yangheng
/

OmniGenome-52M

@@ -1,1761 +0,0 @@
-# coding=utf-8
-# Copyright 2022 ColaLab-UoE (https://colalab.ai/), Meta and The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-""" PyTorch OmniGenome model."""
-import math
-import random
-import warnings
-from typing import List, Optional, Tuple, Union
-import numpy as np
-import torch
-import torch.utils.checkpoint
-from torch import nn
-from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
-from transformers import add_start_docstrings, PreTrainedModel
-from transformers.modeling_outputs import (
-    BaseModelOutputWithPastAndCrossAttentions,
-    BaseModelOutputWithPoolingAndCrossAttentions,
-    MaskedLMOutput,
-    SequenceClassifierOutput,
-    TokenClassifierOutput,
-)
-from transformers.pytorch_utils import (
-    find_pruneable_heads_and_indices,
-    prune_linear_layer,
-)
-from transformers.utils import (
-    logging,
-    add_code_sample_docstrings,
-    add_start_docstrings_to_model_forward,
-)
-from .configuration_omnigenome import OmniGenomeConfig
-logger = logging.get_logger(__name__)
-_CHECKPOINT_FOR_DOC = "yangheng/OmniGenome-52M"
-_CONFIG_FOR_DOC = "OmniGenomeConfig"
-OmniGenome_PRETRAINED_MODEL_ARCHIVE_LIST = [
-    "yangheng/OmniGenome-52M",
-    # This is not a complete list of all OmniGenome models!
-    # See all OmniGenome models at https://huggingface.co/models?filter=OmniGenome
-]
-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)
-def gelu(x):
-    """
-    This is the gelu implementation from the original OmniGenome repo. Using F.gelu yields subtly wrong results.
-    """
-    return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0)))
-def symmetrize(x):
-    "Make layer symmetric in final two dimensions, used for contact prediction."
-    return x + x.transpose(-1, -2)
-def average_product_correct(x):
-    "Perform average product correct, used for contact prediction."
-    a1 = x.sum(-1, keepdims=True)
-    a2 = x.sum(-2, keepdims=True)
-    a12 = x.sum((-1, -2), keepdims=True)
-    avg = a1 * a2
-    avg.div_(a12)  # in-place to reduce memory
-    normalized = x - avg
-    return normalized
-# Copied from transformers.models.esm.modeling_esm.RotaryEmbedding
-class RotaryEmbedding(torch.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),
-        )
-# Copied from transformers.models.esm.modeling_esm.EsmContactPredictionHead with Esm->OmniGenome
-class OmniGenomeContactPredictionHead(nn.Module):
-    """Performs symmetrization, apc, and computes a logistic regression on the output features"""
-    def __init__(
-            self,
-            in_features: int,
-            bias=True,
-            eos_idx: int = 2,
-    ):
-        super().__init__()
-        self.in_features = in_features
-        self.eos_idx = eos_idx
-        self.regression = nn.Linear(in_features, 1, bias)
-        self.activation = nn.Sigmoid()
-    def forward(self, tokens, attentions):
-        # remove eos token attentions
-        eos_mask = tokens.ne(self.eos_idx).to(attentions)
-        eos_mask = eos_mask.unsqueeze(1) * eos_mask.unsqueeze(2)
-        attentions = attentions * eos_mask[:, None, None, :, :]
-        attentions = attentions[..., :-1, :-1]
-        # remove cls token attentions
-        attentions = attentions[..., 1:, 1:]
-        batch_size, layers, heads, seqlen, _ = attentions.size()
-        attentions = attentions.view(batch_size, layers * heads, seqlen, seqlen)
-        # features: batch x channels x tokens x tokens (symmetric)
-        attentions = attentions.to(
-            self.regression.weight.device
-        )  # attentions always float32, may need to convert to float16
-        attentions = average_product_correct(symmetrize(attentions))
-        attentions = attentions.permute(0, 2, 3, 1)
-        return self.activation(self.regression(attentions).squeeze(3))
-# Copied from transformers.models.esm.modeling_esm.EsmEmbeddings with Esm->OmniGenome
-class OmniGenomeEmbeddings(nn.Module):
-    """
-    Same as BertEmbeddings with a tiny tweak for positional embeddings indexing.
-    """
-    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)
-        # position_ids (1, len position emb) is contiguous in memory and exported when serialized
-        self.position_embedding_type = getattr(
-            config, "position_embedding_type", "absolute"
-        )
-        self.register_buffer(
-            "position_ids",
-            torch.arange(config.max_position_embeddings).expand((1, -1)),
-            persistent=False,
-        )
-        self.padding_idx = config.pad_token_id
-        self.position_embeddings = nn.Embedding(
-            config.max_position_embeddings,
-            config.hidden_size,
-            padding_idx=self.padding_idx,
-        )
-        self.token_dropout = config.token_dropout
-        self.mask_token_id = config.mask_token_id
-    def forward(
-            self,
-            input_ids=None,
-            attention_mask=None,
-            position_ids=None,
-            inputs_embeds=None,
-            past_key_values_length=0,
-    ):
-        if position_ids is None:
-            if input_ids is not None:
-                # Create the position ids from the input token ids. Any padded tokens remain padded.
-                position_ids = create_position_ids_from_input_ids(
-                    input_ids, self.padding_idx, past_key_values_length
-                )
-            else:
-                position_ids = self.create_position_ids_from_inputs_embeds(
-                    inputs_embeds
-                )
-        if inputs_embeds is None:
-            inputs_embeds = self.word_embeddings(input_ids)
-        # Note that if we want to support OmniGenome-1 (not 1b!) in future then we need to support an
-        # embedding_scale factor here.
-        embeddings = inputs_embeds
-        # Matt: OmniGenome has the option to handle masking in MLM in a slightly unusual way. If the token_dropout
-        # flag is False then it is handled in the same was as BERT/RoBERTa. If it is set to True, however,
-        # masked tokens are treated as if they were selected for input dropout and zeroed out.
-        # This "mask-dropout" is compensated for when masked tokens are not present, by scaling embeddings by
-        # a factor of (fraction of unmasked tokens during training) / (fraction of unmasked tokens in sample).
-        # This is analogous to the way that dropout layers scale down outputs during evaluation when not
-        # actually dropping out values (or, equivalently, scale up their un-dropped outputs in training).
