from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals

from caffe2.python import (
    core, gradient_checker, rnn_cell, workspace, scope, utils
)
from caffe2.python.attention import AttentionType
from caffe2.python.model_helper import ModelHelper, ExtractPredictorNet
from caffe2.python.rnn.rnn_cell_test_util import sigmoid, tanh, _prepare_rnn
from caffe2.proto import caffe2_pb2
import caffe2.python.hypothesis_test_util as hu

from functools import partial
from hypothesis import assume, given
from hypothesis import settings as ht_settings
import hypothesis.strategies as st
import numpy as np
import unittest


def lstm_unit(*args, **kwargs):
    forget_bias = kwargs.get('forget_bias', 0.0)
    drop_states = kwargs.get('drop_states', False)
    sequence_lengths = kwargs.get('sequence_lengths', True)

    if sequence_lengths:
        hidden_t_prev, cell_t_prev, gates, seq_lengths, timestep = args
    else:
        hidden_t_prev, cell_t_prev, gates, timestep = args
    D = cell_t_prev.shape[2]
    G = gates.shape[2]
    N = gates.shape[1]
    t = (timestep * np.ones(shape=(N, D))).astype(np.int32)
    assert t.shape == (N, D)
    assert G == 4 * D
    # Resize to avoid broadcasting inconsistencies with NumPy
    gates = gates.reshape(N, 4, D)
    cell_t_prev = cell_t_prev.reshape(N, D)
    i_t = gates[:, 0, :].reshape(N, D)
    f_t = gates[:, 1, :].reshape(N, D)
    o_t = gates[:, 2, :].reshape(N, D)
    g_t = gates[:, 3, :].reshape(N, D)
    i_t = sigmoid(i_t)
    f_t = sigmoid(f_t + forget_bias)
    o_t = sigmoid(o_t)
    g_t = tanh(g_t)
    if sequence_lengths:
        seq_lengths = (np.ones(shape=(N, D)) *
                       seq_lengths.reshape(N, 1)).astype(np.int32)
        assert seq_lengths.shape == (N, D)
        valid = (t < seq_lengths).astype(np.int32)
    else:
        valid = np.ones(shape=(N, D))
    assert valid.shape == (N, D)
    cell_t = ((f_t * cell_t_prev) + (i_t * g_t)) * (valid) + \
        (1 - valid) * cell_t_prev * (1 - drop_states)
    assert cell_t.shape == (N, D)
    hidden_t = (o_t * tanh(cell_t)) * valid + hidden_t_prev * (
        1 - valid) * (1 - drop_states)
    hidden_t = hidden_t.reshape(1, N, D)
    cell_t = cell_t.reshape(1, N, D)
    return hidden_t, cell_t


def layer_norm_with_scale_and_bias_ref(X, scale, bias, axis=-1, epsilon=1e-4):
    left = np.prod(X.shape[:axis])
    reshaped = np.reshape(X, [left, -1])
    mean = np.mean(reshaped, axis=1).reshape([left, 1])
    stdev = np.sqrt(
        np.mean(np.square(reshaped), axis=1).reshape([left, 1]) -
        np.square(mean) + epsilon
    )
    norm = (reshaped - mean) / stdev
    norm = np.reshape(norm, X.shape)
    adjusted = scale * norm + bias

    return adjusted


def layer_norm_lstm_reference(
    input,
    hidden_input,
    cell_input,
    gates_w,
    gates_b,
    gates_t_norm_scale,
    gates_t_norm_bias,
    seq_lengths,
    forget_bias,
    drop_states=False
):
    T = input.shape[0]
    N = input.shape[1]
    G = input.shape[2]
    D = hidden_input.shape[hidden_input.ndim - 1]
    hidden = np.zeros(shape=(T + 1, N, D))
    cell = np.zeros(shape=(T + 1, N, D))
    assert hidden.shape[0] == T + 1
    assert cell.shape[0] == T + 1
    assert hidden.shape[1] == N
    assert cell.shape[1] == N
    cell[0, :, :] = cell_input
    hidden[0, :, :] = hidden_input
    for t in range(T):
        input_t = input[t].reshape(1, N, G)
        print(input_t.shape)
        hidden_t_prev = hidden[t].reshape(1, N, D)
        cell_t_prev = cell[t].reshape(1, N, D)
        gates = np.dot(hidden_t_prev, gates_w.T) + gates_b
        gates = gates + input_t

        gates = layer_norm_with_scale_and_bias_ref(
            gates, gates_t_norm_scale, gates_t_norm_bias
        )

        hidden_t, cell_t = lstm_unit(
            hidden_t_prev,
            cell_t_prev,
            gates,
            seq_lengths,
            t,
            forget_bias=forget_bias,
            drop_states=drop_states,
        )
        hidden[t + 1] = hidden_t
        cell[t + 1] = cell_t
    return (
        hidden[1:],
        hidden[-1].reshape(1, N, D),
        cell[1:],
        cell[-1].reshape(1, N, D)
    )


def lstm_reference(input, hidden_input, cell_input,
                   gates_w, gates_b, seq_lengths, forget_bias,
                   drop_states=False):
    T = input.shape[0]
    N = input.shape[1]
    G = input.shape[2]
    D = hidden_input.shape[hidden_input.ndim - 1]
    hidden = np.zeros(shape=(T + 1, N, D))
    cell = np.zeros(shape=(T + 1, N, D))
    assert hidden.shape[0] == T + 1
    assert cell.shape[0] == T + 1
    assert hidden.shape[1] == N
    assert cell.shape[1] == N
    cell[0, :, :] = cell_input
    hidden[0, :, :] = hidden_input
    for t in range(T):
        input_t = input[t].reshape(1, N, G)
        hidden_t_prev = hidden[t].reshape(1, N, D)
        cell_t_prev = cell[t].reshape(1, N, D)
        gates = np.dot(hidden_t_prev, gates_w.T) + gates_b
        gates = gates + input_t
        hidden_t, cell_t = lstm_unit(
            hidden_t_prev,
            cell_t_prev,
            gates,
            seq_lengths,
            t,
            forget_bias=forget_bias,
            drop_states=drop_states,
        )
        hidden[t + 1] = hidden_t
        cell[t + 1] = cell_t
    return (
        hidden[1:],
        hidden[-1].reshape(1, N, D),
        cell[1:],
        cell[-1].reshape(1, N, D)
    )


def multi_lstm_reference(input, hidden_input_list, cell_input_list,
                            i2h_w_list, i2h_b_list, gates_w_list, gates_b_list,
                            seq_lengths, forget_bias, drop_states=False):
    num_layers = len(hidden_input_list)
    assert len(cell_input_list) == num_layers
    assert len(i2h_w_list) == num_layers
    assert len(i2h_b_list) == num_layers
    assert len(gates_w_list) == num_layers
    assert len(gates_b_list) == num_layers

    for i in range(num_layers):
        layer_input = np.dot(input, i2h_w_list[i].T) + i2h_b_list[i]
        h_all, h_last, c_all, c_last = lstm_reference(
            layer_input,
            hidden_input_list[i],
            cell_input_list[i],
            gates_w_list[i],
            gates_b_list[i],
            seq_lengths,
            forget_bias,
            drop_states=drop_states,
        )
        input = h_all
    return h_all, h_last, c_all, c_last


