# Copyright 2023 The KerasCV Authors
#
# 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
#
#     https://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 keras
import tensorflow as tf
import tree

if hasattr(keras, "src"):
    keras_backend = keras.src.backend
else:
    keras_backend = keras.backend

from keras_cv import bounding_box
from keras_cv.api_export import keras_cv_export
from keras_cv.backend import config
from keras_cv.backend import keras
from keras_cv.backend import ops
from keras_cv.backend import scope
from keras_cv.utils import preprocessing

# In order to support both unbatched and batched inputs, the horizontal
# and vertical axis is reverse indexed
H_AXIS = -3
W_AXIS = -2

IMAGES = "images"
LABELS = "labels"
TARGETS = "targets"
BOUNDING_BOXES = "bounding_boxes"
KEYPOINTS = "keypoints"
SEGMENTATION_MASKS = "segmentation_masks"
IS_DICT = "is_dict"
USE_TARGETS = "use_targets"


@keras_cv_export("keras_cv.layers.BaseImageAugmentationLayer")
class BaseImageAugmentationLayer(keras.layers.Layer):
    """Abstract base layer for image augmentation.

    This layer contains base functionalities for preprocessing layers which
    augment image related data, e.g. image and in the future, label and bounding
    boxes. The subclasses could avoid making certain mistakes and reduce code
    duplications.

    This layer requires you to implement one method: `augment_image()`, which
    augments one single image during the training. There are a few additional
    methods that you can implement for added functionality on the layer:

    `augment_label()`, which handles label augmentation if the layer supports
    that.

    `augment_bounding_boxes()`, which handles the bounding box augmentation, if
    the layer supports that.

    `get_random_transformation()`, which should produce a random transformation
    setting. The transformation object, which could be of any type, will be
    passed to `augment_image`, `augment_label` and `augment_bounding_boxes`, to
    coordinate the randomness behaviour, e.g., in the RandomFlip layer, the
    image and bounding_boxes should be changed in the same way.

    The `call()` method supports two formats of inputs:
    1. A single image tensor with shape (height, width, channels) or
       (batch_size, height, width, channels)
    2. A dict of tensors with any of the following keys (note that `"images"`
       must be present):
       * `"images"` - Image Tensor with shape (height, width, channels) or
        (batch_size, height, width, channels)
       * `"labels"` - One-hot encoded classification labels Tensor with shape
        (num_classes) or (batch_size, num_classes)
       * `"bounding_boxes"` - A dictionary with keys:
         * `"boxes"` - Tensor with shape (num_boxes, 4) or (batch_size,
          num_boxes, 4)
         * `"classes"` - Tensor of class labels for boxes with shape (num_boxes,
          num_classes) or (batch_size, num_boxes, num_classes).
       Any other keys included in this dictionary will be ignored and unmodified
       by an augmentation layer.

    The output of the `call()` will be the same structure as the inputs.

    The `call()` will unpack the inputs, forward to the correct function, and
    pack the output back to the same structure as the inputs.

    By default, the `call()` method leverages the `tf.vectorized_map()`
    function. Auto-vectorization can be disabled by setting
    `self.auto_vectorize = False` in your `__init__()` method. When disabled,
    `call()` instead relies on `tf.map_fn()`. For example:

    ```python
    class SubclassLayer(keras_cv.BaseImageAugmentationLayer):
      def __init__(self):
        super().__init__()
        self.auto_vectorize = False
    ```

    Example:

    ```python
    class RandomContrast(keras_cv.BaseImageAugmentationLayer):

      def __init__(self, factor=(0.5, 1.5), **kwargs):
        super().__init__(**kwargs)
        self._factor = factor

      def augment_image(self, image, transformation):
        random_factor = tf.random.uniform([], self._factor[0], self._factor[1])
        mean = tf.math.reduced_mean(inputs, axis=-1, keep_dim=True)
        return (inputs - mean) * random_factor + mean
    ```

