# https://github.com/shiimizu/ComfyUI_smZNodes
import numpy as np

philox_m = [0xD2511F53, 0xCD9E8D57]
philox_w = [0x9E3779B9, 0xBB67AE85]

two_pow32_inv = np.array([2.3283064e-10], dtype=np.float32)
two_pow32_inv_2pi = np.array([2.3283064e-10 * 6.2831855], dtype=np.float32)


def uint32(x):
    """Converts (N,) np.uint64 array into (2, N) np.unit32 array."""
    return x.view(np.uint32).reshape(-1, 2).transpose(1, 0)


def philox4_round(counter, key):
    """A single round of the Philox 4x32 random number generator."""

    v1 = uint32(counter[0].astype(np.uint64) * philox_m[0])
    v2 = uint32(counter[2].astype(np.uint64) * philox_m[1])

    counter[0] = v2[1] ^ counter[1] ^ key[0]
    counter[1] = v2[0]
    counter[2] = v1[1] ^ counter[3] ^ key[1]
    counter[3] = v1[0]


def philox4_32(counter, key, rounds=10):
    """Generates 32-bit random numbers using the Philox 4x32 random number generator.

    Parameters:
        counter (numpy.ndarray): A 4xN array of 32-bit integers representing the counter values (offset into generation).
        key (numpy.ndarray): A 2xN array of 32-bit integers representing the key values (seed).
        rounds (int): The number of rounds to perform.

    Returns:
        numpy.ndarray: A 4xN array of 32-bit integers containing the generated random numbers.
    """

    for _ in range(rounds - 1):
        philox4_round(counter, key)

        key[0] = key[0] + philox_w[0]
        key[1] = key[1] + philox_w[1]

    philox4_round(counter, key)
    return counter


def box_muller(x, y):
    """Returns just the first out of two numbers generated by Box–Muller transform algorithm."""
    u = x * two_pow32_inv + two_pow32_inv / 2
    v = y * two_pow32_inv_2pi + two_pow32_inv_2pi / 2

    s = np.sqrt(-2.0 * np.log(u))

    r1 = s * np.sin(v)
    return r1.astype(np.float32)


class Generator:
    """RNG that produces same outputs as torch.randn(..., device='cuda') on CPU"""

    def __init__(self, seed):
        self.seed = seed
        self.offset = 0

    def randn(self, shape):
        """Generate a sequence of n standard normal random variables using the Philox 4x32 random number generator and the Box-Muller transform."""

        n = 1
        for x in shape:
            n *= x

        counter = np.zeros((4, n), dtype=np.uint32)
        counter[0] = self.offset
        counter[2] = np.arange(n, dtype=np.uint32)  # up to 2^32 numbers can be generated - if you want more you'd need to spill into counter[3]
        self.offset += 1

        key = np.empty(n, dtype=np.uint64)
        key.fill(self.seed)
        key = uint32(key)

        g = philox4_32(counter, key)

        return box_muller(g[0], g[1]).reshape(shape)  # discard g[2] and g[3]

#=======================================================================================================================
# Monkey Patch "prepare_noise" function
# https://github.com/shiimizu/ComfyUI_smZNodes
import torch
import functools
from comfy.sample import np
import comfy.model_management

def rng_rand_source(rand_source='cpu'):
    device = comfy.model_management.text_encoder_device()

    def prepare_noise(latent_image, seed, noise_inds=None, device='cpu'):
        """
        creates random noise given a latent image and a seed.
        optional arg skip can be used to skip and discard x number of noise generations for a given seed
        """
        generator = torch.Generator(device).manual_seed(seed)
        if rand_source == 'nv':
            rng = Generator(seed)
        if noise_inds is None:
            shape = latent_image.size()
            if rand_source == 'nv':
                return torch.asarray(rng.randn(shape), device=device)
            else:
                return torch.randn(shape, dtype=latent_image.dtype, layout=latent_image.layout, generator=generator,
                                   device=device)

        unique_inds, inverse = np.unique(noise_inds, return_inverse=True)
        noises = []
        for i in range(unique_inds[-1] + 1):
            shape = [1] + list(latent_image.size())[1:]
            if rand_source == 'nv':
                noise = torch.asarray(rng.randn(shape), device=device)
            else:
                noise = torch.randn(shape, dtype=latent_image.dtype, layout=latent_image.layout, generator=generator,
                                    device=device)
            if i in unique_inds:
                noises.append(noise)
        noises = [noises[i] for i in inverse]
        noises = torch.cat(noises, axis=0)
        return noises

    if rand_source == 'cpu':
        if hasattr(comfy.sample, 'prepare_noise_orig'):
            comfy.sample.prepare_noise = comfy.sample.prepare_noise_orig
    else:
        if not hasattr(comfy.sample, 'prepare_noise_orig'):
            comfy.sample.prepare_noise_orig = comfy.sample.prepare_noise
        _prepare_noise = functools.partial(prepare_noise, device=device)
        comfy.sample.prepare_noise = _prepare_noise