def compute_regular_attention_logits(
    hidden_t,
    weighted_decoder_hidden_state_t_w,
    weighted_decoder_hidden_state_t_b,
    attention_weighted_encoder_context_t_prev,
    weighted_prev_attention_context_w,
    weighted_prev_attention_context_b,
    attention_v,
    weighted_encoder_outputs,
    encoder_outputs_for_dot_product,
    coverage_prev,
    coverage_weights,
):
    weighted_hidden_t = np.dot(
        hidden_t,
        weighted_decoder_hidden_state_t_w.T,
    ) + weighted_decoder_hidden_state_t_b
    attention_v = attention_v.reshape([-1])
    return np.sum(
        attention_v * np.tanh(weighted_encoder_outputs + weighted_hidden_t),
        axis=2,
    )


def compute_recurrent_attention_logits(
    hidden_t,
    weighted_decoder_hidden_state_t_w,
    weighted_decoder_hidden_state_t_b,
    attention_weighted_encoder_context_t_prev,
    weighted_prev_attention_context_w,
    weighted_prev_attention_context_b,
    attention_v,
    weighted_encoder_outputs,
    encoder_outputs_for_dot_product,
    coverage_prev,
    coverage_weights,
):
    weighted_hidden_t = np.dot(
        hidden_t,
        weighted_decoder_hidden_state_t_w.T,
    ) + weighted_decoder_hidden_state_t_b
    weighted_prev_attention_context = np.dot(
        attention_weighted_encoder_context_t_prev,
        weighted_prev_attention_context_w.T
    ) + weighted_prev_attention_context_b
    attention_v = attention_v.reshape([-1])
    return np.sum(
        attention_v * np.tanh(
            weighted_encoder_outputs + weighted_hidden_t +
            weighted_prev_attention_context
        ),
        axis=2,
    )


def compute_dot_attention_logits(
    hidden_t,
    weighted_decoder_hidden_state_t_w,
    weighted_decoder_hidden_state_t_b,
    attention_weighted_encoder_context_t_prev,
    weighted_prev_attention_context_w,
    weighted_prev_attention_context_b,
    attention_v,
    weighted_encoder_outputs,
    encoder_outputs_for_dot_product,
    coverage_prev,
    coverage_weights,
):
    hidden_t_for_dot_product = np.transpose(hidden_t, axes=[1, 2, 0])
    if (
        weighted_decoder_hidden_state_t_w is not None and
        weighted_decoder_hidden_state_t_b is not None
    ):
        hidden_t_for_dot_product = np.matmul(
            weighted_decoder_hidden_state_t_w,
            hidden_t_for_dot_product,
        ) + np.expand_dims(weighted_decoder_hidden_state_t_b, axis=1)
    attention_logits_t = np.sum(
        np.matmul(
            encoder_outputs_for_dot_product,
            hidden_t_for_dot_product,
        ),
        axis=2,
    )
    return np.transpose(attention_logits_t)


def compute_coverage_attention_logits(
    hidden_t,
    weighted_decoder_hidden_state_t_w,
    weighted_decoder_hidden_state_t_b,
    attention_weighted_encoder_context_t_prev,
    weighted_prev_attention_context_w,
    weighted_prev_attention_context_b,
    attention_v,
    weighted_encoder_outputs,
    encoder_outputs_for_dot_product,
    coverage_prev,
    coverage_weights,
):
    weighted_hidden_t = np.dot(
        hidden_t,
        weighted_decoder_hidden_state_t_w.T,
    ) + weighted_decoder_hidden_state_t_b
    coverage_part = coverage_prev.T * coverage_weights
    encoder_part = weighted_encoder_outputs + coverage_part
    attention_v = attention_v.reshape([-1])
    return np.sum(
        attention_v * np.tanh(encoder_part + weighted_hidden_t),
        axis=2,
    )


def lstm_with_attention_reference(
    input,
    initial_hidden_state,
    initial_cell_state,
    initial_attention_weighted_encoder_context,
    gates_w,
    gates_b,
    decoder_input_lengths,
    encoder_outputs_transposed,
    weighted_prev_attention_context_w,
    weighted_prev_attention_context_b,
    weighted_decoder_hidden_state_t_w,
    weighted_decoder_hidden_state_t_b,
    weighted_encoder_outputs,
    coverage_weights,
    attention_v,
    attention_zeros,
    compute_attention_logits,
):
    encoder_outputs = np.transpose(encoder_outputs_transposed, axes=[2, 0, 1])
    encoder_outputs_for_dot_product = np.transpose(
        encoder_outputs_transposed,
        [0, 2, 1],
    )
    decoder_input_length = input.shape[0]
    batch_size = input.shape[1]
    decoder_input_dim = input.shape[2]
    decoder_state_dim = initial_hidden_state.shape[2]
    encoder_output_dim = encoder_outputs.shape[2]
    hidden = np.zeros(
        shape=(decoder_input_length + 1, batch_size, decoder_state_dim))
    cell = np.zeros(
        shape=(decoder_input_length + 1, batch_size, decoder_state_dim))
    attention_weighted_encoder_context = np.zeros(
        shape=(decoder_input_length + 1, batch_size, encoder_output_dim))
    cell[0, :, :] = initial_cell_state
    hidden[0, :, :] = initial_hidden_state
    attention_weighted_encoder_context[0, :, :] = (
        initial_attention_weighted_encoder_context
    )
    encoder_length = encoder_outputs.shape[0]
    coverage = np.zeros(
        shape=(decoder_input_length + 1, batch_size, encoder_length))
    for t in range(decoder_input_length):
        input_t = input[t].reshape(1, batch_size, decoder_input_dim)
        hidden_t_prev = hidden[t].reshape(1, batch_size, decoder_state_dim)
        cell_t_prev = cell[t].reshape(1, batch_size, decoder_state_dim)
        attention_weighted_encoder_context_t_prev = (
            attention_weighted_encoder_context[t].reshape(
                1, batch_size, encoder_output_dim)
        )
        gates_input = np.concatenate(
            (hidden_t_prev, attention_weighted_encoder_context_t_prev),
            axis=2,
        )
        gates = np.dot(gates_input, gates_w.T) + gates_b
        gates = gates + input_t
        hidden_t, cell_t = lstm_unit(hidden_t_prev, cell_t_prev, gates,
                                     decoder_input_lengths, t)
        hidden[t + 1] = hidden_t
        cell[t + 1] = cell_t

        coverage_prev = coverage[t].reshape(1, batch_size, encoder_length)

        attention_logits_t = compute_attention_logits(
            hidden_t,
            weighted_decoder_hidden_state_t_w,
            weighted_decoder_hidden_state_t_b,
            attention_weighted_encoder_context_t_prev,
            weighted_prev_attention_context_w,
            weighted_prev_attention_context_b,
            attention_v,
            weighted_encoder_outputs,
            encoder_outputs_for_dot_product,
            coverage_prev,
            coverage_weights,
        )

        attention_logits_t_exp = np.exp(attention_logits_t)
        attention_weights_t = (
            attention_logits_t_exp /
            np.sum(attention_logits_t_exp, axis=0).reshape([1, -1])
        )
        coverage[t + 1, :, :] = coverage[t, :, :] + attention_weights_t.T
        attention_weighted_encoder_context[t + 1] = np.sum(
            (
                encoder_outputs *
                attention_weights_t.reshape([-1, batch_size, 1])
            ),
            axis=0,
        )
    return (
        hidden[1:],
        hidden[-1].reshape(1, batch_size, decoder_state_dim),
        cell[1:],
        cell[-1].reshape(1, batch_size, decoder_state_dim),
        attention_weighted_encoder_context[1:],
        attention_weighted_encoder_context[-1].reshape(
            1,
            batch_size,
            encoder_output_dim,
        )
    )


