import json
import warnings
from pathlib import Path
from typing import List

from docling_core.types.doc import DoclingDocument
from docling_core.types.legacy_doc.document import ExportedCCSDocument as DsDocument
from pydantic import TypeAdapter
from pydantic.json import pydantic_encoder

from docling.datamodel.base_models import ConversionStatus, Page
from docling.datamodel.document import ConversionResult


def levenshtein(str1: str, str2: str) -> int:

    # Ensure str1 is the shorter string to optimize memory usage
    if len(str1) > len(str2):
        str1, str2 = str2, str1

    # Previous and current row buffers
    previous_row = list(range(len(str2) + 1))
    current_row = [0] * (len(str2) + 1)

    # Compute the Levenshtein distance row by row
    for i, c1 in enumerate(str1, start=1):
        current_row[0] = i
        for j, c2 in enumerate(str2, start=1):
            insertions = previous_row[j] + 1
            deletions = current_row[j - 1] + 1
            substitutions = previous_row[j - 1] + (c1 != c2)
            current_row[j] = min(insertions, deletions, substitutions)
        # Swap rows for the next iteration
        previous_row, current_row = current_row, previous_row

    # The result is in the last element of the previous row
    return previous_row[-1]


def verify_text(gt: str, pred: str, fuzzy: bool, fuzzy_threshold: float = 0.4):

    if len(gt) == 0 or not fuzzy:
        assert gt == pred, f"{gt}!={pred}"
    else:
        dist = levenshtein(gt, pred)
        diff = dist / len(gt)
        assert diff < fuzzy_threshold, f"{gt}!~{pred}"
    return True


def verify_cells(doc_pred_pages: List[Page], doc_true_pages: List[Page]):

    assert len(doc_pred_pages) == len(
        doc_true_pages
    ), "pred- and true-doc do not have the same number of pages"

    for pid, page_true_item in enumerate(doc_true_pages):

        num_true_cells = len(page_true_item.cells)
        num_pred_cells = len(doc_pred_pages[pid].cells)

        assert (
            num_true_cells == num_pred_cells
        ), f"num_true_cells!=num_pred_cells {num_true_cells}!={num_pred_cells}"

        for cid, cell_true_item in enumerate(page_true_item.cells):

            cell_pred_item = doc_pred_pages[pid].cells[cid]

            true_text = cell_true_item.text
            pred_text = cell_pred_item.text
            assert true_text == pred_text, f"{true_text}!={pred_text}"

            true_bbox = cell_true_item.bbox.as_tuple()
            pred_bbox = cell_pred_item.bbox.as_tuple()
            assert (
                true_bbox == pred_bbox
            ), f"bbox is not the same: {true_bbox} != {pred_bbox}"

    return True


# def verify_maintext(doc_pred: DsDocument, doc_true: DsDocument):
#     assert doc_true.main_text is not None, "doc_true cannot be None"
#     assert doc_pred.main_text is not None, "doc_true cannot be None"
#
#     assert len(doc_true.main_text) == len(
#         doc_pred.main_text
#     ), f"document has different length of main-text than expected. {len(doc_true.main_text)}!={len(doc_pred.main_text)}"
#
#     for l, true_item in enumerate(doc_true.main_text):
#         pred_item = doc_pred.main_text[l]
#         # Validate type
#         assert (
#             true_item.obj_type == pred_item.obj_type
#         ), f"Item[{l}] type does not match. expected[{true_item.obj_type}] != predicted [{pred_item.obj_type}]"
#
#         # Validate text ceels
#         if isinstance(true_item, BaseText):
#             assert isinstance(
#                 pred_item, BaseText
#             ), f"{pred_item} is not a BaseText element, but {true_item} is."
#             assert true_item.text == pred_item.text
#
#     return True


def verify_tables_v1(doc_pred: DsDocument, doc_true: DsDocument, fuzzy: bool):
    if doc_true.tables is None:
        # No tables to check
        assert doc_pred.tables is None, "not expecting any table on this document"
        return True

    assert doc_pred.tables is not None, "no tables predicted, but expected in doc_true"

    # print("Expected number of tables: {}, result: {}".format(len(doc_true.tables), len(doc_pred.tables)))

    assert len(doc_true.tables) == len(
        doc_pred.tables
    ), "document has different count of tables than expected."

