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

from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import session_ops
from tensorflow.python.ops import state_ops
from tensorflow.python.ops import variables
from tensorflow.python.platform import test


class SessionOpsTest(test.TestCase):

  def testHandleBasic(self):
    with self.test_session() as sess:
      # Return a handle.
      a = constant_op.constant(10)
      b = constant_op.constant(5)
      c = math_ops.multiply(a, b)
      h = session_ops.get_session_handle(c)
      h = sess.run(h)

      # Feed a tensor handle.
      f, x = session_ops.get_session_tensor(h.handle, dtypes.int32)
      y = math_ops.multiply(x, 10)
      self.assertEqual(500, sess.run(y, feed_dict={f: h.handle}))

  def testHandleEval(self):
    with self.test_session() as sess:
      # Return a handle.
      a = constant_op.constant(10)
      b = constant_op.constant(5)
      c = math_ops.multiply(a, b)
      h = session_ops.get_session_handle(c)
      h = sess.run(h)

      # Get the tensor from its handle.
      self.assertEqual(50, h.eval())

  def testHandleAndValue(self):
    with self.test_session() as sess:
      # Return a handle and a value.
      a = constant_op.constant(10)
      b = constant_op.constant(5)
      c = math_ops.multiply(a, b)
      h = session_ops.get_session_handle(c)
      v = math_ops.multiply(a, c)
      h, v = sess.run([h, v])

      self.assertEqual(50, h.eval())
      self.assertEqual(500, v)

  def testHandleCond(self):
    with self.test_session() as sess:
      # Return a handle and a value
      a = constant_op.constant(10)
      b = constant_op.constant(5)
      p = math_ops.less(a, b)
      c = math_ops.multiply(a, b)
      h = session_ops.get_session_handle(c)
      p, h = sess.run([p, h])

      # Run by feeding a tensor handle.
      f, x = session_ops.get_session_tensor(h.handle, dtypes.int32)
      if p:
        y = math_ops.multiply(x, 10)
      else:
        y = math_ops.multiply(x, 100)
      result = sess.run(y, feed_dict={f: h.handle})

      self.assertEqual(5000, result)

  def testHandleForLoop(self):
    with self.test_session() as sess:
      # Initialize a handle.
      a = constant_op.constant(0)
      h = session_ops.get_session_handle(a)
      h = sess.run(h)

      # Do some computation.
      f, x = session_ops.get_session_tensor(h.handle, dtypes.int32)
      # Must define the loop body outside the loop.
      h_x = session_ops.get_session_handle(math_ops.add(x, 1))
      for _ in range(100):
        # This exercises garbage collection.
        h = sess.run(h_x, feed_dict={f: h.handle})

      self.assertEqual(100, h.eval())

  def testHandleWhileLoop(self):
    with self.test_session() as sess:
      # Initialize a handle.
      a = constant_op.constant(0)
      h = session_ops.get_session_handle(a)
      h = sess.run(h)

      # Do some computation.
      f, x = session_ops.get_session_tensor(h.handle, dtypes.int32)
      b = constant_op.constant(100)
      p = math_ops.less(x, b)
      # Must define the loop body outside the loop.
      h_x = session_ops.get_session_handle(math_ops.add(x, 1))
      while True:
        rp, h = sess.run([p, h_x], feed_dict={f: h.handle})
        if not rp:
          break

      self.assertEqual(101, h.eval())

  def testHandleMover(self):
    with self.test_session() as sess:
      # Return a handle.
      a = constant_op.constant(10)
      b = constant_op.constant(5)
      c = math_ops.multiply(a, b)
      h = session_ops.get_session_handle(c)
      h = sess.run(h)

      # Feed a tensor handle.
      f, x = session_ops.get_session_tensor(h.handle, dtypes.int32)
      y = math_ops.multiply(x, 10)
      self.assertEqual(500, sess.run(y, feed_dict={f: h.handle}))

      # Feed another tensor handle.
      with ops.device(test.gpu_device_name()):
        a = constant_op.constant(10)
        h = session_ops.get_session_handle(a)
        h = sess.run(h)
        self.assertEqual(100, sess.run(y, feed_dict={f: h.handle}))

  def testHandleDelete(self):
    with self.test_session() as sess:
      # Return a handle.
      a = constant_op.constant(10)
      b = constant_op.constant(5)
      c = math_ops.multiply(a, b)
      h = session_ops.get_session_handle(c)
      sess.run(h).delete()

  def testHandleDeleteRaw(self):
    with self.test_session() as sess:
      # Return a handle.
      a = constant_op.constant(10)
      b = constant_op.constant(5)
      c = math_ops.multiply(a, b)
      h = session_ops.get_session_handle(c)
      h = sess.run(h)

