code: 
  
  
  from functools import partial
  from typing import Any, Callable, Dict, List, Type, TypeVar, Union, overload
  
  import torch
  import torch.nn as nn
  import torch.types
  def _A		(		lowercase ,					lowercase ,					lowercase	):
  
  
  
  
  
  
  
      """simple docstring"""
      # The first operation in a checkpoint can't be in-place, but it's
      # nice to have in-place addition during inference. Thus...
      if not inplace:
          a					=ma + ma
      else:
          ma += ma
  
      return ma
  def _A		(		lowercase ,					lowercase	):
  
  
  
  
  
  
  
      """simple docstring"""
      a					=-1 * len(lowercase	)
      a					=list(range(len(tensor.shape[:zero_index]	)	)	)
      return tensor.permute(first_inds + [zero_index + i for i in inds]	)
  def _A		(		lowercase ,					lowercase	):
  
  
  
  
  
  
  
      """simple docstring"""
      return t.reshape(t.shape[:-no_dims] + (-1,)	)
  def _A		(		lowercase ,					lowercase ,					lowercase ,					lowercase = 1E-4	):
  
  
  
  
  
  
  
      """simple docstring"""
      a					=mask.expand(*value.shape	)
      return torch.sum(mask * value ,					dim=lowercase	) / (eps + torch.sum(lowercase ,					dim=lowercase	))
  def _A		(		lowercase ,					lowercase = 2.3125 ,					lowercase = 21.6875 ,					lowercase = 64	):
  
  
  
  
  
  
  
      """simple docstring"""
      a					=torch.linspace(lowercase ,					lowercase ,					no_bins - 1 ,					device=pts.device	)
      a					=torch.sqrt(torch.sum((pts.unsqueeze(-2	) - pts.unsqueeze(-3	)) ** 2 ,					dim=-1	)	)
      return torch.bucketize(lowercase ,					lowercase	)
  def _A		(		lowercase ,					lowercase	):
  
  
  
  
  
  
  
      """simple docstring"""
      a					=dicts[0]
      a					={}
      for k, v in first.items():
          a					=[d[k] for d in dicts]
          if isinstance(lowercase ,					lowercase	):
              a					=dict_multimap(lowercase ,					lowercase	)
          else:
              a					=fn(lowercase	)
  
      return new_dict
  def _A		(		lowercase ,					lowercase	):
  
  
  
  
  
  
  
      """simple docstring"""
      a					=v_bins.view(((1,) * len(x.shape	)) + (len(lowercase	),)	)
      a					=x[..., None] - reshaped_bins
      a					=torch.argmin(torch.abs(lowercase	) ,					dim=-1	)
      return nn.functional.one_hot(lowercase ,					num_classes=len(lowercase	)	).float()
  def _A		(		lowercase ,					lowercase ,					lowercase = 0 ,					lowercase = 0	):
  
  
  
  
  
  
  
      """simple docstring"""
      a					=[]
      for i, s in enumerate(data.shape[:no_batch_dims]	):
          a					=torch.arange(lowercase	)
          a					=r.view(*(*((1,) * i), -1, *((1,) * (len(inds.shape	) - i - 1)))	)
          ranges.append(lowercase	)
  
      a					=[slice(lowercase	) for _ in range(len(data.shape	) - no_batch_dims	)]
      a					=inds
      ranges.extend(lowercase	)
      # Matt note: Editing this to get around the behaviour of using a list as an array index changing
      # in recent Numpy versions
      return data[tuple(lowercase	)]
  
  
  lowerCamelCase_     :     Any    		=						TypeVar("""T""")
  def _A		(		lowercase ,					lowercase ,					lowercase	):
  
  
  
  
  
  
  
      """simple docstring"""
      a					={}
      for k, v in dic.items():
          if isinstance(lowercase ,					lowercase	):
              a					=dict_map(lowercase ,					lowercase ,					lowercase	)
          else:
              a					=tree_map(lowercase ,					lowercase ,					lowercase	)
  
      return new_dict
  @overload
  def _A		(		lowercase ,					lowercase ,					lowercase	):
  
  
  
  
  
  
  
      """simple docstring"""
      ...
  @overload
  def _A		(		lowercase ,					lowercase ,					lowercase	):
  
  
  
  
  
  
  
      """simple docstring"""
      ...
  @overload
  def _A		(		lowercase ,					lowercase ,					lowercase	):
  
  
  
  
  
  
  
      """simple docstring"""
      ...
  @overload
  def _A		(		lowercase ,					lowercase ,					lowercase	):
  
  
  
  
  
  
  
      """simple docstring"""
      ...
  def _A		(		lowercase ,					lowercase ,					lowercase	):
  
  
  
  
  
  
  
      """simple docstring"""
      if isinstance(lowercase ,					lowercase	):
          return dict_map(lowercase ,					lowercase ,					lowercase	)
      elif isinstance(lowercase ,					lowercase	):
          return [tree_map(lowercase ,					lowercase ,					lowercase	) for x in tree]
      elif isinstance(lowercase ,					lowercase	):
          return tuple(tree_map(lowercase ,					lowercase ,					lowercase	) for x in tree	)
      elif isinstance(lowercase ,					lowercase	):
          return fn(lowercase	)
      else:
          print(type(lowercase	)	)
          raise ValueError('''Not supported'''	)
  
  
  
  
  
  lowerCamelCase_     :     Any    		=						partial(tree_map, leaf_type=torch.Tensor)
style_context: 
  
  
  """simple docstring"""
  
  
  
  
  
  
  
  from __future__ import annotations
  
  from random import randrange
  def _A		(		lowercase	):
  
  
  
  
  
  
  
      """simple docstring"""
      if len(lowercase	) < 2:
          return collection
      a					=randrange(len(lowercase	)	)  # Use random element as pivot
      a					=collection[pivot_index]
      a					=[]  # All elements greater than pivot
      a					=[]  # All elements less than or equal to pivot
  
      for element in collection[:pivot_index]:
          (greater if element > pivot else lesser).append(lowercase	)
  
      for element in collection[pivot_index + 1 :]:
          (greater if element > pivot else lesser).append(lowercase	)
  
      return [*quick_sort(lowercase	), pivot, *quick_sort(lowercase	)]
  
  
  if __name__ == "__main__":
         lowerCamelCase_     :     Union[str, Any]    		=						input("""Enter numbers separated by a comma:\n""").strip()
         lowerCamelCase_     :     Union[str, Any]    		=						[int(item) for item in user_input.split(""",""")]
         print(quick_sort(unsorted))