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config.json

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+{
+  "architectures": [
+    "UniRNAForMaskedLM"
+  ],
+  "attention_probs_dropout_prob": 0.0,
+  "emb_layer_norm_before": true,
+  "hidden_dropout_prob": 0.0,
+  "hidden_size": 1024,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-05,
+  "mask_token_id": 4,
+  "max_position_embeddings": 1026,
+  "model_type": "unirna",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 16,
+  "pad_token_id": 0,
+  "position_embedding_type": "rotary",
+  "sep_token_id": 1,
+  "token_dropout": true,
+  "transformers_version": "4.26.1",
+  "vocab_size": 10,
+  "auto_map": {
+    "AutoConfig": "config.UniRNAConfig",
+    "AutoModel": "model.UniRNAModels",
+    "AutoModelForMaskedLM": "model.UniRNAForMaskedLM"
+  }
+}

config.py

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+import os
+
+from transformers import PretrainedConfig
+
+
+class UniRNAConfig(PretrainedConfig):
+    """Configuration for UniRNA models."""
+
+    model_type: str = "unirna"
+
+    def __init__(
+        self,
+        vocab_size: int = 10,
+        hidden_size: int = 768,
+        num_hidden_layers: int = 12,
+        num_attention_heads: int = 12,
+        intermediate_size: int = 3072,
+        hidden_dropout_prob: float = 0.0,
+        attention_probs_dropout_prob: float = 0.0,
+        max_position_embeddings: int = 1026,
+        layer_norm_eps: float = 1e-5,
+        pad_token_id: int = 0,
+        sep_token_id: int = 1,
+        cls_token_id: int = 3,
+        mask_token_id: int = 4,
+        emb_layer_norm_before: bool = True,
+        token_dropout: bool = True,
+        position_embedding_type: str = "rotary",
+        use_flash_attention: bool = False,
+        tie_word_embeddings: bool = False,
+        is_decoder: bool = False,
+        **kwargs,
+    ):
+        # Ensure attribute exists before any access.
+        self.architectures = kwargs.get("architectures", None)
+        super().__init__(
+            pad_token_id=pad_token_id,
+            sep_token_id=sep_token_id,
+            cls_token_id=cls_token_id,
+            mask_token_id=mask_token_id,
+            tie_word_embeddings=tie_word_embeddings,
+            is_decoder=is_decoder,
+            **kwargs,
+        )
+        self.vocab_size = vocab_size
+        self.hidden_size = hidden_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.intermediate_size = intermediate_size
+        self.hidden_dropout_prob = hidden_dropout_prob
+        self.attention_probs_dropout_prob = attention_probs_dropout_prob
+        self.max_position_embeddings = max_position_embeddings
+        self.layer_norm_eps = layer_norm_eps
+        self.emb_layer_norm_before = emb_layer_norm_before
+        self.token_dropout = token_dropout
+        self.position_embedding_type = position_embedding_type
+        self.use_flash_attention = use_flash_attention
+        if self.architectures is None:
+            self.architectures = ["UniRNAForMaskedLM"]
+
+
+def build_config(path):
+    path = os.path.splitext(path)[0]
+    name = os.path.basename(path)
+    model_type, num_hidden_layers, hidden_size, _ = name.split("_")[:4]
+    num_hidden_layers = int(num_hidden_layers[1:])
+    hidden_size = int(hidden_size[1:])
+    num_attention_heads = hidden_size // 64
+    intermediate_size = hidden_size * 3
+    config = UniRNAConfig(
+        model_type=model_type,
+        num_hidden_layers=num_hidden_layers,
+        hidden_size=hidden_size,
+        num_attention_heads=num_attention_heads,
+        intermediate_size=intermediate_size,
+        pad_token_id=0,
+        sep_token_id=1,
+        mask_token_id=4,
+        cls_token_id=3,
+        vocab_size=10,
+        emb_layer_norm_before=True,
+        layer_norm_eps=1e-5,
+        hidden_dropout_prob=0.0,
+        attention_probs_dropout_prob=0.0,
+        token_dropout=True,
+        initializer_range=0.02,
+        use_flash_attention=True,
+        max_position_embeddings=1026,
+        position_embedding_type="rotary",
+        tie_word_embeddings=False,
+    )
+    config._name_or_path = name
+    return config
+
+
+def build_config_GENE(path, num_hidden_layers: int, hidden_size: int, vocab_size: int, model_type="GENE"):
+    path = os.path.splitext(path)[0]
+    name = os.path.basename(path)
+    # model_type, num_hidden_layers, hidden_size, _ = name.split("_")[:4]
+    num_hidden_layers = int(num_hidden_layers)
+    hidden_size = int(hidden_size)
+    num_attention_heads = hidden_size // 64
+    intermediate_size = hidden_size * 4
+    config = UniRNAConfig(
+        model_type=model_type,
+        num_hidden_layers=num_hidden_layers,
+        hidden_size=hidden_size,
+        num_attention_heads=num_attention_heads,
+        intermediate_size=intermediate_size,
+        pad_token_id=0,
+        sep_token_id=1,
+        mask_token_id=4,
+        cls_token_id=3,
+        vocab_size=vocab_size,
+        emb_layer_norm_before=True,
+        layer_norm_eps=1e-5,
+        hidden_dropout_prob=0.0,
+        attention_probs_dropout_prob=0.0,
+        token_dropout=True,
+        initializer_range=0.02,
+        use_flash_attention=True,
+        max_position_embeddings=1026,
+        position_embedding_type="rotary",
+        tie_word_embeddings=False,
+    )
+    config._name_or_path = name
+    return config

model.py

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+"""
+    The code is modified from the EsmModel in the transformers library.