def lstm_with_regular_attention_reference(
    input,
    initial_hidden_state,
    initial_cell_state,
    initial_attention_weighted_encoder_context,
    gates_w,
    gates_b,
    decoder_input_lengths,
    weighted_decoder_hidden_state_t_w,
    weighted_decoder_hidden_state_t_b,
    weighted_encoder_outputs,
    attention_v,
    attention_zeros,
    encoder_outputs_transposed,
):
    return lstm_with_attention_reference(
        input=input,
        initial_hidden_state=initial_hidden_state,
        initial_cell_state=initial_cell_state,
        initial_attention_weighted_encoder_context=(
            initial_attention_weighted_encoder_context
        ),
        gates_w=gates_w,
        gates_b=gates_b,
        decoder_input_lengths=decoder_input_lengths,
        encoder_outputs_transposed=encoder_outputs_transposed,
        weighted_prev_attention_context_w=None,
        weighted_prev_attention_context_b=None,
        weighted_decoder_hidden_state_t_w=weighted_decoder_hidden_state_t_w,
        weighted_decoder_hidden_state_t_b=weighted_decoder_hidden_state_t_b,
        weighted_encoder_outputs=weighted_encoder_outputs,
        coverage_weights=None,
        attention_v=attention_v,
        attention_zeros=attention_zeros,
        compute_attention_logits=compute_regular_attention_logits,
    )


def lstm_with_recurrent_attention_reference(
    input,
    initial_hidden_state,
    initial_cell_state,
    initial_attention_weighted_encoder_context,
    gates_w,
    gates_b,
    decoder_input_lengths,
    weighted_prev_attention_context_w,
    weighted_prev_attention_context_b,
    weighted_decoder_hidden_state_t_w,
    weighted_decoder_hidden_state_t_b,
    weighted_encoder_outputs,
    attention_v,
    attention_zeros,
    encoder_outputs_transposed,
):
    return lstm_with_attention_reference(
        input=input,
        initial_hidden_state=initial_hidden_state,
        initial_cell_state=initial_cell_state,
        initial_attention_weighted_encoder_context=(
            initial_attention_weighted_encoder_context
        ),
        gates_w=gates_w,
        gates_b=gates_b,
        decoder_input_lengths=decoder_input_lengths,
        encoder_outputs_transposed=encoder_outputs_transposed,
        weighted_prev_attention_context_w=weighted_prev_attention_context_w,
        weighted_prev_attention_context_b=weighted_prev_attention_context_b,
        weighted_decoder_hidden_state_t_w=weighted_decoder_hidden_state_t_w,
        weighted_decoder_hidden_state_t_b=weighted_decoder_hidden_state_t_b,
        weighted_encoder_outputs=weighted_encoder_outputs,
        coverage_weights=None,
        attention_v=attention_v,
        attention_zeros=attention_zeros,
        compute_attention_logits=compute_recurrent_attention_logits,
    )


def lstm_with_dot_attention_reference(
    input,
    initial_hidden_state,
    initial_cell_state,
    initial_attention_weighted_encoder_context,
    gates_w,
    gates_b,
    decoder_input_lengths,
    encoder_outputs_transposed,
    weighted_decoder_hidden_state_t_w,
    weighted_decoder_hidden_state_t_b,
):
    return lstm_with_attention_reference(
        input=input,
        initial_hidden_state=initial_hidden_state,
        initial_cell_state=initial_cell_state,
        initial_attention_weighted_encoder_context=(
            initial_attention_weighted_encoder_context
        ),
        gates_w=gates_w,
        gates_b=gates_b,
        decoder_input_lengths=decoder_input_lengths,
        encoder_outputs_transposed=encoder_outputs_transposed,
        weighted_prev_attention_context_w=None,
        weighted_prev_attention_context_b=None,
        weighted_decoder_hidden_state_t_w=weighted_decoder_hidden_state_t_w,
        weighted_decoder_hidden_state_t_b=weighted_decoder_hidden_state_t_b,
        weighted_encoder_outputs=None,
        coverage_weights=None,
        attention_v=None,
        attention_zeros=None,
        compute_attention_logits=compute_dot_attention_logits,
    )


def lstm_with_dot_attention_reference_same_dim(
    input,
    initial_hidden_state,
    initial_cell_state,
    initial_attention_weighted_encoder_context,
    gates_w,
    gates_b,
    decoder_input_lengths,
    encoder_outputs_transposed,
):
    return lstm_with_dot_attention_reference(
        input=input,
        initial_hidden_state=initial_hidden_state,
        initial_cell_state=initial_cell_state,
        initial_attention_weighted_encoder_context=(
            initial_attention_weighted_encoder_context
        ),
        gates_w=gates_w,
        gates_b=gates_b,
        decoder_input_lengths=decoder_input_lengths,
        encoder_outputs_transposed=encoder_outputs_transposed,
        weighted_decoder_hidden_state_t_w=None,
        weighted_decoder_hidden_state_t_b=None,
    )


def lstm_with_dot_attention_reference_different_dim(
    input,
    initial_hidden_state,
    initial_cell_state,
    initial_attention_weighted_encoder_context,
    gates_w,
    gates_b,
    decoder_input_lengths,
    weighted_decoder_hidden_state_t_w,
    weighted_decoder_hidden_state_t_b,
    encoder_outputs_transposed,
):
    return lstm_with_dot_attention_reference(
        input=input,
        initial_hidden_state=initial_hidden_state,
        initial_cell_state=initial_cell_state,
        initial_attention_weighted_encoder_context=(
            initial_attention_weighted_encoder_context
        ),
        gates_w=gates_w,
        gates_b=gates_b,
        decoder_input_lengths=decoder_input_lengths,
        encoder_outputs_transposed=encoder_outputs_transposed,
        weighted_decoder_hidden_state_t_w=weighted_decoder_hidden_state_t_w,
        weighted_decoder_hidden_state_t_b=weighted_decoder_hidden_state_t_b,
    )


def lstm_with_coverage_attention_reference(
    input,
    initial_hidden_state,
    initial_cell_state,
    initial_attention_weighted_encoder_context,
    initial_coverage,
    gates_w,
    gates_b,
    decoder_input_lengths,
    weighted_decoder_hidden_state_t_w,
    weighted_decoder_hidden_state_t_b,
    weighted_encoder_outputs,
    coverage_weights,
    attention_v,
    attention_zeros,
    encoder_outputs_transposed,
):
    return lstm_with_attention_reference(
        input=input,
        initial_hidden_state=initial_hidden_state,
        initial_cell_state=initial_cell_state,
        initial_attention_weighted_encoder_context=(
            initial_attention_weighted_encoder_context
        ),
        gates_w=gates_w,
        gates_b=gates_b,
        decoder_input_lengths=decoder_input_lengths,
        encoder_outputs_transposed=encoder_outputs_transposed,
        weighted_prev_attention_context_w=None,
        weighted_prev_attention_context_b=None,
        weighted_decoder_hidden_state_t_w=weighted_decoder_hidden_state_t_w,
        weighted_decoder_hidden_state_t_b=weighted_decoder_hidden_state_t_b,
        weighted_encoder_outputs=weighted_encoder_outputs,
        coverage_weights=coverage_weights,
        attention_v=attention_v,
        attention_zeros=attention_zeros,
        compute_attention_logits=compute_coverage_attention_logits,
    )


