import numpy as np
import tensorflow as tf

def tf_test(op_test):
    def run_test():
        g1 = tf.Graph()
        op_test(g1)
        tf.io.write_graph(g1, '.', '{}.pb'.format(op_test.__name__), as_text=False)
    return run_test

@tf_test
def add_test(g1):
    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_test
def add_bcast_test(g1):
    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_test
def assert_less_equal_test(g1):
    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_test
def batchmatmul_test(g1):
    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_test
def batchnorm_test(g1):
    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_test
def biasadd_test(g1):
    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_test
def cast_test(g1):
    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_test
def concat_test(g1):
    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_test
def const_test(g1):
    with g1.as_default():
        tf.constant(1.0, dtype=tf.float32 ,name='constant1')

@tf_test
def conv_test(g1):
    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_test
def depthwiseconv_test(g1):
    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_test
def expanddims_test(g1):
    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_test
def gather_test(g1):
    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_test
def identity_test(g1):
    with g1.as_default():
        g1_input = tf.placeholder(tf.float32, shape=(1,3,16,16), name = '0')
        tf.identity(g1_input, 'identity')

@tf_test
def matmul_test(g1):
    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_test
def mean_test(g1):
    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_test
def mean_test_nhwc(g1):
    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_test
def mul_test(g1):
    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_test
def pack_test(g1):
    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_test
def pack_test_nhwc(g1):
    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_test
def pooling_test(g1):
    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_test
def pow_test(g1):
    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_test
def relu_test(g1):
    with g1.as_default():
        g1_input = tf.placeholder(tf.float32, shape=(1,3,16,16), name = '0')
        tf.nn.relu(g1_input, 'relu')

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

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

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

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

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

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

@tf_test
def stridedslice_test(g1):
    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_test
def stridedslice_masks_test(g1):
    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_test
def sub_test(g1):
    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_test
def tanh_test(g1):
    with g1.as_default():
        g1_input = tf.placeholder(tf.float32, shape=(1,3,16,16), name = '0')
        tf.tanh(g1_input, 'tanh')

@tf_test
def transpose_test(g1):
    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')