text: # Copyright (c) 2020 PaddlePaddle 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.
  
  from __future__ import print_function
  
  import collections
  from paddle.utils import gast
  import inspect
  import six
  import textwrap
  import threading
  import weakref
  
  from paddle.fluid import framework
  from paddle.fluid import _non_static_mode
  from paddle.fluid.dygraph import layers
  from paddle.fluid.data_feeder import check_type
  from paddle.fluid.layers.utils import flatten
  from paddle.fluid.dygraph.base import param_guard
  from paddle.fluid.dygraph.base import switch_to_static_graph
  from paddle.fluid.dygraph.dygraph_to_static import DygraphToStaticAst
  from paddle.fluid.dygraph.dygraph_to_static import error
  from paddle.fluid.dygraph.dygraph_to_static import logging_utils
  from paddle.fluid.dygraph.dygraph_to_static.origin_info import attach_origin_info
  from paddle.fluid.dygraph.dygraph_to_static.origin_info import create_and_update_origin_info_map
  from paddle.fluid.dygraph.dygraph_to_static.origin_info import update_op_callstack_with_origin_info
  from paddle.fluid.dygraph.dygraph_to_static.partial_program import partial_program_from
  from paddle.fluid.dygraph.dygraph_to_static.utils import ast_to_func
  from paddle.fluid.dygraph.dygraph_to_static.utils import ast_to_source_code
  from paddle.fluid.dygraph.dygraph_to_static.utils import func_to_source_code
  from paddle.fluid.dygraph.dygraph_to_static.utils import input_specs_compatible
  from paddle.fluid.dygraph.dygraph_to_static.utils import type_name
  from paddle.fluid.dygraph.dygraph_to_static.utils import unwrap
  from paddle.fluid.dygraph.dygraph_to_static.utils import make_hashable
  from paddle.fluid.dygraph.dygraph_to_static.function_spec import FunctionSpec, _hash_spec_names
  from paddle.fluid.dygraph.dygraph_to_static.function_spec import get_buffers, get_parameters
  from paddle.fluid.wrapped_decorator import signature_safe_contextmanager
  
  __all__ = ['ProgramTranslator', 'convert_to_static']
  
  # For each traced function, we set `max_traced_program_count` = 10 to consider caching performance.
  # Once exceeding the threshold, we will raise warning to users to make sure the conversion is as expected.
  MAX_TRACED_PROGRAM_COUNT = 10
  
  
  class FunctionCache(object):
      """
      Caches the transformed functions to avoid redundant conversions of the same function.
      """
  
      def __init__(self):
          # Caches the converted static functions. {dygraph_func: static_func}
          self._converted_static_func_caches = dict()
          # Caches the converted ast node for same source code. {source_code: ast_root}
          self._code_to_ast_caches = dict()
          self._dygraph_to_static = DygraphToStaticAst()
  
      def convert_with_cache(self, func):
          """
          Returns the cached static function or converts it when first encounters the function.
          """
          # If hit cache, return it directly.
          static_func = self._converted_static_func_caches.get(func, None)
  
          if static_func is None:
              static_func = self._convert(func)
              self._converted_static_func_caches[func] = static_func
  
          return static_func
  
      def _convert(self, func):
          """
          Converts dygraph function into static function. For two functions with same dedent code,
          the second function will reuse the transformed ast node of previous one.
  
          For example:
              # A.py
              def foo(x, y):
                  z = x + y
                  return z
  
              # B.py
              def foo(x, y):
                  z = x + y
                  return z
  
          If the conversion of A.foo happens after B.foo, it will reuse the transformed ast node of B.foo
          to speed up the conversion.
          """
          # Note: In Python2, it will raise OSError when inspect function
          # with decorator directly and function.__wrapped__ holds the actual function.
          func = unwrap(func)
          source_code = func_to_source_code(func)
  
          # TODO(liym27):
          #  Consider this case: source_code in self._code_to_ast_caches,
          #  but actually they are methods in different classes.
          #  Maybe use (__class__, source_code) as key
          if source_code in self._code_to_ast_caches:
              root_wrapper = self._code_to_ast_caches[source_code]
          else:
              root = gast.parse(source_code)
              root = attach_origin_info(root, func)
              root_wrapper = self._dygraph_to_static.get_static_ast(root)
              self._code_to_ast_caches[source_code] = root_wrapper
  
          # Get static function from AST
          static_func, file_name = ast_to_func(root_wrapper.node, func)
  
          create_and_update_origin_info_map(root_wrapper.node, static_func)
          return static_func
  
      def exist(self, func):
          return func in self._converted_static_func_caches
  
  
  _CACHE_LOCK = threading.Lock()
  _FUNCTION_CACHE = FunctionCache()
  
  
  def convert_to_static(function):
      """
      Transforms function of dygraph into static function using the cache mechanism.
  
