# 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.
# =============================================================================
"""Contains the base Layer class, from which all layers inherit."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import copy

from tensorflow.python.eager import context
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.keras import backend
from tensorflow.python.keras.engine import base_layer
from tensorflow.python.keras.mixed_precision.experimental import policy
from tensorflow.python.ops import variable_scope as vs
from tensorflow.python.ops import variables as tf_variables
from tensorflow.python.training.tracking import base as trackable
from tensorflow.python.util import deprecation
from tensorflow.python.util import function_utils
from tensorflow.python.util import nest
from tensorflow.python.util import object_identity
from tensorflow.python.util import tf_contextlib
from tensorflow.python.util.tf_export import tf_export

# Avoid breaking users who directly import this symbol from this file.
# TODO(fchollet): remove this.
InputSpec = base_layer.InputSpec  # pylint: disable=invalid-name

_KERAS_STYLE_SCOPE = False


@tf_export(v1=['layers.experimental.keras_style_scope'])
@tf_contextlib.contextmanager
def keras_style_scope():
  """Use Keras-style variable management.

  All tf.layers and tf RNN cells created in this scope use Keras-style
  variable management.  Creating such layers with a scope= argument is
  disallowed, and reuse=True is disallowed.

  The purpose of this scope is to allow users of existing layers to
  slowly transition to a Keras layers API without breaking existing
  functionality.

  One example of this is when using TensorFlow's RNN classes with Keras
  Models or Networks.  Because Keras models do not properly set variable
  scopes, users of RNNs may either accidentally share scopes between two
  different models, or get errors about variables that already exist.

  Example:

  ```python
  class RNNModel(tf.keras.Model):

    def __init__(self, name):
      super(RNNModel, self).__init__(name=name)
      self.rnn = tf.compat.v1.nn.rnn_cell.MultiRNNCell(
        [tf.compat.v1.nn.rnn_cell.LSTMCell(64) for _ in range(2)])

    def call(self, input, state):
      return self.rnn(input, state)

  model_1 = RNNModel("model_1")
  model_2 = RNNModel("model_2")

  # OK
  output_1, next_state_1 = model_1(input, state)
  # Raises an error about trying to create an already existing variable.
  output_2, next_state_2 = model_2(input, state)
  ```

  The solution is to wrap the model construction and execution in a keras-style
  scope:

  ```python
  with keras_style_scope():
    model_1 = RNNModel("model_1")
    model_2 = RNNModel("model_2")

    # model_1 and model_2 are guaranteed to create their own variables.
    output_1, next_state_1 = model_1(input, state)
    output_2, next_state_2 = model_2(input, state)

    assert len(model_1.weights) > 0
    assert len(model_2.weights) > 0
    assert(model_1.weights != model_2.weights)
  ```

  Yields:
    A keras layer style scope.
  """
  global _KERAS_STYLE_SCOPE
  stack = _KERAS_STYLE_SCOPE
  _KERAS_STYLE_SCOPE = True
  try:
    yield
  finally:
    _KERAS_STYLE_SCOPE = stack


@tf_export(v1=['layers.experimental.set_keras_style'])
def set_keras_style():
  """Use Keras-style variable management.

  All tf.layers and tf RNN cells created after keras style ha been enabled
  use Keras-style variable management.  Creating such layers with a
  scope= argument is disallowed, and reuse=True is disallowed.

  The purpose of this function is to allow users of existing layers to
  slowly transition to Keras layers API without breaking existing
  functionality.

  For more details, see the documentation for `keras_style_scope`.

  Note, once keras style has been set, it is set globally for the entire
  program and cannot be unset.