-        if self.token_dropout:
-            embeddings = embeddings.masked_fill(
-                (input_ids == self.mask_token_id).unsqueeze(-1), 0.0
-            )
-            mask_ratio_train = (
-                    0.15 * 0.8
-            )  # Hardcoded as the ratio used in all OmniGenome model training runs
-            src_lengths = attention_mask.sum(-1)
-            mask_ratio_observed = (input_ids == self.mask_token_id).sum(
-                -1
-            ).float() / src_lengths
-            embeddings = (
-                    embeddings
-                    * (1 - mask_ratio_train)
-                    / (1 - mask_ratio_observed)[:, None, None]
-            ).to(embeddings.dtype)
-        if self.position_embedding_type == "absolute":
-            position_embeddings = self.position_embeddings(position_ids)
-            embeddings = embeddings + position_embeddings
-        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
-            )
-        # Matt: I think this line was copied incorrectly from BERT, disabling it for now.
-        # embeddings = self.dropout(embeddings)
-        return embeddings
-    def create_position_ids_from_inputs_embeds(self, inputs_embeds):
-        """
-        We are provided embeddings directly. We cannot infer which are padded so just generate sequential position ids.
-        Args:
-            inputs_embeds: torch.Tensor
-        Returns: torch.Tensor
-        """
-        input_shape = inputs_embeds.size()[:-1]
-        sequence_length = input_shape[1]
-        position_ids = torch.arange(
-            self.padding_idx + 1,
-            sequence_length + self.padding_idx + 1,
-            dtype=torch.long,
-            device=inputs_embeds.device,
-        )
-        return position_ids.unsqueeze(0).expand(input_shape)
-# Copied from transformers.models.esm.modeling_esm.EsmSelfAttention with Esm->OmniGenome
-class OmniGenomeSelfAttention(nn.Module):
-    def __init__(self, config, position_embedding_type=None):
-        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.position_embedding_type = position_embedding_type or getattr(
-            config, "position_embedding_type", "absolute"
-        )
-        self.rotary_embeddings = 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
-            )
-        elif self.position_embedding_type == "rotary":
-            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,
-            head_mask: Optional[torch.FloatTensor] = None,
-            encoder_hidden_states: Optional[torch.FloatTensor] = None,
-            encoder_attention_mask: Optional[torch.FloatTensor] = None,
-            past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
-            output_attentions: Optional[bool] = False,
-    ) -> Tuple[torch.Tensor]:
-        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 and past_key_value is not None:
-            # reuse k,v, cross_attentions
-            key_layer = past_key_value[0]
-            value_layer = past_key_value[1]
-            attention_mask = encoder_attention_mask
-        elif is_cross_attention:
-            key_layer = self.transpose_for_scores(self.key(encoder_hidden_states))
-            value_layer = self.transpose_for_scores(self.value(encoder_hidden_states))
-            attention_mask = encoder_attention_mask
-        elif past_key_value is not None:
-            key_layer = self.transpose_for_scores(self.key(hidden_states))
-            value_layer = self.transpose_for_scores(self.value(hidden_states))
-            key_layer = torch.cat([past_key_value[0], key_layer], dim=2)
-            value_layer = torch.cat([past_key_value[1], value_layer], dim=2)
-        else:
-            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)
-        # Matt: Our BERT model (which this code was derived from) scales attention logits down by sqrt(head_dim).
-        # OmniGenome scales the query down by the same factor instead. Modulo numerical stability these are equivalent,
-        # but not when rotary embeddings get involved. Therefore, we scale the query here to match the original
-        # OmniGenome code and fix rotary embeddings.
-        query_layer = query_layer * self.attention_head_size ** -0.5
-        if self.is_decoder:
-            # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states.
-            # Further calls to cross_attention layer can then reuse all cross-attention
-            # key/value_states (first "if" case)
-            # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of
-            # all previous decoder key/value_states. Further calls to uni-directional self-attention
-            # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
-            # if encoder bi-directional self-attention `past_key_value` is always `None`
-            past_key_value = (key_layer, value_layer)
-        if self.position_embedding_type == "rotary":
-            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.
-        attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
-        if (
-                self.position_embedding_type == "relative_key"
-                or self.position_embedding_type == "relative_key_query"
-        ):
-            seq_length = hidden_states.size()[1]
-            position_ids_l = torch.arange(
-                seq_length, dtype=torch.long, device=hidden_states.device
-            ).view(-1, 1)
-            position_ids_r = torch.arange(
-                seq_length, dtype=torch.long, device=hidden_states.device
-            ).view(1, -1)
-            distance = position_ids_l - position_ids_r
-            positional_embedding = self.distance_embedding(
-                distance + self.max_position_embeddings - 1
-            )
-            positional_embedding = positional_embedding.to(
-                dtype=query_layer.dtype
-            )  # fp16 compatibility
-            if self.position_embedding_type == "relative_key":
-                relative_position_scores = torch.einsum(
-                    "bhld,lrd->bhlr", query_layer, positional_embedding
-                )
-                attention_scores = attention_scores + relative_position_scores
-            elif self.position_embedding_type == "relative_key_query":
-                relative_position_scores_query = torch.einsum(
-                    "bhld,lrd->bhlr", query_layer, positional_embedding
-                )
-                relative_position_scores_key = torch.einsum(
-                    "bhrd,lrd->bhlr", key_layer, positional_embedding
-                )
-                attention_scores = (
-                        attention_scores
-                        + relative_position_scores_query
-                        + relative_position_scores_key
-                )
-        if attention_mask is not None:
-            # Apply the attention mask is (precomputed for all layers in OmniGenomeModel 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)
-        # Mask heads if we want to
-        if head_mask is not None:
-            attention_probs = attention_probs * head_mask
-        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,)
-        )
-        if self.is_decoder:
-            outputs = outputs + (past_key_value,)
-        return outputs
-# Copied from transformers.models.esm.modeling_esm.EsmSelfOutput with Esm->OmniGenome
-class OmniGenomeSelfOutput(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
-# Copied from transformers.models.esm.modeling_esm.EsmAttention with Esm->OmniGenome
-class OmniGenomeAttention(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.self = OmniGenomeSelfAttention(config)
-        self.output = OmniGenomeSelfOutput(config)
-        self.pruned_heads = set()
-        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
-    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,
-    ):
-        hidden_states_ln = self.LayerNorm(hidden_states)
-        self_outputs = self.self(
-            hidden_states_ln,
-            attention_mask,
-            head_mask,