    Note that since the randomness is also a common functionality, this layer
    also includes a keras_backend.RandomGenerator, which can be used to
    produce the random numbers. The random number generator is stored in the
    `self._random_generator` attribute.
    """

    def __init__(self, seed=None, **kwargs):
        if seed is not None:
            self._random_generator = tf.random.Generator.from_seed(seed=seed)
        else:
            self._random_generator = tf.random.get_global_generator()
        super().__init__(**kwargs)

        self._allow_non_tensor_positional_args = True
        self.built = True
        self._convert_input_args = False

    @property
    def force_output_ragged_images(self):
        """Control whether to force outputting of ragged images."""
        return getattr(self, "_force_output_ragged_images", False)

    @force_output_ragged_images.setter
    def force_output_ragged_images(self, force_output_ragged_images):
        self._force_output_ragged_images = force_output_ragged_images

    @property
    def force_output_dense_images(self):
        """Control whether to force outputting of dense images."""
        return getattr(self, "_force_output_dense_images", False)

    @force_output_dense_images.setter
    def force_output_dense_images(self, force_output_dense_images):
        self._force_output_dense_images = force_output_dense_images

    @property
    def auto_vectorize(self):
        """Control whether automatic vectorization occurs.

        By default, the `call()` method leverages the `tf.vectorized_map()`
        function. Auto-vectorization can be disabled by setting
        `self.auto_vectorize = False` in your `__init__()` method. When
        disabled, `call()` instead relies on `tf.map_fn()`. For example:

        ```python
        class SubclassLayer(BaseImageAugmentationLayer):
          def __init__(self):
            super().__init__()
            self.auto_vectorize = False
        ```
        """
        return getattr(self, "_auto_vectorize", True)

    @auto_vectorize.setter
    def auto_vectorize(self, auto_vectorize):
        self._auto_vectorize = auto_vectorize

    def compute_image_signature(self, images):
        """Computes the output image signature for the `augment_image()`
        function.

        Must be overridden to return tensors with different shapes than the
        input images. By default, returns either a `tf.RaggedTensorSpec`
        matching the input image spec, or a `tf.TensorSpec` matching the input
        image spec.
        """
        if self.force_output_dense_images:
            return tf.TensorSpec(images.shape[1:], self.compute_dtype)
        if self.force_output_ragged_images or isinstance(
            images, tf.RaggedTensor
        ):
            ragged_spec = tf.RaggedTensorSpec(
                shape=images.shape[1:],
                ragged_rank=1,
                dtype=self.compute_dtype,
            )
            return ragged_spec
        return tf.TensorSpec(images.shape[1:], self.compute_dtype)

    # TODO(lukewood): promote to user facing API if needed
    def _compute_bounding_box_signature(self, bounding_boxes):
        return {
            "boxes": tf.RaggedTensorSpec(
                shape=[None, 4],
                ragged_rank=1,
                dtype=self.compute_dtype,
            ),
            "classes": tf.RaggedTensorSpec(
                shape=[None], dtype=self.compute_dtype
            ),
        }

    # TODO(lukewood): promote to user facing API if needed
    def _compute_keypoints_signature(self, keypoints):
        if isinstance(keypoints, tf.RaggedTensor):
            ragged_spec = tf.RaggedTensorSpec(
                shape=keypoints.shape[1:],
                ragged_rank=1,
                dtype=self.compute_dtype,
            )
            return ragged_spec

        return tf.TensorSpec(
            shape=keypoints.shape[1:],
            dtype=self.compute_dtype,
        )

