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import os
import torch
import numpy as np
import pandas as pd
import h5py
import re
from omegaconf import OmegaConf
import h5py
import lightning as L
from pera.nn import BidirectionalModel, sample_components_from_bidirectional_transformer, sample_perturbations, sample_embedding_perturbations
from esm.tokenization.sequence_tokenizer import EsmSequenceTokenizer
from Bio.Seq import Seq
device = torch.device("cuda:0")
sequence_tokenizer = EsmSequenceTokenizer()
import argparse
# set up parser
parser = parser = argparse.ArgumentParser(description="Calculating the log-likelihood of a sequence")
parser.add_argument('--target', type=str, required=True, help='Dataset as a string')
parser.add_argument('--num_samples', type=int, required=False, default=384, help='Number of samples to process (default: 100000)')
parser.add_argument('--alignment_round', type=int, required=False, default=1, help='Alignment round as an integer')
parser.add_argument('--version_number', type=str, required=False, default=1, help='Version number as a string')
parser.add_argument('--replicate', type=int, required=False, default=1, help='Replicate number as an integer')
args = parser.parse_args()
target = args.target
alignment_round = args.alignment_round
version_number = args.version_number
num_samples = args.num_samples
replicate = args.replicate
cfg_filename = f"{target}/lightning_logs_round_{alignment_round}/{version_number}/config.yaml"
network_filename = f"{target}/lightning_logs_round_{alignment_round}/{version_number}/checkpoints/best_model.ckpt"
save_folder_name = f"{target}/aligned_{alignment_round}_{num_samples}_{replicate}"
cfg = OmegaConf.load(cfg_filename)
sampling_temperature=1
OmegaConf.update(cfg, "train.lightning_model_args.sampling_temperature", sampling_temperature)
OmegaConf.update(cfg, "train.lightning_model_args.better_energy", "lower")
esm_model = BidirectionalModel(cfg["nn"]["model"],
cfg["nn"]["model_args"],
**cfg["train"]["lightning_model_args"]).to(device)
esm_model.load_model_from_ckpt(network_filename)
esm_model.eval()
print("")
mask_token_sequence = cfg["nn"]["model_args"]["residue_token_info"]["mask"]
bos_token_sequence = cfg["nn"]["model_args"]["residue_token_info"]["bos"]
eos_token_sequence = cfg["nn"]["model_args"]["residue_token_info"]["eos"]
pad_token_sequence = cfg["nn"]["model_args"]["residue_token_info"]["pad"]
os.makedirs(save_folder_name, exist_ok=True)
past_generations =[f"{target}/base_model_{num_samples}"]
for i in range(alignment_round):
past_generations.append(f"{target}/aligned_{i}_{num_samples}_{replicate}")
previous_unmasked_sequences_decoded = []
for round in past_generations:
trpb = torch.load(f"{round}/trpb_{replicate}.pt")
previous_unmasked_sequences_decoded.extend(trpb['all_unmasked_sequences_decoded'])
assert len(previous_unmasked_sequences_decoded) == len(set(previous_unmasked_sequences_decoded)), "There are duplicate sequences in previous_unmasked_sequences_decoded"
print("All elements in previous_unmasked_sequences_decoded are unique.")
data = target # "GB1", "ParD2", "TEV", "TrpB3F", "TrpB3I", "TrpB4"
data_root_path = "/scratch/groups/rotskoff/sebastian/era/protein_era/data"
sequence_tokenizer = EsmSequenceTokenizer()
if data.startswith("TrpB"):
df = pd.read_csv(f"{data_root_path}/TrpB/scale2max/{data}.csv")
with open(f"{data_root_path}/TrpB/TrpB.fasta", "r") as file:
parent_sequence_decoded = file.readlines()[1].strip()
elif data == "DHFR":
df = pd.read_csv(f"{data_root_path}/{data}/scale2max/{data}.csv")
with open(f"{data_root_path}/{data}/{data}.fasta", "r") as file:
nucleotide_seq = file.readlines()[1].strip()
nucleotide_seq = Seq(nucleotide_seq)
parent_sequence_decoded = str(nucleotide_seq.translate()) # Translate to amino acid sequence
else:
df = pd.read_csv(f"{data_root_path}/{data}/scale2max/{data}.csv")
with open(f"{data_root_path}/{data}/{data}.fasta", "r") as file:
parent_sequence_decoded = file.readlines()[1].strip()
if data != "GB1":
muts = df["muts"].iloc[0]
else:
muts = df["muts"].iloc[100000]
