Intelegentny_Pszczelarz/.venv/Lib/site-packages/keras/layers/preprocessing/preprocessing_utils.py

# Copyright 2021 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.
# ==============================================================================
"""Utils for preprocessing layers."""

import numpy as np
import tensorflow.compat.v2 as tf

from keras.utils import tf_utils

INT = "int"
ONE_HOT = "one_hot"
MULTI_HOT = "multi_hot"
COUNT = "count"
TF_IDF = "tf_idf"


def ensure_tensor(inputs, dtype=None):
    """Ensures the input is a Tensor, SparseTensor or RaggedTensor."""
    if not isinstance(inputs, (tf.Tensor, tf.RaggedTensor, tf.SparseTensor)):
        inputs = tf.convert_to_tensor(inputs, dtype)
    if dtype is not None and inputs.dtype != dtype:
        inputs = tf.cast(inputs, dtype)
    return inputs


def listify_tensors(x):
    """Convert any tensors or numpy arrays to lists for config serialization."""
    if tf.is_tensor(x):
        x = x.numpy()
    if isinstance(x, np.ndarray):
        x = x.tolist()
    return x


def sparse_bincount(inputs, depth, binary_output, dtype, count_weights=None):
    """Apply binary or count encoding to an input and return a sparse tensor."""
    result = tf.sparse.bincount(
        inputs,
        weights=count_weights,
        minlength=depth,
        maxlength=depth,
        axis=-1,
        binary_output=binary_output,
    )
    result = tf.cast(result, dtype)
    if inputs.shape.rank == 1:
        output_shape = (depth,)
    else:
        batch_size = tf.shape(result)[0]
        output_shape = (batch_size, depth)
    result = tf.SparseTensor(
        indices=result.indices, values=result.values, dense_shape=output_shape
    )
    return result


def dense_bincount(inputs, depth, binary_output, dtype, count_weights=None):
    """Apply binary or count encoding to an input."""
    result = tf.math.bincount(
        inputs,
        weights=count_weights,
        minlength=depth,
        maxlength=depth,
        dtype=dtype,
        axis=-1,
        binary_output=binary_output,
    )
    if inputs.shape.rank == 1:
        result.set_shape(tf.TensorShape((depth,)))
    else:
        batch_size = inputs.shape.as_list()[0]
        result.set_shape(tf.TensorShape((batch_size, depth)))
    return result


def expand_dims(inputs, axis):
    """Expand dims on sparse, ragged, or dense tensors."""
    if tf_utils.is_sparse(inputs):
        return tf.sparse.expand_dims(inputs, axis)
    else:
        return tf.expand_dims(inputs, axis)


def encode_categorical_inputs(
    inputs,
    output_mode,
    depth,
    dtype="float32",
    sparse=False,
    count_weights=None,
    idf_weights=None,
):
    """Encodes categoical inputs according to output_mode."""
    if output_mode == INT:
        return tf.identity(tf.cast(inputs, dtype))

    original_shape = inputs.shape
    # In all cases, we should uprank scalar input to a single sample.
    if inputs.shape.rank == 0:
        inputs = expand_dims(inputs, -1)
    # One hot will unprank only if the final output dimension is not already 1.
    if output_mode == ONE_HOT:
        if inputs.shape[-1] != 1:
            inputs = expand_dims(inputs, -1)

    # TODO(b/190445202): remove output rank restriction.
    if inputs.shape.rank > 2:
        raise ValueError(
            "When output_mode is not `'int'`, maximum supported output rank "
            f"is 2. Received output_mode {output_mode} and input shape "
            f"{original_shape}, "
            f"which would result in output rank {inputs.shape.rank}."
        )

    binary_output = output_mode in (MULTI_HOT, ONE_HOT)
    if sparse:
        bincounts = sparse_bincount(
            inputs, depth, binary_output, dtype, count_weights
        )
    else:
        bincounts = dense_bincount(
            inputs, depth, binary_output, dtype, count_weights
        )

    if output_mode != TF_IDF:
        return bincounts

    if idf_weights is None:
        raise ValueError(
            "When output mode is `'tf_idf'`, idf_weights must be provided. "
            f"Received: output_mode={output_mode} and idf_weights={idf_weights}"
        )

    if sparse:
        value_weights = tf.gather(idf_weights, bincounts.indices[:, -1])
        return tf.SparseTensor(
            bincounts.indices,
            value_weights * bincounts.values,
            bincounts.dense_shape,
        )
    else:
        return tf.multiply(bincounts, idf_weights)


def compute_shape_for_encode_categorical(shape, output_mode, depth):
    """Computes the output shape of `encode_categorical_inputs`."""
    if output_mode == INT:
        return tf.TensorShape(shape)
    if not shape:
        return tf.TensorShape([depth])
    if output_mode == ONE_HOT and shape[-1] != 1:
        return tf.TensorShape(shape + [depth])
    else:
        return tf.TensorShape(shape[:-1] + [depth])