-            encoder_hidden_states,
-            encoder_attention_mask,
-            past_key_value,
-            output_attentions,
-        )
-        attention_output = self.output(self_outputs[0], hidden_states)
-        outputs = (attention_output,) + self_outputs[
-                                        1:
-                                        ]  # add attentions if we output them
-        return outputs
-# Copied from transformers.models.esm.modeling_esm.EsmIntermediate with Esm->OmniGenome
-class OmniGenomeIntermediate(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 = gelu(hidden_states)
-        return hidden_states
-# Copied from transformers.models.esm.modeling_esm.EsmOutput with Esm->OmniGenome
-class OmniGenomeOutput(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
-# Copied from transformers.models.esm.modeling_esm.EsmLayer with Esm->OmniGenome
-class OmniGenomeLayer(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 = OmniGenomeAttention(config)
-        self.is_decoder = config.is_decoder
-        self.add_cross_attention = config.add_cross_attention
-        if self.add_cross_attention:
-            if not self.is_decoder:
-                raise RuntimeError(
-                    f"{self} should be used as a decoder model if cross attention is added"
-                )
-            self.crossattention = OmniGenomeAttention(config)
-        self.intermediate = OmniGenomeIntermediate(config)
-        self.output = OmniGenomeOutput(config)
-        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
-    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,
-    ):
-        # decoder uni-directional self-attention cached key/values tuple is at positions 1,2
-        self_attn_past_key_value = (
-            past_key_value[:2] if past_key_value is not None else None
-        )
-        self_attention_outputs = self.attention(
-            hidden_states,
-            attention_mask,
-            head_mask,
-            output_attentions=output_attentions,
-            past_key_value=self_attn_past_key_value,
-        )
-        attention_output = self_attention_outputs[0]
-        # if decoder, the last output is tuple of self-attn cache
-        if self.is_decoder:
-            outputs = self_attention_outputs[1:-1]
-            present_key_value = self_attention_outputs[-1]
-        else:
-            outputs = self_attention_outputs[
-                      1:
-                      ]  # add self attentions if we output attention weights
-        cross_attn_present_key_value = None
-        if self.is_decoder and encoder_hidden_states is not None:
-            if not hasattr(self, "crossattention"):
-                raise AttributeError(
-                    f"If `encoder_hidden_states` are passed, {self} has to be instantiated"
-                    " with cross-attention layers by setting `config.add_cross_attention=True`"
-                )
-            # cross_attn cached key/values tuple is at positions 3,4 of past_key_value tuple
-            cross_attn_past_key_value = (
-                past_key_value[-2:] if past_key_value is not None else None
-            )
-            cross_attention_outputs = self.crossattention(
-                attention_output,
-                attention_mask,
-                head_mask,
-                encoder_hidden_states,
-                encoder_attention_mask,
-                cross_attn_past_key_value,
-                output_attentions,
-            )
-            attention_output = cross_attention_outputs[0]
-            outputs = (
-                    outputs + cross_attention_outputs[1:-1]
-            )  # add cross attentions if we output attention weights
-            # add cross-attn cache to positions 3,4 of present_key_value tuple
-            cross_attn_present_key_value = cross_attention_outputs[-1]
-            present_key_value = present_key_value + cross_attn_present_key_value
-        layer_output = self.feed_forward_chunk(attention_output)
-        outputs = (layer_output,) + outputs
-        # if decoder, return the attn key/values as the last output
-        if self.is_decoder:
-            outputs = outputs + (present_key_value,)
-        return outputs
-    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
-# Copied from transformers.models.esm.modeling_esm.EsmEncoder with Esm->OmniGenome
-class OmniGenomeEncoder(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.config = config
-        self.layer = nn.ModuleList(
-            [OmniGenomeLayer(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,
-            head_mask=None,
-            encoder_hidden_states=None,
-            encoder_attention_mask=None,
-            past_key_values=None,
-            use_cache=None,
-            output_attentions=False,
-            output_hidden_states=False,
-            return_dict=True,
-    ):
-        if self.gradient_checkpointing and self.training:
-            if use_cache:
-                logger.warning_once(
-                    "`use_cache=True` is incompatible with `config.gradient_checkpointing=True`. Setting "
-                    "`use_cache=False`..."
-                )
-                use_cache = False
-        all_hidden_states = () if output_hidden_states else None
-        all_self_attentions = () if output_attentions else None
-        all_cross_attentions = (
-            () if output_attentions and self.config.add_cross_attention else None
-        )
-        next_decoder_cache = () if use_cache else None
-        for i, layer_module in enumerate(self.layer):
-            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
-            if self.gradient_checkpointing and self.training:
-                layer_outputs = self._gradient_checkpointing_func(
-                    layer_module.__call__,
-                    hidden_states,
-                    attention_mask,
-                    layer_head_mask,
-                    encoder_hidden_states,
-                    encoder_attention_mask,
-                    past_key_value,
-                    output_attentions,
-                )
-            else:
-                layer_outputs = layer_module(
-                    hidden_states,
-                    attention_mask,
-                    layer_head_mask,
-                    encoder_hidden_states,
-                    encoder_attention_mask,
-                    past_key_value,
-                    output_attentions,
-                )
-            hidden_states = layer_outputs[0]
-            if use_cache:
-                next_decoder_cache = next_decoder_cache + (layer_outputs[-1],)
-            if output_attentions:
-                all_self_attentions = all_self_attentions + (layer_outputs[1],)
-                if self.config.add_cross_attention:
-                    all_cross_attentions = all_cross_attentions + (layer_outputs[2],)
-        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,)
-        if not return_dict:
-            return tuple(
-                v
-                for v in [
-                    hidden_states,
-                    next_decoder_cache,
-                    all_hidden_states,
-                    all_self_attentions,
-                    all_cross_attentions,
-                ]
-                if v is not None
-            )
-        return BaseModelOutputWithPastAndCrossAttentions(
-            last_hidden_state=hidden_states,
-            past_key_values=next_decoder_cache,
-            hidden_states=all_hidden_states,
-            attentions=all_self_attentions,
-            cross_attentions=all_cross_attentions,
-        )
-# Copied from transformers.models.bert.modeling_bert.BertPooler with Bert->OmniGenome
-class OmniGenomePooler(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
-# Copied from transformers.models.esm.modeling_esm.EsmPreTrainedModel with Esm->OmniGenome
-class OmniGenomePreTrainedModel(PreTrainedModel):
-    """
-    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
-    models.
