# 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 tensorflow.ops.tf.cast."""

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

import numpy as np
import sys

from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import sparse_tensor
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import gradient_checker
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import variables
from tensorflow.python.platform import test


class CastOpTest(test.TestCase):

  def _toDataType(self, dtype):
    """Returns TensorFlow data type for numpy type."""
    if dtype == np.float32:
      return dtypes.float32
    elif dtype == np.float64:
      return dtypes.float64
    elif dtype == np.int32:
      return dtypes.int32
    elif dtype == np.int64:
      return dtypes.int64
    elif dtype == np.bool:
      return dtypes.bool
    elif dtype == np.complex64:
      return dtypes.complex64
    elif dtype == np.complex128:
      return dtypes.complex128
    else:
      return None

  def _cast(self, x, dtype, use_gpu=False):
    with self.test_session(use_gpu=use_gpu):
      val = constant_op.constant(x, self._toDataType(np.array([x]).dtype))
      return math_ops.cast(val, self._toDataType(dtype), name="cast").eval()

  def _test(self, x, dtype, use_gpu=False):
    """Tests cast(x) to dtype behaves the same as numpy.astype."""
    np_ans = x.astype(dtype)
    tf_ans = self._cast(x, dtype, use_gpu)
    self.assertAllEqual(np_ans, tf_ans)

  def _testTypes(self, x, use_gpu=False):
    """Tests cast(x) to different tf."""
    if use_gpu:
      type_list = [
          np.float32, np.float64, np.int64, np.complex64, np.complex128
      ]
    else:
      type_list = [
          np.float32, np.float64, np.int32, np.int64, np.complex64,
          np.complex128
      ]
    for from_type in type_list:
      for to_type in type_list:
        self._test(x.astype(from_type), to_type, use_gpu)

    self._test(x.astype(np.bool), np.float32, use_gpu)
    self._test(x.astype(np.uint8), np.float32, use_gpu)
    if not use_gpu:
      self._test(x.astype(np.bool), np.int32, use_gpu)
      self._test(x.astype(np.int32), np.int32, use_gpu)

  def _testAll(self, x):
    self._testTypes(x, use_gpu=False)
    if x.dtype == np.float32 or x.dtype == np.float64:
      self._testTypes(x, use_gpu=True)

  def testBasic(self):
    self._testAll(np.arange(-10, 10).reshape(2, 10))
    self._testAll(np.linspace(-10, 10, 17))

  def testSmallValues(self):
    f4 = np.finfo(np.float32)
    f8 = np.finfo(np.float64)
    self._testAll(
        np.array([
            0, -1, 1, -f4.resolution, f4.resolution, f8.resolution,
            -f8.resolution
        ]))

  def testBfloat16(self):
    a = np.random.uniform(-100, 100, 100).astype(np.float32)
    with self.test_session(use_gpu=False):
      b = math_ops.cast(math_ops.cast(a, dtypes.bfloat16), dtypes.float32)
      self.assertAllClose(a, b.eval(), rtol=1 / 128.)
    with self.test_session(use_gpu=True):
      b = math_ops.cast(math_ops.cast(a, dtypes.bfloat16), dtypes.float32)
      self.assertAllClose(a, b.eval(), rtol=1 / 128.)

  def testRandom(self):
    self._testAll(np.random.normal(0, 10, 210).reshape([2, 3, 5, 7]))
    self._testAll(np.random.normal(0, 1e6, 210).reshape([2, 3, 5, 7]))

  # Special values like int32max, int64min, inf, -inf, nan casted to
  # integer values in somewhat unexpected ways. And they behave
  # differently on CPU and GPU.
  def _compare(self, x, dst_dtype, expected, use_gpu=False):
    np.testing.assert_equal(
        self._cast(
            x, dst_dtype, use_gpu=use_gpu), dst_dtype(expected))

  def testIntToFloatBoundary(self):
    i4 = np.iinfo(np.int32)
    i8 = np.iinfo(np.int64)

    self._compare(i4.min, np.float32, i4.min, False)
    self._compare(i4.max, np.float32, i4.max, False)
    self._compare(i8.min, np.float32, i8.min, False)
    self._compare(i8.max, np.float32, i8.max, False)
    self._compare(i4.min, np.float64, i4.min, False)
    self._compare(i4.max, np.float64, i4.max, False)
    self._compare(i8.min, np.float64, i8.min, False)
    self._compare(i8.max, np.float64, i8.max, False)
    # NOTE: GPU does not support int32/int64 for casting.