      Args:
          function(callable): The function with dygraph layers that will be converted into static layers.
      """
      with _CACHE_LOCK:
          static_func = _FUNCTION_CACHE.convert_with_cache(function)
          return static_func
  
  
  class CacheKey(object):
      """
      Cached key for ProgramCache.
      """
      __slots__ = [
          'function_spec', 'input_args_with_spec', 'input_kwargs_with_spec',
          'class_instance', 'kwargs', '_spec_names_id'
      ]
  
      def __init__(self, function_spec, input_args_with_spec,
                   input_kwargs_with_spec, class_instance, **kwargs):
          """
          Initializes a cache key.
  
          Args:
              functions_spec(FunctionSpec): a FunctionSpec instance of decorated function.
              input_args_with_spec(list[InputSpec]): actual input args with some arguments replaced by InputSpec.
              input_kwargs_with_spec(list[{string:InputSpec}]): actual input kwargs with some arguments replaced by InputSpec.
              class_instance(object): a instance of class `Layer`.
              **kwargs(dict): manage other arguments used for better scalability
          """
          self.function_spec = function_spec
          self.input_args_with_spec = input_args_with_spec
          self.input_kwargs_with_spec = input_kwargs_with_spec
          self.class_instance = class_instance
          # NOTE: `kwargs` is usually not considered as basic member for `__hash__`
          self.kwargs = kwargs
          self._spec_names_id = _hash_spec_names(input_args_with_spec,
                                                 input_kwargs_with_spec)
  
      @classmethod
      def from_func_and_args(cls, function_spec, args, kwargs, class_instance):
          """
          Generated a CacheKey instance by given inputs.
  
          Args:
              functions_spec(FunctionSpec): a FunctionSpec instance of decorated function.
              args(tuple): tuple of actual inputs arguments.
              kwargs(dict): dict of actual inputs keyword arguments.
              class_instance(object): a instance of class `Layer`.
          """
          # 1. filter `self` in args
          if args and isinstance(args[0], layers.Layer):
              args = args[1:]
          # 2. convert tensor and numpy array into InputSpec
          _args, _kwargs = function_spec.unified_args_and_kwargs(args, kwargs)
          input_args_with_spec, input_kwargs_with_spec = function_spec.args_to_input_spec(
              _args, _kwargs)
  
          # 3. check whether hit the cache or build a new program for the input arguments
          return CacheKey(function_spec, input_args_with_spec,
                          input_kwargs_with_spec, class_instance)
  
      def __hash__(self):
          error_msg = "Arguments to a `@paddle.jit.to_static` must be a hashable Python objects (or nested structures of these types)."
          with_hook = self.kwargs.get("with_hook", False)
          is_train = self.kwargs.get("is_train", False)
          return hash(
              (id(self.function_spec),
               make_hashable(self.input_args_with_spec, error_msg),
               make_hashable(self.input_kwargs_with_spec, error_msg),
               self._spec_names_id, self.class_instance, with_hook, is_train))
  
      def __eq__(self, other):
          return (type(self) is type(other)) and hash(self) == hash(other)
  
      def __neq__(self, other):
          return not self == other
  
      def __repr__(self):
          return "id(function_spec): {}, input_args_with_spec: {}, input_kwargs_with_spec: {}, class_instance: {}".format(
              id(self.function_spec), self.input_args_with_spec,
              self.input_kwargs_with_spec, self.class_instance)
  
  
  def unwrap_decorators(func):
      """
      Unwraps a decorated function and returns the decorator list and inner target.
      """
      decorators = []
      cur = func
      while True:
          if isinstance(cur, StaticFunction):
              decorators.append(cur)
              # Note: if `cur` is a method, keep it as bound method of class.
              instance = cur._class_instance
              if instance is not None:
                  cur = cur.dygraph_function.__get__(instance)
              else:
                  cur = cur.dygraph_function
          else:
              break
      return decorators, cur
  
  
  class StaticFunction(object):
      """
      Wrapper class to Manage program conversion of decorated function.
  
      """
  
      def __init__(self, function, input_spec=None, **kwargs):
          """
          Initializes a `StaticFunction`.
  
          Args:
              function(callable): A function or method that will be converted into static program.
              input_spec(list[InputSpec]): list of InputSpec to specify the `shape/dtype/name` information for each input argument, default None.
              **kwargs(dict): other arguments like `build_strategy` et.al.
          """
          # save the instance `self` while decorating a method of class.
          if inspect.ismethod(function):
              self._dygraph_function = getattr(function, '__func__')
              self._class_instance = getattr(function, '__self__')
          else:
              self._dygraph_function = function
              self._class_instance = None
  
          self._input_spec = input_spec
          self._function_spec = FunctionSpec(function, input_spec)
          self._program_cache = ProgramCache()
          self._descriptor_cache = weakref.WeakKeyDictionary()
          # Note: Hold a reference to ProgramTranslator for switching `enable_to_static`.
          self._program_trans = ProgramTranslator()
          self._kwargs = kwargs
          self._training = True
  
      def train(self):
          if isinstance(self._class_instance,
                        layers.Layer) and self._class_instance.training == False:
              raise RuntimeError(
                  "Failed to switch train mode. {} is a Layer's method, "
                  "please use Layer.train() to switch train mode.".format(
                      self.dygraph_function))
          self._training = True
  
      def eval(self):
          if isinstance(self._class_instance,
                        layers.Layer) and self._class_instance.training == True:
              raise RuntimeError(
                  "Failed to switch eval mode. {} is a Layer's method, "
                  "please use Layer.eval() to switch eval mode.".format(
                      self.dygraph_function))
          self._training = False
  
      def __get__(self, instance, owner):
          """
          Overrides this method to parse the class instance and call bound method correctly.
  
