import numpy as np
from dataclasses import dataclass
from typing import ClassVar
from src.models import KeypadSize


@dataclass
class CustomerCipher:
    property_key: np.ndarray
    position_key: np.ndarray
    keypad_size: KeypadSize
    MAX_KEYS: ClassVar[int] = 256
    MAX_PROP_PER_KEY: ClassVar[int] = 256

    def __post_init__(self):
        if self.keypad_size.is_dispersable:
            raise ValueError("number of keys must be less than the number of "
                             "properties per key to be dispersion resistant")

    @classmethod
    def create(cls, keypad_size: KeypadSize) -> 'CustomerCipher':
        if keypad_size.numb_of_keys > cls.MAX_KEYS or keypad_size.props_per_key > cls.MAX_PROP_PER_KEY:
            raise ValueError(f"Keys and properties per key must not exceed {cls.MAX_KEYS}")
        # Using numpy to generate non-repeating random integers
        prop_key = np.random.choice(2 ** 16, size=keypad_size.total_props, replace=False)
        pos_key = np.random.choice(2 ** 16, size=keypad_size.props_per_key, replace=False)
        return cls(
            property_key=prop_key,
            position_key=pos_key,
            keypad_size=keypad_size,
        )

    def renew(self):
        self.property_key = np.random.choice(2 ** 16, size=self.keypad_size.total_props, replace=False)
        self.position_key = np.random.choice(2 ** 16, size=self.keypad_size.props_per_key, replace=False)

    def get_props_position_vals(self, props: np.ndarray | list[int]) -> np.ndarray:
        if not all([prop in self.property_key for prop in props]):
            raise ValueError("Property values must be within valid range")
        pos_vals = [self._get_prop_position_val(prop) for prop in props]
        return np.array(pos_vals)

    def _get_prop_position_val(self, prop: int) -> int:
        assert prop in self.property_key
        prop_idx = np.where(self.property_key == prop)[0][0]
        pos_idx = prop_idx % self.keypad_size.props_per_key
        return int(self.position_key[pos_idx])

    def get_position_index(self, pos_val: int) -> int:
        if not np.isin(pos_val, self.position_key):
            raise ValueError(f"Position value {pos_val} not found in customer cipher position_key")
        return int(np.where(self.position_key == pos_val)[0][0])

    def get_passcode_position_indices_padded(self, passcode_indices: list[int], max_nkode_len: int) -> list[int]:
        if not all(0 <= idx < self.keypad_size.total_props for idx in passcode_indices):
            raise ValueError("invalid passcode index")
        pos_indices = [idx % self.keypad_size.props_per_key for idx in passcode_indices]
        pad_len = max_nkode_len - len(passcode_indices)
        pad = np.random.choice(self.keypad_size.props_per_key, pad_len, replace=True)
        return pos_indices +  pad.tolist()