#
# 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.
#

model = Model()
in0 = Input("op1", "TENSOR_QUANT8_ASYMM_SIGNED", "{4, 1, 5, 1}, 0.5f, -1")
weights = Parameter("op2", "TENSOR_QUANT8_ASYMM_SIGNED", "{3, 10}, 0.5f, -1",
         [1, 3, 5, 7, 9, 11, 13, 15, 17, 19,
          1, 3, 5, 7, 9, 11, 13, 15, 17, 19,
          1, 3, 5, 7, 9, 11, 13, 15, 17, 19])
bias = Parameter("b0", "TENSOR_INT32", "{3}, 0.25f, 0", [4, 8, 12])
out0 = Output("op3", "TENSOR_QUANT8_ASYMM_SIGNED", "{2, 3}, 1.f, -1")
act_relu = Int32Scalar("act_relu", 1)
model = model.Operation("FULLY_CONNECTED", in0, weights, bias, act_relu).To(out0)

# Example 1. Input in operand 0,
input0 = {in0: # input 0
          [1, 3, 5, 7, 9, 11, 13, 15, -19, -21,
           1, 3, 5, 7, 9, 11, 13, -17, 17, -21]}
output0 = {out0: # output 0
           [23, 24, 25, 57, 58, 59]}

# Instantiate an example
Example((input0, output0))

#######################################################

model = Model()
in0 = Input("op1", "TENSOR_QUANT8_ASYMM_SIGNED", "{1, 5}, 0.2, -128") # batch = 1, input_size = 5
weights = Parameter("op2", "TENSOR_QUANT8_ASYMM_SIGNED", "{1, 5}, 0.2, -128", [-118, -108, -108, -108, -118]) # num_units = 1, input_size = 5
bias = Parameter("b0", "TENSOR_INT32", "{1}, 0.04, 0", [10])
out0 = Output("op3", "TENSOR_QUANT8_ASYMM_SIGNED", "{1, 1}, 1.f, -128") # batch = 1, number_units = 1
act = Int32Scalar("act", 0)
model = model.Operation("FULLY_CONNECTED", in0, weights, bias, act).To(out0)

# Example 1. Input in operand 0,
input0 = {in0: # input 0
          [-118, -118, -118, -118, -118]}
output0 = {out0: # output 0
           [-96]}

# Instantiate an example
Example((input0, output0))

#######################################################

model = Model()
in0 = Input("op1", "TENSOR_QUANT8_ASYMM_SIGNED", "{1, 5}, 0.2, -128") # batch = 1, input_size = 5
weights = Input("op2", "TENSOR_QUANT8_ASYMM_SIGNED", "{1, 5}, 0.2, -128") # num_units = 1, input_size = 5
bias = Input("b0", "TENSOR_INT32", "{1}, 0.04, 0")
out0 = Output("op3", "TENSOR_QUANT8_ASYMM_SIGNED", "{1, 1}, 1.f, -128") # batch = 1, number_units = 1
act = Int32Scalar("act", 0)
model = model.Operation("FULLY_CONNECTED", in0, weights, bias, act).To(out0)

# Example 1. Input in operand 0,
input0 = {in0: # input 0
          [-118, -118, -118, -118, -118],
          weights:
          [-118, -108, -108, -108, -118],
          bias:
          [10]}
output0 = {out0: # output 0
           [-96]}

# Instantiate an example
Example((input0, output0))

#######################################################

model = Model()
in0 = Input("op1", "TENSOR_QUANT8_ASYMM_SIGNED", "{3, 1}, 0.5f, -128")
weights = Parameter("op2", "TENSOR_QUANT8_ASYMM_SIGNED", "{1, 1}, 0.5f, -128", [-126])
bias = Parameter("b0", "TENSOR_INT32", "{1}, 0.25f, 0", [4])
out0 = Output("op3", "TENSOR_QUANT8_ASYMM_SIGNED", "{3, 1}, 1.f, -128")
act = Int32Scalar("act", 0)
model = model.Operation("FULLY_CONNECTED", in0, weights, bias, act).To(out0)

# Example 1. Input in operand 0,
input0 = {in0: # input 0
          [-126, -96, -112]}
output0 = {out0: # output 0
               [-126, -111, -119]}

# Instantiate an example
Example((input0, output0))

#######################################################

model = Model()
in0 = Input("op1", "TENSOR_QUANT8_ASYMM_SIGNED", "{3, 1}, 0.5f, -128")
weights = Input("op2", "TENSOR_QUANT8_ASYMM_SIGNED", "{1, 1}, 0.5f, -128")
bias = Input("b0", "TENSOR_INT32", "{1}, 0.25f, 0")
out0 = Output("op3", "TENSOR_QUANT8_ASYMM_SIGNED", "{3, 1}, 1.f, -128")
act = Int32Scalar("act", 0)
model = model.Operation("FULLY_CONNECTED", in0, weights, bias, act).To(out0)