-    """
-    config_class = OmniGenomeConfig
-    base_model_prefix = "OmniGenome"
-    supports_gradient_checkpointing = True
-    _no_split_modules = [
-        "OmniGenomeLayer",
-        "OmniGenomeFoldTriangularSelfAttentionBlock",
-        "OmniGenomeEmbeddings",
-    ]
-    # Copied from transformers.models.bert.modeling_bert.BertPreTrainedModel._init_weights
-    def _init_weights(self, module):
-        """Initialize the weights"""
-        if isinstance(module, nn.Linear):
-            # 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)
-            if module.bias is not None:
-                module.bias.data.zero_()
-        elif isinstance(module, nn.Embedding):
-            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
-            if module.padding_idx is not None:
-                module.weight.data[module.padding_idx].zero_()
-        elif isinstance(module, nn.LayerNorm):
-            module.bias.data.zero_()
-            module.weight.data.fill_(1.0)
-OmniGenome_START_DOCSTRING = r"""
-    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
-    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
-    etc.)
-    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
-    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
-    and behavior.
-    Parameters:
-        config ([`OmniGenomeConfig`]): Model configuration class with all the parameters of the
-            model. Initializing with a config file does not load the weights associated with the model, only the
-            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
-"""
-OmniGenome_INPUTS_DOCSTRING = r"""
-    Args:
-        input_ids (`torch.LongTensor` of shape `({0})`):
-            Indices of input sequence tokens in the vocabulary.
-            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
-            [`PreTrainedTokenizer.__call__`] for details.
-            [What are input IDs?](../glossary#input-ids)
-        attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*):
-            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
-            - 1 for tokens that are **not masked**,
-            - 0 for tokens that are **masked**.
-            [What are attention masks?](../glossary#attention-mask)
-        position_ids (`torch.LongTensor` of shape `({0})`, *optional*):
-            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
-            config.max_position_embeddings - 1]`.
-            [What are position IDs?](../glossary#position-ids)
-        head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
-            Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
-            - 1 indicates the head is **not masked**,
-            - 0 indicates the head is **masked**.
-        inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*):
-            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
-            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
-            model's internal embedding lookup matrix.
-        output_attentions (`bool`, *optional*):
-            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
-            tensors for more detail.
-        output_hidden_states (`bool`, *optional*):
-            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
-            more detail.
-        return_dict (`bool`, *optional*):
-            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
-"""
-@add_start_docstrings(
-    "The bare OmniGenome Model transformer outputting raw hidden-states without any specific head on top.",
-    OmniGenome_START_DOCSTRING,
-)
-# Copied from transformers.models.esm.modeling_esm.EsmModel with Esm->OmniGenome
-class OmniGenomeModel(OmniGenomePreTrainedModel):
-    """
-    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.
-    To behave as an decoder the model needs to be initialized with the `is_decoder` argument of the configuration set
-    to `True`. To be used in a Seq2Seq model, the model needs to initialized with both `is_decoder` argument and
-    `add_cross_attention` set to `True`; an `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 = OmniGenomeEmbeddings(config)
-        self.encoder = OmniGenomeEncoder(config)
-        self.pooler = OmniGenomePooler(config) if add_pooling_layer else None
-        self.contact_head = OmniGenomeContactPredictionHead(
-            in_features=config.num_hidden_layers * config.num_attention_heads, bias=True
-        )
-        # Initialize weights and apply final processing
-        self.post_init()
-    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)
-    @add_start_docstrings_to_model_forward(
-        OmniGenome_INPUTS_DOCSTRING.format("(batch_size, sequence_length)")
-    )
-    @add_code_sample_docstrings(
-        checkpoint=_CHECKPOINT_FOR_DOC,
-        output_type=BaseModelOutputWithPoolingAndCrossAttentions,
-        config_class=_CONFIG_FOR_DOC,
-    )
-    def forward(
-            self,
-            input_ids: Optional[torch.Tensor] = None,
-            attention_mask: Optional[torch.Tensor] = None,
-            position_ids: Optional[torch.Tensor] = None,
-            head_mask: Optional[torch.Tensor] = None,
-            inputs_embeds: Optional[torch.Tensor] = None,
-            encoder_hidden_states: Optional[torch.Tensor] = None,
-            encoder_attention_mask: Optional[torch.Tensor] = None,
-            past_key_values: Optional[List[torch.FloatTensor]] = None,
-            use_cache: Optional[bool] = 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))` of length `config.n_layers` with each tuple having 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
-        )
-        if self.config.is_decoder:
-            use_cache = use_cache if use_cache is not None else self.config.use_cache
-        else:
-            use_cache = False
-        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
-        # past_key_values_length
-        past_key_values_length = (
-            past_key_values[0][0].shape[2] if past_key_values is not None else 0
-        )
-        if attention_mask is None:
-            attention_mask = torch.ones(
-                ((batch_size, seq_length + past_key_values_length)), device=device
-            )
-        # 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.
-        extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(
-            attention_mask, input_shape
-        )
-        # If a 2D or 3D attention mask is provided for the cross-attention
-        # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
-        if self.config.is_decoder and encoder_hidden_states is not None:
-            (
-                encoder_batch_size,
-                encoder_sequence_length,
-                _,
-            ) = encoder_hidden_states.size()
-            encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)
-            if encoder_attention_mask is None:
-                encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device)
-            encoder_extended_attention_mask = self.invert_attention_mask(
-                encoder_attention_mask
-            )
-        else:
-            encoder_extended_attention_mask = None
-        # Prepare head mask if needed
-        # 1.0 in head_mask indicate we keep the head
-        # attention_probs has shape bsz x n_heads x N x N
-        # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
-        # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
-        head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
-        embedding_output = self.embeddings(
-            input_ids=input_ids,
-            position_ids=position_ids,
-            attention_mask=attention_mask,
-            inputs_embeds=inputs_embeds,
-            past_key_values_length=past_key_values_length,
-        )
-        encoder_outputs = self.encoder(
-            embedding_output,
-            attention_mask=extended_attention_mask,
-            head_mask=head_mask,
-            encoder_hidden_states=encoder_hidden_states,
-            encoder_attention_mask=encoder_extended_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,
-        )
-        sequence_output = encoder_outputs[0]
-        pooled_output = (
-            self.pooler(sequence_output) if self.pooler is not None else None
-        )
-        if not return_dict:
-            return (sequence_output, pooled_output) + encoder_outputs[1:]
-        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 predict_contacts(self, tokens, attention_mask):
-        attns = self(
-            tokens,
-            attention_mask=attention_mask,
-            return_dict=True,
-            output_attentions=True,
-        ).attentions
-        attns = torch.stack(attns, dim=1)  # Matches the original model layout
-        # In the original model, attentions for padding tokens are completely zeroed out.