    # TODO(lukewood): promote to user facing API if needed
    def _compute_target_signature(self, targets):
        return tf.TensorSpec(targets.shape[1:], self.compute_dtype)

    def _compute_output_signature(self, inputs):
        fn_output_signature = {
            IMAGES: self.compute_image_signature(inputs[IMAGES])
        }
        bounding_boxes = inputs.get(BOUNDING_BOXES, None)

        if bounding_boxes is not None:
            fn_output_signature[BOUNDING_BOXES] = (
                self._compute_bounding_box_signature(bounding_boxes)
            )

        segmentation_masks = inputs.get(SEGMENTATION_MASKS, None)
        if segmentation_masks is not None:
            fn_output_signature[SEGMENTATION_MASKS] = (
                self.compute_image_signature(segmentation_masks)
            )

        keypoints = inputs.get(KEYPOINTS, None)
        if keypoints is not None:
            fn_output_signature[KEYPOINTS] = self._compute_keypoints_signature(
                keypoints
            )

        labels = inputs.get(LABELS, None)
        if labels is not None:
            fn_output_signature[LABELS] = self._compute_target_signature(labels)

        return fn_output_signature

    @staticmethod
    def _any_ragged(inputs):
        if isinstance(inputs[IMAGES], tf.RaggedTensor):
            return True
        if BOUNDING_BOXES in inputs:
            return True
        if KEYPOINTS in inputs:
            return True
        return False

    def _map_fn(self, func, inputs):
        """Returns either tf.map_fn or tf.vectorized_map based on the provided
        inputs.

        Args:
            inputs: dictionary of inputs provided to map_fn.
        """
        if self._any_ragged(inputs) or self.force_output_ragged_images:
            return tf.map_fn(
                func,
                inputs,
                fn_output_signature=self._compute_output_signature(inputs),
            )
        if self.auto_vectorize:
            return tf.vectorized_map(func, inputs)
        return tf.map_fn(func, inputs)

    def augment_image(self, image, transformation, **kwargs):
        """Augment a single image during training.

        Args:
          image: 3D image input tensor to the layer. Forwarded from
            `layer.call()`.
          transformation: The transformation object produced by
            `get_random_transformation`. Used to coordinate the randomness
            between image, label, bounding box, keypoints, and segmentation
            mask.

        Returns:
          output 3D tensor, which will be forward to `layer.call()`.
        """
        raise NotImplementedError()

    def augment_label(self, label, transformation, **kwargs):
        """Augment a single label during training.

        Args:
          label: 1D label to the layer. Forwarded from `layer.call()`.
          transformation: The transformation object produced by
            `get_random_transformation`. Used to coordinate the randomness
            between image, label, bounding box, keypoints, and segmentation
            mask.

        Returns:
          output 1D tensor, which will be forward to `layer.call()`.
        """
        raise NotImplementedError()

    def augment_target(self, target, transformation, **kwargs):
        """Augment a single target during training.

        Args:
          target: 1D label to the layer. Forwarded from `layer.call()`.
          transformation: The transformation object produced by
            `get_random_transformation`. Used to coordinate the randomness
            between image, label, bounding box, keypoints, and segmentation
            mask.

        Returns:
          output 1D tensor, which will be forward to `layer.call()`.
        """
        return self.augment_label(target, transformation)

    def augment_bounding_boxes(self, bounding_boxes, transformation, **kwargs):
        """Augment bounding boxes for one image during training.

        Args:
          bounding_boxes: 2D bounding boxes to the layer. Forwarded from
            `call()`.
          transformation: The transformation object produced by
            `get_random_transformation`. Used to coordinate the randomness
            between image, label, bounding box, keypoints, and segmentation
            mask.

        Returns:
          output 2D tensor, which will be forward to `layer.call()`.
        """
        raise NotImplementedError()

    def augment_keypoints(self, keypoints, transformation, **kwargs):
        """Augment keypoints for one image during training.

        Args:
          keypoints: 2D keypoints input tensor to the layer. Forwarded from
            `layer.call()`.
          transformation: The transformation object produced by
            `get_random_transformation`. Used to coordinate the randomness
            between image, label, bounding box, keypoints, and segmentation
            mask.

        Returns:
          output 2D tensor, which will be forward to `layer.call()`.
        """
        raise NotImplementedError()

    def augment_segmentation_mask(
        self, segmentation_mask, transformation, **kwargs
    ):
        """Augment a single image's segmentation mask during training.

