# Copyright 2018 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 ragged_map_ops.map_fn."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

from absl.testing import parameterized
import numpy as np

from tensorflow.python.framework import dtypes
from tensorflow.python.framework import sparse_tensor
from tensorflow.python.framework import test_util
from tensorflow.python.keras import backend
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import math_ops as mo
from tensorflow.python.ops import string_ops
from tensorflow.python.ops.ragged import ragged_factory_ops
from tensorflow.python.ops.ragged import ragged_functional_ops
from tensorflow.python.ops.ragged import ragged_map_ops
from tensorflow.python.ops.ragged import ragged_math_ops
from tensorflow.python.ops.ragged import ragged_tensor
from tensorflow.python.ops.ragged import ragged_test_util
from tensorflow.python.platform import googletest


@test_util.run_all_in_graph_and_eager_modes
class RaggedMapOpTest(ragged_test_util.RaggedTensorTestCase,
                      parameterized.TestCase):

  @parameterized.parameters([
      # The following test sets map over a RaggedTensor and apply a
      # transformation that returns with shape:
      # [d1, (d2)] -> [d1]
      dict(
          fn=mo.reduce_mean,
          elems=[[1, 2, 3], [4, 5], [6, 7]],
          expected_output=[2, 4, 6],
      ),
      dict(
          fn=string_ops.reduce_join,
          elems=[['foo', 'bar', 'baz'], ['a'], ['b', 'c']],
          expected_output=[b'foobarbaz', b'a', b'bc'],
          dtype=dtypes.string,
      ),
      # [d1, (d2)] -> [d1, 2]
      dict(
          fn=lambda x: array_ops.stack([mo.reduce_mean(x), mo.reduce_sum(x)]),
          # fn=self.stack_mean_and_sum,
          elems=[[1, 2, 3], [4, 5], [6, 7]],
          expected_output=[[2, 6], [4.5, 9], [6.5, 13]],
          dtype=dtypes.float32,
          expected_ragged_rank=0,
      ),
      # [d1, (d2)] -> [d1, (d2)]
      dict(
          fn=lambda x: x + np.int64(1),
          elems=[[1, 2, 3], [4, 5], [6, 7]],
          expected_output=[[2, 3, 4], [5, 6], [7, 8]],
          dtype=dtypes.int64,
          result_dtype=ragged_tensor.RaggedTensorType(
              dtype=dtypes.int64, ragged_rank=1),
      ),
      # [d1, (d2), d3] -> [d1, (d2), d3]
      dict(
          fn=lambda x: x + np.int64(1),
          elems=[[[1, 2], [3, 4]], [], [[5, 6], [7, 8], [9, 0]]],
          elems_ragged_rank=1,
          expected_ragged_rank=1,
          result_dtype=ragged_tensor.RaggedTensorType(
              dtype=dtypes.int64, ragged_rank=1),
          expected_output=[[[2, 3], [4, 5]], [], [[6, 7], [8, 9], [10, 1]]],
      ),
      # [d1, (d2)] -> [d1, (d2), (d3)]
      dict(
          fn=lambda x: ragged_tensor.RaggedTensor.from_row_starts(x, [0]),
          elems=[[1, 2, 3], [4, 5], [6, 7]],
          expected_output=[[[1, 2, 3]], [[4, 5]], [[6, 7]]],
          result_dtype=ragged_tensor.RaggedTensorType(
              dtype=dtypes.int64, ragged_rank=2),
      ),
      # [d1, (d2), (d3)] -> [d1, (d2), (d3)]
      dict(
          fn=lambda x: ragged_functional_ops.map_flat_values(mo.add, x, 1),
          elems=[[[1, 2, 3]], [[4, 5], [6, 7]]],
          expected_output=[[[2, 3, 4]], [[5, 6], [7, 8]]],
          result_dtype=ragged_tensor.RaggedTensorType(
              dtype=dtypes.int64, ragged_rank=2),
      ),
      # [d1, (d2), (d3)] -> [d1, (d2)]
      dict(
          fn=lambda x: ragged_math_ops.reduce_sum(x, axis=1),
          elems=[[[1, 2, 3]], [[4, 5], [6, 7]]],
          expected_output=[[6], [9, 13]],
          result_dtype=ragged_tensor.RaggedTensorType(
              dtype=dtypes.int64, ragged_rank=1),
      ),
      # [d1, (d2), (d3)] -> [d1, (d3)]
      dict(
          fn=lambda x: ragged_math_ops.reduce_sum(x, axis=0),
          elems=[[[1, 2, 3]], [[4, 5], [6, 7]]],
          expected_output=[[1, 2, 3], [10, 12]],
          result_dtype=ragged_tensor.RaggedTensorType(
              dtype=dtypes.int64, ragged_rank=1),
      ),
      # [d1, (d2), (d3)] -> [d1]
      dict(
          fn=ragged_math_ops.reduce_sum,
          elems=[[[1, 2, 3]], [[4, 5], [6, 7]]],
          expected_output=[6, 22],
          result_dtype=dtypes.int64,
      ),
      # [d1] -> [d1, (d2)]
      dict(
          fn=mo.range,
          elems=[4, 0, 2],
          expected_output=[[0, 1, 2, 3], [], [0, 1]],
          result_dtype=ragged_tensor.RaggedTensorType(
              dtype=dtypes.int64, ragged_rank=1),
      ),
      # [d1] -> [d1, (d2), (d3)]
      dict(
          fn=lambda x: ragged_math_ops.range(mo.range(x)),
          elems=[5, 0, 3],
          expected_output=[[[], [0], [0, 1], [0, 1, 2], [0, 1, 2, 3]], [],
                           [[], [0], [0, 1]]],
          result_dtype=ragged_tensor.RaggedTensorType(
              dtype=dtypes.int64, ragged_rank=2),
      ),
      # [d1, (d2), (d3), (d4a), (d5)] ->  [d1, (d2), (d3), (d4b), (d5)]
      dict(
          fn=lambda x: x + np.int64(1),
          elems=[[[[[1, 2, 3]], [[4], [5]]]], [[[[6, 7]]], [[[8], []]]]],
          expected_output=[[[[[2, 3, 4]], [[5], [6]]]], [[[[7, 8]]], [[[9],
                                                                       []]]]],
          result_dtype=ragged_tensor.RaggedTensorType(
              dtype=dtypes.int64, ragged_rank=4),
      ),
  ])