-        # This makes no difference most of the time because the other tokens won't attend to them,
-        # but it does for the contact prediction task, which takes attentions as input,
-        # so we have to mimic that here.
-        attns *= attention_mask.unsqueeze(1).unsqueeze(2).unsqueeze(3)
-        attns *= attention_mask.unsqueeze(1).unsqueeze(2).unsqueeze(4)
-        return self.contact_head(tokens, attns)
-@add_start_docstrings(
-    """OmniGenome Model with a `language modeling` head on top.""", OmniGenome_START_DOCSTRING
-)
-# Copied from transformers.models.esm.modeling_esm.EsmForMaskedLM with Esm->OmniGenome
-class OmniGenomeForMaskedLM(OmniGenomePreTrainedModel):
-    _tied_weights_keys = ["lm_head.decoder.weight"]
-    def __init__(self, config):
-        super().__init__(config)
-        if config.is_decoder:
-            logger.warning(
-                "If you want to use `OmniGenomeForMaskedLM` make sure `config.is_decoder=False` for "
-                "bi-directional self-attention."
-            )
-        self.OmniGenome = OmniGenomeModel(config, add_pooling_layer=False)
-        self.lm_head = OmniGenomeLMHead(config)
-        self.init_weights()
-    def get_output_embeddings(self):
-        return self.lm_head.decoder
-    def set_output_embeddings(self, new_embeddings):
-        self.lm_head.decoder = new_embeddings
-    @add_start_docstrings_to_model_forward(
-        OmniGenome_INPUTS_DOCSTRING.format("batch_size, sequence_length")
-    )
-    @add_code_sample_docstrings(
-        checkpoint=_CHECKPOINT_FOR_DOC,
-        output_type=MaskedLMOutput,
-        config_class=_CONFIG_FOR_DOC,
-        mask="<mask>",
-    )
-    def forward(
-            self,
-            input_ids: Optional[torch.LongTensor] = None,
-            attention_mask: Optional[torch.Tensor] = None,
-            position_ids: Optional[torch.LongTensor] = None,
-            head_mask: Optional[torch.Tensor] = None,
-            inputs_embeds: Optional[torch.FloatTensor] = None,
-            encoder_hidden_states: Optional[torch.FloatTensor] = None,
-            encoder_attention_mask: Optional[torch.Tensor] = None,
-            labels: Optional[torch.LongTensor] = 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.
-        """
-        return_dict = (
-            return_dict if return_dict is not None else self.config.use_return_dict
-        )
-        outputs = self.OmniGenome(
-            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,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-        sequence_output = outputs[0]
-        prediction_scores = self.lm_head(sequence_output)
-        masked_lm_loss = None
-        if labels is not None:
-            loss_fct = CrossEntropyLoss()
-            labels = labels.to(prediction_scores.device)
-            masked_lm_loss = loss_fct(
-                prediction_scores.view(-1, self.config.vocab_size), labels.view(-1)
-            )
-        if not return_dict:
-            output = (prediction_scores,) + outputs[2:]
-            return (
-                ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
-            )
-        return MaskedLMOutput(
-            loss=masked_lm_loss,
-            logits=prediction_scores,
-            hidden_states=outputs.hidden_states,
-            attentions=outputs.attentions,
-        )
-    def predict_contacts(self, tokens, attention_mask):
-        return self.OmniGenome.predict_contacts(tokens, attention_mask=attention_mask)
-# Copied from transformers.models.esm.modeling_esm.EsmLMHead with Esm->OmniGenome
-class OmniGenomeLMHead(nn.Module):
-    """OmniGenome 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, bias=False)
-        self.bias = nn.Parameter(torch.zeros(config.vocab_size))
-    def forward(self, features, **kwargs):
-        x = self.dense(features)
-        x = gelu(x)
-        x = self.layer_norm(x)
-        # project back to size of vocabulary with bias
-        x = self.decoder(x) + self.bias
-        return x
-@add_start_docstrings(
-    """
-    OmniGenome Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled
-    output) e.g. for GLUE tasks.
