import os
import re
import hashlib
import shutil
import tempfile
from pathlib import Path

import fitz
import pytesseract
from PIL import Image
from huggingface_hub import snapshot_download, HfApi


# ============================================================
# CONFIGURATION
# ============================================================

DPI = 300
OCR_LANGUAGE = "eng"


if os.environ.get("TESSERACT_CMD"):
    pytesseract.pytesseract.tesseract_cmd = os.environ["TESSERACT_CMD"]


# ============================================================
# INGESTION
# ============================================================

def load_dataset():

    print("\n" + "=" * 55)
    print("       PDF OCR PROCESSING PIPELINE")
    print("=" * 55)

    source = input(
        "\nEnter a Hugging Face dataset ID "
        "or local directory path:\n> "
    ).strip().strip('"').strip("'")

    if not source:
        raise ValueError("No dataset source was provided.")

    source_path = Path(source).expanduser()

    if source_path.is_dir():

        print("\n[INGESTION] Using local dataset directory.")
        dataset_path = source_path.resolve()

    else:

        print("\n[INGESTION] Downloading Hugging Face dataset...")

        try:
            dataset_path = Path(
                snapshot_download(
                    repo_id=source,
                    repo_type="dataset",
                    allow_patterns=["*.pdf", "*.PDF"]
                )
            )

        except Exception as error:
            raise RuntimeError(
                f"Could not retrieve dataset: {error}"
            )

    pdf_files = sorted(
        [
            file for file in dataset_path.rglob("*")
            if file.is_file() and file.suffix.lower() == ".pdf"
        ]
    )

    if not pdf_files:
        raise FileNotFoundError(
            "No PDF files were found in the supplied dataset."
        )

    print(f"\n[INGESTION] Found {len(pdf_files)} PDF files.")

    return dataset_path, pdf_files


# ============================================================
# DOCUMENT DIRECTORY CREATION
# ============================================================

def create_document_folder(
    pdf_path,
    dataset_path,
    output_path,
    pdf_files
):

    relative_path = pdf_path.relative_to(dataset_path)

    document_name = pdf_path.stem

    safe_name = re.sub(
        r'[<>:"/\\|?*]',
        "_",
        document_name
    )

    matching_files = [
        p for p in pdf_files
        if p.stem.casefold() == document_name.casefold()
    ]

    # Disambiguate PDFs with identical filenames.
    if len(matching_files) > 1:

        suffix = hashlib.sha1(
            str(relative_path).encode("utf-8")
        ).hexdigest()[:8]

        safe_name = f"{safe_name}_{suffix}"

    document_folder = output_path / safe_name

    png_folder = document_folder / "png"
    txt_folder = document_folder / "txt"

    png_folder.mkdir(parents=True, exist_ok=True)
    txt_folder.mkdir(parents=True, exist_ok=True)

    return document_folder, png_folder, txt_folder, safe_name


# ============================================================
# PDF TO PNG CONVERSION
# ============================================================

def convert_pdf_to_png(
    pdf_path,
    png_folder,
    document_name
):

    print(f"\n[CONVERSION] Processing: {pdf_path.name}")

    pdf = fitz.open(pdf_path)

    try:

        page_count = len(pdf)

        for page_number, page in enumerate(pdf, start=1):

            filename = (
                f"{document_name}_page_{page_number:04d}.png"
            )

            image_path = png_folder / filename

            # Skip existing images.
            if image_path.exists():
                continue

            matrix = fitz.Matrix(
                DPI / 72,
                DPI / 72
            )

            pixmap = page.get_pixmap(
                matrix=matrix,
                alpha=False
            )

            pixmap.save(str(image_path))

        print(
            f"[CONVERSION] Generated/verified "
            f"{page_count} page images."
        )

        return page_count

    finally:
        pdf.close()


