# Copyright 2017 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 SavedModelCLI tool."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import contextlib
import os
import pickle
import shutil
import sys

from absl.testing import parameterized
import numpy as np
from six import StringIO

from tensorflow.core.example import example_pb2
from tensorflow.core.framework import types_pb2
from tensorflow.core.protobuf import meta_graph_pb2
from tensorflow.python.debug.wrappers import local_cli_wrapper
from tensorflow.python.eager import def_function
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import tensor_spec
from tensorflow.python.lib.io import file_io
from tensorflow.python.ops import variables
from tensorflow.python.platform import test
from tensorflow.python.platform import tf_logging as logging
from tensorflow.python.saved_model import save
from tensorflow.python.tools import saved_model_cli
from tensorflow.python.training.tracking import tracking

SAVED_MODEL_PATH = ('cc/saved_model/testdata/half_plus_two/00000123')


@contextlib.contextmanager
def captured_output():
  new_out, new_err = StringIO(), StringIO()
  old_out, old_err = sys.stdout, sys.stderr
  try:
    sys.stdout, sys.stderr = new_out, new_err
    yield sys.stdout, sys.stderr
  finally:
    sys.stdout, sys.stderr = old_out, old_err


class SavedModelCLITestCase(test.TestCase, parameterized.TestCase):

  def testShowCommandAll(self):
    base_path = test.test_src_dir_path(SAVED_MODEL_PATH)
    self.parser = saved_model_cli.create_parser()
    args = self.parser.parse_args(['show', '--dir', base_path, '--all'])
    with captured_output() as (out, err):
      saved_model_cli.show(args)
    output = out.getvalue().strip()
    # pylint: disable=line-too-long
    exp_out = """MetaGraphDef with tag-set: 'serve' contains the following SignatureDefs:

signature_def['classify_x2_to_y3']:
  The given SavedModel SignatureDef contains the following input(s):
    inputs['inputs'] tensor_info:
        dtype: DT_FLOAT
        shape: (-1, 1)
        name: x2:0
  The given SavedModel SignatureDef contains the following output(s):
    outputs['scores'] tensor_info:
        dtype: DT_FLOAT
        shape: (-1, 1)
        name: y3:0
  Method name is: tensorflow/serving/classify

signature_def['classify_x_to_y']:
  The given SavedModel SignatureDef contains the following input(s):
    inputs['inputs'] tensor_info:
        dtype: DT_STRING
        shape: unknown_rank
        name: tf_example:0
  The given SavedModel SignatureDef contains the following output(s):
    outputs['scores'] tensor_info:
        dtype: DT_FLOAT
        shape: (-1, 1)
        name: y:0
  Method name is: tensorflow/serving/classify

signature_def['regress_x2_to_y3']:
  The given SavedModel SignatureDef contains the following input(s):
    inputs['inputs'] tensor_info:
        dtype: DT_FLOAT
        shape: (-1, 1)
        name: x2:0
  The given SavedModel SignatureDef contains the following output(s):
    outputs['outputs'] tensor_info:
        dtype: DT_FLOAT
        shape: (-1, 1)
        name: y3:0
  Method name is: tensorflow/serving/regress

signature_def['regress_x_to_y']:
  The given SavedModel SignatureDef contains the following input(s):
    inputs['inputs'] tensor_info:
        dtype: DT_STRING
        shape: unknown_rank
        name: tf_example:0
  The given SavedModel SignatureDef contains the following output(s):
    outputs['outputs'] tensor_info:
        dtype: DT_FLOAT
        shape: (-1, 1)
        name: y:0
  Method name is: tensorflow/serving/regress

signature_def['regress_x_to_y2']:
  The given SavedModel SignatureDef contains the following input(s):
    inputs['inputs'] tensor_info:
        dtype: DT_STRING
        shape: unknown_rank
        name: tf_example:0
  The given SavedModel SignatureDef contains the following output(s):
    outputs['outputs'] tensor_info:
        dtype: DT_FLOAT
        shape: (-1, 1)
        name: y2:0
  Method name is: tensorflow/serving/regress

