


"""simple docstring"""







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)



"""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))