diff --git "a/modeling_isoformer.py" "b/modeling_isoformer.py"
--- "a/modeling_isoformer.py"
+++ "b/modeling_isoformer.py"
@@ -1,3369 +1,12 @@
-from isoformer_config import IsoformerConfig
-from modeling_esm import NTForMaskedLM, MultiHeadAttention
-from modeling_esm_original import EsmForMaskedLM
-from enformer_pytorch import Enformer, str_to_one_hot, EnformerConfig
-from typing import Dict
-import torch.utils.checkpoint
-from torch import nn
-from torch.nn import SiLU
-from transformers.utils import logging
-
-logger = logging.get_logger(__name__)
-
-_CHECKPOINT_FOR_DOC = "facebook/esm2_t6_8M_UR50D"
-_CONFIG_FOR_DOC = "NTConfig"
-
-ESM_PRETRAINED_MODEL_ARCHIVE_LIST = [
-    "facebook/esm2_t6_8M_UR50D",
-    "facebook/esm2_t12_35M_UR50D",
-    # This is not a complete list of all ESM models!
-    # See all ESM models at https://huggingface.co/models?filter=esm
-]
-
-
-import math
-from typing import List, Optional, Tuple, Union
-
-import torch
-import torch.utils.checkpoint
-from torch import nn
-from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
-from transformers.file_utils import (
-    add_code_sample_docstrings,
-    add_start_docstrings,
-    add_start_docstrings_to_model_forward,
-)
-from transformers.modeling_outputs import (
-    BaseModelOutputWithPastAndCrossAttentions,
-    BaseModelOutputWithPoolingAndCrossAttentions,
-    MaskedLMOutput,
-    SequenceClassifierOutput,
-    TokenClassifierOutput,
-)
-from transformers.modeling_utils import (
-    PreTrainedModel,
-    find_pruneable_heads_and_indices,
-    prune_linear_layer,
-)
-from transformers.models.esm.configuration_esm import EsmConfig
-from transformers.utils import logging
-
-logger = logging.get_logger(__name__)
-
-_CHECKPOINT_FOR_DOC = "facebook/esm2_t6_8M_UR50D"
-_CONFIG_FOR_DOC = "EsmConfig"
-
-
-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 ESM 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
-
-
-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, dtype=torch.int64).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 EsmContactPredictionHead(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))
-
-
-class EsmEmbeddings(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 ESM-1 (not 1b!) in future then we need to support an
-        # embedding_scale factor here.
-        embeddings = inputs_embeds
-
-        # Matt: ESM 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 ESM 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
-            )
-        # FIRST DIFF BETWEEN JAX AND TORCH
-        # 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)
-
-
-class EsmSelfAttention(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).
-        # ESM 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
-        # ESM 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 EsmModel forward() function)
-            attention_scores = attention_scores + attention_mask
-
-        # Normalize the attention scores to probabilities.
-        attention_probs = nn.functional.softmax(attention_scores, dim=-1)
-
-        attention_mask_widened = (
-            attention_mask.repeat(
-                attention_probs.shape[0],
-                attention_probs.shape[1],
-                attention_probs.shape[2],
-                1,
-            ).permute(0, 1, 3, 2)
-            == 0
-        )
-        attention_probs = torch.where(
-            attention_mask_widened, attention_probs, 0.00097656
-        )
-
-        # SECOND DIFF BETWEEN JAX AND TORCH
-        # 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.to(value_layer.dtype), 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
-
-
-class EsmSelfOutput(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 EsmAttention(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.self = EsmSelfAttention(config)
-        self.output = EsmSelfOutput(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
-
-
-class EsmIntermediate(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
-
-
-class EsmOutput(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 EsmLayer(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 = EsmAttention(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 = EsmAttention(config)
-        self.intermediate = EsmIntermediate(config)
-        self.output = EsmOutput(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
-
-
-class EsmEncoder(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.config = config
-        self.layer = nn.ModuleList(
-            [EsmLayer(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
-class EsmPooler(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 EsmPreTrainedModel(PreTrainedModel):
-    """
-    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
-    models.
-    """
-
-    config_class = EsmConfig
-    base_model_prefix = "esm"
-    supports_gradient_checkpointing = True
-    _no_split_modules = [
-        "EsmLayer",
-        "EsmFoldTriangularSelfAttentionBlock",
-        "EsmEmbeddings",
-    ]
-
-    # 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)
-
-
-ESM_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 ([`EsmConfig`]): 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.
