import numpy as np
import tensorflow as tf


def add_test(g1=tf.Graph()):
    with g1.as_default():
        g1_input = tf.placeholder(tf.float32, shape=(1,2,2,3), name = '0')
        g2_input = tf.placeholder(tf.float32, shape=(1,2,2,3), name = '1')
        tf.add(g1_input, g2_input, name = 'add1')

    tf.train.write_graph(g1, '.', 'add_test.pb', as_text=False)

def add_bcast_test(g1=tf.Graph()):
    with g1.as_default():
        g1_input = tf.placeholder(tf.float32, shape=(2,3), name = '0')
        g2_input = tf.placeholder(tf.float32, shape=(2,1), name = '1')
        tf.math.add(g1_input, g2_input, name = 'add_bcast1')

    tf.train.write_graph(g1, '.', 'add_bcast_test.pb', as_text=False)

def assert_less_equal_test(g1=tf.Graph()):
    with g1.as_default():
        g1_input = tf.placeholder(tf.float32, shape=(2,3), name = '0')
        g2_input = tf.placeholder(tf.float32, shape=(2,3), name = '1')
        with tf.control_dependencies([tf.assert_less_equal(g1_input, g2_input)]):
            tf.add(g1_input, g2_input, name = 'add1')
    
    tf.train.write_graph(g1, '.', 'assert_less_equal_test.pb', as_text=False)

def batchmatmul_test(g1=tf.Graph()):
    with g1.as_default():
        g1_input = tf.placeholder(tf.float32, shape=(1,2,8,4), name = '0')
        g2_input = tf.placeholder(tf.float32, shape=(1,2,4,8), name = '1')
        tf.matmul(g1_input, g2_input, transpose_a=True, transpose_b=True, name='batchmatmul1')

    tf.train.write_graph(g1, '.', 'batchmatmul_test.pb', as_text=False)

def batchnorm_test(g1=tf.Graph()):
    with g1.as_default():
        g1_input = tf.placeholder(tf.float32, shape=(1, 16, 16, 32), name = '0')
        g1_scale = tf.constant(1.0, dtype=tf.float32, shape=[32], name = '1')
        g1_offset = tf.placeholder(tf.float32, shape=(32), name = '2')
        g1_mean = tf.placeholder(tf.float32, shape=(32), name = '3')
        g1_variance = tf.placeholder(tf.float32, shape=(32), name = '4')
        tf.nn.fused_batch_norm(
                g1_input, g1_scale, g1_offset, g1_mean, g1_variance,
                epsilon=0.00001, is_training=False, name='batchnorm1')

    tf.train.write_graph(g1, '.', 'batchnorm_test.pb', as_text=False)

def biasadd_test(g1=tf.Graph()):
    with g1.as_default():
        g1_input = tf.placeholder(tf.float32, shape=(1,1,1,500), name = '0')
        g2_input = tf.placeholder(tf.float32, shape=(500), name = '1')
        tf.nn.bias_add(g1_input, g2_input, name = 'bias_add1')

    tf.train.write_graph(g1, '.', 'biasadd_test.pb', as_text=False)

def cast_test(g1=tf.Graph()):
    with g1.as_default():
        g1_input = tf.placeholder(tf.float32, shape=(1,3,16,16), name = '0')
        tf.cast(g1_input, dtype=tf.int32, name='cast1')

    tf.train.write_graph(g1, '.', 'cast_test.pb', as_text=False)
def concat_test(g1=tf.Graph()):
    with g1.as_default():
        g1_input = tf.placeholder(tf.float32, shape=(4,7,3), name = '0')
        g2_input = tf.placeholder(tf.float32, shape=(4,2,3), name = '1')
        tf.concat([g1_input, g2_input], axis=1, name = 'concat1')

    tf.train.write_graph(g1, '.', 'concat_test.pb', as_text=False)

def const_test(g1=tf.Graph()):
    with g1.as_default():
        tf.constant(1.0, dtype=tf.float32 ,name='constant1')

    tf.train.write_graph(g1, '.', 'constant_test.pb', as_text=False)

def conv_test(g1=tf.Graph()):
    with g1.as_default():
        g1_input = tf.placeholder(tf.float32, shape=(1,16,16,3), name = '0')
        g1_weights = tf.constant(value=1.0, dtype=tf.float32, shape=(3,3,3,32), name = '1')
        tf.nn.conv2d(g1_input, g1_weights, [1,1,1,1], "SAME", name = 'conv1')

    tf.train.write_graph(g1, '.', 'conv_test.pb', as_text=False)

def depthwiseconv_test(g1=tf.Graph()):
    with g1.as_default():
        g1_input = tf.placeholder(tf.float32, shape=(1,16,16,3), name = '0')
        g1_weights = tf.constant(value=1.0, dtype=tf.float32, shape=(3,3,3,1), name = '1')
        tf.nn.depthwise_conv2d_native(g1_input, g1_weights, [1,1,1,1], "SAME", name = 'depthwiseconv1')

