# 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 os
import collections
import pickle
import warnings
import sys
import numpy as np
import copyreg
import paddle

# deprecated module import
from paddle import fluid
from paddle.fluid import core
from paddle.fluid.io import _unpack_saved_dict, _pack_loaded_dict, _pickle_loads_mac
from paddle.fluid.io import _legacy_save as _legacy_static_save
from paddle.fluid.io import _open_file_buffer, _is_file_path, _is_memory_buffer

from paddle.fluid.framework import Variable, _varbase_creator, _dygraph_tracer, _non_static_mode, ParamBase, EagerParamBase, _current_expected_place, Program
from paddle.fluid.dygraph.jit import _SaveLoadConfig
from paddle.fluid.dygraph.io import _construct_program_holders, _construct_params_and_buffers
from paddle.fluid.dygraph.io import INFER_MODEL_SUFFIX, INFER_PARAMS_SUFFIX, INFER_PARAMS_INFO_SUFFIX

__all__ = []


def _build_saved_state_dict(state_dict):
 save_dict = {}
 name_table = {}
 for key, value in state_dict.items():
 if isinstance(value, (Variable, core.VarBase, core.eager.Tensor)):
 if value.type == core.VarDesc.VarType.VOCAB:
 save_dict[key] = value.value().get_map_tensor()
 else:
 if not value.value().get_tensor()._is_initialized():
 raise ValueError(
 "The saved tensor is not initialized. If you used group sharded, please use save_group_sharded_model."
 )
 save_dict[key] = value.numpy()
 name_table[key] = value.name
 else:
 save_dict[key] = value
 save_dict["StructuredToParameterName@@"] = name_table

 return save_dict


def _load_state_dict_from_save_inference_model(model_path, config):
 # 1. load program desc & construct _ProgramHolder
 programs = _construct_program_holders(model_path, config.model_filename)

 # 2. load layer parameters & buffers
 with fluid.dygraph.guard():
 persistable_var_dict = _construct_params_and_buffers(
 model_path, programs, config.params_filename, append_suffix=False)

 # 3. construct state_dict
 load_param_dict = dict()
 for var_name in persistable_var_dict:
 load_param_dict[var_name] = persistable_var_dict[var_name].numpy()

 # if *.info exists, we can recover structured_name
 var_info_filename = str(config.params_filename) + ".info"
 var_info_path = os.path.join(model_path, var_info_filename)
 if os.path.exists(var_info_path):
 with open(var_info_path, 'rb') as f:
 extra_var_info = pickle.load(f)
 structured_para_dict = dict()
 for var_name in load_param_dict:
 structured_name = extra_var_info[var_name].get(
 'structured_name', None)
 assert structured_name is not None, "Cannot find saved variable (%s)'s structured name in saved model." % var_name
 structured_para_dict[structured_name] = load_param_dict[
 var_name]
 load_param_dict = structured_para_dict

 return load_param_dict


def _load_state_dict_from_save_params(model_path):
 # Try to load all the files in the directory in VarBase format, 
 # the file name is used as the name of VarBase
 load_var_list = []

 # 1. load file names
 var_name_list = []
 for root, _, files in os.walk(model_path):
 for filename in files:
 file_path = os.path.join(root, filename)
 tmp_var_name = os.path.relpath(file_path, model_path)
 var_name = tmp_var_name.replace("\\", "/")
 var_name_list.append(var_name)

 # 2. create and load VarBase
 with fluid.dygraph.guard():
 for name in var_name_list:
 new_var = _varbase_creator(name=name, persistable=True)
 _dygraph_tracer().trace_op(
 type='load',
 inputs={},
 outputs={'Out': new_var},
 attrs={'file_path': os.path.join(model_path, name)})
 load_var_list.append(new_var)