-"""
-
-ESM_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 ESM Model transformer outputting raw hidden-states without any specific head on top.",
-    ESM_START_DOCSTRING,
-)
-class EsmModel(EsmPreTrainedModel):
-    """
-
-    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 = EsmEmbeddings(config)
-        self.encoder = EsmEncoder(config)
-
-        self.pooler = EsmPooler(config) if add_pooling_layer else None
-
-        self.contact_head = EsmContactPredictionHead(
-            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(
-        ESM_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(
-    """ESM Model with a `language modeling` head on top.""", ESM_START_DOCSTRING
-)
-class EsmForMaskedLM(EsmPreTrainedModel):
-    _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 `EsmForMaskedLM` make sure `config.is_decoder=False` for "
-                "bi-directional self-attention."
-            )
-
-        self.esm = EsmModel(config, add_pooling_layer=False)
-        self.lm_head = EsmLMHead(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(
-        ESM_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.esm(
-            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.esm.predict_contacts(tokens, attention_mask=attention_mask)
-
-
-class EsmLMHead(nn.Module):
-    """ESM 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(
-    """
-    ESM Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled
-    output) e.g. for GLUE tasks.
-    """,
-    ESM_START_DOCSTRING,
-)
-class EsmForSequenceClassification(EsmPreTrainedModel):
-    def __init__(self, config):
-        super().__init__(config)
-        self.num_labels = config.num_labels
-        self.config = config
-
-        self.esm = EsmModel(config, add_pooling_layer=False)
-        self.classifier = EsmClassificationHead(config)
-
-        self.init_weights()
-
-    @add_start_docstrings_to_model_forward(
-        ESM_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.esm(
-            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,
-        )
-        sequence_output = outputs[0]
-        logits = self.classifier(sequence_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(
-    """
-    ESM Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for
-    Named-Entity-Recognition (NER) tasks.
-    """,
-    ESM_START_DOCSTRING,
-)
-class EsmForTokenClassification(EsmPreTrainedModel):
-    def __init__(self, config):
-        super().__init__(config)
-        self.num_labels = config.num_labels
-
-        self.esm = EsmModel(config, add_pooling_layer=False)
-        self.dropout = nn.Dropout(config.hidden_dropout_prob)
-        self.classifier = nn.Linear(config.hidden_size, config.num_labels)
-
-        self.init_weights()
-
-    @add_start_docstrings_to_model_forward(
-        ESM_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.esm(
-            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,
-        )
-
-        sequence_output = outputs[0]
-
-        sequence_output = self.dropout(sequence_output)
-        logits = self.classifier(sequence_output)
-
-        loss = None
-        if labels is not None:
-            loss_fct = CrossEntropyLoss()
-
-            labels = labels.to(logits.device)
-            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,
-        )
-
-
-class EsmClassificationHead(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
-
-
-from dataclasses import asdict, dataclass
-from typing import Optional
-
-from transformers import PretrainedConfig, logging
-
-logger = logging.get_logger(__name__)
-
-# TODO Update this
-ESM_PRETRAINED_CONFIG_ARCHIVE_MAP = {
-    "facebook/esm-1b": "https://huggingface.co/facebook/esm-1b/resolve/main/config.json",
-    # See all ESM models at https://huggingface.co/models?filter=esm
-}
-
-
-class NTConfig(PretrainedConfig):
-    r"""
-    This is the configuration class to store the configuration of a [`ESMModel`]. It is used to instantiate a ESM model
-    according to the specified arguments, defining the model architecture. Instantiating a configuration with the
-    defaults will yield a similar configuration to that of the ESM
-    [facebook/esm-1b](https://huggingface.co/facebook/esm-1b) architecture.
-    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
-    documentation from [`PretrainedConfig`] for more information.
-    Args:
-        vocab_size (`int`, *optional*):
-            Vocabulary size of the ESM model. Defines the number of different tokens that can be represented by the
-            `inputs_ids` passed when calling [`ESMModel`].
-        mask_token_id (`int`, *optional*):
-            The index of the mask token in the vocabulary. This must be included in the config because of the
-            "mask-dropout" scaling trick, which will scale the inputs depending on the number of masked tokens.
