# 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.
# ==============================================================================

"""Provides templates which allow variable sharing."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import functools
import traceback

from tensorflow.python.eager import context
from tensorflow.python.eager import function
from tensorflow.python.framework import ops
from tensorflow.python.ops import variable_scope
from tensorflow.python.platform import tf_logging as logging
from tensorflow.python.training.checkpointable import base as checkpointable
from tensorflow.python.training.checkpointable import util as checkpointable_util
from tensorflow.python.util import tf_contextlib
from tensorflow.python.util import tf_decorator
from tensorflow.python.util.deprecation import deprecated
from tensorflow.python.util.tf_export import tf_export


__all__ = ["make_template"]


@tf_export(v1=["make_template"])
def make_template(name_, func_, create_scope_now_=False, unique_name_=None,
                  custom_getter_=None, **kwargs):
  """Given an arbitrary function, wrap it so that it does variable sharing.

  This wraps `func_` in a Template and partially evaluates it. Templates are
  functions that create variables the first time they are called and reuse them
  thereafter. In order for `func_` to be compatible with a `Template` it must
  have the following properties:

  * The function should create all trainable variables and any variables that
     should be reused by calling `tf.get_variable`. If a trainable variable is
     created using `tf.Variable`, then a ValueError will be thrown. Variables
     that are intended to be locals can be created by specifying
     `tf.Variable(..., trainable=false)`.
  * The function may use variable scopes and other templates internally to
      create and reuse variables, but it shouldn't use `tf.global_variables` to
      capture variables that are defined outside of the scope of the function.
  * Internal scopes and variable names should not depend on any arguments that
      are not supplied to `make_template`. In general you will get a ValueError
      telling you that you are trying to reuse a variable that doesn't exist
      if you make a mistake.

  In the following example, both `z` and `w` will be scaled by the same `y`. It
  is important to note that if we didn't assign `scalar_name` and used a
  different name for z and w that a `ValueError` would be thrown because it
  couldn't reuse the variable.

  ```python
  def my_op(x, scalar_name):
    var1 = tf.get_variable(scalar_name,
                           shape=[],
                           initializer=tf.constant_initializer(1))
    return x * var1

  scale_by_y = tf.make_template('scale_by_y', my_op, scalar_name='y')

  z = scale_by_y(input1)
  w = scale_by_y(input2)
  ```

  As a safe-guard, the returned function will raise a `ValueError` after the
  first call if trainable variables are created by calling `tf.Variable`.

  If all of these are true, then 2 properties are enforced by the template:

  1. Calling the same template multiple times will share all non-local
      variables.
  2. Two different templates are guaranteed to be unique, unless you reenter the
      same variable scope as the initial definition of a template and redefine
      it. An examples of this exception:

  ```python
  def my_op(x, scalar_name):
    var1 = tf.get_variable(scalar_name,
                           shape=[],
                           initializer=tf.constant_initializer(1))
    return x * var1

  with tf.variable_scope('scope') as vs:
    scale_by_y = tf.make_template('scale_by_y', my_op, scalar_name='y')
    z = scale_by_y(input1)
    w = scale_by_y(input2)

  # Creates a template that reuses the variables above.
  with tf.variable_scope(vs, reuse=True):
    scale_by_y2 = tf.make_template('scale_by_y', my_op, scalar_name='y')
    z2 = scale_by_y2(input1)
    w2 = scale_by_y2(input2)
  ```

  Depending on the value of `create_scope_now_`, the full variable scope may be
  captured either at the time of first call or at the time of construction. If
  this option is set to True, then all Tensors created by repeated calls to the
  template will have an extra trailing _N+1 to their name, as the first time the
  scope is entered in the Template constructor no Tensors are created.

  Note: `name_`, `func_` and `create_scope_now_` have a trailing underscore to
  reduce the likelihood of collisions with kwargs.