def milstm_reference(
        input,
        hidden_input,
        cell_input,
        gates_w,
        gates_b,
        alpha,
        beta1,
        beta2,
        b,
        seq_lengths,
        forget_bias,
        drop_states=False):
    T = input.shape[0]
    N = input.shape[1]
    G = input.shape[2]
    D = hidden_input.shape[hidden_input.ndim - 1]
    hidden = np.zeros(shape=(T + 1, N, D))
    cell = np.zeros(shape=(T + 1, N, D))
    assert hidden.shape[0] == T + 1
    assert cell.shape[0] == T + 1
    assert hidden.shape[1] == N
    assert cell.shape[1] == N
    cell[0, :, :] = cell_input
    hidden[0, :, :] = hidden_input
    for t in range(T):
        input_t = input[t].reshape(1, N, G)
        hidden_t_prev = hidden[t].reshape(1, N, D)
        cell_t_prev = cell[t].reshape(1, N, D)
        gates = np.dot(hidden_t_prev, gates_w.T) + gates_b
        gates = (alpha * gates * input_t) + \
                    (beta1 * gates) + \
                    (beta2 * input_t) + \
                    b
        hidden_t, cell_t = lstm_unit(
            hidden_t_prev,
            cell_t_prev,
            gates,
            seq_lengths,
            t,
            forget_bias=forget_bias,
            drop_states=drop_states,
        )
        hidden[t + 1] = hidden_t
        cell[t + 1] = cell_t
    return (
        hidden[1:],
        hidden[-1].reshape(1, N, D),
        cell[1:],
        cell[-1].reshape(1, N, D)
    )


def layer_norm_milstm_reference(
        input,
        hidden_input,
        cell_input,
        gates_w,
        gates_b,
        alpha,
        beta1,
        beta2,
        b,
        gates_t_norm_scale,
        gates_t_norm_bias,
        seq_lengths,
        forget_bias,
        drop_states=False):
    T = input.shape[0]
    N = input.shape[1]
    G = input.shape[2]
    D = hidden_input.shape[hidden_input.ndim - 1]
    hidden = np.zeros(shape=(T + 1, N, D))
    cell = np.zeros(shape=(T + 1, N, D))
    assert hidden.shape[0] == T + 1
    assert cell.shape[0] == T + 1
    assert hidden.shape[1] == N
    assert cell.shape[1] == N
    cell[0, :, :] = cell_input
    hidden[0, :, :] = hidden_input
    for t in range(T):
        input_t = input[t].reshape(1, N, G)
        hidden_t_prev = hidden[t].reshape(1, N, D)
        cell_t_prev = cell[t].reshape(1, N, D)
        gates = np.dot(hidden_t_prev, gates_w.T) + gates_b
        gates = (alpha * gates * input_t) + \
                    (beta1 * gates) + \
                    (beta2 * input_t) + \
                    b
        gates = layer_norm_with_scale_and_bias_ref(
            gates, gates_t_norm_scale, gates_t_norm_bias
        )
        hidden_t, cell_t = lstm_unit(
            hidden_t_prev,
            cell_t_prev,
            gates,
            seq_lengths,
            t,
            forget_bias=forget_bias,
            drop_states=drop_states,
        )
        hidden[t + 1] = hidden_t
        cell[t + 1] = cell_t
    return (
        hidden[1:],
        hidden[-1].reshape(1, N, D),
        cell[1:],
        cell[-1].reshape(1, N, D)
    )


def lstm_input():
    '''
    Create input tensor where each dimension is from 1 to 4, ndim=3 and
    last dimension size is a factor of 4
    '''
    dims_ = st.tuples(
        st.integers(min_value=1, max_value=4),  # t
        st.integers(min_value=1, max_value=4),  # n
        st.integers(min_value=1, max_value=4),  # d
    )

    def create_input(dims):
        dims = list(dims)
        dims[2] *= 4
        return hu.arrays(dims)

    return dims_.flatmap(create_input)


def _prepare_attention(t, n, dim_in, encoder_dim,
                          forward_only=False, T=None,
                          dim_out=None, residual=False,
                          final_dropout=False):
    if dim_out is None:
        dim_out = [dim_in]
    print("Dims: t={} n={} dim_in={} dim_out={}".format(t, n, dim_in, dim_out))

    model = ModelHelper(name='external')

    def generate_input_state(shape):
        return np.random.random(shape).astype(np.float32)

    initial_states = []
    for layer_id, d in enumerate(dim_out):
        h, c = model.net.AddExternalInputs(
            "hidden_init_{}".format(layer_id),
            "cell_init_{}".format(layer_id),
        )
        initial_states.extend([h, c])
        workspace.FeedBlob(h, generate_input_state((1, n, d)))
        workspace.FeedBlob(c, generate_input_state((1, n, d)))

    awec_init = model.net.AddExternalInputs([
        'initial_attention_weighted_encoder_context',
    ])
    initial_states.append(awec_init)
    workspace.FeedBlob(
        awec_init,
        generate_input_state((1, n, encoder_dim)),
    )

    # Due to convoluted RNN scoping logic we make sure that things
    # work from a namescope
    with scope.NameScope("test_name_scope"):
        (
            input_blob,
            seq_lengths,
            encoder_outputs,
            weighted_encoder_outputs,
        ) = model.net.AddScopedExternalInputs(
            'input_blob',
            'seq_lengths',
            'encoder_outputs',
            'weighted_encoder_outputs',
        )

        layer_input_dim = dim_in
        cells = []
        for layer_id, d in enumerate(dim_out):

            cell = rnn_cell.MILSTMCell(
                name='decoder_{}'.format(layer_id),
                forward_only=forward_only,
                input_size=layer_input_dim,
                hidden_size=d,
                forget_bias=0.0,
                memory_optimization=False,
            )
            cells.append(cell)
            layer_input_dim = d

        decoder_cell = rnn_cell.MultiRNNCell(
            cells,
            name='decoder',
            residual_output_layers=range(1, len(cells)) if residual else None,
        )

        attention_cell = rnn_cell.AttentionCell(
            encoder_output_dim=encoder_dim,
            encoder_outputs=encoder_outputs,
            encoder_lengths=None,
            decoder_cell=decoder_cell,
            decoder_state_dim=dim_out[-1],
            name='attention_decoder',
            attention_type=AttentionType.Recurrent,
            weighted_encoder_outputs=weighted_encoder_outputs,
            attention_memory_optimization=True,
        )
        if final_dropout:
            # dropout ratio of 0.0 used to test mechanism but not interfere
            # with numerical tests
            attention_cell = rnn_cell.DropoutCell(
                internal_cell=attention_cell,
                dropout_ratio=0.0,
                name='dropout',
                forward_only=forward_only,
                is_test=False,
            )

        attention_cell = (
            attention_cell if T is None
            else rnn_cell.UnrolledCell(attention_cell, T)
        )

        output_indices = decoder_cell.output_indices
        output_indices.append(2 * len(cells))
        outputs_with_grads = [2 * i for i in output_indices]

        final_output, state_outputs = attention_cell.apply_over_sequence(
            model=model,
            inputs=input_blob,
            seq_lengths=seq_lengths,
            initial_states=initial_states,
            outputs_with_grads=outputs_with_grads,
        )

    workspace.RunNetOnce(model.param_init_net)