          For example:
              
              '''
              class Net(Layer):
                  def __init__(self):
                      pass
                  
                  @paddle.jit.to_static
                  def forward(self, x, y):
                      return x + y
  
              net = Net()
              out = net(x, y)
              '''
          
          In above case, `net(x, y)` will call `net.forward(x, y)` firstly that is a bound method
          of `Net` instance. After decorated by `@paddle.jit.to_static`, it will firstly to call `__get__`
          to parse the class instance correctly instead of the `StaticFunction` instance.
          """
          if instance not in self._descriptor_cache:
              if instance is None:
                  return self
              # Note(Aurelius84): To construct new instance of StaticFunction when we
              # first encouter the bound function of layer and cache it.
              new_static_layer = self._clone()
              new_static_layer._class_instance = instance
              self._descriptor_cache[instance] = new_static_layer
  
          return self._descriptor_cache[instance]
  
      def _clone(self):
          return self.__class__(self._dygraph_function, self._input_spec)
  
      def __call__(self, *args, **kwargs):
          """
          Supports to call the returned instance with input `args` and `kwargs` directly.
  
          Args:
              *args(tuple): tuple of all input arguments from original decorated function.
              **kwargs(dict): dict of all input keyward arguments from original decorated function. 
  
          Return:
              Outputs of decorated function.
          """
  
          # 1. call dygraph function directly if not enable `declarative`
          if not self._program_trans.enable_to_static:
              # NOTE(liym27):
              # Here calls `warnings.warn` but not `logging_utils.warn` because by default warnings.warn(message)
              # will show up **only once**. StaticFunction.__call__ will run many times, it is appropriate to
              # display this warning message only once.
              logging_utils.warn(
                  "The decorator '@paddle.jit.to_static' does NOT work when setting ProgramTranslator.enable to False. "
                  "We will just return dygraph output. If you would like to get static graph output, please call API "
                  "ProgramTranslator.enable(True)")
              return self._call_dygraph_function(*args, **kwargs)
  
          if not _non_static_mode():
              raise RuntimeError(
                  "Failed to run the callable object {} decorated by '@paddle.jit.to_static', "
                  "because it is NOT in dynamic mode. Please disable the static mode to enter dynamic mode with the "
                  "following API: paddle.disable_static().".format(
                      self.dygraph_function))
  
          # 2. trace ops from dygraph layers and cache the generated program.
          args, kwargs = self._function_spec.unified_args_and_kwargs(args, kwargs)
  
          try:
              concrete_program, partial_program_layer = self.get_concrete_program(
                  *args, **kwargs, is_train=self._is_train_mode())
  
              # 3. synchronize self.training attribute.
              if isinstance(self._class_instance, layers.Layer):
                  partial_program_layer.training = self._class_instance.training
              else:
                  partial_program_layer.training = self._training
  
              # 4. return outputs.
              try:
                  return partial_program_layer(args)
              except Exception as e:
                  if not hasattr(e, error.ERROR_DATA):
                      # runtime error
                      error.attach_error_data(e, in_runtime=True)
                      raise
          except Exception as e:
              error_data = getattr(e, error.ERROR_DATA, None)
              if error_data:
                  error_data.raise_new_exception()
              else:
                  logging_utils.warn(
                      "Please file an issue at 'https://github.com/PaddlePaddle/Paddle/issues'"
                      " if you can't handle this {} yourself.".format(type(e)))
                  raise e
  
      def _is_train_mode(self):
          if self._class_instance is not None:
              return self._class_instance.training
          else:
              return self._training
  
      def _call_dygraph_function(self, *args, **kwargs):
          """
          Calls dygraph function directly and returns the outputs.
  
          Args:
              *args(tuple): tuple of all input arguments from original decorated function.
              **kwargs(dict): dict of all input keyward arguments from original decorated function. 
  
          Return:
              Outputs of dygraph function.
          """
          if self._class_instance is not None:
              dygraph_function = self._dygraph_function.__get__(
                  self._class_instance)
          else:
              dygraph_function = self._dygraph_function
  
          return dygraph_function(*args, **kwargs)
  
      def get_concrete_program(self, *args, **kwargs):
          """
          Returns traced concrete program and inner executable partial layer.
  
          Args:
              *args(tuple): input arguments values or InputSpec
              **kwargs(dict) : input kwargs values.
  
