import os
import uuid

import pytest

from cognee.modules.data.processing.document_types.TextDocument import TextDocument

GROUND_TRUTH = {
    "code.txt": [
        {"word_count": 205, "len_text": 1024, "cut_type": "sentence_cut"},
        {"word_count": 104, "len_text": 833, "cut_type": "paragraph_end"},
    ],
    "Natural_language_processing.txt": [
        {"word_count": 128, "len_text": 984, "cut_type": "paragraph_end"},
        {"word_count": 1, "len_text": 1, "cut_type": "paragraph_end"},
    ],
}


@pytest.mark.parametrize(
    "input_file,chunk_size",
    [("code.txt", 256), ("Natural_language_processing.txt", 128)],
)
def test_TextDocument(input_file, chunk_size):
    test_file_path = os.path.join(
        os.sep,
        *(os.path.dirname(__file__).split(os.sep)[:-2]),
        "test_data",
        input_file,
    )
    document = TextDocument(
        id=uuid.uuid4(), name=input_file, raw_data_location=test_file_path
    )

    for ground_truth, paragraph_data in zip(
        GROUND_TRUTH[input_file], document.read(chunk_size=chunk_size)
    ):
        assert (
            ground_truth["word_count"] == paragraph_data.word_count
        ), f'{ground_truth["word_count"] = } != {paragraph_data.word_count = }'
        assert ground_truth["len_text"] == len(
            paragraph_data.text
        ), f'{ground_truth["len_text"] = } != {len(paragraph_data.text) = }'
        assert (
            ground_truth["cut_type"] == paragraph_data.cut_type
        ), f'{ground_truth["cut_type"] = } != {paragraph_data.cut_type = }'