+    Sources: https://github.com/huggingface/transformers/blob/main/src/transformers/models/esm/modeling_esm.py
+"""
+
+from dataclasses import dataclass
+from functools import partial
+from typing import Optional, Sequence, Tuple, Union
+
+import torch
+import torch.utils.checkpoint
+from torch import nn
+from torch.nn import CrossEntropyLoss
+from transformers.modeling_outputs import (
+    BaseModelOutputWithPastAndCrossAttentions,
+    BaseModelOutputWithPoolingAndCrossAttentions,
+    MaskedLMOutput,
+    ModelOutput,
+)
+from transformers.modeling_utils import PreTrainedModel
+from transformers.utils import logging
+
+from .config import UniRNAConfig
+
+logger = logging.get_logger(__name__)
+
+
+@dataclass
+class UniRNASSPredictionOutput(ModelOutput):
+    loss: Optional[torch.FloatTensor] = None
+    logits: Optional[torch.FloatTensor] = None
+    hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
+    attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
+    pair_mask: Optional[torch.BoolTensor] = None
+
+
+def rotate_half(x):
+    x1, x2 = x.chunk(2, dim=-1)
+    return torch.cat((-x2, x1), dim=-1)
+
+
+def apply_rotary_pos_emb(x, cos, sin):
+    cos = cos[:, :, : x.shape[-2], :]
+    sin = sin[:, :, : x.shape[-2], :]
+
+    return (x * cos) + (rotate_half(x) * sin)
+
+
+class RotaryEmbedding(nn.Module):
+    """
+    Rotary position embeddings based on those in
+    [RoFormer](https://huggingface.co/docs/transformers/model_doc/roformer). Query and keys are transformed by rotation
+    matrices which depend on their relative positions.
+    """
+
+    def __init__(self, dim: int):
+        super().__init__()
+        # Generate and save the inverse frequency buffer (non trainable)
+        inv_freq = 1.0 / (10000 ** (torch.arange(0, dim, 2).float() / dim))
+        inv_freq = inv_freq
+        self.register_buffer("inv_freq", inv_freq)
+
+        self._seq_len_cached = None
+        self._cos_cached = None
+        self._sin_cached = None
+
+    def _update_cos_sin_tables(self, x, seq_dimension=2):
+        seq_len = x.shape[seq_dimension]
+
+        # Reset the tables if the sequence length has changed,
+        # or if we're on a new device (possibly due to tracing for instance)
+        if seq_len != self._seq_len_cached or self._cos_cached.device != x.device:
+            self._seq_len_cached = seq_len
+            t = torch.arange(x.shape[seq_dimension], device=x.device).type_as(self.inv_freq)
+            freqs = torch.outer(t, self.inv_freq)
+            emb = torch.cat((freqs, freqs), dim=-1).to(x.device)
+
+            self._cos_cached = emb.cos()[None, None, :, :]
+            self._sin_cached = emb.sin()[None, None, :, :]
+
+        return self._cos_cached, self._sin_cached
+
+    def forward(self, q: torch.Tensor, k: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
+        self._cos_cached, self._sin_cached = self._update_cos_sin_tables(k, seq_dimension=-2)
+
+        return (
+            apply_rotary_pos_emb(q, self._cos_cached, self._sin_cached),
+            apply_rotary_pos_emb(k, self._cos_cached, self._sin_cached),
+        )
+
+
+class UniRNAEmbedding(nn.Module):
+    """
+    Same as BertEmbeddings with a additional token_dropout.
+    """
+
+    def __init__(self, config):
+        super().__init__()
+        self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
+
+        if config.emb_layer_norm_before:
+            self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        else:
+            self.layer_norm = None
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+        self.padding_idx = config.pad_token_id
+        self.token_dropout = config.token_dropout
+        self.mask_token_id = config.mask_token_id
+
+    def forward(self, input_ids=None, attention_mask=None, inputs_embeds=None):
+        if inputs_embeds is None:
+            inputs_embeds = self.word_embeddings(input_ids)
+
+        embeddings = inputs_embeds
+        if attention_mask is None:
+            attention_mask = torch.ones(embeddings.shape[:2], device=embeddings.device)
+
+        # By default, we use token dropout, similar to UniRNA.