  Args:
    name_: A name for the scope created by this template. If necessary, the name
      will be made unique by appending `_N` to the name.
    func_: The function to wrap.
    create_scope_now_: Boolean controlling whether the scope should be created
      when the template is constructed or when the template is called. Default
      is False, meaning the scope is created when the template is called.
    unique_name_: When used, it overrides name_ and is not made unique. If a
      template of the same scope/unique_name already exists and reuse is false,
      an error is raised. Defaults to None.
    custom_getter_: Optional custom getter for variables used in `func_`. See
      the `tf.get_variable` `custom_getter` documentation for
      more information.
    **kwargs: Keyword arguments to apply to `func_`.

  Returns:
    A function to encapsulate a set of variables which should be created once
    and reused. An enclosing scope will be created either when `make_template`
    is called or when the result is called, depending on the value of
    `create_scope_now_`. Regardless of the value, the first time the template
    is called it will enter the scope with no reuse, and call `func_` to create
    variables, which are guaranteed to be unique. All subsequent calls will
    re-enter the scope and reuse those variables.

  Raises:
    ValueError: if `name_` is None.
  """
  return make_template_internal(
      name_,
      func_,
      create_scope_now_,
      unique_name_,
      custom_getter_,
      create_graph_function_=False,
      **kwargs)


def make_template_internal(name_,
                           func_,
                           create_scope_now_=False,
                           unique_name_=None,
                           custom_getter_=None,
                           create_graph_function_=False,
                           **kwargs):
  """Make a template, optionally compiling func_ into a graph function.

  See `make_template` for full documentation.

  Args:
    name_: A name for the scope created by this template. If necessary, the name
      will be made unique by appending `_N` to the name.
    func_: The function to wrap.
    create_scope_now_: Boolean controlling whether the scope should be created
      when the template is constructed or when the template is called. Default
      is False, meaning the scope is created when the template is called.
    unique_name_: When used, it overrides name_ and is not made unique. If a
      template of the same scope/unique_name already exists and reuse is false,
      an error is raised. Defaults to None. If executing eagerly, must be None.
    custom_getter_: Optional custom getter for variables used in `func_`. See
      the `tf.get_variable` `custom_getter` documentation for
      more information.
    create_graph_function_: When True, `func_` will be executed as a graph
      function. This implies that `func_` must satisfy the properties that
      `function.defun` requires of functions: See the documentation of
      `function.defun` for details. When executing eagerly, setting this flag to
      True can improve performance. Regardless of whether eager execution is
      enabled, enabling this flag gives the caller access to graph-function
      semantics, i.e., accesses to variables are totally ordered and
      side-effecting ops are not pruned.
    **kwargs: Keyword arguments to apply to `func_`.

  Returns:
    A function to encapsulate a set of variables which should be created once
    and reused. An enclosing scope will be created either when `make_template`
    is called or when the result is called, depending on the value of
    `create_scope_now_`. Regardless of the value, the first time the template
    is called it will enter the scope with no reuse, and call `func_` to create
    variables, which are guaranteed to be unique. All subsequent calls will
    re-enter the scope and reuse those variables.

  Raises:
    ValueError: if `name_` is None.
    ValueError: if `unique_name_` is not None and eager execution is enabled.
  """

  if kwargs:
    func_ = tf_decorator.make_decorator(func_, functools.partial(
        func_, **kwargs))
  if context.executing_eagerly():
    if unique_name_ is not None:
      raise ValueError(
          "unique_name_ cannot be used when eager exeuction is enabled.")
    return EagerTemplate(
        name_,
        func_,
        create_scope_now=create_scope_now_,
        custom_getter=custom_getter_,
        create_graph_function=create_graph_function_)
  return Template(
      name_,
      func_,
      create_scope_now=create_scope_now_,
      unique_name=unique_name_,
      custom_getter=custom_getter_,
      create_graph_function=create_graph_function_)


def _skip_common_stack_elements(stacktrace, base_case):
  """Skips items that the target stacktrace shares with the base stacktrace."""
  for i, (trace, base) in enumerate(zip(stacktrace, base_case)):
    if trace != base:
      return stacktrace[i:]
  return stacktrace[-1:]


class Template(checkpointable.CheckpointableBase):
  """Wrap a function to aid in variable sharing.