        Args:
          segmentation_mask: 3D segmentation mask input tensor to the layer.
            This should generally have the shape [H, W, 1], or in some cases
            [H, W, C] for multilabeled data. Forwarded from `layer.call()`.
          transformation: The transformation object produced by
            `get_random_transformation`. Used to coordinate the randomness
            between image, label, bounding box, keypoints, and segmentation
            mask.

        Returns:
          output 3D tensor containing the augmented segmentation mask, which
          will be forward to `layer.call()`.
        """
        raise NotImplementedError()

    def get_random_transformation(
        self,
        image=None,
        label=None,
        bounding_boxes=None,
        keypoints=None,
        segmentation_mask=None,
    ):
        """Produce random transformation config for one single input.

        This is used to produce same randomness between
        image/label/bounding_box.

        Args:
          image: 3D image tensor from inputs.
          label: optional 1D label tensor from inputs.
          bounding_boxes: optional 2D bounding boxes tensor from inputs.
          segmentation_mask: optional 3D segmentation mask tensor from inputs.

        Returns:
          Any type of object, which will be forwarded to `augment_image`,
          `augment_label` and `augment_bounding_box` as the `transformation`
          parameter.
        """
        return None

    def call(self, inputs):
        # try to convert a given backend native tensor to TensorFlow tensor
        # before passing it over to TFDataScope
        is_tf_backend = config.backend() == "tensorflow"
        is_in_tf_graph = not tf.executing_eagerly()
        contains_ragged = lambda y: any(
            tree.map_structure(
                lambda x: isinstance(x, (tf.RaggedTensor, tf.SparseTensor)),
                tree.flatten(y),
            )
        )
        inputs_contain_ragged = contains_ragged(inputs)
        if not is_tf_backend and not inputs_contain_ragged:
            inputs = tree.map_structure(
                lambda x: tf.convert_to_tensor(x), inputs
            )
        with scope.TFDataScope():
            inputs = self._ensure_inputs_are_compute_dtype(inputs)
            inputs, metadata = self._format_inputs(inputs)
            images = inputs[IMAGES]
            if images.shape.rank == 3:
                outputs = self._format_output(self._augment(inputs), metadata)
            elif images.shape.rank == 4:
                outputs = self._format_output(
                    self._batch_augment(inputs), metadata
                )
            else:
                raise ValueError(
                    "Image augmentation layers are expecting inputs to be "
                    "rank 3 (HWC) or 4D (NHWC) tensors. Got shape: "
                    f"{images.shape}"
                )
        # convert the outputs to backend native tensors if none of them
        # contain RaggedTensors. Note that if the user passed in Raggeds
        # but the outputs are dense, we still don't want to convert to
        # backend native tensors. This is to avoid breaking TF data
        # pipelines that can't easily be ported to become backend
        # agnostic.
        # Skip this step for TF backend or if in `tf.graph` like `tf.data`.
        if not is_tf_backend and not is_in_tf_graph:
            if not inputs_contain_ragged and not contains_ragged(outputs):
                outputs = tree.map_structure(
                    # some layers return None, handle that case when
                    # converting to tensors
                    lambda x: ops.convert_to_tensor(x) if x is not None else x,
                    outputs,
                )
        return outputs

    def _augment(self, inputs):
        raw_image = inputs.get(IMAGES, None)
        image = raw_image
        label = inputs.get(LABELS, None)
        bounding_boxes = inputs.get(BOUNDING_BOXES, None)
        keypoints = inputs.get(KEYPOINTS, None)
        segmentation_mask = inputs.get(SEGMENTATION_MASKS, None)

        image_ragged = isinstance(image, tf.RaggedTensor)
        # At this point, the tensor is not actually ragged as we have mapped
        # over the batch axis. This call is required to make `tf.shape()` behave
        # as users subclassing the layer expect.
        if image_ragged:
            image = image.to_tensor()

        transformation = self.get_random_transformation(
            image=image,
            label=label,
            bounding_boxes=bounding_boxes,
            keypoints=keypoints,
            segmentation_mask=segmentation_mask,
        )

