def class_to_graph(c, conversion_map):
  """Specialization of `entity_to_graph` for classes."""
  converted_members = {}
  method_filter = lambda m: tf_inspect.isfunction(m) or tf_inspect.ismethod(m)
  members = tf_inspect.getmembers(c, predicate=method_filter)
  if not members:
    raise ValueError('Cannot convert %s: it has no member methods.' % c)

  class_namespace = None
  for _, m in members:
    node, _ = function_to_graph(
        m,
        conversion_map=conversion_map,
        arg_values={},
        arg_types={'self': (c.__name__, c)},
        owner_type=c)
    # TODO(mdan): Do not assume all members have the same view of globals.
    if class_namespace is None:
      class_namespace = inspect_utils.getnamespace(m)
    converted_members[m] = node
  namer = conversion_map.new_namer(class_namespace)
  class_name = namer.compiled_class_name(c.__name__, c)
  node = gast.ClassDef(
      class_name,
      bases=[],
      keywords=[],
      body=list(converted_members.values()),
      decorator_list=[])

  return node, class_name

def to_graph(o, arg_value_hints=None):
  """Compile a Python entity into equivalent TensorFlow code.

  Currently supported entities:
    * functions
    * classes

  Classes are handled by converting all their methods into a new class.

  Args:
    o: A Python function or class.
    arg_value_hints: A dict mapping parameter names to objects that can hint
        at the type of those parameters.

  Returns:
    A function with a signature identical to `o`, but which when executed it
  creates TF a graph that has the same functionality as the original entity.
  """
  conversion_map = conversion.ConversionMap()
  _, name = conversion.object_to_graph(o, conversion_map, arg_value_hints)

  module = gast.Module([])
  for import_line in config.COMPILED_IMPORT_STATEMENTS:
    module.body.append(parser.parse_str(import_line))
  for dep in conversion_map.dependency_cache.values():
    module.body.append(dep)
  compiled_node = compiler.ast_to_object(module)

  # The compiled code should see everything the entry function saw.
  # TODO(mdan): This might not work well if the call tree spans modules?
  if tf_inspect.isfunction(o):
    compiled_node.__dict__.update(six.get_function_globals(o))

  compiled_fn = getattr(compiled_node, name)
  return compiled_fn

def is_supported_type_for_deprecation(symbol):
  # Exclude Exception subclasses since users should be able to
  # "except" the same type of exception that was "raised" (i.e. we
  # shouldn't wrap it with deprecation alias).
  # Also, exclude subclasses of namedtuples for now.
  return tf_inspect.isfunction(symbol) or (
      tf_inspect.isclass(symbol) and not issubclass(symbol, Exception)
      and tuple not in tf_inspect.getmro(symbol))

def to_graph(e,
             recursive=True,
             verbose=False,
             arg_values=None,
             arg_types=None,
             partial_types=None):
  """Compile a Python entity into equivalent TensorFlow code.

  Currently supported entities:
    * functions
    * classes

  Classes are handled by converting all their methods into a new class.

  Args:
    e: A Python entity.
    recursive: Whether to recusrively convert any functions that the decorator
        function may call.
    verbose: Whether to output the compiled code in the logs.
    arg_values: A dict containing value hints for symbols like function
        parameters.
    arg_types: A dict containing type hints for symbols like function
        parameters.
    partial_types: A set of types (e.g. classes) that will not be converted
        entirely. Calls to member functions for these types will be renamed
        independently.

  Returns:
    A function with a signature identical to `o`, but which when executed it
  creates TF a graph that has the same functionality as the original entity.
  """
  conversion_map = conversion.ConversionMap(
      recursive=recursive,
      nocompile_decorators=(convert, do_not_convert, converted_call),
      partial_types=partial_types,
      api_module=tf_inspect.getmodule(to_graph))
  _, name = conversion.entity_to_graph(e, conversion_map, arg_values, arg_types)

  module = gast.Module([])
  for import_line in config.COMPILED_IMPORT_STATEMENTS:
    module.body.extend(parser.parse_str(import_line).body)
  for dep in conversion_map.dependency_cache.values():
    module.body.append(dep)
  compiled_node, compiled_src = compiler.ast_to_object(module)

