import hashlib
import numpy as np
import os
import random as _random
import struct
import sys

from gym import error



def np_random(seed=None):
    if seed is not None and not (isinstance(seed, int) and 0 <= seed):
        raise error.Error(
            "Seed must be a non-negative integer or omitted, not {}".format(seed)
        )

    seed = create_seed(seed)

    rng = np.random.RandomState()
    rng.seed(_int_list_from_bigint(hash_seed(seed)))
    return rng, seed


def hash_seed(seed=None, max_bytes=8):
    """Any given evaluation is likely to have many PRNG's active at
    once. (Most commonly, because the environment is running in
    multiple processes.) There's literature indicating that having
    linear correlations between seeds of multiple PRNG's can correlate
    the outputs:

    http://blogs.unity3d.com/2015/01/07/a-primer-on-repeatable-random-numbers/
    http://stackoverflow.com/questions/1554958/how-different-do-random-seeds-need-to-be
    http://dl.acm.org/citation.cfm?id=1276928

    Thus, for sanity we hash the seeds before using them. (This scheme
    is likely not crypto-strength, but it should be good enough to get
    rid of simple correlations.)

    Args:
        seed (Optional[int]): None seeds from an operating system specific randomness source.
        max_bytes: Maximum number of bytes to use in the hashed seed.
    """
    if seed is None:
        seed = create_seed(max_bytes=max_bytes)
    hash = hashlib.sha512(str(seed).encode("utf8")).digest()
    return _bigint_from_bytes(hash[:max_bytes])


def create_seed(a=None, max_bytes=8):
    """Create a strong random seed. Otherwise, Python 2 would seed using
    the system time, which might be non-robust especially in the
    presence of concurrency.

    Args:
        a (Optional[int, str]): None seeds from an operating system specific randomness source.
        max_bytes: Maximum number of bytes to use in the seed.
    """
    # Adapted from https://svn.python.org/projects/python/tags/r32/Lib/random.py
    if a is None:
        a = _bigint_from_bytes(os.urandom(max_bytes))
    elif isinstance(a, str):
        a = a.encode("utf8")
        a += hashlib.sha512(a).digest()
        a = _bigint_from_bytes(a[:max_bytes])
    elif isinstance(a, int):
        a = a % 2 ** (8 * max_bytes)
    else:
        raise error.Error("Invalid type for seed: {} ({})".format(type(a), a))

    return a


# TODO: don't hardcode sizeof_int here
def _bigint_from_bytes(bytes):
    sizeof_int = 4
    padding = sizeof_int - len(bytes) % sizeof_int
    bytes += b"\0" * padding
    int_count = int(len(bytes) / sizeof_int)
    unpacked = struct.unpack("{}I".format(int_count), bytes)
    accum = 0
    for i, val in enumerate(unpacked):
        accum += 2 ** (sizeof_int * 8 * i) * val
    return accum


def _int_list_from_bigint(bigint):
    # Special case 0
    if bigint < 0:
        raise error.Error("Seed must be non-negative, not {}".format(bigint))
    elif bigint == 0:
        return [0]

    ints = []
    while bigint > 0:
        bigint, mod = divmod(bigint, 2 ** 32)
        ints.append(mod)
    return ints

# TODO: Remove this class and make it alias to `Generator` in a future Gym release
# RandomNumberGenerator = np.random.Generator
class RandomNumberGenerator(np.random.Generator):
    """Random number generator class that inherits from numpy's random Generator class."""

    def rand(self, *size):
        """Deprecated rand function using random."""
        deprecation(
            "Function `rng.rand(*size)` is marked as deprecated "
            "and will be removed in the future. "
            "Please use `Generator.random(size)` instead."
        )

        return self.random(size)

    random_sample = rand

    def randn(self, *size):
        """Deprecated random standard normal function use standard_normal."""
        deprecation(
            "Function `rng.randn(*size)` is marked as deprecated "
            "and will be removed in the future. "
            "Please use `rng.standard_normal(size)` instead."
        )

        return self.standard_normal(size)

    def randint(self, low, high=None, size=None, dtype=int):
        """Deprecated random integer function use integers."""
        deprecation(
            "Function `rng.randint(low, [high, size, dtype])` is marked as deprecated "
            "and will be removed in the future. "
            "Please use `rng.integers(low, [high, size, dtype])` instead."
        )

        return self.integers(low=low, high=high, size=size, dtype=dtype)

    random_integers = randint

    def get_state(self):
        """Deprecated get rng state use bit_generator.state."""
        deprecation(
            "Function `rng.get_state()` is marked as deprecated "
            "and will be removed in the future. "
            "Please use `rng.bit_generator.state` instead."
        )

        return self.bit_generator.state

    def set_state(self, state):
        """Deprecated set rng state function use bit_generator.state = state."""
        deprecation(
            "Function `rng.set_state(state)` is marked as deprecated "
            "and will be removed in the future. "
            "Please use `rng.bit_generator.state = state` instead."
        )

        self.bit_generator.state = state

    def seed(self, seed=None):
        """Deprecated seed function use gym.utils.seeding.np_random(seed)."""
        deprecation(
            "Function `rng.seed(seed)` is marked as deprecated "
            "and will be removed in the future. "
            "Please use `rng, seed = gym.utils.seeding.np_random(seed)` to create a separate generator instead."
        )

        self.bit_generator.state = type(self.bit_generator)(seed).state

    rand.__doc__ = np.random.rand.__doc__
    randn.__doc__ = np.random.randn.__doc__
    randint.__doc__ = np.random.randint.__doc__
    get_state.__doc__ = np.random.get_state.__doc__
    set_state.__doc__ = np.random.set_state.__doc__
    seed.__doc__ = np.random.seed.__doc__

    def __reduce__(self):
        """Reduces the Random Number Generator to a RandomNumberGenerator, init_args and additional args."""
        # np.random.Generator defines __reduce__, but it's hard-coded to
        # return a Generator instead of its subclass RandomNumberGenerator.
        # We need to override it here, otherwise sampling from a Space will
        # be broken after pickling and unpickling, due to using the deprecated
        # methods defined above.
        # See: https://github.com/numpy/numpy/blob/41d37b714caa1eef72f984d529f1d40ed48ce535/numpy/random/_generator.pyx#L221-L223
        # And: https://github.com/numpy/numpy/blob/41d37b714caa1eef72f984d529f1d40ed48ce535/numpy/random/_pickle.py#L17-L37
        _, init_args, *args = np.random.Generator.__reduce__(self)
        return (RandomNumberGenerator._generator_ctor, init_args, *args)

    @staticmethod
    def _generator_ctor(bit_generator_name="MT19937"):
        # Workaround method for RandomNumberGenerator pickling, see __reduce__ above.
        # Ported from numpy.random._pickle.__generator_ctor function.
        from numpy.random._pickle import BitGenerators

        if bit_generator_name in BitGenerators:
            bit_generator = BitGenerators[bit_generator_name]
        else:
            raise ValueError(
                f"{bit_generator_name} is not a known BitGenerator module."
            )
        return RandomNumberGenerator(bit_generator())