signature_def['serving_default']:
  The given SavedModel SignatureDef contains the following input(s):
    inputs['x'] tensor_info:
        dtype: DT_FLOAT
        shape: (-1, 1)
        name: x:0
  The given SavedModel SignatureDef contains the following output(s):
    outputs['y'] tensor_info:
        dtype: DT_FLOAT
        shape: (-1, 1)
        name: y:0
  Method name is: tensorflow/serving/predict"""
    # pylint: enable=line-too-long
    self.maxDiff = None  # Produce a useful error msg if the comparison fails
    self.assertMultiLineEqual(output, exp_out)
    self.assertEqual(err.getvalue().strip(), '')

  def testShowAllWithFunctions(self):

    class DummyModel(tracking.AutoTrackable):
      """Model with callable polymorphic functions specified."""

      @def_function.function
      def func1(self, a, b, c):
        if c:
          return a + b
        else:
          return a * b

      @def_function.function(input_signature=[
          tensor_spec.TensorSpec(shape=(2, 2), dtype=dtypes.float32)
      ])
      def func2(self, x):
        return x + 2

      @def_function.function
      def __call__(self, y, c=7):
        return y + 2 * c

    saved_model_dir = os.path.join(test.get_temp_dir(), 'dummy_model')
    dummy_model = DummyModel()
    # Call with specific values to create new polymorphic function traces.
    dummy_model.func1(constant_op.constant(5), constant_op.constant(9), True)
    dummy_model(constant_op.constant(5))
    save.save(dummy_model, saved_model_dir)
    self.parser = saved_model_cli.create_parser()
    args = self.parser.parse_args(['show', '--dir', saved_model_dir, '--all'])
    with captured_output() as (out, err):
      saved_model_cli.show(args)
    output = out.getvalue().strip()
    exp_out = """MetaGraphDef with tag-set: 'serve' contains the following SignatureDefs:

signature_def['__saved_model_init_op']:
  The given SavedModel SignatureDef contains the following input(s):
  The given SavedModel SignatureDef contains the following output(s):
    outputs['__saved_model_init_op'] tensor_info:
        dtype: DT_INVALID
        shape: unknown_rank
        name: NoOp
  Method name is: 

signature_def['serving_default']:
  The given SavedModel SignatureDef contains the following input(s):
    inputs['x'] tensor_info:
        dtype: DT_FLOAT
        shape: (2, 2)
        name: serving_default_x:0
  The given SavedModel SignatureDef contains the following output(s):
    outputs['output_0'] tensor_info:
        dtype: DT_FLOAT
        shape: (2, 2)
        name: PartitionedCall:0
  Method name is: tensorflow/serving/predict

Defined Functions:
  Function Name: '__call__'
    Option #1
      Callable with:
        Argument #1
          y: TensorSpec(shape=(), dtype=tf.int32, name='y')
        Argument #2
          DType: int
          Value: 7

  Function Name: 'func1'
    Option #1
      Callable with:
        Argument #1
          a: TensorSpec(shape=(), dtype=tf.int32, name='a')
        Argument #2
          b: TensorSpec(shape=(), dtype=tf.int32, name='b')
        Argument #3
          DType: bool
          Value: True

  Function Name: 'func2'
    Option #1
      Callable with:
        Argument #1
          x: TensorSpec(shape=(2, 2), dtype=tf.float32, name='x')
""".strip()  # pylint: enable=line-too-long
    self.maxDiff = None  # Produce a useful error msg if the comparison fails
    self.assertMultiLineEqual(output, exp_out)
    self.assertEqual(err.getvalue().strip(), '')

  def testShowAllWithPureConcreteFunction(self):

    class DummyModel(tracking.AutoTrackable):
      """Model with a callable concrete function."""

