` from multiprocessing.sharedctypes import Value
from typing import Any

import numpy as np
import pandas as pd
from clearml import Task
import pickle
from sklearn.linear_model import LinearRegression, RANSACRegressor

Notice Preprocess class Must be named "Preprocess"

class Preprocess(object):
def init(self):
# set internal state, this will be called only once. (i.e. not per request)
self.task = Task.get_task(project_name='serving examples', task_id='bfc1ae4d242b4d5a9955adde1c9e5a58')
if self.task.artifacts.get('seasonality').size is not None:
self.seasonality = eval(self.task.artifacts.get('seasonality').preview)
self.z_score = eval(self.task.artifacts.get('z_score').preview)
else:
self.z_score = None

    self.threshold = eval(self.task.artifacts.get('threshold').preview)
    self.timesteps = eval(self.task.artifacts.get('time_steps').preview)
    self.trend_step = eval(self.task.artifacts.get('trend_step').preview)

    regressor_path = self.task.artifacts['regressor'].get_local_copy()
    file_regressor = open(regressor_path, 'rb')
    self.regressor = pickle.load(file_regressor)
    pass

def z_score_normalization(self, df,seasonality, normal_z_score=None):
    indexer = 0
    if seasonality == 'hourly':
        season = df.groupby(df.index.hour)
    if seasonality == 'daily':
        season = df.groupby(df.index.dayofweek)
    if seasonality == 'weekly':
        season = df.groupby(df.index.isocalendar().week)
        indexer = 1
    if seasonality == 'monthly':
        season = df.groupby(df.index.month)
        indexer = 1

    if normal_z_score is not None:
        for index, time in season:
            index -= indexer
            df.loc[time.index, 'value'] = (time['value'] - normal_z_score[index]['mean'])/normal_z_score[index]['std']
        return df
    else:  
        z_score = []
        for _, time in season:
            mean = np.mean(time['value'])
            std = np.std(time['value'])
            df.loc[time.index, 'value'] = (time['value'] - mean)/std
            z_score.append({'mean': mean, 'std': std})
        return df, z_score

def create_sequences(self, X, y):
    Xs, ys = [], []
    for i in range(len(X)-self.timesteps):
        Xs.append(X.iloc[i:(i+self.timesteps)].values)
        ys.append(y.iloc[i+self.timesteps])
    
    return np.array(Xs), np.array(ys)

def preprocess(self, body: dict, state: dict, collect_custom_statistics_fn=None) -> Any:
    df = pd.DataFrame({'date': body.get("date"), 'value': body.get("value")}, columns=['date','value'])
    
    df['date'] = pd.to_datetime(df['date'], dayfirst = True)
    df = df.set_index('date')

    df = df.resample('1T').mean()
    # detrend regression here
    y_test_value = df['value'].values
    X_test_value = [i + self.trend_step for i in range(0, len(df['value']))]
    X_test_value = np.reshape(X_test_value, (len(X_test_value), 1))
    # X_test_poly = preprocessing.get_polynomial_features(X_test_value)
    trendp = self.regressor.predict(X_test_value)
    detrended = [y_test_value[i]-trendp[i] for i in range(0, len(df['value']))]
    df['value'] = detrended
    self.task.upload_artifact('trend_step', self.trend_step + 1)
            
    if self.z_score is not None:
        df = self.z_score_normalization(df, self.seasonality, self.z_score)

    X, _ = self.create_sequences(df[['value']],  df['value'])
    state['X'] = X
    state['df_test'] = df
    return X

def postprocess(self, data: Any, state: dict, collect_custom_statistics_fn=None) -> dict:
    # post process the data returned from the model inference engine
    # data is the return value from model.predict we will put is inside a return value as Y
    
    X_test = state['X']
    df_test = state['df_test']
    
    test_mae_loss = np.mean(np.abs(data-X_test), axis=1)
    test_score_df = pd.DataFrame(df_test[self.timesteps:])
    test_score_df['loss'] = test_mae_loss
    test_score_df['threshold'] = self.threshold
    test_score_df['anomaly'] = test_score_df['loss'] > test_score_df['threshold']
    test_score_df['Close'] = df_test[self.timesteps:]['value']

    return test_score_df.to_dict() `