"""
.. _reading_page_and_xml:

Reading PAGE- and ALTO-XML
==========================

Here, we show how you can load PAGE- and ALTO-XML files and open them in Stringalign.
Again, we are using the `Lærebog i de forskjellige Grene af Huusholdningen <https://www.nb.no/items/URN:NBN:no-nb_digibok_2009111210001>`_ by Hanna Winsnes (1846) example, but this time with PAGE- and ALTO-XML files generated by OCR-D.
"""

import xml.etree.ElementTree as ET
from pathlib import Path

import stringalign

# %%
# Set parameters and load the references from a text file
data_path = Path("synthetic_transcription_data")
predictions_page = data_path / "predicted.page.xml"
predictions_alto = data_path / "predicted.alto.xml"
reference_path = data_path / "reference.txt"


references = reference_path.read_text().splitlines()


# %%
# Loading PAGE XML
# ----------------
#
# You can use the following function to parse PAGE XML-files.
# Note that the namespace (``ns``) must match the namespace tag in the PAGE XML-file.
# You can find it by searching for ``xmlns`` in the PAGE XML file.
def get_page_text(file: Path) -> list[str]:
    root = ET.parse(file)
    ns = {"PAGE": "http://schema.primaresearch.org/PAGE/gts/pagecontent/2019-07-15"}
    unicode_tags = root.findall(".//PAGE:TextRegion/PAGE:TextLine/PAGE:TextEquiv/PAGE:Unicode", ns)

    return [unicode_tag.text.strip() for unicode_tag in unicode_tags if unicode_tag.text.strip()]


page_predictions = get_page_text(predictions_page)

analyzer = stringalign.evaluate.MultiAlignmentAnalyzer.from_strings(
    predictions=page_predictions, references=references, tokenizer=stringalign.tokenize.GraphemeClusterTokenizer()
)
print(analyzer.compute_ter())


# %%
# Loading ALTO XML
# ----------------
#
# ALTO XML is a bit more complicated than PAGE XML.
# We need to find the text line objects, iterate over its children and append the content to a list.
# Still, you can copy this (and potentially update the namespace) function and use it in your code as needed.


def get_alto_text(file: Path) -> list[str]:
    root = ET.parse(file)
    ns = "http://www.loc.gov/standards/alto/ns-v4#"
    text_line_tags = root.findall(".//ALTO:TextLine", {"ALTO": ns})

    out = []
    for text_line_tag in text_line_tags:
        line = []
        for tag in text_line_tag:
            tag_type = tag.tag.casefold()
            if tag_type == f"{{{ns}}}string":
                line.append(tag.attrib["CONTENT"])
            elif tag_type == f"{{{ns}}}sp":
                line.append(" ")
            elif tag_type == f"{{{ns}}}hyp":
                line.append(tag.attrib["CONTENT"])
            elif tag_type == f"{{{ns}}}shape":
                continue
            else:
                raise ValueError(tag)

        if text := "".join(line).strip():
            out.append(text)

    return out


alto_predictions = get_alto_text(predictions_alto)

analyzer = stringalign.evaluate.MultiAlignmentAnalyzer.from_strings(
    predictions=alto_predictions, references=references, tokenizer=stringalign.tokenize.GraphemeClusterTokenizer()
)
print(analyzer.compute_ter())