  Templates are functions that create variables the first time they are called
  and reuse them thereafter. See `make_template` for full documentation.

  Note: By default, the full variable scope is captured at the time of first
  call. If `create_scope_now_` is passed as True to the constructor, the full
  scope will be captured there, but no variables will created until the first
  call.
  """

  def __init__(self, name, func, create_scope_now=False, unique_name=None,
               custom_getter=None, create_graph_function=False):
    """Creates a template for the given function.

    Args:
      name: A name for the scope created by this template. The
        name will be made unique by appending `_N` to the it (see how
        `tf.variable_scope` treats the `default_name` for details).
      func: The function to apply each time.
      create_scope_now: Whether to create the scope at Template construction
        time, rather than first call. Defaults to false. Creating the scope at
        construction time may be more convenient if the template is to passed
        through much lower level code, and you want to be sure of the scope
        name without knowing exactly where it will be first called. If set to
        True, the scope will be created in the constructor, and all subsequent
        times in `__call__`, leading to a trailing numeral being added to the
        names of all created Tensors. If set to False, the scope will be created
        at the first call location.
      unique_name: When used, it overrides `name` and is not made unique. If a
        template of the same scope/unique_name already exists and reuse is
        false, an error is raised. Defaults to None.
      custom_getter: optional custom getter to pass to `variable_scope()`
      create_graph_function: When True, `func` will be executed as a graph
        function. Enabling this flag gives the caller access to graph-function
        semantics, i.e., accesses to variables are totally ordered and
        side-effecting ops are not pruned.

    Raises:
      ValueError: if `name` is None.
    """
    if create_graph_function:
      self._func = function.defun(func)
    else:
      self._func = func
    self._stacktrace = traceback.format_stack()[:-2]
    self._name = name
    self._unique_name = unique_name
    self._custom_getter = custom_getter
    if name is None:
      raise ValueError("name cannot be None.")
    if create_scope_now:
      with variable_scope._pure_variable_scope(  # pylint:disable=protected-access
          (self._unique_name or
           variable_scope._get_unique_variable_scope(self._name)),  # pylint:disable=protected-access
          custom_getter=self._custom_getter) as vs:
        self._variable_scope = vs
    else:
      self._variable_scope = None
    # This variable keeps track of whether the template has been called yet,
    # which is not the same as whether the scope has been created.
    self._variables_created = False

  def _call_func(self, args, kwargs):
    try:
      vars_at_start = len(
          ops.get_collection_ref(ops.GraphKeys.GLOBAL_VARIABLES))
      trainable_at_start = len(
          ops.get_collection_ref(ops.GraphKeys.TRAINABLE_VARIABLES))
      if self._variables_created:
        result = self._func(*args, **kwargs)
      else:
        # The first time we run, restore variables if necessary (via
        # Checkpointable).
        with checkpointable_util.capture_dependencies(template=self):
          result = self._func(*args, **kwargs)

      if self._variables_created:
        # Variables were previously created, implying this is not the first
        # time the template has been called. Check to make sure that no new
        # trainable variables were created this time around.
        trainable_variables = ops.get_collection_ref(
            ops.GraphKeys.TRAINABLE_VARIABLES)
        # If a variable that we intend to train is created as a side effect
        # of creating a template, then that is almost certainly an error.
        if trainable_at_start != len(trainable_variables):
          raise ValueError("Trainable variable created when calling a template "
                           "after the first time, perhaps you used tf.Variable "
                           "when you meant tf.get_variable: %s" %
                           (trainable_variables[trainable_at_start:],))