 # 3. construct state_dict
 load_param_dict = dict()
 for var in load_var_list:
 load_param_dict[var.name] = var.numpy()

 return load_param_dict


# NOTE(chenweihang): [ Handling of use cases of API paddle.load ]
# `paddle.load` may be used to load saved results of:
# 1. Expected cases:
# - need [full filename] when loading
# - paddle.save
# - paddle.static.save
# - paddle.fluid.save_dygraph
# - need [prefix] when loading [compatible for paddle 2.x]
# - paddle.jit.save
# - paddle.static.save_inference_model
# - need [directory] when loading [compatible for paddle 1.x]
# - paddle.fluid.io.save_inference_model
# - paddle.fluid.io.save_params/save_persistable
# 2. Error cases:
# - no error case
def _build_load_path_and_config(path, config):
 # NOTE(chenweihang): If both [prefix save format] and [directory save format] exist,
 # raise error, avoid confusing behavior
 prefix_format_path = path + INFER_MODEL_SUFFIX
 prefix_format_exist = os.path.exists(prefix_format_path)
 directory_format_exist = os.path.isdir(path)
 if prefix_format_exist and directory_format_exist:
 raise ValueError(
 "The %s.pdmodel and %s directory exist at the same time, "
 "don't know which one to load, please make sure that the specified target "
 "of ``path`` is unique." % (path, path))
 elif not prefix_format_exist and not directory_format_exist:
 error_msg = "The ``path`` (%s) to load model not exists."
 # if current path is a prefix, and the path.pdparams or path.pdopt
 # is exist, users may want use `paddle.load` load the result of 
 # `fluid.save_dygraph`, we raise error here for users
 params_file_path = path + ".pdparams"
 opti_file_path = path + ".pdopt"
 if os.path.exists(params_file_path) or os.path.exists(opti_file_path):
 error_msg += " If you want to load the results saved by `fluid.save_dygraph`, " \
 "please specify the full file name, not just the file name prefix. For " \
 "example, it should be written as `paddle.load('model.pdparams')` instead of " \
 "`paddle.load('model')`."
 raise ValueError(error_msg % path)
 else:
 if prefix_format_exist:
 file_prefix = os.path.basename(path)
 model_path = os.path.dirname(path)
 if config.model_filename is not None:
 warnings.warn(
 "When loading the result saved with the "
 "specified file prefix, the ``model_filename`` config does "
 "not take effect.")
 config.model_filename = file_prefix + INFER_MODEL_SUFFIX
 if config.params_filename is not None:
 warnings.warn(
 "When loading the result saved with the "
 "specified file prefix, the ``params_filename`` config does "
 "not take effect.")
 config.params_filename = file_prefix + INFER_PARAMS_SUFFIX
 else:
 # Compatible with the old save_inference_model format
 model_path = path

 return model_path, config


def _parse_load_config(configs):
 supported_configs = [
 'model_filename', 'params_filename', 'keep_name_table', 'return_numpy'
 ]

 # input check
 for key in configs:
 if key not in supported_configs:
 raise ValueError(
 "The additional config (%s) of `paddle.load` is not supported."
 % key)

 # construct inner config
 inner_config = _SaveLoadConfig()
 inner_config.model_filename = configs.get('model_filename', None)
 inner_config.params_filename = configs.get('params_filename', None)
 inner_config.keep_name_table = configs.get('keep_name_table', None)
 inner_config.return_numpy = configs.get('return_numpy', False)

 return inner_config


def _parse_save_config(configs):
 supported_configs = ['use_binary_format', 'pickle_protocol']

 # input check
 for key in configs:
 if key not in supported_configs:
 raise ValueError(
 "The additional config (%s) of `paddle.save` is not supported."
 % key)

 # construct inner config
 inner_config = _SaveLoadConfig()
 inner_config.use_binary_format = configs.get('use_binary_format', False)
 inner_config.pickle_protocol = configs.get('pickle_protocol', None)

 return inner_config


def _pickle_save(obj, f, protocol):
 # TODO(weixin):add support for BytesIO.
 if not isinstance(protocol, int):
 raise ValueError("The 'protocol' MUST be `int`, but received {}".format(
 type(protocol)))

 if protocol < 2 or protocol > 4:
 raise ValueError("Expected 1<'protocol'<5, but received protocol={}".
 format(protocol))

 def reduce_varbase(self):
 data = self.numpy()
 name = self.name

 return (tuple, ((name, data), ))

 def reduce_LoDTensor(self):
 data = np.array(self)

 return (eval, ('data', {'data': data}))

 def reduce_Layer(self):
 raise ValueError(
 "paddle do not support saving `paddle.nn.Layer` object.")

 dispatch_table_layer = dict()

 def create_layer_dispatch_table(layer):
 dispatch_table_layer[layer.__class__] = reduce_Layer
 return layer

 _parse_every_object(obj, lambda v: isinstance(v, fluid.Layer),
 create_layer_dispatch_table)

 def add_dispatch_table():
 # This is not a good method, because the pickle module has been modified.
 pickle.dispatch_table[core.VarBase] = reduce_varbase
 pickle.dispatch_table[ParamBase] = reduce_varbase
 pickle.dispatch_table[core.eager.Tensor] = reduce_varbase
 pickle.dispatch_table[EagerParamBase] = reduce_varbase
 pickle.dispatch_table[core.LoDTensor] = reduce_LoDTensor
 pickle.dispatch_table.update(dispatch_table_layer)

 def pop_dispatch_table():
 pickle.dispatch_table.pop(core.VarBase)
 pickle.dispatch_table.pop(core.LoDTensor)
 pickle.dispatch_table.pop(ParamBase)
 pickle.dispatch_table.pop(core.eager.Tensor)
 pickle.dispatch_table.pop(EagerParamBase)
 for k in dispatch_table_layer:
 pickle.dispatch_table.pop(k)

