pip install --upgrade git+https://github.com/huggingface/transformers.git
!pip install torch einops enformer_pytorch==0.7.6

import torch
from transformers import AutoModel

model = AutoModel.from_pretrained("InstaDeepAI/segment_enformer", trust_remote_code=True)

def encode_sequences(sequences):
    one_hot_map = {
        'a': torch.tensor([1., 0., 0., 0.]),
        'c': torch.tensor([0., 1., 0., 0.]),
        'g': torch.tensor([0., 0., 1., 0.]),
        't': torch.tensor([0., 0., 0., 1.]),
        'n': torch.tensor([0., 0., 0., 0.]),
        'A': torch.tensor([1., 0., 0., 0.]),
        'C': torch.tensor([0., 1., 0., 0.]),
        'G': torch.tensor([0., 0., 1., 0.]),
        'T': torch.tensor([0., 0., 0., 1.]),
        'N': torch.tensor([0., 0., 0., 0.])
    }

    def encode_sequence(seq_str):
        one_hot_list = []
        for char in seq_str:
            one_hot_vector = one_hot_map.get(char, torch.tensor([0.25, 0.25, 0.25, 0.25]))
            one_hot_list.append(one_hot_vector)
        return torch.stack(one_hot_list)

    if isinstance(sequences, list):
        return torch.stack([encode_sequence(seq) for seq in sequences])
    else:
        return encode_sequence(sequences)

sequences = ["A"*196608, "G"*196608]
one_hot_encoding = encode_sequences(sequences)
preds = model(one_hot_encoding)
print(preds['logits'])