  # The compiled code should see everything the entry function saw.
  # TODO(mdan): This might not work well if the call tree spans modules?
  if tf_inspect.isfunction(e):
    for key, val in inspect_utils.getnamespace(e).items():
      # Avoid overwriting entities that have been transformed.
      if key not in compiled_node.__dict__:
        compiled_node.__dict__[key] = val
  compiled_fn = getattr(compiled_node, name)

  if verbose:
    logging.info('Compiled output of %s:\n\n%s\n', e, compiled_src)

  return compiled_fn

def parse_entity(entity):
  """Returns the AST of given entity."""
  source = tf_inspect.getsource(entity)

  def fail(comment):
    raise ValueError(
        'Failed to parse source code of {}, which Python reported as:\n{}\n'
        '{}'.format(entity, source, comment))

  # Comments and multiline strings can appear at arbitrary indentation levels,
  # causing textwrap.dedent to not correctly dedent source code.
  # TODO(b/115884650): Automatic handling of comments/multiline strings.
  source = textwrap.dedent(source)

  try:
    return parse_str(source), source

  except IndentationError:
    # The text below lists the causes of this error known to us. There may
    # be more.
    fail('This may be caused by multiline strings or comments not indented at'
         'the same level as the code.')

  except SyntaxError as e:
    if not tf_inspect.isfunction(entity) or entity.__name__ != '<lambda>':
      raise

    # Certain entities, like lambdas, only hold the raw code lines which defined
    # them, which may include surrounding tokens and may be syntactically
    # invalid out of context. For example:
    #
    #     l = (
    #         lambda x: x,)[0]
    #
    # will have the dedented source "lambda x: x,)[0]"
    # Here we make an attempt to stip away the garbage by looking at the
    # information in the syntax error.
    lines = source.split('\n')
    lineno, offset = e.lineno, e.offset  # 1-based

    # Give up if there's nothing we can chip away.
    if len(lines) == lineno and len(lines[-1]) == offset:
      fail('If this is a lambda function, the error may be avoided by creating'
           ' the lambda in a standalone statement.')

    # Drop all lines following the error location
    # TODO(mdan): What's with the pylint errors?
    lines = lines[:lineno]  # pylint:disable=invalid-slice-index
    # Drop all characters following the error location
    lines[-1] = lines[-1][:offset - 1]  # pylint:disable=invalid-slice-index
    new_source = '\n'.join(lines)

    try:
      return parse_str(new_source), new_source
    except SyntaxError as e:
      fail('If this is a lambda function, the error may be avoided by creating'
           ' the lambda in a standalone statement. Tried to strip down the'
           ' source to:\n{}\nBut that did not work.'.format(new_source))

def entity_to_graph(o, conversion_map, arg_values, arg_types):
  """Compile a Python entity into equivalent TensorFlow.

  The function will also recursively compile all the entities that `o`
  references, updating `dependency_cache`.

  This function is reentrant, and relies on dependency_cache to avoid
  generating duplicate code.

  Args:
    o: A Python entity.
    conversion_map: A ConversionMap object.
    arg_values: A dict containing value hints for symbols like function
        parameters.
    arg_types: A dict containing type hints for symbols like function
        parameters.

  Returns:
    A tuple (ast, new_name, namespace):
        * ast: An AST representing an entity with interface equivalent to `o`,
            but which when executed it creates TF a graph.
        * new_name: The symbol name under which the new entity can be found.
        * namespace: A dict mapping all symbols visible to the converted entity,
            keyed by their symbol name.

  Raises:
    ValueError: if the entity type is not supported.
  """
  if tf_inspect.isclass(o):
    node, name, ns = class_to_graph(o, conversion_map)
  elif tf_inspect.isfunction(o):
    node, name, ns = function_to_graph(o, conversion_map, arg_values, arg_types)
  elif tf_inspect.ismethod(o):
    node, name, ns = function_to_graph(o, conversion_map, arg_values, arg_types)
  else:
    raise ValueError(
        'Entity "%s" has unsupported type "%s". Only functions and classes are '
        'supported for now.' % (o, type(o)))