# ============================================================
# OCR PROCESSING
# ============================================================

def ocr_images(
    png_folder,
    txt_folder,
    document_name
):

    print(f"\n[OCR] Processing: {document_name}")

    image_files = sorted(
        png_folder.glob(f"{document_name}_page_*.png")
    )

    for image_path in image_files:

        text_filename = image_path.stem + ".txt"

        text_path = txt_folder / text_filename

        # Skip existing OCR outputs.
        if text_path.exists():
            continue

        with Image.open(image_path) as image:

            extracted_text = pytesseract.image_to_string(
                image,
                lang=OCR_LANGUAGE
            )

        with open(
            text_path,
            "w",
            encoding="utf-8"
        ) as text_file:

            text_file.write(extracted_text)

    print(
        f"[OCR] Generated/verified "
        f"{len(image_files)} page-level TXT files."
    )


# ============================================================
# MASTER DOCUMENT CREATION
# ============================================================

def create_master_document(
    txt_folder,
    document_folder,
    document_name,
    page_count
):

    print(f"\n[STITCHING] Reconstructing: {document_name}")

    master_path = document_folder / f"{document_name}.txt"

    with open(
        master_path,
        "w",
        encoding="utf-8"
    ) as master_file:

        for page_number in range(1, page_count + 1):

            filename = (
                f"{document_name}_page_{page_number:04d}.txt"
            )

            text_path = txt_folder / filename

            if not text_path.exists():
                raise FileNotFoundError(
                    f"Missing OCR output: {text_path}"
                )

            with open(
                text_path,
                "r",
                encoding="utf-8"
            ) as page_file:

                page_text = page_file.read()

            master_file.write(
                f"\n\n========== PAGE {page_number} ==========\n\n"
            )

            master_file.write(page_text)

    print("[STITCHING] Master document created.")

    return master_path


# ============================================================
# DOCUMENT VALIDATION
# ============================================================

def validate_document(
    png_folder,
    txt_folder,
    master_path,
    page_count,
    document_name
):

    png_count = len(
        list(png_folder.glob(f"{document_name}_page_*.png"))
    )

    txt_count = len(
        list(txt_folder.glob(f"{document_name}_page_*.txt"))
    )

    master_exists = master_path.exists()

    valid = (
        png_count == page_count
        and txt_count == page_count
        and master_exists
    )

    print("\n[VALIDATION]")

    print(f"Expected pages: {page_count}")
    print(f"PNG files:      {png_count}")
    print(f"TXT files:      {txt_count}")
    print(f"Master TXT:     {master_exists}")

    if valid:
        print("[VALIDATION] SUCCESS")
    else:
        print("[VALIDATION] FAILED")

    return valid


# ============================================================
# HUGGING FACE UPLOAD AND PULL REQUEST
# ============================================================

def upload_results(
    successful_folders
):

    print("\n" + "=" * 55)
    print("             HUGGING FACE UPLOAD")
    print("=" * 55)

    if not successful_folders:

        print(
            "\n[UPLOAD] No successfully validated documents. "
            "Skipping upload."
        )

        return

    repo_id = input(
        "\nEnter destination Hugging Face dataset ID "
        "(e.g. sample-organization/sample-dataset):\n> "
    ).strip()

    if not repo_id:

        print("\n[UPLOAD] No repository specified. Skipping upload.")

        return

    try:

        api = HfApi()

        print(f"\n[UPLOAD] Destination: {repo_id}")
        print(
            f"[UPLOAD] Documents to upload: "
            f"{len(successful_folders)}"
        )

        # Create a temporary staging directory containing
        # only successfully validated documents.
        with tempfile.TemporaryDirectory(
            prefix="pdf_ocr_upload_"
        ) as temporary_directory:

            staging_path = Path(temporary_directory)

            for document_folder in successful_folders:

                destination = staging_path / document_folder.name

                shutil.copytree(
                    document_folder,
                    destination
                )

            print("\n[UPLOAD] Uploading OCR corpus...")

