import numpy as np
import time
import os, sys

from pathlib import Path

from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split

from concrete.ml.deployment import FHEModelClient

import requests


API_URL = "https://puqif7goarh132kl.us-east-1.aws.endpoints.huggingface.cloud"
headers = {
    "Authorization": "Bearer " + os.environ.get("HF_TOKEN"),
    "Content-Type": "application/octet-stream",
}


def query(payload):
    response = requests.post(API_URL, headers=headers, data=payload)
    return response.json()


path_to_model = Path("compiled_model")
x, y = make_classification(n_samples=1000, class_sep=2, n_features=30, random_state=42)
_, X_test, _, y_test = train_test_split(x, y, test_size=0.2, random_state=42)

# Recover parameters for client side
fhemodel_client = FHEModelClient(path_to_model)

# Generate the keys
fhemodel_client.generate_private_and_evaluation_keys()
evaluation_keys = fhemodel_client.get_serialized_evaluation_keys()

# Test the handler
nb_good = 0
nb_samples = len(X_test)
verbose = False
time_start = time.time()
duration = 0

for i in range(nb_samples):

    # Quantize the input and encrypt it
    encrypted_inputs = fhemodel_client.quantize_encrypt_serialize([X_test[i]])

    # Prepare the payload, including the evaluation keys which are needed server side
    payload = {
        "inputs": "fake",
        "encrypted_inputs": encrypted_inputs,
        "evaluation_keys": evaluation_keys,
    }

    # Run the inference on HF servers
    duration -= time.time()
    encrypted_prediction = query(payload)
    duration += time.time()

    encrypted_prediction = encrypted_prediction

    # Decrypt the result and dequantize
    prediction_proba = fhemodel_client.deserialize_decrypt_dequantize(encrypted_prediction)[0]
    prediction = np.argmax(prediction_proba)

    if verbose or True:
        print(f"for {i}-th input, {prediction=} with expected {y_test[i]}")

    # Measure accuracy
    nb_good += y_test[i] == prediction

print(f"Accuracy on {nb_samples} samples is {nb_good * 1. / nb_samples}")
print(f"Total time: {time.time() - time_start} seconds")
print(f"Duration in inferences: {duration} seconds")
print(f"Duration per inference: {duration / nb_samples} seconds")