        # Non-trainable tracking variables are a legitimate reason why a new
        # variable would be created, but it is a relatively advanced use-case,
        # so log it.
        variables = ops.get_collection_ref(ops.GraphKeys.GLOBAL_VARIABLES)
        if vars_at_start != len(variables):
          logging.info("New variables created when calling a template after "
                       "the first time, perhaps you used tf.Variable when you "
                       "meant tf.get_variable: %s",
                       variables[vars_at_start:])
      else:
        self._variables_created = True
      return result
    except Exception as exc:
      # Reraise the exception, but append the original definition to the
      # trace.
      args = exc.args
      if not args:
        arg0 = ""
      else:
        arg0 = args[0]
      trace = "".join(_skip_common_stack_elements(self._stacktrace,
                                                  traceback.format_stack()))
      arg0 = "%s\n\noriginally defined at:\n%s" % (arg0, trace)
      new_args = [arg0]
      new_args.extend(args[1:])
      exc.args = tuple(new_args)
      raise

  def __call__(self, *args, **kwargs):
    if self._variable_scope:
      # Only reuse variables if they were already created.
      with variable_scope.variable_scope(
          self._variable_scope, reuse=self._variables_created):
        return self._call_func(args, kwargs)
    else:
      # The scope was not created at construction time, so create it here.
      # Subsequent calls should reuse variables.
      with variable_scope.variable_scope(
          self._unique_name, self._name,
          custom_getter=self._custom_getter) as vs:
        self._variable_scope = vs
        return self._call_func(args, kwargs)

  @property
  def name(self):
    """Returns the name given to this Template."""
    return self._name

  @property
  def func(self):
    """Returns the func given to this Template."""
    return self._func

  @property
  def variable_scope(self):
    """Returns the variable scope object created by this Template."""
    return self._variable_scope

  @property
  def variable_scope_name(self):
    """Returns the variable scope name created by this Template."""
    if self._variable_scope:
      name = self._variable_scope.name
      # To prevent partial matches on the scope_name, we add '/' at the end.
      return name if name[-1] == "/" else name + "/"

  @property
  def variables(self):
    """Returns the list of global and local variables created by the Template.
    """
    return self.global_variables + self.local_variables

  @property
  def trainable_variables(self):
    """Returns the list of trainable variables created by the Template."""
    if self._variables_created:
      return ops.get_collection(ops.GraphKeys.TRAINABLE_VARIABLES,
                                self.variable_scope_name)
    else:
      return []

  @property
  def non_trainable_variables(self):
    """Returns the list of non-trainable variables created by the Template."""
    # TODO(apassos) Make sure it matches Eager when using local variables.
    global_variables = self.global_variables
    trainable_variables = set(self.trainable_variables)
    return [x for x in global_variables if x not in trainable_variables]

  @property
  def global_variables(self):
    """Returns the list of global variables created by the Template."""
    if self._variables_created:
      return ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES,
                                self.variable_scope_name)
    else:
      return []

  @property
  def local_variables(self):
    """Returns the list of global variables created by the Template."""
    if self._variables_created:
      return ops.get_collection(ops.GraphKeys.LOCAL_VARIABLES,
                                self.variable_scope_name)
    else:
      return []

  @property
  def weights(self):
    """List of weights/variables created by the Template."""
    return self.variables

  @property
  def trainable_weights(self):
    """List of trainable weights/variables created by the Template."""
    return self.trainable_variables

  @property
  def non_trainable_weights(self):
    """List of non-trainable weights/variables created by the Template."""
    return self.non_trainable_variables

  @property
  @deprecated(
      "2017-02-21", "The .var_scope property is deprecated. Please change your "
      "code to use the .variable_scope property")
  def var_scope(self):
    """Returns the variable scope object created by this Template."""
    return self._variable_scope


class _EagerTemplateVariableStore(object):
  """Wrapper around EagerVariableStore to support nesting EagerTemplates.
  """

  def __init__(self, variable_scope_name):
    self._variable_scope_name = variable_scope_name
    default = variable_scope._get_default_variable_store()  # pylint: disable=protected-access
    if default._store_eager_variables:  # pylint: disable=protected-access
      self._eager_variable_store = variable_scope.EagerVariableStore(default)
    else:
      self._eager_variable_store = variable_scope.EagerVariableStore()

  def set_variable_scope_name(self, variable_scope_name):
    self._variable_scope_name = variable_scope_name