          Returns:
              Traced ConcreteProgram and executable translated Layer.
          """
  
          with_hook = kwargs.get("with_hook", False)
          is_train = kwargs.get("is_train", True)
          if "is_train" in kwargs: kwargs.pop("is_train")
          if "with_hook" in kwargs: kwargs.pop("with_hook")
          # 1. unify args/kwargs and replace Tensor with InputSpec
          if len(args) != len(self._function_spec.args_name):
              args, kwargs = self._function_spec.unified_args_and_kwargs(args,
                                                                         kwargs)
          input_args_with_spec, input_kwargs_with_spec = self._function_spec.args_to_input_spec(
              args, kwargs)
  
          # 2. generate cache key
          cache_key = CacheKey(
              self._function_spec,
              input_args_with_spec,
              input_kwargs_with_spec,
              self._class_instance,
              **self._kwargs,
              with_hook=with_hook,
              is_train=is_train)
  
          # 3. check whether hit the cache or build a new program for the input arguments
          concrete_program, partial_program_layer = self._program_cache[cache_key]
          return concrete_program, partial_program_layer
  
      def get_traced_count(self):
          """
          Returns the number of traced programs for the decorated function.
          """
          return len(self._program_cache)
  
      @property
      def code(self):
          """
          Returns the source code of transformed static function for debugging.
          """
          static_func = convert_to_static(self._dygraph_function)
          source_code = func_to_source_code(static_func)
          return source_code
  
      @property
      def dygraph_function(self):
          """
          Returns the original decorated function.
          """
          return self._dygraph_function
  
      @property
      def concrete_program(self):
          """
          Returns recent ConcreteProgram instance of decorated function.
  
          Examples:
              .. code-block:: python
  
                  import paddle
                  from paddle.jit import to_static
                  from paddle.static import InputSpec
  
                  paddle.disable_static()
  
                  def foo(x, y):
                      z = x + y
                      return z
                  
                  # usage 1:
                  decorated_foo = to_static(foo, input_spec=[InputSpec([10], name='x'), InputSpec([10], name='y')])
                  print(decorated_foo.concrete_program)
  
                  # usage 2:
                  decorated_foo = to_static(foo)
                  out_foo = decorated_foo(paddle.rand([10]), paddle.rand([10]))
                  print(decorated_foo.concrete_program)
          """
          return self.concrete_program_specify_input_spec(input_spec=None)
  
      def concrete_program_specify_input_spec(self,
                                              input_spec=None,
                                              with_hook=False):
          """
          Returns recent ConcreteProgram instance of decorated function while
          specifying input_spec. If the self._function_spec already has
          input_spec, it will check the compatibility of input input_spec and
          the self._function_spec.input_spec. If input input_spec=None, then
          this method uses self._function_spec.input_spec
  
          args:
              input_spec (list[InputSpec], optional): Describes the input of
                  the translate function.
          """
          # if specific the `input_spec`, the length of program_cache will always 1,
          # else, return the last one.
          cached_program_len = len(self._program_cache)
          # If specific `input_spec`, apply convertion from dygraph layers into static Program.
          if cached_program_len == 0:
              desired_input_spec = input_spec
              if self._function_spec.input_spec is not None:
                  if input_spec is not None and not input_specs_compatible(
                          flatten(input_spec),
                          flatten(self._function_spec.input_spec)):
                      raise ValueError(
                          "The `input_spec`: {} used to construct concrete_program is conflict with the `input_spec`: {} in `@paddle.jit.to_static`".
                          format(input_spec, self._function_spec.input_spec))
                  # NOTE(chenweihang): we should always translated program based on the `input_spec`
                  # decorated on forward if it is valid
                  desired_input_spec = self._function_spec.input_spec
                  if input_spec is not None:
                      logging_utils.warn(
                          "\n\nYou have specified `input_spec` both in function definition (higher priority) and `paddle.jit.save` (will be ignored.)\n\n\t Using: {}\n\n\t Ignore: {}\n".
                          format(desired_input_spec, input_spec))
  
              has_input_spec = (desired_input_spec is not None)
              if has_input_spec:
                  concrete_program, _ = self.get_concrete_program(
                      *desired_input_spec,
                      with_hook=with_hook,
                      is_train=self._is_train_mode())
                  return concrete_program
              else:
                  raise ValueError(
                      "No valid transformed program for {}.\n\t    Please specific `input_spec` in `@paddle.jit.to_static` or feed input tensor to call the decorated function at once.\n".
                      format(self._function_spec))
          elif with_hook:
              cache_key = self._program_cache._recent_cache_key
              cache_key.kwargs["with_hook"] = True
              concrete_program, _ = self._program_cache[cache_key]
              return concrete_program
  
          # If more than one programs have been cached, return the recent converted program by default.
          elif cached_program_len > 1:
              logging_utils.warn(
                  "Current {} has more than one cached programs: {}, the last traced progam will be return by default.".
                  format(self._function_spec, cached_program_len))
  
          cache_key, (concrete_program,
                      partial_layer) = self._program_cache.last()
          return concrete_program
  