      def __init__(self):
        function = def_function.function(
            self.multiply,
            input_signature=[
                tensor_spec.TensorSpec(shape=(), dtype=dtypes.float32),
                tensor_spec.TensorSpec(shape=(), dtype=dtypes.float32)
            ])
        self.pure_concrete_function = function.get_concrete_function()
        super(DummyModel, self).__init__()

      def multiply(self, a, b):
        return a * b

    saved_model_dir = os.path.join(test.get_temp_dir(), 'dummy_model')
    dummy_model = DummyModel()
    save.save(dummy_model, saved_model_dir)
    self.parser = saved_model_cli.create_parser()
    args = self.parser.parse_args(['show', '--dir', saved_model_dir, '--all'])
    with captured_output() as (out, err):
      saved_model_cli.show(args)
    output = out.getvalue().strip()
    exp_out = """MetaGraphDef with tag-set: 'serve' contains the following SignatureDefs:

signature_def['__saved_model_init_op']:
  The given SavedModel SignatureDef contains the following input(s):
  The given SavedModel SignatureDef contains the following output(s):
    outputs['__saved_model_init_op'] tensor_info:
        dtype: DT_INVALID
        shape: unknown_rank
        name: NoOp
  Method name is: 

signature_def['serving_default']:
  The given SavedModel SignatureDef contains the following input(s):
    inputs['a'] tensor_info:
        dtype: DT_FLOAT
        shape: ()
        name: serving_default_a:0
    inputs['b'] tensor_info:
        dtype: DT_FLOAT
        shape: ()
        name: serving_default_b:0
  The given SavedModel SignatureDef contains the following output(s):
    outputs['output_0'] tensor_info:
        dtype: DT_FLOAT
        shape: ()
        name: PartitionedCall:0
  Method name is: tensorflow/serving/predict

Defined Functions:
  Function Name: 'pure_concrete_function'
    Option #1
      Callable with:
        Argument #1
          a: TensorSpec(shape=(), dtype=tf.float32, name='a')
        Argument #2
          b: TensorSpec(shape=(), dtype=tf.float32, name='b')
""".strip()  # pylint: enable=line-too-long
    self.maxDiff = None  # Produce a useful error msg if the comparison fails
    self.assertMultiLineEqual(output, exp_out)
    self.assertEqual(err.getvalue().strip(), '')

  def testShowCommandTags(self):
    base_path = test.test_src_dir_path(SAVED_MODEL_PATH)
    self.parser = saved_model_cli.create_parser()
    args = self.parser.parse_args(['show', '--dir', base_path])
    with captured_output() as (out, err):
      saved_model_cli.show(args)
    output = out.getvalue().strip()
    exp_out = 'The given SavedModel contains the following tag-sets:\n\'serve\''
    self.assertMultiLineEqual(output, exp_out)
    self.assertEqual(err.getvalue().strip(), '')

  def testShowCommandSignature(self):
    base_path = test.test_src_dir_path(SAVED_MODEL_PATH)
    self.parser = saved_model_cli.create_parser()
    args = self.parser.parse_args(
        ['show', '--dir', base_path, '--tag_set', 'serve'])
    with captured_output() as (out, err):
      saved_model_cli.show(args)
    output = out.getvalue().strip()
    exp_header = ('The given SavedModel MetaGraphDef contains SignatureDefs '
                  'with the following keys:')
    exp_start = 'SignatureDef key: '
    exp_keys = [
        '"classify_x2_to_y3"', '"classify_x_to_y"', '"regress_x2_to_y3"',
        '"regress_x_to_y"', '"regress_x_to_y2"', '"serving_default"'
    ]
    # Order of signatures does not matter
    self.assertMultiLineEqual(
        output,
        '\n'.join([exp_header] + [exp_start + exp_key for exp_key in exp_keys]))
    self.assertEqual(err.getvalue().strip(), '')

  def testShowCommandErrorNoTagSet(self):
    base_path = test.test_src_dir_path(SAVED_MODEL_PATH)
    self.parser = saved_model_cli.create_parser()
    args = self.parser.parse_args(
        ['show', '--dir', base_path, '--tag_set', 'badtagset'])
    with self.assertRaises(RuntimeError):
      saved_model_cli.show(args)