+        if self.layer_norm is not None:
+            embeddings = self.layer_norm(embeddings)
+        if attention_mask is not None:
+            embeddings = (embeddings * attention_mask.unsqueeze(-1)).to(embeddings.dtype)
+
+        embeddings = self.dropout(embeddings)
+        if self.token_dropout and input_ids is not None:
+            embeddings = embeddings.masked_fill((input_ids == self.mask_token_id).unsqueeze(-1), 0.0)
+            # 0.15 is MaskedLM's default mask probability, and 0.8 is the default keep probability
+            mask_ratio_train = 0.15 * 0.8
+            src_lengths = attention_mask.sum(-1).clamp(min=1).to(embeddings.dtype)
+            mask_ratio_observed = (input_ids == self.mask_token_id).sum(-1).to(embeddings.dtype) / src_lengths
+            denom = (1 - mask_ratio_observed).clamp(min=1e-6)
+            embeddings = (embeddings * (1 - mask_ratio_train) / denom[:, None, None]).to(embeddings.dtype)
+
+        return embeddings
+
+
+class UniRNASelfAttention(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
+            raise ValueError(
+                f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention "
+                f"heads ({config.num_attention_heads})"
+            )
+
+        self.num_attention_heads = config.num_attention_heads
+        self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
+        self.all_head_size = self.num_attention_heads * self.attention_head_size
+
+        self.query = nn.Linear(config.hidden_size, self.all_head_size)
+        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.rotary_embeddings = RotaryEmbedding(dim=self.attention_head_size)
+
+        self.is_decoder = config.is_decoder
+
+    def transpose_for_scores(self, x: torch.Tensor) -> torch.Tensor:
+        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: torch.Tensor,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        output_attentions: Optional[bool] = False,
+    ) -> Tuple[torch.Tensor]:
+        mixed_query_layer = self.query(hidden_states)
+
+        key_layer = self.transpose_for_scores(self.key(hidden_states))
+        value_layer = self.transpose_for_scores(self.value(hidden_states))
+        query_layer = self.transpose_for_scores(mixed_query_layer)
+
+        # Hardcoded from EsmModel provided by transformers
+        query_layer = query_layer * self.attention_head_size**-0.5
+
+        # Apply rotary embeddings
+        query_layer, key_layer = self.rotary_embeddings(query_layer, key_layer)
+
+        # Take the dot product between "query" and "key" to get the raw attention scores.
+        # For faster computation, you can used torch.nn.functional.scaled_dot_product_attention
+
+        attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
+
+        if attention_mask is not None:
+            # Apply the attention mask is (precomputed for all layers in UniRNAModel forward() function)
+            attention_scores = attention_scores + attention_mask
+
+        # Normalize the attention scores to probabilities.
+        attention_probs = nn.functional.softmax(attention_scores, dim=-1)
+
+        # This is actually dropping out entire tokens to attend to, which might
+        # seem a bit unusual, but is taken from the original Transformer paper.
+        attention_probs = self.dropout(attention_probs)
+
+        context_layer = torch.matmul(attention_probs, value_layer)
+
+        context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
+        new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
+        context_layer = context_layer.view(new_context_layer_shape)
+
+        outputs = (context_layer, attention_probs) if output_attentions else (context_layer, None)
+
+        return outputs
+
+
+class UniRNAFlashSelfAttention(UniRNASelfAttention):
+    """Self-attention using PyTorch's scaled_dot_product_attention (SDPA) backend."""
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.dropout_prob = config.attention_probs_dropout_prob
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        output_attentions: Optional[bool] = False,
+    ) -> Tuple[torch.Tensor]:
+        if output_attentions:
+            raise ValueError("SDPA attention does not support output_attentions=True")
+
+        mixed_query_layer = self.query(hidden_states)
+        key_layer = self.transpose_for_scores(self.key(hidden_states))
+        value_layer = self.transpose_for_scores(self.value(hidden_states))
+        query_layer = self.transpose_for_scores(mixed_query_layer)
+
+        # Same manual scaling as UniRNASelfAttention
+        query_layer = query_layer * self.attention_head_size**-0.5
+
+        # Apply rotary embeddings
+        query_layer, key_layer = self.rotary_embeddings(query_layer, key_layer)
+
+        # Use PyTorch SDPA; scale=1.0 because we already scaled query above
+        attn_output = torch.nn.functional.scaled_dot_product_attention(
+            query_layer,
+            key_layer,
+            value_layer,
+            attn_mask=attention_mask,
+            dropout_p=self.dropout_prob if self.training else 0.0,
+            scale=1.0,
+        )
+
+        attn_output = attn_output.permute(0, 2, 1, 3).contiguous()
+        new_shape = attn_output.size()[:-2] + (self.all_head_size,)
+        attn_output = attn_output.view(new_shape)
+
+        return (attn_output, None)
+
+
+class UniRNASelfOutput(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+        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 = hidden_states + input_tensor
+        return hidden_states
+
+
+class UniRNA_Attention(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+
+        if getattr(config, "use_flash_attention", False):
+            self.self = UniRNAFlashSelfAttention(config)
+        else:
+            self.self = UniRNASelfAttention(config)
+        self.output = UniRNASelfOutput(config)
+        self.pruned_heads = set()
+        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+
+    # TODO: add pruning heads
+    # 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,
+        output_attentions=False,
+    ):
+        hidden_states_ln = self.LayerNorm(hidden_states)