      @property
      def inputs(self):
          """
          Returns input tensors of recent converted static program.
          """
          concrete_program = self.concrete_program
          inputs = [
              var for var in flatten(concrete_program.inputs)
              if isinstance(var, framework.Variable)
          ]
          return inputs
  
      @property
      def outputs(self):
          """
          Returns output tensors of recent converted static program.
          """
          concrete_program = self.concrete_program
          outputs = [
              var for var in flatten(concrete_program.outputs)
              if isinstance(var, framework.Variable)
          ]
  
          return outputs
  
      @property
      def main_program(self):
          """
          Returns recent converted static main program.
          """
          concrete_program = self.concrete_program
          main_program = concrete_program.main_program
          return main_program
  
      @property
      def program_cache(self):
          return self._program_cache
  
      @property
      def function_spec(self):
          return self._function_spec
  
  
  def _verify_init_in_dynamic_mode(class_instance):
      """
      Verifies the instance is initialized in dynamic mode.
      """
      if isinstance(class_instance, layers.Layer):
          if not class_instance._init_in_dynamic_mode:
              raise RuntimeError(
                  " `paddle.jit.to_static` is only available in dynamic mode. Please call `paddle.disable_static()` before "
                  "initializing your Layer class `{}` . Because parameters of Layer class should be initialized firstly "
                  "in dynamic mode while applying transformation.".format(
                      class_instance))
  
  
  class HookHelper(object):
      """
      Only For converting pre/post hooks operation in outermost layer while jit.save.
      Because hooks in sublayer have been processed automatically.
      """
  
      def __init__(self, func, class_instance, with_hook=False):
          self.func = func
          self.class_instance = class_instance
          self.with_hook = with_hook
          self.need_apply_hook = with_hook and isinstance(
              self.class_instance,
              layers.Layer) and getattr(func, "__name__") == "forward"
  
      def apply_pre_hooks(self, inputs):
          """
          Apply _forward_pre_hooks from outermost layer
          """
          if not self.need_apply_hook: return inputs
  
          inputs = inputs[1:]
          for forward_pre_hook in self.class_instance._forward_pre_hooks.values():
              hook_result = forward_pre_hook(self.class_instance, inputs)
              if hook_result is not None:
                  if not isinstance(hook_result, tuple):
                      hook_result = (hook_result, )
                  inputs = hook_result
  
          return [self.class_instance] + list(inputs)
  
      def apply_post_hooks(self, inputs, outputs):
          """
          Apply _forward_post_hooks from outermost layer
          """
          if not self.need_apply_hook: return outputs
  
          inputs = inputs[1:]
          for forward_post_hook in self.class_instance._forward_post_hooks.values(
          ):
              hook_result = forward_post_hook(self.class_instance, inputs,
                                              outputs)
              if hook_result is not None:
                  outputs = hook_result
  
          inputs.insert(0, self.class_instance)
          return outputs
  
  
  class ConcreteProgram(object):
  
      __slots__ = [
          'inputs', 'outputs', 'main_program', "startup_program", "parameters",
          "function", 'kwargs'
      ]
  
      def __init__(self,
                   inputs,
                   outputs,
                   parameters,
                   function,
                   main_program,
                   startup_program=None,
                   **kwargs):
          self.inputs = inputs
          self.outputs = outputs
          self.main_program = main_program
          self.startup_program = startup_program
          self.parameters = parameters
          self.function = function
          self.kwargs = kwargs
  
      @staticmethod
      @switch_to_static_graph
      def from_func_spec(func_spec, input_spec, input_kwargs_spec, class_instance,
                         **kwargs):
          """
          Builds the main_program with specialized inputs and returns outputs
          of program as fetch_list.
  
          Args:
              func_spec(FunctionSpec): A FunctionSpec instance for decorated function.
              input_spec(list[InputSpec]): 
          """
          # verify the instance is initialized in imperative mode.
          _verify_init_in_dynamic_mode(class_instance)
  
          # Transforms dygraph function into static function and caches it.
          dygraph_function = func_spec.dygraph_function
          static_func = convert_to_static(dygraph_function)
          # apply pre\post hook for outermost layer
          hook_helper = HookHelper(dygraph_function, class_instance,
                                   kwargs.get("with_hook", False))
  
          main_program, startup_program = framework.Program(), framework.Program()
          # Note: The random seed should be synchronized into cached program
          # if set in `fluid.dygraph_guard` because some ops rely on it, such as
          # `fluid.layers.dropout`.
          main_program.random_seed = framework.default_main_program().random_seed
          startup_program.random_seed = framework.default_startup_program(
          ).random_seed
  
          from paddle.fluid.dygraph.base import _switch_declarative_mode_guard_
          with framework.program_guard(main_program, startup_program):
              with _switch_declarative_mode_guard_(is_declarative=True):
                  # 1. Adds `fluid.data` layers for input if needed
                  static_inputs = func_spec.to_static_inputs_with_spec(
                      input_spec, main_program)
                  _kwargs = func_spec.to_static_inputs_with_spec(
                      input_kwargs_spec, main_program)
                  if class_instance:
                      static_inputs = tuple([class_instance] + list(
                          static_inputs))
  