 # When value of dict is lager than 4GB ,there is a Bug on 'MAC python3'
 if sys.platform == 'darwin' and sys.version_info.major == 3:
 add_dispatch_table()
 pickle_bytes = pickle.dumps(obj)
 pop_dispatch_table()

 max_bytes = 2**30
 for i in range(0, len(pickle_bytes), max_bytes):
 f.write(pickle_bytes[i:i + max_bytes])
 else:
 pickler = pickle.Pickler(f, protocol)
 pickler.dispatch_table = copyreg.dispatch_table.copy()

 pickler.dispatch_table[core.VarBase] = reduce_varbase
 pickler.dispatch_table[core.LoDTensor] = reduce_LoDTensor
 pickler.dispatch_table[ParamBase] = reduce_varbase
 pickler.dispatch_table[core.eager.Tensor] = reduce_varbase
 pickler.dispatch_table[EagerParamBase] = reduce_varbase
 pickler.dispatch_table.update(dispatch_table_layer)
 pickler.dump(obj)


def _contain_x(obj, condition_func):
 if isinstance(obj, core.SelectedRows):
 raise NotImplementedError(
 "`paddle.save` do not support saving 'SelectedRows'.")

 if condition_func(obj):
 return True
 elif type(obj) in (dict, collections.OrderedDict, list, tuple):
 if type(obj) in (dict, collections.OrderedDict):
 keys = list(obj.keys())
 else:
 keys = range(len(obj))
 flag = False
 for key in keys:
 flag |= _contain_x(obj[key], condition_func)
 if flag:
 return True
 return flag
 else:
 return False


def _is_state_dict(obj):
 if isinstance(obj, dict):

 def condition(obj):
 return isinstance(obj, (fluid.Layer, Program, core.VarBase,
 core.eager.Tensor, core.LoDTensor,
 core.SelectedRows))

 # If the value of a dict is a core.VarBase/LoDTensor or a dict 
 # that does not contain a paddle type(Layer, Program, VarBase, LoDTensor, SelectedRows), 
 # the dict is considered to be a state_ dict.
 for key, value in obj.items():
 if isinstance(value, dict):
 for k, v in value.items():
 if _contain_x(v, condition):
 return False
 elif not isinstance(value, (core.VarBase, core.eager.Tensor,
 core.LoDTensor)):
 return False
 return True

 return False


def _transformed_from_varbase(obj):
 # In paddle2.1 version, VarBase is saved as tuple(tensor.name, tensor.numpy()).
 # When executing paddle.load, use this function to determine whether to restore to VarBase/LoDTensor.
 if isinstance(obj, tuple) and len(obj) == 2:
 name_types = str
 if isinstance(obj[0], name_types) and isinstance(obj[1], np.ndarray):
 return True
 return False


def _transformed_from_lodtensor(obj):
 # In paddle2.1 version, LoDTensor is saved as np.array(tensor).
 # When executing paddle.load, use this function to determine whether to restore to VarBase/LoDTensor.
 if isinstance(obj, np.ndarray):
 return True
 return False


def _to_LodTensor(ndarray):
 if not isinstance(ndarray, np.ndarray):
 raise TypeError(
 'Type of `ndarray` should be numpy.ndarray, but received {}.'.
 format(type(ndarray)))
 t = core.LoDTensor()
 place = _current_expected_place()
 t.set(ndarray, place)
 return t


def _tuple_to_tensor(obj, return_numpy):
 if return_numpy:
 return obj[1]
 if _non_static_mode():
 t = paddle.to_tensor(obj[1])
 # This function does modify the name of return value.
 # Loading the same variable multiple times may cause the same name.
 t.name = obj[0]
 return t
 else:
 return _to_LodTensor(obj[1])


def _ndarray_to_tensor(obj, return_numpy):
 if return_numpy:
 return obj
 if _non_static_mode():
 return paddle.to_tensor(obj)
 else:
 return _to_LodTensor(obj)


def _lod_tensor2varbase(tensor):
 return_var = _varbase_creator()
 return_var.value().get_tensor().set(tensor, _current_expected_place())
 return return_var


