# Copyright 2020 The TensorFlow Authors. All Rights Reserved.
#
# 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.
# ==============================================================================
"""Keras hashing preprocessing layer."""


import tensorflow.compat.v2 as tf

from keras.src import backend
from keras.src.engine import base_layer
from keras.src.engine import base_preprocessing_layer
from keras.src.layers.preprocessing import preprocessing_utils as utils
from keras.src.utils import layer_utils

# isort: off
from tensorflow.python.util.tf_export import keras_export

INT = utils.INT
MULTI_HOT = utils.MULTI_HOT
ONE_HOT = utils.ONE_HOT
COUNT = utils.COUNT


@keras_export(
    "keras.layers.Hashing", "keras.layers.experimental.preprocessing.Hashing"
)
class Hashing(base_layer.Layer):
    """A preprocessing layer which hashes and bins categorical features.

    This layer transforms categorical inputs to hashed output. It element-wise
    converts a ints or strings to ints in a fixed range. The stable hash
    function uses `tensorflow::ops::Fingerprint` to produce the same output
    consistently across all platforms.

    This layer uses [FarmHash64](https://github.com/google/farmhash) by default,
    which provides a consistent hashed output across different platforms and is
    stable across invocations, regardless of device and context, by mixing the
    input bits thoroughly.

    If you want to obfuscate the hashed output, you can also pass a random
    `salt` argument in the constructor. In that case, the layer will use the
    [SipHash64](https://github.com/google/highwayhash) hash function, with
    the `salt` value serving as additional input to the hash function.

    For an overview and full list of preprocessing layers, see the preprocessing
    [guide](https://www.tensorflow.org/guide/keras/preprocessing_layers).

    **Example (FarmHash64)**

    >>> layer = tf.keras.layers.Hashing(num_bins=3)
    >>> inp = [['A'], ['B'], ['C'], ['D'], ['E']]
    >>> layer(inp)
    <tf.Tensor: shape=(5, 1), dtype=int64, numpy=
      array([[1],
             [0],
             [1],
             [1],
             [2]])>

    **Example (FarmHash64) with a mask value**

    >>> layer = tf.keras.layers.Hashing(num_bins=3, mask_value='')
    >>> inp = [['A'], ['B'], [''], ['C'], ['D']]
    >>> layer(inp)
    <tf.Tensor: shape=(5, 1), dtype=int64, numpy=
      array([[1],
             [1],
             [0],
             [2],
             [2]])>

    **Example (SipHash64)**

    >>> layer = tf.keras.layers.Hashing(num_bins=3, salt=[133, 137])
    >>> inp = [['A'], ['B'], ['C'], ['D'], ['E']]
    >>> layer(inp)
    <tf.Tensor: shape=(5, 1), dtype=int64, numpy=
      array([[1],
             [2],
             [1],
             [0],
             [2]])>

    **Example (Siphash64 with a single integer, same as `salt=[133, 133]`)**

    >>> layer = tf.keras.layers.Hashing(num_bins=3, salt=133)
    >>> inp = [['A'], ['B'], ['C'], ['D'], ['E']]
    >>> layer(inp)
    <tf.Tensor: shape=(5, 1), dtype=int64, numpy=
      array([[0],
             [0],
             [2],
             [1],
             [0]])>

    Args:
      num_bins: Number of hash bins. Note that this includes the `mask_value`
        bin, so the effective number of bins is `(num_bins - 1)` if `mask_value`
        is set.
      mask_value: A value that represents masked inputs, which are mapped to
        index 0. `None` means no mask term will be added and the
        hashing will start at index 0. Defaults to `None`.
      salt: A single unsigned integer or None.
        If passed, the hash function used will be SipHash64, with these values
        used as an additional input (known as a "salt" in cryptography).
        These should be non-zero. If `None`, uses the FarmHash64 hash function.
        It also supports tuple/list of 2 unsigned integer numbers, see
        reference paper for details. Defaults to `None`.
      output_mode: Specification for the output of the layer. Values can bes
        `"int"`, `"one_hot"`, `"multi_hot"`, or
        `"count"` configuring the layer as follows:
          - `"int"`: Return the integer bin indices directly.
          - `"one_hot"`: Encodes each individual element in the input into an
            array the same size as `num_bins`, containing a 1 at the input's bin
            index. If the last dimension is size 1, will encode on that
            dimension.  If the last dimension is not size 1, will append a new
            dimension for the encoded output.
          - `"multi_hot"`: Encodes each sample in the input into a single array
            the same size as `num_bins`, containing a 1 for each bin index
            index present in the sample. Treats the last dimension as the sample
            dimension, if input shape is `(..., sample_length)`, output shape
            will be `(..., num_tokens)`.
          - `"count"`: As `"multi_hot"`, but the int array contains a count of
            the number of times the bin index appeared in the sample.
        Defaults to `"int"`.
      sparse: Boolean. Only applicable to `"one_hot"`, `"multi_hot"`,
        and `"count"` output modes. If True, returns a `SparseTensor` instead of
        a dense `Tensor`. Defaults to `False`.
      **kwargs: Keyword arguments to construct a layer.

