import json
import warnings

import numpy as np

from keras import activations
from keras import backend
from keras import ops
from keras.api_export import keras_export
from keras.utils import file_utils

CLASS_INDEX = None
CLASS_INDEX_PATH = (
    "https://storage.googleapis.com/download.tensorflow.org/"
    "data/imagenet_class_index.json"
)


PREPROCESS_INPUT_DOC = """
  Preprocesses a tensor or Numpy array encoding a batch of images.

  Usage example with `applications.MobileNet`:

  ```python
  i = keras.layers.Input([None, None, 3], dtype="uint8")
  x = ops.cast(i, "float32")
  x = keras.applications.mobilenet.preprocess_input(x)
  core = keras.applications.MobileNet()
  x = core(x)
  model = keras.Model(inputs=[i], outputs=[x])
  result = model(image)
  ```

  Args:
        x: A floating point `numpy.array` or a backend-native tensor,
            3D or 4D with 3 color
            channels, with values in the range [0, 255].
            The preprocessed data are written over the input data
        if the data types are compatible. To avoid this
        behaviour, `numpy.copy(x)` can be used.
        data_format: Optional data format of the image tensor/array. None, means
        the global setting `keras.backend.image_data_format()` is used
        (unless you changed it, it uses "channels_last").{mode}
        Defaults to `None`.

  Returns:
      Preprocessed array with type `float32`.
      {ret}

  Raises:
      {error}
  """

PREPROCESS_INPUT_MODE_DOC = """
    mode: One of "caffe", "tf" or "torch".
      - caffe: will convert the images from RGB to BGR,
          then will zero-center each color channel with
          respect to the ImageNet dataset,
          without scaling.
      - tf: will scale pixels between -1 and 1,
          sample-wise.
      - torch: will scale pixels between 0 and 1 and then
          will normalize each channel with respect to the
          ImageNet dataset.
      Defaults to `"caffe"`.
  """

PREPROCESS_INPUT_DEFAULT_ERROR_DOC = """
    ValueError: In case of unknown `mode` or `data_format` argument."""

PREPROCESS_INPUT_ERROR_DOC = """
    ValueError: In case of unknown `data_format` argument."""

PREPROCESS_INPUT_RET_DOC_TF = """
      The inputs pixel values are scaled between -1 and 1, sample-wise."""

PREPROCESS_INPUT_RET_DOC_TORCH = """
      The input pixels values are scaled between 0 and 1 and each channel is
      normalized with respect to the ImageNet dataset."""

PREPROCESS_INPUT_RET_DOC_CAFFE = """
      The images are converted from RGB to BGR, then each color channel is
      zero-centered with respect to the ImageNet dataset, without scaling."""


@keras_export("keras.applications.imagenet_utils.preprocess_input")
def preprocess_input(x, data_format=None, mode="caffe"):
    """Preprocesses a tensor or Numpy array encoding a batch of images."""
    if mode not in {"caffe", "tf", "torch"}:
        raise ValueError(
            "Expected mode to be one of `caffe`, `tf` or `torch`. "
            f"Received: mode={mode}"
        )

    if data_format is None:
        data_format = backend.image_data_format()
    elif data_format not in {"channels_first", "channels_last"}:
        raise ValueError(
            "Expected data_format to be one of `channels_first` or "
            f"`channels_last`. Received: data_format={data_format}"
        )

    if isinstance(x, np.ndarray):
        return _preprocess_numpy_input(x, data_format=data_format, mode=mode)
    else:
        return _preprocess_tensor_input(x, data_format=data_format, mode=mode)


preprocess_input.__doc__ = PREPROCESS_INPUT_DOC.format(
    mode=PREPROCESS_INPUT_MODE_DOC,
    ret="",
    error=PREPROCESS_INPUT_DEFAULT_ERROR_DOC,
)


@keras_export("keras.applications.imagenet_utils.decode_predictions")
def decode_predictions(preds, top=5):
    """Decodes the prediction of an ImageNet model.

    Args:
        preds: NumPy array encoding a batch of predictions.
        top: Integer, how many top-guesses to return. Defaults to `5`.

    Returns:
        A list of lists of top class prediction tuples
        `(class_name, class_description, score)`.
        One list of tuples per sample in batch input.

    Raises:
        ValueError: In case of invalid shape of the `pred` array
            (must be 2D).
    """
    global CLASS_INDEX

    if len(preds.shape) != 2 or preds.shape[1] != 1000:
        raise ValueError(
            "`decode_predictions` expects "
            "a batch of predictions "
            "(i.e. a 2D array of shape (samples, 1000)). "
            f"Received array with shape: {preds.shape}"
        )
    if CLASS_INDEX is None:
        fpath = file_utils.get_file(
            "imagenet_class_index.json",
            CLASS_INDEX_PATH,
            cache_subdir="models",
            file_hash="c2c37ea517e94d9795004a39431a14cb",
        )
        with open(fpath) as f:
            CLASS_INDEX = json.load(f)
    results = []
    preds = ops.convert_to_numpy(preds)
    for pred in preds:
        top_indices = pred.argsort()[-top:][::-1]
        result = [tuple(CLASS_INDEX[str(i)]) + (pred[i],) for i in top_indices]
        result.sort(key=lambda x: x[2], reverse=True)
        results.append(result)
    return results


def _preprocess_numpy_input(x, data_format, mode):
    """Preprocesses a NumPy array encoding a batch of images.