  def testShowCommandInputsOutputs(self):
    base_path = test.test_src_dir_path(SAVED_MODEL_PATH)
    self.parser = saved_model_cli.create_parser()
    args = self.parser.parse_args([
        'show', '--dir', base_path, '--tag_set', 'serve', '--signature_def',
        'serving_default'
    ])
    with captured_output() as (out, err):
      saved_model_cli.show(args)
    output = out.getvalue().strip()
    expected_output = (
        'The given SavedModel SignatureDef contains the following input(s):\n'
        '  inputs[\'x\'] tensor_info:\n'
        '      dtype: DT_FLOAT\n      shape: (-1, 1)\n      name: x:0\n'
        'The given SavedModel SignatureDef contains the following output(s):\n'
        '  outputs[\'y\'] tensor_info:\n'
        '      dtype: DT_FLOAT\n      shape: (-1, 1)\n      name: y:0\n'
        'Method name is: tensorflow/serving/predict')
    self.assertEqual(output, expected_output)
    self.assertEqual(err.getvalue().strip(), '')

  def testPrintREFTypeTensor(self):
    ref_tensor_info = meta_graph_pb2.TensorInfo()
    ref_tensor_info.dtype = types_pb2.DT_FLOAT_REF
    with captured_output() as (out, err):
      saved_model_cli._print_tensor_info(ref_tensor_info)
    self.assertTrue('DT_FLOAT_REF' in out.getvalue().strip())
    self.assertEqual(err.getvalue().strip(), '')

  def testInputPreProcessFormats(self):
    input_str = 'input1=/path/file.txt[ab3];input2=file2'
    input_expr_str = 'input3=np.zeros([2,2]);input4=[4,5]'
    input_dict = saved_model_cli.preprocess_inputs_arg_string(input_str)
    input_expr_dict = saved_model_cli.preprocess_input_exprs_arg_string(
        input_expr_str)
    self.assertTrue(input_dict['input1'] == ('/path/file.txt', 'ab3'))
    self.assertTrue(input_dict['input2'] == ('file2', None))
    print(input_expr_dict['input3'])
    self.assertAllClose(input_expr_dict['input3'], np.zeros([2, 2]))
    self.assertAllClose(input_expr_dict['input4'], [4, 5])
    self.assertTrue(len(input_dict) == 2)
    self.assertTrue(len(input_expr_dict) == 2)

  def testInputPreProcessExamplesWithStrAndBytes(self):
    input_examples_str = 'inputs=[{"text":["foo"], "bytes":[b"bar"]}]'
    input_dict = saved_model_cli.preprocess_input_examples_arg_string(
        input_examples_str)
    feature = example_pb2.Example.FromString(input_dict['inputs'][0])
    self.assertProtoEquals(
        """
          features {
            feature {
              key: "bytes"
              value {
                bytes_list {
                  value: "bar"
                }
              }
            }
            feature {
              key: "text"
              value {
                bytes_list {
                  value: "foo"
                }
              }
            }
          }
    """, feature)

  def testInputPreProcessFileNames(self):
    input_str = (r'inputx=C:\Program Files\data.npz[v:0];'
                 r'input:0=c:\PROGRA~1\data.npy')
    input_dict = saved_model_cli.preprocess_inputs_arg_string(input_str)
    self.assertTrue(input_dict['inputx'] == (r'C:\Program Files\data.npz',
                                             'v:0'))
    self.assertTrue(input_dict['input:0'] == (r'c:\PROGRA~1\data.npy', None))

  def testInputPreProcessErrorBadFormat(self):
    input_str = 'inputx=file[[v1]v2'
    with self.assertRaises(RuntimeError):
      saved_model_cli.preprocess_inputs_arg_string(input_str)
    input_str = 'inputx:file'
    with self.assertRaises(RuntimeError):
      saved_model_cli.preprocess_inputs_arg_string(input_str)
    input_str = 'inputx:np.zeros((5))'
    with self.assertRaises(RuntimeError):
      saved_model_cli.preprocess_input_exprs_arg_string(input_str)