    workspace.FeedBlob(
        seq_lengths,
        np.random.randint(1, t + 1, size=(n,)).astype(np.int32)
    )

    return {
        'final_output': final_output,
        'net': model.net,
        'initial_states': initial_states,
        'input_blob': input_blob,
        'encoder_outputs': encoder_outputs,
        'weighted_encoder_outputs': weighted_encoder_outputs,
        'outputs_with_grads': outputs_with_grads,
    }


class MulCell(rnn_cell.RNNCell):
    def _apply(self, model, input_t,
               seq_lengths, states, timestep, extra_inputs):
        assert len(states) == 1
        result = model.net.Mul([input_t, states[0]])
        model.net.AddExternalOutput(result)
        return [result]

    def get_state_names(self):
        return [self.scope("state")]


def prepare_mul_rnn(model, input_blob, shape, T, outputs_with_grad, num_layers):
    print("Shape: ", shape)
    t, n, d = shape
    cells = [MulCell(name="layer_{}".format(i)) for i in range(num_layers)]
    cell = rnn_cell.MultiRNNCell(name="multi_mul_rnn", cells=cells)
    if T is not None:
        cell = rnn_cell.UnrolledCell(cell, T=T)
    states = [
        model.param_init_net.ConstantFill(
            [], "initial_state_{}".format(i), value=1.0, shape=[1, n, d])
        for i in range(num_layers)]
    _, results = cell.apply_over_sequence(
        model=model,
        inputs=input_blob,
        initial_states=states,
        outputs_with_grads=[
            x + 2 * (num_layers - 1) for x in outputs_with_grad
        ],
        seq_lengths=None,
    )
    return results[-2:]


class RNNCellTest(hu.HypothesisTestCase):
    @given(
        input_tensor=hu.tensor(min_dim=3, max_dim=3, max_value=3),
        num_layers=st.integers(1, 4),
        outputs_with_grad=st.sampled_from(
            [[0], [1], [0, 1]]
        ),
    )
    @ht_settings(max_examples=10)
    def test_unroll_mul(self, input_tensor, num_layers, outputs_with_grad):
        outputs = []
        nets = []
        input_blob = None
        for T in [input_tensor.shape[0], None]:
            model = ModelHelper("rnn_mul_{}".format(
                "unroll" if T else "dynamic"))
            input_blob = model.net.AddExternalInputs("input_blob")
            outputs.append(
                prepare_mul_rnn(model, input_blob, input_tensor.shape, T,
                                outputs_with_grad, num_layers))
            workspace.RunNetOnce(model.param_init_net)
            nets.append(model.net)
            workspace.blobs[input_blob] = input_tensor

        gradient_checker.NetGradientChecker.CompareNets(
            nets, outputs, outputs_with_grad_ids=outputs_with_grad,
            inputs_with_grads=[input_blob],
        )

    @given(
        input_tensor=hu.tensor(min_dim=3, max_dim=3, max_value=3),
        forget_bias=st.floats(-10.0, 10.0),
        drop_states=st.booleans(),
        dim_out=st.lists(
            elements=st.integers(min_value=1, max_value=3),
            min_size=1, max_size=3,
        ),
        outputs_with_grads=st.sampled_from(
            [[0], [1], [0, 1], [0, 2], [0, 1, 2, 3]]
        )
    )
    @ht_settings(max_examples=10)
    @utils.debug
    def test_unroll_lstm(self, input_tensor, dim_out, outputs_with_grads,
                         **kwargs):
        lstms = [
            _prepare_rnn(
                *input_tensor.shape,
                create_rnn=rnn_cell.LSTM,
                outputs_with_grads=outputs_with_grads,
                T=T,
                two_d_initial_states=False,
                dim_out=dim_out,
                **kwargs
            ) for T in [input_tensor.shape[0], None]
        ]
        outputs, nets, inputs = zip(*lstms)
        workspace.FeedBlob(inputs[0][-1], input_tensor)

        assert inputs[0] == inputs[1]
        gradient_checker.NetGradientChecker.CompareNets(
            nets, outputs, outputs_with_grads,
            inputs_with_grads=inputs[0],
        )

    @given(
        input_tensor=hu.tensor(min_dim=3, max_dim=3, max_value=3),
        encoder_length=st.integers(min_value=1, max_value=3),
        encoder_dim=st.integers(min_value=1, max_value=3),
        hidden_units=st.integers(min_value=1, max_value=3),
        num_layers=st.integers(min_value=1, max_value=3),
        residual=st.booleans(),
        final_dropout=st.booleans(),
    )
    @ht_settings(max_examples=10)
    @utils.debug
    def test_unroll_attention(self, input_tensor, encoder_length,
                                    encoder_dim, hidden_units,
                                    num_layers, residual,
                                    final_dropout):

        dim_out = [hidden_units] * num_layers
        encoder_tensor = np.random.random(
            (encoder_length, input_tensor.shape[1], encoder_dim),
        ).astype('float32')

        print('Decoder input shape: {}'.format(input_tensor.shape))
        print('Encoder output shape: {}'.format(encoder_tensor.shape))

        # Necessary because otherwise test fails for networks with fewer
        # layers than previous test. TODO: investigate why.
        workspace.ResetWorkspace()

        net, unrolled = [
            _prepare_attention(
                t=input_tensor.shape[0],
                n=input_tensor.shape[1],
                dim_in=input_tensor.shape[2],
                encoder_dim=encoder_dim,
                T=T,
                dim_out=dim_out,
                residual=residual,
                final_dropout=final_dropout,
            ) for T in [input_tensor.shape[0], None]
        ]

        workspace.FeedBlob(net['input_blob'], input_tensor)
        workspace.FeedBlob(net['encoder_outputs'], encoder_tensor)
        workspace.FeedBlob(
            net['weighted_encoder_outputs'],
            np.random.random(encoder_tensor.shape).astype('float32'),
        )

        for input_name in [
            'input_blob',
            'encoder_outputs',
            'weighted_encoder_outputs',
        ]:
            assert net[input_name] == unrolled[input_name]
        for state_name, unrolled_state_name in zip(
            net['initial_states'],
            unrolled['initial_states'],
        ):
            assert state_name == unrolled_state_name

        inputs_with_grads = net['initial_states'] + [
            net['input_blob'],
            net['encoder_outputs'],
            net['weighted_encoder_outputs'],
        ]

        gradient_checker.NetGradientChecker.CompareNets(
            [net['net'], unrolled['net']],
            [[net['final_output']], [unrolled['final_output']]],
            [0],
            inputs_with_grads=inputs_with_grads,
            threshold=0.000001,
        )

    @given(
        input_tensor=hu.tensor(min_dim=3, max_dim=3),
        forget_bias=st.floats(-10.0, 10.0),
        forward_only=st.booleans(),
        drop_states=st.booleans(),
    )
    @ht_settings(max_examples=10)
    def test_layered_lstm(self, input_tensor, **kwargs):
        for outputs_with_grads in [[0], [1], [0, 1, 2, 3]]:
            for memory_optim in [False, True]:
                _, net, inputs = _prepare_rnn(
                    *input_tensor.shape,
                    create_rnn=rnn_cell.LSTM,
                    outputs_with_grads=outputs_with_grads,
                    memory_optim=memory_optim,
                    **kwargs
                )
                workspace.FeedBlob(inputs[-1], input_tensor)
                workspace.RunNetOnce(net)
                workspace.ResetWorkspace()

    def test_lstm(self):
        self.lstm_base(lstm_type=(rnn_cell.LSTM, lstm_reference))

    def test_milstm(self):
        self.lstm_base(lstm_type=(rnn_cell.MILSTM, milstm_reference))