                  # 2. Gets all ParamBases and buffered VarBases in the function
                  all_parameters_and_buffers = _extract_indeed_params_buffers(
                      class_instance)
  
                  # 3. Builds program only once and returns the output Variables.
                  with param_guard(get_parameters(
                          class_instance, False)), param_guard(
                              get_buffers(class_instance, False)):
                      try:
                          # only for jit.save, do nothing while train and eval process
                          inputs = hook_helper.apply_pre_hooks(static_inputs)
                          if _kwargs:
                              outputs = static_func(*inputs, **_kwargs)
                          else:
                              outputs = static_func(*inputs)
                          outputs = hook_helper.apply_post_hooks(inputs, outputs)
                      except BaseException as e:
                          # NOTE: If e is raised in compile time, e should be attached to ERROR_DATA here.
                          error.attach_error_data(e)
                          error_data = getattr(e, error.ERROR_DATA, None)
                          if error_data:
                              error_data.raise_new_exception()
                          raise
  
                  if outputs is not None:
                      need_wrap_into_list = not isinstance(outputs, (
                          tuple, list)) or len(outputs) == 1
                      if need_wrap_into_list:
                          outputs = [outputs]
  
          main_program = update_op_callstack_with_origin_info(main_program)
  
          return ConcreteProgram(
              inputs=static_inputs,
              outputs=outputs,
              parameters=all_parameters_and_buffers,
              function=dygraph_function,
              main_program=main_program,
              startup_program=startup_program,
              **kwargs)
  
  
  def _extract_indeed_params_buffers(class_instance):
      """
      To filter not initialzed buffers.
      """
      params = list(get_parameters(class_instance).values())
      buffers = list(get_buffers(class_instance).values())
      buffers = [buffer for buffer in buffers if len(buffer.shape) != 0]
  
      return params + buffers
  
  
  class ProgramCache(object):
      """
      Wrapper class for the program functions defined by dygraph function.
      """
  
      def __init__(self):
          # {hash_id : (concrete_program, partial_layer)}
          self._caches = collections.OrderedDict()
          # trace mostly recent used program 
          self._recent_key = None
          self._recent_cache_key = None
  
      def _build_once(self, cache_key):
          concrete_program = ConcreteProgram.from_func_spec(
              func_spec=cache_key.function_spec,
              input_spec=cache_key.input_args_with_spec,
              input_kwargs_spec=cache_key.input_kwargs_with_spec,
              class_instance=cache_key.class_instance,
              **cache_key.kwargs)
          return concrete_program, partial_program_from(concrete_program)
  
      def __getitem__(self, item):
          if not isinstance(item, CacheKey):
              raise ValueError('type(item) should be CacheKey, but received %s' %
                               type_name(item))
          item_id = hash(item)
          self._recent_cache_key = item
          self._recent_key = item_id
          if item_id not in self._caches:
              self._caches[item_id] = self._build_once(item)
              # Note: raise warnings if number of traced program is more than `max_tracing_count`
              current_tracing_count = len(self._caches)
              if current_tracing_count > MAX_TRACED_PROGRAM_COUNT:
                  logging_utils.warn(
                      "Current traced program number: {} > `max_tracing_count`:{}. Too much cached programs will bring expensive overhead. "
                      "The reason may be: (1) passing tensors with different shapes, (2) passing python objects instead of tensors.".
                      format(current_tracing_count, MAX_TRACED_PROGRAM_COUNT))
  
          return self._caches[item_id]
  
      def get_program(self, item):
          if not isinstance(item, CacheKey):
              raise ValueError(
                  "Input item's type should be FunctionSpec, but received %s" %
                  type_name(item))
          item_id = hash(item)
          if item_id not in self._caches:
              raise RuntimeError(
                  "Failed to find program for input item, please decorate input function by `@paddle.jit.to_static`."
              )
          return self._caches[item_id]
  
      def last(self):
          assert len(
              self._caches) >= 1, "No valid cached program in ProgramCache."
          assert self._recent_key is not None
          return self._recent_key, self._caches[self._recent_key]
  
      def __len__(self):
          return len(self._caches)
  
      def concrete_programs(self):
          return [cp for key, (cp, _) in six.iteritems(self._caches)]
  
  
  def synchronized(func):
      func.__lock__ = threading.Lock()
  
      def lock_func(*args, **kwargs):
          with func.__lock__:
              return func(*args, **kwargs)
  
      return lock_func
  
  
  class ProgramTranslator(object):
      """
      Class to translate dygraph function into static graph function. The object
      of this class is a singleton.
  
      Args:
          None.
  
      Returns:
          ProgramTranslator: the singleton object.
  