-        pad_token_id (`int`, *optional*):
-            The index of the padding token in the vocabulary. This must be included in the config because certain parts
-            of the ESM code use this instead of the attention mask.
-        hidden_size (`int`, *optional*, defaults to 768):
-            Dimensionality of the encoder layers and the pooler layer.
-        num_hidden_layers (`int`, *optional*, defaults to 12):
-            Number of hidden layers in the Transformer encoder.
-        num_attention_heads (`int`, *optional*, defaults to 12):
-            Number of attention heads for each attention layer in the Transformer encoder.
-        intermediate_size (`int`, *optional*, defaults to 3072):
-            Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
-        hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
-            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
-        attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
-            The dropout ratio for the attention probabilities.
-        max_position_embeddings (`int`, *optional*, defaults to 1026):
-            The maximum sequence length that this model might ever be used with. Typically set this to something large
-            just in case (e.g., 512 or 1024 or 2048).
-        initializer_range (`float`, *optional*, defaults to 0.02):
-            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
-        layer_norm_eps (`float`, *optional*, defaults to 1e-12):
-            The epsilon used by the layer normalization layers.
-        position_embedding_type (`str`, *optional*, defaults to `"absolute"`):
-            Type of position embedding. Choose one of `"absolute"`, `"relative_key"`, `"relative_key_query", "rotary"`.
-            For positional embeddings use `"absolute"`. For more information on `"relative_key"`, please refer to
-            [Self-Attention with Relative Position Representations (Shaw et al.)](https://arxiv.org/abs/1803.02155).
-            For more information on `"relative_key_query"`, please refer to *Method 4* in [Improve Transformer Models
-            with Better Relative Position Embeddings (Huang et al.)](https://arxiv.org/abs/2009.13658).
-        is_decoder (`bool`, *optional*, defaults to `False`):
-            Whether the model is used as a decoder or not. If `False`, the model is used as an encoder.
-        use_cache (`bool`, *optional*, defaults to `True`):
-            Whether or not the model should return the last key/values attentions (not used by all models). Only
-            relevant if `config.is_decoder=True`.
-        emb_layer_norm_before (`bool`, *optional*):
-            Whether to apply layer normalization after embeddings but before the main stem of the network.
-        token_dropout (`bool`, defaults to `False`):
-            When this is enabled, masked tokens are treated as if they had been dropped out by input dropout.
-    Examples:
-    ```python
-    >>> from transformers import EsmModel, EsmConfig
-    >>> # Initializing a ESM facebook/esm-1b style configuration >>> configuration = EsmConfig()
-    >>> # Initializing a model from the configuration >>> model = ESMModel(configuration)
-    >>> # Accessing the model configuration >>> configuration = model.config
-    ```"""
-    model_type = "esm"
-
-    def __init__(
-        self,
-        vocab_size=None,
-        mask_token_id=None,
-        pad_token_id=None,
-        hidden_size=768,
-        num_hidden_layers=12,
-        num_attention_heads=12,
-        intermediate_size=3072,
-        hidden_dropout_prob=0.1,
-        attention_probs_dropout_prob=0.1,
-        max_position_embeddings=1026,
-        initializer_range=0.02,
-        layer_norm_eps=1e-12,
-        position_embedding_type="absolute",
-        use_cache=True,
-        emb_layer_norm_before=None,
-        token_dropout=False,
-        is_folding_model=False,
-        esmfold_config=None,
-        vocab_list=None,
-        add_bias_fnn=True,
-        **kwargs,
-    ):
-        super().__init__(
-            pad_token_id=pad_token_id, mask_token_id=mask_token_id, **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.initializer_range = initializer_range
-        self.layer_norm_eps = layer_norm_eps
-        self.position_embedding_type = position_embedding_type
-        self.use_cache = use_cache
-        self.emb_layer_norm_before = emb_layer_norm_before
-        self.token_dropout = token_dropout
-        self.is_folding_model = is_folding_model
-
-        # Arguments needed for Dalmatian
-        self.add_bias_fnn = add_bias_fnn
-        if is_folding_model:
-            if esmfold_config is None:
-                logger.info(
-                    "No esmfold_config supplied for folding model, using default values."
