import paddle.v2.framework.core as core
import paddle.v2.framework.proto.framework_pb2 as framework_pb2
import collections
import numpy as np
import copy

__all__ = ['Block', 'Variable', 'Program', 'Operator']


class Variable(object):
    def __init__(self,
                 block,
                 type=core.VarDesc.VarType.LOD_TENSOR,
                 name=None,
                 shape=None,
                 dtype=None,
                 lod_level=None,
                 persistable=None,
                 **kwargs):
        self.block = block

        if name is None:
            name = Variable._unique_var_name_()
        is_new_var = False
        self.desc = self.block.desc.find_var(name)

        if self.desc is None:
            self.desc = self.block.desc.var(name)
            is_new_var = True

        if is_new_var:
            self.desc.set_type(type)
        elif self.desc.type() != type:
            raise ValueError("Variable {0} has been created before. The "
                             "previous type is {1}; the new type is {2}. They"
                             " are not matched".format(self.name,
                                                       self.desc.type(), type))

        if shape is not None:
            if is_new_var:
                self.desc.set_shape(shape)
            else:
                old_shape = self.shape
                shape = tuple(shape)
                if shape != old_shape:
                    raise ValueError(
                        "Variable {0} has been created before. the previous "
                        "shape is {1}; the new shape is {2}. They are not "
                        "matched.".format(self.name, old_shape, shape))
        if dtype is not None:
            if not isinstance(dtype, core.DataType):
                dtype = Variable._convert_np_dtype_to_dtype_(dtype)
            if is_new_var:
                self.desc.set_data_type(dtype)
            else:
                old_dtype = self.data_type()
                if dtype != old_shape:
                    raise ValueError("Variable {0} has been created before. "
                                     "The previous data type is {1}; the new "
                                     "data type is {2}. They are not "
                                     "matched.".format(self.name, old_dtype,
                                                       dtype))

        if lod_level is not None:
            if is_new_var:
                self.desc.set_lod_level(lod_level)
            else:
                if lod_level != self.lod_level:
                    raise ValueError("Variable {0} has been created before. "
                                     "The previous lod_level is {1}; the new "
                                     "lod_level is {2}. They are not "
                                     "matched".format(self.name, self.lod_level,
                                                      lod_level))
        if persistable is not None:
            if is_new_var:
                self.desc.set_persistable(persistable)
            else:
                if persistable != self.persistable:
                    raise ValueError(
                        "Variable {0} has been created before."
                        "The previous persistable is {1}; the new "
                        "persistable is {2}. They are not matched".format(
                            self.name, self.persistable, persistable))

        self.block.vars[name] = self
        self.op = None

    def __str__(self):
        protostr = self.desc.serialize_to_string()
        proto = framework_pb2.VarDesc.FromString(str(protostr))
        return proto.__str__()

    __repr__ = __str__

    @property
    def persistable(self):
        return self.desc.persistable()

    @property
    def name(self):
        return self.desc.name()

    @property
    def shape(self):
        # convert to tuple, make it as same as numpy API.
        return tuple(self.desc.shape())

    @property
    def data_type(self):
        return self.desc.data_type()

    @property
    def lod_level(self):
        return self.desc.lod_level()

    @staticmethod
    def _unique_var_name_():
        uid = core.unique_integer()  # unique during whole process.
        return "_generated_var_%d" % uid

    @staticmethod
    def _convert_np_dtype_to_dtype_(np_dtype):
        dtype = np.dtype(np_dtype)
        if dtype == np.float32:
            return core.DataType.FP32
        elif dtype == np.float64:
            return core.DataType.FP64
        elif dtype == np.float16:
            return core.DataType.FP16
        elif dtype == np.int32:
            return core.DataType.INT32
        elif dtype == np.int16:
            return core.DataType.INT16
        elif dtype == np.int64:
            return core.DataType.INT64
        elif dtype == np.bool:
            return core.DataType.BOOL
        else:
            raise ValueError("Not supported numpy dtype " + str(dtype))


def get_all_op_protos():
    """
    Get all registered op proto from PaddlePaddle C++ end.
    :return: A list of registered OpProto.
    """
    protostrs = core.get_all_op_protos()
    ret_values = []
    for pbstr in protostrs:
        op_proto = framework_pb2.OpProto.FromString(str(pbstr))
        ret_values.append(op_proto)
    return ret_values


class OpProtoHolder(object):
    @classmethod
    def instance(cls):
        if not hasattr(cls, '_instance'):
            cls._instance = cls()
        return cls._instance

    def __init__(self):
        assert not hasattr(
            self.__class__,
            '_instance'), 'Please use `instance()` to get OpProtoHolder opject!'
        op_protos = get_all_op_protos()
        self.op_proto_map = {}
        for proto in op_protos:
            self.op_proto_map[proto.type] = proto

