#
# Copyright (C) 2019 The Android Open Source Project
#
# 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.
#

# Unidirectional Sequence LSTM Test:
# 3 Time Step, No Layer Normalization, Cifg, Peephole, No Projection, and No Clipping.
import copy

model = Model()

max_time = 3
n_batch = 1
n_input = 2
# n_cell and n_output have the same size when there is no projection.
n_cell = 4
n_output = 4

input = Input("input", "TENSOR_FLOAT32", "{%d, %d, %d}" % (max_time, n_batch, n_input))

input_to_input_weights = Input("input_to_input_weights", "TENSOR_FLOAT32",
                               "{%d, %d}" % (n_cell, n_input))
input_to_forget_weights = Input("input_to_forget_weights", "TENSOR_FLOAT32",
                                "{%d, %d}" % (n_cell, n_input))
input_to_cell_weights = Input("input_to_cell_weights", "TENSOR_FLOAT32",
                              "{%d, %d}" % (n_cell, n_input))
input_to_output_weights = Input("input_to_output_weights", "TENSOR_FLOAT32",
                                "{%d, %d}" % (n_cell, n_input))

recurrent_to_input_weights = Input("recurrent_to_input_weights",
                                   "TENSOR_FLOAT32",
                                   "{%d, %d}" % (n_cell, n_output))
recurrent_to_forget_weights = Input("recurrent_to_forget_weights",
                                    "TENSOR_FLOAT32",
                                    "{%d, %d}" % (n_cell, n_output))
recurrent_to_cell_weights = Input("recurrent_to_cell_weights", "TENSOR_FLOAT32",
                                  "{%d, %d}" % (n_cell, n_output))
recurrent_to_output_weights = Input("recurrent_to_output_weights",
                                    "TENSOR_FLOAT32",
                                    "{%d, %d}" % (n_cell, n_output))

cell_to_input_weights = Input("cell_to_input_weights", "TENSOR_FLOAT32",
                              "{%d}" % (n_cell))
cell_to_forget_weights = Input("cell_to_forget_weights", "TENSOR_FLOAT32",
                               "{%d}" % (n_cell))
cell_to_output_weights = Input("cell_to_output_weights", "TENSOR_FLOAT32",
                               "{%d}" % (n_cell))

input_gate_bias = Input("input_gate_bias", "TENSOR_FLOAT32", "{%d}" % (n_cell))
forget_gate_bias = Input("forget_gate_bias", "TENSOR_FLOAT32",
                         "{%d}" % (n_cell))
cell_gate_bias = Input("cell_gate_bias", "TENSOR_FLOAT32", "{%d}" % (n_cell))
output_gate_bias = Input("output_gate_bias", "TENSOR_FLOAT32",
                         "{%d}" % (n_cell))

projection_weights = Input("projection_weights", "TENSOR_FLOAT32",
                           "{%d,%d}" % (n_output, n_cell))
projection_bias = Input("projection_bias", "TENSOR_FLOAT32", "{0}")

output_state_in = Input("output_state_in", "TENSOR_FLOAT32",
                        "{%d, %d}" % (n_batch, n_output))
cell_state_in = Input("cell_state_in", "TENSOR_FLOAT32",
                      "{%d, %d}" % (n_batch, n_cell))

activation_param = Int32Scalar("activation_param", 4)  # Tanh
cell_clip_param = Float32Scalar("cell_clip_param", 0.)
proj_clip_param = Float32Scalar("proj_clip_param", 0.)
time_major_param = BoolScalar("time_major_param", True)

input_layer_norm_weights = Input("input_layer_norm_weights", "TENSOR_FLOAT32",
                                 "{%d}" % n_cell)
forget_layer_norm_weights = Input("forget_layer_norm_weights", "TENSOR_FLOAT32",
                                  "{%d}" % n_cell)
cell_layer_norm_weights = Input("cell_layer_norm_weights", "TENSOR_FLOAT32",
                                "{%d}" % n_cell)
output_layer_norm_weights = Input("output_layer_norm_weights", "TENSOR_FLOAT32",
                                  "{%d}" % n_cell)