def _parse_every_object(obj, condition_func, convert_func):
 if condition_func(obj):
 return convert_func(obj)
 elif type(obj) in (dict, collections.OrderedDict, list):
 if type(obj) == list:
 keys = range(len(obj))
 else:
 keys = list(obj.keys())
 for key in keys:
 if condition_func(obj[key]):
 obj[key] = convert_func(obj[key])
 else:
 obj[key] = _parse_every_object(obj[key], condition_func,
 convert_func)
 return obj
 elif type(obj) == tuple:
 return tuple(
 _parse_every_object(list(obj), condition_func, convert_func))
 elif type(obj) == set:
 return set(_parse_every_object(list(obj), condition_func, convert_func))
 else:
 if isinstance(obj, collections.Iterable) and not isinstance(
 obj,
 (str, np.ndarray, core.VarBase, core.eager.Tensor, core.LoDTensor)):
 raise NotImplementedError(
 "The iteratable objects supported are tuple, list, dict, OrderedDict, string. But received {}.".
 format(type(obj)))
 return obj


def _parse_load_result(obj, return_numpy):
 def is_layer(obj):
 return isinstance(obj, fluid.Layer)

 def parse_layer(obj):
 temp_dict = _parse_load_result(obj.__dict__, False)
 obj.__dict__.update(temp_dict)
 return obj

 if _contain_x(obj, is_layer):
 if not _non_static_mode():
 raise ValueError(
 "Layer can only be loaded in dynamic graph mode, but now in static graph mode."
 )

 _parse_every_object(obj, is_layer, parse_layer)

 def tuple_to_tensor(obj):
 return _tuple_to_tensor(obj, return_numpy=return_numpy)

 def ndarray_to_tensor(obj):
 return _ndarray_to_tensor(obj, return_numpy=return_numpy)

 # tuple(name, ndarry) was converted from varbase of paddle2.1, 
 # and all tuple(name, ndarry) are converted to tensor.
 if _contain_x(obj, _transformed_from_varbase):
 return _parse_every_object(obj, _transformed_from_varbase,
 tuple_to_tensor)
 # If there is no tuple(name, ndary), it is considered to be saved by paddle2.0 
 # or converted from LoDTensor, and all ndarrays are converted to tensor.
 else:
 return _parse_every_object(obj, _transformed_from_lodtensor,
 ndarray_to_tensor)


def _save_lod_tensor(tensor, file_name):
 if not tensor._is_initialized():
 raise ValueError(
 "The saved tensor is not initialized. If you used group sharded, please use save_group_sharded_model firstly."
 )
 if _is_file_path(file_name):
 _seek = core.save_lod_tensor(tensor, file_name)
 # '_seek' is the end position of this tensor in the file.

 elif _is_memory_buffer(file_name):
 tensor_bytes = core.save_lod_tensor_to_memory(tensor)

 with _open_file_buffer(file_name, 'wb') as f:
 f.write(tensor_bytes)
 _seek = f.tell()

 else:
 raise NotImplementedError(
 'Only supports saving objects to file or BytesIO, but received {}'.
 format(type(file_name)))
 return _seek


def _load_lod_tensor(file_name):
 temp_t = paddle.fluid.core.LoDTensor()
 if _is_file_path(file_name):
 # '_seek' is the end position of this tensor in the file.
 _seek = paddle.fluid.core.load_lod_tensor(temp_t, file_name)

 elif _is_memory_buffer(file_name):
 with _open_file_buffer(file_name, 'rb') as f:
 tensor_bytes = f.read()
 paddle.fluid.core.load_lod_tensor_from_memory(temp_t, tensor_bytes)
 _seek = f.tell()

 else:
 raise NotImplementedError(
 'Only supports load objects from file or BytesIO, but received {}'.
 format(type(file_name)))

 return temp_t, _seek


def _save_selected_rows(selected_rows, file_name):
 if not selected_rows.get_tensor()._is_initialized():
 raise ValueError("The saved tensor is not initialized.")
 if _is_file_path(file_name):
 # '_seek' is the end position of this SelectedRows in the file.
 _seek = core.save_selected_rows(selected_rows, file_name)

 elif _is_memory_buffer(file_name):
 selected_rows_bytes = core.save_selected_rows_to_memory(selected_rows)
 with _open_file_buffer(file_name, 'wb') as f:
 f.write(selected_rows_bytes)
 _seek = f.tell()
 else:
 raise NotImplementedError(
 'Only supports saving objects to file or BytesIO, but received {}'.
 format(type(file_name)))
 return _seek