  conversion_map.add_to_cache(o, node)
  if conversion_map.recursive:
    while True:
      candidate = None
      for obj in conversion_map.name_map.keys():
        if obj not in conversion_map.dependency_cache:
          candidate = obj
          break
      if candidate is None:
        break
      if (hasattr(candidate, 'im_class') and
          getattr(candidate, 'im_class') not in conversion_map.partial_types):
        # Class members are converted with their objects, unless they're
        # only converted partially.
        continue
      entity_to_graph(candidate, conversion_map, {}, {})

  return node, name, ns

    def conversion_visitor(unused_path, unused_parent, children):
      for child in children:
        _, attr = tf_decorator.unwrap(child[1])
        if not tf_inspect.isfunction(attr):
          continue
        names_v1 = tf_export.get_v1_names(attr)
        arg_names_v1 = get_args(attr)

        for name in names_v1:
          tf_name = "tf.%s" % name
          if tf_name in function_warnings or tf_name in function_transformers:
            continue  # These require manual change
          if tf_name in v1_name_exceptions:
            continue
          # Assert that arg names after converting to v2 are present in
          # v2 function.
          # 1. First, create an input of the form:
          #    tf.foo(arg1=val1, arg2=val2, ...)
          args = ",".join(
              ["%s=%d" % (from_name, from_index)
               for from_index, from_name in enumerate(arg_names_v1)])
          text_input = "%s(%s)" % (tf_name, args)
          # 2. Convert the input to V2.
          _, _, _, text = self._upgrade(text_input)
          new_function_name, new_args = get_func_and_args_from_str(text)
          if new_function_name == "tf.compat.v1.%s" % name:
            if tf_name in keyword_renames:
              # If we rename arguments, new function must be available in 2.0.
              # We should not be using compat.v1 in this case.
              self.assertFalse(
                  "Function '%s' is not in 2.0 when converting\n%s\nto\n%s" %
                  (new_function_name, text_input, text))
            continue
          # 3. Verify V2 function and arguments.
          args_v2 = get_args(self.v2_symbols[new_function_name])
          args_v2.extend(v2_arg_exceptions)
          for new_arg in new_args:
            self.assertIn(
                new_arg, args_v2,
                "Invalid argument '%s' in 2.0 when converting\n%s\nto\n%s.\n"
                "Supported arguments: %s" % (
                    new_arg, text_input, text, str(args_v2)))
          # 4. Verify that the argument exists in v1 as well.
          if new_function_name in set(["tf.nn.ctc_loss",
                                       "tf.saved_model.save"]):
            continue
          args_v1 = get_args(self.v1_symbols[new_function_name])
          args_v1.extend(v2_arg_exceptions)
          for new_arg in new_args:
            self.assertIn(
                new_arg, args_v1,
                "Invalid argument '%s' in 1.0 when converting\n%s\nto\n%s.\n"
                "Supported arguments: %s" % (
                    new_arg, text_input, text, str(args_v1)))

def _get_func_name(func):
  _, func = tf_decorator.unwrap(func)
  if callable(func):
    if tf_inspect.isfunction(func):
      return func.__name__
    elif tf_inspect.ismethod(func):
      return "%s.%s" % (func.__self__.__name__, func.__name__)
    else:  # Probably a class instance with __call__
      return type(func)
  else:
    raise ValueError("Argument must be callable")

def get_func_code(func):
  """Returns func_code of passed callable."""
  _, func = tf_decorator.unwrap(func)
  if callable(func):
    if tf_inspect.isfunction(func) or tf_inspect.ismethod(func):
      return six.get_function_code(func)
    elif hasattr(func, '__call__'):
      return six.get_function_code(func.__call__)
    else:
      raise ValueError('Unhandled callable, type=%s' % type(func))
  else:
    raise ValueError('Argument must be callable')

def getfutureimports(entity):
  """Detects what future imports are necessary to safely execute entity source.

