import pymupdf
from scripts.utility_functions import color_mapping, get_best_fuzzy_match


def add_infos_to_pdf_agentic(doc, changes, successful_annotations, extraction_method="Landing AI", nlp_preprocessing=True):
    type_counts = {
        "addition": 0,
        "modification": 0,
        "deletion": 0,
        "unspecified": 0,
    }
    for change in changes:
        change_type = change.get("type", "unspecified")
        if change_type in type_counts:
            type_counts[change_type] += 1
        else:
            type_counts["unspecified"] += 1

    summary_text = (
        "Regulatory Summary:\n"
        f"- Extraction Method: {extraction_method}\n"
        f"- Nlp preprocessing: {'yes' if nlp_preprocessing else 'no'}\n"
        f"- Total Changes: {len(changes)}, Successful Annotations: {successful_annotations}\n"
        f"- Additions: {type_counts.get('addition', 0)}\n"
        f"- Deletions: {type_counts.get('deletion', 0)}\n"
        f"- Modifications: {type_counts.get('modification', 0)}\n"
    )
    page = doc.load_page(0)
    rect = pymupdf.Rect(10, 10, 550, 150)
    page.insert_textbox(
        rect,
        summary_text,
        fontsize=9,
        fontname="helv",
        align=pymupdf.TEXT_ALIGN_LEFT,
        color=(0, 0, 0.7),
        overlay=True,
    )

    metadata = doc.metadata
    metadata["title"] = "Annotated " + (
        metadata["title"] if metadata["title"] else "PDF"
    )
    metadata["author"] = "Fortiss ReguLens" + (
        " & " + metadata["author"] if metadata["author"] else ""
    )
    metadata["subject"] = "Annotated PDF with regulatory changes"
    metadata["keywords"] = "regulatory, changes, annotations, pdf"
    doc.set_metadata(metadata)


def add_failed_annotations_to_pdf_agentic(doc, failed_annotations):
    """
    Doc is edited in place.
    Adds failed annotations to the end of the PDF document.

    :param doc: The PyMuPDF document object.
    :type doc: pymupdf.Document
    :param failed_annotations: The failed annotations to be added.
    :type failed_annotations: array
    """
    if not failed_annotations:
        return
    page = doc.new_page(pno=-1)
    annotation_str = "Failed Annotations:\n"
    for failed_annotation in failed_annotations:
        text = failed_annotation["change"]["text"]
        change_type = failed_annotation["change"]["type"]
        change_str = failed_annotation["change"]["category"]
        page_num = failed_annotation["page"]
        annotation_str += (
            f"Page {page_num}: {text} ({change_type}) Change: {change_str}\n"
        )

    rect = pymupdf.Rect(20, 20, 580, 822)
    page.insert_textbox(
        rect,
        annotation_str,
        fontsize=9,
        fontname="helv",
        align=pymupdf.TEXT_ALIGN_LEFT,
        color=(0, 0, 0.7),
    )


def agentic_pdf_annotator(changes, file_bytes, extraction_method="Landing AI", nlp_preprocessing=True):
    changes = [
        c for c in changes if c.get("confirmed", False) and c.get("validated", False)
    ]
    if not changes:
        return ""
    successful_annotations = 0
    failed_annotations = []
    try:
        doc = pymupdf.open(stream=file_bytes, filetype="pdf")
    except Exception as e:
        return ""

    # Sort by length of relevant_text in descending order to avoid overlapping highlights
    changes = sorted(changes, key=lambda c: -len(c["text"]))
    annotated_areas = {}
    full_text = ""
    for page_num in range(len(doc)):
        page = doc[page_num]
        full_text += page.get_text()
    for change in changes:
        page_num = int(change["grounding"][0]["page"])
        text = change["text"]
        change_type = change["type"]
        change_str = change["category"]
        comment = change["context"]
        if page_num < 0 or page_num >= len(doc):
            results = []
            for pnr in range(len(doc)):  # search all pages
                annotated_areas.setdefault(f"{pnr}", [])
                page = doc.load_page(pnr)
                text_instances = page.search_for(text)
                for inst in text_instances:
                    page_num = pnr# remove?
                    results.append({"page": pnr, "bbox": inst})
                results = list(
                    filter(
                        lambda result: not any(
                            result["bbox"].intersects(area)
                            for area in annotated_areas[f"{result['page']}"]
                        ),
                        results,
                    )
                )
            if not results:
                best_match = get_best_fuzzy_match(full_text, change)
                if best_match and len(best_match) > 0:
                    print("found best fuzzy match: ", best_match)
                    for page_num in range(len(doc)):  # search all pages
                        page = doc.load_page(page_num)
                        text_instances = page.search_for(best_match)
                        for inst in text_instances:
                            results.append({"page": page_num, "bbox": inst})
                    # we only want the results that do not overlap with already annotated areas
                    results = list(
                        filter(
                            lambda result: not any(
                                result["bbox"].intersects(area)
                                for area in annotated_areas[f"{result['page']}"]
                            ),
                            results,
                        )
                    )
            if results:  # "flattenning" the results
                page_num = results[0]["page"]
                doc_page = doc.load_page(page_num)
                results = [r["bbox"] for r in results if r["page"] == page_num]
        else:
            doc_page = doc.load_page(page_num)
            annotated_areas.setdefault(f"{page_num}", [])
            # Search for the relevant text on the page
            results = doc_page.search_for(text)
            # we only want the results that do not overlap with already annotated areas
            results = list(
                filter(
                    lambda result: not any(
                        result.intersects(area)
                        for area in annotated_areas[f"{page_num}"]
                    ),
                    results,
                )
            )
            if not results:
                best_match = get_best_fuzzy_match(
                    doc_page.get_text(option="text"), change
                )
                if best_match and len(best_match) > 0:
                    results = doc_page.search_for(best_match)
                    print("found best fuzzy match: ", best_match)
                    # we only want the results that do not overlap with already annotated areas
                    results = list(
                        filter(
                            lambda result: not any(
                                result.intersects(area)
                                for area in annotated_areas[f"{page_num}"]
                            ),
                            results,
                        )
                    )
        if not results:
            print(f"No non-overlapping match found on page {page_num} for: '{text}'")
            failed_annotations.append({"change": change, "page": page_num})
            continue

        color = color_mapping.get(change_type, (1, 1, 0))

        annotated_areas[f"{page_num}"].append(results[0])
        highlight = doc_page.add_highlight_annot(results[0])
        highlight.set_colors({"stroke": color})
        highlight.set_info(
            info={
                "title": "Comment",
                "content": f"{change_type} - {change_str}\n{comment}",
                "name": change_type,
            }
        )
        highlight.update()
        successful_annotations += 1

        # if the resulting rects contain anything other than our search text we know it is a multiline highlight because for each line
        # we will have a new result rect. We need to check if the text in the rect is not equal to our search text but is inside of it
        for result in results[1:]:
            resulttext = doc_page.get_textbox(result)
            if (
                (resulttext.strip() != text.strip())
                & (resulttext.strip() in text.strip())
                # & (
                #     not any(
                #         result.intersects(area)
                #         for area in annotated_areas[f"{page_num}"]
                #     )
                # )
            ):
                highlight = doc_page.add_highlight_annot(result)
                highlight.set_colors({"stroke": color})
                highlight.update()
                annotated_areas[f"{page_num}"].append(result)
    add_infos_to_pdf_agentic(doc, changes, successful_annotations, extraction_method, nlp_preprocessing)
    add_failed_annotations_to_pdf_agentic(doc, failed_annotations)
    result_bytes = doc.tobytes()
    return result_bytes