# 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

from doctr.file_utils import is_tf_available, is_torch_available

from .. import classification
from ..preprocessor import PreProcessor
from .predictor import OrientationPredictor

__all__ = ["crop_orientation_predictor", "page_orientation_predictor"]

ARCHS: list[str] = [
    "magc_resnet31",
    "mobilenet_v3_small",
    "mobilenet_v3_small_r",
    "mobilenet_v3_large",
    "mobilenet_v3_large_r",
    "resnet18",
    "resnet31",
    "resnet34",
    "resnet50",
    "resnet34_wide",
    "textnet_tiny",
    "textnet_small",
    "textnet_base",
    "vgg16_bn_r",
    "vit_s",
    "vit_b",
]
ORIENTATION_ARCHS: list[str] = ["mobilenet_v3_small_crop_orientation", "mobilenet_v3_small_page_orientation"]


def _orientation_predictor(
    arch: Any, pretrained: bool, model_type: str, disabled: bool = False, **kwargs: Any
) -> OrientationPredictor:
    if disabled:
        # Case where the orientation predictor is disabled
        return OrientationPredictor(None, None)

    if isinstance(arch, str):
        if arch not in ORIENTATION_ARCHS:
            raise ValueError(f"unknown architecture '{arch}'")

        # Load directly classifier from backbone
        _model = classification.__dict__[arch](pretrained=pretrained)
    else:
        allowed_archs = [classification.MobileNetV3]
        if is_torch_available():
            # Adding the type for torch compiled models to the allowed architectures
            from doctr.models.utils import _CompiledModule

            allowed_archs.append(_CompiledModule)

        if not isinstance(arch, tuple(allowed_archs)):
            raise ValueError(f"unknown architecture: {type(arch)}")
        _model = arch

    kwargs["mean"] = kwargs.get("mean", _model.cfg["mean"])
    kwargs["std"] = kwargs.get("std", _model.cfg["std"])
    kwargs["batch_size"] = kwargs.get("batch_size", 128 if model_type == "crop" else 4)
    input_shape = _model.cfg["input_shape"][:-1] if is_tf_available() else _model.cfg["input_shape"][1:]
    predictor = OrientationPredictor(
        PreProcessor(input_shape, preserve_aspect_ratio=True, symmetric_pad=True, **kwargs), _model
    )
    return predictor


def crop_orientation_predictor(
    arch: Any = "mobilenet_v3_small_crop_orientation", pretrained: bool = False, batch_size: int = 128, **kwargs: Any
) -> OrientationPredictor:
    """Crop orientation classification architecture.

    >>> import numpy as np
    >>> from doctr.models import crop_orientation_predictor
    >>> model = crop_orientation_predictor(arch='mobilenet_v3_small_crop_orientation', pretrained=True)
    >>> input_crop = (255 * np.random.rand(256, 256, 3)).astype(np.uint8)
    >>> out = model([input_crop])

    Args:
        arch: name of the architecture to use (e.g. 'mobilenet_v3_small_crop_orientation')
        pretrained: If True, returns a model pre-trained on our recognition crops dataset
        batch_size: number of samples the model processes in parallel
        **kwargs: keyword arguments to be passed to the OrientationPredictor

    Returns:
        OrientationPredictor
    """
    return _orientation_predictor(arch=arch, pretrained=pretrained, batch_size=batch_size, model_type="crop", **kwargs)


def page_orientation_predictor(
    arch: Any = "mobilenet_v3_small_page_orientation", pretrained: bool = False, batch_size: int = 4, **kwargs: Any
) -> OrientationPredictor:
    """Page orientation classification architecture.

    >>> import numpy as np
    >>> from doctr.models import page_orientation_predictor
    >>> model = page_orientation_predictor(arch='mobilenet_v3_small_page_orientation', pretrained=True)
    >>> input_page = (255 * np.random.rand(512, 512, 3)).astype(np.uint8)
    >>> out = model([input_page])

    Args:
        arch: name of the architecture to use (e.g. 'mobilenet_v3_small_page_orientation')
        pretrained: If True, returns a model pre-trained on our recognition crops dataset
        batch_size: number of samples the model processes in parallel
        **kwargs: keyword arguments to be passed to the OrientationPredictor

    Returns:
        OrientationPredictor
    """
    return _orientation_predictor(arch=arch, pretrained=pretrained, batch_size=batch_size, model_type="page", **kwargs)
