import math

CONV_KERNELS = [
    # Filter 0
    [
        [-0.18, 0.83, 0.09],
        [0.57, 0.28, -0.1],
        [0.62, 0.26, -0.9],
    ],
    # Filter 1
    [
        [0.27, 1.04, 1.0],
        [0.16, 0.38, 0.51],
        [-2.1, -1.44, -1.54],
    ],
    # Filter 2
    [
        [0.39, 0.56, -0.14],
        [0.15, 0.7, 0.54],
        [-0.09, -0.06, 0.39],
    ],
    # Filter 3
    [
        [-0.52, 0.65, 0.72],
        [-1.5, -0.15, 0.66],
        [-0.87, 0.1, 0.38],
    ],
    # Filter 4
    [
        [1.51, -1.3, -1.35],
        [-1.39, -1.81, -0.45],
        [-1.0, -0.61, 1.36],
    ],
]
CONV_BIAS = [-0.1, -0.03, -0.16, 0.05, 0.33]
DENSE_WEIGHTS = 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DENSE_BIAS = [0.02, 0.2, 0.01, -0.08, -0.04, 0.13, -0.03, 0.11, -0.23, -0.06]


class Conv2D:
    """Convolutional 2D Layer"""
    
    def __init__(self, num_filters, kernel_size, padding='same'):
        self.num_filters = num_filters
        self.kernel_size = kernel_size
        self.padding = padding
        self.kernels = None
        self.bias = None
    
    def set_weights(self, kernels, bias):
        """Set weights directly"""
        self.kernels = kernels
        self.bias = bias
    
    def apply_padding(self, image):
        """Apply padding to image"""
        if self.padding == 'same':
            pad = self.kernel_size // 2
            height = len(image)
            width = len(image[0])
            
            # Create padded image
            padded = [[0.0 for _ in range(width + 2*pad)] for _ in range(height + 2*pad)]
            
            # Copy original image to center
            for i in range(height):
                for j in range(width):
                    padded[i + pad][j + pad] = image[i][j]
            
            return padded
        else:
            return image
    
    def forward(self, image):
        padded_image = self.apply_padding(image)
        
        height = len(padded_image)
        width = len(padded_image[0])
        
        if self.padding == 'same':
            out_height = len(image)
            out_width = len(image[0])
        else:
            out_height = height - self.kernel_size + 1
            out_width = width - self.kernel_size + 1
        
        output = []
        for f in range(self.num_filters):
            feature_map = []
            for i in range(out_height):
                row = []
                for j in range(out_width):
                    sum_val = self.bias[f]
                    for ki in range(self.kernel_size):
                        for kj in range(self.kernel_size):
                            sum_val += padded_image[i + ki][j + kj] * self.kernels[f][ki][kj]
                    
                    row.append(max(0, sum_val))
                feature_map.append(row)
            output.append(feature_map)
        
        return output

class MaxPooling2D:
    def __init__(self, pool_size=2):
        self.pool_size = pool_size
    
    def forward(self, input_maps):
        output = []
        
        for feature_map in input_maps:
            height = len(feature_map)
            width = len(feature_map[0])
            
            out_height = height // self.pool_size
            out_width = width // self.pool_size
            
            pooled = []
            for i in range(out_height):
                row = []
                for j in range(out_width):
                    max_val = feature_map[i * self.pool_size][j * self.pool_size]
                    for pi in range(self.pool_size):
                        for pj in range(self.pool_size):
                            y = i * self.pool_size + pi
                            x = j * self.pool_size + pj
                            if y < height and x < width:
                                max_val = max(max_val, feature_map[y][x])
                    row.append(max_val)
                pooled.append(row)
            output.append(pooled)
        
        return output

class Dense:
    def __init__(self, output_size):
        self.output_size = output_size
        self.weights = None
        self.bias = None
    
    def set_weights(self, weights, bias):
        self.weights = weights
        self.bias = bias
    
    def forward(self, input_vector):
        output = []
        
        for j in range(self.output_size):
            sum_val = self.bias[j]
            for i in range(len(input_vector)):
                sum_val += input_vector[i] * self.weights[i][j]
            output.append(sum_val)
        
        return output

class SimpleCNN:
    def __init__(self):
        self.conv = Conv2D(num_filters=5, kernel_size=3, padding='same')
        self.maxpool = MaxPooling2D(pool_size=2)
        self.dense = Dense(output_size=10)
        
        self.conv.set_weights(CONV_KERNELS, CONV_BIAS)
        self.dense.set_weights(DENSE_WEIGHTS, DENSE_BIAS)
    
    def flatten(self, feature_maps):
        flat = []
        h = len(feature_maps[0])
        w = len(feature_maps[0][0])
        for i in range(h):
            for j in range(w):
                for f in range(len(feature_maps)):
                    flat.append(feature_maps[f][i][j])
        return flat
    
    def softmax(self, logits):
        max_val = max(logits)
        exp_vals = []
        for val in logits:
            exp_vals.append(math.exp(val - max_val))
        
        sum_exp = sum(exp_vals)
        probabilities = []
        for exp_val in exp_vals:
            probabilities.append(exp_val / sum_exp)
        
        return probabilities
    
    def forward(self, image):
        conv_output = self.conv.forward(image)
        pool_output = self.maxpool.forward(conv_output)
        flat = self.flatten(pool_output)
        logits = self.dense.forward(flat)
        probabilities = self.softmax(logits)
        
        return probabilities
    
    def predict(self, image):
        probabilities = self.forward(image)
        
        max_prob = probabilities[0]
        predicted_class = 0
        
        for i in range(1, len(probabilities)):
            if probabilities[i] > max_prob:
                max_prob = probabilities[i]
                predicted_class = i
        
        return predicted_class, max_prob, probabilities

def get_image_from_user():
    image = []
    
    for _ in range(40):
        while True:
            line = input().strip()
            
            values = line.split()
            
            if len(values) != 40:
                continue
            
            valid = True
            row = []
            for val in values:
                if val not in ['0', '1']:
                    valid = False
                    break
                row.append(float(val))
            
            if valid:
                image.append(row)
                break
    
    return image

def resize_image_bilinear(image, new_size):
    old_size = len(image)
    scale = (old_size - 1) / (new_size - 1) if new_size > 1 else 0
    
    resized = []
    for i in range(new_size):
        row = []
        for j in range(new_size):
            y = i * scale
            x = j * scale
            
            y0 = int(y)
            x0 = int(x)
            y1 = min(y0 + 1, old_size - 1)
            x1 = min(x0 + 1, old_size - 1)
            
            dy = y - y0
            dx = x - x0
            
            val = (1 - dx) * (1 - dy) * image[y0][x0] + \
                  dx * (1 - dy) * image[y0][x1] + \
                  (1 - dx) * dy * image[y1][x0] + \
                  dx * dy * image[y1][x1]
            
            row.append(1.0 if val > 0.5 else 0.0)
        resized.append(row)
    
    return resized

def main():
    model = SimpleCNN()
    image = get_image_from_user()
    image = resize_image_bilinear(image, 28)
    predicted_class, _, _ = model.predict(image)
    
    print(predicted_class)

if __name__ == "__main__":
    main()