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import numpy as np
import matplotlib.pyplot as plt
import time
from qiskit import QuantumCircuit
from qiskit_aer import AerSimulator
from qiskit.quantum_info import Operator

# Define basic quantum gates
def get_X():
    return np.array([[0, 1], [1, 0]])

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# Credit : gpt-4o , Claude-3.5-Sonnet-200k , Gemini-Pro-1.5

# Reference :
# [Protein Discovery with Discrete Walk-Jump Sampling](http://arxiv.org/abs/2306.12360)
# [Diffusion Forcing: Next-token Prediction Meets Full-Sequence Diffusion](http://arxiv.org/abs/2407.01392)


import torch
import torch.nn as nn
import torch.nn.functional as F

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import ceviche
import matplotlib.pyplot as plt
import pandas as pd

from skimage.draw import disk as circle
from autograd.scipy.signal import convolve as conv

from scipy.optimize import minimize
from autograd import grad
import autograd.numpy as anp

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# [Nash Learning from Human Feedback](http://arxiv.org/abs/2312.00886)


import os
import math
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader, Dataset

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# Reference: [Half-Quadratic Quantization of Large Machine Learning Models](https://mobiusml.github.io/hqq_blog/)


import numpy as np

# Define the shrinkage function for soft-thresholding
def shrink(x, beta, p):
    return np.sign(x) * np.maximum(np.abs(x) - (np.abs(x)**(p-1))/beta, 0)

# Define the quantization and dequantization operators

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# [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://arxiv.org/abs/2312.00752)

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader, Dataset
from torch.nn import functional as F
from einops import rearrange, repeat
from tqdm import tqdm

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import torch
from torch import nn
import torch.nn.functional as F
import torchvision
import torchvision.transforms as transforms
from tqdm import tqdm

# Custom fast linear layer
class FastLinear(nn.Module):
    def __init__(self, in_features, out_features):

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sequence_id,sequence,experiment_type,dataset_name,reads,signal_to_noise,SN_filter,reactivity_0001,reactivity_0002,reactivity_0003,reactivity_0004,reactivity_0005,reactivity_0006,reactivity_0007,reactivity_0008,reactivity_0009,reactivity_0010,reactivity_0011,reactivity_0012,reactivity_0013,reactivity_0014,reactivity_0015,reactivity_0016,reactivity_0017,reactivity_0018,reactivity_0019,reactivity_0020,reactivity_0021,reactivity_0022,reactivity_0023,reactivity_0024,reactivity_0025,reactivity_0026,reactivity_0027,reactivity_0028,reactivity_0029,reactivity_0030,reactivity_0031,reactivity_0032,reactivity_0033,reactivity_0034,reactivity_0035,reactivity_0036,reactivity_0037,reactivity_0038,reactivity_0039,reactivity_0040,reactivity_0041,reactivity_0042,reactivity_0043,reactivity_0044,reactivity_0045,reactivity_0046,reactivity_0047,reactivity_0048,reactivity_0049,reactivity_0050,reactivity_0051,reactivity_0052,reactivity_0053,reactivity_0054,reactivity_0055,reactivity_0056,reactivity_0057,reactivity_0058,reactivity_005

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