# Copyright (c) Microsoft Corporation. All rights reserved.
#
# MIT License
#
# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and
# associated documentation files (the "Software"), to deal in the Software without restriction,
# including without limitation the rights to use, copy, modify, merge, publish, distribute,
# sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all copies or
# substantial portions of the Software.
#
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# NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
# NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM,
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# OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
# ==================================================================================================


import inspect
import math
import random

from .common import env_args
from . import trial


__all__ = [
    'choice',
    'randint',
    'uniform',
    'quniform',
    'loguniform',
    'qloguniform',
    'normal',
    'qnormal',
    'lognormal',
    'qlognormal',
    'function_choice'
]


# pylint: disable=unused-argument

if env_args.platform is None:
    def choice(*options, name=None):
        return random.choice(options)

    def randint(upper, name=None):
        return random.randrange(upper)

    def uniform(low, high, name=None):
        return random.uniform(low, high)

    def quniform(low, high, q, name=None):
        assert high > low, 'Upper bound must be larger than lower bound'
        return round(random.uniform(low, high) / q) * q

    def loguniform(low, high, name=None):
        assert low > 0, 'Lower bound must be positive'
        return np.exp(random.uniform(np.log(low), np.log(high)))

    def qloguniform(low, high, q, name=None):
        return round(loguniform(low, high) / q) * q

    def normal(mu, sigma, name=None):
        return random.gauss(mu, sigma)

    def qnormal(mu, sigma, q, name=None):
        return round(random.gauss(mu, sigma) / q) * q

    def lognormal(mu, sigma, name=None):
        return np.exp(random.gauss(mu, sigma))

    def qlognormal(mu, sigma, q, name=None):
        return round(lognormal(mu, sigma) / q) * q

    def function_choice(*funcs, name=None):
        return random.choice(funcs)()

else:

    def choice(options, name=None):
        return options[_get_param('choice', name)]

    def randint(upper, name=None):
        return _get_param('randint', name)

    def uniform(low, high, name=None):
        return _get_param('uniform', name)

    def quniform(low, high, q, name=None):
        return _get_param('quniform', name)

    def loguniform(low, high, name=None):
        return _get_param('loguniform', name)

    def qloguniform(low, high, q, name=None):
        return _get_param('qloguniform', name)

    def normal(mu, sigma, name=None):
        return _get_param('normal', name)

    def qnormal(mu, sigma, q, name=None):
        return _get_param('qnormal', name)

    def lognormal(mu, sigma, name=None):
        return _get_param('lognormal', name)

    def qlognormal(mu, sigma, q, name=None):
        return _get_param('qlognormal', name)

    def function_choice(funcs, name=None):
        return funcs[_get_param('function_choice', name)]()

    def _get_param(func, name):
        # frames:
        #   layer 0: this function
        #   layer 1: the API function (caller of this function)
        #   layer 2: caller of the API function
        frame = inspect.stack(0)[2]
        filename = frame.filename
        lineno = frame.lineno  # NOTE: this is the lineno of caller's last argument
        del frame  # see official doc
        module = inspect.getmodulename(filename)
        if name is None:
            name = '__line{:d}'.format(lineno)
        key = '{}/{}/{}'.format(module, name, func)
        if trial._params is None:
            trial.get_next_parameter()
        return trial.get_current_parameter(key)
