'''
    #################################################################################################################################################################################################
    # This code library is an adaptation of the original Transformers and was designed, developed and programmed by Sapiens Technology®.                                                            #
    # Any alteration and/or disclosure of this code without prior authorization is strictly prohibited and is subject to legal action that will be forwarded by the Sapiens Technology® legal team. #
    # This set of algorithms aims to download, train, fine-tune and/or infer large language models from various sources and slopes.                                                                 #
    #################################################################################################################################################################################################
'''
from typing import Union
import torch
import torch.nn as nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models.autoencoders.vae import DecoderOutput, VectorQuantizer
from ...models.modeling_utils import ModelMixin
from ...models.vq_model import VQEncoderOutput
from ...utils.sapiens_accelerator_utils import apply_forward_hook
class MixingResidualBlock(nn.Module):
    def __init__(self, inp_channels, embed_dim):
        super().__init__()
        self.norm1 = nn.LayerNorm(inp_channels, elementwise_affine=False, eps=1e-06)
        self.depthwise = nn.Sequential(nn.ReplicationPad2d(1), nn.Conv2d(inp_channels, inp_channels, kernel_size=3, groups=inp_channels))
        self.norm2 = nn.LayerNorm(inp_channels, elementwise_affine=False, eps=1e-06)
        self.channelwise = nn.Sequential(nn.Linear(inp_channels, embed_dim), nn.GELU(), nn.Linear(embed_dim, inp_channels))
        self.gammas = nn.Parameter(torch.zeros(6), requires_grad=True)
    def forward(self, x):
        mods = self.gammas
        x_temp = self.norm1(x.permute(0, 2, 3, 1)).permute(0, 3, 1, 2) * (1 + mods[0]) + mods[1]
        x = x + self.depthwise(x_temp) * mods[2]
        x_temp = self.norm2(x.permute(0, 2, 3, 1)).permute(0, 3, 1, 2) * (1 + mods[3]) + mods[4]
        x = x + self.channelwise(x_temp.permute(0, 2, 3, 1)).permute(0, 3, 1, 2) * mods[5]
        return x
class PaellaVQModel(ModelMixin, ConfigMixin):
    @register_to_config
    def __init__(self, in_channels: int=3, out_channels: int=3, up_down_scale_factor: int=2, levels: int=2, bottleneck_blocks: int=12, embed_dim: int=384,
    latent_channels: int=4, num_vq_embeddings: int=8192, scale_factor: float=0.3764):
        super().__init__()
        c_levels = [embed_dim // 2 ** i for i in reversed(range(levels))]
        self.in_block = nn.Sequential(nn.PixelUnshuffle(up_down_scale_factor), nn.Conv2d(in_channels * up_down_scale_factor ** 2, c_levels[0], kernel_size=1))
        down_blocks = []
        for i in range(levels):
            if i > 0: down_blocks.append(nn.Conv2d(c_levels[i - 1], c_levels[i], kernel_size=4, stride=2, padding=1))
            block = MixingResidualBlock(c_levels[i], c_levels[i] * 4)
            down_blocks.append(block)
        down_blocks.append(nn.Sequential(nn.Conv2d(c_levels[-1], latent_channels, kernel_size=1, bias=False), nn.BatchNorm2d(latent_channels)))
        self.down_blocks = nn.Sequential(*down_blocks)
        self.vquantizer = VectorQuantizer(num_vq_embeddings, vq_embed_dim=latent_channels, legacy=False, beta=0.25)
        up_blocks = [nn.Sequential(nn.Conv2d(latent_channels, c_levels[-1], kernel_size=1))]
        for i in range(levels):
            for j in range(bottleneck_blocks if i == 0 else 1):
                block = MixingResidualBlock(c_levels[levels - 1 - i], c_levels[levels - 1 - i] * 4)
                up_blocks.append(block)
            if i < levels - 1: up_blocks.append(nn.ConvTranspose2d(c_levels[levels - 1 - i], c_levels[levels - 2 - i], kernel_size=4, stride=2, padding=1))
        self.up_blocks = nn.Sequential(*up_blocks)
        self.out_block = nn.Sequential(nn.Conv2d(c_levels[0], out_channels * up_down_scale_factor ** 2, kernel_size=1), nn.PixelShuffle(up_down_scale_factor))
    @apply_forward_hook
    def encode(self, x: torch.Tensor, return_dict: bool=True) -> VQEncoderOutput:
        h = self.in_block(x)
        h = self.down_blocks(h)
        if not return_dict: return (h,)
        return VQEncoderOutput(latents=h)
    @apply_forward_hook
    def decode(self, h: torch.Tensor, force_not_quantize: bool=True, return_dict: bool=True) -> Union[DecoderOutput, torch.Tensor]:
        if not force_not_quantize: quant, _, _ = self.vquantizer(h)
        else: quant = h
        x = self.up_blocks(quant)
        dec = self.out_block(x)
        if not return_dict: return (dec,)
        return DecoderOutput(sample=dec)
    def forward(self, sample: torch.Tensor, return_dict: bool=True) -> Union[DecoderOutput, torch.Tensor]:
        """Args:"""
        x = sample
        h = self.encode(x).latents
        dec = self.decode(h).sample
        if not return_dict: return (dec,)
        return DecoderOutput(sample=dec)
'''
    #################################################################################################################################################################################################
    # This code library is an adaptation of the original Transformers and was designed, developed and programmed by Sapiens Technology®.                                                            #
    # Any alteration and/or disclosure of this code without prior authorization is strictly prohibited and is subject to legal action that will be forwarded by the Sapiens Technology® legal team. #
    # This set of algorithms aims to download, train, fine-tune and/or infer large language models from various sources and slopes.                                                                 #
    #################################################################################################################################################################################################
'''