-                )
-                esmfold_config = EsmFoldConfig()
-            elif isinstance(esmfold_config, dict):
-                esmfold_config = EsmFoldConfig(**esmfold_config)
-            self.esmfold_config = esmfold_config
-            if vocab_list is None:
-                logger.warning(
-                    "No vocab_list supplied for folding model, assuming the ESM-2 vocabulary!"
-                )
-                self.vocab_list = get_default_vocab_list()
-            else:
-                self.vocab_list = vocab_list
-        else:
-            self.esmfold_config = None
-            self.vocab_list = None
-        if self.esmfold_config is not None and getattr(
-            self.esmfold_config, "use_esm_attn_map", False
-        ):
-            raise ValueError(
-                "The HuggingFace port of ESMFold does not support use_esm_attn_map at this time!"
-            )
-
-    def to_dict(self):
-        """
-        Serializes this instance to a Python dictionary. Override the default [`~PretrainedConfig.to_dict`].
-        Returns:
-            `Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance,
-        """
-        output = super().to_dict()
-        if isinstance(self.esmfold_config, EsmFoldConfig):
-            output["esmfold_config"] = self.esmfold_config.to_dict()
-        return output
-
-
-@dataclass
-class EsmFoldConfig:
-    esm_type: str = None
-    fp16_esm: bool = True
-    use_esm_attn_map: bool = False
-    esm_ablate_pairwise: bool = False
-    esm_ablate_sequence: bool = False
-    esm_input_dropout: float = 0
-
-    embed_aa: bool = True
-    bypass_lm: bool = False
-
-    lddt_head_hid_dim: int = 128
-    trunk: "TrunkConfig" = None
-
-    def __post_init__(self):
-        if self.trunk is None:
-            self.trunk = TrunkConfig()
-        elif isinstance(self.trunk, dict):
-            self.trunk = TrunkConfig(**self.trunk)
-
-    def to_dict(self):
-        """
-        Serializes this instance to a Python dictionary. Override the default [`~PretrainedConfig.to_dict`].
-        Returns:
-            `Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance,
-        """
-        output = asdict(self)
-        output["trunk"] = self.trunk.to_dict()
-        return output
-
-
-@dataclass
-class TrunkConfig:
-    num_blocks: int = 48
-    sequence_state_dim: int = 1024
-    pairwise_state_dim: int = 128
-    sequence_head_width: int = 32
-    pairwise_head_width: int = 32
-    position_bins: int = 32
-    dropout: float = 0
-    layer_drop: float = 0
-    cpu_grad_checkpoint: bool = False
-    max_recycles: int = 4
-    chunk_size: Optional[int] = 128
-    structure_module: "StructureModuleConfig" = None
-
-    def __post_init__(self):
-        if self.structure_module is None:
-            self.structure_module = StructureModuleConfig()
-        elif isinstance(self.structure_module, dict):
-            self.structure_module = StructureModuleConfig(**self.structure_module)
-
-        if self.max_recycles <= 0:
-            raise ValueError(
-                f"`max_recycles` should be positive, got {self.max_recycles}."
-            )
-        if self.sequence_state_dim % self.sequence_state_dim != 0:
-            raise ValueError(
-                "`sequence_state_dim` should be a round multiple of `sequence_state_dim`, got"
-                f" {self.sequence_state_dim} and {self.sequence_state_dim}."
-            )
-        if self.pairwise_state_dim % self.pairwise_state_dim != 0:
-            raise ValueError(
-                "`pairwise_state_dim` should be a round multiple of `pairwise_state_dim`, got"
-                f" {self.pairwise_state_dim} and {self.pairwise_state_dim}."
-            )
-
-        sequence_num_heads = self.sequence_state_dim // self.sequence_head_width
-        pairwise_num_heads = self.pairwise_state_dim // self.pairwise_head_width
-
-        if self.sequence_state_dim != sequence_num_heads * self.sequence_head_width:
-            raise ValueError(
-                "`sequence_state_dim` should be equal to `sequence_num_heads * sequence_head_width, got"
-                f" {self.sequence_state_dim} != {sequence_num_heads} * {self.sequence_head_width}."
-            )
-        if self.pairwise_state_dim != pairwise_num_heads * self.pairwise_head_width:
-            raise ValueError(
-                "`pairwise_state_dim` should be equal to `pairwise_num_heads * pairwise_head_width, got"
-                f" {self.pairwise_state_dim} != {pairwise_num_heads} * {self.pairwise_head_width}."