    @unittest.skip("This is currently numerically unstable")
    def test_norm_lstm(self):
        self.lstm_base(
            lstm_type=(rnn_cell.LayerNormLSTM, layer_norm_lstm_reference),
        )

    @unittest.skip("This is currently numerically unstable")
    def test_norm_milstm(self):
        self.lstm_base(
            lstm_type=(rnn_cell.LayerNormMILSTM, layer_norm_milstm_reference)
        )

    @given(
        seed=st.integers(0, 2**32 - 1),
        input_tensor=lstm_input(),
        forget_bias=st.floats(-10.0, 10.0),
        fwd_only=st.booleans(),
        drop_states=st.booleans(),
        memory_optim=st.booleans(),
        outputs_with_grads=st.sampled_from([[0], [1], [0, 1, 2, 3]]),
    )
    def lstm_base(self, seed, lstm_type, outputs_with_grads, memory_optim,
                  input_tensor, forget_bias, fwd_only, drop_states):
        np.random.seed(seed)
        create_lstm, ref = lstm_type
        ref = partial(ref, forget_bias=forget_bias)

        t, n, d = input_tensor.shape
        assert d % 4 == 0
        d = d // 4
        ref = partial(ref, forget_bias=forget_bias, drop_states=drop_states)

        net = _prepare_rnn(t, n, d, create_lstm,
                            outputs_with_grads=outputs_with_grads,
                            memory_optim=memory_optim,
                            forget_bias=forget_bias,
                            forward_only=fwd_only,
                            drop_states=drop_states)[1]
        # here we don't provide a real input for the net but just for one of
        # its ops (RecurrentNetworkOp). So have to hardcode this name
        workspace.FeedBlob("test_name_scope/external/recurrent/i2h",
                           input_tensor)
        op = net._net.op[-1]
        inputs = [workspace.FetchBlob(name) for name in op.input]

        # Validate forward only mode is in effect
        if fwd_only:
            for arg in op.arg:
                self.assertFalse(arg.name == 'backward_step_net')

        self.assertReferenceChecks(
            hu.cpu_do,
            op,
            inputs,
            ref,
            outputs_to_check=list(range(4)),
        )

        # Checking for input, gates_t_w and gates_t_b gradients
        if not fwd_only:
            for param in range(5):
                self.assertGradientChecks(
                    device_option=hu.cpu_do,
                    op=op,
                    inputs=inputs,
                    outputs_to_check=param,
                    outputs_with_grads=outputs_with_grads,
                    threshold=0.01,
                    stepsize=0.005,
                )

    def test_lstm_extract_predictor_net(self):
        model = ModelHelper(name="lstm_extract_test")

        with core.DeviceScope(core.DeviceOption(caffe2_pb2.CPU, 0)):
            output, _, _, _ = rnn_cell.LSTM(
                model=model,
                input_blob="input",
                seq_lengths="seqlengths",
                initial_states=("hidden_init", "cell_init"),
                dim_in=20,
                dim_out=40,
                scope="test",
                drop_states=True,
                return_last_layer_only=True,
            )
        # Run param init net to get the shapes for all inputs
        shapes = {}
        workspace.RunNetOnce(model.param_init_net)
        for b in workspace.Blobs():
            shapes[b] = workspace.FetchBlob(b).shape

        # But export in CPU
        (predict_net, export_blobs) = ExtractPredictorNet(
            net_proto=model.net.Proto(),
            input_blobs=["input"],
            output_blobs=[output],
            device=core.DeviceOption(caffe2_pb2.CPU, 1),
        )

        # Create the net and run once to see it is valid
        # Populate external inputs with correctly shaped random input
        # and also ensure that the export_blobs was constructed correctly.
        workspace.ResetWorkspace()
        shapes['input'] = [10, 4, 20]
        shapes['cell_init'] = [1, 4, 40]
        shapes['hidden_init'] = [1, 4, 40]

        print(predict_net.Proto().external_input)
        self.assertTrue('seqlengths' in predict_net.Proto().external_input)
        for einp in predict_net.Proto().external_input:
            if einp == 'seqlengths':
                workspace.FeedBlob(
                    "seqlengths",
                    np.array([10] * 4, dtype=np.int32)
                )
            else:
                workspace.FeedBlob(
                    einp,
                    np.zeros(shapes[einp]).astype(np.float32),
                )
                if einp != 'input':
                    self.assertTrue(einp in export_blobs)

        print(str(predict_net.Proto()))
        self.assertTrue(workspace.CreateNet(predict_net.Proto()))
        self.assertTrue(workspace.RunNet(predict_net.Proto().name))

        # Validate device options set correctly for the RNNs
        for op in predict_net.Proto().op:
            if op.type == 'RecurrentNetwork':
                for arg in op.arg:
                    if arg.name == "step_net":
                        for step_op in arg.n.op:
                            self.assertEqual(0, step_op.device_option.device_type)
                            self.assertEqual(1, step_op.device_option.cuda_gpu_id)
                    elif arg.name == 'backward_step_net':
                        self.assertEqual(caffe2_pb2.NetDef(), arg.n)

    def test_lstm_params(self):
        model = ModelHelper(name="lstm_params_test")

        with core.DeviceScope(core.DeviceOption(caffe2_pb2.CPU, 0)):
            output, _, _, _ = rnn_cell.LSTM(
                model=model,
                input_blob="input",
                seq_lengths="seqlengths",
                initial_states=None,
                dim_in=20,
                dim_out=40,
                scope="test",
                drop_states=True,
                return_last_layer_only=True,
            )
        for param in model.GetParams():
            self.assertNotEqual(model.get_param_info(param), None)

    def test_milstm_params(self):
        model = ModelHelper(name="milstm_params_test")

        with core.DeviceScope(core.DeviceOption(caffe2_pb2.CPU, 0)):
            output, _, _, _ = rnn_cell.MILSTM(
                model=model,
                input_blob="input",
                seq_lengths="seqlengths",
                initial_states=None,
                dim_in=20,
                dim_out=[40, 20],
                scope="test",
                drop_states=True,
                return_last_layer_only=True,
            )
        for param in model.GetParams():
            self.assertNotEqual(model.get_param_info(param), None)

    def test_layer_norm_lstm_params(self):
        model = ModelHelper(name="layer_norm_lstm_params_test")

        with core.DeviceScope(core.DeviceOption(caffe2_pb2.CPU, 0)):
            output, _, _, _ = rnn_cell.LayerNormLSTM(
                model=model,
                input_blob="input",
                seq_lengths="seqlengths",
                initial_states=None,
                dim_in=20,
                dim_out=40,
                scope="test",
                drop_states=True,
                return_last_layer_only=True,
            )
        for param in model.GetParams():
            self.assertNotEqual(model.get_param_info(param), None)

    @given(encoder_output_length=st.integers(1, 3),
           encoder_output_dim=st.integers(1, 3),
           decoder_input_length=st.integers(1, 3),
           decoder_state_dim=st.integers(1, 3),
           batch_size=st.integers(1, 3),
           **hu.gcs)
    def test_lstm_with_regular_attention(
        self,
        encoder_output_length,
        encoder_output_dim,
        decoder_input_length,
        decoder_state_dim,
        batch_size,
        gc,
        dc,
    ):
        self.lstm_with_attention(
            partial(
                rnn_cell.LSTMWithAttention,
                attention_type=AttentionType.Regular,
            ),
            encoder_output_length,
            encoder_output_dim,
            decoder_input_length,
            decoder_state_dim,
            batch_size,
            lstm_with_regular_attention_reference,
            gc,
        )

