import os
from os import path
import pandas as pd
from pdf2image import convert_from_path
from itertools import chain
import json
from cv_analysis.locations import PDF_FOR_TESTING, TEST_DATA_DIR, PNG_FOR_TESTING, DVC_DATA_DIR, HASHED_PDFS_FOR_TESTING
from cv_analysis.utils.deduplicate_pdfs import hash_pdf_files

def read_json(path):
    with open(path, encoding='utf-8') as file:
        data = json.load(file)
    return data


# def collect_metadata(example_pages, save=False):
#     metadata = []
#     i = 0
#     for name, document_sections in example_pages.items():
#         for pages in document_sections:
#             span = list(range(pages[0], pages[1] + 1))
#             for page_nr in span:
#                 metadata.append(["fig_table" + str(i), name, page_nr])
#                 i += 1
#     if save:
#         df = pd.DataFrame(data=metadata, columns=["image_name", "pdf_name", "page"])
#         df.to_csv("/exported_files/test_pages.csv")
#     else:
#         return pd.DataFrame(data=metadata, columns=["image_name", "pdf_name", "page"])



def collect_metadata(example_pages, save=False):
    metadata = []
    make_metadata_entry = make_metadata_entry_maker()
    for name, document_sections in example_pages.items():
        metadata.append(f(name, document_sections, make_metadata_entry))
    metadata = list(chain.from_iterable(metadata))
    if save:
        df = pd.DataFrame(data=metadata, columns=["image_name", "pdf_name", "page"])
        df.to_csv(path.join(DVC_DATA_DIR, "metadata_testing_files.csv"))
    else:
        return pd.DataFrame(data=metadata, columns=["image_name", "pdf_name", "page"])


def f(name, document_sections, make_metadata_entry):
    for pages in document_sections:
        span = list(range(pages[0], pages[1] + 1))
        for page_nr in span:
            yield make_metadata_entry(name, page_nr)


def make_metadata_entry_maker():
    i = -1

    def make_metadata_entry(name, page_nr):
        nonlocal i
        i += 1
        return [f"fig_table{i:0>3}", name, page_nr]

    return make_metadata_entry


def split_pdf(example_pages):
    dir_path = PDF_FOR_TESTING
    i = 0
    for name, document_sections in example_pages.items():
        for pages in document_sections:
            images = convert_from_path(pdf_path=path.join(dir_path, name), dpi=300, first_page=pages[0],
                                       last_page=pages[1])
            for image in images:
                fp = path.join(PNG_FOR_TESTING, f"fig_table{i:0>3}.png")
                image.save(fp=fp, dpi=(300, 300))
                i += 1

def rename_files_with_hash(example_pages, hashes):

    files_to_rename = list(example_pages.keys())
    folder = HASHED_PDFS_FOR_TESTING

    # Iterate through the folder
    for file in os.listdir(folder):
        # Checking if the file is present in the list
        if file in files_to_rename:
            # construct current name using file name and path
            old_name = path.join(folder, file)
            # get file name without extension
            only_name = path.splitext(file)[0]

            # Adding the new name with extension
            new_base = only_name + '_new' + '.txt'
            # construct full file path
            new_name = path.join(folder, new_base)

            # Renaming the file
            os.rename(old_name, new_name)

    # verify the result
    res = os.listdir(folder)
    print(res)

def hash_pdfs(example_pages):
    pdf_paths = list(path.join(PDF_FOR_TESTING, pdf_name) for pdf_name in example_pages.keys())
    hashes = hash_pdf_files(paths=pdf_paths, verbose=0)
    example_pages = dict(zip(hashes, example_pages.values()))
    return example_pages

def main():
    examples_pages = read_json(path.join(TEST_DATA_DIR, "example_pages.json"))
    # examples_pages = hash_pdfs(examples_pages)
    collect_metadata(examples_pages, save=True)
    split_pdf(examples_pages)


if __name__ == "__main__":
    main()