    Input shape:
      A single or list of string, int32 or int64 `Tensor`,
      `SparseTensor` or `RaggedTensor` of shape `(batch_size, ...,)`

    Output shape:
      An int64 `Tensor`, `SparseTensor` or `RaggedTensor` of shape
      `(batch_size, ...)`. If any input is `RaggedTensor` then output is
      `RaggedTensor`, otherwise if any input is `SparseTensor` then output is
      `SparseTensor`, otherwise the output is `Tensor`.

    Reference:
      - [SipHash with salt](https://www.131002.net/siphash/siphash.pdf)

    """

    def __init__(
        self,
        num_bins,
        mask_value=None,
        salt=None,
        output_mode="int",
        sparse=False,
        **kwargs,
    ):
        if num_bins is None or num_bins <= 0:
            raise ValueError(
                "The `num_bins` for `Hashing` cannot be `None` or "
                f"non-positive values. Received: num_bins={num_bins}."
            )

        # By default, output int64 when output_mode='int' and floats otherwise.
        if "dtype" not in kwargs or kwargs["dtype"] is None:
            kwargs["dtype"] = (
                tf.int64 if output_mode == INT else backend.floatx()
            )
        elif (
            output_mode == "int" and not tf.as_dtype(kwargs["dtype"]).is_integer
        ):
            # Compat for when dtype was always floating and ignored by the
            # layer.
            kwargs["dtype"] = tf.int64

        super().__init__(**kwargs)
        base_preprocessing_layer.keras_kpl_gauge.get_cell("Hashing").set(True)

        # Check dtype only after base layer parses it; dtype parsing is complex.
        if (
            output_mode == INT
            and not tf.as_dtype(self.compute_dtype).is_integer
        ):
            input_dtype = kwargs["dtype"]
            raise ValueError(
                'When `output_mode="int"`, `dtype` should be an integer '
                f"type. Received: dtype={input_dtype}"
            )

        # 'output_mode' must be one of (INT, ONE_HOT, MULTI_HOT, COUNT)
        layer_utils.validate_string_arg(
            output_mode,
            allowable_strings=(INT, ONE_HOT, MULTI_HOT, COUNT),
            layer_name=self.__class__.__name__,
            arg_name="output_mode",
        )

        if sparse and output_mode == INT:
            raise ValueError(
                "`sparse` may only be true if `output_mode` is "
                '`"one_hot"`, `"multi_hot"`, or `"count"`. '
                f"Received: sparse={sparse} and "
                f"output_mode={output_mode}"
            )

        self.num_bins = num_bins
        self.mask_value = mask_value
        self.strong_hash = True if salt is not None else False
        self.output_mode = output_mode
        self.sparse = sparse
        self.salt = None
        if salt is not None:
            if isinstance(salt, (tuple, list)) and len(salt) == 2:
                self.salt = salt
            elif isinstance(salt, int):
                self.salt = [salt, salt]
            else:
                raise ValueError(
                    "The `salt` argument for `Hashing` can only be a tuple of "
                    "size 2 integers, or a single integer. "
                    f"Received: salt={salt}."
                )

    def call(self, inputs):
        inputs = utils.ensure_tensor(inputs)
        if isinstance(inputs, tf.SparseTensor):
            indices = tf.SparseTensor(
                indices=inputs.indices,
                values=self._hash_values_to_bins(inputs.values),
                dense_shape=inputs.dense_shape,
            )
        else:
            indices = self._hash_values_to_bins(inputs)
        return utils.encode_categorical_inputs(
            indices,
            output_mode=self.output_mode,
            depth=self.num_bins,
            sparse=self.sparse,
            dtype=self.compute_dtype,
        )

    def _hash_values_to_bins(self, values):
        """Converts a non-sparse tensor of values to bin indices."""
        hash_bins = self.num_bins
        mask = None
        # If mask_value is set, the zeroth bin is reserved for it.
        if self.mask_value is not None and hash_bins > 1:
            hash_bins -= 1
            mask = tf.equal(values, self.mask_value)
        # Convert all values to strings before hashing.
        if values.dtype.is_integer:
            values = tf.as_string(values)
        # Hash the strings.
        if self.strong_hash:
            values = tf.strings.to_hash_bucket_strong(
                values, hash_bins, name="hash", key=self.salt
            )
        else:
            values = tf.strings.to_hash_bucket_fast(
                values, hash_bins, name="hash"
            )
        if mask is not None:
            values = tf.add(values, tf.ones_like(values))
            values = tf.where(mask, tf.zeros_like(values), values)
        return values

    def compute_output_shape(self, input_shape):
        return input_shape

    def compute_output_signature(self, input_spec):
        output_shape = self.compute_output_shape(input_spec.shape)
        if isinstance(input_spec, tf.SparseTensorSpec):
            return tf.SparseTensorSpec(
                shape=output_shape, dtype=self.compute_dtype
            )
        else:
            return tf.TensorSpec(shape=output_shape, dtype=self.compute_dtype)

    def get_config(self):
        config = super().get_config()
        config.update(
            {
                "num_bins": self.num_bins,
                "salt": self.salt,
                "mask_value": self.mask_value,
                "output_mode": self.output_mode,
                "sparse": self.sparse,
            }
        )
        return config