  Args:
    entity: Any object

  Returns:
    A tuple of future strings
  """
  if not (tf_inspect.isfunction(entity) or tf_inspect.ismethod(entity)):
    return tuple()
  return tuple(sorted(name for name, value in entity.__globals__.items()
                      if getattr(value, '__module__', None) == '__future__'))

def get_func_name(func):
  """Returns name of passed callable."""
  _, func = tf_decorator.unwrap(func)
  if callable(func):
    if tf_inspect.isfunction(func):
      return func.__name__
    elif tf_inspect.ismethod(func):
      return '%s.%s' % (six.get_method_self(func).__class__.__name__,
                        six.get_method_function(func).__name__)
    else:  # Probably a class instance with __call__
      return str(type(func))
  else:
    raise ValueError('Argument must be callable')

def convert_entity_to_ast(o, program_ctx):
  """Compile a Python entity into equivalent TensorFlow.

  Args:
    o: A Python entity.
    program_ctx: A ProgramContext object.

  Returns:
    A tuple (ast, new_name, namespace):
        * ast: An AST representing an entity with interface equivalent to `o`,
            but which when executed it creates TF a graph.
        * new_name: The symbol name under which the new entity can be found.
        * namespace: A dict mapping all symbols visible to the converted entity,
            keyed by their symbol name.

  Raises:
    ValueError: if the entity type is not supported.
  """
  logging.log(1, 'Converting %s', o)

  if tf_inspect.isclass(o):
    nodes, name, entity_info = convert_class_to_ast(o, program_ctx)
  elif tf_inspect.isfunction(o):
    nodes, name, entity_info = convert_func_to_ast(o, program_ctx)
  elif tf_inspect.ismethod(o):
    nodes, name, entity_info = convert_func_to_ast(o, program_ctx)
  # TODO(mdan,yashkatariya): Remove when object conversion is implemented.
  elif hasattr(o, '__class__'):
    raise NotImplementedError(
        'Object conversion is not yet supported. If you are '
        'trying to convert code that uses an existing object, '
        'try including the creation of that object in the '
        'conversion. For example, instead of converting the method '
        'of a class, try converting the entire class instead. '
        'See https://github.com/tensorflow/tensorflow/blob/master/tensorflow/'
        'python/autograph/README.md#using-the-functional-api '
        'for more information.')
  else:
    raise ValueError(
        'Entity "%s" has unsupported type "%s". Only functions and classes are '
        'supported for now.' % (o, type(o)))

  if logging.has_verbosity(2):
    logging.log(2, 'Compiled output of %s:\n\n%s\n', o,
                compiler.ast_to_source(nodes))
  if logging.has_verbosity(4):
    for n in nodes:
      logging.log(4, 'Compiled AST of %s:\n\n%s\n\n', o,
                  pretty_printer.fmt(n, color=False))

  return nodes, name, entity_info

def _instantiate(entity, converted_entity_info, free_nonglobal_var_names):
  """Creates a converted instance and binds it to match original entity."""
  factory = converted_entity_info.get_factory()

  # `factory` is currently bound to the empty module it was loaded from.
  # It must instead be bound to the globals and closure from the original
  # entity.
  if tf_inspect.isfunction(entity) or tf_inspect.ismethod(entity):
    entity_globals = entity.__globals__
    entity_closure = entity.__closure__ or ()
  elif hasattr(entity, '__module__'):
    entity_globals = sys.modules[entity.__module__].__dict__
    entity_closure = ()
  assert len(entity_closure) == len(free_nonglobal_var_names)

  # Fit the original entity's cells to match the order of factory's cells.
  original_names_and_cells = dict(zip(free_nonglobal_var_names, entity_closure))
  new_factory_cells = tuple(
      original_names_and_cells[name] for name in factory.__code__.co_freevars)

  bound_factory = types.FunctionType(
      code=factory.__code__,
      globals=entity_globals,
      name=factory.__name__,
      argdefs=(),
      closure=new_factory_cells)

  # Two other free vars: the internal "ag__" module and the source
  # map. These are wired via the parameters of the factory.
  converted_entity = bound_factory(  # pylint:disable=not-callable
      ag_internal, converted_entity_info.source_map,
      converted_entity_info.get_module())

  if tf_inspect.isfunction(entity) or tf_inspect.ismethod(entity):
    # Attach the default argument to the converted function.
    converted_entity.__defaults__ = entity.__defaults__

  return converted_entity

def object_to_graph(o, conversion_map, value_hints):
  """Compile a Python object into equivalent TensorFlow.

