# 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.argmax_op."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import numpy as np

from tensorflow.python.ops import math_ops
from tensorflow.python.platform import test


class ArgMaxTest(test.TestCase):

  def _testArg(self,
               method,
               x,
               dimension,
               expected_values,
               use_gpu=False,
               expected_err_re=None):
    with self.test_session(use_gpu=use_gpu):
      ans = method(x, dimension=dimension)
      if expected_err_re is None:
        tf_ans = ans.eval()
        self.assertAllEqual(tf_ans, expected_values)
        self.assertShapeEqual(expected_values, ans)
      else:
        with self.assertRaisesOpError(expected_err_re):
          ans.eval()

  def _testBothArg(self,
                   method,
                   x,
                   dimension,
                   expected_values,
                   expected_err_re=None):
    self._testArg(method, x, dimension, expected_values, True, expected_err_re)
    self._testArg(method, x, dimension, expected_values, False, expected_err_re)

  def _testBasic(self, dtype):
    x = np.asarray(100 * np.random.randn(200), dtype=dtype)

    # Check that argmin and argmax match numpy along the primary
    # dimension
    self._testBothArg(math_ops.argmax, x, 0, x.argmax())
    self._testBothArg(math_ops.argmin, x, 0, x.argmin())

  def _testDim(self, dtype):
    x = np.asarray(100 * np.random.randn(3, 2, 4, 5, 6), dtype=dtype)

    # Check that argmin and argmax match numpy along all dimensions
    for dim in range(-5, 5):
      self._testBothArg(math_ops.argmax, x, dim, x.argmax(dim))
      self._testBothArg(math_ops.argmin, x, dim, x.argmin(dim))

  def testFloat(self):
    self._testBasic(np.float32)
    self._testDim(np.float32)

  def testDouble(self):
    self._testBasic(np.float64)
    self._testDim(np.float64)

  def testInt32(self):
    self._testBasic(np.int32)
    self._testDim(np.int32)

  def testInt64(self):
    self._testBasic(np.int64)
    self._testDim(np.int64)

  def testEmpty(self):
    with self.test_session():
      for op in math_ops.argmin, math_ops.argmax:
        with self.assertRaisesOpError(
            r"Reduction axis 0 is empty in shape \[0\]"):
          op([], 0).eval()

  def testDefaultAxis(self):
    with self.test_session():
      for op in math_ops.argmin, math_ops.argmax:
        ans = op([1]).eval()
        self.assertAllEqual(ans, 0)


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