
"""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 		a__						(      a_						):
   __lowerCAmelCase            =				42


class 		a__						(      a_,			a_						):
   @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_8_2_1_5	,    _a = "group"	,    ):
          super().__init__()

          # pass init params to Encoder
          lowercase    :				List[Any]       		=							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	,    )

          lowercase    :				str       		=							vq_embed_dim if vq_embed_dim is not None else latent_channels

          lowercase    :				Union[str, Any]       		=							nn.Convad(_a	,    _a	,    1				)
          lowercase    :				List[str]       		=							VectorQuantizer(_a	,    _a	,    beta=0.2_5	,    remap=_a	,    sane_index_shape=_a				)
          lowercase    :				Optional[Any]       		=							nn.Convad(_a	,    _a	,    1				)

          # pass init params to Decoder
          lowercase    :				List[Any]       		=							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 			__magic_name__			(			self	,    _a	,    _a = True				):
          lowercase    :				Dict       		=							self.encoder(_a				)
          lowercase    :				List[Any]       		=							self.quant_conv(_a				)

          if not return_dict:
                 return (h,)

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

          if not return_dict:
                 return (dec,)

          return DecoderOutput(sample=_a				)

   def 			__magic_name__			(			self	,    _a	,    _a = True				):
          lowercase    :				Dict       		=							sample
          lowercase    :				Any       		=							self.encode(_a				).latents
          lowercase    :				List[Any]       		=							self.decode(_a				).sample

          if not return_dict:
                 return (dec,)

          return DecoderOutput(sample=_a				)




"""simple docstring"""




from __future__ import annotations

from bisect import bisect_left
from functools import total_ordering
from heapq import merge

@total_ordering
class 		a__						(      a_						):
   def __lt__(			self	,    _a				):
          return self[-1] < other[-1]

   def __eq__(			self	,    _a				):
          return self[-1] == other[-1]


def   __magic_name__	(      __snake_case				:       list  ) ->				list:
       lowercase    :				list[Stack]       		=							[]
       # sort into stacks
       for element in collection:
              lowercase    :				Union[str, Any]       		=							Stack([element]  )
              lowercase    :				Dict       		=							bisect_left(__snake_case     , __snake_case  )
              if i != len(__snake_case  ):
                     stacks[i].append(__snake_case  )
              else:
                     stacks.append(__snake_case  )

    # use a heap-based merge to merge stack efficiently
       lowercase    :				Dict       		=							merge(*(reversed(__snake_case  ) for stack in stacks)  )
       return collection


if __name__ == "__main__":
   _A			:       Optional[int]   		=       input("""Enter numbers separated by a comma:\n""").strip()
   _A			:       Optional[Any]   		=       [int(item) for item in user_input.split(""",""")]
   print(patience_sort(unsorted))