      Examples:
          .. code-block:: python
  
              import paddle
  
              # Two methods get same object because ProgramTranslator is a singleton
              paddle.jit.ProgramTranslator()
              paddle.jit.ProgramTranslator.get_instance()
  
      """
  
      _singleton_lock = threading.Lock()
      _instance = None
  
      @synchronized
      def __new__(cls, *args, **kwargs):
          if cls._instance is None:
              cls._instance = object.__new__(cls, *args, **kwargs)
              cls._instance._initialized = False
          return cls._instance
  
      @classmethod
      def get_instance(cls):
          if cls._instance is None:
              with cls._singleton_lock:
                  cls._instance = cls()
          return cls._instance
  
      @classmethod
      def reset(cls):
          if cls._instance is not None:
              cls._instance._initialized = False
              cls._instance.__init__()
  
      def __init__(self):
          # To make sure that calls __init__ only once.
          if self._initialized:
              return
          self._initialized = True
          self._program_cache = ProgramCache()
          self.enable_to_static = True
  
      def enable(self, enable_to_static):
          """
          Enable or disable the converting from imperative to static graph by
          ProgramTranslator globally.
  
          Args:
              enable_to_static (bool): True or False to enable or disable converting to static.
  
          Returns:
              None.
  
          Examples:
              .. code-block:: python
  
                  import paddle
  
  
                  @paddle.jit.to_static
                  def func(x):
                      if paddle.mean(x) > 0:
                          x_v = x - 1
                      else:
                          x_v = x + 1
                      return x_v
  
  
                  prog_trans = paddle.jit.ProgramTranslator()
                  prog_trans.enable(False)
  
                  x = paddle.ones([1, 2])
                  # ProgramTranslator is disabled so the func is run in dygraph
                  print(func(x))  # [[0. 0.]]
  
          """
          check_type(enable_to_static, "enable_to_static", bool,
                     "ProgramTranslator.enable")
          self.enable_to_static = enable_to_static
  
      def get_output(self, dygraph_func, *args, **kwargs):
          """
          Returns the output dygraph Tensor for dygraph function. The dygraph
          function will be translated into static graph function so the under
          beneath numerical result will be calculated by static graph mode.
  
          Args:
              dygraph_func (callable): the dygraph function.
              *args (tuple): the input argument of dygraph_func.
              **kwargs (dict): the input argument of dygraph_func.
  
          Returns:
              Tensor or tuple of Tensors: the dygraph Tensor containing digital result.
  
          Examples:
              .. code-block:: python
  
                  import paddle
  
  
                  def func(x):
                      if paddle.mean(x) > 0:
                          x_v = x - 1
                      else:
                          x_v = x + 1
                      return x_v
  
  
                  prog_trans = paddle.jit.ProgramTranslator()
  
                  x = paddle.ones([1, 2])
                  x_v = prog_trans.get_output(func, x)
                  print(x_v)  # [[0. 0.]]
  
          """
          assert callable(
              dygraph_func
          ), "Input dygraph_func is not a callable in ProgramTranslator.get_output"
  
          if not self.enable_to_static:
              # Here calls `warnings.warn` but not `logging_utils.warn` because by default warnings.warn(message)
              # will show up **only once**.
              logging_utils.warn(
                  "The ProgramTranslator.get_output doesn't work when setting ProgramTranslator.enable to False. "
                  "We will just return dygraph output. "
                  "Please call ProgramTranslator.enable(True) if you would like to get static output."
              )
              return dygraph_func(*args, **kwargs)
          try:
              function_spec = FunctionSpec(dygraph_func)
              cache_key = CacheKey.from_func_and_args(function_spec, args, kwargs,
                                                      getattr(dygraph_func,
                                                              '__self__', None))
              _, partial_program_layer = self._program_cache[cache_key]
  
              if args and isinstance(args[0], layers.Layer):
                  # Synchronize self.training attribute.
                  partial_program_layer.training = args[0].training
                  args = args[1:]
              try:
                  return partial_program_layer(args)
              except BaseException as e:
                  # NOTE:
                  # 1. If e is raised in compile time, e should have been attached to ERROR_DATA before;
                  # 2. If e raised in runtime, e should be attached to ERROR_DATA here.
                  if not hasattr(e, error.ERROR_DATA):
                      # runtime error
                      error.attach_error_data(e, in_runtime=True)
                  raise
          except BaseException as e:
              error_data = getattr(e, error.ERROR_DATA, None)
              if error_data:
                  error_data.raise_new_exception()
              else:
                  logging_utils.warn(
                      "Please file an issue at 'https://github.com/PaddlePaddle/Paddle/issues'"
                      " if you can't handle this {} yourself.".format(type(e)))
                  raise e
  
      def get_func(self, dygraph_func):
          """
          Returns a callable function which converts imperative dygraph APIs of
          the input dygraph_func into declarative net-building APIs, which means
          it doesn't return immediate digital result as get_output does.
          Users should handle Program and Executor by themselves.
  
          Args:
              dygraph_func (callable): the dygraph function.
  
          Returns:
              callable: converting imperative dygraph APIs into declarative
              net-building APIs.
  