  Example:

  ```python
  set_keras_style()

  model_1 = RNNModel(name="model_1")
  model_2 = RNNModel(name="model_2")

  # model_1 and model_2 are guaranteed to create their own variables.
  output_1, next_state_1 = model_1(input, state)
  output_2, next_state_2 = model_2(input, state)

  assert len(model_1.weights) > 0
  assert len(model_2.weights) > 0
  assert(model_1.weights != model_2.weights)
  ```
  """
  global _KERAS_STYLE_SCOPE
  _KERAS_STYLE_SCOPE = True


def _is_in_keras_style_scope():
  global _KERAS_STYLE_SCOPE
  return _KERAS_STYLE_SCOPE


@tf_export(v1=['layers.Layer'])
class Layer(base_layer.Layer):
  """Base layer class.

  It is considered legacy, and we recommend the use of `tf.keras.layers.Layer`
  instead.

  Arguments:
    trainable: Boolean, whether the layer's variables should be trainable.
    name: String name of the layer.
    dtype: Default dtype of the layer's weights (default of `None` means use the
      type of the first input).

  Read-only properties:
    name: The name of the layer (string).
    dtype: Default dtype of the layer's weights (default of `None` means use the
      type of the first input).
    trainable_variables: List of trainable variables.
    non_trainable_variables: List of non-trainable variables.
    variables: List of all variables of this layer, trainable and
      non-trainable.
    updates: List of update ops of this layer.
    losses: List of losses added by this layer.
    trainable_weights: List of variables to be included in backprop.
    non_trainable_weights: List of variables that should not be
      included in backprop.
    weights: The concatenation of the lists trainable_weights and
      non_trainable_weights (in this order).

  Mutable properties:
    trainable: Whether the layer should be trained (boolean).
    input_spec: Optional (list of) `InputSpec` object(s) specifying the
      constraints on inputs that can be accepted by the layer.
  """

  def __init__(self, trainable=True, name=None, dtype=None,
               **kwargs):
    # For backwards compatibility, legacy layers do not use `ResourceVariable`
    # by default.
    self._use_resource_variables = False
    scope = kwargs.pop('_scope', None)
    self._reuse = kwargs.pop('_reuse', None)

    # Avoid an incorrect lint error
    self._trainable_weights = []
    self.built = False

    if dtype is None:
      # Indicates to infer dtype from inputs. When the V2 dtype behavior is
      # enabled, Keras layers default their dtype to floatx instead, so we pass
      # an "infer" policy to keep the old V1 behavior.
      dtype = policy.Policy('infer')

    if 'autocast' not in kwargs:
      kwargs['autocast'] = False

    super(Layer, self).__init__(trainable=trainable, name=name, dtype=dtype,
                                **kwargs)

    if _is_in_keras_style_scope():
      if scope is not None:
        raise ValueError(
            'scope argument not allowed when keras style layers are enabled, '
            'but saw: {}'.format(scope))
      if self._reuse is not None:
        raise ValueError(
            'reuse argument not allowed when keras style layers are enabled, '
            'but saw: {}'.format(self._reuse))
      self._keras_style = True
    else:
      self._keras_style = False

    self._call_has_scope_arg = 'scope' in self._call_fn_args
    if scope:
      with vs.variable_scope(scope) as captured_scope:
        self._scope = captured_scope
    else:
      self._scope = None
    self._current_scope = None

  # We no longer track graph in tf.layers layers. This property is only kept to
  # maintain API backward compatibility.
  @property
  @deprecation.deprecated(
      date=None,
      instructions='Stop using this property because tf.layers layers no '
      'longer track their graph.')
  def graph(self):
    if context.executing_eagerly():
      raise RuntimeError('Layer.graph not supported when executing eagerly.')
    return None

  def _init_set_name(self, name):
    # Determine layer name (non-unique).
    if isinstance(name, vs.VariableScope):
      base_name = name.name
      self._name, _ = self._make_unique_name()
    else:
      base_name = name
      self._name = name
    if not name:
      self._name, base_name = self._make_unique_name()
    self._base_name = base_name

  def _make_unique_name(self, name_uid_map=None, avoid_names=None,
                        namespace='', zero_based=False):
    base_name = base_layer.to_snake_case(self.__class__.__name__)
    name = backend.unique_object_name(
        base_name,
        name_uid_map=name_uid_map,
        avoid_names=avoid_names,
        namespace=namespace,
        zero_based=zero_based)
    return (name, base_name)

