# 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.
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# ==============================================================================
"""Keras text dataset generation utilities."""

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

from keras.src.utils import dataset_utils

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


@keras_export(
    "keras.utils.text_dataset_from_directory",
    "keras.preprocessing.text_dataset_from_directory",
    v1=[],
)
def text_dataset_from_directory(
    directory,
    labels="inferred",
    label_mode="int",
    class_names=None,
    batch_size=32,
    max_length=None,
    shuffle=True,
    seed=None,
    validation_split=None,
    subset=None,
    follow_links=False,
):
    """Generates a `tf.data.Dataset` from text files in a directory.

    If your directory structure is:

    ```
    main_directory/
    ...class_a/
    ......a_text_1.txt
    ......a_text_2.txt
    ...class_b/
    ......b_text_1.txt
    ......b_text_2.txt
    ```

    Then calling `text_dataset_from_directory(main_directory,
    labels='inferred')` will return a `tf.data.Dataset` that yields batches of
    texts from the subdirectories `class_a` and `class_b`, together with labels
    0 and 1 (0 corresponding to `class_a` and 1 corresponding to `class_b`).

    Only `.txt` files are supported at this time.

    Args:
        directory: Directory where the data is located.
            If `labels` is "inferred", it should contain
            subdirectories, each containing text files for a class.
            Otherwise, the directory structure is ignored.
        labels: Either "inferred"
            (labels are generated from the directory structure),
            None (no labels),
            or a list/tuple of integer labels of the same size as the number of
            text files found in the directory. Labels should be sorted according
            to the alphanumeric order of the text file paths
            (obtained via `os.walk(directory)` in Python).
        label_mode: String describing the encoding of `labels`. Options are:
            - 'int': means that the labels are encoded as integers
                (e.g. for `sparse_categorical_crossentropy` loss).
            - 'categorical' means that the labels are
                encoded as a categorical vector
                (e.g. for `categorical_crossentropy` loss).
            - 'binary' means that the labels (there can be only 2)
                are encoded as `float32` scalars with values 0 or 1
                (e.g. for `binary_crossentropy`).
            - None (no labels).
        class_names: Only valid if "labels" is "inferred". This is the explicit
            list of class names (must match names of subdirectories). Used
            to control the order of the classes
            (otherwise alphanumerical order is used).
        batch_size: Size of the batches of data. Default: 32.
            If `None`, the data will not be batched
            (the dataset will yield individual samples).
        max_length: Maximum size of a text string. Texts longer than this will
            be truncated to `max_length`.
        shuffle: Whether to shuffle the data. Default: True.
            If set to False, sorts the data in alphanumeric order.
        seed: Optional random seed for shuffling and transformations.
        validation_split: Optional float between 0 and 1,
            fraction of data to reserve for validation.
        subset: Subset of the data to return.
            One of "training", "validation" or "both".
            Only used if `validation_split` is set.
            When `subset="both"`, the utility returns a tuple of two datasets
            (the training and validation datasets respectively).
        follow_links: Whether to visits subdirectories pointed to by symlinks.
            Defaults to False.

    Returns:
        A `tf.data.Dataset` object.
        - If `label_mode` is None, it yields `string` tensors of shape
          `(batch_size,)`, containing the contents of a batch of text files.
        - Otherwise, it yields a tuple `(texts, labels)`, where `texts`
          has shape `(batch_size,)` and `labels` follows the format described
          below.

    Rules regarding labels format:
        - if `label_mode` is `int`, the labels are an `int32` tensor of shape
          `(batch_size,)`.
        - if `label_mode` is `binary`, the labels are a `float32` tensor of
          1s and 0s of shape `(batch_size, 1)`.
        - if `label_mode` is `categorical`, the labels are a `float32` tensor
          of shape `(batch_size, num_classes)`, representing a one-hot
          encoding of the class index.
    """
    if labels not in ("inferred", None):
        if not isinstance(labels, (list, tuple)):
            raise ValueError(
                "`labels` argument should be a list/tuple of integer labels, "
                "of the same size as the number of text files in the target "
                "directory. If you wish to infer the labels from the "
                "subdirectory names in the target directory, "
                'pass `labels="inferred"`. '
                "If you wish to get a dataset that only contains text samples "
                f"(no labels), pass `labels=None`. Received: labels={labels}"
            )
        if class_names:
            raise ValueError(
                "You can only pass `class_names` if "
                f'`labels="inferred"`. Received: labels={labels}, and '
                f"class_names={class_names}"
            )
    if label_mode not in {"int", "categorical", "binary", None}:
        raise ValueError(
            '`label_mode` argument must be one of "int", '
            '"categorical", "binary", '
            f"or None. Received: label_mode={label_mode}"
        )
    if labels is None or label_mode is None:
        labels = None
        label_mode = None
    dataset_utils.check_validation_split_arg(
        validation_split, subset, shuffle, seed
    )

