"""This module contains inference-related abstract class and its Torch and OpenVINO implementations."""

# Copyright (C) 2020 Intel Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions
# and limitations under the License.

import importlib
from pathlib import Path
from typing import Dict, Optional, Tuple, Union

import cv2
import numpy as np
from omegaconf import DictConfig, ListConfig

from anomalib.pre_processing import PreProcessor

from .base import Inferencer

if importlib.util.find_spec("openvino") is not None:
    from openvino.inference_engine import IECore  # pylint: disable=no-name-in-module


class OpenVINOInferencer(Inferencer):
    """OpenVINO implementation for the inference.

    Args:
        config (DictConfig): Configurable parameters that are used
            during the training stage.
        path (Union[str, Path]): Path to the openvino onnx, xml or bin file.
        meta_data_path (Union[str, Path], optional): Path to metadata file. Defaults to None.
    """

    def __init__(
        self,
        config: Union[DictConfig, ListConfig],
        path: Union[str, Path, Tuple[bytes, bytes]],
        meta_data_path: Union[str, Path] = None,
    ):
        self.config = config
        self.input_blob, self.output_blob, self.network = self.load_model(path)
        self.meta_data = super()._load_meta_data(meta_data_path)

    def load_model(self, path: Union[str, Path, Tuple[bytes, bytes]]):
        """Load the OpenVINO model.

        Args:
            path (Union[str, Path, Tuple[bytes, bytes]]): Path to the onnx or xml and bin files
                                                        or tuple of .xml and .bin data as bytes.

        Returns:
            [Tuple[str, str, ExecutableNetwork]]: Input and Output blob names
                together with the Executable network.
        """
        ie_core = IECore()
        # If tuple of bytes is passed

        if isinstance(path, tuple):
            network = ie_core.read_network(model=path[0], weights=path[1], init_from_buffer=True)
        else:
            path = path if isinstance(path, Path) else Path(path)
            if path.suffix in (".bin", ".xml"):
                if path.suffix == ".bin":
                    bin_path, xml_path = path, path.with_suffix(".xml")
                elif path.suffix == ".xml":
                    xml_path, bin_path = path, path.with_suffix(".bin")
                network = ie_core.read_network(xml_path, bin_path)
            elif path.suffix == ".onnx":
                network = ie_core.read_network(path)
            else:
                raise ValueError(f"Path must be .onnx, .bin or .xml file. Got {path.suffix}")

        input_blob = next(iter(network.input_info))
        output_blob = next(iter(network.outputs))
        executable_network = ie_core.load_network(network=network, device_name="CPU")

        return input_blob, output_blob, executable_network

    def pre_process(self, image: np.ndarray) -> np.ndarray:
        """Pre process the input image by applying transformations.

        Args:
            image (np.ndarray): Input image.

        Returns:
            np.ndarray: pre-processed image.
        """
        config = self.config.transform if "transform" in self.config.keys() else None
        image_size = tuple(self.config.dataset.image_size)
        pre_processor = PreProcessor(config, image_size)
        processed_image = pre_processor(image=image)["image"]

        if len(processed_image.shape) == 3:
            processed_image = np.expand_dims(processed_image, axis=0)

        if processed_image.shape[-1] == 3:
            processed_image = processed_image.transpose(0, 3, 1, 2)

        return processed_image

    def forward(self, image: np.ndarray) -> np.ndarray:
        """Forward-Pass input tensor to the model.

        Args:
            image (np.ndarray): Input tensor.

        Returns:
            np.ndarray: Output predictions.
        """
        return self.network.infer(inputs={self.input_blob: image})

    def post_process(
        self, predictions: np.ndarray, meta_data: Optional[Union[Dict, DictConfig]] = None
    ) -> Tuple[np.ndarray, float]:
        """Post process the output predictions.

        Args:
            predictions (np.ndarray): Raw output predicted by the model.
            meta_data (Dict, optional): Meta data. Post-processing step sometimes requires
                additional meta data such as image shape. This variable comprises such info.
                Defaults to None.

        Returns:
            np.ndarray: Post processed predictions that are ready to be visualized.
        """
        if meta_data is None:
            meta_data = self.meta_data

        predictions = predictions[self.output_blob]
        anomaly_map = predictions.squeeze()
        pred_score = anomaly_map.reshape(-1).max()

        anomaly_map, pred_score = self._normalize(anomaly_map, pred_score, meta_data)

        if "image_shape" in meta_data and anomaly_map.shape != meta_data["image_shape"]:
            anomaly_map = cv2.resize(anomaly_map, meta_data["image_shape"])

        return anomaly_map, float(pred_score)