  def testInfNan(self):
    i4 = np.iinfo(np.int32)
    i8 = np.iinfo(np.int64)

    self._compare(np.inf, np.float32, np.inf, False)
    self._compare(np.inf, np.float64, np.inf, False)
    if sys.byteorder == "big":  
      self._compare(np.inf, np.int32, i4.max, False)  
      self._compare(np.inf, np.int64, i8.max, False)  
    else:  
      self._compare(np.inf, np.int32, i4.min, False)  
      self._compare(np.inf, np.int64, i8.min, False)  
    self._compare(-np.inf, np.float32, -np.inf, False)
    self._compare(-np.inf, np.float64, -np.inf, False)
    self._compare(-np.inf, np.int32, i4.min, False)
    self._compare(-np.inf, np.int64, i8.min, False)
    self.assertAllEqual(np.isnan(self._cast(np.nan, np.float32, False)), True)
    self.assertAllEqual(np.isnan(self._cast(np.nan, np.float64, False)), True)
    self._compare(np.nan, np.int32, i4.min, False)
    self._compare(np.nan, np.int64, i8.min, False)

    self._compare(np.inf, np.float32, np.inf, True)
    self._compare(np.inf, np.float64, np.inf, True)
    self._compare(-np.inf, np.float32, -np.inf, True)
    self._compare(-np.inf, np.float64, -np.inf, True)
    self.assertAllEqual(np.isnan(self._cast(np.nan, np.float32, True)), True)
    self.assertAllEqual(np.isnan(self._cast(np.nan, np.float64, True)), True)

  def _OpError(self, x, dtype, err):
    with self.test_session():
      with self.assertRaisesOpError(err):
        math_ops.cast(x, dtype).eval()

  def testNotImplemented(self):
    self._OpError(np.arange(0, 10), dtypes.string, "Cast.*int64.*string.*")

  def testCastToTypeOfVariable(self):
    with self.test_session() as sess:
      x = variables.Variable(5, dtype=dtypes.float32)
      y = variables.Variable(True, dtype=dtypes.bool)
      cast = math_ops.cast(y, x.dtype)
      variables.global_variables_initializer().run()
      self.assertEqual(1.0, sess.run(cast))

  def testGradients(self):
    t = [dtypes.float32, dtypes.float64, dtypes.complex64, dtypes.complex128]
    for src_t in t:
      for dst_t in t:
        with self.test_session():
          x = constant_op.constant(1.0, src_t)
          z = array_ops.identity(x)
          y = math_ops.cast(z, dst_t)
          err = gradient_checker.compute_gradient_error(x, [], y, [])
          self.assertLess(err, 1e-3)


class SparseTensorCastTest(test.TestCase):

  def testCast(self):
    indices = constant_op.constant([[0], [1], [2]], dtypes.int64)
    values = constant_op.constant(np.array([1, 2, 3], np.int64))
    shape = constant_op.constant([3], dtypes.int64)
    st = sparse_tensor.SparseTensor(indices, values, shape)
    st_cast = math_ops.cast(st, dtypes.float32)
    with self.test_session():
      self.assertAllEqual(st_cast.indices.eval(), [[0], [1], [2]])
      self.assertAllEqual(st_cast.values.eval(),
                          np.array([1, 2, 3], np.float32))
      self.assertAllEqual(st_cast.dense_shape.eval(), [3])


class SaturateCastTest(test.TestCase):

  def testSaturate(self):
    in_types = dtypes.float32,
    out_types = dtypes.int8, dtypes.uint8, dtypes.int16, dtypes.float32
    with self.test_session() as sess:
      for in_type in in_types:
        for out_type in out_types:
          lo, hi = in_type.min, in_type.max
          x = constant_op.constant(
              [lo, lo + 1, lo // 2, hi // 2, hi - 1, hi], dtype=in_type)
          y = math_ops.saturate_cast(x, dtype=out_type)
          self.assertEqual(y.dtype, out_type)
          x, y = sess.run([x, y])
          correct = np.maximum(out_type.min, np.minimum(out_type.max, x))
          self.assertAllEqual(correct, y)


if __name__ == "__main__":
  test.main()