        image = self.augment_image(
            image,
            transformation=transformation,
            bounding_boxes=bounding_boxes,
            label=label,
        )

        if (
            image_ragged and not self.force_output_dense_images
        ) or self.force_output_ragged_images:
            image = tf.RaggedTensor.from_tensor(image)

        result = {IMAGES: image}
        if label is not None:
            label = self.augment_target(
                label,
                transformation=transformation,
                bounding_boxes=bounding_boxes,
                image=image,
            )
            result[LABELS] = label

        if bounding_boxes is not None:
            bounding_boxes = bounding_box.to_dense(bounding_boxes)

            bounding_boxes = self.augment_bounding_boxes(
                bounding_boxes,
                transformation=transformation,
                label=label,
                image=raw_image,
            )

            bounding_boxes = bounding_box.to_ragged(
                bounding_boxes, dtype=self.compute_dtype
            )
            result[BOUNDING_BOXES] = bounding_boxes

        if keypoints is not None:
            keypoints = self.augment_keypoints(
                keypoints,
                transformation=transformation,
                label=label,
                bounding_boxes=bounding_boxes,
                image=image,
            )
            result[KEYPOINTS] = keypoints
        if segmentation_mask is not None:
            segmentation_mask = self.augment_segmentation_mask(
                segmentation_mask,
                transformation=transformation,
            )
            result[SEGMENTATION_MASKS] = segmentation_mask

        # preserve any additional inputs unmodified by this layer.
        for key in inputs.keys() - result.keys():
            result[key] = inputs[key]
        return result

    def _batch_augment(self, inputs):
        return self._map_fn(self._augment, inputs)

    def _format_inputs(self, inputs):
        metadata = {IS_DICT: True, USE_TARGETS: False}
        if tf.is_tensor(inputs):
            # single image input tensor
            metadata[IS_DICT] = False
            inputs = {IMAGES: inputs}
            return inputs, metadata

        if not isinstance(inputs, dict):
            raise ValueError(
                "Expect the inputs to be image tensor or dict. Got "
                f"inputs={inputs} of type {type(inputs)}"
            )

        if BOUNDING_BOXES in inputs:
            inputs[BOUNDING_BOXES] = self._format_bounding_boxes(
                inputs[BOUNDING_BOXES]
            )

        if isinstance(inputs, dict) and TARGETS in inputs:
            # TODO(scottzhu): Check if it only contains the valid keys
            inputs[LABELS] = inputs[TARGETS]
            del inputs[TARGETS]
            metadata[USE_TARGETS] = True
            return inputs, metadata

        return inputs, metadata

    def _format_bounding_boxes(self, bounding_boxes):
        # We can't catch the case where this is None, sometimes RaggedTensor
        # drops this dimension
        if "classes" not in bounding_boxes:
            raise ValueError(
                "Bounding boxes are missing class_id. If you would like to pad "
                "the bounding boxes with class_id, use: "
                "`bounding_boxes['classes'] = "
                "tf.ones_like(bounding_boxes['boxes'])`."
            )
        return bounding_boxes

    def _format_output(self, output, metadata):
        if not metadata[IS_DICT]:
            return output[IMAGES]
        elif metadata[USE_TARGETS]:
            output[TARGETS] = output[LABELS]
            del output[LABELS]
        return output

    def _ensure_inputs_are_compute_dtype(self, inputs):
        if not isinstance(inputs, dict):
            return preprocessing.ensure_tensor(
                inputs,
                self.compute_dtype,
            )
        # Copy the input dict before we mutate it.
        inputs = dict(inputs)
        inputs[IMAGES] = preprocessing.ensure_tensor(
            inputs[IMAGES],
            self.compute_dtype,
        )
        if BOUNDING_BOXES in inputs:
            inputs[BOUNDING_BOXES] = bounding_box.ensure_tensor(
                inputs[BOUNDING_BOXES], dtype=self.compute_dtype
            )
        return inputs