def _load_selected_rows(file_name):
 temp_sr = core.SelectedRows()
 if _is_file_path(file_name):
 # '_seek' is the end position of this SelectedRows in the file.
 _seek = core.load_selected_rows(temp_sr, file_name)

 elif _is_memory_buffer(file_name):
 with _open_file_buffer(file_name, 'rb') as f:
 selected_rows_bytes = f.read()
 paddle.fluid.core.load_selected_rows_from_memory(
 temp_sr, selected_rows_bytes)
 _seek = f.tell()

 else:
 raise NotImplementedError(
 'Only supports load objects from file or BytesIO, but received {}'.
 format(type(file_name)))

 return temp_sr, _seek


def _save_binary_var(obj, path):
 if isinstance(obj, core.LoDTensor):
 _save_lod_tensor(obj, path)
 elif isinstance(obj, core.SelectedRows):
 _save_selected_rows(obj, path)
 elif isinstance(obj, (core.VarBase, core.eager.Tensor)):
 _save_lod_tensor(obj.value().get_tensor(), path)
 else:
 # Since the concept of 'Tensor' is only exposed to users, the error message can only contain tensor instead of 'LoDTensor' or 'SelectedRows'
 raise NotImplementedError(
 "When use_binary_format = True, `paddle.save` expected Tensor, but received {}.".
 format(type(obj)))


def save(obj, path, protocol=4, **configs):
 '''
 Save an object to the specified path.

 .. note::
 Now supports saving ``state_dict`` of Layer/Optimizer, Tensor and nested structure containing Tensor, Program.

 .. note::
 Different from ``paddle.jit.save``, since the save result of ``paddle.save`` is a single file, 
 there is no need to distinguish multiple saved files by adding a suffix. The argument ``path`` 
 of ``paddle.save`` will be directly used as the saved file name instead of a prefix. 
 In order to unify the saved file name format, we recommend using the paddle standard suffix:
 1. for ``Layer.state_dict`` , recommend to use ``.pdparams`` ; 
 2. for ``Optimizer.state_dict`` , recommend to use ``.pdopt`` . 
 For specific examples, please refer to API code examples.

 Args:
 obj(Object) : The object to be saved.
 path(str|BytesIO) : The path/buffer of the object to be saved. 
 If saved in the current directory, the input path string will be used as the file name. 
 protocol(int, optional): The protocol version of pickle module must be greater than 1 and less than 5.
 Default: 4
 **configs(dict, optional): optional keyword arguments. The following options are currently supported:
 use_binary_format(bool): When the saved object is static graph variable, you can specify ``use_binary_for_var``. 
 If True, save the file in the c++ binary format when saving a single static graph variable; otherwise, save it in pickle format.
 Default: False

 Returns:
 None

 Examples:
 .. code-block:: python

 # example 1: dynamic graph
 import paddle
 emb = paddle.nn.Embedding(10, 10)
 layer_state_dict = emb.state_dict()

 # save state_dict of emb
 paddle.save(layer_state_dict, "emb.pdparams")

 scheduler = paddle.optimizer.lr.NoamDecay(
 d_model=0.01, warmup_steps=100, verbose=True)
 adam = paddle.optimizer.Adam(
 learning_rate=scheduler,
 parameters=emb.parameters())
 opt_state_dict = adam.state_dict()

 # save state_dict of optimizer
 paddle.save(opt_state_dict, "adam.pdopt")
 # save weight of emb
 paddle.save(emb.weight, "emb.weight.pdtensor")

 # example 2: Save multiple state_dict at the same time
 from paddle import nn
 from paddle.optimizer import Adam

 layer = paddle.nn.Linear(3, 4)
 adam = Adam(learning_rate=0.001, parameters=layer.parameters())
 obj = {'model': layer.state_dict(), 'opt': adam.state_dict(), 'epoch': 100}
 path = 'example/model.pdparams'
 paddle.save(obj, path)


 # example 3: static graph
 import paddle
 import paddle.static as static

 paddle.enable_static()

 # create network
 x = paddle.static.data(name="x", shape=[None, 224], dtype='float32')
 z = paddle.static.nn.fc(x, 10)

 place = paddle.CPUPlace()
 exe = paddle.static.Executor(place)
 exe.run(paddle.static.default_startup_program())
 prog = paddle.static.default_main_program()
 for var in prog.list_vars():
 if list(var.shape) == [224, 10]:
 tensor = var.get_value()
 break