    if seed is None:
        seed = np.random.randint(1e6)
    file_paths, labels, class_names = dataset_utils.index_directory(
        directory,
        labels,
        formats=(".txt",),
        class_names=class_names,
        shuffle=shuffle,
        seed=seed,
        follow_links=follow_links,
    )

    if label_mode == "binary" and len(class_names) != 2:
        raise ValueError(
            'When passing `label_mode="binary"`, there must be exactly 2 '
            f"class_names. Received: class_names={class_names}"
        )

    if subset == "both":
        (
            file_paths_train,
            labels_train,
        ) = dataset_utils.get_training_or_validation_split(
            file_paths, labels, validation_split, "training"
        )
        (
            file_paths_val,
            labels_val,
        ) = dataset_utils.get_training_or_validation_split(
            file_paths, labels, validation_split, "validation"
        )
        if not file_paths_train:
            raise ValueError(
                f"No training text files found in directory {directory}. "
                "Allowed format: .txt"
            )
        if not file_paths_val:
            raise ValueError(
                f"No validation text files found in directory {directory}. "
                "Allowed format: .txt"
            )
        train_dataset = paths_and_labels_to_dataset(
            file_paths=file_paths_train,
            labels=labels_train,
            label_mode=label_mode,
            num_classes=len(class_names),
            max_length=max_length,
        )
        val_dataset = paths_and_labels_to_dataset(
            file_paths=file_paths_val,
            labels=labels_val,
            label_mode=label_mode,
            num_classes=len(class_names),
            max_length=max_length,
        )

        train_dataset = train_dataset.prefetch(tf.data.AUTOTUNE)
        val_dataset = val_dataset.prefetch(tf.data.AUTOTUNE)
        if batch_size is not None:
            if shuffle:
                # Shuffle locally at each iteration
                train_dataset = train_dataset.shuffle(
                    buffer_size=batch_size * 8, seed=seed
                )
            train_dataset = train_dataset.batch(batch_size)
            val_dataset = val_dataset.batch(batch_size)
        else:
            if shuffle:
                train_dataset = train_dataset.shuffle(
                    buffer_size=1024, seed=seed
                )
        # Users may need to reference `class_names`.
        train_dataset.class_names = class_names
        val_dataset.class_names = class_names
        dataset = [train_dataset, val_dataset]
    else:
        file_paths, labels = dataset_utils.get_training_or_validation_split(
            file_paths, labels, validation_split, subset
        )
        if not file_paths:
            raise ValueError(
                f"No text files found in directory {directory}. "
                "Allowed format: .txt"
            )
        dataset = paths_and_labels_to_dataset(
            file_paths=file_paths,
            labels=labels,
            label_mode=label_mode,
            num_classes=len(class_names),
            max_length=max_length,
        )
        dataset = dataset.prefetch(tf.data.AUTOTUNE)
        if batch_size is not None:
            if shuffle:
                # Shuffle locally at each iteration
                dataset = dataset.shuffle(buffer_size=batch_size * 8, seed=seed)
            dataset = dataset.batch(batch_size)
        else:
            if shuffle:
                dataset = dataset.shuffle(buffer_size=1024, seed=seed)
        # Users may need to reference `class_names`.
        dataset.class_names = class_names
    return dataset


def paths_and_labels_to_dataset(
    file_paths, labels, label_mode, num_classes, max_length
):
    """Constructs a dataset of text strings and labels."""
    path_ds = tf.data.Dataset.from_tensor_slices(file_paths)
    string_ds = path_ds.map(
        lambda x: path_to_string_content(x, max_length),
        num_parallel_calls=tf.data.AUTOTUNE,
    )
    if label_mode:
        label_ds = dataset_utils.labels_to_dataset(
            labels, label_mode, num_classes
        )
        string_ds = tf.data.Dataset.zip((string_ds, label_ds))
    return string_ds


def path_to_string_content(path, max_length):
    txt = tf.io.read_file(path)
    if max_length is not None:
        txt = tf.compat.v1.strings.substr(txt, 0, max_length)
    return txt

