import numpy as np

### ex1
def update_parameters_with_gd_test_case():
    np.random.seed(1)
    learning_rate = 0.01
    W1 = np.random.randn(2,3)
    b1 = np.random.randn(2,1)
    W2 = np.random.randn(3,2)
    b2 = np.random.randn(3,1)

    dW1 = np.random.randn(2,3)
    db1 = np.random.randn(2,1)
    dW2 = np.random.randn(3,2)
    db2 = np.random.randn(3,1)
    
    parameters = {"W1": W1, "b1": b1, "W2": W2, "b2": b2}
    grads = {"dW1": dW1, "db1": db1, "dW2": dW2, "db2": db2}
    
    return parameters, grads, learning_rate

"""
def update_parameters_with_sgd_checker(function, inputs, outputs):
    if function(inputs) == outputs:
        print("Correct")
    else:
        print("Incorrect")
"""


### ex 2
def random_mini_batches_test_case():
    np.random.seed(1)
    mini_batch_size = 64
    X = np.random.randn(12288, 148)
    Y = np.random.randn(1, 148) < 0.5
    return X, Y, mini_batch_size


### ex 3
def initialize_velocity_test_case():
    np.random.seed(1)
    W1 = np.random.randn(3,2)
    b1 = np.random.randn(3,1)
    W2 = np.random.randn(3,3)
    b2 = np.random.randn(3,1)
    parameters = {"W1": W1, "b1": b1, "W2": W2, "b2": b2}
    return parameters


### ex 4
def update_parameters_with_momentum_test_case():
    np.random.seed(1)
    W1 = np.random.randn(2,3)
    b1 = np.random.randn(2,1)
    W2 = np.random.randn(3,2)
    b2 = np.random.randn(3,1)

    dW1 = np.random.randn(2,3)
    db1 = np.random.randn(2,1)
    dW2 = np.random.randn(3,2)
    db2 = np.random.randn(3,1)
   
    parameters = {"W1": W1, "b1": b1, "W2": W2, "b2": b2}
    grads = {"dW1": dW1, "db1": db1, "dW2": dW2, "db2": db2}
    
    v = {'dW1': np.array([[ 0.,  0.,  0.],
                          [ 0.,  0.,  0.]]), 
         'dW2': np.array([[ 0.,  0.],
                          [ 0.,  0.],
                          [ 0.,  0.]]), 
         'db1': np.array([[ 0.],
                          [ 0.]]), 
         'db2': np.array([[ 0.],
                          [ 0.],
                          [ 0.]])}
    
    return parameters, grads, v


### ex 5
def initialize_adam_test_case():
    np.random.seed(1)
    W1 = np.random.randn(2,3)
    b1 = np.random.randn(2,1)
    W2 = np.random.randn(3,2)
    b2 = np.random.randn(3,1)
    parameters = {"W1": W1, "b1": b1, "W2": W2, "b2": b2}
    return parameters


### ex 6
def update_parameters_with_adam_test_case():
    np.random.seed(1)
    v, s = ({'dW1': np.array([[ 0.,  0.,  0.], # (2, 3)
                              [ 0.,  0.,  0.]]), 
             'dW2': np.array([[ 0.,  0.],      # (3, 2)
                              [ 0.,  0.],
                              [ 0.,  0.]]), 
             'db1': np.array([[ 0.],           # (2, 1)
                              [ 0.]]), 
             'db2': np.array([[ 0.],          # (3, 1)
                              [ 0.],
                              [ 0.]])}, 
            {'dW1': np.array([[ 0.,  0.,  0.], # (2, 3)
                              [ 0.,  0.,  0.]]), 
             'dW2': np.array([[ 0.,  0.],      # (3, 2)
                              [ 0.,  0.],
                              [ 0.,  0.]]), 
             'db1': np.array([[ 0.],           # (2, 1)
                              [ 0.]]), 
             'db2': np.array([[ 0.],           # (3, 1)
                              [ 0.],
                              [ 0.]])})
    W1 = np.random.randn(2,3)
    b1 = np.random.randn(2,1)
    W2 = np.random.randn(3,2)
    b2 = np.random.randn(3,1)

    dW1 = np.random.randn(2,3)
    db1 = np.random.randn(2,1)
    dW2 = np.random.randn(3,2)
    db2 = np.random.randn(3,1)
    
    parameters = {"W1": W1, "b1": b1, "W2": W2, "b2": b2}
    grads = {"dW1": dW1, "db1": db1, "dW2": dW2, "db2": db2}
    
    t = 2
    learning_rate = 0.02
    beta1 = 0.8
    beta2 = 0.888
    epsilon = 1e-2
    
    return parameters, grads, v, s, t, learning_rate, beta1, beta2, epsilon