    def get_op_proto(self, type):
        if type not in self.op_proto_map:
            raise ValueError("Operator \"%s\" has not been registered." % type)
        return self.op_proto_map[type]


class Operator(object):
    def __init__(self,
                 block,
                 desc,
                 type=None,
                 inputs=None,
                 outputs=None,
                 attrs=None):
        self.block = block
        self.desc = desc
        if len(self.desc.type()) != 0:
            return
        if type is None:
            raise ValueError(
                "`type` to initilized an Operator can not be None.")
        self.desc.set_type(type)
        proto = OpProtoHolder.instance().get_op_proto(type)

        if inputs is not None:
            given = set()
            need = set()
            for n in inputs:
                given.add(n)
            for m in proto.inputs:
                need.add(m.name)
            if not given == need:
                raise ValueError(
                    "Incorrect setting for input(s) of operator \"%s\". Need: [%s] Given: [%s]"
                    % (type, ", ".join(str(e) for e in need), ", ".join(
                        str(e) for e in given)))

            for in_proto in proto.inputs:
                in_argus = inputs[in_proto.name]
                if not isinstance(in_argus, list):
                    in_argus = [in_argus]
                if not in_proto.duplicable and len(in_argus) > 1:
                    raise ValueError(
                        "Input %s expects only one input, but %d are given." %
                        (in_proto.name, len(in_argus)))
                in_argu_names = []
                for argu in in_argus:
                    in_argu_names.append(argu.name)
                self.desc.set_input(in_proto.name, in_argu_names)

        if outputs is not None:
            given = set()
            need = set()
            for n in outputs:
                given.add(n)
            for m in proto.outputs:
                need.add(m.name)
            if not given == need:
                raise ValueError(
                    "Incorrect setting for output(s) of operator \"%s\". Need: [%s] Given: [%s]"
                    % (type, ", ".join(str(e) for e in need), ", ".join(
                        str(e) for e in given)))

            for out_proto in proto.outputs:
                out_argus = outputs[out_proto.name]
                if not isinstance(out_argus, list):
                    out_argus = [out_argus]
                if not out_proto.duplicable and len(out_argus) > 1:
                    raise ValueError(
                        "Output %s expects only one output, but %d are given." %
                        (out_proto.name, len(out_argus)))
                out_argu_names = []
                for argu in out_argus:
                    out_argu_names.append(argu.name)
                    argu.op = self
                self.desc.set_output(out_proto.name, out_argu_names)

        if attrs is not None:
            for attr in proto.attrs:
                attr_name = attr.name
                if (not attr_name in attrs) or (attrs[attr_name] is None):
                    continue
                if not isinstance(attrs[attr_name], Block):
                    self.desc.set_attr(attr_name, attrs[attr_name])
                else:
                    self.desc.set_block_attr(attr_name, attrs[attr_name].desc)

        self.desc.check_attrs()
        if type not in {'feed', 'fetch'}:
            self.desc.infer_shape(self.block.desc)

    def __str__(self):
        protostr = self.desc.serialize_to_string()
        proto = framework_pb2.OpDesc.FromString(str(protostr))
        return proto.__str__()

    __repr__ = __str__

    @property
    def type(self):
        return self.desc.type()

    def input(self, name):
        return self.desc.input(name)

    @property
    def input_names(self):
        return self.desc.input_names()

    def output(self, name):
        return self.desc.output(name)

    @property
    def output_names(self):
        return self.desc.output_names()

    def has_attr(self, name):
        return self.desc.has_attr(name)

    def attr_type(self, name):
        return self.desc.attr_type(name)

    @property
    def attr_names(self):
        return self.desc.attr_names()

    def attr(self, name):
        return self.desc.attr(name)

    def block_attr(self, name):
        return self.desc.block_attr(name)


class Block(object):
    def __init__(self, program, idx):
        self.desc = program.desc.block(idx)
        self.vars = dict()  # var_name --> var
        self.ops = collections.deque()  # operator list
        self.program = program

    def __str__(self):
        protostr = self.desc.serialize_to_string()
        proto = framework_pb2.BlockDesc.FromString(str(protostr))
        return proto.__str__()