  def testInputParserNPY(self):
    x0 = np.array([[1], [2]])
    x1 = np.array(range(6)).reshape(2, 3)
    input0_path = os.path.join(test.get_temp_dir(), 'input0.npy')
    input1_path = os.path.join(test.get_temp_dir(), 'input1.npy')
    np.save(input0_path, x0)
    np.save(input1_path, x1)
    input_str = 'x0=' + input0_path + '[x0];x1=' + input1_path
    feed_dict = saved_model_cli.load_inputs_from_input_arg_string(
        input_str, '', '')
    self.assertTrue(np.all(feed_dict['x0'] == x0))
    self.assertTrue(np.all(feed_dict['x1'] == x1))

  def testInputParserNPZ(self):
    x0 = np.array([[1], [2]])
    input_path = os.path.join(test.get_temp_dir(), 'input.npz')
    np.savez(input_path, a=x0)
    input_str = 'x=' + input_path + '[a];y=' + input_path
    feed_dict = saved_model_cli.load_inputs_from_input_arg_string(
        input_str, '', '')
    self.assertTrue(np.all(feed_dict['x'] == x0))
    self.assertTrue(np.all(feed_dict['y'] == x0))

  def testInputParserPickle(self):
    pkl0 = {'a': 5, 'b': np.array(range(4))}
    pkl1 = np.array([1])
    pkl2 = np.array([[1], [3]])
    input_path0 = os.path.join(test.get_temp_dir(), 'pickle0.pkl')
    input_path1 = os.path.join(test.get_temp_dir(), 'pickle1.pkl')
    input_path2 = os.path.join(test.get_temp_dir(), 'pickle2.pkl')
    with open(input_path0, 'wb') as f:
      pickle.dump(pkl0, f)
    with open(input_path1, 'wb') as f:
      pickle.dump(pkl1, f)
    with open(input_path2, 'wb') as f:
      pickle.dump(pkl2, f)
    input_str = 'x=' + input_path0 + '[b];y=' + input_path1 + '[c];'
    input_str += 'z=' + input_path2
    feed_dict = saved_model_cli.load_inputs_from_input_arg_string(
        input_str, '', '')
    self.assertTrue(np.all(feed_dict['x'] == pkl0['b']))
    self.assertTrue(np.all(feed_dict['y'] == pkl1))
    self.assertTrue(np.all(feed_dict['z'] == pkl2))

  def testInputParserPythonExpression(self):
    x1 = np.ones([2, 10])
    x2 = np.array([[1], [2], [3]])
    x3 = np.mgrid[0:5, 0:5]
    x4 = [[3], [4]]
    input_expr_str = ('x1=np.ones([2,10]);x2=np.array([[1],[2],[3]]);'
                      'x3=np.mgrid[0:5,0:5];x4=[[3],[4]]')
    feed_dict = saved_model_cli.load_inputs_from_input_arg_string(
        '', input_expr_str, '')
    self.assertTrue(np.all(feed_dict['x1'] == x1))
    self.assertTrue(np.all(feed_dict['x2'] == x2))
    self.assertTrue(np.all(feed_dict['x3'] == x3))
    self.assertTrue(np.all(feed_dict['x4'] == x4))

  def testInputParserBoth(self):
    x0 = np.array([[1], [2]])
    input_path = os.path.join(test.get_temp_dir(), 'input.npz')
    np.savez(input_path, a=x0)
    x1 = np.ones([2, 10])
    input_str = 'x0=' + input_path + '[a]'
    input_expr_str = 'x1=np.ones([2,10])'
    feed_dict = saved_model_cli.load_inputs_from_input_arg_string(
        input_str, input_expr_str, '')
    self.assertTrue(np.all(feed_dict['x0'] == x0))
    self.assertTrue(np.all(feed_dict['x1'] == x1))

