.k_proj.weight)
            nn.init.xavier_uniform_(self.v_proj.weight)
            nn.init.xavier_uniform_(self.q_proj.weight)

        nn.init.xavier_uniform_(self.out_proj.weight)
        if self.out_proj.bias is not None:
            nn.init.constant_(self.out_proj.bias, 0.0)
        if self.bias_k is not None:
            nn.init.xavier_normal_(self.bias_k)
        if self.bias_v is not None:
            nn.init.xavier_normal_(self.bias_v)
        if self.has_relative_attention_bias:
            nn.init.xavier_normal_(self.relative_attention_bias.weight)

    def _relative_positions_bucket(self, relative_positions, bidirectional=True):
        num_buckets = self.num_buckets
        max_distance = self.max_distance
        relative_buckets = 0

        if bidirectional:
            num_buckets = num_buckets // 2
            relative_buckets += (relative_positions > 0).to(torch.long) * num_buckets
            relative_positions = torch.abs(relative_positions)
        else:
            relative_positions = -torch.min(relative_positions, torch.zeros_like(relative_positions))

        max_exact = num_buckets // 2
        is_small = relative_positions < max_exact

        relative_postion_if_large = max_exact + (
                torch.log(relative_positions.float() / max_exact)
                / math.log(max_distance / max_exact)
                * (num_buckets - max_exact)
        ).to(torch.long)
        relative_postion_if_large = torch.min(
            relative_postion_if_large, torch.full_like(relative_postion_if_large, num_buckets - 1)
        )

        relative_buckets += torch.where(is_small, relative_positions, relative_postion_if_large)
        return relative_buckets

    def compute_bias(self, query_length, key_length):
        context_position = torch.arange(query_length, dtype=torch.long)[:, None]
        memory_position = torch.arange(key_length, dtype=torch.long)[None, :]
        relative_position = memory_position - context_position
        relative_position_bucket = self._relative_positions_bucket(
            relative_position,
            bidirectional=True
        )
        relative_position_bucket = relative_position_bucket.to(self.relative_attention_bias.weight.device)
        values = self.relative_attention_bias(relative_position_bucket)
        values = values.permute([2, 0, 1])
        return values

    def forward(
            self,
            query,
            key: Optional[Tensor],
            value: Optional[Tensor],
            key_padding_mask: Optional[Tensor] = None,
            incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None,
            need_weights: bool = True,
            static_kv: bool = False,
            attn_mask: Optional[Tensor] = None,
            before_softmax: bool = False,
            need_head_weights: bool = False,
            position_bias: Optional[Tensor] = None
    ) -> Tuple[Tensor, Optional[Tensor], Optional[Tensor]]:
        """Input shape: Time x Batch x Channel

        Args:
            key_padding_mask (ByteTensor, optional): mask to exclude
                keys that are pads, of shape `(batch, src_len)`, where
                padding elements are indicated by 1s.
            need_weights (bool, optional): return the attention weights,
                averaged over heads (default: False).
            attn_mask (ByteTensor, optional): typically used to
                implement causal attention, where the mask prevents the
                attention from looking forward in time (default: None).
            before_softmax (bool, optional): return the raw attention
                weights and values before the attention softmax.
            need_head_weights (bool, optional): return the attention
                weights for each head. Implies *need_weights*. Default:
                return the average attention weights over all heads.
        """
        if need_head_weights:
            need_weights = True

        is_tpu = query.device.type == "xla"

        tgt_len, bsz, embed_dim = query.size()
        src_len = tgt_len
        assert embed_dim == self.embed_dim
        assert list(query.size()) == [tgt_len, bsz, embed_dim]
        if key is not None:
            src_len, key_bsz, _ = key.size()
            if not torch.jit.is_scripting():
                assert key_bsz == bsz
                assert value is not None
                assert src_len, bsz == value.shape[:2]

        if self.has_relative_attention_bias and position_bias is None:
            position_bias = self.compute_bias(tgt_len, src_len)
            position_bias = position_bias.unsqueeze(0).repeat(bsz, 1, 1, 1).view(bsz * self.num_heads, tgt_len, src_len)