            result = api.upload_folder(

                folder_path=str(staging_path),

                repo_id=repo_id,

                repo_type="dataset",

                path_in_repo="ocr-output",

                commit_message="Add OCR processing results",

                commit_description=(
                    "Automatically generated OCR corpus "
                    "containing page images, page-level text "
                    "files, and consolidated master documents."
                ),

                create_pr=True
            )

        print("\n[SUCCESS] Upload completed!")

        print(f"Pull Request URL: {result}")

    except Exception as error:

        print(f"\n[UPLOAD ERROR] {error}")

        print(
            "[UPLOAD] Local OCR results have been preserved."
        )

        print(
            "[UPLOAD] Check your Hugging Face authentication "
            "and repository write permissions."
        )


# ============================================================
# MAIN PIPELINE
# ============================================================

def run_pipeline():

    try:

        pytesseract.get_tesseract_version()

    except Exception as error:

        raise RuntimeError(
            "Tesseract OCR is not installed or is not accessible. "
            "Check your installation and PATH."
        ) from error

    dataset_path, pdf_files = load_dataset()

    output_input = input(
        "\nEnter output directory "
        "(press Enter for ./PDF_OCR_CORPUS):\n> "
    ).strip().strip('"').strip("'")

    if output_input:

        output_path = Path(
            output_input
        ).expanduser().resolve()

    else:

        output_path = Path.cwd() / "PDF_OCR_CORPUS"

    output_path.mkdir(
        parents=True,
        exist_ok=True
    )

    print("\n" + "=" * 55)

    print(f"PDF FILES: {len(pdf_files)}")
    print(f"OUTPUT:    {output_path}")

    print("=" * 55)

    successful = 0
    failed = 0

    successful_folders = []

    for index, pdf_path in enumerate(pdf_files, start=1):

        print("\n" + "-" * 55)

        print(
            f"DOCUMENT {index}/{len(pdf_files)}: "
            f"{pdf_path.name}"
        )

        print("-" * 55)

        try:

            (
                document_folder,
                png_folder,
                txt_folder,
                document_name
            ) = create_document_folder(
                pdf_path,
                dataset_path,
                output_path,
                pdf_files
            )

            page_count = convert_pdf_to_png(
                pdf_path,
                png_folder,
                document_name
            )

            ocr_images(
                png_folder,
                txt_folder,
                document_name
            )

            master_path = create_master_document(
                txt_folder,
                document_folder,
                document_name,
                page_count
            )

            valid = validate_document(
                png_folder,
                txt_folder,
                master_path,
                page_count,
                document_name
            )

            if valid:

                successful += 1

                successful_folders.append(
                    document_folder
                )

            else:

                failed += 1

        except Exception as error:

            failed += 1

            print(
                f"\n[ERROR] Could not process "
                f"{pdf_path.name}: {error}"
            )

            continue

    # ========================================================
    # PROCESSING SUMMARY
    # ========================================================

    print("\n" + "=" * 55)
    print("             PIPELINE COMPLETE")
    print("=" * 55)

    print(f"Total PDFs:       {len(pdf_files)}")
    print(f"Successful:       {successful}")
    print(f"Failed:           {failed}")
    print(f"Output directory: {output_path}")

    print("=" * 55)

    # ========================================================
    # OPTIONAL HUGGING FACE UPLOAD
    # ========================================================

    if successful > 0:

        upload_choice = input(
            "\nWould you like to upload the results "
            "to Hugging Face and create a Pull Request? "
            "(y/n):\n> "
        ).strip().lower()

        if upload_choice in ("y", "yes"):

            upload_results(successful_folders)

        else:

            print("\n[UPLOAD] Skipped by user.")

    else:

        print(
            "\n[UPLOAD] No successful documents to upload."
        )


# ============================================================
# EXECUTION
# ============================================================

if __name__ == "__main__":

    try:

        run_pipeline()

    except KeyboardInterrupt:

        print("\n[INTERRUPTED] Pipeline stopped by user.")

    except Exception as error:

        print(f"\n[FATAL ERROR] {error}")