# Import necessary libraries
from autogonai.development.base import BaseBlock, gen_params


# Define class ArtificialNeuralNetworkInit
class ArtificialNeuralNetworkInit(BaseBlock):
    def set_params(self, layer_list: list = None):
        """
        Sets parameters for ArtificialNeuralNetworkInit class.

        Args:
            layer_list (list, optional): List of layers for the neural network. Defaults to None.

        Returns:
            dict: Parameters dictionary.
        """
        self.params = gen_params(locals())
        return self.params


# Define class ArtificialNeuralNetworkTrain
class ArtificialNeuralNetworkTrain(BaseBlock):
    def set_params(
        self, model_name: str = None, add_dim: bool = None, hyp_params: dict = None
    ):
        """
        Sets parameters for ArtificialNeuralNetworkTrain class.

        Args:
            model_name (str, optional): Name of the neural network model. Defaults to None.
            add_dim (bool, optional): Flag for adding dimensions. Defaults to None.
            hyp_params (dict, optional): Hyperparameters for training. Defaults to None.

        Returns:
            dict: Parameters dictionary.
        """
        self.params = gen_params(locals())
        return self.params


# Define class ArtificialNeuralNetworkEvaluate
class ArtificialNeuralNetworkEvaluate(BaseBlock):
    def set_params(self, hyp_params: dict = None):
        """
        Sets parameters for ArtificialNeuralNetworkEvaluate class.

        Args:
            hyp_params (dict, optional): Hyperparameters for evaluation. Defaults to None.

        Returns:
            dict: Parameters dictionary.
        """
        self.params = gen_params(locals())
        return self.params


# Define class ArtificialNeuralNetworkPredict
class ArtificialNeuralNetworkPredict(BaseBlock):
    def set_params(self, test_data: str = None, hyp_params: dict = None):
        """
        Sets parameters for ArtificialNeuralNetworkPredict class.

        Args:
            test_data (str, optional): Test data for prediction. Defaults to None.
            hyp_params (dict, optional): Hyperparameters for prediction. Defaults to None.

        Returns:
            dict: Parameters dictionary.
        """
        self.params = gen_params(locals())
        return self.params


# Define class SelfOrganizingMapsInit
class SelfOrganizingMapsInit(BaseBlock):
    def set_params(self, hyp_params: dict = None):
        """
        Sets parameters for SelfOrganizingMapsInit class.

        Args:
            hyp_params (dict, optional): Hyperparameters for SOM initialization. Defaults to None.

        Returns:
            dict: Parameters dictionary.
        """
        self.params = gen_params(locals())
        return self.params


# Define class SelfOrganizingMapsTrain
class SelfOrganizingMapsTrain(BaseBlock):
    def set_params(self, model_name: str = None, hyp_params: dict = None):
        """
        Sets parameters for SelfOrganizingMapsTrain class.

        Args:
            model_name (str, optional): Name of the SOM model. Defaults to None.
            hyp_params (dict, optional): Hyperparameters for SOM training. Defaults to None.

        Returns:
            dict: Parameters dictionary.
        """
        self.params = gen_params(locals())
        return self.params


# Define class SelfOrganizingMapsPredict
class SelfOrganizingMapsPredict(BaseBlock):
    def set_params(self, test_data: str = None, row: int = None):
        """
        Sets parameters for SelfOrganizingMapsPredict class.

        Args:
            test_data (str, optional): Test data for prediction. Defaults to None.
            row (int, optional): Row for SOM prediction. Defaults to None.

        Returns:
            dict: Parameters dictionary.
        """
        self.params = gen_params(locals())
        return self.params


# Define class RestrictedBoltzmannMachineInit
class RestrictedBoltzmannMachineInit(BaseBlock):
    def set_params(self, hyp_params: dict = None):
        """
        Sets parameters for RestrictedBoltzmannMachineInit class.

        Args:
            hyp_params (dict, optional): Hyperparameters for RBM initialization. Defaults to None.

        Returns:
            dict: Parameters dictionary.
        """
        self.params = gen_params(locals())
        return self.params


# Define class RestrictedBoltzmannMachineTrain
class RestrictedBoltzmannMachineTrain(BaseBlock):
    def set_params(self, model_name: str = None, hyp_params: dict = None):
        """
        Sets parameters for RestrictedBoltzmannMachineTrain class.

        Args:
            model_name (str, optional): Name of the RBM model. Defaults to None.
            hyp_params (dict, optional): Hyperparameters for RBM training. Defaults to None.

        Returns:
            dict: Parameters dictionary.
        """
        self.params = gen_params(locals())
        return self.params


# Define class RestrictedBoltzmannMachineTransform
class RestrictedBoltzmannMachineTransform(BaseBlock):
    def set_params(self, test_data: str = None):
        """
        Sets parameters for RestrictedBoltzmannMachineTransform class.

        Args:
            test_data (str, optional): Test data for transformation. Defaults to None.

        Returns:
            dict: Parameters dictionary.
        """
        self.params = gen_params(locals())
        return self.params
