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	# coding=utf-8
	# Copyright 2022 Meta and The HuggingFace Inc. team. All rights reserved.
	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#
	# Unless required by applicable law or agreed to in writing, software
	# distributed under the License is distributed on an "AS IS" BASIS,
	# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	# See the License for the specific language governing permissions and
	# limitations under the License.
	""" PyTorch ESM model."""

	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, SiLU
	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.utils import logging

	from .esm_config import EsmConfig

	logger = logging.get_logger(__name__)

	_CHECKPOINT_FOR_DOC = "facebook/esm2_t6_8M_UR50D"
	_CONFIG_FOR_DOC = "EsmConfig"

	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
	]


	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
	)
	# 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)

	# 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 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 = EsmConfig
	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 ([`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.
	"""

	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 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