-            )
-        if self.pairwise_state_dim % 2 != 0:
-            raise ValueError(
-                f"`pairwise_state_dim` should be even, got {self.pairwise_state_dim}."
-            )
-
-        if self.dropout >= 0.4:
-            raise ValueError(
-                f"`dropout` should not be greater than 0.4, got {self.dropout}."
-            )
-
-    def to_dict(self):
-        """
-        Serializes this instance to a Python dictionary. Override the default [`~PretrainedConfig.to_dict`].
-        Returns:
-            `Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance,
-        """
-        output = asdict(self)
-        output["structure_module"] = self.structure_module.to_dict()
-        return output
-
-
-@dataclass
-class StructureModuleConfig:
-    """
-    Args:
-        sequence_dim:
-            Single representation channel dimension
-        pairwise_dim:
-            Pair representation channel dimension
-        ipa_dim:
-            IPA hidden channel dimension
-        resnet_dim:
-            Angle resnet (Alg. 23 lines 11-14) hidden channel dimension
-        num_heads_ipa:
-            Number of IPA heads
-        num_qk_points:
-            Number of query/key points to generate during IPA
-        num_v_points:
-            Number of value points to generate during IPA
-        dropout_rate:
-            Dropout rate used throughout the layer
-        num_blocks:
-            Number of structure module blocks
-        num_transition_layers:
-            Number of layers in the single representation transition (Alg. 23 lines 8-9)
-        num_resnet_blocks:
-            Number of blocks in the angle resnet
-        num_angles:
-            Number of angles to generate in the angle resnet
-        trans_scale_factor:
-            Scale of single representation transition hidden dimension
-        epsilon:
-            Small number used in angle resnet normalization
-        inf:
-            Large number used for attention masking
-    """
-
-    sequence_dim: int = 384
-    pairwise_dim: int = 128
-    ipa_dim: int = 16
-    resnet_dim: int = 128
-    num_heads_ipa: int = 12
-    num_qk_points: int = 4
-    num_v_points: int = 8
-    dropout_rate: float = 0.1
-    num_blocks: int = 8
-    num_transition_layers: int = 1
-    num_resnet_blocks: int = 2
-    num_angles: int = 7
-    trans_scale_factor: int = 10
-    epsilon: float = 1e-8
-    inf: float = 1e5
-
-    def to_dict(self):
-        return asdict(self)
-
-
-def get_default_vocab_list():
-    return (
-        "<cls>",
-        "<pad>",
-        "<eos>",
-        "<unk>",
-        "L",
-        "A",
-        "G",
-        "V",
-        "S",
-        "E",
-        "R",
-        "T",
-        "I",
-        "D",
-        "P",
-        "K",
-        "Q",
-        "N",
-        "F",
-        "Y",
-        "M",
-        "H",
-        "W",
-        "C",
-        "X",
-        "B",
-        "U",
-        "Z",
-        "O",
-        ".",
-        "-",
-        "<null_1>",
-        "<mask>",
-    )
-
-
-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 ESM 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
-
-
-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),
-        )
-
-
-class EsmContactPredictionHead(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))
-
-
-class EsmEmbeddings(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 ESM-1 (not 1b!) in future then we need to support an
-        # embedding_scale factor here.
-        embeddings = inputs_embeds
-
-        # Matt: ESM 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.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 ESM 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 += 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
-        #     )
-        # FIRST DIFF BETWEEN JAX AND TORCH
-        # 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)
-
-
-class EsmSelfAttention(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).
-        # ESM 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
-        # ESM 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:
-            attention_scores = attention_scores + attention_mask
-
-        # Normalize the attention scores to probabilities.
-        attention_probs = nn.functional.softmax(attention_scores, dim=-1)
-        attention_mask_widened = attention_mask.repeat(
-            attention_probs.shape[0],
-            attention_probs.shape[1],
-            attention_probs.shape[2],
-            1
-        ).permute(0,1,3,2) == 0
-        attention_probs = torch.where(attention_mask_widened, attention_probs, 0.00097656)
-        # SECOND DIFF BETWEEN JAX AND TORCH
-
-        # 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
-
-
-class EsmSelfOutput(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 += input_tensor
-        return hidden_states
-
-
-class EsmAttention(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.self = EsmSelfAttention(config)
-        self.output = EsmSelfOutput(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
-
-
-class MultiHeadAttention(nn.Module):
-    def __init__(self, config, omics_of_interest_size: int, other_omic_size: int, 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(omics_of_interest_size, omics_of_interest_size) # 3072, 3072
-
-        self.key = nn.Linear(other_omic_size, omics_of_interest_size) # 768, 3072
-
-        self.value = nn.Linear(other_omic_size, omics_of_interest_size) # 768, 3072
-
-        self.dense = nn.Linear(omics_of_interest_size, omics_of_interest_size) # 3072, 3072
-
-
-        #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,
-    ) -> Dict[str, 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).