    __repr__ = __str__

    @property
    def parent_idx(self):
        return self.desc.parent

    @property
    def idx(self):
        return self.desc.id

    def var(self, name):
        if not isinstance(name, basestring):
            raise TypeError()
        v = self.vars.get(name, None)
        if v is None:
            raise ValueError("var %s not in this block" % name)
        return v

    def all_parameters(self):
        return {v for k, v in self.vars.iteritems() if isinstance(v, Parameter)}

    def create_var(self, *args, **kwargs):
        return Variable(self, *args, **kwargs)

    def has_var(self, name):
        return name in self.vars

    def create_parameter(self, *args, **kwargs):
        global_block = self.program.global_block()
        param = Parameter(global_block, *args, **kwargs)
        if 'init_attr' in kwargs:
            self._prepend_initialize_ops_(param, kwargs['init_attr'])
        return param

    def append_op(self, *args, **kwargs):
        op_desc = self.desc.append_op()
        op = Operator(self, op_desc, *args, **kwargs)
        self.ops.append(op)
        return op

    def prepend_op(self, *args, **kwargs):
        op_desc = self.desc.prepend_op()
        op = Operator(self, op_desc, *args, **kwargs)
        self.ops.appendleft(op)
        return op

    def sync_with_cpp(self):
        # sync variables from cpp
        for var in self.desc.all_vars():
            if not self.has_var(var.name()):
                self.create_var(name=var.name(), desc=var, type=var.type())

        # sync operators from cpp
        ops_in_cpp = []
        for op_idx in range(0, self.desc.op_size()):
            ops_in_cpp.append(self.desc.op(op_idx))

        if len(self.ops) != 0:
            first_op_in_python = self.ops[0].desc
            last_op_in_python = self.ops[len(self.ops) - 1].desc
            start_index = None
            end_index = None
            for index in range(len(ops_in_cpp)):
                if first_op_in_python == ops_in_cpp[index]:
                    start_index = index
                if last_op_in_python == ops_in_cpp[index]:
                    end_index = index
            assert start_index is not None
            assert end_index is not None
            assert start_index <= end_index
        else:
            start_index = 0
            end_index = -1

        # sync ops append to the head of cpp_ops
        for index in range((start_index - 1 - 1), -1, -1):
            op_desc = ops_in_cpp[index]
            op = Operator(self, op_desc)
            self.ops.appendleft(op)

        # sync ops append to the end of cpp_ops
        for index in range((end_index + 1), len(ops_in_cpp)):
            op_desc = ops_in_cpp[index]
            op = Operator(self, op_desc)
            self.ops.append(op)

        assert len(self.ops) == len(ops_in_cpp)
        for index in range(len(self.ops)):
            assert self.ops[index].desc == ops_in_cpp[index]

    def _prepend_initialize_ops_(self, param, init_attr):
        op_type = init_attr['type']
        init_attr['shape'] = param.shape
        init_attr['data_type'] = int(param.data_type)
        op = self.prepend_op(
            type=op_type,
            inputs=None,
            outputs={'Out': [param]},
            attrs=init_attr)
        param.op = op


class Program(object):
    def __init__(self):
        self.desc = core.ProgramDesc()
        self.blocks = [Block(self, 0)]
        self.current_block_idx = 0

    def __str__(self):
        protostr = self.desc.serialize_to_string()
        proto = framework_pb2.ProgramDesc.FromString(str(protostr))
        return proto.__str__()

    def clone(self):
        p = Program()
        p.desc = core.ProgramDesc(self.desc)
        p.blocks = [Block(p, i) for i in xrange(self.desc.num_blocks())]
        p.sync_with_cpp()
        return p

    def __repr__(self):
        return str(self)

    def global_block(self):
        return self.blocks[0]

    def block(self, index):
        return self.blocks[index]

    def current_block(self):
        return self.blocks[self.current_block_idx]

    def append_backward(self, target, no_grad_set=None):
        """
        return map(param_name -> (grad_name, block_index, op_index))
        """
        assert isinstance(target, Variable)
        if no_grad_set is None:
            no_grad_set = set()
        param_to_grad_info = self.desc.append_backward(target.desc, no_grad_set)
        self.sync_with_cpp()
        return param_to_grad_info

    def create_block(self):
        new_block_idx = len(self.blocks)
        self.desc.append_block(self.current_block().desc)
        self.current_block_idx = new_block_idx
        self.blocks.append(Block(self, self.current_block_idx))
        return self.current_block()

    def rollback(self):
        self.current_block_idx = self.current_block().parent_idx

    def sync_with_cpp(self):
        for block_idx in range(len(self.blocks), self.desc.num_blocks()):
            self.blocks.append(Block(self, block_idx))
        for block in self.blocks:
            block.sync_with_cpp()


class Parameter(Variable):
    def __init__(self, block, shape, dtype, **kwargs):
        if shape is None or dtype is None:
            raise ValueError("Parameter must set shape and dtype")
        if len(shape) == 0:
            raise ValueError("Parameter shape cannot be empty")

        for each in shape:
            if each < 0:
                raise ValueError("Parameter shape should not be related with "
                                 "batch-size")

        Variable.__init__(
            self, block, persistable=True, shape=shape, dtype=dtype, **kwargs)
        self.trainable = kwargs.get('trainable', True)

        self.optimize_attr = kwargs.get('optimize_attr', {'learning_rate': 1.0})


# program is a global instance.
g_program = Program()
g_init_program = Program()
