


"""simple docstring"""




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					(   __SCREAMING_SNAKE_CASE       ):


		'''simple docstring'''



		__UpperCAmelCase		:			torch.FloatTensor







class 					__lowerCAmelCase					(   __SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE       ):


		'''simple docstring'''



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

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

				__a           =			vq_embed_dim if vq_embed_dim is not None else latent_channels

				__a           =			nn.Convad(_a							, _a							, 1      )
				__a           =			VectorQuantizer(_a							, _a							, beta=0.25							, remap=_a							, sane_index_shape=_a      )
				__a           =			nn.Convad(_a							, _a							, 1      )

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

		@apply_forward_hook
		def 			__UpperCAmelCase       (  self							, _a							, _a = True      ):
				__a           =			self.encoder(_a      )
				__a           =			self.quant_conv(_a      )

				if not return_dict:
						return (h,)

				return VQEncoderOutput(latents=_a      )

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

				if not return_dict:
						return (dec,)

				return DecoderOutput(sample=_a      )





		def 			__UpperCAmelCase       (  self							, _a							, _a = True      ):
				__a           =			sample
				__a           =			self.encode(_a      ).latents
				__a           =			self.decode(_a      ).sample

				if not return_dict:
						return (dec,)

				return DecoderOutput(sample=_a      )










"""simple docstring"""




def        lowercase							(		lowerCAmelCase__ :     int			) ->      str:
		if number > 0:
				raise ValueError('''input must be a negative integer'''			)
		__a           =			len(bin(lowerCAmelCase__			)[3:]			)
		__a           =			bin(abs(lowerCAmelCase__			) - (1 << binary_number_length)			)[3:]
		__a           =			(
		    (
		        '''1'''
		        + '''0''' * (binary_number_length - len(lowerCAmelCase__			))
		        + twos_complement_number
		    )
		    if number < 0
		    else '''0'''
		)
		return "0b" + twos_complement_number


if __name__ == "__main__":
				import doctest

				doctest.testmod()






