# Copyright (C) 2021-2024, Mindee.

# This program is licensed under the Apache License 2.0.
# See LICENSE or go to <https://opensource.org/licenses/Apache-2.0> for full license details.

from typing import Any, List, Union

import numpy as np
import torch
from torch import nn

from doctr.io.elements import Document
from doctr.models._utils import estimate_orientation, get_language
from doctr.models.detection.predictor import DetectionPredictor
from doctr.models.recognition.predictor import RecognitionPredictor
from doctr.utils.geometry import rotate_image

from .base import _OCRPredictor

__all__ = ["OCRPredictor"]


class OCRPredictor(nn.Module, _OCRPredictor):
    """Implements an object able to localize and identify text elements in a set of documents

    Args:
    ----
        det_predictor: detection module
        reco_predictor: recognition module
        assume_straight_pages: if True, speeds up the inference by assuming you only pass straight pages
            without rotated textual elements.
        straighten_pages: if True, estimates the page general orientation based on the median line orientation.
            Then, rotates page before passing it to the deep learning modules. The final predictions will be remapped
            accordingly. Doing so will improve performances for documents with page-uniform rotations.
        detect_orientation: if True, the estimated general page orientation will be added to the predictions for each
            page. Doing so will slightly deteriorate the overall latency.
        detect_language: if True, the language prediction will be added to the predictions for each
            page. Doing so will slightly deteriorate the overall latency.
        **kwargs: keyword args of `DocumentBuilder`
    """

    def __init__(
        self,
        det_predictor: DetectionPredictor,
        reco_predictor: RecognitionPredictor,
        assume_straight_pages: bool = True,
        straighten_pages: bool = False,
        preserve_aspect_ratio: bool = True,
        symmetric_pad: bool = True,
        detect_orientation: bool = False,
        detect_language: bool = False,
        **kwargs: Any,
    ) -> None:
        nn.Module.__init__(self)
        self.det_predictor = det_predictor.eval()  # type: ignore[attr-defined]
        self.reco_predictor = reco_predictor.eval()  # type: ignore[attr-defined]
        _OCRPredictor.__init__(
            self, assume_straight_pages, straighten_pages, preserve_aspect_ratio, symmetric_pad, **kwargs
        )
        self.detect_orientation = detect_orientation
        self.detect_language = detect_language

    @torch.inference_mode()
    def forward(
        self,
        pages: List[Union[np.ndarray, torch.Tensor]],
        **kwargs: Any,
    ) -> Document:
        # Dimension check
        if any(page.ndim != 3 for page in pages):
            raise ValueError("incorrect input shape: all pages are expected to be multi-channel 2D images.")

        origin_page_shapes = [page.shape[:2] if isinstance(page, np.ndarray) else page.shape[-2:] for page in pages]

        # Localize text elements
        loc_preds, out_maps = self.det_predictor(pages, return_maps=True, **kwargs)

        # Detect document rotation and rotate pages
        seg_maps = [
            np.where(out_map > getattr(self.det_predictor.model.postprocessor, "bin_thresh"), 255, 0).astype(np.uint8)
            for out_map in out_maps
        ]
        if self.detect_orientation:
            origin_page_orientations = [estimate_orientation(seq_map) for seq_map in seg_maps]
            orientations = [
                {"value": orientation_page, "confidence": None} for orientation_page in origin_page_orientations
            ]
        else:
            orientations = None
        if self.straighten_pages:
            origin_page_orientations = (
                origin_page_orientations
                if self.detect_orientation
                else [estimate_orientation(seq_map) for seq_map in seg_maps]
            )
            pages = [rotate_image(page, -angle, expand=False) for page, angle in zip(pages, origin_page_orientations)]  # type: ignore[arg-type]
            # Forward again to get predictions on straight pages
            loc_preds = self.det_predictor(pages, **kwargs)

        assert all(
            len(loc_pred) == 1 for loc_pred in loc_preds
        ), "Detection Model in ocr_predictor should output only one class"

        loc_preds = [list(loc_pred.values())[0] for loc_pred in loc_preds]
        # Check whether crop mode should be switched to channels first
        channels_last = len(pages) == 0 or isinstance(pages[0], np.ndarray)

        # Rectify crops if aspect ratio
        loc_preds = self._remove_padding(pages, loc_preds)  # type: ignore[arg-type]

        # Apply hooks to loc_preds if any
        for hook in self.hooks:
            loc_preds = hook(loc_preds)

        # Crop images
        crops, loc_preds = self._prepare_crops(
            pages,  # type: ignore[arg-type]
            loc_preds,
            channels_last=channels_last,
            assume_straight_pages=self.assume_straight_pages,
        )
        # Rectify crop orientation and get crop orientation predictions
        crop_orientations: Any = []
        if not self.assume_straight_pages:
            crops, loc_preds, _crop_orientations = self._rectify_crops(crops, loc_preds)
            crop_orientations = [
                {"value": orientation[0], "confidence": orientation[1]} for orientation in _crop_orientations
            ]

        # Identify character sequences
        word_preds = self.reco_predictor([crop for page_crops in crops for crop in page_crops], **kwargs)
        if not crop_orientations:
            crop_orientations = [{"value": 0, "confidence": None} for _ in word_preds]

        boxes, text_preds, crop_orientations = self._process_predictions(loc_preds, word_preds, crop_orientations)

        if self.detect_language:
            languages = [get_language(" ".join([item[0] for item in text_pred])) for text_pred in text_preds]
            languages_dict = [{"value": lang[0], "confidence": lang[1]} for lang in languages]
        else:
            languages_dict = None

        out = self.doc_builder(
            pages,  # type: ignore[arg-type]
            boxes,
            text_preds,
            origin_page_shapes,  # type: ignore[arg-type]
            crop_orientations,
            orientations,
            languages_dict,
        )
        return out