    for l, true_item in enumerate(doc_true.tables):
        pred_item = doc_pred.tables[l]

        assert (
            true_item.num_rows == pred_item.num_rows
        ), "table does not have the same #-rows"
        assert (
            true_item.num_cols == pred_item.num_cols
        ), "table does not have the same #-cols"

        assert true_item.data is not None, "documents are expected to have table data"
        assert pred_item.data is not None, "documents are expected to have table data"

        print("True: \n", true_item.export_to_dataframe().to_markdown())
        print("Pred: \n", true_item.export_to_dataframe().to_markdown())

        for i, row in enumerate(true_item.data):
            for j, col in enumerate(true_item.data[i]):

                # print("true: ", true_item.data[i][j].text)
                # print("pred: ", pred_item.data[i][j].text)
                # print("")

                verify_text(
                    true_item.data[i][j].text, pred_item.data[i][j].text, fuzzy=fuzzy
                )

                assert (
                    true_item.data[i][j].obj_type == pred_item.data[i][j].obj_type
                ), "table-cell does not have the same type"

    return True


def verify_tables_v2(doc_pred: DoclingDocument, doc_true: DoclingDocument, fuzzy: bool):
    if not len(doc_true.tables) > 0:
        # No tables to check
        assert len(doc_pred.tables) == 0, "not expecting any table on this document"
        return True
    else:
        assert len(doc_pred.tables) > 0, "no tables predicted, but expected in doc_true"

    # print("Expected number of tables: {}, result: {}".format(len(doc_true.tables), len(doc_pred.tables)))

    assert len(doc_true.tables) == len(
        doc_pred.tables
    ), "document has different count of tables than expected."

    for l, true_item in enumerate(doc_true.tables):
        pred_item = doc_pred.tables[l]

        assert (
            true_item.data.num_rows == pred_item.data.num_rows
        ), "table does not have the same #-rows"
        assert (
            true_item.data.num_cols == pred_item.data.num_cols
        ), "table does not have the same #-cols"

        assert true_item.data is not None, "documents are expected to have table data"
        assert pred_item.data is not None, "documents are expected to have table data"

        print("True: \n", true_item.export_to_dataframe().to_markdown())
        print("Pred: \n", true_item.export_to_dataframe().to_markdown())

        for i, row in enumerate(true_item.data.grid):
            for j, col in enumerate(true_item.data.grid[i]):

                # print("true: ", true_item.data[i][j].text)
                # print("pred: ", pred_item.data[i][j].text)
                # print("")

                verify_text(
                    true_item.data.grid[i][j].text,
                    pred_item.data.grid[i][j].text,
                    fuzzy=fuzzy,
                )

                assert (
                    true_item.data.grid[i][j].column_header
                    == pred_item.data.grid[i][j].column_header
                ), "table-cell should be a column_header but prediction isn't"

                assert (
                    true_item.data.grid[i][j].row_header
                    == pred_item.data.grid[i][j].row_header
                ), "table-cell should be a row_header but prediction isn't"

                assert (
                    true_item.data.grid[i][j].row_section
                    == pred_item.data.grid[i][j].row_section
                ), "table-cell should be a row_section but prediction isn't"

    return True


# def verify_output(doc_pred: DsDocument, doc_true: DsDocument):
#     #assert verify_maintext(doc_pred, doc_true), "verify_maintext(doc_pred, doc_true)"
#     assert verify_tables_v1(doc_pred, doc_true), "verify_tables(doc_pred, doc_true)"
#     return True


def verify_md(doc_pred_md: str, doc_true_md: str, fuzzy: bool):
    return verify_text(doc_true_md, doc_pred_md, fuzzy)


def verify_dt(doc_pred_dt: str, doc_true_dt: str, fuzzy: bool):
    return verify_text(doc_true_dt, doc_pred_dt, fuzzy)


def verify_conversion_result_v1(
    input_path: Path,
    doc_result: ConversionResult,
    generate: bool = False,
    ocr_engine: str = None,
    fuzzy: bool = False,
):
    PageList = TypeAdapter(List[Page])

    assert (
        doc_result.status == ConversionStatus.SUCCESS
    ), f"Doc {input_path} did not convert successfully."

    doc_pred_pages: List[Page] = doc_result.pages
    doc_pred: DsDocument = doc_result.legacy_document
    with warnings.catch_warnings():
        warnings.simplefilter("ignore", DeprecationWarning)
        doc_pred_md = doc_result.legacy_document.export_to_markdown()
        doc_pred_dt = doc_result.legacy_document.export_to_document_tokens()

    engine_suffix = "" if ocr_engine is None else f".{ocr_engine}"
    gt_subpath = input_path.parent / "groundtruth" / "docling_v1" / input_path.name
    pages_path = gt_subpath.with_suffix(f"{engine_suffix}.pages.json")
    json_path = gt_subpath.with_suffix(f"{engine_suffix}.json")
    md_path = gt_subpath.with_suffix(f"{engine_suffix}.md")
    dt_path = gt_subpath.with_suffix(f"{engine_suffix}.doctags.txt")