  @tf_contextlib.contextmanager
  def as_default(self):
    try:
      with self._eager_variable_store.as_default():
        yield
    finally:
      # Each _EagerTemplateVariableStore object lives underneath a variable
      # scope (see EagerTemplate.__call__). This variable scope's subscopes are
      # closed when the EagerTemplate object returns from __call__. For
      # top-level _EagerTemplateVariableStore objects, the variable store to
      # which the variable scope is attached is different from the
      # EagerVariableStore; as such it is necessary to close its subscopes
      # here as well.
      if self._variable_scope_name is None:
        raise RuntimeError("A variable scope must be set before an "
                           "_EagerTemplateVariableStore object exits.")
      variable_scope.get_variable_scope_store().close_variable_subscopes(
          self._variable_scope_name)

  def _variables_in_scope(self, variable_list):
    if self._variable_scope_name is None:
      raise RuntimeError(
          "A variable scope must be set before variables can be accessed.")
    return [
        v for v in variable_list
        if v.name.startswith(self._variable_scope_name + "/")
    ]

  def variables(self):
    return self._variables_in_scope(self._eager_variable_store.variables())

  def trainable_variables(self):
    return self._variables_in_scope(
        self._eager_variable_store.trainable_variables())

  def non_trainable_variables(self):
    return self._variables_in_scope(
        self._eager_variable_store.non_trainable_variables())


class EagerTemplate(Template):
  """Wrap a function to aid in variable sharing in Eager mode.

  Templates are functions that create variables the first time they are called
  and reuse them thereafter. See `make_template` for full documentation.

  Note: By default, the full variable scope is captured at the time of first
  call. If `create_scope_now` is passed as True to the constructor, the full
  scope will be captured there, but no variables will be created until the first
  call.
  """

  def __init__(self, name, func, create_scope_now=False, custom_getter=None,
               create_graph_function=False):
    """Creates a template for the given function.

    Args:
      name: A name for the scope created by this template. The
        name will be made unique by appending `_N` to the it (see how
        `tf.variable_scope` treats the `default_name` for details).
      func: The function to apply each time.
      create_scope_now: Whether to create the scope at Template construction
        time, rather than first call. Defaults to false. Creating the scope at
        construction time may be more convenient if the template is passed
        through much lower level code, and you want to be sure of the scope
        name without knowing exactly where it will be first called. If set to
        True, the scope will be created in the constructor, and all subsequent
        times in `__call__`, leading to a trailing numeral being added to the
        names of all created Tensors. If set to False, the scope will be created
        at the first call location.
      custom_getter: optional custom getter to pass to `variable_scope()`
      create_graph_function: When True, `func` will be executed as a graph
        function. Enabling this flag allows the caller to reap the performance
        benefits associated with executing graphs, at the cost of sacrificing
        debuggability; however, not all Python functions can be compiled into
        graph functions. See the documentation for `function.defun` for details.

    Raises:
      RuntimeError: if eager execution is not enabled.
    """
    if not context.executing_eagerly():
      raise RuntimeError(
          "{} objects can only be used when eager execution is enabled, use "
          "tf.Template for graph construction".
          format(type(self)))
    super(EagerTemplate, self).__init__(name, func, create_scope_now, None,
                                        custom_getter, create_graph_function)
    if self._variable_scope is not None:
      variable_scope_name = self._variable_scope.name
    else:
      # Defer setting the variable scope name until the variable scope
      # is created in __call__.
      variable_scope_name = None
    self._template_store = _EagerTemplateVariableStore(variable_scope_name)
    self._variable_scope_context_manager = None

  def _call_func(self, args, kwargs):
    try:
      vars_at_start = self._template_store.variables()
      trainable_at_start = self._template_store.trainable_variables()
      if self._variables_created:
        result = self._func(*args, **kwargs)
      else:
        # The first time we run, restore variables if necessary (via
        # Checkpointable).
        with checkpointable_util.capture_dependencies(template=self):
          result = self._func(*args, **kwargs)

      if self._variables_created:
        # Variables were previously created, implying this is not the first
        # time the template has been called. Check to make sure that no new
        # trainable variables were created this time around.
        trainable_variables = self._template_store.trainable_variables()
        # If a variable that we intend to train is created as a side effect
        # of creating a template, then that is almost certainly an error.
        if len(trainable_at_start) != len(trainable_variables):
          raise ValueError("Trainable variable created when calling a template "
                           "after the first time, perhaps you used tf.Variable "
                           "when you meant tf.get_variable: %s" %
                           list(set(trainable_variables) -
                                set(trainable_at_start)))