 # save/load tensor
 path_tensor = 'temp/tensor.pdtensor'
 paddle.save(tensor, path_tensor)

 # save/load state_dict
 path_state_dict = 'temp/model.pdparams'
 paddle.save(prog.state_dict("param"), path_tensor)

 # example 4: save program
 import paddle

 paddle.enable_static()

 data = paddle.static.data(
 name='x_static_save', shape=(None, 224), dtype='float32')
 y_static = z = paddle.static.nn.fc(data, 10)
 main_program = paddle.static.default_main_program()
 path = "example/main_program.pdmodel"
 paddle.save(main_program, path)


 # example 5: save object to memory
 from io import BytesIO
 import paddle
 from paddle.nn import Linear
 paddle.disable_static()

 linear = Linear(5, 10)
 state_dict = linear.state_dict()
 byio = BytesIO()
 paddle.save(state_dict, byio)
 tensor = paddle.randn([2, 3], dtype='float32')
 paddle.save(tensor, byio)

 '''
 if _is_file_path(path):
 # 1. input check
 filename = os.path.basename(path)
 if filename == "":
 raise ValueError(
 "The input path MUST be format of dirname/filename "
 "[dirname\\filename in Windows system], but received "
 "filename is empty string.")

 # 2. save object
 dirname = os.path.dirname(path)
 if dirname and not os.path.exists(dirname):
 os.makedirs(dirname)
 elif not _is_memory_buffer(path):
 raise ValueError(
 "only supports saving objects to file and `BytesIO`, but got {}".
 format(type(path)))

 config = _parse_save_config(configs)

 if not isinstance(config.use_binary_format, bool):
 raise TypeError(
 "Type of `use_binary_format` should be bool, but received {}.".
 format(type(config.use_binary_format)))

 if config.use_binary_format:
 _save_binary_var(obj, path)
 else:
 # `protocol` need to be used, `pickle_protocol` is a deprecated arg.
 if config.pickle_protocol is not None:
 protocol = config.pickle_protocol
 warnings.warn(
 "'pickle_protocol' is a deprecated argument. Please use 'protocol' instead."
 )

 if isinstance(obj, Program):
 obj.desc.flush()
 with _open_file_buffer(path, "wb") as f:
 f.write(obj.desc.serialize_to_string())

 elif _is_state_dict(obj):
 if _non_static_mode():
 _legacy_save(obj, path, protocol)
 else:
 _legacy_static_save(obj, path, protocol)
 else:
 with _open_file_buffer(path, 'wb') as f:
 _pickle_save(obj, f, protocol)


def _legacy_save(obj, path, protocol=2):
 # 1. input check
 if not isinstance(obj, dict):
 raise NotImplementedError(
 "Now only supports save state_dict of Layer or Optimizer, "
 "expect dict, but received %s." % type(obj))

 if len(obj) == 0:
 warnings.warn("The input state dict is empty, no need to save.")

 if not isinstance(protocol, int):
 raise ValueError("The 'protocol' MUST be `int`, but received {}".format(
 type(protocol)))

 if protocol < 2 or protocol > 4:
 raise ValueError("Expected 1<'protocol'<5, but received protocol={}".
 format(protocol))

 if _is_file_path(path):
 filename = os.path.basename(path)
 if filename == "":
 raise ValueError(
 "The input path MUST be format of dirname/filename "
 "[dirname\\filename in Windows system], but received "
 "filename is empty string.")
 # 2. save object
 dirname = os.path.dirname(path)
 if dirname and not os.path.exists(dirname):
 os.makedirs(dirname)

 if isinstance(obj, dict):
 saved_obj = _build_saved_state_dict(obj)

 saved_obj = _unpack_saved_dict(saved_obj, protocol)

 # When value of dict is lager than 4GB ,there is a Bug on 'MAC python3'
 if _is_file_path(
 path) and sys.platform == 'darwin' and sys.version_info.major == 3:
 pickle_bytes = pickle.dumps(saved_obj, protocol=protocol)
 with open(path, 'wb') as f:
 max_bytes = 2**30
 for i in range(0, len(pickle_bytes), max_bytes):
 f.write(pickle_bytes[i:i + max_bytes])
 else:
 with _open_file_buffer(path, 'wb') as f:
 pickle.dump(saved_obj, f, protocol=protocol)


def load(path, **configs):
 '''
 Load an object can be used in paddle from specified path.

 .. note::
 Now supports loading ``state_dict`` of Layer/Optimizer, Tensor and nested structure containing Tensor, Program.

