remove unneeded functions
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@@ -26,7 +26,7 @@ class CustomerCipher:
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raise ValueError(f"Keys and properties per key must not exceed {cls.MAX_KEYS}")
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# Using numpy to generate non-repeating random integers
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prop_key = np.random.choice(2 ** 16, size=keypad_size.numb_of_props, replace=False)
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prop_key = np.random.choice(2 ** 16, size=keypad_size.total_props, replace=False)
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set_key = np.random.choice(2 ** 16, size=keypad_size.props_per_key, replace=False)
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return cls(
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@@ -36,7 +36,7 @@ class CustomerCipher:
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)
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def renew(self):
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self.prop_key = np.random.choice(2 ** 16, size=self.keypad_size.numb_of_props, replace=False)
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self.prop_key = np.random.choice(2 ** 16, size=self.keypad_size.total_props, replace=False)
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self.set_key = np.random.choice(2 ** 16, size=self.keypad_size.props_per_key, replace=False)
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def get_prop_set_val(self, prop: int) -> int:
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@@ -23,7 +23,7 @@ class KeypadSize:
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numb_of_keys: int
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@property
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def numb_of_props(self) -> int:
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def total_props(self) -> int:
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return self.props_per_key * self.numb_of_keys
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@property
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@@ -23,13 +23,13 @@ class UserCipher:
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raise ValueError("Invalid set values")
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set_values_array = np.array(set_values, dtype=np.uint16)
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set_key = generate_random_nonrepeating_array(keypad_size.props_per_key)
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set_key = np.random.choice(2**16,size=keypad_size.props_per_key, replace=False)
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set_key = np.bitwise_xor(set_key, set_values_array)
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return UserCipher(
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prop_key=generate_random_nonrepeating_array(keypad_size.props_per_key * keypad_size.numb_of_keys),
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pass_key=generate_random_nonrepeating_array(max_nkode_len),
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mask_key=generate_random_nonrepeating_array(max_nkode_len),
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prop_key=np.random.choice(2 ** 16, size=keypad_size.total_props, replace=False),
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pass_key=np.random.choice(2 ** 16, size=max_nkode_len, replace=False),
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mask_key=np.random.choice(2**16, size=max_nkode_len, replace=False),
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set_key=set_key,
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salt=bcrypt.gensalt(),
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max_nkode_len=max_nkode_len
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@@ -63,7 +63,7 @@ class UserCipher:
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return np.array(int_list, dtype=np.uint16)
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def _hash_passcode(self, passcode: np.ndarray) -> str:
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passcode_bytes = int_array_to_bytes(passcode)
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passcode_bytes = passcode.astype(np.uint16).tobytes()
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passcode_digest = base64.b64encode(hashlib.sha256(passcode_bytes).digest())
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hashed_data = bcrypt.hashpw(passcode_digest, self.salt)
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return hashed_data.decode("utf-8")
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@@ -88,20 +88,20 @@ class UserCipher:
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passcode_prop_idx: list[int],
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customer_prop: CustomerCipher,
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) -> str:
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passcode_prop_idx_array = np.array(passcode_prop_idx, dtype=np.uint16)
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passcode_len = len(passcode_prop_idx_array)
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passcode_props = np.array([customer_prop.prop_key[idx] for idx in passcode_prop_idx_array], dtype=np.uint16)
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passcode_len = len(passcode_prop_idx)
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passcode_cipher = self.pass_key.copy()
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for idx in range(passcode_len):
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prop_idx = passcode_prop_idx_array[idx]
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passcode_cipher[idx] = passcode_cipher[idx] ^ self.prop_key[prop_idx] ^ passcode_props[idx]
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passcode_cipher[:passcode_len] = (
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passcode_cipher[:passcode_len] ^
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self.prop_key[passcode_prop_idx] ^
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customer_prop.prop_key[passcode_prop_idx]
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)
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return self._hash_passcode(passcode_cipher)
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def encipher_mask(
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self,
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passcode_sets: list[int],
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passcode_sets: np.ndarray,
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customer_properites: CustomerCipher
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) -> str:
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padded_passcode_sets = self.pad_user_mask(passcode_sets, customer_properites.set_key)
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@@ -131,21 +131,7 @@ class UserCipher:
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set_idx = np.where(set_key_rand_component == set_cipher)[0][0]
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passcode_sets.append(set_vals[set_idx])
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return passcode_sets
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# NumPy utility functions to replace the existing ones
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def generate_random_nonrepeating_array(array_len: int, min_val: int = 0, max_val: int = 2 ** 16) -> np.ndarray:
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if max_val - min_val < array_len:
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raise ValueError("Range of values is less than the array length requested")
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# Generate array of random unique integers
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return np.random.choice(
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np.arange(min_val, max_val, dtype=np.uint16),
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size=array_len,
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replace=False
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)
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return np.array(passcode_sets)
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def int_array_to_bytes(int_arr: np.ndarray, byte_size: int = 2) -> bytes:
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return b"".join([int(num).to_bytes(byte_size, byteorder='big') for num in int_arr])
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@@ -11,7 +11,7 @@ class UserKeypad:
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@classmethod
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def create(cls, keypad_size: KeypadSize) -> 'UserKeypad':
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keypad = UserKeypad(
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keypad=np.arange(keypad_size.numb_of_props),
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keypad=np.arange(keypad_size.total_props),
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keypad_size=keypad_size
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)
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keypad.random_keypad_shuffle()
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