modeling_segment_nt.py

# 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 dataclasses import dataclass
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 .segment_nt_config import SegmentNTConfig

logger = logging.get_logger(__name__)

_CHECKPOINT_FOR_DOC = "facebook/esm2_t6_8M_UR50D"
_CONFIG_FOR_DOC = "SegmentNTConfig"

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

@dataclass
class RotaryEmbeddingConfig:
    """
    Parameters to initialize the RotaryEmbedding layer. The rescaling factor allows
    to adapt the rotary embeddings to larger lengths than what was used for training.
    One of this strategy is presented in the Yarn paper: https://arxiv.org/pdf/2309.00071.pdf. # noqa

    Args:

    """

    rescaling_factor: Optional[float]

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, rotary_embedding_config: RotaryEmbeddingConfig):
        super().__init__()

        # Extract argument from the config
        self.rescaling_factor = rotary_embedding_config.rescaling_factor
        self.upper_freq = 10000
        self.dim = dim

        self._seq_len_cached = None
        self._cos_cached = None
        self._sin_cached = None


        
    def _compute_cos_sin_tables(self, x, inv_freq, 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)
        self._seq_len_cached = seq_len
        t = torch.arange(x.shape[seq_dimension], device=x.device).type_as(
            inv_freq
        )
        freqs = torch.outer(t, 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]:
        
        if self.rescaling_factor is None:
            inv_freq = 1.0 / (self.upper_freq ** (torch.arange(0, self.dim, 2).float() / self.dim))
        else:
            updated_base = self.upper_freq * (
                self.rescaling_factor ** (self.dim / (self.dim - 2))
            )
            inv_freq = 1.0 / (
                updated_base ** (torch.arange(0, self.dim, 2).float()  / self.dim)
            )

        self._cos_cached, self._sin_cached = self._compute_cos_sin_tables(
            k, inv_freq, 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":
            # Initiliaze rotary embedding config
            rescaling_factor = config.rescaling_factor
            rotary_embedding_config = RotaryEmbeddingConfig(rescaling_factor=rescaling_factor)

            self.rotary_embeddings = RotaryEmbedding(dim=self.attention_head_size, rotary_embedding_config=rotary_embedding_config)

        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 = SegmentNTConfig
    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)

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


    

class SegmentNT(EsmPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.num_labels = config.num_labels
        self.config = config
        self.num_features = len(config.features)

        self.esm = EsmModel(config, add_pooling_layer=False)

        embed_dim = config.hidden_size
        num_layers = config.num_layers_head
        self.unet = UNET1DSegmentationHead(
            embed_dim=embed_dim,
            num_classes=embed_dim // 2,
            output_channels_list=tuple(
                embed_dim * (2**i) for i in range(num_layers)
            ),
        )
        self.fc = nn.Linear(in_features=embed_dim, out_features=6 * 2 * self.num_features)
        self.activation_fn = nn.SiLU()

        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]
        # Remove CLS token
        sequence_output = sequence_output[:,1:,:]
        

        # Invert the channels and sequence length channel
        sequence_output = torch.transpose(sequence_output, 2,1)

        x = self.activation_fn(self.unet(sequence_output))

        # Invert the channels and sequence length channel
        x = torch.transpose(x, 2,1)

        logits = self.fc(x)

        # Final reshape to have logits per nucleotides, per feature
        logits = torch.reshape(logits, (x.shape[0], x.shape[1] * 6, self.num_features, 2))

        # Add logits to the ESM outputs
        outputs["logits"] = logits

        return outputs


class DownSample1D(nn.Module):
    """
    1D-UNET downsampling block.
    """

    def __init__(
        self,
        input_channels: int,
        output_channels: int,
        num_layers: int = 2,
    ):
        """
        Args:
            output_channels: number of output channels.
            activation_fn: name of the activation function to use.
                Should be one of "gelu",
                "gelu-no-approx", "relu", "swish", "silu", "sin".
            num_layers: number of convolution layers.
            name: module name.
        """
        
        super().__init__()
        self.first_layer = [nn.Conv1d(
                in_channels=input_channels,
                out_channels=output_channels,
                kernel_size=3,
                stride=1,
                dilation=1,
                padding="same",
            )]
        

        self.next_layers = [
            nn.Conv1d(
                in_channels=output_channels,
                out_channels=output_channels,
                kernel_size=3,
                stride=1,
                dilation=1,
                padding="same",
            )
            for _ in range(num_layers-1)
        ]
        self.conv_layers = nn.ModuleList(self.first_layer + self.next_layers)

        self.avg_pool = nn.AvgPool1d(
            kernel_size=2,
            stride=2,
            padding=0,
        )
        self.activation_fn = nn.SiLU()


    def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
        for i, conv_layer in enumerate(self.conv_layers):
            x = self.activation_fn(conv_layer(x))

        hidden = x
        x = self.avg_pool(hidden)
        return x, hidden
    


class UpSample1D(nn.Module):
    """
    1D-UNET upsampling block.
    """