    @given(encoder_output_length=st.integers(1, 3),
           encoder_output_dim=st.integers(1, 3),
           decoder_input_length=st.integers(1, 3),
           decoder_state_dim=st.integers(1, 3),
           batch_size=st.integers(1, 3),
           **hu.gcs)
    def test_lstm_with_recurrent_attention(
        self,
        encoder_output_length,
        encoder_output_dim,
        decoder_input_length,
        decoder_state_dim,
        batch_size,
        gc,
        dc,
    ):
        self.lstm_with_attention(
            partial(
                rnn_cell.LSTMWithAttention,
                attention_type=AttentionType.Recurrent,
            ),
            encoder_output_length,
            encoder_output_dim,
            decoder_input_length,
            decoder_state_dim,
            batch_size,
            lstm_with_recurrent_attention_reference,
            gc,
        )

    @given(encoder_output_length=st.integers(2, 2),
           encoder_output_dim=st.integers(4, 4),
           decoder_input_length=st.integers(3, 3),
           decoder_state_dim=st.integers(4, 4),
           batch_size=st.integers(5, 5),
           **hu.gcs)
    def test_lstm_with_dot_attention_same_dim(
        self,
        encoder_output_length,
        encoder_output_dim,
        decoder_input_length,
        decoder_state_dim,
        batch_size,
        gc,
        dc,
    ):
        self.lstm_with_attention(
            partial(
                rnn_cell.LSTMWithAttention,
                attention_type=AttentionType.Dot,
            ),
            encoder_output_length,
            encoder_output_dim,
            decoder_input_length,
            decoder_state_dim,
            batch_size,
            lstm_with_dot_attention_reference_same_dim,
            gc,
        )

    @given(encoder_output_length=st.integers(1, 3),
           encoder_output_dim=st.integers(4, 4),
           decoder_input_length=st.integers(1, 3),
           decoder_state_dim=st.integers(5, 5),
           batch_size=st.integers(1, 3),
           **hu.gcs)
    def test_lstm_with_dot_attention_different_dim(
        self,
        encoder_output_length,
        encoder_output_dim,
        decoder_input_length,
        decoder_state_dim,
        batch_size,
        gc,
        dc,
    ):
        self.lstm_with_attention(
            partial(
                rnn_cell.LSTMWithAttention,
                attention_type=AttentionType.Dot,
            ),
            encoder_output_length,
            encoder_output_dim,
            decoder_input_length,
            decoder_state_dim,
            batch_size,
            lstm_with_dot_attention_reference_different_dim,
            gc,
        )

    @given(encoder_output_length=st.integers(2, 3),
           encoder_output_dim=st.integers(1, 3),
           decoder_input_length=st.integers(1, 3),
           decoder_state_dim=st.integers(1, 3),
           batch_size=st.integers(1, 3),
           **hu.gcs)
    def test_lstm_with_coverage_attention(
        self,
        encoder_output_length,
        encoder_output_dim,
        decoder_input_length,
        decoder_state_dim,
        batch_size,
        gc,
        dc,
    ):
        self.lstm_with_attention(
            partial(
                rnn_cell.LSTMWithAttention,
                attention_type=AttentionType.SoftCoverage,
            ),
            encoder_output_length,
            encoder_output_dim,
            decoder_input_length,
            decoder_state_dim,
            batch_size,
            lstm_with_coverage_attention_reference,
            gc,
        )

    def lstm_with_attention(
        self,
        create_lstm_with_attention,
        encoder_output_length,
        encoder_output_dim,
        decoder_input_length,
        decoder_state_dim,
        batch_size,
        ref,
        gc,
    ):
        model = ModelHelper(name='external')
        with core.DeviceScope(gc):
            (
                encoder_outputs,
                decoder_inputs,
                decoder_input_lengths,
                initial_decoder_hidden_state,
                initial_decoder_cell_state,
                initial_attention_weighted_encoder_context,
            ) = model.net.AddExternalInputs(
                'encoder_outputs',
                'decoder_inputs',
                'decoder_input_lengths',
                'initial_decoder_hidden_state',
                'initial_decoder_cell_state',
                'initial_attention_weighted_encoder_context',
            )
            create_lstm_with_attention(
                model=model,
                decoder_inputs=decoder_inputs,
                decoder_input_lengths=decoder_input_lengths,
                initial_decoder_hidden_state=initial_decoder_hidden_state,
                initial_decoder_cell_state=initial_decoder_cell_state,
                initial_attention_weighted_encoder_context=(
                    initial_attention_weighted_encoder_context
                ),
                encoder_output_dim=encoder_output_dim,
                encoder_outputs=encoder_outputs,
                encoder_lengths=None,
                decoder_input_dim=decoder_state_dim,
                decoder_state_dim=decoder_state_dim,
                scope='external/LSTMWithAttention',
            )
            op = model.net._net.op[-2]
        workspace.RunNetOnce(model.param_init_net)

        # This is original decoder_inputs after linear layer
        decoder_input_blob = op.input[0]

        workspace.FeedBlob(
            decoder_input_blob,
            np.random.randn(
                decoder_input_length,
                batch_size,
                decoder_state_dim * 4,
            ).astype(np.float32))
        workspace.FeedBlob(
            'external/LSTMWithAttention/encoder_outputs_transposed',
            np.random.randn(
                batch_size,
                encoder_output_dim,
                encoder_output_length,
            ).astype(np.float32),
        )
        workspace.FeedBlob(
            'external/LSTMWithAttention/weighted_encoder_outputs',
            np.random.randn(
                encoder_output_length,
                batch_size,
                encoder_output_dim,
            ).astype(np.float32),
        )
        workspace.FeedBlob(
            'external/LSTMWithAttention/coverage_weights',
            np.random.randn(
                encoder_output_length,
                batch_size,
                encoder_output_dim,
            ).astype(np.float32),
        )
        workspace.FeedBlob(
            decoder_input_lengths,
            np.random.randint(
                0,
                decoder_input_length + 1,
                size=(batch_size,)
            ).astype(np.int32))
        workspace.FeedBlob(
            initial_decoder_hidden_state,
            np.random.randn(1, batch_size, decoder_state_dim).astype(np.float32)
        )
        workspace.FeedBlob(
            initial_decoder_cell_state,
            np.random.randn(1, batch_size, decoder_state_dim).astype(np.float32)
        )
        workspace.FeedBlob(
            initial_attention_weighted_encoder_context,
            np.random.randn(
                1, batch_size, encoder_output_dim).astype(np.float32)
        )
        workspace.FeedBlob(
            'external/LSTMWithAttention/initial_coverage',
            np.zeros((1, batch_size, encoder_output_length)).astype(np.float32),
        )
        inputs = [workspace.FetchBlob(name) for name in op.input]
        self.assertReferenceChecks(
            device_option=gc,
            op=op,
            inputs=inputs,
            reference=ref,
            grad_reference=None,
            output_to_grad=None,
            outputs_to_check=list(range(6)),
        )
        gradients_to_check = [
            index for (index, input_name) in enumerate(op.input)
            if input_name != 'decoder_input_lengths'
        ]
        for param in gradients_to_check:
            self.assertGradientChecks(
                device_option=gc,
                op=op,
                inputs=inputs,
                outputs_to_check=param,
                outputs_with_grads=[0, 4],
                threshold=0.01,
                stepsize=0.001,
            )

