# Path: deeppy/expr/base.py
# class Op(PickleMixin):
#     _pickle_ignore = ['_tmp_', 'array', 'grad_array']
#     inputs = []
#     bpropable = True
#     shape = None
#     array = None
#     grad_array = None
# 
#     def setup(self):
#         pass
# 
#     def fprop(self):
#         raise NotImplementedError()
# 
#     def bprop(self):
#         raise NotImplementedError()
# 
#     def __add__(self, other):
#         return deeppy.expr.Add()(self, other)
# 
#     def __radd__(self, other):
#         return deeppy.expr.Add()(other, self)
# 
#     def __iadd__(self, other):
#         return self.__add__(other)
# 
#     def __sub__(self, other):
#         return deeppy.expr.Subtract()(self, other)
# 
#     def __rsub__(self, other):
#         return deeppy.expr.Subtract()(other, self)
# 
#     def __isub__(self, other):
#         return self.__sub__(other)
# 
#     def __mul__(self, other):
#         return deeppy.expr.Multiply()(self, other)
# 
#     def __rmul__(self, other):
#         return deeppy.expr.Multiply()(other, self)
# 
#     def __imul__(self, other):
#         return self.__mul__(other)
# 
#     def __div__(self, other):
#         return deeppy.expr.Divide()(self, other)
# 
#     def __rdiv__(self, other):
#         return deeppy.expr.Divide()(other, self)
# 
#     def __idiv__(self, other):
#         return self.__div__(other)
# 
#     def __truediv__(self, other):
#         return deeppy.expr.Divide()(self, other)
# 
#     def __rtruediv__(self, other):
#         return deeppy.expr.Divide()(other, self)
# 
#     def __itruediv__(self, other):
#         return self.__truediv__(other)
# 
#     def __pow__(self, other):
#         return deeppy.expr.Power()(self, other)
# 
#     def __rpow__(self, other):
#         return deeppy.expr.Power()(other, self)
# 
#     def __ipow__(self, other):
#         return self.__pow__(other)
# 
#     def __neg__(self):
#         return deeppy.expr.Negative()(self)
# 
#     @property
#     def T(self):
#         return deeppy.expr.transpose(self)
# 
# def _require_op(x):
#     if isinstance(x, Op):
#         return x
#     if isinstance(x, (np.ndarray, ca.ndarray, int, float)):
#         return Constant(x)
#     else:
#         raise ValueError('Cannot process %s' % str(x))


import numpy as np
from .base import Op, _require_op
                 measures={}):
        self.i = 0
        self.rate = rate
        self.label = label
        self.print_fprop = fprop
        self.print_bprop = bprop
        self.measures = measures

    def __call__(self, x):
        x = _require_op(x)
        self.x = x
        self.inputs = [x]
        self.bpropable = x.bpropable or self.print_bprop
        return self

    def setup(self):
        self.shape = self.x.shape
        self.array = self.x.array
        if self.bpropable:
            self.grad_array = self.x.grad_array
        if self.label is None:
            self.label = self.x.__class__.__name__

    def _message(self, val):
        msg = self.label + ' '
        for name, (s, fun) in dict(_measures, **self.measures).items():
            msg += ' ' + name + ':' + (s % fun(val))
        return msg

    def fprop(self):

# Path: deeppy/expr/base.py
# class Op(PickleMixin):
#     _pickle_ignore = ['_tmp_', 'array', 'grad_array']
#     inputs = []
#     bpropable = True
#     shape = None
#     array = None
#     grad_array = None
# 
#     def setup(self):
#         pass
# 
#     def fprop(self):
#         raise NotImplementedError()
# 
#     def bprop(self):
#         raise NotImplementedError()
# 
#     def __add__(self, other):
#         return deeppy.expr.Add()(self, other)
# 
#     def __radd__(self, other):
#         return deeppy.expr.Add()(other, self)
# 
#     def __iadd__(self, other):
#         return self.__add__(other)
# 
#     def __sub__(self, other):
#         return deeppy.expr.Subtract()(self, other)
# 
#     def __rsub__(self, other):
#         return deeppy.expr.Subtract()(other, self)
# 
#     def __isub__(self, other):
#         return self.__sub__(other)
# 
#     def __mul__(self, other):
#         return deeppy.expr.Multiply()(self, other)
# 
#     def __rmul__(self, other):
#         return deeppy.expr.Multiply()(other, self)
# 
#     def __imul__(self, other):
#         return self.__mul__(other)
# 
#     def __div__(self, other):
#         return deeppy.expr.Divide()(self, other)
# 
#     def __rdiv__(self, other):
#         return deeppy.expr.Divide()(other, self)
# 
#     def __idiv__(self, other):
#         return self.__div__(other)
# 
#     def __truediv__(self, other):
#         return deeppy.expr.Divide()(self, other)
# 
#     def __rtruediv__(self, other):
#         return deeppy.expr.Divide()(other, self)
# 
#     def __itruediv__(self, other):
#         return self.__truediv__(other)
# 
#     def __pow__(self, other):
#         return deeppy.expr.Power()(self, other)
# 
#     def __rpow__(self, other):
#         return deeppy.expr.Power()(other, self)
# 
#     def __ipow__(self, other):
#         return self.__pow__(other)
# 
#     def __neg__(self):
#         return deeppy.expr.Negative()(self)
# 
#     @property
#     def T(self):
#         return deeppy.expr.transpose(self)
# 
# def _require_op(x):
#     if isinstance(x, Op):
#         return x
#     if isinstance(x, (np.ndarray, ca.ndarray, int, float)):
#         return Constant(x)
#     else:
#         raise ValueError('Cannot process %s' % str(x))


# Path: deeppy/expr/util.py
import numpy as np
from .base import Op, _require_op

                 measures={}):
        self.i = 0
        self.rate = rate
        self.label = label
        self.print_fprop = fprop
        self.print_bprop = bprop
        self.measures = measures

    def __call__(self, x):
        x = _require_op(x)
        self.x = x
        self.inputs = [x]
        self.bpropable = x.bpropable or self.print_bprop
        return self

    def setup(self):
        self.shape = self.x.shape
        self.array = self.x.array
        if self.bpropable:
            self.grad_array = self.x.grad_array
        if self.label is None:
            self.label = self.x.__class__.__name__

    def _message(self, val):
        msg = self.label + ' '
        for name, (s, fun) in dict(_measures, **self.measures).items():
            msg += ' ' + name + ':' + (s % fun(val))
        return msg

    def fprop(self):

        self.array = self.x.array