

import bisect
import copy
from collections import defaultdict

import numpy as np
from torch.utils.data import BatchSampler, Sampler

from utils import logger


def   __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__	):
						snake_case_       =     copy.deepcopy(SCREAMING_SNAKE_CASE__	)
						snake_case_       =     sorted(SCREAMING_SNAKE_CASE__	)
						snake_case_       =     [bisect.bisect_right(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__	) for y in x]
						return quantized


def   __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__=0	):
						snake_case_       =     np.arange(start=3  ,		stop=SCREAMING_SNAKE_CASE__  ,		step=4	).tolist() if k > 0 else [10]
						snake_case_       =     _quantize(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__	)
						# count number of elements per group
						snake_case_       =     np.unique(SCREAMING_SNAKE_CASE__  ,		return_counts=SCREAMING_SNAKE_CASE__	)[1]
						snake_case_       =     [0] + bins + [np.inf]
						logger.info('''Using {} as bins for aspect lengths quantization'''.format(SCREAMING_SNAKE_CASE__	)	)
						logger.info('''Count of instances per bin: {}'''.format(SCREAMING_SNAKE_CASE__	)	)
						return groups





class      snake_case_       (		__A ):







			'''simple docstring'''

			def __init__(					self			:    Tuple							,       _UpperCamelCase			:    Dict							,       _UpperCamelCase			:    Any							,       _UpperCamelCase			:    str							)				->Optional[int]:
									if not isinstance(_UpperCamelCase							,       _UpperCamelCase							):
															raise ValueError(
															    '''sampler should be an instance of torch.utils.data.Sampler, but got sampler={}'''.format(_UpperCamelCase							)							)
									snake_case_       =     sampler
									snake_case_       =     group_ids
									snake_case_       =     batch_size







			def __iter__(					self			:    Dict							)				->int:
									snake_case_       =     defaultdict(_UpperCamelCase							)
									snake_case_       =     defaultdict(_UpperCamelCase							)

									snake_case_       =     0
									for idx in self.sampler:
															snake_case_       =     self.group_ids[idx]
															buffer_per_group[group_id].append(_UpperCamelCase							)
															samples_per_group[group_id].append(_UpperCamelCase							)
															if len(buffer_per_group[group_id]							) == self.batch_size:
																					yield buffer_per_group[group_id]  # TODO
																					num_batches += 1
																					del buffer_per_group[group_id]
															assert len(buffer_per_group[group_id]							) < self.batch_size

									# now we have run out of elements that satisfy
									# the group criteria, let's return the remaining
									# elements so that the size of the sampler is
									# deterministic
									snake_case_       =     len(self							)
									snake_case_       =     expected_num_batches - num_batches
									if num_remaining > 0:
															# for the remaining batches, group the batches by similar lengths
															snake_case_       =     []
															for group_id, idxs in sorted(buffer_per_group.items()							,       key=lambda _UpperCamelCase							: x[0]							):
																					batch_idx.extend(_UpperCamelCase							)
																					if len(_UpperCamelCase							) >= self.batch_size:
																											yield batch_idx[: self.batch_size]
																											snake_case_       =     batch_idx[self.batch_size :]
																											num_remaining -= 1
															if len(_UpperCamelCase							) > 0:
																					yield batch_idx
																					num_remaining -= 1
									assert num_remaining == 0







			def __len__(					self			:    Dict							)				->Optional[int]:
									return (len(self.sampler							) + self.batch_size - 1) // self.batch_size


from __future__ import annotations

from random import randrange


def   __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__	):
						if len(SCREAMING_SNAKE_CASE__	) < 2:
												return collection
						snake_case_       =     randrange(len(SCREAMING_SNAKE_CASE__	)	)  # Use random element as pivot
						snake_case_       =     collection[pivot_index]
						snake_case_       =     []  # All elements greater than pivot
						snake_case_       =     []  # All elements less than or equal to pivot

						for element in collection[:pivot_index]:
												(greater if element > pivot else lesser).append(SCREAMING_SNAKE_CASE__	)

						for element in collection[pivot_index + 1 :]:
												(greater if element > pivot else lesser).append(SCREAMING_SNAKE_CASE__	)

						return [*quick_sort(SCREAMING_SNAKE_CASE__	), pivot, *quick_sort(SCREAMING_SNAKE_CASE__	)]


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