+        self_outputs = self.self(
+            hidden_states_ln,
+            attention_mask,
+            output_attentions,
+        )
+        attention_output = self.output(self_outputs[0], hidden_states)
+        return (attention_output, self_outputs[1])
+
+
+class UniRNAIntermediate(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        hidden_states = self.dense(hidden_states)
+        hidden_states = nn.functional.gelu(hidden_states)
+        return hidden_states
+
+
+class UniRNAOutput(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
+        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 = hidden_states + input_tensor
+        return hidden_states
+
+
+class UniRNALayer(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.chunk_size_feed_forward = config.chunk_size_feed_forward
+        self.seq_len_dim = 1
+        self.attention = UniRNA_Attention(config)
+        self.intermediate = UniRNAIntermediate(config)
+        self.output = UniRNAOutput(config)
+        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+
+    def forward(
+        self,
+        hidden_states,
+        attention_mask=None,
+        output_attentions=False,
+    ):
+        self_attention_outputs = self.attention(hidden_states, attention_mask, output_attentions=output_attentions)
+        layer_output = self.feed_forward_chunk(self_attention_outputs[0])
+        return (layer_output, self_attention_outputs[1])
+
+    def feed_forward_chunk(self, attention_output):
+        attention_output_ln = self.LayerNorm(attention_output)
+        intermediate_output = self.intermediate(attention_output_ln)
+        layer_output = self.output(intermediate_output, attention_output)
+        return layer_output
+
+
+class UniRNAEncoder(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        self.layer = nn.ModuleList([UniRNALayer(config) for _ in range(config.num_hidden_layers)])
+        self.emb_layer_norm_after = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.gradient_checkpointing = False
+
+    def forward(
+        self,
+        hidden_states,
+        attention_mask=None,
+        output_attentions=False,
+        output_hidden_states=False,
+    ):
+
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attentions = () if output_attentions else None
+
+        for layer_module in self.layer:
+            if output_hidden_states:
+                all_hidden_states = all_hidden_states + (hidden_states,)
+
+            if self.gradient_checkpointing and self.training:
+                layer_outputs = self._gradient_checkpointing_func(
+                    layer_module.__call__,
+                    hidden_states,
+                    attention_mask,
+                    output_attentions,
+                )
+            else:
+                layer_outputs = layer_module(
+                    hidden_states,
+                    attention_mask,
+                    output_attentions,
+                )
+            hidden_states = layer_outputs[0]
+            if output_attentions:
+                all_self_attentions = all_self_attentions + (layer_outputs[1],)
+
+        if self.emb_layer_norm_after:
+            hidden_states = self.emb_layer_norm_after(hidden_states)
+
+        if output_hidden_states:
+            all_hidden_states = all_hidden_states + (hidden_states,)
+
+        return BaseModelOutputWithPastAndCrossAttentions(
+            last_hidden_state=hidden_states,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attentions,
+        )
+
+
+# Copied from transformers.models.bert.modeling_bert.BertPooler
+class UniRNAPooler(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+        self.activation = nn.Tanh()
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        # We "pool" the model by simply taking the hidden state corresponding
+        # to the first token.
+        first_token_tensor = hidden_states[:, 0]
+        pooled_output = self.dense(first_token_tensor)
+        pooled_output = self.activation(pooled_output)
+        return pooled_output
+
+
+class UniRNAModel(PreTrainedModel):
+    config_class = UniRNAConfig
+    supports_gradient_checkpointing = True
+    main_input_name = "input_ids"
+    """
+
+    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.
+    """
+
+    def __init__(self, config, add_pooling_layer=True):
+        super().__init__(config)
+        self.config = config
+        self.embeddings = UniRNAEmbedding(config)
+        self.encoder = UniRNAEncoder(config)
+        self.pooler = UniRNAPooler(config) if add_pooling_layer else None
+
+        use_flash_attention = getattr(config, "use_flash_attention", False)
+        if use_flash_attention:
+            logger.info("Using Uni-RNA SDPA Attention")
+        else:
+            logger.info("Using Uni-RNA Attention")
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def _set_gradient_checkpointing(self, enable: bool, gradient_checkpointing_func=None):
+        self.encoder.gradient_checkpointing = enable
+        if gradient_checkpointing_func is not None:
+            self.encoder._gradient_checkpointing_func = gradient_checkpointing_func
+
+    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 forward(
+        self,
+        input_ids: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPoolingAndCrossAttentions]:
+        r"""
+        encoder_hidden_states  (`torch.FloatTensor` of shape `(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 (`torch.FloatTensor` of shape `(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**.
+        past_key_values (`Tuple[Tuple[torch.FloatTensor]]`, *optional*):
+            Tuple of length `config.n_layers`. Each tuple has 4 tensors of shape
+            `(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 `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
+            don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
+            `decoder_input_ids` of shape `(batch_size, sequence_length)`.
+        use_cache (`bool`, *optional*):
+            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
+            `past_key_values`).