    tf.train.write_graph(g1, '.', 'depthwise_conv_test.pb', as_text=False)

def expanddims_test(g1=tf.Graph()):
    with g1.as_default():
        g1_input = tf.placeholder(tf.float32, shape=(2,3,4), name = '0')
        tf.expand_dims(g1_input, axis=-1, name='expanddims_neg')

    tf.train.write_graph(g1, '.', 'expanddims_neg_test.pb', as_text=False)

def gather_test(g1=tf.Graph()):
    with g1.as_default():
        g1_input = tf.placeholder(tf.float32, shape=(2,4), name = '0')
        tf.gather(g1_input, [1,1], axis=1, name='gather1')

    tf.train.write_graph(g1, '.', 'gather_test.pb', as_text=False)

def identity_test(g1=tf.Graph()):
    with g1.as_default():
        g1_input = tf.placeholder(tf.float32, shape=(1,3,16,16), name = '0')
        tf.identity(g1_input, 'identity')

    tf.train.write_graph(g1, '.', 'identity_test.pb', as_text=False)

def matmul_test(g1=tf.Graph()):
    with g1.as_default():
        g1_input = tf.placeholder(tf.float32, shape=(8,4), name = '0')
        g2_input = tf.placeholder(tf.float32, shape=(4,8), name = '1')
        tf.matmul(g1_input, g2_input, transpose_a=True, transpose_b=True, name='matmul1')

    tf.train.write_graph(g1, '.', 'matmul_test.pb', as_text=False)

def mean_test(g1=tf.Graph()):
    with g1.as_default():
        g1_input = tf.placeholder(tf.float32, shape=(1,3,16,16), name = '0')
        tf.math.reduce_mean(
                g1_input,
                axis=(2,3),
                keepdims=True,
                name='mean1'
            )
        tf.math.reduce_mean(
                g1_input,
                axis=(2,3),
                keepdims=False,
                name='mean2'
            )

    tf.train.write_graph(g1, '.', 'mean_test.pb', as_text=False)

def mean_test_nhwc(g1=tf.Graph()):
    with g1.as_default():
        g1_input = tf.placeholder(tf.float32, shape=(1,16,16,3), name = '0')
        tf.math.reduce_mean(
                g1_input,
                axis=(1,2),
                keepdims=True,
                name='mean1'
            )
        tf.math.reduce_mean(
                g1_input,
                axis=(1,2),
                keepdims=False,
                name='mean2'
            )

    tf.train.write_graph(g1, '.', 'mean_test_nhwc.pb', as_text=False)

def mul_test(g1=tf.Graph()):
    with g1.as_default():
        g1_input = tf.placeholder(tf.float32, shape=(1,1,1,16), name = '0')
        g2_input = tf.placeholder(tf.float32, shape=(1,1,1,16), name = '1')
        tf.multiply(g1_input, g2_input, name='mul1')
    tf.train.write_graph(g1, '.', 'mul_test.pb', as_text=False)

def pack_test(g1=tf.Graph()):
    with g1.as_default():
        g1_input = tf.placeholder(tf.float32, shape=(2), name = '0')
        g2_input = tf.placeholder(tf.float32, shape=(2), name = '1')
        g3_input = tf.placeholder(tf.float32, shape=(2), name = '2')
        tf.stack([g1_input, g2_input, g3_input], axis=1, name = 'pack1')

    tf.train.write_graph(g1, '.', 'pack_test.pb', as_text=False)

def pack_test_nhwc(g1=tf.Graph()):
    with g1.as_default():
        g1_input = tf.placeholder(tf.float32, shape=(1,1,1,2), name = '0')
        g2_input = tf.placeholder(tf.float32, shape=(1,1,1,2), name = '1')
        g3_input = tf.placeholder(tf.float32, shape=(1,1,1,2), name = '2')
        tf.stack([g1_input, g2_input, g3_input], axis=3, name = 'pack1')

    tf.train.write_graph(g1, '.', 'pack_test_nhwc.pb', as_text=False)

def pooling_test(g1=tf.Graph()):
    with g1.as_default():
        g1_input = tf.placeholder(tf.float32, shape=(1,16,16,3), name = '0')
        tf.nn.avg_pool(
                value=g1_input,
                ksize=(1,2,2,1),
                strides=(1,2,2,1),
                padding='VALID',
                data_format='NHWC',
                name='avg_pooling'
            )
        tf.nn.max_pool(
                value=g1_input,
                ksize=(1,2,2,1),
                strides=(1,2,2,1),
                padding='VALID',
                data_format='NHWC',
                name='max_pooling'
            )

    tf.train.write_graph(g1, '.', 'pooling_test.pb', as_text=False)

def pow_test(g1=tf.Graph()):
    with g1.as_default():
        g1_input = tf.placeholder(tf.float32, shape=(1,2,2,3), name = '0')
        g2_input = tf.placeholder(tf.float32, shape=(1,2,2,3), name = '1')
        tf.pow(g1_input, g2_input, name = 'pow1')