  def testRaggedMap(
      self,
      fn,
      elems,
      expected_output,
      expected_ragged_rank=None,
      result_ragged_rank=None,
      elems_ragged_rank=None,
      dtype=dtypes.int64,
      result_dtype=None,
      infer_shape=False,
  ):
    elems = ragged_factory_ops.constant(elems, dtype, elems_ragged_rank)
    output = ragged_map_ops.map_fn(
        fn=fn, elems=elems, dtype=result_dtype, infer_shape=infer_shape)

    expected_rt = ragged_factory_ops.constant(
        expected_output, ragged_rank=expected_ragged_rank)
    self.assertRaggedEqual(expected_rt, output)

  def testRaggedMapOnStructure(self):
    batman = ragged_factory_ops.constant([[1, 2, 3], [4], [5, 6, 7]])
    # [[10, 20, 30], [40], [50, 60, 70]]
    robin = ragged_functional_ops.map_flat_values(mo.multiply, batman, 10)

    features = {'batman': batman, 'robin': robin}

    def _reduce_sum_from_all(f):
      return mo.reduce_sum(f['batman']) + mo.reduce_sum(f['robin'])

    output = ragged_map_ops.map_fn(
        fn=_reduce_sum_from_all,
        elems=features,
        dtype=dtypes.int32,
    )

    self.assertRaggedEqual(output, [66, 44, 198])

