text: # 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.
  # ==============================================================================
  """Base class for tests in this module."""
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import contextlib
  import imp
  import sys
  
  import six
  
  from tensorflow.python.autograph import operators
  from tensorflow.python.autograph import utils
  from tensorflow.python.autograph.core import config
  from tensorflow.python.autograph.core import converter
  from tensorflow.python.autograph.core import errors
  from tensorflow.python.autograph.core import function_wrapping
  from tensorflow.python.autograph.lang import special_functions
  from tensorflow.python.autograph.pyct import compiler
  from tensorflow.python.autograph.pyct import inspect_utils
  from tensorflow.python.autograph.pyct import origin_info
  from tensorflow.python.autograph.pyct import parser
  from tensorflow.python.autograph.pyct import pretty_printer
  from tensorflow.python.autograph.pyct import transformer
  from tensorflow.python.platform import test
  
  RESULT_OF_MOCK_CONVERTED_CALL = 7
  
  
  # TODO(mdan): We should use the real namer here.
  class FakeNamer(object):
    """A fake namer that uses a global counter to generate unique names."""
  
    def __init__(self):
      self.i = 0
      self.partial_types = ()
  
    def new_symbol(self, name_root, used):
      while True:
        self.i += 1
        name = '%s%d' % (name_root, self.i)
        if name not in used:
          return name
  
    def compiled_function_name(self,
                               original_fqn,
                               live_entity=None,
                               owner_type=None):
      if inspect_utils.islambda(live_entity):
        return None, False
      if owner_type is not None:
        return None, False
      return ('renamed_%s' % '_'.join(original_fqn)), True
  
  
  class FakeNoRenameNamer(FakeNamer):
  
    def compiled_function_name(self, original_fqn, **_):
      return str(original_fqn), False
  
  
  class TestCase(test.TestCase):
    """Base class for unit tests in this module. Contains relevant utilities."""
  
    @contextlib.contextmanager
    def assertPrints(self, expected_result):
      try:
        out_capturer = six.StringIO()
        sys.stdout = out_capturer
        yield
        self.assertEqual(out_capturer.getvalue(), expected_result)
      finally:
        sys.stdout = sys.__stdout__
  
    @contextlib.contextmanager
    def compiled(self, node, namespace, *symbols):
      source = None
  
      self.dynamic_calls = []
      def converted_call(*args):
        """Mock version of api.converted_call."""
        self.dynamic_calls.append(args[3:])  # args only; see api.converted_call
        return RESULT_OF_MOCK_CONVERTED_CALL
  
      try:
        result, source = compiler.ast_to_object(node, include_source_map=True)
  
        # TODO(mdan): Move this into self.prepare()
        result.tf = self.make_fake_mod('fake_tf', *symbols)
        fake_ag = self.make_fake_mod('fake_ag', converted_call,
                                     converter.ConversionOptions)
        fake_ag.__dict__.update(operators.__dict__)
        fake_ag.__dict__.update(special_functions.__dict__)
        fake_ag.ConversionOptions = converter.ConversionOptions
        fake_ag.Feature = converter.Feature
        fake_ag.utils = utils
        fake_ag.rewrite_graph_construction_error = (
            errors.rewrite_graph_construction_error)
        fake_ag.function_scope = function_wrapping.function_scope
        result.ag__ = fake_ag
        for k, v in namespace.items():
          result.__dict__[k] = v
        yield result
      except Exception:  # pylint:disable=broad-except
        if source is None:
          print('Offending AST:\n%s' % pretty_printer.fmt(node, color=False))
        else:
          print('Offending compiled code:\n%s' % source)
        raise
  
    @contextlib.contextmanager
    def converted(self, entity, converter_module, namespace, *tf_symbols):
      node, ctx = self.prepare(entity, namespace)
  
      if not isinstance(converter_module, (list, tuple)):
        converter_module = (converter_module,)
      for i, m in enumerate(converter_module):
        node = converter.standard_analysis(node, ctx, is_initial=not i)
        node = m.transform(node, ctx)
  
      with self.compiled(node, namespace, *tf_symbols) as result:
        yield result
  
    def make_fake_mod(self, name, *symbols):
      fake_mod = imp.new_module(name)
      for s in symbols:
        if hasattr(s, '__name__'):
          setattr(fake_mod, s.__name__, s)
        elif hasattr(s, 'name'):
          # This is a bit of a hack, but works for things like tf.int32
          setattr(fake_mod, s.name, s)
        else:
          raise ValueError('can not attach %s - what should be its name?' % s)
      return fake_mod
  
    def attach_namespace(self, module, **ns):
      for k, v in ns.items():
        setattr(module, k, v)
  
    def prepare(self,
                test_fn,
                namespace,
                namer=None,
                arg_types=None,
                owner_type=None,
                recursive=True,
                strip_decorators=()):
      namespace['ConversionOptions'] = converter.ConversionOptions
  
      node, source = parser.parse_entity(test_fn)
      node = node.body[0]
      if namer is None:
        namer = FakeNamer()
      program_ctx = converter.ProgramContext(
          options=converter.ConversionOptions(
              recursive=recursive,
              strip_decorators=strip_decorators,
              verbose=True),
          partial_types=None,
          autograph_module=None,
          uncompiled_modules=config.DEFAULT_UNCOMPILED_MODULES)
      entity_info = transformer.EntityInfo(
          source_code=source,
          source_file='<fragment>',
          namespace=namespace,
          arg_values=None,
          arg_types=arg_types,
          owner_type=owner_type)
      ctx = converter.EntityContext(namer, entity_info, program_ctx)
      origin_info.resolve(node, source, test_fn)
      node = converter.standard_analysis(node, ctx, is_initial=True)
      return node, ctx
  