# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# 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.
# ==============================================================================
"""Tests for py_func op."""

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

import numpy as np
from six.moves import queue
from six.moves import xrange  # pylint: disable=redefined-builtin

from tensorflow.python.client import session as session_lib
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import errors
from tensorflow.python.framework import ops
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import script_ops
from tensorflow.python.platform import test


class PyOpTest(test.TestCase):

  def testBasic(self):

    def my_func(x, y):
      return np.sinh(x) + np.cosh(y)

    # single type
    with self.test_session():
      x = constant_op.constant(1.0, dtypes.float32)
      y = constant_op.constant(2.0, dtypes.float32)
      z = script_ops.py_func(my_func, [x, y], dtypes.float32)
      self.assertEqual(z.eval(), my_func(1.0, 2.0).astype(np.float32))

    # scalar
    with self.test_session():
      x = constant_op.constant(1.0, dtypes.float32)
      y = constant_op.constant(2.0, dtypes.float32)
      z = script_ops.py_func(my_func, [x, y], [dtypes.float32])
      self.assertEqual(z[0].eval(), my_func(1.0, 2.0).astype(np.float32))

    # array
    with self.test_session():
      x = constant_op.constant([1.0, 2.0], dtypes.float64)
      y = constant_op.constant([2.0, 3.0], dtypes.float64)
      z = script_ops.py_func(my_func, [x, y], [dtypes.float64])
      self.assertAllEqual(z[0].eval(),
                          my_func([1.0, 2.0], [2.0, 3.0]).astype(np.float64))

    # a bit exotic type (complex64)
    with self.test_session():
      x = constant_op.constant(1 + 2j, dtypes.complex64)
      y = constant_op.constant(3 + 4j, dtypes.complex64)
      z, = script_ops.py_func(my_func, [x, y], [dtypes.complex64])
      self.assertAllClose(z.eval(), my_func(1 + 2j, 3 + 4j))

    # a bit excotic function (rfft)
    with self.test_session():
      x = constant_op.constant([1., 2., 3., 4.], dtypes.float32)

      def rfft(x):
        return np.fft.rfft(x).astype(np.complex64)

      y, = script_ops.py_func(rfft, [x], [dtypes.complex64])
      self.assertAllClose(y.eval(), np.fft.rfft([1., 2., 3., 4.]))

    # returns a python literal.
    with self.test_session():

      def literal(x):
        return 1.0 if x == 0.0 else 0.0

      x = constant_op.constant(0.0, dtypes.float64)
      y, = script_ops.py_func(literal, [x], [dtypes.float64])
      self.assertAllClose(y.eval(), 1.0)

    # returns a list
    with self.test_session():

      def list_func(x):
        return [x, x + 1]

      x = constant_op.constant(0.0, dtypes.float64)
      y, z = script_ops.py_func(list_func, [x], [dtypes.float64] * 2)
      self.assertAllClose(y.eval(), 0.0)
      self.assertAllClose(z.eval(), 1.0)

    # returns a tuple
    with self.test_session():

      def tuple_func(x):
        return x, x + 1

      x = constant_op.constant(0.0, dtypes.float64)
      y, z = script_ops.py_func(tuple_func, [x], [dtypes.float64] * 2)
      self.assertAllClose(y.eval(), 0.0)
      self.assertAllClose(z.eval(), 1.0)

    # returns a tuple, Tout and inp a tuple
    with self.test_session():
      x = constant_op.constant(0.0, dtypes.float64)
      y, z = script_ops.py_func(tuple_func, (x,), (dtypes.float64,
                                                   dtypes.float64))
      self.assertAllClose(y.eval(), 0.0)
      self.assertAllClose(z.eval(), 1.0)

  def testStrings(self):

    def read_fixed_length_numpy_strings():
      return np.array([b" there"])

    def read_and_return_strings(x, y):
      return x + y

    with self.test_session():
      x = constant_op.constant([b"hello", b"hi"], dtypes.string)
      y, = script_ops.py_func(read_fixed_length_numpy_strings, [],
                              [dtypes.string])
      z, = script_ops.py_func(read_and_return_strings, [x, y], [dtypes.string])
      self.assertListEqual(list(z.eval()), [b"hello there", b"hi there"])

  def testStringPadding(self):
    correct = [b"this", b"is", b"a", b"test"]
    with self.test_session():
      s, = script_ops.py_func(lambda: [correct], [], [dtypes.string])
      self.assertAllEqual(s.eval(), correct)

  def testLarge(self):
    with self.test_session() as sess:
      x = array_ops.zeros([1000000], dtype=np.float32)
      y = script_ops.py_func(lambda x: x + 1, [x], [dtypes.float32])
      z = script_ops.py_func(lambda x: x * 2, [x], [dtypes.float32])
      for _ in xrange(100):
        sess.run([y[0].op, z[0].op])

  def testNoInput(self):
    with self.test_session():
      x, = script_ops.py_func(lambda: 42.0, [], [dtypes.float64])
      self.assertAllClose(x.eval(), 42.0)

  def testCleanup(self):
    for _ in xrange(1000):
      g = ops.Graph()
      with g.as_default():
        c = constant_op.constant([1.], dtypes.float32)
        _ = script_ops.py_func(lambda x: x + 1, [c], [dtypes.float32])
    self.assertTrue(script_ops._py_funcs.size() < 100)

  def testAlias(self):
    with self.test_session():
      np_array = np.array([1.0, 2.0], dtype=np.float32)