    Args:
      x: Input array, 3D or 4D.
      data_format: Data format of the image array.
      mode: One of "caffe", "tf" or "torch".
        - caffe: will convert the images from RGB to BGR,
            then will zero-center each color channel with
            respect to the ImageNet dataset,
            without scaling.
        - tf: will scale pixels between -1 and 1,
            sample-wise.
        - torch: will scale pixels between 0 and 1 and then
            will normalize each channel with respect to the
            ImageNet dataset.

    Returns:
        Preprocessed Numpy array.
    """
    if not issubclass(x.dtype.type, np.floating):
        x = x.astype(backend.floatx(), copy=False)

    if mode == "tf":
        x /= 127.5
        x -= 1.0
        return x
    elif mode == "torch":
        x /= 255.0
        mean = [0.485, 0.456, 0.406]
        std = [0.229, 0.224, 0.225]
    else:
        if data_format == "channels_first":
            # 'RGB'->'BGR'
            if len(x.shape) == 3:
                x = x[::-1, ...]
            else:
                x = x[:, ::-1, ...]
        else:
            # 'RGB'->'BGR'
            x = x[..., ::-1]
        mean = [103.939, 116.779, 123.68]
        std = None

    # Zero-center by mean pixel
    if data_format == "channels_first":
        if len(x.shape) == 3:
            x[0, :, :] -= mean[0]
            x[1, :, :] -= mean[1]
            x[2, :, :] -= mean[2]
            if std is not None:
                x[0, :, :] /= std[0]
                x[1, :, :] /= std[1]
                x[2, :, :] /= std[2]
        else:
            x[:, 0, :, :] -= mean[0]
            x[:, 1, :, :] -= mean[1]
            x[:, 2, :, :] -= mean[2]
            if std is not None:
                x[:, 0, :, :] /= std[0]
                x[:, 1, :, :] /= std[1]
                x[:, 2, :, :] /= std[2]
    else:
        x[..., 0] -= mean[0]
        x[..., 1] -= mean[1]
        x[..., 2] -= mean[2]
        if std is not None:
            x[..., 0] /= std[0]
            x[..., 1] /= std[1]
            x[..., 2] /= std[2]
    return x


def _preprocess_tensor_input(x, data_format, mode):
    """Preprocesses a tensor encoding a batch of images.

    Args:
      x: Input tensor, 3D or 4D.
      data_format: Data format of the image tensor.
      mode: One of "caffe", "tf" or "torch".
        - caffe: will convert the images from RGB to BGR,
            then will zero-center each color channel with
            respect to the ImageNet dataset,
            without scaling.
        - tf: will scale pixels between -1 and 1,
            sample-wise.
        - torch: will scale pixels between 0 and 1 and then
            will normalize each channel with respect to the
            ImageNet dataset.

    Returns:
        Preprocessed tensor.
    """
    ndim = len(x.shape)

    if mode == "tf":
        x /= 127.5
        x -= 1.0
        return x
    elif mode == "torch":
        x /= 255.0
        mean = [0.485, 0.456, 0.406]
        std = [0.229, 0.224, 0.225]
    else:
        if data_format == "channels_first":
            # 'RGB'->'BGR'
            if len(x.shape) == 3:
                x = ops.stack([x[i, ...] for i in (2, 1, 0)], axis=0)
            else:
                x = ops.stack([x[:, i, :] for i in (2, 1, 0)], axis=1)
        else:
            # 'RGB'->'BGR'
            x = ops.stack([x[..., i] for i in (2, 1, 0)], axis=-1)
        mean = [103.939, 116.779, 123.68]
        std = None

    mean_tensor = ops.convert_to_tensor(-np.array(mean), dtype=x.dtype)

    # Zero-center by mean pixel
    if data_format == "channels_first":
        mean_tensor = ops.reshape(mean_tensor, (1, 3) + (1,) * (ndim - 2))
    else:
        mean_tensor = ops.reshape(mean_tensor, (1,) * (ndim - 1) + (3,))
    x += mean_tensor
    if std is not None:
        std_tensor = ops.convert_to_tensor(np.array(std), dtype=x.dtype)
        if data_format == "channels_first":
            std_tensor = ops.reshape(std_tensor, (-1, 1, 1))
        x /= std_tensor
    return x


def obtain_input_shape(
    input_shape,
    default_size,
    min_size,
    data_format,
    require_flatten,
    weights=None,
):
    """Internal utility to compute/validate a model's input shape.