        if incremental_state is not None:
            saved_state = self._get_input_buffer(incremental_state)
            if saved_state is not None and "prev_key" in saved_state:
                # previous time steps are cached - no need to recompute
                # key and value if they are static
                if static_kv:
                    assert self.encoder_decoder_attention and not self.self_attention
                    key = value = None
        else:
            saved_state = None

        if self.self_attention:
            q = self.q_proj(query)
            k = self.k_proj(query)
            v = self.v_proj(query)
        elif self.encoder_decoder_attention:
            # encoder-decoder attention
            q = self.q_proj(query)
            if key is None:
                assert value is None
                k = v = None
            else:
                k = self.k_proj(key)
                v = self.v_proj(key)

        else:
            assert key is not None and value is not None
            q = self.q_proj(query)
            k = self.k_proj(key)
            v = self.v_proj(value)
        q *= self.scaling
        alpha = 32
        q *= 1 / alpha

        if self.bias_k is not None:
            assert self.bias_v is not None
            k = torch.cat([k, self.bias_k.repeat(1, bsz, 1)])
            v = torch.cat([v, self.bias_v.repeat(1, bsz, 1)])
            if attn_mask is not None:
                attn_mask = torch.cat(
                    [attn_mask, attn_mask.new_zeros(attn_mask.size(0), 1)], dim=1
                )
            if key_padding_mask is not None:
                key_padding_mask = torch.cat(
                    [
                        key_padding_mask,
                        key_padding_mask.new_zeros(key_padding_mask.size(0), 1),
                    ],
                    dim=1,
                )

        q = (
# ... truncated ...


TransformerEncoder

# Based on fairseq code bases
# https://github.com/pytorch/fairseq
# --------------------------------------------------------

import math
import numpy as np
from typing import Dict, Optional, Tuple
import torch
from torch import Tensor, nn
import torch.nn.functional as F
from torch.nn import LayerNorm, Parameter
from lavis.models.beats.modules import (
    GradMultiply,
    SamePad,
    get_activation_fn,
    GLU_Linear,
    quant_noise,
)

class TransformerEncoder(nn.Module):
    def __init__(self, args):
        super().__init__()

        self.dropout = args.dropout
        self.embedding_dim = args.encoder_embed_dim

        self.pos_conv = nn.Conv1d(
            self.embedding_dim,
            self.embedding_dim,
            kernel_size=args.conv_pos,
            padding=args.conv_pos // 2,
            groups=args.conv_pos_groups,
        )
        dropout = 0
        std = math.sqrt((4 * (1.0 - dropout)) / (args.conv_pos * self.embedding_dim))
        nn.init.normal_(self.pos_conv.weight, mean=0, std=std)
        nn.init.constant_(self.pos_conv.bias, 0)

        self.pos_conv = nn.utils.weight_norm(self.pos_conv, name="weight", dim=2)
        self.pos_conv = nn.Sequential(self.pos_conv, SamePad(args.conv_pos), nn.GELU())

        if hasattr(args, "relative_position_embedding"):
            self.relative_position_embedding = args.relative_position_embedding
            self.num_buckets = args.num_buckets
            self.max_distance = args.max_distance
        else:
            self.relative_position_embedding = False
            self.num_buckets = 0
            self.max_distance = 0

        self.layers = nn.ModuleList(
            [
                TransformerSentenceEncoderLayer(
                    embedding_dim=self.embedding_dim,
                    ffn_embedding_dim=args.encoder_ffn_embed_dim,
                    num_attention_heads=args.encoder_attention_heads,
                    dropout=self.dropout,
                    attention_dropout=args.attention_dropout,
                    activation_dropout=args.activation_dropout,
                    activation_fn=args.activation_fn,
                    layer_norm_first=args.layer_norm_first,
                    deep_norm=args.deep_norm,
                    has_relative_attention_bias=self.relative_position_embedding,
                    num_buckets=self.num_buckets,
                    max_distance=self.max_distance,
                    gru_rel_pos=args.gru_rel_pos,
                    encoder_layers=args.encoder_layers,
                )
                for i in range(args.encoder_layers)
            ]
        )
        if self.relative_position_embedding:
            for i in range(1, args.encoder_layers):
                del self.layers[i].self_attn.relative_attention_bias
                self.layers[i].self_attn.relative_attention_bias = self.layers[0].self_attn.relative_attention_bias