+        """
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        input_shape, attention_mask = self._validate_and_shape_inputs(input_ids, inputs_embeds, attention_mask)
+        extended_attention_mask = self._prepare_attention_mask(attention_mask, input_shape)
+        embedding_output = self._compute_embedding_output(input_ids, attention_mask, inputs_embeds)
+        encoder_outputs = self.encoder(
+            embedding_output,
+            attention_mask=extended_attention_mask,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+        )
+        sequence_output, pooled_output = self._pool_outputs(encoder_outputs[0], attention_mask)
+
+        if not return_dict:
+            output = (sequence_output, pooled_output) + encoder_outputs[1:]
+            return output
+
+        return BaseModelOutputWithPoolingAndCrossAttentions(
+            last_hidden_state=sequence_output,
+            pooler_output=pooled_output,
+            past_key_values=encoder_outputs.past_key_values,
+            hidden_states=encoder_outputs.hidden_states,
+            attentions=encoder_outputs.attentions,
+            cross_attentions=encoder_outputs.cross_attentions,
+        )
+
+    def _validate_and_shape_inputs(
+        self,
+        input_ids: Optional[torch.Tensor],
+        inputs_embeds: Optional[torch.Tensor],
+        attention_mask: Optional[torch.Tensor],
+    ) -> Tuple[Tuple[int, ...], torch.Tensor]:
+        if input_ids is not None and inputs_embeds is not None:
+            raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
+        if input_ids is None and inputs_embeds is None:
+            raise ValueError("You have to specify either input_ids or inputs_embeds")
+
+        if input_ids is not None:
+            self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
+            input_shape = input_ids.size()
+            device = input_ids.device
+        else:
+            input_shape = inputs_embeds.size()[:-1]
+            device = inputs_embeds.device
+
+        batch_size, seq_length = input_shape
+        if attention_mask is None:
+            attention_mask = torch.ones((batch_size, seq_length), device=device)
+        return input_shape, attention_mask
+
+    def _prepare_attention_mask(self, attention_mask: torch.Tensor, input_shape: Tuple[int, ...]) -> torch.Tensor:
+        return self.get_extended_attention_mask(attention_mask, input_shape)
+
+    def _compute_embedding_output(
+        self,
+        input_ids: Optional[torch.Tensor],
+        attention_mask: torch.Tensor,
+        inputs_embeds: Optional[torch.Tensor],
+    ) -> torch.Tensor:
+        return self.embeddings(input_ids=input_ids, attention_mask=attention_mask, inputs_embeds=inputs_embeds)
+
+    def _pool_outputs(
+        self, sequence_output: torch.Tensor, attention_mask: torch.Tensor
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
+        # make it compatible with deepprotein which wraps the model with different pooler
+        try:
+            pooled_output = self.pooler(sequence_output, attention_mask) if self.pooler is not None else None
+        except TypeError:
+            pooled_output = self.pooler(sequence_output) if self.pooler is not None else None
+        return sequence_output, pooled_output
+
+
+class UniRNAForMaskedLM(PreTrainedModel):
+    _tied_weights_keys = ["lm_head.decoder.weight"]
+    config_class = UniRNAConfig
+    supports_gradient_checkpointing = True
+    main_input_name = "input_ids"
+
+    def __init__(self, config):
+        super().__init__(config)
+
+        self.config = config
+        self.embeddings = UniRNAEmbedding(config)
+        self.encoder = UniRNAEncoder(config)
+        self.lm_head = UniRNALMHead(config)
+
+        self.post_init()
+
+    def _set_gradient_checkpointing(self, enable: bool, gradient_checkpointing_func=None):
+        self.encoder.gradient_checkpointing = enable
+        if gradient_checkpointing_func is not None:
+            self.encoder._gradient_checkpointing_func = gradient_checkpointing_func
+
+    def get_input_embeddings(self):
+        return self.embeddings.word_embeddings
+
+    def set_input_embeddings(self, value):
+        self.embeddings.word_embeddings = value
+
+    def get_output_embeddings(self):
+        return self.lm_head.decoder
+
+    def set_output_embeddings(self, new_embeddings):
+        self.lm_head.decoder = new_embeddings
+
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        labels: Optional[torch.Tensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, MaskedLMOutput]:
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Labels for computing the masked language modeling loss. 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 in `[0, ..., config.vocab_size]`
+        kwargs (`Dict[str, any]`, optional, defaults to *{}*):
+            Used to hide legacy arguments that have been deprecated.
+        """
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        if input_ids is not None and inputs_embeds is not None:
+            raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
+        elif input_ids is not None:
+            self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
+            input_shape = input_ids.size()
+        elif inputs_embeds is not None:
+            input_shape = inputs_embeds.size()[:-1]
+        else:
+            raise ValueError("You have to specify either input_ids or inputs_embeds")
+
+        batch_size, seq_length = input_shape
+        device = input_ids.device if input_ids is not None else inputs_embeds.device
+
+        if attention_mask is None:
+            attention_mask = torch.ones(((batch_size, seq_length)), device=device)
+
+        extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape)
+
+        embedding_output = self.embeddings(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            inputs_embeds=inputs_embeds,
+        )
+
+        encoder_outputs = self.encoder(
+            embedding_output,
+            attention_mask=extended_attention_mask,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+        )
+        sequence_output = encoder_outputs[0]
+
+        prediction_scores = self.lm_head(sequence_output)
+
+        loss = None
+        if labels is not None:
+            loss_fct = CrossEntropyLoss()
+            loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
+
+        if not return_dict:
+            output = (prediction_scores,) + encoder_outputs[1:]
+            return ((loss,) + output) if loss is not None else output
+
+        return MaskedLMOutput(
+            loss=loss,
+            logits=prediction_scores,
+            hidden_states=encoder_outputs.hidden_states,
+            attentions=encoder_outputs.attentions,
+        )
+
+
+class UniRNAForSSPredict(PreTrainedModel):
+    """
+    TODO: make it compatible with transformers, create new 'modeling_outputs' class for SS prediction
+    """
+
+    config_class = UniRNAConfig
+    supports_gradient_checkpointing = True
+    main_input_name = "input_ids"
+
+    def __init__(self, config):
+        # Explicitly block usage until this head is trained and validated.
