import numpy as np
import time
import os, sys

from pathlib import Path

from concrete.ml.deployment import FHEModelClient

import requests


def to_json(python_object):
    if isinstance(python_object, bytes):
        return {"__class__": "bytes", "__value__": list(python_object)}
    raise TypeError(repr(python_object) + " is not JSON serializable")


def from_json(python_object):
    if "__class__" in python_object:
        return bytes(python_object["__value__"])


API_URL = "https://zkmlo9jbfzj9ep1j.eu-west-1.aws.endpoints.huggingface.cloud"
headers = {
    "Authorization": "Bearer " + os.environ.get("HF_TOKEN"),
    "Content-Type": "application/json",
}


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

    if "error" in response:
        assert False, f"Got an error: {response=}"

    return response.json()


path_to_model = Path("compiled_model")

# Iris and NeuralNetClassifier in FHE
from sklearn.model_selection import train_test_split
from sklearn.datasets import load_iris

X, y = load_iris(return_X_y=True)
_, X_test, _, Y_test = train_test_split(X, y, test_size=0.25, random_state=42)
X_test = X_test.astype("float32")

# 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()

# Save the key in the database
evaluation_keys_remaining = evaluation_keys[:]
uid = None
is_first = True
is_finished = False
i = 0
packet_size = 1024 * 1024 * 100

while not is_finished:

    # Send by packets of 100M
    if sys.getsizeof(evaluation_keys_remaining) > packet_size:
        evaluation_keys_piece = evaluation_keys_remaining[:packet_size]
        evaluation_keys_remaining = evaluation_keys_remaining[packet_size:]
    else:
        evaluation_keys_piece = evaluation_keys_remaining
        is_finished = True

    print(
        f"Sending {i}-th piece of the key (remaining size is {sys.getsizeof(evaluation_keys_remaining) / 1024:.2f} kbytes)"
    )
    i += 1

    if is_first:
        is_first = False
        payload = {
            "inputs": "fake",
            "evaluation_keys": to_json(evaluation_keys_piece),
            "method": "save_key",
        }

        uid = query(payload)["uid"]
        print(f"Storing the key in the database under {uid=}")

    else:
        payload = {
            "inputs": "fake",
            "evaluation_keys": to_json(evaluation_keys_piece),
            "method": "append_key",
            "uid": uid,
        }

        query(payload)

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

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
    payload = {
        "inputs": "fake",
        "encrypted_inputs": to_json(encrypted_inputs),
        "method": "inference",
        "uid": uid,
    }

    if is_first:
        print(f"Size of the payload: {sys.getsizeof(payload) / 1024:.2f} kilobytes")
        is_first = False

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

    encrypted_prediction = from_json(encrypted_prediction)

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

    if verbose:
        print(
            f"for {i}-th input, {prediction=} with expected {Y_test[i]} in {duration_inference:.3f} seconds"
        )

    # 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:.3f} seconds")
print(f"Duration per inference: {duration / nb_samples:.3f} seconds")