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#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#
#     http://www.apache.org/licenses/LICENSE-2.0
#
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# distributed under the License is distributed on an "AS IS" BASIS,
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"""
Helper functions for converting sympy equations to numpy
"""

import types
import inspect
import numpy as np
import symengine as se
import sympy as sp

NP_LAMBDA_STORE = {}


[docs]def np_lambdify(f, r):
    """
    generates a numpy function from a sympy equation

    Parameters
    ----------
    f : Sympy Exp, float, int, bool or list of the previous
      the equation to convert to a numpy function.
      If float, int, or bool this gets converted
      to a constant function of value `f`. If f is a list
      then output for each element in list is is
      concatenated on axis -1.
    r : list, dict
      A list of the arguments for `f`. If dict then
      the keys of the dict are used.

    Returns
    -------
    np_f : numpy function
    """

    # possibly lambdify list of f
    if not isinstance(f, list):
        f = [f]

    # convert r to a list if dictionary
    # break up any tuples to elements in list
    if isinstance(r, dict):
        r = list(r.keys())
    no_tuple_r = []
    for key in r:
        if isinstance(key, tuple):
            for k in key:
                no_tuple_r.append(k)
        else:
            no_tuple_r.append(key)

    # lambidfy all functions in list
    lambdify_f = []
    for f_i in f:
        # check if already a numpy function
        if isinstance(f_i, types.FunctionType):
            # add r inputs to function
            args = inspect.getargspec(f_i).args

            def lambdify_f_i(**x):
                return f_i(**{key: x[key] for key in args})

        else:
            # check if already lambdified equation
            if (f_i, tuple(no_tuple_r)) in NP_LAMBDA_STORE.keys():
                lambdify_f_i = NP_LAMBDA_STORE[(f_i, tuple(no_tuple_r))]
            else:  # if not lambdify it
                try:
                    if not isinstance(f_i, bool):
                        f_i = float(f_i)
                except:
                    pass
                if isinstance(f_i, (float, int)):  # constant function

                    def loop_lambda(constant):
                        return (
                            lambda **x: np.zeros_like(next(iter(x.items()))[1])
                            + constant
                        )

                    lambdify_f_i = loop_lambda(f_i)
                elif type(f_i) in [
                    type((se.Symbol("x") > 0).subs(se.Symbol("x"), 1)),
                    type((se.Symbol("x") > 0).subs(se.Symbol("x"), -1)),
                    bool,
                ]:  # TODO hacky sympy boolian check

                    def loop_lambda(constant):
                        if constant:
                            return lambda **x: np.ones_like(
                                next(iter(x.items()))[1], dtype=bool
                            )
                        else:
                            return lambda **x: np.zeros_like(
                                next(iter(x.items()))[1], dtype=bool
                            )

                    lambdify_f_i = loop_lambda(f_i)
                else:
                    try:  # first try to compile with Symengine
                        kk = []
                        for k in no_tuple_r:
                            if isinstance(k, str):
                                kk.append(se.Symbol(k))
                            else:
                                kk.append(k)
                        kk = [se.Symbol(name) for name in sorted([x.name for x in kk])]
                        se_lambdify_f_i = se.lambdify(kk, [f_i], backend="llvm")

                        def lambdify_f_i(**x):
                            if len(x) == 1:
                                v = list(x.values())[0]
                            else:
                                v = np.stack(
                                    [v for v in dict(sorted(x.items())).values()],
                                    axis=-1,
                                )
                            out = se_lambdify_f_i(v)
                            if isinstance(out, list):
                                out = np.concatenate(out, axis=-1)
                            return out

                    except:  # fall back on older SymPy compile
                        sp_lambdify_f_i = sp.lambdify(
                            [k for k in no_tuple_r], f_i, [NP_SYMPY_PRINTER, "numpy"]
                        )

                        def lambdify_f_i(**x):
                            v = sp_lambdify_f_i(**x)
                            if isinstance(v, list):
                                v = np.concatenate(v, axis=-1)
                            return v

            # add new lambdified function to dictionary
            NP_LAMBDA_STORE[(f_i, tuple(no_tuple_r))] = lambdify_f_i

        # add new list of lambda functions
        lambdify_f.append(lambdify_f_i)

    # construct master lambda function for all
    def loop_grouped_lambda(lambdify_f):
        def grouped_lambda(**invar):
            output = []
            for lambdify_f_i in lambdify_f:
                output.append(lambdify_f_i(**invar))
            return np.concatenate(output, axis=-1)

        return grouped_lambda

    return loop_grouped_lambda(lambdify_f)
def _xor_np(x):
    return np.logical_xor(x)


def _min_np(x):
    return_value = x[0]
    for value in x:
        return_value = np.minimum(return_value, value)
    return return_value


def _max_np(x):
    return_value = x[0]
    for value in x:
        return_value = np.maximum(return_value, value)
    return return_value


def _heaviside_np(x):
    return np.heaviside(x, 0)


def _equal_np(x, y):
    return np.isclose(x, y)


NP_SYMPY_PRINTER = {
    "amin": _min_np,
    "amax": _max_np,
    "Heaviside": _heaviside_np,
    "equal": _equal_np,
    "Xor": _xor_np,
}

SYMENGINE_BLACKLIST = [sp.Heaviside, sp.DiracDelta]