


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    ,						_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_8215    ,						_lowercase = "group"    ,						):
       super().__init__()

       # pass init params to Encoder
       lowerCAmelCase_					:       Dict    =		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    ,						)

       lowerCAmelCase_					:       Any    =		vq_embed_dim if vq_embed_dim is not None else latent_channels

       lowerCAmelCase_					:       Tuple    =		nn.Convad(_lowercase    ,						_lowercase    ,						1						)
       lowerCAmelCase_					:       Tuple    =		VectorQuantizer(_lowercase    ,						_lowercase    ,						beta=0.25    ,						remap=_lowercase    ,						sane_index_shape=_lowercase						)
       lowerCAmelCase_					:       Tuple    =		nn.Convad(_lowercase    ,						_lowercase    ,						1						)

       # pass init params to Decoder
       lowerCAmelCase_					:       Optional[Any]    =		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 	UpperCAmelCase__		(	self    ,						_lowercase    ,						_lowercase = True						):
       lowerCAmelCase_					:       str    =		self.encoder(_lowercase						)
       lowerCAmelCase_					:       Optional[Any]    =		self.quant_conv(_lowercase						)

       if not return_dict:
            return (h,)

       return VQEncoderOutput(latents=_lowercase						)







  @apply_forward_hook
  def 	UpperCAmelCase__		(	self    ,						_lowercase    ,						_lowercase = False    ,						_lowercase = True						):
       # also go through quantization layer
       if not force_not_quantize:
            lowerCAmelCase_					:       Dict    =		self.quantize(_lowercase						)
       else:
            lowerCAmelCase_					:       Optional[int]    =		h
       lowerCAmelCase_					:       Tuple    =		self.post_quant_conv(_lowercase						)
       lowerCAmelCase_					:       str    =		self.decoder(_lowercase    ,						quant if self.config.norm_type == """spatial""" else None						)

       if not return_dict:
            return (dec,)

       return DecoderOutput(sample=_lowercase						)







  def 	UpperCAmelCase__		(	self    ,						_lowercase    ,						_lowercase = True						):
       lowerCAmelCase_					:       Dict    =		sample
       lowerCAmelCase_					:       List[str]    =		self.encode(_lowercase						).latents
       lowerCAmelCase_					:       List[Any]    =		self.decode(_lowercase						).sample

       if not return_dict:
            return (dec,)

       return DecoderOutput(sample=_lowercase						)







from __future__ import annotations






def 							_lowerCAmelCase (  _a   :					list[int]			)	->			list[int]:  # This function is recursive
     lowerCAmelCase_					:       List[Any]    =		len(_a			)
     # If the array contains only one element, we return it (it's the stop condition of
     # recursion)
     if array_length <= 1:
          return array
          # Else
     lowerCAmelCase_					:       Union[str, Any]    =		array[0]
     lowerCAmelCase_					:       str    =		False
     lowerCAmelCase_					:       Optional[int]    =		1
     lowerCAmelCase_					:       list[int]    =		[]
     while not is_found and i < array_length:
          if array[i] < pivot:
               lowerCAmelCase_					:       Optional[Any]    =		True
               lowerCAmelCase_					:       Tuple    =		[element for element in array[i:] if element >= array[i]]
               lowerCAmelCase_					:       Any    =		longest_subsequence(_a			)
               if len(_a			) > len(_a			):
                    lowerCAmelCase_					:       List[Any]    =		temp_array
          else:
               i += 1

     lowerCAmelCase_					:       Tuple    =		[element for element in array[1:] if element >= pivot]
     lowerCAmelCase_					:       List[Any]    =		[pivot, *longest_subsequence(_a			)]
     if len(_a			) > len(_a			):
          return temp_array
     else:
          return longest_subseq


if __name__ == "__main__":
      import doctest

      doctest.testmod()