        # Non-trainable tracking variables are a legitimate reason why a new
        # variable would be created, but it is a relatively advanced use-case,
        # so log it.
        variables = self._template_store.variables()
        if len(vars_at_start) != len(variables):
          logging.info("New variables created when calling a template after "
                       "the first time, perhaps you used tf.Variable when you "
                       "meant tf.get_variable: %s",
                       list(set(variables) - set(vars_at_start)))
      else:
        self._variables_created = True
      return result
    except Exception as exc:
      # Reraise the exception, but append the original definition to the
      # trace.
      args = exc.args
      if not args:
        arg0 = ""
      else:
        arg0 = args[0]
      trace = "".join(_skip_common_stack_elements(self._stacktrace,
                                                  traceback.format_stack()))
      arg0 = "%s\n\noriginally defined at:\n%s" % (arg0, trace)
      new_args = [arg0]
      new_args.extend(args[1:])
      exc.args = tuple(new_args)
      raise

  def __call__(self, *args, **kwargs):
    # In both branches below, the template store is installed as default after
    # the variable scope is opened in order to ensure that templates nested at
    # the same level correctly uniquify lower variable scope names.
    if self._variable_scope:
      # Create a cache for the variable scope context manager the first time
      # around so that we don't have to keep recreating it.
      if not self._variable_scope_context_manager:
        self._variable_scope_context_manager = variable_scope.variable_scope(
            self._variable_scope, reuse=variable_scope.AUTO_REUSE)
      with self._variable_scope_context_manager:
        with self._template_store.as_default():
          return self._call_func(args, kwargs)
    else:
      # The scope was not created at construction time, so create it here.
      # Subsequent calls should reuse variables.
      with variable_scope.variable_scope(
          self._unique_name, self._name,
          custom_getter=self._custom_getter) as vs:
        self._variable_scope = vs
        # Because the scope was not created at construction time, the template
        # store's variable scope name is unset; set it here.
        self._template_store.set_variable_scope_name(vs.name)
        with self._template_store.as_default():
          return self._call_func(args, kwargs)

  @property
  def name(self):
    """Returns the name given to this Template."""
    return self._name

  @property
  def func(self):
    """Returns the func given to this Template."""
    return self._func

  @property
  def variable_scope(self):
    """Returns the variable scope object created by this Template."""
    return self._variable_scope

  @property
  def variable_scope_name(self):
    """Returns the variable scope name created by this Template."""
    if self._variable_scope:
      name = self._variable_scope.name
      # To prevent partial matches on the scope_name, we add '/' at the end.
      return name if name[-1] == "/" else name + "/"

  @property
  def variables(self):
    """Returns the list of variables created by the Template."""
    # Currently there is no local variable in Eager mode.
    if not self._variables_created:
      return []
    return self._template_store.variables()

  @property
  def trainable_variables(self):
    """Returns the list of trainable variables created by the Template."""
    # Currently there is no local variable in Eager mode.
    if not self._variables_created:
      return []
    return self._template_store.trainable_variables()

  @property
  def non_trainable_variables(self):
    """Returns the list of non-trainable variables created by the Template."""
    # Currently there is no local variable in Eager mode.
    if not self._variables_created:
      return []
    return self._template_store.non_trainable_variables()

  @property
  def global_variables(self):
    """Returns the list of global variables created by the Template."""
    # Currently there is no local variable in Eager mode.
    if not self._variables_created:
      return []
    return self.variables

  @property
  def local_variables(self):
    """Returns the list of global variables created by the Template."""
    # Currently there is no local variable in Eager mode.
    return []