    Args:
      input_shape: Either None (will return the default network input shape),
        or a user-provided shape to be validated.
      default_size: Default input width/height for the model.
      min_size: Minimum input width/height accepted by the model.
      data_format: Image data format to use.
      require_flatten: Whether the model is expected to
        be linked to a classifier via a Flatten layer.
      weights: One of `None` (random initialization)
        or 'imagenet' (pre-training on ImageNet).
        If weights='imagenet' input channels must be equal to 3.

    Returns:
      An integer shape tuple (may include None entries).

    Raises:
      ValueError: In case of invalid argument values.
    """
    if weights != "imagenet" and input_shape and len(input_shape) == 3:
        if data_format == "channels_first":
            correct_channel_axis = 1 if len(input_shape) == 4 else 0
            if input_shape[correct_channel_axis] not in {1, 3}:
                warnings.warn(
                    "This model usually expects 1 or 3 input channels. "
                    "However, it was passed an input_shape "
                    f"with {input_shape[0]} input channels.",
                    stacklevel=2,
                )
            default_shape = (input_shape[0], default_size, default_size)
        else:
            if input_shape[-1] not in {1, 3}:
                warnings.warn(
                    "This model usually expects 1 or 3 input channels. "
                    "However, it was passed an input_shape "
                    f"with {input_shape[-1]} input channels.",
                    stacklevel=2,
                )
            default_shape = (default_size, default_size, input_shape[-1])
    else:
        if data_format == "channels_first":
            default_shape = (3, default_size, default_size)
        else:
            default_shape = (default_size, default_size, 3)
    if weights == "imagenet" and require_flatten:
        if input_shape is not None:
            if input_shape != default_shape:
                raise ValueError(
                    "When setting `include_top=True` "
                    "and loading `imagenet` weights, "
                    f"`input_shape` should be {default_shape}.  "
                    f"Received: input_shape={input_shape}"
                )
        return default_shape
    if input_shape:
        if data_format == "channels_first":
            if input_shape is not None:
                if len(input_shape) != 3:
                    raise ValueError(
                        "`input_shape` must be a tuple of three integers."
                    )
                if input_shape[0] != 3 and weights == "imagenet":
                    raise ValueError(
                        "The input must have 3 channels; Received "
                        f"`input_shape={input_shape}`"
                    )
                if (
                    input_shape[1] is not None and input_shape[1] < min_size
                ) or (input_shape[2] is not None and input_shape[2] < min_size):
                    raise ValueError(
                        f"Input size must be at least {min_size}"
                        f"x{min_size}; Received: "
                        f"input_shape={input_shape}"
                    )
        else:
            if input_shape is not None:
                if len(input_shape) != 3:
                    raise ValueError(
                        "`input_shape` must be a tuple of three integers."
                    )
                if input_shape[-1] != 3 and weights == "imagenet":
                    raise ValueError(
                        "The input must have 3 channels; Received "
                        f"`input_shape={input_shape}`"
                    )
                if (
                    input_shape[0] is not None and input_shape[0] < min_size
                ) or (input_shape[1] is not None and input_shape[1] < min_size):
                    raise ValueError(
                        "Input size must be at least "
                        f"{min_size}x{min_size}; Received: "
                        f"input_shape={input_shape}"
                    )
    else:
        if require_flatten:
            input_shape = default_shape
        else:
            if data_format == "channels_first":
                input_shape = (3, None, None)
            else:
                input_shape = (None, None, 3)
    if require_flatten:
        if None in input_shape:
            raise ValueError(
                "If `include_top` is True, "
                "you should specify a static `input_shape`. "
                f"Received: input_shape={input_shape}"
            )
    return input_shape


def correct_pad(inputs, kernel_size):
    """Returns a tuple for zero-padding for 2D convolution with downsampling.

    Args:
      inputs: Input tensor.
      kernel_size: An integer or tuple/list of 2 integers.

    Returns:
      A tuple.
    """
    img_dim = 2 if backend.image_data_format() == "channels_first" else 1
    input_size = inputs.shape[img_dim : (img_dim + 2)]
    if isinstance(kernel_size, int):
        kernel_size = (kernel_size, kernel_size)
    if input_size[0] is None:
        adjust = (1, 1)
    else:
        adjust = (1 - input_size[0] % 2, 1 - input_size[1] % 2)
    correct = (kernel_size[0] // 2, kernel_size[1] // 2)
    return (
        (correct[0] - adjust[0], correct[0]),
        (correct[1] - adjust[1], correct[1]),
    )


def validate_activation(classifier_activation, weights):
    """validates that the classifer_activation is compatible with the weights.

    Args:
      classifier_activation: str or callable activation function
      weights: The pretrained weights to load.

    Raises:
      ValueError: if an activation other than `None` or `softmax` are used with
        pretrained weights.
    """
    if weights is None:
        return

    classifier_activation = activations.get(classifier_activation)
    if classifier_activation not in {
        activations.get("softmax"),
        activations.get(None),
    }:
        raise ValueError(
            "Only `None` and `softmax` activations are allowed "
            "for the `classifier_activation` argument when using "
            "pretrained weights, with `include_top=True`; Received: "
            f"classifier_activation={classifier_activation}"
        )
