"""PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localization.

Paper https://arxiv.org/abs/2011.08785
"""

# 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 logging
from typing import List, Union

import torch
from omegaconf import DictConfig, ListConfig
from torch import Tensor

from anomalib.models.components import AnomalyModule
from anomalib.models.padim.torch_model import PadimModel

logger = logging.getLogger(__name__)

__all__ = ["PadimLightning"]


class PadimLightning(AnomalyModule):
    """PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localization.

    Args:
        hparams (Union[DictConfig, ListConfig]): Model params
    """

    def __init__(self, hparams: Union[DictConfig, ListConfig]):
        super().__init__(hparams)
        logger.info("Initializing Padim Lightning model.")

        self.layers = hparams.model.layers
        self.model: PadimModel = PadimModel(
            layers=hparams.model.layers,
            input_size=hparams.model.input_size,
            tile_size=hparams.dataset.tiling.tile_size,
            tile_stride=hparams.dataset.tiling.stride,
            apply_tiling=hparams.dataset.tiling.apply,
            backbone=hparams.model.backbone,
        ).eval()

        self.stats: List[Tensor] = []
        self.embeddings: List[Tensor] = []

    @staticmethod
    def configure_optimizers():  # pylint: disable=arguments-differ
        """PADIM doesn't require optimization, therefore returns no optimizers."""
        return None

    def training_step(self, batch, _batch_idx):  # pylint: disable=arguments-differ
        """Training Step of PADIM. For each batch, hierarchical features are extracted from the CNN.

        Args:
            batch (Dict[str, Any]): Batch containing image filename, image, label and mask
            _batch_idx: Index of the batch.

        Returns:
            Hierarchical feature map
        """
        self.model.feature_extractor.eval()
        embedding = self.model(batch["image"])

        # NOTE: `self.embedding` appends each batch embedding to
        #   store the training set embedding. We manually append these
        #   values mainly due to the new order of hooks introduced after PL v1.4.0
        #   https://github.com/PyTorchLightning/pytorch-lightning/pull/7357
        self.embeddings.append(embedding.cpu())

    def on_validation_start(self) -> None:
        """Fit a Gaussian to the embedding collected from the training set."""
        # NOTE: Previous anomalib versions fit Gaussian at the end of the epoch.
        #   This is not possible anymore with PyTorch Lightning v1.4.0 since validation
        #   is run within train epoch.
        logger.info("Aggregating the embedding extracted from the training set.")
        embeddings = torch.vstack(self.embeddings)

        logger.info("Fitting a Gaussian to the embedding collected from the training set.")
        self.stats = self.model.gaussian.fit(embeddings)

    def validation_step(self, batch, _):  # pylint: disable=arguments-differ
        """Validation Step of PADIM.

        Similar to the training step, hierarchical features are extracted from the CNN for each batch.

        Args:
            batch: Input batch
            _: Index of the batch.

        Returns:
            Dictionary containing images, features, true labels and masks.
            These are required in `validation_epoch_end` for feature concatenation.
        """

        batch["anomaly_maps"] = self.model(batch["image"])
        return batch