    def __init__(
        self,
        input_channels: int,
        output_channels: int,
        num_layers: int = 2,
    ):
        """
        Args:
            output_channels: number of output channels.
            activation_fn: name of the activation function to use.
                Should be one of "gelu",
                "gelu-no-approx", "relu", "swish", "silu", "sin".
            interpolation_method: Method to be used for upsampling interpolation.
                Should be one of "nearest", "linear", "cubic", "lanczos3", "lanczos5".
            num_layers: number of convolution layers.
            name: module name.
        """
        super().__init__()

        self._first_layer = [nn.ConvTranspose1d(
                in_channels=input_channels,
                out_channels=output_channels,
                kernel_size=3,
                stride=1,
                padding=1,
            )]


        self._next_layers = [
            nn.ConvTranspose1d(
                in_channels=output_channels,
                out_channels=output_channels,
                kernel_size=3,
                stride=1,
                padding=1,
            )
            for _ in range(num_layers-1)
        ]

        self.conv_layers = nn.ModuleList(self._first_layer + self._next_layers)

        self._activation_fn = nn.SiLU()



    def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
        for i, conv_layer in enumerate(self.conv_layers):
            x = self._activation_fn(conv_layer(x))      

        # Different order than in Haiku because the channels are changed when going 
        # from Haiku to Torch.
        x = nn.functional.interpolate(x, size=2 * x.shape[2], mode="nearest")


        return x



class FinalConv1D(nn.Module):
    """
    Final output block of the 1D-UNET.
    """

    def __init__(
        self,
        input_channels: int,
        output_channels: int,
        num_layers: int = 2,
    ):
        """
        Args:
            output_channels: number of output channels.
            activation_fn: name of the activation function to use.
                Should be one of "gelu",
                "gelu-no-approx", "relu", "swish", "silu", "sin".
            num_layers: number of convolution layers.
            name: module name.
        """
        super().__init__()

        self._first_layer = [nn.Conv1d(
                in_channels=input_channels,
                out_channels=output_channels,
                kernel_size=3,
                stride=1,
                dilation=1,
                padding="same",
            )]

        self._next_layers = [
            nn.Conv1d(
                in_channels=output_channels,
                out_channels=output_channels,
                kernel_size=3,
                stride=1,
                dilation=1,
                padding="same",
            )
            for _ in range(num_layers-1)
        ]
        self.conv_layers = nn.ModuleList(self._first_layer + self._next_layers)

        self._activation_fn = nn.SiLU()



    def forward(self, x: torch.Tensor) -> torch.Tensor:
        for i, conv_layer in enumerate(self.conv_layers):
            x = conv_layer(x)
            if i < len(self.conv_layers) - 1:
                x = self._activation_fn(x)
        return x


class UNET1DSegmentationHead(nn.Module):
    """
    1D-UNET based head to be plugged on top of a pretrained model to perform
    semantic segmentation.
    """

    def __init__(
        self,
        embed_dim: int,
        num_classes: int,
        output_channels_list: Tuple[int, ...] = (64, 128, 256),
        num_conv_layers_per_block: int = 2,
    ):
        """
        Args:
            num_classes: number of classes to segment
            output_channels_list: list of the number of output channel at each level of
                the UNET
            num_conv_layers_per_block: number of convolution layers per block.
        """
        super().__init__()
        self._num_pooling_layers = len(output_channels_list)


        downsample_input_channels_list = (embed_dim, ) + output_channels_list[:-1]

        output_channels_list_reversed = tuple(reversed(output_channels_list))
        upsample_input_channels_list = (output_channels_list[-1],) + output_channels_list_reversed
        upsample_output_channels_list =  output_channels_list_reversed

        self._downsample_blocks = nn.ModuleList([
            DownSample1D(
                input_channels= input_channels,
                output_channels=output_channels,
                num_layers=num_conv_layers_per_block,
            )
            for input_channels, output_channels in zip(downsample_input_channels_list, output_channels_list)
        ])

        self._upsample_blocks = nn.ModuleList([
            UpSample1D(
                input_channels = input_channels,
                output_channels=output_channels,
                num_layers=num_conv_layers_per_block,
            )
            for input_channels, output_channels in zip(upsample_input_channels_list, upsample_output_channels_list)
        ])

        self.final_block = FinalConv1D(
            input_channels=output_channels_list[0],
            output_channels=num_classes * 2,
            num_layers=num_conv_layers_per_block,
        )

    def forward(self, x: torch.Tensor) -> torch.Tensor:

        if x.shape[2] % 2**self._num_pooling_layers:
            raise ValueError(
                "Input length must be divisible by the 2 to the power of"
                " number of poolign layers."
            )

        hiddens = []
        for downsample_block in self._downsample_blocks:
            x, hidden = downsample_block(x)
            hiddens.append(hidden)
        
        

        for i, (upsample_block, hidden) in enumerate(zip(self._upsample_blocks, reversed(hiddens))):
            x = upsample_block(x) + hidden
        x = self.final_block(x)
        return x