-        # ESM 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
-        # ESM 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 NTModel forward() function)
-            #attention_scores = attention_scores + attention_mask
-            attention_scores = torch.where(attention_mask, attention_scores, -1e30)
-            #attention_logits = jnp.where(attention_mask, attention_logits, -1e30)
-
-        # 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 {
-            "embeddings": self.dense(context_layer) + hidden_states,
-            "query_heads": self.transpose_for_scores(mixed_query_layer),
-            "value_heads": self.transpose_for_scores(self.value(encoder_hidden_states)),
-            "key_heads": self.transpose_for_scores(self.key(encoder_hidden_states)),
-            "attention_probs": attention_probs,
-            "attention_scores": attention_scores,
-            "context_layer": context_layer,
-        }
-
-class EsmIntermediate(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.dense = nn.Linear(
-            config.hidden_size,
-            int(config.intermediate_size * 2),
-            bias=config.add_bias_fnn,
-        )
-        self.activation_fn = SiLU()
-
-    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
-        hidden_states = self.dense(hidden_states)
-
-        # GLU
-        x1, x2 = hidden_states.split(int(hidden_states.size(-1) / 2), -1)
-        hidden_states = self.activation_fn(x1) * x2
-
-        return hidden_states
-
-
-class EsmOutput(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.dense = nn.Linear(
-            config.intermediate_size, config.hidden_size, bias=config.add_bias_fnn
-        )
-        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 += input_tensor
-        return hidden_states
-
-
-class EsmLayer(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 = EsmAttention(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 = EsmAttention(config)
-        self.intermediate = EsmIntermediate(config)
-        self.output = EsmOutput(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
-
-
-class EsmEncoder(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.config = config
-        self.layer = nn.ModuleList(
-            [EsmLayer(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:
-
-                def create_custom_forward(module):
-                    def custom_forward(*inputs):
-                        return module(*inputs, past_key_value, output_attentions)
-
-                    return custom_forward
-
-                layer_outputs = torch.utils.checkpoint.checkpoint(
-                    create_custom_forward(layer_module),
-                    hidden_states,
-                    attention_mask,
-                    layer_head_mask,
-                    encoder_hidden_states,
-                    encoder_attention_mask,
-                )
-            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 += (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
-class EsmPooler(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 EsmPreTrainedModel(PreTrainedModel):
-    """
-    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
-    models.
-    """
-
-    config_class = NTConfig
-    base_model_prefix = "esm"
-    _no_split_modules = ["EsmLayer", "EsmFoldTriangularSelfAttentionBlock"]
-
-    # 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)
-
-
-ESM_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 ([`NTConfig`]): 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.
-"""
-
-ESM_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 ESM Model transformer outputting raw hidden-states without any specific head on top.",
-    ESM_START_DOCSTRING,
-)
-class NTModel(EsmPreTrainedModel):
-    """
-    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.
-    """
-
-    supports_gradient_checkpointing = False
-
-    def __init__(self, config, add_pooling_layer=True):
-        super().__init__(config)
-        self.config = config
-
-        self.embeddings = EsmEmbeddings(config)
-        self.encoder = EsmEncoder(config)
-
-        self.pooler = EsmPooler(config) if add_pooling_layer else None
-
-        self.contact_head = EsmContactPredictionHead(
-            in_features=config.num_hidden_layers * config.num_attention_heads, bias=True
-        )
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    def _set_gradient_checkpointing(self, module, value=False):
-        if isinstance(module, EsmEncoder):
-            module.gradient_checkpointing = value
-
-    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(
-        ESM_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:
-            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(
-    """ESM Model with a `language modeling` head on top.""", ESM_START_DOCSTRING
-)
-class NTForMaskedLM(EsmPreTrainedModel):
-    _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 `EsmForMaskedLM` make sure `config.is_decoder=False` for "
-                "bi-directional self-attention."
