import csv
import numpy as np
from typing import Optional

def load_vector_from_csv(file_path: str) -> np.ndarray:
    """
    Loads a single-column vector from a CSV file.

    Args:
        file_path: The path to the CSV file.

    Returns:
        A NumPy array containing the vector data.

    Raises:
        FileNotFoundError: If the specified file does not exist.
        ValueError: If the data in the file cannot be converted to float
                    or if the file is empty.
        Exception: For other potential I/O errors.
    """
    data = []
    try:
        with open(file_path, 'r', newline='') as file:
            reader = csv.reader(file)
            for row in reader:
                if not row:  # Skip empty rows
                    continue
                data.append(float(row[0]))
    except FileNotFoundError:
        # Re-raise the exception to be caught by the caller
        raise
    except (ValueError, IndexError) as e:
        # Catch conversion errors (ValueError) or empty rows causing IndexError
        raise ValueError(f"Error converting data to float in {file_path}: {e}")
    except Exception as e:
        # Catch other unforeseen errors during file processing
        raise IOError(f"An unexpected error occurred while reading {file_path}: {e}")

    if not data:
        raise ValueError(f"No data found in file: {file_path}")

    return np.array(data)


def load_matrix_from_csv(file_path: str) -> np.ndarray:
    """
    Loads a matrix from a custom CSV format.

    The expected format is:
    - Row 1: "rows",<number_of_rows>
    - Row 2: "cols",<number_of_cols>
    - Subsequent rows: Matrix data, comma-separated.

    Args:
        file_path: The path to the CSV file.

    Returns:
        A NumPy array containing the matrix data.

    Raises:
        FileNotFoundError: If the specified file does not exist.
        ValueError: If the file format is incorrect, dimensions are invalid,
                    or data cannot be converted to float.
        Exception: For other potential I/O errors.
    """
    try:
        with open(file_path, 'r', newline='') as file:
            reader = csv.reader(file)
            
            # Read and validate header for rows
            header_rows = next(reader)
            if not (len(header_rows) == 2 and header_rows[0].strip().lower() == 'rows'):
                raise ValueError("Invalid header format: Expected 'rows, <value>'")
            num_rows = int(header_rows[1])

            # Read and validate header for columns
            header_cols = next(reader)
            if not (len(header_cols) == 2 and header_cols[0].strip().lower() == 'cols'):
                raise ValueError("Invalid header format: Expected 'cols, <value>'")
            num_cols = int(header_cols[1])

            if num_rows <= 0 or num_cols <= 0:
                raise ValueError("Matrix dimensions must be positive.")

            # Read matrix data
            data = []
            for i, row in enumerate(reader):
                if i >= num_rows:
                    # Optional: Add a warning here later with the logger
                    break 
                
                # Convert each item in the row to float
                float_row = [float(x) for x in row]
                
                if len(float_row) != num_cols:
                    raise ValueError(f"Row {i} has incorrect number of columns. "
                                     f"Expected {num_cols}, found {len(float_row)}.")
                data.append(float_row)

            if len(data) != num_rows:
                raise ValueError(f"Incorrect number of data rows. "
                                 f"Expected {num_rows}, found {len(data)}.")
            
            return np.array(data)
            
    except FileNotFoundError:
        raise
    except (ValueError, IndexError, StopIteration) as e:
        # StopIteration for empty/short files, ValueError for conversion/format,
        # IndexError for malformed rows.
        raise ValueError(f"Invalid file format or data in {file_path}: {e}")
    except Exception as e:
        raise IOError(f"An unexpected error occurred while reading {file_path}: {e}")