from cpython.pycapsule cimport PyCapsule_New

import numpy as np
cimport numpy as np

from libc.stdint cimport uint32_t, uint64_t
from ._common cimport uint64_to_double, int_to_array, wrap_int
from numpy.random cimport BitGenerator

__all__ = ['Philox']

np.import_array()

DEF PHILOX_BUFFER_SIZE=4

cdef extern from 'src/philox/philox.h':
    struct s_r123array2x64:
        uint64_t v[2]

    struct s_r123array4x64:
        uint64_t v[4]

    ctypedef s_r123array4x64 r123array4x64
    ctypedef s_r123array2x64 r123array2x64

    ctypedef r123array4x64 philox4x64_ctr_t
    ctypedef r123array2x64 philox4x64_key_t

    struct s_philox_state:
        philox4x64_ctr_t *ctr
        philox4x64_key_t *key
        int buffer_pos
        uint64_t buffer[PHILOX_BUFFER_SIZE]
        int has_uint32
        uint32_t uinteger

    ctypedef s_philox_state philox_state

    uint64_t philox_next64(philox_state *state)  nogil
    uint32_t philox_next32(philox_state *state)  nogil
    void philox_jump(philox_state *state)
    void philox_advance(uint64_t *step, philox_state *state)


cdef uint64_t philox_uint64(void*st) nogil:
    return philox_next64(<philox_state *> st)

cdef uint32_t philox_uint32(void *st) nogil:
    return philox_next32(<philox_state *> st)

cdef double philox_double(void*st) nogil:
    return uint64_to_double(philox_next64(<philox_state *> st))

cdef class Philox(BitGenerator):
    """
    Philox(seed=None, counter=None, key=None)

    Container for the Philox (4x64) pseudo-random number generator.

    Parameters
    ----------
    seed : {None, int, array_like[ints], SeedSequence}, optional
        A seed to initialize the `BitGenerator`. If None, then fresh,
        unpredictable entropy will be pulled from the OS. If an ``int`` or
        ``array_like[ints]`` is passed, then it will be passed to
        `SeedSequence` to derive the initial `BitGenerator` state. One may also
        pass in a `SeedSequence` instance.
    counter : {None, int, array_like}, optional
        Counter to use in the Philox state. Can be either
        a Python int (long in 2.x) in [0, 2**256) or a 4-element uint64 array.
        If not provided, the RNG is initialized at 0.
    key : {None, int, array_like}, optional
        Key to use in the Philox state.  Unlike ``seed``, the value in key is
        directly set. Can be either a Python int in [0, 2**128) or a 2-element
        uint64 array. `key` and ``seed`` cannot both be used.

    Attributes
    ----------
    lock: threading.Lock
        Lock instance that is shared so that the same bit git generator can
        be used in multiple Generators without corrupting the state. Code that
        generates values from a bit generator should hold the bit generator's
        lock.

    Notes
    -----
    Philox is a 64-bit PRNG that uses a counter-based design based on weaker
    (and faster) versions of cryptographic functions [1]_. Instances using
    different values of the key produce independent sequences.  Philox has a
    period of :math:`2^{256} - 1` and supports arbitrary advancing and jumping
    the sequence in increments of :math:`2^{128}`. These features allow
    multiple non-overlapping sequences to be generated.

    ``Philox`` provides a capsule containing function pointers that produce
    doubles, and unsigned 32 and 64- bit integers. These are not
    directly consumable in Python and must be consumed by a ``Generator``
    or similar object that supports low-level access.

    **State and Seeding**

    The ``Philox`` state vector consists of a 256-bit value encoded as
    a 4-element uint64 array and a 128-bit value encoded as a 2-element uint64
    array. The former is a counter which is incremented by 1 for every 4 64-bit
    randoms produced. The second is a key which determined the sequence
    produced. Using different keys produces independent sequences.

    The input ``seed`` is processed by `SeedSequence` to generate the key. The
    counter is set to 0.

    Alternately, one can omit the ``seed`` parameter and set the ``key`` and
    ``counter`` directly.

    **Parallel Features**

    The preferred way to use a BitGenerator in parallel applications is to use
    the `SeedSequence.spawn` method to obtain entropy values, and to use these
    to generate new BitGenerators:

    >>> from numpy.random import Generator, Philox, SeedSequence
    >>> sg = SeedSequence(1234)
    >>> rg = [Generator(Philox(s)) for s in sg.spawn(10)]

    ``Philox`` can be used in parallel applications by calling the ``jumped``
    method  to advances the state as-if :math:`2^{128}` random numbers have
    been generated. Alternatively, ``advance`` can be used to advance the
    counter for any positive step in [0, 2**256). When using ``jumped``, all
    generators should be chained to ensure that the segments come from the same
    sequence.

    >>> from numpy.random import Generator, Philox
    >>> bit_generator = Philox(1234)
    >>> rg = []
    >>> for _ in range(10):
    ...    rg.append(Generator(bit_generator))
    ...    bit_generator = bit_generator.jumped()

    Alternatively, ``Philox`` can be used in parallel applications by using
    a sequence of distinct keys where each instance uses different key.

    >>> key = 2**96 + 2**33 + 2**17 + 2**9
    >>> rg = [Generator(Philox(key=key+i)) for i in range(10)]

    **Compatibility Guarantee**

    ``Philox`` makes a guarantee that a fixed ``seed`` will always produce
    the same random integer stream.

