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"""Keras string lookup preprocessing layer."""
# pylint: disable=g-classes-have-attributes

import numpy as np

from tensorflow.python.framework import dtypes
from tensorflow.python.keras.engine import base_preprocessing_layer
from tensorflow.python.keras.layers.preprocessing import index_lookup
from tensorflow.python.keras.layers.preprocessing import table_utils
from tensorflow.python.util import compat
from tensorflow.python.util.tf_export import keras_export


@keras_export("keras.layers.experimental.preprocessing.StringLookup", v1=[])
class StringLookup(index_lookup.IndexLookup):
  """Maps strings from a vocabulary to integer indices.

  This layer translates a set of arbitrary strings into an integer output via a
  table-based vocabulary lookup.

  The vocabulary for the layer can be supplied on construction or learned via
  `adapt()`. During `adapt()`, the layer will analyze a data set, determine the
  frequency of individual strings tokens, and create a vocabulary from them. If
  the vocabulary is capped in size, the most frequent tokens will be used to
  create the vocabulary and all others will be treated as out-of-vocabulary
  (OOV).

  There are two possible output modes for the layer.
  When `output_mode` is `"int"`,
  input strings are converted to their index in the vocabulary (an integer).
  When `output_mode` is `"multi_hot"`, `"count"`, or `"tf_idf"`, input strings
  are encoded into an array where each dimension corresponds to an element in
  the vocabulary.

  The vocabulary can optionally contain a mask token as well as an OOV token
  (which can optionally occupy multiple indices in the vocabulary, as set
  by `num_oov_indices`).
  The position of these tokens in the vocabulary is fixed. When `output_mode` is
  `"int"`, the vocabulary will begin with the mask token (if set), followed by
  OOV indices, followed by the rest of the vocabulary. When `output_mode` is
  `"multi_hot"`, `"count"`, or `"tf_idf"` the vocabulary will begin with OOV
  indices and instances of the mask token will be dropped.

  Args:
    max_tokens: The maximum size of the vocabulary for this layer. If None,
      there is no cap on the size of the vocabulary. Note that this size
      includes the OOV and mask tokens. Default to None.
    num_oov_indices: The number of out-of-vocabulary tokens to use. If this
      value is more than 1, OOV inputs are hashed to determine their OOV value.
      If this value is 0, OOV inputs will cause an error when calling the layer.
      Defaults to 1.
    mask_token: A token that represents masked inputs. When `output_mode` is
      `"int"`, the token is included in vocabulary and mapped to index 0. In
      other output modes, the token will not appear in the vocabulary and
      instances of the mask token in the input will be dropped. If set to None,
      no mask term will be added. Defaults to `None`.
    oov_token: Only used when `invert` is True. The token to return for OOV
      indices. Defaults to `"[UNK]"`.
    vocabulary: An optional list of tokens, or a path to a text file containing
      a vocabulary to load into this layer. The file should contain one token
      per line. If the list or file contains the same token multiple times, an
      error will be thrown.
    invert: Only valid when `output_mode` is `"int"`. If True, this layer will
      map indices to vocabulary items instead of mapping vocabulary items to
      indices. Default to False.
    output_mode: Specification for the output of the layer. Defaults to `"int"`.
      Values can be `"int"`, `"one_hot"`, `"multi_hot"`, `"count"`, or
      `"tf_idf"` configuring the layer as follows:
        - `"int"`: Return the raw integer indices of the input tokens.
        - `"one_hot"`: Encodes each individual element in the input into an
          array the same size as the vocabulary, containing a 1 at the element
          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 the vocabulary, containing a 1 for each vocabulary
          term 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 token at that index appeared in the sample.
        - `"tf_idf"`: As `"multi_hot"`, but the TF-IDF algorithm is applied to
          find the value in each token slot.
    pad_to_max_tokens: Only applicable when `output_mode` is `"multi_hot"`,
      `"count"`, or `"tf_idf"`. If True, the output will have its feature axis
      padded to `max_tokens` even if the number of unique tokens in the
      vocabulary is less than max_tokens, resulting in a tensor of shape
      [batch_size, max_tokens] regardless of vocabulary size. Defaults to False.
    sparse: Boolean. Only applicable when `output_mode` is `"multi_hot"`,
      `"count"`, or `"tf_idf"`. If True, returns a `SparseTensor` instead of a
      dense `Tensor`. Defaults to False.

  Examples:

  **Creating a lookup layer with a known vocabulary**

  This example creates a lookup layer with a pre-existing vocabulary.

  >>> vocab = ["a", "b", "c", "d"]
  >>> data = tf.constant([["a", "c", "d"], ["d", "z", "b"]])
  >>> layer = StringLookup(vocabulary=vocab)
  >>> layer(data)
  <tf.Tensor: shape=(2, 3), dtype=int64, numpy=
  array([[1, 3, 4],
         [4, 0, 2]])>

  **Creating a lookup layer with an adapted vocabulary**

  This example creates a lookup layer and generates the vocabulary by analyzing
  the dataset.