          Examples:
              .. code-block:: python
  
                  import paddle
  
  
                  def func(x):
                      if paddle.mean(x) > 0:
                          x_v = x - 1
                      else:
                          x_v = x + 1
                      return x_v
  
  
                  prog_trans = paddle.jit.ProgramTranslator()
                  static_func = prog_trans.get_func(func)
                  print(callable(static_func)) # True
  
          """
          assert callable(
              dygraph_func
          ), "Input dygraph_func is not a callable in ProgramTranslator.get_func"
  
          if not self.enable_to_static:
              logging_utils.warn(
                  "The ProgramTranslator.get_func doesn't work when setting ProgramTranslator.enable to False. We will "
                  "just return dygraph output. Please call ProgramTranslator.enable(True) if you would like to get static output."
              )
              return dygraph_func
  
          static_func = convert_to_static(dygraph_func)
          return static_func
  
      def get_program(self, dygraph_func, *args, **kwargs):
          """
          Returns the translated static program and input/output Tensors from
          dygraph function. The users can use the program to run by executor.
  
          Args:
              dygraph_func (callable): the dygraph function.
              *args (tuple): the input argument of dygraph_func.
              **kwargs (dict): the input argument of dygraph_func.
  
          Returns:
              tuple of (main_program, startup_program, inputs, outputs) whose
              types are (Program, Program, list of Tensors, list of Tensors).
              main_program: the converted main program.
              startup_program: the converted startup program.
              inputs: list of input Tensors which need to be fed.
              outputs: list of output Tensors which users can fetch.
  
          Examples:
              .. code-block:: python
  
                  import paddle
  
  
                  def func(x):
                      if paddle.mean(x) > 0:
                          x_v = x - 1
                      else:
                          x_v = x + 1
                      return x_v
  
  
                  prog_trans = paddle.jit.ProgramTranslator()
                  x = paddle.ones([1, 2])
                  main_prog, start_prog, inputs, outputs = prog_trans.get_program(func, x)
                  print([i.name for i in inputs])
                  # [u'generated_tensor_0'] the feed input Tensor name representing x
                  print([o.name for o in outputs])
                  # [u'_generated_var_4'] the fetch output Tensor name representing x_v        
  
          """
          assert callable(
              dygraph_func
          ), "Input dygraph_func is not a callable in ProgramTranslator.get_program"
  
          if not self.enable_to_static:
              logging_utils.warn(
                  "The ProgramTranslator.get_program doesn't work when setting ProgramTranslator.enable to False."
                  "We will just return dygraph output. "
                  "Please call ProgramTranslator.enable(True) if you would like to get static output."
              )
              return dygraph_func(*args, **kwargs)
  
          function_spec = FunctionSpec(dygraph_func)
          cache_key = CacheKey.from_func_and_args(function_spec, args, kwargs,
                                                  getattr(dygraph_func,
                                                          '__self__', None))
          concrete_program, partial_program_layer = self._program_cache[cache_key]
  
          # Note: concrete_program hold all input/output infos include non-Variable
          input_vars = [
              var for var in concrete_program.inputs
              if isinstance(var, framework.Variable)
          ]
          output_vars = [
              var for var in concrete_program.outputs
              if isinstance(var, framework.Variable)
          ]
  
          return concrete_program.main_program, \
                 concrete_program.startup_program, \
                 input_vars, \
                 output_vars
  
      def get_code(self, dygraph_func):
          """
          Returns the translated static function string code from dygraph function.
  
          Args:
              dygraph_func (callable): the dygraph function.
  
          Returns:
              str: the string code of translated static function.
  
          Examples:
              .. code-block:: python
  
                  import paddle
  
  
                  def func(x):
                      if paddle.mean(x) > 0:
                          x_v = x - 1
                      else:
                          x_v = x + 1
                      return x_v
  
  
                  prog_trans = paddle.jit.ProgramTranslator()
  
                  code = prog_trans.get_code(func)
                  print(type(code)) # <class 'str'>
  
          """
          assert callable(
              dygraph_func
          ), "Input dygraph_func is not a callable in ProgramTranslator.get_code"
          # Gets AST from dygraph function
  
          unwrap_func = unwrap(dygraph_func)
          raw_code = inspect.getsource(unwrap_func)
          code = textwrap.dedent(raw_code)
          root = gast.parse(code)
  
          # Transform AST
          dygraph_to_static = DygraphToStaticAst()
          root_wrapper = dygraph_to_static.get_static_ast(root)
  
          # Get source_code
          source_code = ast_to_source_code(root_wrapper.node)
          return source_code
  
      def get_program_cache(self):
          """
          Returns the ProgramCache instance. This method is used by PaddlePaddle
          developers to manage program cache in ProgramTranslator. Normal users
          don't have to call this method.
  
          Returns:
              ProgramCache: ProgramCache instance of ProgramTranslator.
  
          Examples:
              .. code-block:: python
  
                  import paddle
  
                  prog_trans = paddle.jit.ProgramTranslator()
                  prog_cache = prog_trans.get_program_cache()
  
          """
          return self._program_cache
  