+        raise RuntimeError(
+            "UniRNAForSSPredict is disabled and not supported. This head is untrained and must not be called."
+        )
+
+    def _set_gradient_checkpointing(self, enable: bool, gradient_checkpointing_func=None):
+        self.encoder.gradient_checkpointing = enable
+        if gradient_checkpointing_func is not None:
+            self.encoder._gradient_checkpointing_func = gradient_checkpointing_func
+
+    def get_input_embeddings(self):
+        return self.embeddings.word_embeddings
+
+    def set_input_embeddings(self, value):
+        self.embeddings.word_embeddings = value
+
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        labels: Optional[torch.Tensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, UniRNASSPredictionOutput]:
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Labels for computing the masked language modeling loss. 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 in `[0, ..., config.vocab_size]`
+        kwargs (`Dict[str, any]`, optional, defaults to *{}*):
+            Used to hide legacy arguments that have been deprecated.
+        """
+
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        if input_ids is not None and inputs_embeds is not None:
+            raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
+        elif input_ids is not None:
+            self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
+            input_shape = input_ids.size()
+        elif inputs_embeds is not None:
+            input_shape = inputs_embeds.size()[:-1]
+        else:
+            raise ValueError("You have to specify either input_ids or inputs_embeds")
+
+        batch_size, seq_length = input_shape
+        device = input_ids.device if input_ids is not None else inputs_embeds.device
+
+        if attention_mask is None:
+            attention_mask = torch.ones(((batch_size, seq_length)), device=device)
+
+        extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape)
+
+        embedding_output = self.embeddings(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            inputs_embeds=inputs_embeds,
+        )
+
+        encoder_outputs = self.encoder(
+            embedding_output,
+            attention_mask=extended_attention_mask,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+        )
+
+        sequence_output = encoder_outputs[0]
+        logits, pair_mask = self.heads(sequence_output, attention_mask=attention_mask, return_mask=True)
+
+        loss = None
+        if labels is not None:
+            if labels.dim() == 3:
+                labels = labels.unsqueeze(-1)
+            if labels.shape[1] == logits.shape[1] + 2 and labels.shape[2] == logits.shape[2] + 2:
+                labels = labels[:, 1:-1, 1:-1, :]
+            labels = labels.to(logits.dtype)
+            loss_fct = nn.BCEWithLogitsLoss()
+            if pair_mask is not None:
+                loss = loss_fct(logits[pair_mask], labels[pair_mask])
+            else:
+                loss = loss_fct(logits, labels)
+
+        if not return_dict:
+            output = (logits, encoder_outputs.hidden_states, encoder_outputs.attentions, pair_mask)
+            return ((loss,) + output) if loss is not None else output
+
+        return UniRNASSPredictionOutput(
+            loss=loss,
+            logits=logits,
+            hidden_states=encoder_outputs.hidden_states,
+            attentions=encoder_outputs.attentions,
+            pair_mask=pair_mask,
+        )
+
+
+class UniRNALMHead(nn.Module):
+    """UniRNA Head for masked language modeling."""
+
+    def __init__(self, config):
+        super().__init__()
+        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+        self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.decoder = nn.Linear(config.hidden_size, config.vocab_size)
+
+    def forward(self, features):
+        x = self.dense(features)
+        x = nn.functional.gelu(x)
+        x = self.layer_norm(x)
+
+        # project back to size of vocabulary with bias
+        x = self.decoder(x)
+        return x
+
+
+class Dense(nn.Module):
+    def __init__(
+        self,
+        in_features: int,
+        out_features: int,
+        norm: str = "LayerNorm",
+        activation: str = "ReLU",
+        dropout: float = 0.1,
+        pool: str = "AdaptiveAvgPool1d",
+        bias: bool = True,
+        residual: bool = True,
+    ) -> None:
+        super().__init__()
+        self.residual = residual
+        self.linear = nn.Linear(in_features, out_features, bias=bias)
+        self.norm = getattr(nn, norm)(out_features) if norm else nn.Identity()
+        self.activation = getattr(nn, activation)() if activation else nn.Identity()
+        self.dropout = nn.Dropout(dropout)
+        self.pool = getattr(nn, pool)(out_features) if pool else nn.Identity() if self.residual else None
+
+    def forward(self, x):
+        out = self.linear(x)
+        out = self.norm(out)
+        out = self.activation(out)
+        out = self.dropout(out)
+        if self.residual:
+            out = out + self.pool(x)
+        return out
+
+
+class MLP(nn.Module):
+    def __init__(
+        self,
+        *features: Sequence[int],
+        norm: str = "LayerNorm",
+        activation: str = "ReLU",
+        dropout: float = 0.1,
+        pool: str = "AdaptiveAvgPool1d",
+        bias: bool = True,
+        residual: bool = True,
+        linear_output: bool = True
+    ) -> None:
