from .common import Benchmark, get_squares_

import numpy as np


ufuncs = ['abs', 'absolute', 'add', 'arccos', 'arccosh', 'arcsin', 'arcsinh',
          'arctan', 'arctan2', 'arctanh', 'bitwise_and', 'bitwise_not',
          'bitwise_or', 'bitwise_xor', 'cbrt', 'ceil', 'conj', 'conjugate',
          'copysign', 'cos', 'cosh', 'deg2rad', 'degrees', 'divide', 'divmod',
          'equal', 'exp', 'exp2', 'expm1', 'fabs', 'float_power', 'floor',
          'floor_divide', 'fmax', 'fmin', 'fmod', 'frexp', 'gcd', 'greater',
          'greater_equal', 'heaviside', 'hypot', 'invert', 'isfinite',
          'isinf', 'isnan', 'isnat', 'lcm', 'ldexp', 'left_shift', 'less',
          'less_equal', 'log', 'log10', 'log1p', 'log2', 'logaddexp',
          'logaddexp2', 'logical_and', 'logical_not', 'logical_or',
          'logical_xor', 'matmul', 'maximum', 'minimum', 'mod', 'modf', 'multiply',
          'negative', 'nextafter', 'not_equal', 'positive', 'power',
          'rad2deg', 'radians', 'reciprocal', 'remainder', 'right_shift',
          'rint', 'sign', 'signbit', 'sin', 'sinh', 'spacing', 'sqrt',
          'square', 'subtract', 'tan', 'tanh', 'true_divide', 'trunc']


for name in dir(np):
    if isinstance(getattr(np, name, None), np.ufunc) and name not in ufuncs:
        print("Missing ufunc %r" % (name,))


class Broadcast(Benchmark):
    def setup(self):
        self.d = np.ones((50000, 100), dtype=np.float64)
        self.e = np.ones((100,), dtype=np.float64)

    def time_broadcast(self):
        self.d - self.e


class UFunc(Benchmark):
    params = [ufuncs]
    param_names = ['ufunc']
    timeout = 10

    def setup(self, ufuncname):
        np.seterr(all='ignore')
        try:
            self.f = getattr(np, ufuncname)
        except AttributeError:
            raise NotImplementedError()
        self.args = []
        for t, a in get_squares_().items():
            arg = (a,) * self.f.nin
            try:
                self.f(*arg)
            except TypeError:
                continue
            self.args.append(arg)

    def time_ufunc_types(self, ufuncname):
        [self.f(*arg) for arg in self.args]


class Custom(Benchmark):
    def setup(self):
        self.b = np.ones(20000, dtype=bool)

    def time_nonzero(self):
        np.nonzero(self.b)

    def time_not_bool(self):
        (~self.b)

    def time_and_bool(self):
        (self.b & self.b)

    def time_or_bool(self):
        (self.b | self.b)


class CustomInplace(Benchmark):
    def setup(self):
        self.c = np.ones(500000, dtype=np.int8)
        self.i = np.ones(150000, dtype=np.int32)
        self.f = np.zeros(150000, dtype=np.float32)
        self.d = np.zeros(75000, dtype=np.float64)
        # fault memory
        self.f *= 1.
        self.d *= 1.

    def time_char_or(self):
        np.bitwise_or(self.c, 0, out=self.c)
        np.bitwise_or(0, self.c, out=self.c)

    def time_char_or_temp(self):
        0 | self.c | 0

    def time_int_or(self):
        np.bitwise_or(self.i, 0, out=self.i)
        np.bitwise_or(0, self.i, out=self.i)

    def time_int_or_temp(self):
        0 | self.i | 0

    def time_float_add(self):
        np.add(self.f, 1., out=self.f)
        np.add(1., self.f, out=self.f)

    def time_float_add_temp(self):
        1. + self.f + 1.

    def time_double_add(self):
        np.add(self.d, 1., out=self.d)
        np.add(1., self.d, out=self.d)

    def time_double_add_temp(self):
        1. + self.d + 1.


class CustomScalar(Benchmark):
    params = [np.float32, np.float64]
    param_names = ['dtype']

    def setup(self, dtype):
        self.d = np.ones(20000, dtype=dtype)

    def time_add_scalar2(self, dtype):
        np.add(self.d, 1)

    def time_divide_scalar2(self, dtype):
        np.divide(self.d, 1)

    def time_divide_scalar2_inplace(self, dtype):
        np.divide(self.d, 1, out=self.d)

    def time_less_than_scalar2(self, dtype):
        (self.d < 1)


class Scalar(Benchmark):
    def setup(self):
        self.x = np.asarray(1.0)
        self.y = np.asarray((1.0 + 1j))
        self.z = complex(1.0, 1.0)

    def time_add_scalar(self):
        (self.x + self.x)

    def time_add_scalar_conv(self):
        (self.x + 1.0)

    def time_add_scalar_conv_complex(self):
        (self.y + self.z)


class ArgPack:
    __slots__ = ['args', 'kwargs']
    def __init__(self, *args, **kwargs):
        self.args = args
        self.kwargs = kwargs
    def __repr__(self):
        return '({})'.format(', '.join(
            [repr(a) for a in self.args] +
            ['{}={}'.format(k, repr(v)) for k, v in self.kwargs.items()]
        ))


class ArgParsing(Benchmark):
    # In order to benchmark the speed of argument parsing, all but the
    # out arguments are chosen such that they have no effect on the
    # calculation.  In particular, subok=True and where=True are
    # defaults, and the dtype is the correct one (the latter will
    # still have some effect on the search for the correct inner loop).
    x = np.array(1.)
    y = np.array(2.)
    out = np.array(3.)
    param_names = ['arg_kwarg']
    params = [[
        ArgPack(x, y),
        ArgPack(x, y, out),
        ArgPack(x, y, out=out),
        ArgPack(x, y, out=(out,)),
        ArgPack(x, y, out=out, subok=True, where=True),
        ArgPack(x, y, subok=True),
        ArgPack(x, y, subok=True, where=True),
        ArgPack(x, y, out, subok=True, where=True)
    ]]

    def time_add_arg_parsing(self, arg_pack):
        np.add(*arg_pack.args, **arg_pack.kwargs)


class ArgParsingReduce(Benchmark):
    # In order to benchmark the speed of argument parsing, all but the
    # out arguments are chosen such that they have minimal effect on the
    # calculation.
    a = np.arange(2.)
    out = np.array(0.)
    param_names = ['arg_kwarg']
    params = [[
        ArgPack(a,),
        ArgPack(a, 0),
        ArgPack(a, axis=0),
        ArgPack(a, 0, None),
        ArgPack(a, axis=0, dtype=None),
        ArgPack(a, 0, None, out),
        ArgPack(a, axis=0, dtype=None, out=out),
        ArgPack(a, out=out)
    ]]

    def time_add_reduce_arg_parsing(self, arg_pack):
        np.add.reduce(*arg_pack.args, **arg_pack.kwargs)