  @property
  def scope_name(self):
    if not self._scope:
      raise ValueError('No name available for layer scope because the layer "' +
                       self._name + '" has not been used yet. The scope name ' +
                       ' is determined the first time the layer instance is ' +
                       'called. You must therefore call the layer before ' +
                       'querying `scope_name`.')
    return self._scope.name

  def add_loss(self, losses, inputs=None):
    previous_losses_length = len(self._losses)
    previous_callable_losses_length = len(self._callable_losses)
    super(Layer, self).add_loss(losses, inputs=inputs)
    if not context.executing_eagerly():
      # TODO(fchollet): deprecate collection below.
      new_losses = self._losses[previous_losses_length:]
      new_callable_losses = self._callable_losses[
          previous_callable_losses_length:]
      for regularizer in new_callable_losses:
        loss_tensor = regularizer()
        if loss_tensor is not None:
          new_losses.append(loss_tensor)
      _add_elements_to_collection(
          new_losses,
          ops.GraphKeys.REGULARIZATION_LOSSES)

  def _name_scope(self):
    """Determines op naming for the Layer."""
    if self._keras_style:
      return super(Layer, self)._name_scope()
    return self._current_scope.original_name_scope

  def _set_scope(self, scope=None):
    if self._scope is None:
      # If constructed with _scope=None, lazy setting of scope.
      if self._reuse:
        with vs.variable_scope(
            scope if scope is not None else self._base_name) as captured_scope:
          self._scope = captured_scope
      else:
        with vs.variable_scope(
            scope, default_name=self._base_name) as captured_scope:
          self._scope = captured_scope

  def add_weight(self,
                 name,
                 shape,
                 dtype=None,
                 initializer=None,
                 regularizer=None,
                 trainable=None,
                 constraint=None,
                 use_resource=None,
                 synchronization=vs.VariableSynchronization.AUTO,
                 aggregation=vs.VariableAggregation.NONE,
                 partitioner=None,
                 **kwargs):
    """Adds a new variable to the layer, or gets an existing one; returns it.

    Arguments:
      name: variable name.
      shape: variable shape.
      dtype: The type of the variable. Defaults to `self.dtype` or `float32`.
      initializer: initializer instance (callable).
      regularizer: regularizer instance (callable).
      trainable: whether the variable should be part of the layer's
        "trainable_variables" (e.g. variables, biases)
        or "non_trainable_variables" (e.g. BatchNorm mean, stddev).
        Note, if the current variable scope is marked as non-trainable
        then this parameter is ignored and any added variables are also
        marked as non-trainable. `trainable` defaults to `True` unless
        `synchronization` is set to `ON_READ`.
      constraint: constraint instance (callable).
      use_resource: Whether to use `ResourceVariable`.
      synchronization: Indicates when a distributed a variable will be
        aggregated. Accepted values are constants defined in the class
        `tf.VariableSynchronization`. By default the synchronization is set to
        `AUTO` and the current `DistributionStrategy` chooses
        when to synchronize. If `synchronization` is set to `ON_READ`,
        `trainable` must not be set to `True`.
      aggregation: Indicates how a distributed variable will be aggregated.
        Accepted values are constants defined in the class
        `tf.VariableAggregation`.
      partitioner: (optional) partitioner instance (callable).  If
        provided, when the requested variable is created it will be split
        into multiple partitions according to `partitioner`.  In this case,
        an instance of `PartitionedVariable` is returned.  Available
        partitioners include `tf.compat.v1.fixed_size_partitioner` and
        `tf.compat.v1.variable_axis_size_partitioner`.  For more details, see
        the documentation of `tf.compat.v1.get_variable` and the  "Variable
        Partitioners and Sharding" section of the API guide.
      **kwargs: Additional keyword arguments.

    Returns:
      The created variable.  Usually either a `Variable` or `ResourceVariable`
      instance.  If `partitioner` is not `None`, a `PartitionedVariable`
      instance is returned.

