





from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder, VectorQuantizer







@dataclass
class      lowerCamelCase(						__snake_case	):
     '''simple docstring'''


     __magic_name__   =			42







class      lowerCamelCase(						__snake_case			, __snake_case	):
     '''simple docstring'''


     @register_to_config
     def __init__(    self				,     snake_case_ = 3				,     snake_case_ = 3				,     snake_case_ = ("DownEncoderBlock2D",)				,     snake_case_ = ("UpDecoderBlock2D",)				,     snake_case_ = (64,)				,     snake_case_ = 1				,     snake_case_ = "silu"				,     snake_case_ = 3				,     snake_case_ = 32				,     snake_case_ = 256				,     snake_case_ = 32				,     snake_case_ = None				,     snake_case_ = 0.1_8215				,     snake_case_ = "group"				,     ):
         super().__init__()

         # pass init params to Encoder
         _A     =							Encoder(
             in_channels=snake_case_				,     out_channels=snake_case_				,     down_block_types=snake_case_				,     block_out_channels=snake_case_				,     layers_per_block=snake_case_				,     act_fn=snake_case_				,     norm_num_groups=snake_case_				,     double_z=snake_case_				,     )

         _A     =							vq_embed_dim if vq_embed_dim is not None else latent_channels

         _A     =							nn.Convad(snake_case_				,     snake_case_				,     1							)
         _A     =							VectorQuantizer(snake_case_				,     snake_case_				,     beta=0.25				,     remap=snake_case_				,     sane_index_shape=snake_case_							)
         _A     =							nn.Convad(snake_case_				,     snake_case_				,     1							)

         # pass init params to Decoder
         _A     =							Decoder(
             in_channels=snake_case_				,     out_channels=snake_case_				,     up_block_types=snake_case_				,     block_out_channels=snake_case_				,     layers_per_block=snake_case_				,     act_fn=snake_case_				,     norm_num_groups=snake_case_				,     norm_type=snake_case_				,     )




     @apply_forward_hook
     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_ = True							):
         _A     =							self.encoder(snake_case_							)
         _A     =							self.quant_conv(snake_case_							)

         if not return_dict:
             return (h,)

         return VQEncoderOutput(latents=snake_case_							)




     @apply_forward_hook
     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_ = False				,     snake_case_ = True							):
         # also go through quantization layer
         if not force_not_quantize:
             _A,			_A,			_A     =							self.quantize(snake_case_							)
         else:
             _A     =							h
         _A     =							self.post_quant_conv(snake_case_							)
         _A     =							self.decoder(snake_case_				,     quant if self.config.norm_type == 'spatial' else None							)

         if not return_dict:
             return (dec,)

         return DecoderOutput(sample=snake_case_							)




     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_ = True							):
         _A     =							sample
         _A     =							self.encode(snake_case_							).latents
         _A     =							self.decode(snake_case_							).sample

         if not return_dict:
             return (dec,)

         return DecoderOutput(sample=snake_case_							)












import numpy as np





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE				)							-> np.array:




    """simple docstring"""




    return (2 / (1 + np.exp(-2 * vector				))) - 1


if __name__ == "__main__":
 import doctest

 doctest.testmod()