  def testInputParserBothDuplicate(self):
    x0 = np.array([[1], [2]])
    input_path = os.path.join(test.get_temp_dir(), 'input.npz')
    np.savez(input_path, a=x0)
    x1 = np.ones([2, 10])
    input_str = 'x0=' + input_path + '[a]'
    input_expr_str = 'x0=np.ones([2,10])'
    feed_dict = saved_model_cli.load_inputs_from_input_arg_string(
        input_str, input_expr_str, '')
    self.assertTrue(np.all(feed_dict['x0'] == x1))

  def testInputParserErrorNoName(self):
    x0 = np.array([[1], [2]])
    x1 = np.array(range(5))
    input_path = os.path.join(test.get_temp_dir(), 'input.npz')
    np.savez(input_path, a=x0, b=x1)
    input_str = 'x=' + input_path
    with self.assertRaises(RuntimeError):
      saved_model_cli.load_inputs_from_input_arg_string(input_str, '', '')

  def testInputParserErrorWrongName(self):
    x0 = np.array([[1], [2]])
    x1 = np.array(range(5))
    input_path = os.path.join(test.get_temp_dir(), 'input.npz')
    np.savez(input_path, a=x0, b=x1)
    input_str = 'x=' + input_path + '[c]'
    with self.assertRaises(RuntimeError):
      saved_model_cli.load_inputs_from_input_arg_string(input_str, '', '')

  def testRunCommandInputExamples(self):
    self.parser = saved_model_cli.create_parser()
    base_path = test.test_src_dir_path(SAVED_MODEL_PATH)
    output_dir = os.path.join(test.get_temp_dir(), 'new_dir')
    args = self.parser.parse_args([
        'run', '--dir', base_path, '--tag_set', 'serve', '--signature_def',
        'regress_x_to_y', '--input_examples',
        'inputs=[{"x":[8.0],"x2":[5.0]}, {"x":[4.0],"x2":[3.0]}]', '--outdir',
        output_dir
    ])
    saved_model_cli.run(args)
    y_actual = np.load(os.path.join(output_dir, 'outputs.npy'))
    y_expected = np.array([[6.0], [4.0]])
    self.assertAllEqual(y_expected, y_actual)

  def testRunCommandExistingOutdir(self):
    self.parser = saved_model_cli.create_parser()
    base_path = test.test_src_dir_path(SAVED_MODEL_PATH)
    x = np.array([[1], [2]])
    x_notused = np.zeros((6, 3))
    input_path = os.path.join(test.get_temp_dir(), 'testRunCommand_inputs.npz')
    np.savez(input_path, x0=x, x1=x_notused)
    output_file = os.path.join(test.get_temp_dir(), 'outputs.npy')
    if os.path.exists(output_file):
      os.remove(output_file)
    args = self.parser.parse_args([
        'run', '--dir', base_path, '--tag_set', 'serve', '--signature_def',
        'regress_x2_to_y3', '--inputs', 'inputs=' + input_path + '[x0]',
        '--outdir',
        test.get_temp_dir()
    ])
    saved_model_cli.run(args)
    y_actual = np.load(output_file)
    y_expected = np.array([[3.5], [4.0]])
    self.assertAllClose(y_expected, y_actual)

  def testRunCommandNewOutdir(self):
    self.parser = saved_model_cli.create_parser()
    base_path = test.test_src_dir_path(SAVED_MODEL_PATH)
    x = np.array([[1], [2]])
    x_notused = np.zeros((6, 3))
    input_path = os.path.join(test.get_temp_dir(),
                              'testRunCommandNewOutdir_inputs.npz')
    output_dir = os.path.join(test.get_temp_dir(), 'new_dir')
    if os.path.isdir(output_dir):
      shutil.rmtree(output_dir)
    np.savez(input_path, x0=x, x1=x_notused)
    args = self.parser.parse_args([
        'run', '--dir', base_path, '--tag_set', 'serve', '--signature_def',
        'serving_default', '--inputs', 'x=' + input_path + '[x0]', '--outdir',
        output_dir
    ])
    saved_model_cli.run(args)
    y_actual = np.load(os.path.join(output_dir, 'y.npy'))
    y_expected = np.array([[2.5], [3.0]])
    self.assertAllClose(y_expected, y_actual)