  >>> data = tf.constant([["a", "c", "d"], ["d", "z", "b"]])
  >>> layer = StringLookup()
  >>> layer.adapt(data)
  >>> layer.get_vocabulary()
  ['[UNK]', 'd', 'z', 'c', 'b', 'a']

  Note that the OOV token [UNK] has been added to the vocabulary. The remaining
  tokens are sorted by frequency ('d', which has 2 occurrences, is first) then
  by inverse sort order.

  >>> data = tf.constant([["a", "c", "d"], ["d", "z", "b"]])
  >>> layer = StringLookup()
  >>> layer.adapt(data)
  >>> layer(data)
  <tf.Tensor: shape=(2, 3), dtype=int64, numpy=
  array([[5, 3, 1],
         [1, 2, 4]])>

  **Lookups with multiple OOV indices**

  This example demonstrates how to use a lookup layer with multiple OOV indices.
  When a layer is created with more than one OOV index, any OOV values are
  hashed into the number of OOV buckets, distributing OOV values in a
  deterministic fashion across the set.

  >>> vocab = ["a", "b", "c", "d"]
  >>> data = tf.constant([["a", "c", "d"], ["m", "z", "b"]])
  >>> layer = StringLookup(vocabulary=vocab, num_oov_indices=2)
  >>> layer(data)
  <tf.Tensor: shape=(2, 3), dtype=int64, numpy=
  array([[2, 4, 5],
         [0, 1, 3]])>

  Note that the output for OOV value 'm' is 0, while the output for OOV value
  'z' is 1. The in-vocab terms have their output index increased by 1 from
  earlier examples (a maps to 2, etc) in order to make space for the extra OOV
  value.

  **One-hot output**

  Configure the layer with `output_mode='one_hot'`. Note that the first
  `num_oov_indices` dimensions in the ont_hot encoding represent OOV values.

  >>> vocab = ["a", "b", "c", "d"]
  >>> data = tf.constant(["a", "b", "c", "d", "z"])
  >>> layer = StringLookup(vocabulary=vocab, output_mode='one_hot')
  >>> layer(data)
  <tf.Tensor: shape=(5, 5), dtype=float32, numpy=
    array([[0., 1., 0., 0., 0.],
           [0., 0., 1., 0., 0.],
           [0., 0., 0., 1., 0.],
           [0., 0., 0., 0., 1.],
           [1., 0., 0., 0., 0.]], dtype=float32)>

  **Multi-hot output**

  Configure the layer with `output_mode='multi_hot'`. Note that the first
  `num_oov_indices` dimensions in the multi_hot encoding represent OOV values.

  >>> vocab = ["a", "b", "c", "d"]
  >>> data = tf.constant([["a", "c", "d", "d"], ["d", "z", "b", "z"]])
  >>> layer = StringLookup(vocabulary=vocab, output_mode='multi_hot')
  >>> layer(data)
  <tf.Tensor: shape=(2, 5), dtype=float32, numpy=
    array([[0., 1., 0., 1., 1.],
           [1., 0., 1., 0., 1.]], dtype=float32)>

  **Token count output**

  Configure the layer with `output_mode='count'`. As with multi_hot output, the
  first `num_oov_indices` dimensions in the output represent OOV values.

  >>> vocab = ["a", "b", "c", "d"]
  >>> data = tf.constant([["a", "c", "d", "d"], ["d", "z", "b", "z"]])
  >>> layer = StringLookup(vocabulary=vocab, output_mode='count')
  >>> layer(data)
  <tf.Tensor: shape=(2, 5), dtype=float32, numpy=
    array([[0., 1., 0., 1., 2.],
           [2., 0., 1., 0., 1.]], dtype=float32)>

  **TF-IDF output**

  Configure the layer with `output_mode='tf_idf'`. As with multi_hot output, the
  first `num_oov_indices` dimensions in the output represent OOV values.

  Each token bin will output `token_count * idf_weight`, where the idf weights
  are the inverse document frequency weights per token. These should be provided
  along with the vocabulary. Note that the `idf_weight` for OOV values will
  default to the average of all idf weights passed in.

  >>> vocab = ["a", "b", "c", "d"]
  >>> idf_weights = [0.25, 0.75, 0.6, 0.4]
  >>> data = tf.constant([["a", "c", "d", "d"], ["d", "z", "b", "z"]])
  >>> layer = StringLookup(output_mode='tf_idf')
  >>> layer.set_vocabulary(vocab, idf_weights=idf_weights)
  >>> layer(data)
  <tf.Tensor: shape=(2, 5), dtype=float32, numpy=
    array([[0.  , 0.25, 0.  , 0.6 , 0.8 ],
           [1.0 , 0.  , 0.75, 0.  , 0.4 ]], dtype=float32)>

  To specify the idf weights for oov values, you will need to pass the entire
  vocabularly including the leading oov token.

