input: Write a python function to train a linear regression model on a given dataset and evaluate its performance using RMSE and R2 score. 
  assert linear_regression_evaluation('sales_data.csv') == (expected_rmse, expected_r2)
output: def linear_regression_evaluation(file_path):
      import pandas as pd
      from sklearn.model_selection import train_test_split
      from sklearn.linear_model import LinearRegression
      from sklearn.metrics import mean_squared_error, r2_score
      import numpy as np
  
      # Load the dataset
      data = pd.read_csv(file_path)
  
      # Split the dataset into features and target variable
      X = data.drop(columns=['target'])
      y = data['target']
  
      # Split the data into training and testing sets
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
  
      # Create a linear regression model
      model = LinearRegression()
  
      # Train the model
      model.fit(X_train, y_train)
  
      # Make predictions on the test set
      y_pred = model.predict(X_test)
  
      # Calculate RMSE
      rmse = np.sqrt(mean_squared_error(y_test, y_pred))
  
      # Calculate R2 score
      r2 = r2_score(y_test, y_pred)
  
      return rmse, r2