  def testRunCommandOutOverwrite(self):
    self.parser = saved_model_cli.create_parser()
    base_path = test.test_src_dir_path(SAVED_MODEL_PATH)
    x = np.array([[1], [2]])
    x_notused = np.zeros((6, 3))
    input_path = os.path.join(test.get_temp_dir(),
                              'testRunCommandOutOverwrite_inputs.npz')
    np.savez(input_path, x0=x, x1=x_notused)
    output_file = os.path.join(test.get_temp_dir(), 'y.npy')
    open(output_file, 'a').close()
    args = self.parser.parse_args([
        'run', '--dir', base_path, '--tag_set', 'serve', '--signature_def',
        'serving_default', '--inputs', 'x=' + input_path + '[x0]', '--outdir',
        test.get_temp_dir(), '--overwrite'
    ])
    saved_model_cli.run(args)
    y_actual = np.load(output_file)
    y_expected = np.array([[2.5], [3.0]])
    self.assertAllClose(y_expected, y_actual)

  def testRunCommandInvalidInputKeyError(self):
    self.parser = saved_model_cli.create_parser()
    base_path = test.test_src_dir_path(SAVED_MODEL_PATH)
    args = self.parser.parse_args([
        'run', '--dir', base_path, '--tag_set', 'serve', '--signature_def',
        'regress_x2_to_y3', '--input_exprs', 'x2=np.ones((3,1))'
    ])
    with self.assertRaises(ValueError):
      saved_model_cli.run(args)

  def testRunCommandInputExamplesNotListError(self):
    self.parser = saved_model_cli.create_parser()
    base_path = test.test_src_dir_path(SAVED_MODEL_PATH)
    output_dir = os.path.join(test.get_temp_dir(), 'new_dir')
    args = self.parser.parse_args([
        'run', '--dir', base_path, '--tag_set', 'serve', '--signature_def',
        'regress_x_to_y', '--input_examples', 'inputs={"x":8.0,"x2":5.0}',
        '--outdir', output_dir
    ])
    with self.assertRaisesRegex(ValueError, 'must be a list'):
      saved_model_cli.run(args)

  def testRunCommandInputExamplesFeatureValueNotListError(self):
    self.parser = saved_model_cli.create_parser()
    base_path = test.test_src_dir_path(SAVED_MODEL_PATH)
    output_dir = os.path.join(test.get_temp_dir(), 'new_dir')
    args = self.parser.parse_args([
        'run', '--dir', base_path, '--tag_set', 'serve', '--signature_def',
        'regress_x_to_y', '--input_examples', 'inputs=[{"x":8.0,"x2":5.0}]',
        '--outdir', output_dir
    ])
    with self.assertRaisesRegex(ValueError, 'feature value must be a list'):
      saved_model_cli.run(args)

  def testRunCommandInputExamplesFeatureBadType(self):
    self.parser = saved_model_cli.create_parser()
    base_path = test.test_src_dir_path(SAVED_MODEL_PATH)
    output_dir = os.path.join(test.get_temp_dir(), 'new_dir')
    args = self.parser.parse_args([
        'run', '--dir', base_path, '--tag_set', 'serve', '--signature_def',
        'regress_x_to_y', '--input_examples', 'inputs=[{"x":[[1],[2]]}]',
        '--outdir', output_dir
    ])
    with self.assertRaisesRegex(ValueError, 'is not supported'):
      saved_model_cli.run(args)

  def testRunCommandOutputFileExistError(self):
    self.parser = saved_model_cli.create_parser()
    base_path = test.test_src_dir_path(SAVED_MODEL_PATH)
    x = np.array([[1], [2]])
    x_notused = np.zeros((6, 3))
    input_path = os.path.join(test.get_temp_dir(),
                              'testRunCommandOutOverwrite_inputs.npz')
    np.savez(input_path, x0=x, x1=x_notused)
    output_file = os.path.join(test.get_temp_dir(), 'y.npy')