  # Test mapping over a dict of RTs can produce a dict of RTs.
  def testRaggedMapOnStructure_RaggedOutputs(self):
    batman = ragged_factory_ops.constant([[1, 2, 3], [4], [5, 6, 7]])
    # [[10, 20, 30], [40], [50, 60, 70]]
    robin = ragged_functional_ops.map_flat_values(mo.multiply, batman, 10)

    features = {'batman': batman, 'robin': robin}

    def _increment(f):
      return {
          'batman': f['batman'] + 1,
          'robin': f['robin'] + 1,
      }

    output = ragged_map_ops.map_fn(
        fn=_increment,
        elems=features,
        infer_shape=False,
        dtype={
            'batman':
                ragged_tensor.RaggedTensorType(
                    dtype=dtypes.int32, ragged_rank=1),
            'robin':
                ragged_tensor.RaggedTensorType(
                    dtype=dtypes.int32, ragged_rank=1)
        },
    )

    self.assertRaggedEqual(output['batman'], [[2, 3, 4], [5], [6, 7, 8]])
    self.assertRaggedEqual(output['robin'], [[11, 21, 31], [41], [51, 61, 71]])

  def testZip(self):
    x = ragged_factory_ops.constant(
        [[10, 20], [30, 40], [50, 60], [70], [80, 90, 100]], dtypes.int64)
    y = array_ops.expand_dims(mo.range(x.nrows(out_type=dtypes.int64)), axis=1)

    def _zip(foo):
      y_val, x_val = foo
      bar = backend.tile(y_val, array_ops.shape(x_val))
      return array_ops.stack([bar, x_val], axis=1)

    output = ragged_map_ops.map_fn(
        _zip, (y, x),
        dtype=ragged_tensor.RaggedTensorType(dtype=dtypes.int64, ragged_rank=1),
        infer_shape=False)

    self.assertRaggedEqual(
        output, [[[0, 10], [0, 20]], [[1, 30], [1, 40]], [[2, 50], [2, 60]],
                 [[3, 70]], [[4, 80], [4, 90], [4, 100]]])

  def testBatchGather(self):
    tokens = ragged_factory_ops.constant([['hello', '.', 'there'], ['merhaba'],
                                          ['bonjour', '.', 'ca va', '?']])
    indices = ragged_factory_ops.constant([[0, 2], [0], [0, 2]])

    def gather(x):
      tokens_val, indices_val = x
      return array_ops.gather(tokens_val, indices_val)

    data = tokens, indices
    out = ragged_map_ops.map_fn(
        gather,
        data,
        dtype=ragged_tensor.RaggedTensorType(
            dtype=dtypes.string, ragged_rank=1),
        infer_shape=False)

    self.assertRaggedEqual(
        out, [[b'hello', b'there'], [b'merhaba'], [b'bonjour', b'ca va']])

  def testMismatchRaggedRank(self):
    elems = ragged_factory_ops.constant([[[1, 2, 3]], [[4, 5], [6, 7]]])
    fn = lambda x: ragged_math_ops.reduce_sum(x, axis=0)
    with self.assertRaisesWithLiteralMatch(
        ValueError, r'The declared ragged rank (23) mismatches the result (1)'):
      _ = ragged_map_ops.map_fn(
          fn,
          elems,
          dtype=ragged_tensor.RaggedTensorType(
              dtype=dtypes.int64, ragged_rank=23))

  def testMismatchRaggedRank2(self):
    elems = ragged_factory_ops.constant([[1, 2, 3], [4, 5], [6, 7]])
    fn = lambda x: ragged_tensor.RaggedTensor.from_row_starts(x, [0])
    with self.assertRaisesWithLiteralMatch(
        ValueError, r'The declared ragged rank (10) mismatches the result (2)'):
      _ = ragged_map_ops.map_fn(
          fn,
          elems,
          dtype=ragged_tensor.RaggedTensorType(
              dtype=dtypes.int64, ragged_rank=10))

  def testMapOnSparseTensor(self):
    s = sparse_tensor.SparseTensor(
        indices=[[0, 0], [0, 1], [1, 0], [1, 1]],
        values=[0, 5, 0, 4],
        dense_shape=[2, 2],
    )
    t2 = ragged_tensor.RaggedTensor.from_sparse(s)
    id_t2 = ragged_map_ops.map_fn(
        lambda x: x, t2,
    )
    self.assertRaggedEqual(id_t2, [[0, 5], [0, 4]])


if __name__ == '__main__':
  googletest.main()