-            )
-
-        self.esm = NTModel(config, add_pooling_layer=False)
-        self.lm_head = EsmLMHead(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(
-        ESM_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.esm(
-            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.esm.predict_contacts(tokens, attention_mask=attention_mask)
-
-
-class EsmLMHead(nn.Module):
-    """ESM 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(
-    """
-    ESM Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled
-    output) e.g. for GLUE tasks.
-    """,
-    ESM_START_DOCSTRING,
-)
-class EsmForSequenceClassification(EsmPreTrainedModel):
-    def __init__(self, config):
-        super().__init__(config)
-        self.num_labels = config.num_labels
-        self.config = config
-
-        self.esm = NTModel(config, add_pooling_layer=False)
-        self.classifier = EsmClassificationHead(config)
-
-        self.init_weights()
-
-    @add_start_docstrings_to_model_forward(
-        ESM_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.esm(
-            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,
-        )
-        sequence_output = outputs[0]
-        logits = self.classifier(sequence_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(
-    """
-    ESM Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for
-    Named-Entity-Recognition (NER) tasks.
-    """,
-    ESM_START_DOCSTRING,
-)
-class EsmForTokenClassification(EsmPreTrainedModel):
-    def __init__(self, config):
-        super().__init__(config)
-        self.num_labels = config.num_labels
-
-        self.esm = NTModel(config, add_pooling_layer=False)
-        self.dropout = nn.Dropout(config.hidden_dropout_prob)
-        self.classifier = nn.Linear(config.hidden_size, config.num_labels)
-
-        self.init_weights()
-
-    @add_start_docstrings_to_model_forward(
-        ESM_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.esm(
-            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,
-        )
-
-        sequence_output = outputs[0]
-
-        sequence_output = self.dropout(sequence_output)
-        logits = self.classifier(sequence_output)
-
-        loss = None
-        if labels is not None:
-            loss_fct = CrossEntropyLoss()
-
-            labels = labels.to(logits.device)
-            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,
-        )
-
-
-class EsmClassificationHead(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
-
+from .isoformer_config import IsoformerConfig
+from transformers import PreTrainedModel
+from .modeling_esm import NTForMaskedLM, MultiHeadAttention
+from .esm_config import NTConfig
+from .modeling_esm_original import EsmForMaskedLM
+from transformers.models.esm.configuration_esm import EsmConfig
+from enformer_pytorch import Enformer, str_to_one_hot, EnformerConfig
+import torch
+from torch import nn
 
 class Isoformer(PreTrainedModel):
     config_class = IsoformerConfig
@@ -3411,30 +54,8 @@ class Isoformer(PreTrainedModel):
         )
         self.config = config
 
-        # self.enformer_config = EnformerConfig(
-        #     dim=config.enformer_dim,
-        #     depth=config.enformer_depth,
-        #     heads=config.enformer_heads,
-        #     output_heads=dict(
-        #         human=1,
-        #         mouse=1 # TODO CHANGE
-        #     ),
-        #     target_length=config.enformer_target_length,  # 896,
-        #     attn_dim_key=config.enformer_attn_dim_key,
-        #     dropout_rate=0.4,
-        #     attn_dropout=0.05,
-        #     pos_dropout=0.01,
-        #     use_checkpointing=config.enformer_use_checkpointing,
-        #     use_convnext=config.enformer_use_convnext,
-        #     num_downsamples=config.enformer_num_downsamples,
-        #     # genetic sequence is downsampled 2 ** 7 == 128x in default Enformer - can be changed for higher resolution
-        #     dim_divisible_by=config.enformer_dim_divisible_by,
-        #     use_tf_gamma=False,
-        # )
-
-        self.esm_model = EsmForMaskedLM(self.esm_config) # protein encoder
-        self.nt_model = NTForMaskedLM(self.nt_config) # rna encoder
-        #self.enformer_model = Enformer(self.enformer_config) # dna encoder
+        self.esm_model = EsmForMaskedLM(self.esm_config) 
+        self.nt_model = NTForMaskedLM(self.nt_config) 
         self.enformer_model = Enformer.from_pretrained("EleutherAI/enformer-official-rough")
 
         self.cross_attention_layer_rna = MultiHeadAttention(