output = Output("output", "TENSOR_FLOAT32", "{%d, %d, %d}" % (max_time, n_batch, n_output))

model = model.Operation(
    "UNIDIRECTIONAL_SEQUENCE_LSTM", input, input_to_input_weights, input_to_forget_weights,
    input_to_cell_weights, input_to_output_weights, recurrent_to_input_weights,
    recurrent_to_forget_weights, recurrent_to_cell_weights,
    recurrent_to_output_weights, cell_to_input_weights, cell_to_forget_weights,
    cell_to_output_weights, input_gate_bias, forget_gate_bias, cell_gate_bias,
    output_gate_bias, projection_weights, projection_bias, output_state_in,
    cell_state_in, activation_param, cell_clip_param, proj_clip_param, time_major_param,
    input_layer_norm_weights, forget_layer_norm_weights,
    cell_layer_norm_weights, output_layer_norm_weights).To([output])

# Example 1. Input in operand 0,
input0 = {
    input_to_input_weights: [],
    input_to_forget_weights: [
        -0.55291498, -0.42866567, 0.13056988, -0.3633365,
        -0.22755712, 0.28253698, 0.24407166,  0.33826375
    ],
    input_to_cell_weights: [
        -0.49770179, -0.27711356, -0.09624726, 0.05100781,
        0.04717243,  0.48944736, -0.38535351, -0.17212132
    ],
    input_to_output_weights: [
        0.10725588,  -0.02335852, -0.55932593, -0.09426838,
        -0.44257352, 0.54939759, 0.01533556,  0.42751634
    ],
    input_gate_bias: [],
    forget_gate_bias: [1., 1., 1., 1.],
    cell_gate_bias: [0., 0., 0., 0.],
    output_gate_bias: [0., 0., 0., 0.],
    recurrent_to_input_weights: [],
    recurrent_to_cell_weights: [
        0.54066205,  -0.32668582, -0.43562764, -0.56094903,
        0.42957711,  0.01841056,  -0.32764608, -0.33027974,
        -0.10826075, 0.20675004,  0.19069612,  -0.03026325,
        -0.54532051, 0.33003211,  0.44901288,  0.21193194
    ],
    recurrent_to_forget_weights: [
        -0.13832897, -0.0515101,  -0.2359007, -0.16661474,
        -0.14340827, 0.36986142,  0.23414481, 0.55899,
        0.10798943,  -0.41174671, 0.17751795, -0.34484994,
        -0.35874045, -0.11352962, 0.27268326, 0.54058349
    ],
    recurrent_to_output_weights: [
        0.41613156, 0.42610586,  -0.16495961, -0.5663873,
        0.30579174, -0.05115908, -0.33941799, 0.23364776,
        0.11178309, 0.09481031,  -0.26424935, 0.46261835,
        0.50248802, 0.26114327,  -0.43736315, 0.33149987
    ],
    cell_to_input_weights: [],
    cell_to_forget_weights: [0.47485286, -0.51955009, -0.24458408, 0.31544167],
    cell_to_output_weights: [-0.17135078, 0.82760304, 0.85573703, -0.77109635],
    projection_weights: [],
    projection_bias: [],
    input_layer_norm_weights: [],
    forget_layer_norm_weights: [],
    cell_layer_norm_weights: [],
    output_layer_norm_weights: []
}

test_input = [2., 3., 3., 4., 1., 1.]

golden_output = [
    -0.36444446, -0.00352185, 0.12886585, -0.05163646,
    -0.42312205, -0.01218222, 0.24201041, -0.08124574,
    -0.358325, -0.04621704, 0.21641694, -0.06471302
]

output0 = {
    output: golden_output,
}

input0[input] = test_input
input0[output_state_in] = [ 0 for _ in range(n_batch * n_output) ]
input0[cell_state_in] = [ 0 for _ in range(n_batch * n_cell) ]

Example((input0, output0))