    Raises:
      RuntimeError: If called with partitioned variable regularization and
        eager execution is enabled.
      ValueError: When trainable has been set to True with synchronization
        set as `ON_READ`.
    """
    for kwarg in kwargs:
      if kwarg != 'experimental_autocast':
        raise TypeError('Unknown keyword argument:', kwarg)
    if self._keras_style:
      return super(Layer, self).add_weight(
          name=name,
          shape=shape,
          dtype=dtype,
          initializer=initializer,
          regularizer=regularizer,
          trainable=trainable and self.trainable,
          constraint=constraint,
          use_resource=use_resource,
          synchronization=vs.VariableSynchronization.AUTO,
          aggregation=vs.VariableAggregation.NONE,
          partitioner=partitioner,
          **kwargs)

    if synchronization == vs.VariableSynchronization.ON_READ:
      if trainable:
        raise ValueError(
            'Synchronization value can be set to '
            'VariableSynchronization.ON_READ only for non-trainable variables. '
            'You have specified trainable=True and '
            'synchronization=VariableSynchronization.ON_READ.')
      else:
        # Set trainable to be false when variable is to be synced on read.
        trainable = False
    elif trainable is None:
      trainable = True

    def _should_add_regularizer(variable, existing_variable_set):
      if isinstance(variable, tf_variables.PartitionedVariable):
        for var in variable:
          if var in existing_variable_set:
            return False
        return True
      else:
        return variable not in existing_variable_set

    init_graph = None
    if not context.executing_eagerly():
      default_graph = ops.get_default_graph()
      if default_graph.building_function:
        with ops.init_scope():
          # Retrieve the variables from the graph into which variables
          # will be lifted; if initialization ops will be lifted into
          # the eager context, then there is nothing to retrieve, since variable
          # collections are not supported when eager execution is enabled.
          if not context.executing_eagerly():
            init_graph = ops.get_default_graph()
            existing_variables = set(tf_variables.global_variables())
      else:
        # Initialization ops will not be lifted out of the default graph.
        init_graph = default_graph
        existing_variables = set(tf_variables.global_variables())

    if dtype is None:
      dtype = self.dtype or dtypes.float32

    self._set_scope(None)
    reuse = self.built or self._reuse
    prev_len_trainable = len(self._trainable_weights)
    with vs.variable_scope(
        self._scope, reuse=reuse, auxiliary_name_scope=False) as scope:
      self._current_scope = scope
      with ops.name_scope(self._name_scope()):
        use_resource = (use_resource or
                        self._use_resource_variables or
                        scope.use_resource)
        if initializer is None:
          initializer = scope.initializer
        variable = super(Layer, self).add_weight(
            name,
            shape,
            dtype=dtypes.as_dtype(dtype),
            initializer=initializer,
            trainable=trainable and self.trainable,
            constraint=constraint,
            partitioner=partitioner,
            use_resource=use_resource,
            synchronization=synchronization,
            aggregation=aggregation,
            getter=vs.get_variable,
            **kwargs)

        if regularizer:
          if (ops.executing_eagerly_outside_functions()
              or _should_add_regularizer(variable, existing_variables)):
            self._handle_weight_regularization(name, variable, regularizer)

        if init_graph is not None:
          # Handle edge case where a custom getter has overridden `trainable`.
          # There is one known occurrence of this, in unit test
          # testBasicRNNCellNotTrainable in
          # contrib.rnn.python.kernel_tests.core_rnn_cell_test
          with init_graph.as_default():
            trainable_variables = tf_variables.trainable_variables()
          if (trainable and self.trainable and
              variable not in trainable_variables):
            # A custom getter / variable scope overrode the trainable flag.
            extra_trainable_vars = self._trainable_weights[prev_len_trainable:]
            self._trainable_weights = self._trainable_weights[
                :prev_len_trainable]
            self._non_trainable_weights += extra_trainable_vars
    return variable

  def __call__(self, inputs, *args, **kwargs):
    """Wraps `call`, applying pre- and post-processing steps.

