"""
Feature Extraction Methods for DimSense
"""

import numpy as np
import tensorflow as tf
from sklearn.decomposition import PCA, FastICA
from sklearn.manifold import TSNE
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.decomposition import LatentDirichletAllocation
from sklearn.base import BaseEstimator, TransformerMixin


class AutoencoderExtractor(BaseEstimator, TransformerMixin):
    """
    AutoencoderExtractor provides feature extraction using autoencoders.
    """
    def __init__(self, encoding_dim=10):
        """
        Initialize the AutoencoderExtractor.

        Parameters:
        - encoding_dim (int): Dimension of the encoded representation.
        """
        self.tf = None 
        self.encoding_dim = encoding_dim

    def _import_tensorflow(self):
        try:
            import tensorflow as tf
            self.tf = tf
        except ImportError:
            raise ImportError("TensorFlow is required for using AutoencoderExtractor.")      

    def build_autoencoder(self):
        if self.tf is not None:
            input_layer = self.tf.keras.layers.Input(shape=(self.input_dim,))
            encoded = tf.keras.layers.Dense(self.encoding_dim, activation='relu')(input_layer)
            decoded = tf.keras.layers.Dense(self.input_dim, activation='sigmoid')(encoded)
            autoencoder = tf.keras.models.Model(input_layer, decoded)
            autoencoder.compile(optimizer='adam', loss='mean_squared_error')
            return autoencoder
        else: return None

    def fit_transform(self, X):
        """
        Fit the autoencoder model and transform the data.

        Parameters:
        - X (array-like): Input data.

        Returns:
        - X_extracted (array-like): Extracted features.
        """
# >>> THIS NEEDS TO BE GENERATED >>>>
        if self.tf is None:
            self._import_tensorflow()
        self.input_dim = X.shape[1]
        self.autoencoder = self.build_autoencoder()
        self.autoencoder.fit(X, X, epochs=50, batch_size=32, shuffle=True, verbose=0)
        encoder = tf.keras.models.Model(inputs=self.autoencoder.input, outputs=self.autoencoder.layers[1].output)
        X_extracted = encoder.predict(X)
        return X_extracted
# <<<< <<<<

    def set_encoding_dim(self, encoding_dim):
        """
        Set the dimension of the encoded representation.

        Parameters:
        - encoding_dim (int): Dimension of the encoded representation.
        """
        self.encoding_dim = encoding_dim
        self.autoencoder = self.build_autoencoder()

    ...

import click
from geekbot_cli.api_client import APIClient
from geekbot_cli.config_manager import ConfigManager
from geekbot_cli.cli import CLI
import sys

@click.command()
@click.option('--clear-api-key', is_flag=True, help='Removes the saved API key from keyring')
def main(clear_api_key):
    """
    Entry point for the CLI that can now handle `--clear-api-key` to remove the saved API key.
    """
    config_manager = ConfigManager()
    if clear_api_key:
        if click.confirm('Are you sure you want to remove the API key?'):
# >>> THIS NEEDS TO BE GENERATED >>>>
            config_manager.delete_api_key()
            click.echo("API key has been removed.")
# <<<< <<<<
        else:
            click.echo("Operation cancelled.")
    else:
        # Normal CLI operation
        try:
            api_client = APIClient()
            cli = CLI(api_client, config_manager)
            cli.start()
        except Exception as e:
            click.echo(f"Error: {e}")
            sys.exit(1)

if __name__ == '__main__':
    main()