  >>> vocab = ["[UNK]", "a", "b", "c", "d"]
  >>> idf_weights = [0.9, 0.25, 0.75, 0.6, 0.4]
  >>> data = tf.constant([["a", "c", "d", "d"], ["d", "z", "b", "z"]])
  >>> layer = StringLookup(output_mode='tf_idf')
  >>> layer.set_vocabulary(vocab, idf_weights=idf_weights)
  >>> layer(data)
  <tf.Tensor: shape=(2, 5), dtype=float32, numpy=
    array([[0.  , 0.25, 0.  , 0.6 , 0.8 ],
           [1.8 , 0.  , 0.75, 0.  , 0.4 ]], dtype=float32)>

  When adapting the layer in tf_idf mode, each input sample will be considered a
  document, and idf weight per token will be calculated as
  `log(1 + num_documents / (1 + token_document_count))`.

  **Inverse lookup**

  This example demonstrates how to map indices to strings using this layer. (You
  can also use adapt() with inverse=True, but for simplicity we'll pass the
  vocab in this example.)

  >>> vocab = ["a", "b", "c", "d"]
  >>> data = tf.constant([[1, 3, 4], [4, 0, 2]])
  >>> layer = StringLookup(vocabulary=vocab, invert=True)
  >>> layer(data)
  <tf.Tensor: shape=(2, 3), dtype=string, numpy=
  array([[b'a', b'c', b'd'],
         [b'd', b'[UNK]', b'b']], dtype=object)>

  Note that the first index correspond to the oov token by default.


  **Forward and inverse lookup pairs**

  This example demonstrates how to use the vocabulary of a standard lookup
  layer to create an inverse lookup layer.

  >>> vocab = ["a", "b", "c", "d"]
  >>> data = tf.constant([["a", "c", "d"], ["d", "z", "b"]])
  >>> layer = StringLookup(vocabulary=vocab)
  >>> i_layer = StringLookup(vocabulary=vocab, invert=True)
  >>> int_data = layer(data)
  >>> i_layer(int_data)
  <tf.Tensor: shape=(2, 3), dtype=string, numpy=
  array([[b'a', b'c', b'd'],
         [b'd', b'[UNK]', b'b']], dtype=object)>

  In this example, the input value 'z' resulted in an output of '[UNK]', since
  1000 was not in the vocabulary - it got represented as an OOV, and all OOV
  values are returned as '[OOV}' in the inverse layer. Also, note that for the
  inverse to work, you must have already set the forward layer vocabulary
  either directly or via adapt() before calling get_vocabulary().
  """

  def __init__(self,
               max_tokens=None,
               num_oov_indices=1,
               mask_token=None,
               oov_token="[UNK]",
               vocabulary=None,
               encoding=None,
               invert=False,
               output_mode=index_lookup.INT,
               sparse=False,
               pad_to_max_tokens=False,
               **kwargs):
    allowed_dtypes = [dtypes.string]

    if "dtype" in kwargs and kwargs["dtype"] not in allowed_dtypes:
      raise ValueError("The value of the dtype argument for StringLookup may "
                       "only be one of %s." % (allowed_dtypes,))

    if "dtype" not in kwargs:
      kwargs["dtype"] = dtypes.string

    if encoding is None:
      encoding = "utf-8"

    self.encoding = encoding

    super(StringLookup, self).__init__(
        max_tokens=max_tokens,
        num_oov_indices=num_oov_indices,
        mask_token=mask_token,
        oov_token=oov_token,
        vocabulary=vocabulary,
        invert=invert,
        output_mode=output_mode,
        sparse=sparse,
        pad_to_max_tokens=pad_to_max_tokens,
        **kwargs)
    base_preprocessing_layer.keras_kpl_gauge.get_cell("StringLookup").set(True)

  def get_config(self):
    config = {"encoding": self.encoding}
    base_config = super(StringLookup, self).get_config()
    return dict(list(base_config.items()) + list(config.items()))

  def set_vocabulary(self, vocabulary, idf_weights=None):
    if isinstance(vocabulary, str):
      if self.output_mode == index_lookup.TF_IDF:
        raise RuntimeError("Setting vocabulary directly from a file is not "
                           "supported in TF-IDF mode, since this layer cannot "
                           "read files containing TF-IDF weight data. Please "
                           "read the file using Python and set the vocabulary "
                           "and weights by passing lists or arrays to the "
                           "set_vocabulary function's `vocabulary` and "
                           "`idf_weights` args.")
      vocabulary = table_utils.get_vocabulary_from_file(vocabulary,
                                                        self.encoding)
    super().set_vocabulary(vocabulary, idf_weights=idf_weights)

  # Overriden methods from IndexLookup.
  def _tensor_vocab_to_numpy(self, vocabulary):
    vocabulary = vocabulary.numpy()
    return np.array([compat.as_text(x, self.encoding) for x in vocabulary])