+        super().__init__()
+        if len(features) == 0 and isinstance(features, Sequence):
+            features = features[0]  # type: ignore[assignment]
+        if not len(features) > 1:
+            raise ValueError(f"`features` of MLP should have at least 2 elements, but got {len(features)}")
+        dense = partial(
+            Dense,
+            norm=norm,
+            activation=activation,
+            dropout=dropout,
+            pool=pool,
+            bias=bias,
+            residual=residual,
+        )
+        if linear_output:
+            layers = [dense(in_features, out_features) for in_features, out_features in zip(features, features[1:-1])]
+            layers.append(nn.Linear(features[-2], features[-1], bias=bias))
+        else:
+            layers = [dense(in_features, out_features) for in_features, out_features in zip(features, features[1:])]
+        self.layers = nn.Sequential(*layers)
+
+    def forward(self, x):
+        return self.layers(x)
+
+
+class UniRNASSHead(nn.Module):
+    """UniRNA head for Secondary Structure Prediction"""
+
+    def __init__(self, config) -> None:
+        super().__init__()
+
+        self.qk_proj = nn.Linear(config.hidden_size, 2 * config.hidden_size)
+        self.ffn = MLP(1, config.hidden_size, residual=False)
+        self.linear = nn.Linear(config.hidden_size, 1)
+
+    def forward(self, features, attention_mask: Optional[torch.Tensor] = None, return_mask: bool = False):
+        x = features[:, 1:-1]  # remove CLS and EOS tokens
+        q, k = self.qk_proj(x).chunk(2, dim=-1)
+        contact_map = (q @ k.transpose(-2, -1)).unsqueeze(-1)
+        contact_map = contact_map + self.ffn(contact_map)
+        logits = self.linear(contact_map)
+
+        pair_mask = None
+        if attention_mask is not None:
+            core_mask = attention_mask[:, 1:-1].bool()
+            pair_mask = core_mask.unsqueeze(-1) & core_mask.unsqueeze(-2)
+            pair_mask = pair_mask.unsqueeze(-1)
+            logits = logits.masked_fill(~pair_mask, 0.0)
+
+        return (logits, pair_mask) if return_mask else logits
+
+
+class AvgPooler(nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    def forward(self, hidden_states, attention_mask=None):
+        if attention_mask is None:
+            attention_mask = torch.ones(hidden_states.shape[:2], device=hidden_states.device, dtype=torch.bool)
+        else:
+            attention_mask = attention_mask.bool()
+
+        if hidden_states.size(1) > 2:
+            core_states = hidden_states[:, 1:-1, :]
+            core_mask = attention_mask[:, 1:-1]
+        else:
+            core_states = hidden_states
+            core_mask = attention_mask
+
+        core_mask = core_mask.unsqueeze(-1)
+        masked_states = core_states * core_mask
+        denom = core_mask.sum(dim=1).clamp(min=1).to(hidden_states.dtype)
+        return masked_states.sum(dim=1) / denom
+
+
+class UniRNAModels(UniRNAModel):
+    config_class = UniRNAConfig
+    supports_gradient_checkpointing = True
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+        # We didn't include weight for original pooler, so we replace it with a simple cls pooler
+        del self.pooler
+        self.pooler = AvgPooler()
+
+
+class UniRNAForMLM(UniRNAForMaskedLM):
+    config_class = UniRNAConfig
+    supports_gradient_checkpointing = True
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+        # We didn't include weight for original pooler, so we replace it with a simple cls pooler
+        self.pooler = AvgPooler()

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0e1f93036483ef3f89e47136f1fce27a05d2178fc4a0659dd0b9e89dea3219e7
+size 676213784

tokenizer.py

@@ -0,0 +1,162 @@
+# Copyright Beijing DP Technology Co.,Ltd. All rights reserved.
+
+"""
+    The code is modified from the original ESM tokenizer provided by HuggingFace.
+    Sources: https://github.com/huggingface/transformers/blob/main/src/transformers/models/esm/tokenization_esm.py
+"""
+
+import os
+from typing import List, Optional, Union
+
+from transformers.tokenization_utils import PreTrainedTokenizer
+from transformers.tokenization_utils_base import AddedToken
+
+VOCAB_FILES_NAMES = {"vocab_file": "vocab.txt"}
+
+
+def load_vocab_file(vocab_file):
+    """Load vocabulary tokens from file into a list of strings."""
+    with open(vocab_file, "r") as f:
+        lines = f.read().splitlines()
+        return [line.strip() for line in lines]
+
+
+class UniRNATokenizer(PreTrainedTokenizer):
+    """
+    Constructs an UniRNA tokenizer, based on ESM tokenizer provided by HuggingFace.
+    """
+
+    vocab_files_names = VOCAB_FILES_NAMES
+    model_input_names = ["input_ids", "attention_mask"]
+
+    def __init__(
+        self,
+        vocab_file,
+        unk_token="N",
+        cls_token="<cls>",
+        pad_token="<pad>",
+        mask_token="<mask>",
+        eos_token="<eos>",
+        replace_uracil: bool = False,
+        **kwargs,
+    ):
+        self.all_tokens = load_vocab_file(vocab_file)
+        self._id_to_token = dict(enumerate(self.all_tokens))
+        self._token_to_id = {tok: ind for ind, tok in enumerate(self.all_tokens)}
+        super().__init__(
+            unk_token=unk_token,
+            cls_token=cls_token,
+            pad_token=pad_token,
+            mask_token=mask_token,
+            eos_token=eos_token,
+            **kwargs,
+        )
+
+        # Optional compatibility switch for DNA-only workflows.