        self.layer_norm_first = args.layer_norm_first
        self.layer_norm = LayerNorm(self.embedding_dim)
        self.layerdrop = args.encoder_layerdrop

        self.apply(init_bert_params)

        if args.deep_norm:
            deep_norm_beta = math.pow(8 * args.encoder_layers, -1 / 4)
            for i in range(args.encoder_layers):
                nn.init.xavier_normal_(self.layers[i].self_attn.k_proj.weight, gain=1)
                nn.init.xavier_normal_(self.layers[i].self_attn.v_proj.weight, gain=deep_norm_beta)
                nn.init.xavier_normal_(self.layers[i].self_attn.q_proj.weight, gain=1)
                nn.init.xavier_normal_(self.layers[i].self_attn.out_proj.weight, gain=deep_norm_beta)
                nn.init.xavier_normal_(self.layers[i].fc1.weight, gain=deep_norm_beta)
                nn.init.xavier_normal_(self.layers[i].fc2.weight, gain=deep_norm_beta)

        self.layer_wise_gradient_decay_ratio = getattr(args, "layer_wise_gradient_decay_ratio", 1)

    def forward(self, x, padding_mask=None, layer=None):
        x, layer_results = self.extract_features(x, padding_mask, layer)

        if self.layer_norm_first and layer is None:
            x = self.layer_norm(x)

        return x, layer_results

    def extract_features(self, x, padding_mask=None, tgt_layer=None):

        if padding_mask is not None:
            x[padding_mask] = 0

        x_conv = self.pos_conv(x.transpose(1, 2))
        x_conv = x_conv.transpose(1, 2)
        x = x + x_conv

        if not self.layer_norm_first:
            x = self.layer_norm(x)

        x = F.dropout(x, p=self.dropout, training=self.training)

        # B x T x C -> T x B x C
        x = x.transpose(0, 1)

        layer_results = []
        z = None
        if tgt_layer is not None:
            layer_results.append((x, z))
        r = None
        pos_bias = None
        for i, layer in enumerate(self.layers):
            if self.layer_wise_gradient_decay_ratio != 1.0:
                x = GradMultiply.apply(x, self.layer_wise_gradient_decay_ratio)
            dropout_probability = np.random.random()
            if not self.training or (dropout_probability > self.layerdrop):
                x, z, pos_bias = layer(x, self_attn_padding_mask=padding_mask, need_weights=False, pos_bias=pos_bias)
            if tgt_layer is not None:
                layer_results.append((x, z))
            if i == tgt_layer:
                r = x
                break

        if r is not None:
            x = r

        # T x B x C -> B x T x C
        x = x.transpose(0, 1)

        return x, layer_results

class TransformerSentenceEncoderLayer(nn.Module):
    def __init__(
            self,
            embedding_dim: float = 768,
            ffn_embedding_dim: float = 3072,
            num_attention_heads: float = 8,
            dropout: float = 0.1,
            attention_dropout: float = 0.1,
            activation_dropout: float = 0.1,
            activation_fn: str = "relu",
            layer_norm_first: bool = False,
            deep_norm: bool = False,
            has_relative_attention_bias: bool = False,
            num_buckets: int = 0,
            max_distance: int = 0,
            rescale_init: bool = False,
            gru_rel_pos: bool = False,
            encoder_layers: int = 0,


TransformerSentenceEncoderLayer

            if self.layer_wise_gradient_decay_ratio != 1.0:
                x = GradMultiply.apply(x, self.layer_wise_gradient_decay_ratio)
            dropout_probability = np.random.random()
            if not self.training or (dropout_probability > self.layerdrop):
                x, z, pos_bias = layer(x, self_attn_padding_mask=padding_mask, need_weights=False, pos_bias=pos_bias)
            if tgt_layer is not None:
                layer_results.append((x, z))
            if i == tgt_layer:
                r = x
                break