-    """,
-    OmniGenome_START_DOCSTRING,
-)
-class OmniGenomeForSequenceClassification(OmniGenomePreTrainedModel):
-    def __init__(self, config):
-        super().__init__(config)
-        self.num_labels = config.num_labels
-        self.config = config
-        self.OmniGenome = OmniGenomeModel(config, add_pooling_layer=False)
-        self.pooler = OmniGenomePooler(config)
-        self.classifier = OmniGenomeClassificationHead(config)
-        self.init_weights()
-    @add_start_docstrings_to_model_forward(
-        OmniGenome_INPUTS_DOCSTRING.format("batch_size, sequence_length")
-    )
-    @add_code_sample_docstrings(
-        checkpoint=_CHECKPOINT_FOR_DOC,
-        output_type=SequenceClassifierOutput,
-        config_class=_CONFIG_FOR_DOC,
-    )
-    def forward(
-            self,
-            input_ids: Optional[torch.LongTensor] = None,
-            attention_mask: Optional[torch.Tensor] = None,
-            position_ids: Optional[torch.LongTensor] = None,
-            head_mask: Optional[torch.Tensor] = None,
-            inputs_embeds: Optional[torch.FloatTensor] = None,
-            labels: Optional[torch.LongTensor] = None,
-            output_attentions: Optional[bool] = None,
-            output_hidden_states: Optional[bool] = None,
-            return_dict: Optional[bool] = None,
-    ) -> Union[Tuple, SequenceClassifierOutput]:
-        r"""
-        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
-            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
-            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
-            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
-        """
-        return_dict = (
-            return_dict if return_dict is not None else self.config.use_return_dict
-        )
-        outputs = self.OmniGenome(
-            input_ids,
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-            head_mask=head_mask,
-            inputs_embeds=inputs_embeds,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-        last_hidden_state = outputs[0]
-        last_hidden_state = self.dense(last_hidden_state)
-        pooled_output = self.pooler(last_hidden_state)
-        logits = self.classifier(pooled_output)
-        loss = None
-        if labels is not None:
-            labels = labels.to(logits.device)
-            if self.config.problem_type is None:
-                if self.num_labels == 1:
-                    self.config.problem_type = "regression"
-                elif self.num_labels > 1 and (
-                        labels.dtype == torch.long or labels.dtype == torch.int
-                ):
-                    self.config.problem_type = "single_label_classification"
-                else:
-                    self.config.problem_type = "multi_label_classification"
-            if self.config.problem_type == "regression":
-                loss_fct = MSELoss()
-                if self.num_labels == 1:
-                    loss = loss_fct(logits.squeeze(), labels.squeeze())
-                else:
-                    loss = loss_fct(logits, labels)
-            elif self.config.problem_type == "single_label_classification":
-                loss_fct = CrossEntropyLoss()
-                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
-            elif self.config.problem_type == "multi_label_classification":
-                loss_fct = BCEWithLogitsLoss()
-                loss = loss_fct(logits, labels)
-        if not return_dict:
-            output = (logits,) + outputs[2:]
-            return ((loss,) + output) if loss is not None else output
-        return SequenceClassifierOutput(
-            loss=loss,
-            logits=logits,
-            hidden_states=outputs.hidden_states,
-            attentions=outputs.attentions,
-        )
-@add_start_docstrings(
-    """
-    OmniGenome Model with a token classification head on top (a linear layer on top of the hidden-states output)
-    Note that this model is pre-trained for RNA secondary structure prediction and can be used for zero-shot RNA
-    secondary structure prediction. Please find more advanced usages at https://github.com/yangheng95/OmniGenome
-    This model can be fine-tuned for other token classification tasks.
-    """,
-    OmniGenome_START_DOCSTRING,
-)
-# Copied from transformers.models.esm.modeling_esm.EsmForTokenClassification with Esm->OmniGenome
-class OmniGenomeForTokenClassification(OmniGenomePreTrainedModel):
-    def __init__(self, config):
-        super().__init__(config)
-        self.num_labels = config.num_labels
-        self.OmniGenome = OmniGenomeModel(config, add_pooling_layer=False)
-        self.dense = torch.nn.Linear(config.hidden_size, config.hidden_size)
-        self.classifier = torch.nn.Linear(self.config.hidden_size, self.num_labels)
-        self.softmax = nn.Softmax(dim=-1)
-        self.init_weights()
-    @add_start_docstrings_to_model_forward(
-        OmniGenome_INPUTS_DOCSTRING.format("batch_size, sequence_length")
-    )
-    @add_code_sample_docstrings(
-        checkpoint=_CHECKPOINT_FOR_DOC,
-        output_type=TokenClassifierOutput,
-        config_class=_CONFIG_FOR_DOC,
-    )
-    def forward(
-            self,
-            input_ids: Optional[torch.LongTensor] = None,
-            attention_mask: Optional[torch.Tensor] = None,
-            position_ids: Optional[torch.LongTensor] = None,
-            head_mask: Optional[torch.Tensor] = None,
-            inputs_embeds: Optional[torch.FloatTensor] = None,
-            labels: Optional[torch.LongTensor] = None,
-            output_attentions: Optional[bool] = None,
-            output_hidden_states: Optional[bool] = None,
-            return_dict: Optional[bool] = None,
-    ) -> Union[Tuple, TokenClassifierOutput]:
-        r"""
-        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
-            Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.
-        """
-        return_dict = (
-            return_dict if return_dict is not None else self.config.use_return_dict
-        )
-        outputs = self.OmniGenome(
-            input_ids,
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-            head_mask=head_mask,
-            inputs_embeds=inputs_embeds,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-        last_hidden_state = outputs[0]
-        last_hidden_state = self.dense(last_hidden_state)
-        logits = self.classifier(last_hidden_state)
-        logits = self.softmax(logits)
-        loss = None
-        if labels is not None:
-            loss_fct = CrossEntropyLoss()
-            loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
-        if not return_dict:
-            output = (logits,) + outputs[2:]
-            return ((loss,) + output) if loss is not None else output
-        return TokenClassifierOutput(
-            loss=loss,
-            logits=logits,
-            hidden_states=outputs.hidden_states,
-            attentions=outputs.attentions,
-        )
-    @staticmethod
-    def verify_secondary_structure(structure):
-        structure = list(structure)
-        left_brackets = []
-        right_brackets = []
-        for i, char in enumerate(structure):
-            if char == "(":
-                left_brackets.append(i)
-            elif char == ")":
-                if left_brackets:
-                    left_brackets.pop()
-                else:
-                    right_brackets.append(i)
-        for i in left_brackets:
-            structure[i] = "."
-        for i in right_brackets:
-            structure[i] = "."
-        structure = "".join(structure)
-        return structure
-    def predict_rna_structure(
-            self,
-            input_ids: Optional[torch.LongTensor] = None,
-            attention_mask: Optional[torch.Tensor] = None,
-            **kwargs
-    ) -> List[str]:
-        """
-        Predicts the secondary structure of a sequence given the logits and attention mask.
-        """
-        outputs = self.forward(input_ids, attention_mask, **kwargs)
-        logits = torch.argmax(outputs.logits, dim=-1)
-        lengths = torch.sum(torch.ne(torch.tensor(0), attention_mask), dim=-1)
-        structures = []
-        for i, length in enumerate(lengths):
-            structure = logits[i, :length].cpu().numpy()
-            structure = "".join(self.config.id2label[label] for label in structure)
-            if self.config.verify_ss:
-                structure = self.verify_secondary_structure(structure)
-            structures.append(structure)
-        return structures
-@add_start_docstrings(
-    """
-    This is not a standard Seq2Seq model. Instead, this model is designed for RNA design tasks.
