import concurrent.futures

import fitz

from magic_pdf.data.dataset import PymuDocDataset
from magic_pdf.data.utils import fitz_doc_to_image  # PyMuPDF


def partition_array_greedy(arr, k):
    """Partition an array into k parts using a simple greedy approach.

    Parameters:
    -----------
    arr : list
        The input array of integers
    k : int
        Number of partitions to create

    Returns:
    --------
    partitions : list of lists
        The k partitions of the array
    """
    # Handle edge cases
    if k <= 0:
        raise ValueError('k must be a positive integer')
    if k > len(arr):
        k = len(arr)  # Adjust k if it's too large
    if k == 1:
        return [list(range(len(arr)))]
    if k == len(arr):
        return [[i] for i in range(len(arr))]

    # Sort the array in descending order
    sorted_indices = sorted(range(len(arr)), key=lambda i: arr[i][1], reverse=True)

    # Initialize k empty partitions
    partitions = [[] for _ in range(k)]
    partition_sums = [0] * k

    # Assign each element to the partition with the smallest current sum
    for idx in sorted_indices:
        # Find the partition with the smallest sum
        min_sum_idx = partition_sums.index(min(partition_sums))

        # Add the element to this partition
        partitions[min_sum_idx].append(idx)  # Store the original index
        partition_sums[min_sum_idx] += arr[idx][1]

    return partitions


def process_pdf_batch(pdf_jobs, idx):
    """Process a batch of PDF pages using multiple threads.

    Parameters:
    -----------
    pdf_jobs : list of tuples
        List of (pdf_path, page_num) tuples
    output_dir : str or None
        Directory to save images to
    num_threads : int
        Number of threads to use
    **kwargs :
        Additional arguments for process_pdf_page

    Returns:
    --------
    images : list
        List of processed images
    """
    images = []

    for pdf_path, _ in pdf_jobs:
        doc = fitz.open(pdf_path)
        tmp = []
        for page_num in range(len(doc)):
            page = doc[page_num]
            tmp.append(fitz_doc_to_image(page))
        images.append(tmp)
    return (idx, images)


def batch_build_dataset(pdf_paths, k, lang=None):
    """Process multiple PDFs by partitioning them into k balanced parts and
    processing each part in parallel.

    Parameters:
    -----------
    pdf_paths : list
        List of paths to PDF files
    k : int
        Number of partitions to create
    output_dir : str or None
        Directory to save images to
    threads_per_worker : int
        Number of threads to use per worker
    **kwargs :
        Additional arguments for process_pdf_page

    Returns:
    --------
    all_images : list
        List of all processed images
    """
    # Get page counts for each PDF
    pdf_info = []
    total_pages = 0

    results = []
    for pdf_path in pdf_paths:
        try:
            with open(pdf_path, 'rb') as f:
                bits = f.read() 
            results.append(PymuDocDataset(bits, lang))
        except Exception as e:
            print(f'Error opening {pdf_path}: {e}')

    return results