



"""simple docstring"""


def  lowercase	(      _SCREAMING_SNAKE_CASE   :						Any				):






				'''simple docstring'''



				if collection == []:
								return []

				# get some information about the collection
				_UpperCAmelCase					=    len(_SCREAMING_SNAKE_CASE				)
				_UpperCAmelCase					=    max(_SCREAMING_SNAKE_CASE				)
				_UpperCAmelCase					=    min(_SCREAMING_SNAKE_CASE				)

				# create the counting array
				_UpperCAmelCase					=    coll_max + 1 - coll_min
				_UpperCAmelCase					=    [0] * counting_arr_length

				# count how much a number appears in the collection
				for number in collection:
								counting_arr[number - coll_min] += 1

				# sum each position with it's predecessors. now, counting_arr[i] tells
				# us how many elements <= i has in the collection
				for i in range(1					,     _SCREAMING_SNAKE_CASE				):
								_UpperCAmelCase					=    counting_arr[i] + counting_arr[i - 1]

				# create the output collection
				_UpperCAmelCase					=    [0] * coll_len

				# place the elements in the output, respecting the original order (stable
				# sort) from end to begin, updating counting_arr
				for i in reversed(range(0					,     _SCREAMING_SNAKE_CASE				)				):
								_UpperCAmelCase					=    collection[i]
								counting_arr[collection[i] - coll_min] -= 1

				return ordered







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						Tuple				):






				'''simple docstring'''



				return "".join([chr(_SCREAMING_SNAKE_CASE				) for i in counting_sort([ord(_SCREAMING_SNAKE_CASE				) for c in string]				)]				)


if __name__ == "__main__":
					# Test string sort
					assert counting_sort_string("thisisthestring") == "eghhiiinrsssttt"

					__A   :							int										=   input("Enter numbers separated by a comma:\n").strip()
					__A   :							int										=   [int(item) for item in user_input.split(",")]
					print(counting_sort(unsorted))








"""simple docstring"""


from typing import Optional

from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class 			_a (					lowerCAmelCase):
			"""simple docstring"""



			def __init__(		self     :    List[str]				,   __UpperCamelCase     :    NestedDataStructureLike[PathLike]				,   __UpperCamelCase     :    Optional[NamedSplit] = None				,   __UpperCamelCase     :    Optional[Features] = None				,   __UpperCamelCase     :    str = None				,   __UpperCamelCase     :    bool = False				,   __UpperCamelCase     :    bool = False				,   __UpperCamelCase     :    Optional[int] = None				,   **__UpperCamelCase     :    Optional[Any]				,   )->Any:
							super().__init__(
							    __UpperCamelCase				,   split=__UpperCamelCase				,   features=__UpperCamelCase				,   cache_dir=__UpperCamelCase				,   keep_in_memory=__UpperCamelCase				,   streaming=__UpperCamelCase				,   num_proc=__UpperCamelCase				,   **__UpperCamelCase				,   )
							_UpperCAmelCase					=    path_or_paths if isinstance(__UpperCamelCase				,   __UpperCamelCase   ) else {self.split: path_or_paths}
							_UpperCAmelCase					=    Text(
							    cache_dir=__UpperCamelCase				,   data_files=__UpperCamelCase				,   features=__UpperCamelCase				,   **__UpperCamelCase				,   )



			def  lowercase__						(		self     :    Optional[Any]   )->str:
							# Build iterable dataset
							if self.streaming:
											_UpperCAmelCase					=    self.builder.as_streaming_dataset(split=self.split   )
							# Build regular (map-style) dataset
							else:
											_UpperCAmelCase					=    None
											_UpperCAmelCase					=    None
											_UpperCAmelCase					=    None
											_UpperCAmelCase					=    None

											self.builder.download_and_prepare(
											    download_config=__UpperCamelCase				,   download_mode=__UpperCamelCase				,   verification_mode=__UpperCamelCase				,   base_path=__UpperCamelCase				,   num_proc=self.num_proc				,   )
											_UpperCAmelCase					=    self.builder.as_dataset(
											    split=self.split				,   verification_mode=__UpperCamelCase				,   in_memory=self.keep_in_memory   )
							return dataset