-    This is the OmniGenome Model with a simple genetic algorithm based RNA design head on top.
-    """,
-    OmniGenome_START_DOCSTRING,
-)
-class OmniGenomeModelForSeq2SeqLM(OmniGenomePreTrainedModel):
-    def __init__(self, config):
-        super().__init__(config)
-        self.num_labels = config.num_labels
-        self.OmniGenome = OmniGenomeModel(config, add_pooling_layer=False)
-        self.lm_head = OmniGenomeLMHead(config)
-        self.num_generation = config.num_generation
-        self.num_population = config.num_population
-        self.init_weights()
-        self.tokenizer = None
-        self.predict_structure = None
-        warnings.warn(f"This model {self.__class__.__name__} is not a real Seq2Seq model. "
-                      f"Instead, this model is designed for RNA design tasks")
-    @add_start_docstrings_to_model_forward(
-        OmniGenome_INPUTS_DOCSTRING.format("batch_size, sequence_length")
-    )
-    @add_code_sample_docstrings(
-        checkpoint=_CHECKPOINT_FOR_DOC,
-        output_type=TokenClassifierOutput,
-        config_class=_CONFIG_FOR_DOC,
-    )
-    def forward(
-            self,
-            input_ids: Optional[torch.LongTensor] = None,
-            attention_mask: Optional[torch.Tensor] = None,
-            position_ids: Optional[torch.LongTensor] = None,
-            head_mask: Optional[torch.Tensor] = None,
-            inputs_embeds: Optional[torch.FloatTensor] = None,
-            labels: Optional[torch.LongTensor] = None,
-            output_attentions: Optional[bool] = None,
-            output_hidden_states: Optional[bool] = True,
-            return_dict: Optional[bool] = None,
-    ) -> Union[Tuple, TokenClassifierOutput]:
-        r"""
-        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
-            Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.
-        """
-        raise NotImplementedError("This model is not designed for standard Seq2Seq tasks. "
-                                  "Use model.rna_sequence_design() for RNA sequences design instead.")
-    def rna_sequence_design(
-            self,
-            structure: str,
-            predict_structure_func=None,
-            **kwargs
-    ) -> List[str]:
-        """
-        Assemble the RNA sequence given the reference sequence structure
-        """
-        if self.tokenizer is None:
-            tokenizer = kwargs.get("tokenizer", None)
-            if tokenizer is None:
-                from transformers import AutoTokenizer
-                self.tokenizer = AutoTokenizer.from_pretrained(self.config.name_or_path)
-            else:
-                self.tokenizer = tokenizer
-        candidates = self.genetic_algorithm_for_rna_design(structure, predict_structure_func=None, **kwargs)
-        return candidates
-    def genetic_algorithm_for_rna_design(self, structure, predict_structure_func=None, **kwargs):
-        if predict_structure_func is None:
-            import ViennaRNA
-            def predict_structure(sequence):
-                return ViennaRNA.fold(sequence)[0]
-            predict_structure_func = predict_structure
-        self.predict_structure = predict_structure_func
-        mutation_ratio = kwargs.get("mutation_ratio", 0.2)
-        num_population = kwargs.get("num_population", self.num_population)
-        num_generation = kwargs.get("num_generation", self.num_generation)
-        import tqdm
-        population = self.init_population(structure, num_population)
-        population = self.mlm_mutate(population, structure, mutation_ratio=mutation_ratio)
-        for generation_id in tqdm.tqdm(range(num_generation), desc="Designing RNA Sequence"):
-            population_fitness = self.sequence_fitness(population, structure)[:num_population]
-            population = sorted(zip(population, population_fitness), key=lambda x: x[1])[:num_population]
-            population = [x[0] for x in population]
-            next_generation = population  # Elitism
-            next_generation += self.crossover(population, structure)
-            next_generation += self.mlm_mutate(next_generation, structure, mutation_ratio)
-            fitness_values = self.sequence_fitness(next_generation, structure)
-            next_generation = sorted(zip(next_generation, fitness_values), key=lambda x: x[1])
-            candidate_sequences = []
-            for sequence, fitness in next_generation:
-                if fitness == 0:
-                    candidate_sequences.append(sequence)
-                else:
-                    break
-            if candidate_sequences:
-                return candidate_sequences
-            print(f"Generation {generation_id}: {next_generation[0][0]} with fitness {next_generation[0][1]}")
-            population = [x[0] for x in next_generation[:num_population]]
-        return []
-    def init_population(self, structure, num_population):
-        # Initialize lists to store population data and inputs for masked language model
-        population = []
-        mlm_inputs = []
-        # Iterate over the number of individuals in the population
-        for _ in range(num_population):  # Changed from self.num_population to num_population
-            # Create a sequence by randomly choosing nucleotides or a mask token for each position in the structure
-            masked_sequence = [
-                random.choice(["A", "G", "C", "T", "<mask>"])
-                for _ in range(len(structure))
-            ]
-            masked_sequence_str = "".join(masked_sequence)
-            mlm_inputs.append(f"{masked_sequence_str}<eos>{''.join(structure)}")
-        # Call a function to predict outputs using the masked language model
-        outputs = self.mlm_predict(mlm_inputs, structure)
-        # Decode the mlm outputs and construct the initial population
-        for i in range(len(outputs)):
-            sequence = self.tokenizer.convert_ids_to_tokens(outputs[i].tolist())
-            fixed_sequence = [
-                x if x in "AGCT" else random.choice(["G", "C"])
-                for x, y in zip(sequence, list(mlm_inputs[i].replace('<mask>', '$')))
-            ]
-            population.append("".join(fixed_sequence))
-        return population
-    def mlm_mutate(self, population, structure, mutation_ratio=0.2):
-        def mutate(sequence, mutation_rate=0.2):
-            sequence = np.array(list(sequence), dtype=np.str_)
-            probability_matrix = np.full(sequence.shape, mutation_rate)
-            masked_indices = np.random.rand(*sequence.shape) < probability_matrix
-            sequence[masked_indices] = "$"
-            mut_seq = "".join(sequence.tolist()).replace("$", "<mask>")
-            return mut_seq
-        def mutate_with_spans_mask(sequence, mutation_rate=0.2):
-            sequence = np.array(list(sequence), dtype=np.str_)
-            length = len(sequence)