# Example 1. Input in operand 0,
input0 = {in0: # input 0
             [-126, -96, -112],
         weights: [-126],
         bias: [4]}
output0 = {out0: # output 0
               [-126, -111, -119]}

# Instantiate an example
Example((input0, output0))

#######################################################

model = Model()
in0 = Input("op1", "TENSOR_FLOAT32", "{3, 1}")
weights = Parameter("op2", "TENSOR_FLOAT32", "{1, 1}", [2])
bias = Parameter("b0", "TENSOR_FLOAT32", "{1}", [4])
out0 = Output("op3", "TENSOR_FLOAT32", "{3, 1}")
act = Int32Scalar("act", 0)
model = model.Operation("FULLY_CONNECTED", in0, weights, bias, act).To(out0)

quant8_signed_mult_gt_1 = DataTypeConverter(name="quant8_mult_gt_1").Identify({
    in0: ("TENSOR_QUANT8_ASYMM_SIGNED", 0.5, -1),
    weights: ("TENSOR_QUANT8_ASYMM_SIGNED", 0.5, -8),
    bias: ("TENSOR_INT32", 0.25, 0),
    out0: ("TENSOR_QUANT8_ASYMM_SIGNED", 0.1, 0),
})

# Example 1. Input in operand 0,
input0 = {in0: # input 0
          [2, 32, 16]}
output0 = {out0: # output 0
               [8, 68, 36]}

# Instantiate an example
Example((input0, output0)).AddVariations(quant8_signed_mult_gt_1, includeDefault=False)

#######################################################
# zero-sized input

# Use BOX_WITH_NMS_LIMIT op to generate a zero-sized internal tensor for box cooridnates.
p1 = Parameter("scores", "TENSOR_FLOAT32", "{1, 2}", [0.90, 0.10]) # scores
p2 = Parameter("roi", "TENSOR_FLOAT32", "{1, 8}", [1, 1, 10, 10, 0, 0, 10, 10]) # roi
o1 = Output("scoresOut", "TENSOR_FLOAT32", "{0}") # scores out
o2 = Output("classesOut", "TENSOR_INT32", "{0}") # classes out
tmp1 = Internal("roiOut", "TENSOR_FLOAT32", "{0, 4}") # roi out
tmp2 = Internal("batchSplitOut", "TENSOR_INT32", "{0}") # batch split out
model = Model("zero_sized").Operation("BOX_WITH_NMS_LIMIT", p1, p2, [0], 0.3,  -1, 0, 0.4, 1.0, 0.3).To(o1, tmp1, o2, tmp2)

# Use ROI_ALIGN op to convert into zero-sized feature map.
layout = BoolScalar("layout", False) # NHWC
i1 = Input("in", "TENSOR_FLOAT32", "{1, 1, 1, 3}")
zero_sized = Internal("featureMap", "TENSOR_FLOAT32", "{0, 2, 2, 3}")
model = model.Operation("ROI_ALIGN", i1, tmp1, tmp2, 2, 2, 2.0, 2.0, 4, 4, layout).To(zero_sized)

# FULLY_CONNECTED op with numBatches = 0.
w = Parameter("weights", "TENSOR_FLOAT32", "{1, 3}", [1, 2, 3]) # weights
b = Parameter("bias", "TENSOR_FLOAT32", "{1}", [1]) # bias
o3 = Output("out", "TENSOR_FLOAT32", "{0, 1}") # out
model = model.Operation("FULLY_CONNECTED", zero_sized, w, b, 0).To(o3)

quant8_signed = DataTypeConverter().Identify({
    p1: ("TENSOR_QUANT8_ASYMM_SIGNED", 0.1, 0),
    p2: ("TENSOR_QUANT16_ASYMM", 0.125, 0),
    o1: ("TENSOR_QUANT8_ASYMM_SIGNED", 0.1, 0),
    tmp1: ("TENSOR_QUANT16_ASYMM", 0.125, 0),
    i1: ("TENSOR_QUANT8_ASYMM_SIGNED", 0.1, 0),
    zero_sized: ("TENSOR_QUANT8_ASYMM_SIGNED", 0.1, 0),
    w: ("TENSOR_QUANT8_ASYMM_SIGNED", 0.1, 0),
    b: ("TENSOR_INT32", 0.01, 0),
    o3: ("TENSOR_QUANT8_ASYMM_SIGNED", 0.1, 0)
})

Example({
    i1: [1, 2, 3],
    o1: [],
    o2: [],
    o3: [],
}).AddNchw(i1, zero_sized, layout).AddVariations(quant8_signed, includeDefault=False)