      # Delete using a raw tensor handle.
      raw_h = h.get_raw_handle()
      f, x = session_ops.delete_session_tensor(raw_h)
      sess.run(x, feed_dict={f: raw_h})

  def testMultiDevices(self):
    with self.test_session() as sess:
      with ops.device(test.gpu_device_name()):
        a = constant_op.constant(1.0)
        a_handle = sess.run(session_ops.get_session_handle(a))
      with ops.device("/cpu:0"):
        b = constant_op.constant(2.0)
        b_handle = sess.run(session_ops.get_session_handle(b))

      a_p, a_t = session_ops.get_session_tensor(a_handle.handle, dtypes.float32)
      b_p, b_t = session_ops.get_session_tensor(b_handle.handle, dtypes.float32)
      c = math_ops.add(a_t, b_t)
      c_handle = sess.run(
          session_ops.get_session_handle(c),
          feed_dict={a_p: a_handle.handle,
                     b_p: b_handle.handle})
      self.assertEqual(3.0, c_handle.eval())

  def testHandleGC(self):
    with self.test_session() as sess:
      # initial values live on CPU
      with ops.device("/cpu:0"):
        one = constant_op.constant(1, dtype=dtypes.float32)
        one_handle = sess.run(session_ops.get_session_handle(one))
        x_handle = sess.run(session_ops.get_session_handle(one))

      # addition lives on GPU
      with ops.device(test.gpu_device_name()):
        add_h1, add_t1 = session_ops.get_session_tensor(one_handle.handle,
                                                        dtypes.float32)
        add_h2, add_t2 = session_ops.get_session_tensor(x_handle.handle,
                                                        dtypes.float32)
        add_op = math_ops.add(add_t1, add_t2)
        add_output = session_ops.get_session_handle(add_op)

      # add 1 to tensor 20 times
      for _ in range(20):
        x_handle = sess.run(
            add_output,
            feed_dict={add_h1: one_handle.handle,
                       add_h2: x_handle.handle})

  def testHandlePlacement(self):
    with self.test_session() as sess:
      a = constant_op.constant(1.0)
      a_handle_op = session_ops.get_session_handle(a)
      b = constant_op.constant(2.0)
      b_handle_op = session_ops.get_session_handle(b)

      a_handle = sess.run(a_handle_op)
      b_handle = sess.run(b_handle_op)

      a_p, a_t = session_ops.get_session_tensor(a_handle.handle, dtypes.float32)
      b_p, b_t = session_ops.get_session_tensor(b_handle.handle, dtypes.float32)

      c = math_ops.add(a_t, b_t)
      c_handle = sess.run(
          session_ops.get_session_handle(c),
          feed_dict={a_p: a_handle.handle,
                     b_p: b_handle.handle})
      self.assertEqual(3.0, c_handle.eval())

  def testFeedOneHandleDirectly(self):
    with self.test_session() as sess:
      a = constant_op.constant(10.0)
      b = constant_op.constant(5.0)
      c = math_ops.multiply(a, b)
      d = math_ops.multiply(c, c)

      h_c = sess.run(session_ops.get_session_handle(c))

      self.assertAllClose(2500.0, sess.run(d, feed_dict={c: h_c}))

  def testFeedTwoHandlesDirectly(self):
    with self.test_session() as sess:
      a = constant_op.constant(10.0)
      b = constant_op.constant(5.0)
      c = math_ops.multiply(a, b)
      d = math_ops.div(a, b)
      e = math_ops.subtract(c, d)

      h_c = sess.run(session_ops.get_session_handle(c))
      h_d = sess.run(session_ops.get_session_handle(d))

      self.assertAllClose(48.0, sess.run(e, feed_dict={c: h_c, d: h_d}))
      self.assertAllClose(-48.0, sess.run(e, feed_dict={c: h_d, d: h_c}))

  def testFeedHandleToVariableDirectly(self):
    with self.test_session() as sess:
      a = variables.Variable(12.0)
      inc_a = state_ops.assign_add(a, 2.0)
      b = math_ops.add(a, 5.0)
      sess.run(a.initializer)

      h_a_read = sess.run(session_ops.get_session_handle(a.read_value()))
      self.assertAllClose(12.0, sess.run(a))

      self.assertAllClose(17.0, sess.run(b, feed_dict={a: h_a_read}))
      sess.run(inc_a)
      self.assertAllClose(19.0, sess.run(b, feed_dict={a: h_a_read}))


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