


"""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 	lowercase__				(					A_				):
   __UpperCAmelCase							       =					42







class 	lowercase__				(					A_ ,A_				):





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

      # pass init params to Encoder
      _lowerCamelCase	:					List[str]   =		Encoder(
          in_channels=SCREAMING_SNAKE_CASE      ,   out_channels=SCREAMING_SNAKE_CASE      ,   down_block_types=SCREAMING_SNAKE_CASE      ,   block_out_channels=SCREAMING_SNAKE_CASE      ,   layers_per_block=SCREAMING_SNAKE_CASE      ,   act_fn=SCREAMING_SNAKE_CASE      ,   norm_num_groups=SCREAMING_SNAKE_CASE      ,   double_z=SCREAMING_SNAKE_CASE      ,   )

      _lowerCamelCase	:					Tuple   =		vq_embed_dim if vq_embed_dim is not None else latent_channels

      _lowerCamelCase	:					Optional[Any]   =		nn.Convad(SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   1)
      _lowerCamelCase	:					str   =		VectorQuantizer(SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   beta=0.25      ,   remap=SCREAMING_SNAKE_CASE      ,   sane_index_shape=SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					Union[str, Any]   =		nn.Convad(SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   1)

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





   @apply_forward_hook
   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE = True)  ->  VQEncoderOutput:
      _lowerCamelCase	:					Optional[Any]   =		self.encoder(SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					Dict   =		self.quant_conv(SCREAMING_SNAKE_CASE)

      if not return_dict:
         return (h,)

      return VQEncoderOutput(latents=SCREAMING_SNAKE_CASE)





   @apply_forward_hook
   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE = False      ,   SCREAMING_SNAKE_CASE = True)  ->  Union[DecoderOutput, torch.FloatTensor]:
      # also go through quantization layer
      if not force_not_quantize:
         _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase	:					List[str]   =		self.quantize(SCREAMING_SNAKE_CASE)
      else:
         _lowerCamelCase	:					int   =		h
      _lowerCamelCase	:					List[Any]   =		self.post_quant_conv(SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					Optional[int]   =		self.decoder(SCREAMING_SNAKE_CASE      ,   quant if self.config.norm_type == """spatial""" else None)

      if not return_dict:
         return (dec,)

      return DecoderOutput(sample=SCREAMING_SNAKE_CASE)





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE = True)  ->  Union[DecoderOutput, torch.FloatTensor]:
      _lowerCamelCase	:					Dict   =		sample
      _lowerCamelCase	:					Any   =		self.encode(SCREAMING_SNAKE_CASE).latents
      _lowerCamelCase	:					Optional[int]   =		self.decode(SCREAMING_SNAKE_CASE).sample

      if not return_dict:
         return (dec,)

      return DecoderOutput(sample=SCREAMING_SNAKE_CASE)





"""simple docstring"""
import random

def 				_snake_case      (   __snake_case							:      List[Any]						, __snake_case							:      List[Any]						, __snake_case							:      int   ):




   """simple docstring"""







   _lowerCamelCase	:					List[str]   =		a[left_index]
   _lowerCamelCase	:					Dict   =		left_index + 1
   for j in range(left_index + 1						, __snake_case   ):
      if a[j] < pivot:
         _lowerCamelCase   ,    _lowerCamelCase	:					List[str]   =		a[i], a[j]
         i += 1
   _lowerCamelCase   ,    _lowerCamelCase	:					Optional[int]   =		a[i - 1], a[left_index]
   return i - 1

def 				_snake_case      (   __snake_case							:      Tuple						, __snake_case							:      List[str]						, __snake_case							:      List[str]   ):




   """simple docstring"""







   if left < right:
      _lowerCamelCase	:					Any   =		random.randint(__snake_case						, right - 1   )
      _lowerCamelCase   ,    _lowerCamelCase	:					Optional[Any]   =		(
          a[left],
          a[pivot],
      )  # switches the pivot with the left most bound
      _lowerCamelCase	:					List[str]   =		partition(__snake_case						, __snake_case						, __snake_case   )
      quick_sort_random(
          __snake_case						, __snake_case						, __snake_case   )  # recursive quicksort to the left of the pivot point
      quick_sort_random(
          __snake_case						, pivot_index + 1						, __snake_case   )  # recursive quicksort to the right of the pivot point

def 				_snake_case      (   ):




   """simple docstring"""







   _lowerCamelCase	:					Union[str, Any]   =		input("""Enter numbers separated by a comma:\n"""   ).strip()
   _lowerCamelCase	:					int   =		[int(__snake_case   ) for item in user_input.split(""","""   )]

   quick_sort_random(__snake_case						, 0						, len(__snake_case   )   )

   print(__snake_case   )


if __name__ == "__main__":
 main()