        if r is not None:
            x = r

        # T x B x C -> B x T x C
        x = x.transpose(0, 1)

        return x, layer_results

class TransformerSentenceEncoderLayer(nn.Module):
    def __init__(
            self,
            embedding_dim: float = 768,
            ffn_embedding_dim: float = 3072,
            num_attention_heads: float = 8,
            dropout: float = 0.1,
            attention_dropout: float = 0.1,
            activation_dropout: float = 0.1,
            activation_fn: str = "relu",
            layer_norm_first: bool = False,
            deep_norm: bool = False,
            has_relative_attention_bias: bool = False,
            num_buckets: int = 0,
            max_distance: int = 0,
            rescale_init: bool = False,
            gru_rel_pos: bool = False,
            encoder_layers: int = 0,
    ) -> None:

        super().__init__()
        self.embedding_dim = embedding_dim
        self.dropout = dropout
        self.activation_dropout = activation_dropout

        self.activation_name = activation_fn
        self.activation_fn = get_activation_fn(activation_fn)
        self.self_attn = MultiheadAttention(
            self.embedding_dim,
            num_attention_heads,
            dropout=attention_dropout,
            self_attention=True,
            has_relative_attention_bias=has_relative_attention_bias,
            num_buckets=num_buckets,
            max_distance=max_distance,
            rescale_init=rescale_init,
            gru_rel_pos=gru_rel_pos,
        )

        self.dropout1 = nn.Dropout(dropout)
        self.dropout2 = nn.Dropout(self.activation_dropout)
        self.dropout3 = nn.Dropout(dropout)

        self.layer_norm_first = layer_norm_first

        self.self_attn_layer_norm = LayerNorm(self.embedding_dim)

        if self.activation_name == "glu":
            self.fc1 = GLU_Linear(self.embedding_dim, ffn_embedding_dim, "swish")
        else:
            self.fc1 = nn.Linear(self.embedding_dim, ffn_embedding_dim)
        self.fc2 = nn.Linear(ffn_embedding_dim, self.embedding_dim)

        self.final_layer_norm = LayerNorm(self.embedding_dim)

        self.deep_norm = deep_norm
        if self.deep_norm:
            self.deep_norm_alpha = math.pow(2 * encoder_layers, 1 / 4)
        else:
            self.deep_norm_alpha = 1

    def forward(
            self,
            x: torch.Tensor,
            self_attn_mask: torch.Tensor = None,
            self_attn_padding_mask: torch.Tensor = None,
            need_weights: bool = False,
            pos_bias=None
    ):
        residual = x

        if self.layer_norm_first:
            x = self.self_attn_layer_norm(x)
            x, attn, pos_bias = self.self_attn(
                query=x,
                key=x,
                value=x,
                key_padding_mask=self_attn_padding_mask,
                need_weights=False,
                attn_mask=self_attn_mask,
                position_bias=pos_bias
            )
            x = self.dropout1(x)
            x = residual + x

            residual = x
            x = self.final_layer_norm(x)
            if self.activation_name == "glu":
                x = self.fc1(x)
            else:
                x = self.activation_fn(self.fc1(x))
            x = self.dropout2(x)
            x = self.fc2(x)
            x = self.dropout3(x)
            x = residual + x
        else:
            x, attn, pos_bias = self.self_attn(
                query=x,
                key=x,
                value=x,
                key_padding_mask=self_attn_padding_mask,
                need_weights=need_weights,
                attn_mask=self_attn_mask,
                position_bias=pos_bias
            )

            x = self.dropout1(x)
            x = residual * self.deep_norm_alpha + x

            x = self.self_attn_layer_norm(x)

            residual = x
            if self.activation_name == "glu":
                x = self.fc1(x)
            else:
                x = self.activation_fn(self.fc1(x))
            x = self.dropout2(x)
            x = self.fc2(x)
            x = self.dropout3(x)
            x = residual * self.deep_norm_alpha + x
            x = self.final_layer_norm(x)

        return x, attn, pos_bias

class MultiheadAttention(nn.Module):
    """Multi-headed attention.

    See "Attention Is All You Need" for more details.
    """

    def __init__(
            self,
            embed_dim,
            num_heads,
            kdim=None,
            vdim=None,
            dropout=0.0,
            bias=True,
            add_bias_kv=False,
            add_zero_attn=False,
            self_attention=False,
            encoder_decoder_attention=False,