

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 	snake_case_   (		__A					):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE     :   torch.FloatTensor






class 	snake_case_   (		__A	,       __A					):







			'''simple docstring'''

			@register_to_config
			def __init__(     self :				Any							,   _UpperCamelCase :				int = 3							,   _UpperCamelCase :				int = 3							,   _UpperCamelCase :				Tuple[str] = ("DownEncoderBlock2D",)							,   _UpperCamelCase :				Tuple[str] = ("UpDecoderBlock2D",)							,   _UpperCamelCase :				Tuple[int] = (6_4,)							,   _UpperCamelCase :				int = 1							,   _UpperCamelCase :				str = "silu"							,   _UpperCamelCase :				int = 3							,   _UpperCamelCase :				int = 3_2							,   _UpperCamelCase :				int = 2_5_6							,   _UpperCamelCase :				int = 3_2							,   _UpperCamelCase :				Optional[int] = None							,   _UpperCamelCase :				float = 0.18215							,   _UpperCamelCase :				str = "group"							,   )      ->int:
									super().__init__()

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

									snake_case_   =	vq_embed_dim if vq_embed_dim is not None else latent_channels

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

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







			@apply_forward_hook
			def 				snake_case__(     self :				Any							,   _UpperCamelCase :				torch.FloatTensor							,   _UpperCamelCase :				bool = True							)      ->VQEncoderOutput:
									snake_case_   =	self.encoder(_UpperCamelCase							)
									snake_case_   =	self.quant_conv(_UpperCamelCase							)

									if not return_dict:
															return (h,)

									return VQEncoderOutput(latents=_UpperCamelCase							)







			@apply_forward_hook
			def 				snake_case__(     self :				str							,   _UpperCamelCase :				torch.FloatTensor							,   _UpperCamelCase :				bool = False							,   _UpperCamelCase :				bool = True							)      ->Union[DecoderOutput, torch.FloatTensor]:
									# also go through quantization layer
									if not force_not_quantize:
															snake_case_,    snake_case_,    snake_case_   =	self.quantize(_UpperCamelCase							)
									else:
															snake_case_   =	h
									snake_case_   =	self.post_quant_conv(_UpperCamelCase							)
									snake_case_   =	self.decoder(_UpperCamelCase							,   quant if self.config.norm_type == '''spatial''' else None							)

									if not return_dict:
															return (dec,)

									return DecoderOutput(sample=_UpperCamelCase							)







			def 				snake_case__(     self :				Any							,   _UpperCamelCase :				torch.FloatTensor							,   _UpperCamelCase :				bool = True							)      ->Union[DecoderOutput, torch.FloatTensor]:
									snake_case_   =	sample
									snake_case_   =	self.encode(_UpperCamelCase							).latents
									snake_case_   =	self.decode(_UpperCamelCase							).sample

									if not return_dict:
															return (dec,)

									return DecoderOutput(sample=_UpperCamelCase							)


import math


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__       ):

						if 1 < number < 4:
												# 2 and 3 are primes
												return True
						elif number < 2 or number % 2 == 0 or number % 3 == 0:
												# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
												return False

						# All primes number are in format of 6k +/- 1
						for i in range(5  ,		int(math.sqrt(SCREAMING_SNAKE_CASE__       ) + 1       )  ,		6       ):
												if number % i == 0 or number % (i + 2) == 0:
																		return False
						return True


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__ = 10001       ):

						try:
												snake_case_   =	int(SCREAMING_SNAKE_CASE__       )
						except (TypeError, ValueError):
												raise TypeError('''Parameter nth must be int or castable to int.'''       ) from None
						if nth <= 0:
												raise ValueError('''Parameter nth must be greater than or equal to one.'''       )
						snake_case_   =	[]
						snake_case_   =	2
						while len(SCREAMING_SNAKE_CASE__       ) < nth:
												if is_prime(SCREAMING_SNAKE_CASE__       ):
																		primes.append(SCREAMING_SNAKE_CASE__       )
																		num += 1
												else:
																		num += 1
						return primes[len(SCREAMING_SNAKE_CASE__       ) - 1]


if __name__ == "__main__":
					print(f"""{solution() = }""")