  The function will also recursively compile all the objects that `o`
  references, updating `dependency_cache`.

  This function is reentrant, and relies on dependency_cache to avoid
  generating duplicate code.

  Args:
    o: A Python object.
    conversion_map: A ConversionMap object.
    value_hints: A dict containing value hints for symbols like function
        parameters.

  Returns:
    A tuple (ast, new_name):
        * ast: An AST representing an object with interface equivalent to `o`,
            but which when executed it creates TF a graph.
        * new_name: The symbol name under which the new object can be found.

  Raises:
    ValueError: if the object is not supported.
  """
  if value_hints is None:
    value_hints = {}

  if tf_inspect.isclass(o):
    node, new_name = class_to_graph(o, conversion_map, value_hints)
  elif tf_inspect.isfunction(o):
    node, new_name = function_to_graph(o, conversion_map, value_hints)
  elif tf_inspect.ismethod(o):
    node, new_name = function_to_graph(o, conversion_map, value_hints)
  else:
    raise ValueError(
        'Entity "%s" has unsupported type "%s". Only functions and classes are '
        'supported for now.' % (o, type(o)))

  conversion_map.add_to_cache(o, node)
  if conversion_map.recursive:
    for obj in conversion_map.name_map.keys():
      if obj not in conversion_map.dependency_cache:
        if (hasattr(obj, 'im_class') and
            getattr(obj, 'im_class') not in conversion_map.partial_types):
          # Class members are converted with their objects, unless they're
          # only converted partially.
          continue
        object_to_graph(obj, conversion_map, None)

  return node, new_name

def to_graph(e,
             recursive=True,
             arg_values=None,
             arg_types=None,
             partial_types=None):
  """Compile a Python entity into equivalent TensorFlow code.

  Currently supported entities:
    * functions
    * classes

  Classes are handled by converting all their methods into a new class.

  Args:
    e: A Python entity.
    recursive: Whether to recusrively convert any functions that the decorator
        function may call.
    arg_values: A dict containing value hints for symbols like function
        parameters.
    arg_types: A dict containing type hints for symbols like function
        parameters.
    partial_types: A set of types (e.g. classes) that will not be converted
        entirely. Calls to member functions for these types will be renamed
        independently.

  Returns:
    A function with a signature identical to `o`, but which when executed it
  creates TF a graph that has the same functionality as the original entity.
  """
  conversion_map = conversion.ConversionMap(
      recursive=recursive,
      nocompile_decorators=(convert, graph_ready, convert_inline),
      partial_types=partial_types)
  _, name = conversion.entity_to_graph(e, conversion_map, arg_values, arg_types)

  module = gast.Module([])
  for import_line in config.COMPILED_IMPORT_STATEMENTS:
    module.body.append(parser.parse_str(import_line))
  for dep in conversion_map.dependency_cache.values():
    module.body.append(dep)
  compiled_node = compiler.ast_to_object(module)

  # The compiled code should see everything the entry function saw.
  # TODO(mdan): This might not work well if the call tree spans modules?
  if tf_inspect.isfunction(e):
    compiled_node.__dict__.update(six.get_function_globals(e))

  compiled_fn = getattr(compiled_node, name)
  return compiled_fn

def _is_free_function(py_object, full_name, index):
  """Check if input is a free function (and not a class- or static method)."""
  if not tf_inspect.isfunction(py_object):
    return False

  # Static methods are functions to tf_inspect (in 2.7), so check if the parent
  # is a class. If there is no parent, it's not a function.
  if '.' not in full_name:
    return False

  parent_name = full_name.rsplit('.', 1)[0]
  if tf_inspect.isclass(index[parent_name]):
    return False

  return True

def generate_global_index(library_name, index, reference_resolver):
  """Given a dict of full names to python objects, generate an index page.

  The index page generated contains a list of links for all symbols in `index`
  that have their own documentation page.

  Args:
    library_name: The name for the documented library to use in the title.
    index: A dict mapping full names to python objects.
    reference_resolver: An instance of ReferenceResolver.

