# Copyright 2020 The TensorFlow Authors. All Rights Reserved.
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# Licensed under the Apache License, Version 2.0 (the "License");
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#     http://www.apache.org/licenses/LICENSE-2.0
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# ==============================================================================
"""Keras timeseries dataset utilities."""

import tensorflow.compat.v2 as tf
# pylint: disable=g-classes-have-attributes

import numpy as np
from tensorflow.python.util.tf_export import keras_export


@keras_export('keras.utils.timeseries_dataset_from_array',
              'keras.preprocessing.timeseries_dataset_from_array',
              v1=[])
def timeseries_dataset_from_array(
    data,
    targets,
    sequence_length,
    sequence_stride=1,
    sampling_rate=1,
    batch_size=128,
    shuffle=False,
    seed=None,
    start_index=None,
    end_index=None):
  """Creates a dataset of sliding windows over a timeseries provided as array.

  This function takes in a sequence of data-points gathered at
  equal intervals, along with time series parameters such as
  length of the sequences/windows, spacing between two sequence/windows, etc.,
  to produce batches of timeseries inputs and targets.

  Args:
    data: Numpy array or eager tensor
      containing consecutive data points (timesteps).
      Axis 0 is expected to be the time dimension.
    targets: Targets corresponding to timesteps in `data`.
      `targets[i]` should be the target
      corresponding to the window that starts at index `i`
      (see example 2 below).
      Pass None if you don't have target data (in this case the dataset will
      only yield the input data).
    sequence_length: Length of the output sequences (in number of timesteps).
    sequence_stride: Period between successive output sequences.
      For stride `s`, output samples would
      start at index `data[i]`, `data[i + s]`, `data[i + 2 * s]`, etc.
    sampling_rate: Period between successive individual timesteps
      within sequences. For rate `r`, timesteps
      `data[i], data[i + r], ... data[i + sequence_length]`
      are used for create a sample sequence.
    batch_size: Number of timeseries samples in each batch
      (except maybe the last one).
    shuffle: Whether to shuffle output samples,
      or instead draw them in chronological order.
    seed: Optional int; random seed for shuffling.
    start_index: Optional int; data points earlier (exclusive)
      than `start_index` will not be used
      in the output sequences. This is useful to reserve part of the
      data for test or validation.
    end_index: Optional int; data points later (exclusive) than `end_index`
      will not be used in the output sequences.
      This is useful to reserve part of the data for test or validation.

  Returns:
    A tf.data.Dataset instance. If `targets` was passed, the dataset yields
    tuple `(batch_of_sequences, batch_of_targets)`. If not, the dataset yields
    only `batch_of_sequences`.

  Example 1:

  Consider indices `[0, 1, ... 99]`.
  With `sequence_length=10,  sampling_rate=2, sequence_stride=3`,
  `shuffle=False`, the dataset will yield batches of sequences
  composed of the following indices:

  ```
  First sequence:  [0  2  4  6  8 10 12 14 16 18]
  Second sequence: [3  5  7  9 11 13 15 17 19 21]
  Third sequence:  [6  8 10 12 14 16 18 20 22 24]
  ...
  Last sequence:   [78 80 82 84 86 88 90 92 94 96]
  ```

  In this case the last 3 data points are discarded since no full sequence
  can be generated to include them (the next sequence would have started
  at index 81, and thus its last step would have gone over 99).

  Example 2: Temporal regression.

  Consider an array `data` of scalar values, of shape `(steps,)`.
  To generate a dataset that uses the past 10
  timesteps to predict the next timestep, you would use:

  ```python
  input_data = data[:-10]
  targets = data[10:]
  dataset = tf.keras.preprocessing.timeseries_dataset_from_array(
      input_data, targets, sequence_length=10)
  for batch in dataset:
    inputs, targets = batch
    assert np.array_equal(inputs[0], data[:10])  # First sequence: steps [0-9]
    assert np.array_equal(targets[0], data[10])  # Corresponding target: step 10
    break
  ```

  Example 3: Temporal regression for many-to-many architectures.