    @given(seed=st.integers(0, 2**32 - 1),
           n=st.integers(1, 10),
           d=st.integers(1, 10),
           t=st.integers(1, 10),
           dtype=st.sampled_from([np.float32, np.float16]),
           use_sequence_lengths=st.booleans(),
           **hu.gcs)
    def test_lstm_unit_recurrent_network(
            self, seed, n, d, t, dtype, dc, use_sequence_lengths, gc):
        np.random.seed(seed)
        if dtype == np.float16:
            # only supported with CUDA
            assume(gc.device_type == caffe2_pb2.CUDA)
            dc = [do for do in dc if do.device_type == caffe2_pb2.CUDA]

        if use_sequence_lengths:
            op_inputs = ['hidden_t_prev', 'cell_t_prev', 'gates_t',
                         'seq_lengths', 'timestep']
        else:
            op_inputs = ['hidden_t_prev', 'cell_t_prev', 'gates_t', 'timestep']
        op = core.CreateOperator(
            'LSTMUnit',
            op_inputs,
            ['hidden_t', 'cell_t'],
            sequence_lengths=use_sequence_lengths,
        )
        cell_t_prev = np.random.randn(1, n, d).astype(dtype)
        hidden_t_prev = np.random.randn(1, n, d).astype(dtype)
        gates = np.random.randn(1, n, 4 * d).astype(dtype)
        seq_lengths = np.random.randint(1, t + 1, size=(n,)).astype(np.int32)
        timestep = np.random.randint(0, t, size=(1,)).astype(np.int32)
        if use_sequence_lengths:
            inputs = [hidden_t_prev, cell_t_prev, gates, seq_lengths, timestep]
        else:
            inputs = [hidden_t_prev, cell_t_prev, gates, timestep]
        input_device_options = {'timestep': hu.cpu_do}
        self.assertDeviceChecks(
            dc, op, inputs, [0],
            input_device_options=input_device_options)

        kwargs = {}
        if dtype == np.float16:
            kwargs['threshold'] = 1e-1  # default is 1e-4

        def lstm_unit_reference(*args, **kwargs):
            return lstm_unit(*args, sequence_lengths=use_sequence_lengths, **kwargs)

        self.assertReferenceChecks(
            gc, op, inputs, lstm_unit_reference,
            input_device_options=input_device_options,
            **kwargs)

        kwargs = {}
        if dtype == np.float16:
            kwargs['threshold'] = 0.5  # default is 0.005

        for i in range(2):
            self.assertGradientChecks(
                gc, op, inputs, i, [0, 1],
                input_device_options=input_device_options,
                **kwargs)

    @given(input_length=st.integers(2, 5),
           dim_in=st.integers(1, 3),
           max_num_units=st.integers(1, 3),
           num_layers=st.integers(2, 3),
           batch_size=st.integers(1, 3))
    def test_multi_lstm(
        self,
        input_length,
        dim_in,
        max_num_units,
        num_layers,
        batch_size,
    ):
        model = ModelHelper(name='external')
        (
            input_sequence,
            seq_lengths,
        ) = model.net.AddExternalInputs(
            'input_sequence',
            'seq_lengths',
        )
        dim_out = [
            np.random.randint(1, max_num_units + 1)
            for _ in range(num_layers)
        ]
        h_all, h_last, c_all, c_last = rnn_cell.LSTM(
            model=model,
            input_blob=input_sequence,
            seq_lengths=seq_lengths,
            initial_states=None,
            dim_in=dim_in,
            dim_out=dim_out,
            # scope='test',
            outputs_with_grads=(0,),
            return_params=False,
            memory_optimization=False,
            forget_bias=0.0,
            forward_only=False,
            return_last_layer_only=True,
        )

        workspace.RunNetOnce(model.param_init_net)

        seq_lengths_val = np.random.randint(
            1,
            input_length + 1,
            size=(batch_size),
        ).astype(np.int32)
        input_sequence_val = np.random.randn(
            input_length,
            batch_size,
            dim_in,
        ).astype(np.float32)
        workspace.FeedBlob(seq_lengths, seq_lengths_val)
        workspace.FeedBlob(input_sequence, input_sequence_val)

        hidden_input_list = []
        cell_input_list = []
        i2h_w_list = []
        i2h_b_list = []
        gates_w_list = []
        gates_b_list = []

        for i in range(num_layers):
            hidden_input_list.append(
                workspace.FetchBlob(
                    'layer_{}/initial_hidden_state'.format(i)),
            )
            cell_input_list.append(
                workspace.FetchBlob(
                    'layer_{}/initial_cell_state'.format(i)),
            )
            # Input projection for the first layer is produced outside
            # of the cell ans thus not scoped
            prefix = 'layer_{}/'.format(i) if i > 0 else ''
            i2h_w_list.append(
                workspace.FetchBlob('{}i2h_w'.format(prefix)),
            )
            i2h_b_list.append(
                workspace.FetchBlob('{}i2h_b'.format(prefix)),
            )
            gates_w_list.append(
                workspace.FetchBlob('layer_{}/gates_t_w'.format(i)),
            )
            gates_b_list.append(
                workspace.FetchBlob('layer_{}/gates_t_b'.format(i)),
            )

        workspace.RunNetOnce(model.net)
        h_all_calc = workspace.FetchBlob(h_all)
        h_last_calc = workspace.FetchBlob(h_last)
        c_all_calc = workspace.FetchBlob(c_all)
        c_last_calc = workspace.FetchBlob(c_last)

        h_all_ref, h_last_ref, c_all_ref, c_last_ref = multi_lstm_reference(
            input_sequence_val,
            hidden_input_list,
            cell_input_list,
            i2h_w_list,
            i2h_b_list,
            gates_w_list,
            gates_b_list,
            seq_lengths_val,
            forget_bias=0.0,
        )

        h_all_delta = np.abs(h_all_ref - h_all_calc).sum()
        h_last_delta = np.abs(h_last_ref - h_last_calc).sum()
        c_all_delta = np.abs(c_all_ref - c_all_calc).sum()
        c_last_delta = np.abs(c_last_ref - c_last_calc).sum()

        self.assertAlmostEqual(h_all_delta, 0.0, places=5)
        self.assertAlmostEqual(h_last_delta, 0.0, places=5)
        self.assertAlmostEqual(c_all_delta, 0.0, places=5)
        self.assertAlmostEqual(c_last_delta, 0.0, places=5)

        input_values = {
            'input_sequence': input_sequence_val,
            'seq_lengths': seq_lengths_val,
        }
        for param in model.GetParams():
            value = workspace.FetchBlob(param)
            input_values[str(param)] = value

        output_sum = model.net.SumElements(
            [h_all],
            'output_sum',
            average=True,
        )
        fake_loss = model.net.Tanh(
            output_sum,
        )
        for param in model.GetParams():
            gradient_checker.NetGradientChecker.Check(
                model.net,
                outputs_with_grad=[fake_loss],
                input_values=input_values,
                input_to_check=str(param),
                print_net=False,
                step_size=0.0001,
                threshold=0.05,
            )


if __name__ == "__main__":
    workspace.GlobalInit([
        'caffe2',
        '--caffe2_log_level=0',
    ])
    unittest.main()
