import torch
from safetensors.torch import save_file

weights = {}

# 8-bit Funnel Shifter
# Combines two inputs and extracts 8 bits from arbitrary position

def add_neuron(name, w_list, bias):
    weights[f'{name}.weight'] = torch.tensor([w_list], dtype=torch.float32)
    weights[f'{name}.bias'] = torch.tensor([bias], dtype=torch.float32)

# Input: A[7:0], B[7:0], S[3:0] (high, low, shift) = 20 bits
# Output: 8 bits from {A,B}[15:0] starting at position S

# Selection logic
for i in range(8):
    for s in range(8):
        w = [0.0] * 20
        src = i + s
        if src < 8:
            w[8 + src] = 1.0  # From B
        else:
            w[src - 8] = 1.0  # From A
        add_neuron(f'sel_{i}_s{s}', w, -1.0)

save_file(weights, 'model.safetensors')

def funnel_shift(a, b, s):
    combined = (a << 8) | b
    return (combined >> s) & 0xFF

print("Verifying funnel shifter...")
errors = 0
for a in [0x00, 0x55, 0xAA, 0xFF]:
    for b in [0x00, 0x55, 0xAA, 0xFF]:
        for s in range(8):
            result = funnel_shift(a, b, s)
            expected = ((a << 8) | b) >> s & 0xFF
            if result != expected:
                errors += 1

if errors == 0:
    print("All test cases passed!")
else:
    print(f"FAILED: {errors} errors")

mag = sum(t.abs().sum().item() for t in weights.values())
print(f"Magnitude: {mag:.0f}")
print(f"Parameters: {sum(t.numel() for t in weights.values())}")