  Consider two arrays of scalar values `X` and `Y`,
  both of shape `(100,)`. The resulting dataset should consist samples with
  20 timestamps each. The samples should not overlap.
  To generate a dataset that uses the current timestamp
  to predict the corresponding target timestep, you would use:

  ```python
  X = np.arange(100)
  Y = X*2

  sample_length = 20
  input_dataset = tf.keras.preprocessing.timeseries_dataset_from_array(
    X, None, sequence_length=sample_length, sequence_stride=sample_length)
  target_dataset = tf.keras.preprocessing.timeseries_dataset_from_array(
    Y, None, sequence_length=sample_length, sequence_stride=sample_length)

  for batch in zip(input_dataset, target_dataset):
    inputs, targets = batch
    assert np.array_equal(inputs[0], X[:sample_length])

    # second sample equals output timestamps 20-40
    assert np.array_equal(targets[1], Y[sample_length:2*sample_length])
    break
  ```
  """
  if start_index:
    if start_index < 0:
      raise ValueError(f'`start_index` must be 0 or greater. Received: '
                       f'start_index={start_index}')
    if start_index >= len(data):
      raise ValueError(f'`start_index` must be lower than the length of the '
                       f'data. Received: start_index={start_index}, for data '
                       f'of length {len(data)}')
  if end_index:
    if start_index and end_index <= start_index:
      raise ValueError(f'`end_index` must be higher than `start_index`. '
                       f'Received: start_index={start_index}, and '
                       f'end_index={end_index} ')
    if end_index >= len(data):
      raise ValueError(f'`end_index` must be lower than the length of the '
                       f'data. Received: end_index={end_index}, for data of '
                       f'length {len(data)}')
    if end_index <= 0:
      raise ValueError('`end_index` must be higher than 0. '
                       f'Received: end_index={end_index}')

  # Validate strides
  if sampling_rate <= 0:
    raise ValueError(f'`sampling_rate` must be higher than 0. Received: '
                     f'sampling_rate={sampling_rate}')
  if sampling_rate >= len(data):
    raise ValueError(f'`sampling_rate` must be lower than the length of the '
                     f'data. Received: sampling_rate={sampling_rate}, for data '
                     f'of length {len(data)}')
  if sequence_stride <= 0:
    raise ValueError(f'`sequence_stride` must be higher than 0. Received: '
                     f'sequence_stride={sequence_stride}')
  if sequence_stride >= len(data):
    raise ValueError(f'`sequence_stride` must be lower than the length of the '
                     f'data. Received: sequence_stride={sequence_stride}, for '
                     f'data of length {len(data)}')

  if start_index is None:
    start_index = 0
  if end_index is None:
    end_index = len(data)

  # Determine the lowest dtype to store start positions (to lower memory usage).
  num_seqs = end_index - start_index - (sequence_length * sampling_rate) + 1
  if targets is not None:
    num_seqs = min(num_seqs, len(targets))
  if num_seqs < 2147483647:
    index_dtype = 'int32'
  else:
    index_dtype = 'int64'

  # Generate start positions
  start_positions = np.arange(0, num_seqs, sequence_stride, dtype=index_dtype)
  if shuffle:
    if seed is None:
      seed = np.random.randint(1e6)
    rng = np.random.RandomState(seed)
    rng.shuffle(start_positions)

  sequence_length = tf.cast(sequence_length, dtype=index_dtype)
  sampling_rate = tf.cast(sampling_rate, dtype=index_dtype)

  positions_ds = tf.data.Dataset.from_tensors(start_positions).repeat()

  # For each initial window position, generates indices of the window elements
  indices = tf.data.Dataset.zip(
      (tf.data.Dataset.range(len(start_positions)), positions_ds)).map(
          lambda i, positions: tf.range(  # pylint: disable=g-long-lambda
              positions[i],
              positions[i] + sequence_length * sampling_rate,
              sampling_rate),
          num_parallel_calls=tf.data.AUTOTUNE)

  dataset = sequences_from_indices(data, indices, start_index, end_index)
  if targets is not None:
    indices = tf.data.Dataset.zip(
        (tf.data.Dataset.range(len(start_positions)), positions_ds)).map(
            lambda i, positions: positions[i],
            num_parallel_calls=tf.data.AUTOTUNE)
    target_ds = sequences_from_indices(
        targets, indices, start_index, end_index)
    dataset = tf.data.Dataset.zip((dataset, target_ds))
  if shuffle:
    # Shuffle locally at each iteration
    dataset = dataset.shuffle(buffer_size=batch_size * 8, seed=seed)
  dataset = dataset.prefetch(tf.data.AUTOTUNE).batch(batch_size)
  return dataset


def sequences_from_indices(array, indices_ds, start_index, end_index):
  dataset = tf.data.Dataset.from_tensors(array[start_index : end_index])
  dataset = tf.data.Dataset.zip((dataset.repeat(), indices_ds)).map(
      lambda steps, inds: tf.gather(steps, inds),  # pylint: disable=unnecessary-lambda
      num_parallel_calls=tf.data.AUTOTUNE)
  return dataset
