



'''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							(      __UpperCAmelCase			):
	__SCREAMING_SNAKE_CASE       :		torch.FloatTensor







class      _lowerCAmelCase							(      __UpperCAmelCase				,      __UpperCAmelCase			):


	@register_to_config
	def __init__(self	,				lowercase = 3	,				lowercase = 3	,				lowercase = ("DownEncoderBlock2D",)	,				lowercase = ("UpDecoderBlock2D",)	,				lowercase = (64,)	,				lowercase = 1	,				lowercase = "silu"	,				lowercase = 3	,				lowercase = 32	,				lowercase = 256	,				lowercase = 32	,				lowercase = None	,				lowercase = 0.1_82_15	,				lowercase = "group"	,				):
				super().__init__()

				# pass init params to Encoder
				A_     :						int     =					Encoder(
				    in_channels=lowercase	,				out_channels=lowercase	,				down_block_types=lowercase	,				block_out_channels=lowercase	,				layers_per_block=lowercase	,				act_fn=lowercase	,				norm_num_groups=lowercase	,				double_z=lowercase	,				)

				A_     :						Union[str, Any]     =					vq_embed_dim if vq_embed_dim is not None else latent_channels

				A_     :						Union[str, Any]     =					nn.Convad(lowercase	,				lowercase	,				1							)
				A_     :						List[Any]     =					VectorQuantizer(lowercase	,				lowercase	,				beta=0.25	,				remap=lowercase	,				sane_index_shape=lowercase							)
				A_     :						List[Any]     =					nn.Convad(lowercase	,				lowercase	,				1							)

				# pass init params to Decoder
				A_     :						int     =					Decoder(
				    in_channels=lowercase	,				out_channels=lowercase	,				up_block_types=lowercase	,				block_out_channels=lowercase	,				layers_per_block=lowercase	,				act_fn=lowercase	,				norm_num_groups=lowercase	,				norm_type=lowercase	,				)


	@apply_forward_hook
	def    _a     (self	,				lowercase	,				lowercase = True							):
				A_     :						Optional[int]     =					self.encoder(lowercase							)
				A_     :						Dict     =					self.quant_conv(lowercase							)

				if not return_dict:
							return (h,)

				return VQEncoderOutput(latents=lowercase							)


	@apply_forward_hook
	def    _a     (self	,				lowercase	,				lowercase = False	,				lowercase = True							):
				# also go through quantization layer
				if not force_not_quantize:
							A_     :						Optional[Any]     =					self.quantize(lowercase							)
				else:
							A_     :						int     =					h
				A_     :						Union[str, Any]     =					self.post_quant_conv(lowercase							)
				A_     :						Optional[Any]     =					self.decoder(lowercase	,				quant if self.config.norm_type == """spatial""" else None							)

				if not return_dict:
							return (dec,)

				return DecoderOutput(sample=lowercase							)


	def    _a     (self	,				lowercase	,				lowercase = True							):
				A_     :						Optional[Any]     =					sample
				A_     :						str     =					self.encode(lowercase							).latents
				A_     :						Dict     =					self.decode(lowercase							).sample

				if not return_dict:
							return (dec,)

				return DecoderOutput(sample=lowercase							)











'''simple docstring'''




from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors



def      a      (			lowerCamelCase__   ):
			'''simple docstring'''





			A_     :						List[Any]     =					prime_factors(lowerCamelCase__   )
			if is_square_free(lowerCamelCase__   ):
						return -1 if len(lowerCamelCase__   ) % 2 else 1
			return 0


if __name__ == "__main__":
						import doctest

						doctest.testmod()