"""RNN utils for RLlib.

The main trick here is that we add the time dimension at the last moment.
The non-LSTM layers of the model see their inputs as one flat batch. Before
the LSTM cell, we reshape the input to add the expected time dimension. During
postprocessing, we dynamically pad the experience batches so that this
reshaping is possible.

Note that this padding strategy only works out if we assume zero inputs don't
meaningfully affect the loss function. This happens to be true for all the
current algorithms: https://github.com/ray-project/ray/issues/2992
"""

import numpy as np

from ray.rllib.utils.annotations import DeveloperAPI
from ray.rllib.utils import try_import_tf

tf = try_import_tf()


@DeveloperAPI
def add_time_dimension(padded_inputs, seq_lens):
    """Adds a time dimension to padded inputs.

    Arguments:
        padded_inputs (Tensor): a padded batch of sequences. That is,
            for seq_lens=[1, 2, 2], then inputs=[A, *, B, B, C, C], where
            A, B, C are sequence elements and * denotes padding.
        seq_lens (Tensor): the sequence lengths within the input batch,
            suitable for passing to tf.nn.dynamic_rnn().

    Returns:
        Reshaped tensor of shape [NUM_SEQUENCES, MAX_SEQ_LEN, ...].
    """

    # Sequence lengths have to be specified for LSTM batch inputs. The
    # input batch must be padded to the max seq length given here. That is,
    # batch_size == len(seq_lens) * max(seq_lens)
    padded_batch_size = tf.shape(padded_inputs)[0]
    max_seq_len = padded_batch_size // tf.shape(seq_lens)[0]

    # Dynamically reshape the padded batch to introduce a time dimension.
    new_batch_size = padded_batch_size // max_seq_len
    new_shape = ([new_batch_size, max_seq_len] +
                 padded_inputs.get_shape().as_list()[1:])
    return tf.reshape(padded_inputs, new_shape)


@DeveloperAPI
def chop_into_sequences(episode_ids,
                        unroll_ids,
                        agent_indices,
                        feature_columns,
                        state_columns,
                        max_seq_len,
                        dynamic_max=True,
                        shuffle=False,
                        _extra_padding=0):
    """Truncate and pad experiences into fixed-length sequences.

    Arguments:
        episode_ids (list): List of episode ids for each step.
        unroll_ids (list): List of identifiers for the sample batch. This is
            used to make sure sequences are cut between sample batches.
        agent_indices (list): List of agent ids for each step. Note that this
            has to be combined with episode_ids for uniqueness.
        feature_columns (list): List of arrays containing features.
        state_columns (list): List of arrays containing LSTM state values.
        max_seq_len (int): Max length of sequences before truncation.
        dynamic_max (bool): Whether to dynamically shrink the max seq len.
            For example, if max len is 20 and the actual max seq len in the
            data is 7, it will be shrunk to 7.
        shuffle (bool): Whether to shuffle the sequence outputs.
        _extra_padding (int): Add extra padding to the end of sequences.

    Returns:
        f_pad (list): Padded feature columns. These will be of shape
            [NUM_SEQUENCES * MAX_SEQ_LEN, ...].
        s_init (list): Initial states for each sequence, of shape
            [NUM_SEQUENCES, ...].
        seq_lens (list): List of sequence lengths, of shape [NUM_SEQUENCES].

    Examples:
        >>> f_pad, s_init, seq_lens = chop_into_sequences(
                episode_ids=[1, 1, 5, 5, 5, 5],
                unroll_ids=[4, 4, 4, 4, 4, 4],
                agent_indices=[0, 0, 0, 0, 0, 0],
                feature_columns=[[4, 4, 8, 8, 8, 8],
                                 [1, 1, 0, 1, 1, 0]],
                state_columns=[[4, 5, 4, 5, 5, 5]],
                max_seq_len=3)
        >>> print(f_pad)
        [[4, 4, 0, 8, 8, 8, 8, 0, 0],
         [1, 1, 0, 0, 1, 1, 0, 0, 0]]
        >>> print(s_init)
        [[4, 4, 5]]
        >>> print(seq_lens)
        [2, 3, 1]
    """

    prev_id = None
    seq_lens = []
    seq_len = 0
    unique_ids = np.add(
        np.add(episode_ids, agent_indices),
        np.array(unroll_ids) << 32)
    for uid in unique_ids:
        if (prev_id is not None and uid != prev_id) or \
                seq_len >= max_seq_len:
            seq_lens.append(seq_len)
            seq_len = 0
        seq_len += 1
        prev_id = uid
    if seq_len:
        seq_lens.append(seq_len)
    assert sum(seq_lens) == len(unique_ids)
    seq_lens = np.array(seq_lens)

    # Dynamically shrink max len as needed to optimize memory usage
    if dynamic_max:
        max_seq_len = max(seq_lens) + _extra_padding

    feature_sequences = []
    for f in feature_columns:
        f = np.array(f)
        f_pad = np.zeros((len(seq_lens) * max_seq_len, ) + np.shape(f)[1:])
        seq_base = 0
        i = 0
        for l in seq_lens:
            for seq_offset in range(l):
                f_pad[seq_base + seq_offset] = f[i]
                i += 1
            seq_base += max_seq_len
        assert i == len(unique_ids), f
        feature_sequences.append(f_pad)

    initial_states = []
    for s in state_columns:
        s = np.array(s)
        s_init = []
        i = 0
        for l in seq_lens:
            s_init.append(s[i])
            i += l
        initial_states.append(np.array(s_init))

    if shuffle:
        permutation = np.random.permutation(len(seq_lens))
        for i, f in enumerate(feature_sequences):
            orig_shape = f.shape
            f = np.reshape(f, (len(seq_lens), -1) + f.shape[1:])
            f = f[permutation]
            f = np.reshape(f, orig_shape)
            feature_sequences[i] = f
        for i, s in enumerate(initial_states):
            s = s[permutation]
            initial_states[i] = s
        seq_lens = seq_lens[permutation]

    return feature_sequences, initial_states, seq_lens