    tf.train.write_graph(g1, '.', 'pow_test.pb', as_text=False)

def relu_test(g1=tf.Graph()):
    with g1.as_default():
        g1_input = tf.placeholder(tf.float32, shape=(1,3,16,16), name = '0')
        tf.nn.relu(g1_input, 'relu')

    tf.train.write_graph(g1, '.', 'relu_test.pb', as_text=False)

def relu6_test(g1=tf.Graph()):
    with g1.as_default():
        g1_input = tf.placeholder(tf.float32, shape=(1,3,16,16), name = '0')
        tf.nn.relu6(g1_input, 'relu6')

    tf.train.write_graph(g1, '.', 'relu6_test.pb', as_text=False)

def reshape_test(g1=tf.Graph()):
    with g1.as_default():
        g1_input = tf.placeholder(tf.float32, shape=(16), name = '0')
        tf.reshape(g1_input, (1,1,1,16), 'reshape')

    tf.train.write_graph(g1, '.', 'reshape_test.pb', as_text=False)

def rsqrt_test(g1=tf.Graph()):
    with g1.as_default():
        g1_input = tf.placeholder(tf.float32, shape=(1,3,16,16), name = '0')
        tf.math.rsqrt(g1_input, 'rsqrt')

    tf.train.write_graph(g1, '.', 'rsqrt_test.pb', as_text=False)

def slice_test(g1=tf.Graph()):
    with g1.as_default():
        g1_input = tf.placeholder(tf.float32, shape=(5,10), name = '0')
        tf.slice(g1_input, [1, 0], [2, -1], name = 'slice1')

    tf.train.write_graph(g1, '.', 'slice_test.pb', as_text=False)

def softmax_test(g1=tf.Graph()):
    with g1.as_default():
        g1_input = tf.placeholder(tf.float32, shape=(1,3), name = '0')
        tf.nn.softmax(g1_input, name='softmax')

    tf.train.write_graph(g1, '.', 'softmax_test.pb', as_text=False)

def sqdiff_test(g1=tf.Graph()):
    with g1.as_default():
        g1_input = tf.placeholder(tf.float32, shape=(1,2,2,3), name = '0')
        g2_input = tf.placeholder(tf.float32, shape=(1,2,2,3), name = '1')
        tf.squared_difference(g1_input, g2_input, name = 'sqdiff')

    tf.train.write_graph(g1, '.', 'sqdiff_test.pb', as_text=False)

def squeeze_test(g1=tf.Graph()):
    with g1.as_default():
        g1_input = tf.placeholder(tf.float32, shape=(1,2,3,1), name = '0')
        tf.squeeze(g1_input, name='squeeze')

    tf.train.write_graph(g1, '.', 'squeeze_test.pb', as_text=False)

def stopgradient_test(g1=tf.Graph()):
    with g1.as_default():
        g1_input = tf.placeholder(tf.float32, shape=(1,3,16,16), name = '0')
        tf.stop_gradient(g1_input, 'stopgradient')

    tf.train.write_graph(g1, '.', 'stopgradient_test.pb', as_text=False)

def stridedslice_test(g1=tf.Graph()):
    with g1.as_default():
        g1_input = tf.placeholder(tf.float32, shape=(1,1,1,10), name = '0')
        tf.strided_slice(g1_input, [0, 0, 0, 0], [1, 1, 1, 5], [1,1,1,1], shrink_axis_mask=2, name = 'stridedslice1')

    tf.train.write_graph(g1, '.', 'stridedslice_test.pb', as_text=False)

def stridedslice_masks_test(g1=tf.Graph()):
    with g1.as_default():
        g1_input = tf.placeholder(tf.float32, shape=(1,3,3,10), name = '0')
        tf.strided_slice(g1_input, [0, 1, 1, 0], [0, 0, 0, 0], [1,1,1,1], begin_mask=9, end_mask=15, name = 'stridedslice1')

    tf.train.write_graph(g1, '.', 'stridedslice_masks_test.pb', as_text=False)

def sub_test(g1=tf.Graph()):
    with g1.as_default():
        g1_input = tf.placeholder(tf.float32, shape=(1,2,2,3), name = '0')
        g2_input = tf.placeholder(tf.float32, shape=(1,2,2,3), name = '1')
        tf.subtract(g1_input, g2_input, name = 'sub1')

    tf.train.write_graph(g1, '.', 'sub_test.pb', as_text=False)

def tanh_test(g1=tf.Graph()):
    with g1.as_default():
        g1_input = tf.placeholder(tf.float32, shape=(1,3,16,16), name = '0')
        tf.tanh(g1_input, 'tanh')

    tf.train.write_graph(g1, '.', 'tanh_test.pb', as_text=False)

def transpose_test(g1=tf.Graph()):
    with g1.as_default():
        g1_input = tf.placeholder(tf.float32, shape=(1,3,16,16), name = '0')
        tf.transpose(g1_input, perm=[0,2,3,1], name = 'transpose')

    tf.train.write_graph(g1, '.', 'transpose_test.pb', as_text=False)