 .. note::
 In order to use the model parameters saved by paddle more efficiently, 
 ``paddle.load`` supports loading ``state_dict`` of Layer from the result of 
 other save APIs except ``paddle.save`` , but the argument ``path`` format is 
 different:
 1. loading from ``paddle.static.save`` or ``paddle.Model().save(training=True)`` , 
 ``path`` needs to be a complete file name, such as ``model.pdparams`` or 
 ``model.pdopt`` ; 
 2. loading from ``paddle.jit.save`` or ``paddle.static.save_inference_model`` 
 or ``paddle.Model().save(training=False)`` , ``path`` need to be a file prefix, 
 such as ``model/mnist``, and ``paddle.load`` will get information from 
 ``mnist.pdmodel`` and ``mnist.pdiparams`` ;
 3. loading from paddle 1.x APIs ``paddle.fluid.io.save_inference_model`` or 
 ``paddle.fluid.io.save_params/save_persistables`` , ``path`` need to be a 
 directory, such as ``model`` and model is a directory.

 .. note::
 If you load ``state_dict`` from the saved result of static mode API such as 
 ``paddle.static.save`` or ``paddle.static.save_inference_model`` , 
 the structured variable name in dynamic mode will cannot be restored. 
 You need to set the argument ``use_structured_name=False`` when using 
 ``Layer.set_state_dict`` later.

 Args:
 path(str|BytesIO) : The path/buffer to load the target object. Generally, the path is the target 
 file path. When loading state_dict from the saved result of the API used to save 
 the inference model, the path may be a file prefix or directory.
 **configs (dict, optional): other load configuration options for compatibility. We do not 
 recommend using these configurations, they may be removed in the future. If not necessary, 
 DO NOT use them. Default None.
 The following options are currently supported:
 (1) model_filename (str): The inference model file name of the paddle 1.x 
 ``save_inference_model`` save format. Default file name is :code:`__model__` . 
 (2) params_filename (str): The persistable variables file name of the paddle 1.x 
 ``save_inference_model`` save format. No default file name, save variables separately 
 by default. 
 (3) return_numpy(bool): If specified as True, return tensor as numpy.ndarray, otherwise return tensor as paddle.Tensor. 
 Default False.

 Returns:
 Object(Object): a target object can be used in paddle

 Examples:
 .. code-block:: python

 # example 1: dynamic graph
 import paddle
 emb = paddle.nn.Embedding(10, 10)
 layer_state_dict = emb.state_dict()

 # save state_dict of emb
 paddle.save(layer_state_dict, "emb.pdparams")

 scheduler = paddle.optimizer.lr.NoamDecay(
 d_model=0.01, warmup_steps=100, verbose=True)
 adam = paddle.optimizer.Adam(
 learning_rate=scheduler,
 parameters=emb.parameters())
 opt_state_dict = adam.state_dict()

 # save state_dict of optimizer
 paddle.save(opt_state_dict, "adam.pdopt")
 # save weight of emb
 paddle.save(emb.weight, "emb.weight.pdtensor")

 # load state_dict of emb
 load_layer_state_dict = paddle.load("emb.pdparams")
 # load state_dict of optimizer
 load_opt_state_dict = paddle.load("adam.pdopt")
 # load weight of emb
 load_weight = paddle.load("emb.weight.pdtensor")


 # example 2: Load multiple state_dict at the same time
 from paddle import nn
 from paddle.optimizer import Adam

 layer = paddle.nn.Linear(3, 4)
 adam = Adam(learning_rate=0.001, parameters=layer.parameters())
 obj = {'model': layer.state_dict(), 'opt': adam.state_dict(), 'epoch': 100}
 path = 'example/model.pdparams'
 paddle.save(obj, path)
 obj_load = paddle.load(path)


 # example 3: static graph
 import paddle
 import paddle.static as static

 paddle.enable_static()

 # create network
 x = paddle.static.data(name="x", shape=[None, 224], dtype='float32')
 z = paddle.static.nn.fc(x, 10)

 place = paddle.CPUPlace()
 exe = paddle.static.Executor(place)
 exe.run(paddle.static.default_startup_program())
 prog = paddle.static.default_main_program()
 for var in prog.list_vars():
 if list(var.shape) == [224, 10]:
 tensor = var.get_value()
 break

 # save/load tensor
 path_tensor = 'temp/tensor.pdtensor'
 paddle.save(tensor, path_tensor)
 load_tensor = paddle.load(path_tensor)

 # save/load state_dict
 path_state_dict = 'temp/model.pdparams'
 paddle.save(prog.state_dict("param"), path_tensor)
 load_state_dict = paddle.load(path_tensor)


