# Copyright (C) 2021-2023, 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.models.preprocessor import PreProcessor
from doctr.models.utils import set_device_and_dtype

__all__ = ["DetectionPredictor"]


class DetectionPredictor(nn.Module):
    """Implements an object able to localize text elements in a document

    Args:
        pre_processor: transform inputs for easier batched model inference
        model: core detection architecture
    """

    def __init__(
        self,
        pre_processor: PreProcessor,
        model: nn.Module,
    ) -> None:
        super().__init__()
        self.pre_processor = pre_processor
        self.model = model.eval()

    @torch.no_grad()
    def forward(
        self,
        pages: List[Union[np.ndarray, torch.Tensor]],
        **kwargs: Any,
    ) -> List[np.ndarray]:
        # 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.")

        processed_batches = self.pre_processor(pages)
        _params = next(self.model.parameters())
        self.model, processed_batches = set_device_and_dtype(
            self.model, processed_batches, _params.device, _params.dtype
        )
        predicted_batches = [self.model(batch, return_preds=True, **kwargs)["preds"] for batch in processed_batches]
        return [pred for batch in predicted_batches for pred in batch]