    Arguments:
      inputs: input tensor(s).
      *args: additional positional arguments to be passed to `self.call`.
      **kwargs: additional keyword arguments to be passed to `self.call`.
        **Note**: kwarg `scope` is reserved for use by the layer.

    Returns:
      Output tensor(s).

    Note:
      - If the layer's `call` method takes a `scope` keyword argument,
        this argument will be automatically set to the current variable scope.
      - If the layer's `call` method takes a `mask` argument (as some Keras
        layers do), its default value will be set to the mask generated
        for `inputs` by the previous layer (if `input` did come from
        a layer that generated a corresponding mask, i.e. if it came from
        a Keras layer with masking support.

    Raises:
      ValueError: if the layer's `call` method returns None (an invalid value).
    """
    scope = kwargs.pop('scope', None)

    if self._keras_style:
      if scope is not None:
        raise ValueError(
            'scope argument not allowed when keras style layers are enabled, '
            'but saw: {}'.format(scope))
      return super(Layer, self).__call__(inputs, *args, **kwargs)

    self._set_scope(scope)

    if self.built:
      try:
        # Some classes which inherit from Layer do not use its constructor, so
        # rather than initializing to None we check for an AttributeError.
        scope_context_manager = self._always_reuse_variable_scope
      except AttributeError:
        # From this point we will always set reuse=True, so create a "final"
        # variable scope with this setting. We avoid re-creating variable scopes
        # after this point as an optimization.
        self._always_reuse_variable_scope = vs.variable_scope(
            self._scope, reuse=True, auxiliary_name_scope=False)
        scope_context_manager = self._always_reuse_variable_scope
    else:
      scope_context_manager = vs.variable_scope(
          self._scope, reuse=self._reuse, auxiliary_name_scope=False)

    with scope_context_manager as scope:
      self._current_scope = scope

      try:
        call_has_scope_arg = self._call_has_scope_arg
      except AttributeError:
        self._call_fn_args = function_utils.fn_args(self.call)
        self._call_has_scope_arg = 'scope' in self._call_fn_args
        call_has_scope_arg = self._call_has_scope_arg
      if call_has_scope_arg:
        kwargs['scope'] = scope

      # Actually call layer
      outputs = super(Layer, self).__call__(inputs, *args, **kwargs)

    if not context.executing_eagerly():
      # Update global default collections.
      _add_elements_to_collection(self.updates, ops.GraphKeys.UPDATE_OPS)
    return outputs

  def __deepcopy__(self, memo):
    no_copy = set(['_graph', '_thread_local'])
    shallow_copy = set(['_scope', '_always_reuse_variable_scope'])
    cls = self.__class__
    result = cls.__new__(cls)
    memo[id(self)] = result
    for k, v in self.__dict__.items():
      if k in no_copy:
        setattr(result, k, v)
      elif k in shallow_copy:
        setattr(result, k, copy.copy(v))
      elif base_layer.is_tensor_or_tensor_list(v):
        setattr(result, k, v)
      else:
        setattr(result, k, copy.deepcopy(v, memo))
    return result

  def __setattr__(self, value, name):
    # By-pass the automatic dependency tracking performed by the parent Layer.
    super(trackable.Trackable, self).__setattr__(value, name)

  @property
  def _is_legacy_layer(self):
    """Used by keras to check compatibility. This should not be overridden."""
    return True


def _add_elements_to_collection(elements, collection_list):
  if context.executing_eagerly():
    raise RuntimeError('Using collections from Layers not supported in Eager '
                       'mode. Tried to add %s to %s' % (elements,
                                                        collection_list))
  elements = nest.flatten(elements)
  collection_list = nest.flatten(collection_list)
  for name in collection_list:
    collection = ops.get_collection_ref(name)
    collection_set = object_identity.ObjectIdentitySet(collection)
    for element in elements:
      if element not in collection_set:
        collection.append(element)