 # example 4: load program
 import paddle

 paddle.enable_static()

 data = paddle.static.data(
 name='x_static_save', shape=(None, 224), dtype='float32')
 y_static = z = paddle.static.nn.fc(data, 10)
 main_program = paddle.static.default_main_program()
 path = "example/main_program.pdmodel"
 paddle.save(main_program, path)
 load_main = paddle.load(path)
 print(load_main)


 # example 5: save object to memory
 from io import BytesIO
 import paddle
 from paddle.nn import Linear
 paddle.disable_static()

 linear = Linear(5, 10)
 state_dict = linear.state_dict()
 byio = BytesIO()
 paddle.save(state_dict, byio)
 tensor = paddle.randn([2, 3], dtype='float32')
 paddle.save(tensor, byio)
 byio.seek(0)
 # load state_dict
 dict_load = paddle.load(byio)

 '''

 if _is_memory_buffer(path) or os.path.isfile(path):
 config = _parse_load_config(configs)
 exception_type = pickle.UnpicklingError
 try:
 with _open_file_buffer(path, 'rb') as f:
 # When value of dict is lager than 4GB ,there is a Bug on 'MAC python3'
 if _is_file_path(
 path
 ) and sys.platform == 'darwin' and sys.version_info.major == 3:
 load_result = _pickle_loads_mac(path, f)
 else:
 load_result = pickle.load(f, encoding='latin1')

 # TODO(weixin):If `obj` is any object, the judgment condition should be more precise.
 if isinstance(load_result, dict):
 load_result = _pack_loaded_dict(load_result)
 # paddle2.0: paddle.save/load
 if "StructuredToParameterName@@" in load_result:

 for key in load_result["StructuredToParameterName@@"]:
 if isinstance(load_result[key], np.ndarray):
 load_result[key] = _ndarray_to_tensor(
 load_result[key], config.return_numpy)

 if not config.keep_name_table and "StructuredToParameterName@@" in load_result:
 del load_result["StructuredToParameterName@@"]
 else:
 # paddle2.1 static.save/load
 load_result = _parse_load_result(load_result,
 config.return_numpy)

 else:
 load_result = _parse_load_result(load_result,
 config.return_numpy)

 except exception_type as msg_pickle:
 try:
 tensor, _ = _load_selected_rows(path)
 return tensor
 except:
 try:
 tensor, _ = _load_lod_tensor(path)
 if config.return_numpy:
 return np.array(tensor)
 else:
 if _non_static_mode():
 return _lod_tensor2varbase(tensor)
 return tensor
 except:
 try:
 with _open_file_buffer(path, "rb") as f:
 program_desc_str = f.read()
 program = Program.parse_from_string(
 program_desc_str)
 return program
 except:
 raise ValueError(
 "`paddle.load` can not parse the file:{}.".format(
 path))

 else:
 load_result = _legacy_load(path, **configs)

 return load_result


def _legacy_load(path, **configs):
 load_result = None
 config = _parse_load_config(configs)

 if os.path.isfile(path) or _is_memory_buffer(path):
 # we think path is file means this file is created by paddle.save
 with _open_file_buffer(path, 'rb') as f:
 load_result = pickle.load(f, encoding='latin1')
 load_result = _pack_loaded_dict(load_result)
 if not config.keep_name_table and "StructuredToParameterName@@" in load_result:
 del load_result["StructuredToParameterName@@"]
 else:
 # file prefix and directory are compatible cases
 model_path, config = _build_load_path_and_config(path, config)
 # check whether model file exists
 if config.model_filename is None:
 model_filename = '__model__'
 else:
 model_filename = config.model_filename
 model_file_path = os.path.join(model_path, model_filename)

 if os.path.exists(model_file_path):
 # Load state dict by `jit.save/io.save_inference_model` save format
 # NOTE(chenweihang): [ Compatibility of save_inference_model save format ]
 # The model saved by `save_inference_model` does not completely correspond to 
 # the information required by the `state_dict` under the dygraph. 
 # `save_inference_model` not save structured name, we need to remind 
 # the user to configure the `use_structured_name` argument when `set_state_dict`
 # NOTE(chenweihang): `jit.save` doesn't save optimizer state 
 load_result = _load_state_dict_from_save_inference_model(model_path,
 config)
 else:
 # load state dict by `io.save_params/persistables` save format
 # TODO(chenweihang): [ Now only supports loading parameters seperately ]
 # If users save all parameters as one file, the [ variable.name -> variable ]
 # mapping info will lost, so users need to give variable list, but users build 
 # variable list in dygraph mode is difficult, we recommend users to use
 # paddle.static.load_program_state in this case
 load_result = _load_state_dict_from_save_params(model_path)

 return load_result