    if generate:  # only used when re-generating truth
        with open(pages_path, "w") as fw:
            fw.write(json.dumps(doc_pred_pages, default=pydantic_encoder))

        with open(json_path, "w") as fw:
            fw.write(json.dumps(doc_pred, default=pydantic_encoder))

        with open(md_path, "w") as fw:
            fw.write(doc_pred_md)

        with open(dt_path, "w") as fw:
            fw.write(doc_pred_dt)
    else:  # default branch in test
        with open(pages_path, "r") as fr:
            doc_true_pages = PageList.validate_json(fr.read())

        with open(json_path, "r") as fr:
            doc_true: DsDocument = DsDocument.model_validate_json(fr.read())

        with open(md_path, "r") as fr:
            doc_true_md = fr.read()

        with open(dt_path, "r") as fr:
            doc_true_dt = fr.read()

        if not fuzzy:
            assert verify_cells(
                doc_pred_pages, doc_true_pages
            ), f"Mismatch in PDF cell prediction for {input_path}"

        # assert verify_output(
        #    doc_pred, doc_true
        # ), f"Mismatch in JSON prediction for {input_path}"

        assert verify_tables_v1(
            doc_pred, doc_true, fuzzy=fuzzy
        ), f"verify_tables(doc_pred, doc_true) mismatch for {input_path}"

        assert verify_md(
            doc_pred_md, doc_true_md, fuzzy=fuzzy
        ), f"Mismatch in Markdown prediction for {input_path}"

        assert verify_dt(
            doc_pred_dt, doc_true_dt, fuzzy=fuzzy
        ), f"Mismatch in DocTags prediction for {input_path}"


def verify_conversion_result_v2(
    input_path: Path,
    doc_result: ConversionResult,
    generate: bool = False,
    ocr_engine: str = None,
    fuzzy: bool = False,
):
    PageList = TypeAdapter(List[Page])

    assert (
        doc_result.status == ConversionStatus.SUCCESS
    ), f"Doc {input_path} did not convert successfully."

    doc_pred_pages: List[Page] = doc_result.pages
    doc_pred: DoclingDocument = doc_result.document
    doc_pred_md = doc_result.document.export_to_markdown()
    doc_pred_dt = doc_result.document.export_to_document_tokens()

    engine_suffix = "" if ocr_engine is None else f".{ocr_engine}"
    gt_subpath = input_path.parent / "groundtruth" / "docling_v2" / input_path.name
    pages_path = gt_subpath.with_suffix(f"{engine_suffix}.pages.json")
    json_path = gt_subpath.with_suffix(f"{engine_suffix}.json")
    md_path = gt_subpath.with_suffix(f"{engine_suffix}.md")
    dt_path = gt_subpath.with_suffix(f"{engine_suffix}.doctags.txt")

    if generate:  # only used when re-generating truth
        with open(pages_path, "w") as fw:
            fw.write(json.dumps(doc_pred_pages, default=pydantic_encoder))

        with open(json_path, "w") as fw:
            fw.write(json.dumps(doc_pred, default=pydantic_encoder))

        with open(md_path, "w") as fw:
            fw.write(doc_pred_md)

        with open(dt_path, "w") as fw:
            fw.write(doc_pred_dt)
    else:  # default branch in test
        with open(pages_path, "r") as fr:
            doc_true_pages = PageList.validate_json(fr.read())

        with open(json_path, "r") as fr:
            doc_true: DoclingDocument = DoclingDocument.model_validate_json(fr.read())

        with open(md_path, "r") as fr:
            doc_true_md = fr.read()

        with open(dt_path, "r") as fr:
            doc_true_dt = fr.read()

        if not fuzzy:
            assert verify_cells(
                doc_pred_pages, doc_true_pages
            ), f"Mismatch in PDF cell prediction for {input_path}"

        # assert verify_output(
        #    doc_pred, doc_true
        # ), f"Mismatch in JSON prediction for {input_path}"

        assert verify_tables_v2(
            doc_pred, doc_true, fuzzy=fuzzy
        ), f"verify_tables(doc_pred, doc_true) mismatch for {input_path}"

        assert verify_md(
            doc_pred_md, doc_true_md, fuzzy=fuzzy
        ), f"Mismatch in Markdown prediction for {input_path}"

        assert verify_dt(
            doc_pred_dt, doc_true_dt, fuzzy=fuzzy
        ), f"Mismatch in DocTags prediction for {input_path}"