numbers = re.findall(r'\d+', muts)
mask_indices = list(map(int, numbers))
num_masks_per_sequence = num_samples // 4
num_to_generate_per_mask = 4
parent_sequence = torch.tensor(sequence_tokenizer.encode(parent_sequence_decoded,
add_special_tokens=True), device=device).unsqueeze(0).long()
sequence_length = parent_sequence.shape[1]
all_masked_sequences = []
all_unmasked_sequences_decoded = []
all_unmasked_sequences = []
all_logps = []
while len(all_unmasked_sequences_decoded) < num_samples:
print(len(all_unmasked_sequences_decoded))
masked_sequences = parent_sequence.clone().repeat(num_to_generate_per_mask, 1)
masked_sequences[:, mask_indices] = mask_token_sequence
sequence_id = torch.ones((num_to_generate_per_mask, sequence_length), device=device).long() * 1
structure_tokens = torch.ones((num_to_generate_per_mask, sequence_length), device=device).long() * 4096
structure_tokens[:, 0] = 4098
structure_tokens[:, -1] = 4097
coords = torch.inf * torch.ones((num_to_generate_per_mask, sequence_length, 3, 3), device=device)
average_plddt = torch.ones((num_to_generate_per_mask), device=device)
per_res_plddt = torch.zeros((num_to_generate_per_mask, sequence_length), device=device)
ss8_tokens = torch.zeros((num_to_generate_per_mask, sequence_length), device=device).long()
sasa_tokens = torch.zeros((num_to_generate_per_mask, sequence_length), device=device).long()
function_tokens = torch.zeros((num_to_generate_per_mask, sequence_length, 8), device=device).long()
residue_annotation_tokens = torch.zeros((num_to_generate_per_mask, sequence_length, 16), device=device).long()
with torch.no_grad():
unmasked_sequences = sample_components_from_bidirectional_transformer(transformer_model=esm_model,
masked_sequence_tokens=masked_sequences,
structure_tokens=structure_tokens,
average_plddt=average_plddt,
per_res_plddt=per_res_plddt,
ss8_tokens=ss8_tokens,
sasa_tokens=sasa_tokens,
function_tokens=function_tokens,
residue_annotation_tokens=residue_annotation_tokens,
bb_coords=coords,
sequence_id=sequence_id,
mask_token_sequence=mask_token_sequence,
bos_token_sequence=bos_token_sequence,
eos_token_sequence=eos_token_sequence,
pad_token_sequence=pad_token_sequence,
inference_batch_size=1)
masked_indices = (masked_sequences == mask_token_sequence).float()
logits = esm_model.nn(sequence_tokens=masked_sequences,
structure_tokens=structure_tokens,
average_plddt=average_plddt,
per_res_plddt=per_res_plddt,
ss8_tokens=ss8_tokens,
sasa_tokens=sasa_tokens,
function_tokens=function_tokens,
residue_annotation_tokens=residue_annotation_tokens,
sequence_id=sequence_id,
bb_coords=coords)["sequence_logits"].detach()
logps = torch.nn.functional.log_softmax(logits/sampling_temperature, dim=-1)
logps = torch.gather(logps, dim=-1, index=unmasked_sequences.unsqueeze(-1)).squeeze(-1)
logps = (logps * masked_indices).sum(-1).detach()
decoded_seqs = [sequence.replace(" ", "") for sequence in sequence_tokenizer.batch_decode(unmasked_sequences[:, 1:-1])]
for seq, logp, masked_seq, unmasked_seq in zip(decoded_seqs, logps, masked_sequences, unmasked_sequences):
if seq in all_unmasked_sequences_decoded or seq in previous_unmasked_sequences_decoded:
continue
else:
all_unmasked_sequences_decoded.append(seq)
all_logps.append(logp)
all_masked_sequences.append(masked_seq)
all_unmasked_sequences.append(unmasked_seq)
all_unmasked_sequences_decoded = all_unmasked_sequences_decoded[:num_samples]
all_masked_sequences = all_masked_sequences[:num_samples]
all_unmasked_sequences = all_unmasked_sequences[:num_samples]
all_logps = all_logps[:num_samples]
all_masked_sequences = torch.stack(all_masked_sequences, dim=0)
all_unmasked_sequences = torch.stack(all_unmasked_sequences, dim=0)
all_logps = torch.stack(all_logps, dim=0)
to_save = {"parent_sequence": parent_sequence,
"all_masked_sequences": all_masked_sequences,
"all_unmasked_sequences": all_unmasked_sequences,
"all_unmasked_sequences_decoded": all_unmasked_sequences_decoded,
"all_logps": all_logps}
torch.save(to_save, f"{save_folder_name}/trpb_{replicate}.pt")
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