+        if replace_uracil and "U" in self._token_to_id and "T" in self._token_to_id:
+            self._token_to_id["U"] = self._token_to_id["T"]
+        self.unique_no_split_tokens = self.all_tokens
+        self._update_trie(self.unique_no_split_tokens)
+
+    def _convert_token_to_id(self, token: str) -> int:
+        token = token.upper() if token not in self.all_special_tokens else token
+        unk_id = self._token_to_id.get(self.unk_token)
+        if unk_id is None:
+            unk_id = self.unk_token_id
+        return self._token_to_id.get(token, unk_id)
+
+    def _convert_id_to_token(self, index: int) -> str:
+        return self._id_to_token.get(index, self.unk_token)
+
+    def token_to_id(self, token: str) -> int:
+        return self._convert_token_to_id(token)
+
+    def id_to_token(self, index: int) -> str:
+        return self._convert_id_to_token(index)
+
+    def _tokenize(self, text, **kwargs):
+        text = text.strip()
+        if not text:
+            return []
+        if any(ch.isspace() for ch in text):
+            return text.split()
+        return list(text)
+
+    def get_vocab_size(self, with_added_tokens=False):
+        if with_added_tokens:
+            return len(self.get_vocab())
+        return len(self._id_to_token)
+
+    def get_vocab(self):
+        vocab = {token: i for i, token in enumerate(self.all_tokens)}
+        vocab.update(self.added_tokens_encoder)
+        return vocab
+
+    def build_inputs_with_special_tokens(
+        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
+    ) -> List[int]:
+        cls = [self.cls_token_id]
+        sep = [self.eos_token_id]
+        if token_ids_1 is None:
+            if self.eos_token_id is None:
+                return cls + token_ids_0
+            else:
+                return cls + token_ids_0 + sep
+        elif self.eos_token_id is None:
+            raise ValueError("Cannot tokenize multiple sequences when EOS token is not set!")
+        return cls + token_ids_0 + sep + token_ids_1 + sep  # Multiple inputs always have an EOS token
+
+    def get_special_tokens_mask(
+        self,
+        token_ids_0: List,
+        token_ids_1: Optional[List] = None,
+        already_has_special_tokens: bool = False,
+    ) -> List[int]:
+        """
+        Retrieves sequence ids from a token list that has no special tokens added. This method is called when adding
+        special tokens using the tokenizer `prepare_for_model` or `encode_plus` methods.
+
+        Args:
+            token_ids_0 (`List[int]`):
+                List of ids of the first sequence.
+            token_ids_1 (`List[int]`, *optional*):
+                List of ids of the second sequence.
+            already_has_special_tokens (`bool`, *optional*, defaults to `False`):
+                Whether or not the token list is already formatted with special tokens for the model.
+
+        Returns:
+            A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
+        """
+        if already_has_special_tokens:
+            if token_ids_1 is not None:
+                raise ValueError(
+                    "You should not supply a second sequence if the provided sequence of "
+                    "ids is already formatted with special tokens for the model."
+                )
+
+            return [1 if token in self.all_special_ids else 0 for token in token_ids_0]
+        mask = [1] + ([0] * len(token_ids_0)) + [1]
+        if token_ids_1 is not None:
+            mask += [0] * len(token_ids_1) + [1]
+        return mask
+
+    def save_vocabulary(self, save_directory, filename_prefix=None):
+        os.makedirs(save_directory, exist_ok=True)
+        vocab_file = os.path.join(
+            save_directory,
+            (filename_prefix + "-" if filename_prefix else "") + "vocab.txt",
+        )
+        with open(vocab_file, "w") as f:
+            f.write("\n".join(self.all_tokens))
+        return (vocab_file,)
+
+    @property
+    def vocab_size(self) -> int:
+        return self.get_vocab_size(with_added_tokens=False)
+
+    def _add_tokens(
+        self,
+        new_tokens: Union[List[str], List[AddedToken]],
+        special_tokens: bool = False,
+    ) -> int:
+        return super()._add_tokens(new_tokens, special_tokens=special_tokens)

tokenizer_config.json

@@ -0,0 +1,60 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<pad>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<eos>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "N",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "<cls>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "4": {
+      "content": "<mask>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "backend": "custom",
+  "cls_token": "<cls>",
+  "eos_token": "<eos>",
+  "is_local": true,
+  "mask_token": "<mask>",
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "<pad>",
+  "sep_token": "<eos>",
+  "tokenizer_class": "UniRNATokenizer",
+  "unk_token": "N",
+  "auto_map": {
+    "AutoTokenizer": [
+      "tokenizer.UniRNATokenizer",
+      null
+    ]
+  }
+}

vocab.txt

@@ -0,0 +1,10 @@
+<pad>
+<eos>
+N
+<cls>
+<mask>
+A
+T
+C
+G
+U