-            num_mutations = int(mutation_rate * length)  # Total number of mutations is based on mutation rate
-            # Decide the average span length; we assume mutation spans about 20% of the total mutations length
-            average_span_length = random.randint(1, max(1, int(length * mutation_rate / 10)))
-            # Initialize mutation points
-            mutation_points = np.random.choice(length, num_mutations, replace=False)  # Start points for mutations
-            # Create the mask
-            mask = np.zeros(length, dtype=bool)
-            for start in mutation_points:
-                end = start + average_span_length
-                if end > length:
-                    end = length
-                mask[start:end] = True  # Masking a span from start to end
-            # Apply mask
-            sequence[mask] = "<mask>"
-            # Combine the masked parts with the rest of the sequence
-            mutated_sequence = ''.join(sequence)
-            # Since multiple consecutive '<mask>'s might occur, replace them with a single '<mask>'
-            mutated_sequence = mutated_sequence.replace('<mask>' * average_span_length, '<mask>')
-            return mutated_sequence
-        # Initialize lists to store population data and inputs for masked language model
-        mlm_inputs = []
-        masked_sequences = []
-        # Iterate over the number of individuals in the population
-        for sequence in population:
-            # Create a sequence by randomly choosing nucleotides or a mask token for each position in the structure
-            if random.random() < 1:
-                masked_sequence = mutate(sequence, mutation_ratio)
-            else:
-                masked_sequence = mutate_with_spans_mask(sequence, mutation_ratio)
-            masked_sequences.append(masked_sequence)
-            mlm_inputs.append(f"{masked_sequence}<eos>{''.join(structure)}")
-        # Call a function to predict outputs using the masked language model
-        outputs = self.mlm_predict(mlm_inputs, structure)
-        mut_population = []
-        # Decode the mlm outputs and construct the initial population
-        for i in range(len(outputs)):
-            sequence = self.tokenizer.convert_ids_to_tokens(outputs[i].tolist())
-            fixed_sequence = [
-                x if x in "AGCT" else random.choice(["G", "C"])
-                for x, y in zip(sequence, list(masked_sequences[i].replace('<mask>', '$')))
-            ]
-            mut_population.append("".join(fixed_sequence))
-        return mut_population
-    def crossover(self, population, structure):
-        crossover_population = []
-        batch_crossover_inputs = []
-        for i in range(len(population)):
-            parent1, parent2 = random.choices(population, k=2)
-            pos = random.randint(1, len(parent1) - 1)
-            child1 = parent1[:pos] + "<mask>" * len(parent2[pos:])
-            child2 = "<mask>" * len(parent1[:pos]) + parent2[pos:]
-            batch_crossover_inputs.append(f"{child1}<eos>{structure}")
-            batch_crossover_inputs.append(f"{child2}<eos>{structure}")
-        outputs = self.mlm_predict(batch_crossover_inputs, structure)
-        for i in range(len(outputs)):
-            sequence = self.tokenizer.convert_ids_to_tokens(outputs[i].tolist())
-            fixed_sequence = [
-                x if x in "AGCT" else random.choice(["G", "C"])
-                for x, y in zip(sequence, list(batch_crossover_inputs[i].replace('<mask>', '$')))
-            ]
-            crossover_population.append("".join(fixed_sequence))
-        return crossover_population
-    def sequence_fitness(self, sequences, structure):
-        fitness_values = []
-        structures = [self.predict_structure(sequence) for sequence in sequences]
-        for predicted_structure in structures:
-            scores = []
-            for i in range(len(predicted_structure)):
-                if predicted_structure[i] == structure[i]:
-                    scores.append(1)
-                elif (
-                        predicted_structure[i] == ")"
-                        and structure[i] == "("
-                        or predicted_structure[i] == "("
-                        and structure[i] == ")"
-                ):
-                    scores.append(-3)
-                else:
-                    scores.append(0)
-            score = 1 - sum(scores) / len(structure)
-            fitness_values.append(score)
-        return fitness_values
-    def mlm_predict(self, mlm_inputs, structure):
-        batch_size = 4
-        all_outputs = []
-        from transformers import set_seed
-        set_seed(random.randint(0, 99999999), deterministic=False)
-        with torch.no_grad():
-            for i in range(0, len(mlm_inputs), batch_size):
-                batch_mlm_inputs = self.tokenizer(
-                    mlm_inputs[i:i + batch_size],
-                    padding=True,
-                    max_length=len(mlm_inputs[0]) // 2,
-                    truncation=True,
-                    return_tensors="pt",
-                )
-                batch_mlm_inputs = batch_mlm_inputs.to(self.device)
-                outputs = self.OmniGenome(**batch_mlm_inputs)[0]
-                outputs = self.lm_head(outputs)
-                outputs = outputs.argmax(dim=-1)
-                all_outputs.append(outputs)
-        outputs = torch.cat(all_outputs, dim=0)
-        return outputs[:, 1:1 + len(structure)]
-# Copied from transformers.models.esm.modeling_esm.EsmClassificationHead with Esm->OmniGenome
-class OmniGenomeClassificationHead(nn.Module):
-    """Head for sentence-level classification tasks."""
-    def __init__(self, config):
-        super().__init__()
-        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
-        self.dropout = nn.Dropout(config.hidden_dropout_prob)
-        self.out_proj = nn.Linear(config.hidden_size, config.num_labels)
-    def forward(self, features, **kwargs):
-        x = features[:, 0, :]  # take <s> token (equiv. to [CLS])
-        x = self.dropout(x)
-        x = self.dense(x)
-        x = torch.tanh(x)
-        x = self.dropout(x)
-        x = self.out_proj(x)
-        return x
-def create_position_ids_from_input_ids(
-        input_ids, padding_idx, past_key_values_length=0
-):
-    """
-    Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols
-    are ignored. This is modified from fairseq's `utils.make_positions`.
-    Args:
-        x: torch.Tensor x:
-    Returns: torch.Tensor
-    """
-    # The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA.
-    mask = input_ids.ne(padding_idx).int()
-    incremental_indices = (
-                                  torch.cumsum(mask, dim=1).type_as(mask) + past_key_values_length
-                          ) * mask
-    return incremental_indices.long() + padding_idx