    Examples
    --------
    >>> from numpy.random import Generator, Philox
    >>> rg = Generator(Philox(1234))
    >>> rg.standard_normal()
    0.123  # random

    References
    ----------
    .. [1] John K. Salmon, Mark A. Moraes, Ron O. Dror, and David E. Shaw,
           "Parallel Random Numbers: As Easy as 1, 2, 3," Proceedings of
           the International Conference for High Performance Computing,
           Networking, Storage and Analysis (SC11), New York, NY: ACM, 2011.
    """
    cdef philox_state rng_state
    cdef philox4x64_key_t philox_key
    cdef philox4x64_ctr_t philox_ctr

    def __init__(self, seed=None, counter=None, key=None):
        if seed is not None and key is not None:
            raise ValueError('seed and key cannot be both used')
        BitGenerator.__init__(self, seed)
        self.rng_state.ctr = &self.philox_ctr
        self.rng_state.key = &self.philox_key
        if key is not None:
            key = int_to_array(key, 'key', 128, 64)
            for i in range(2):
                self.rng_state.key.v[i] = key[i]
            # The seed sequence is invalid.
            self._seed_seq = None
        else:
            key = self._seed_seq.generate_state(2, np.uint64)
            for i in range(2):
                self.rng_state.key.v[i] = key[i]
        counter = 0 if counter is None else counter
        counter = int_to_array(counter, 'counter', 256, 64)
        for i in range(4):
            self.rng_state.ctr.v[i] = counter[i]

        self._reset_state_variables()

        self._bitgen.state = <void *>&self.rng_state
        self._bitgen.next_uint64 = &philox_uint64
        self._bitgen.next_uint32 = &philox_uint32
        self._bitgen.next_double = &philox_double
        self._bitgen.next_raw = &philox_uint64

    cdef _reset_state_variables(self):
        self.rng_state.has_uint32 = 0
        self.rng_state.uinteger = 0
        self.rng_state.buffer_pos = PHILOX_BUFFER_SIZE
        for i in range(PHILOX_BUFFER_SIZE):
            self.rng_state.buffer[i] = 0

    @property
    def state(self):
        """
        Get or set the PRNG state

        Returns
        -------
        state : dict
            Dictionary containing the information required to describe the
            state of the PRNG
        """
        ctr = np.empty(4, dtype=np.uint64)
        key = np.empty(2, dtype=np.uint64)
        buffer = np.empty(PHILOX_BUFFER_SIZE, dtype=np.uint64)
        for i in range(4):
            ctr[i] = self.rng_state.ctr.v[i]
            if i < 2:
                key[i] = self.rng_state.key.v[i]
        for i in range(PHILOX_BUFFER_SIZE):
            buffer[i] = self.rng_state.buffer[i]

        state = {'counter': ctr, 'key': key}
        return {'bit_generator': self.__class__.__name__,
                'state': state,
                'buffer': buffer,
                'buffer_pos': self.rng_state.buffer_pos,
                'has_uint32': self.rng_state.has_uint32,
                'uinteger': self.rng_state.uinteger}

    @state.setter
    def state(self, value):
        if not isinstance(value, dict):
            raise TypeError('state must be a dict')
        bitgen = value.get('bit_generator', '')
        if bitgen != self.__class__.__name__:
            raise ValueError('state must be for a {0} '
                             'PRNG'.format(self.__class__.__name__))
        for i in range(4):
            self.rng_state.ctr.v[i] = <uint64_t> value['state']['counter'][i]
            if i < 2:
                self.rng_state.key.v[i] = <uint64_t> value['state']['key'][i]
        for i in range(PHILOX_BUFFER_SIZE):
            self.rng_state.buffer[i] = <uint64_t> value['buffer'][i]

        self.rng_state.has_uint32 = value['has_uint32']
        self.rng_state.uinteger = value['uinteger']
        self.rng_state.buffer_pos = value['buffer_pos']

    cdef jump_inplace(self, iter):
        """
        Jump state in-place

        Not part of public API

        Parameters
        ----------
        iter : integer, positive
            Number of times to jump the state of the rng.
        """
        self.advance(iter * int(2 ** 128))

    def jumped(self, jumps=1):
        """
        jumped(jumps=1)

        Returns a new bit generator with the state jumped

        The state of the returned big generator is jumped as-if
        2**(128 * jumps) random numbers have been generated.

        Parameters
        ----------
        jumps : integer, positive
            Number of times to jump the state of the bit generator returned

        Returns
        -------
        bit_generator : Philox
            New instance of generator jumped iter times
        """
        cdef Philox bit_generator

        bit_generator = self.__class__()
        bit_generator.state = self.state
        bit_generator.jump_inplace(jumps)

        return bit_generator

    def advance(self, delta):
        """
        advance(delta)

        Advance the underlying RNG as-if delta draws have occurred.

        Parameters
        ----------
        delta : integer, positive
            Number of draws to advance the RNG. Must be less than the
            size state variable in the underlying RNG.

        Returns
        -------
        self : Philox
            RNG advanced delta steps

        Notes
        -----
        Advancing a RNG updates the underlying RNG state as-if a given
        number of calls to the underlying RNG have been made. In general
        there is not a one-to-one relationship between the number output
        random values from a particular distribution and the number of
        draws from the core RNG.  This occurs for two reasons:

        * The random values are simulated using a rejection-based method
          and so, on average, more than one value from the underlying
          RNG is required to generate an single draw.
        * The number of bits required to generate a simulated value
          differs from the number of bits generated by the underlying
          RNG.  For example, two 16-bit integer values can be simulated
          from a single draw of a 32-bit RNG.

        Advancing the RNG state resets any pre-computed random numbers.
        This is required to ensure exact reproducibility.
        """
        delta = wrap_int(delta, 256)

        cdef np.ndarray delta_a
        delta_a = int_to_array(delta, 'step', 256, 64)
        philox_advance(<uint64_t *> delta_a.data, &self.rng_state)
        self._reset_state_variables()
        return self