  Returns:
    A string containing an index page as Markdown.
  """
  symbol_links = []
  for full_name, py_object in six.iteritems(index):
    if (tf_inspect.ismodule(py_object) or tf_inspect.isfunction(py_object) or
        tf_inspect.isclass(py_object)):
      # In Python 3, unbound methods are functions, so eliminate those.
      if tf_inspect.isfunction(py_object):
        if full_name.count('.') == 0:
          parent_name = ''
        else:
          parent_name = full_name[:full_name.rfind('.')]
        if parent_name in index and tf_inspect.isclass(index[parent_name]):
          # Skip methods (=functions with class parents).
          continue
      symbol_links.append((
          full_name, reference_resolver.python_link(full_name, full_name, '.')))

  lines = ['# All symbols in %s' % library_name, '']
  for _, link in sorted(symbol_links, key=lambda x: x[0]):
    lines.append('*  %s' % link)

  # TODO(markdaoust): use a _ModulePageInfo -> prety_docs.build_md_page()
  return '\n'.join(lines)

def _loop_fn_has_config(loop_fn):
  """Test if `loop_fn` has a `pfor_config` argument."""
  if tf_inspect.isfunction(loop_fn):
    argspec = tf_inspect.getargspec(loop_fn)
    return PFOR_CONFIG_ARG in argspec.args
  elif isinstance(loop_fn, functools.partial):
    fn = loop_fn.func
    argspec = tf_inspect.getargspec(fn)
    return (PFOR_CONFIG_ARG in argspec.args and
            PFOR_CONFIG_ARG not in loop_fn.keywords)
  else:
    loop_class = tf_decorator.unwrap(loop_fn)[1]
    if not hasattr(loop_class, "__call__"):
      raise ValueError("loop_fn object did not have a __call__ method")
    argspec = tf_inspect.getargspec(loop_class.__call__)
    return PFOR_CONFIG_ARG in argspec.args

def get_func_code(func):
  """Returns func_code of passed callable, or None if not available."""
  _, func = tf_decorator.unwrap(func)
  if callable(func):
    if tf_inspect.isfunction(func) or tf_inspect.ismethod(func):
      return six.get_function_code(func)
    # Since the object is not a function or method, but is a callable, we will
    # try to access the __call__method as a function.  This works with callable
    # classes but fails with functool.partial objects despite their __call__
    # attribute.
    try:
      return six.get_function_code(func.__call__)
    except AttributeError:
      return None
  else:
    raise ValueError('Argument must be callable')

def convert_entity_to_ast(o, program_ctx):
  """Compile a Python entity into equivalent TensorFlow.

  Args:
    o: A Python entity.
    program_ctx: A ProgramContext object.

  Returns:
    A tuple (ast, new_name, namespace):
        * ast: An AST representing an entity with interface equivalent to `o`,
            but which when executed it creates TF a graph.
        * new_name: The symbol name under which the new entity can be found.
        * namespace: A dict mapping all symbols visible to the converted entity,
            keyed by their symbol name.

  Raises:
    ValueError: if the entity type is not supported.
  """
  logging.log(1, 'Converting %s', o)

  if tf_inspect.isclass(o):
    nodes, name, entity_info = convert_class_to_ast(o, program_ctx)
  elif tf_inspect.isfunction(o):
    nodes, name, entity_info = convert_func_to_ast(o, program_ctx)
  elif tf_inspect.ismethod(o):
    nodes, name, entity_info = convert_func_to_ast(o, program_ctx)
  elif hasattr(o, '__class__'):
    # Note: this should only be raised when attempting to convert the object
    # directly. converted_call should still support it.
    raise NotImplementedError(
        'cannot convert entity "{}": object conversion is not yet'
        ' supported.'.format(o))
  else:
    raise ValueError(
        'Entity "%s" has unsupported type "%s". Only functions and classes are '
        'supported for now.' % (o, type(o)))

  if logging.has_verbosity(2):
    logging.log(2, 'Compiled output of %s:\n\n%s\n', o,
                compiler.ast_to_source(nodes))
  if logging.has_verbosity(4):
    for n in nodes:
      logging.log(4, 'Compiled AST of %s:\n\n%s\n\n', o,
                  pretty_printer.fmt(n, color=False))

  return nodes, name, entity_info