


def     _lowerCamelCase		(				SCREAMING_SNAKE_CASE_					:			list   ):






							"""simple docstring"""



							if len(__A   ) <= 1:
														return lst

							a_							: int								=						1

							while i < len(__A   ):
														if lst[i - 1] <= lst[i]:
																					i += 1
														else:
																					a_							: Union[str, Any]								=						lst[i], lst[i - 1]
																					i -= 1
																					if i == 0:
																												a_							: Dict								=						1

							return lst


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










'''simple docstring'''







import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union

import pandas as pd
import pyarrow as pa

import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast
from datasets.utils.py_utils import Literal


__lowerCAmelCase									=   datasets.utils.logging.get_logger(__name__)

__lowerCAmelCase									=   ['names', 'prefix']
__lowerCAmelCase									=   ['warn_bad_lines', 'error_bad_lines', 'mangle_dupe_cols']
__lowerCAmelCase									=   ['encoding_errors', 'on_bad_lines']
__lowerCAmelCase									=   ['date_format']





@dataclass
class 						SCREAMING_SNAKE_CASE       (       datasets.BuilderConfig							):
			snake_case__	=					","
			snake_case__	=					None
			snake_case__	=					"infer"
			snake_case__	=					None
			snake_case__	=					None
			snake_case__	=					None
			snake_case__	=					None
			snake_case__	=					None
			snake_case__	=					True
			snake_case__	=					None
			snake_case__	=					None
			snake_case__	=					None
			snake_case__	=					None
			snake_case__	=					False
			snake_case__	=					None
			snake_case__	=					None
			snake_case__	=					None
			snake_case__	=					True
			snake_case__	=					True
			snake_case__	=					False
			snake_case__	=					True
			snake_case__	=					None
			snake_case__	=					"."
			snake_case__	=					None
			snake_case__	=					'"'
			snake_case__	=					0
			snake_case__	=					None
			snake_case__	=					None
			snake_case__	=					None
			snake_case__	=					None
			snake_case__	=					True
			snake_case__	=					True
			snake_case__	=					0
			snake_case__	=					True
			snake_case__	=					False
			snake_case__	=					None
			snake_case__	=					10000
			snake_case__	=					None
			snake_case__	=					"strict"
			snake_case__	=					"error"
			snake_case__	=					None






			def       SCREAMING_SNAKE_CASE   (			self   :      int       )      ->      Optional[int]:
							if self.delimiter is not None:
											a_		:	Optional[Any]									=   self.delimiter
							if self.column_names is not None:
											a_		:	Optional[Any]									=   self.column_names







			@property
			def       SCREAMING_SNAKE_CASE   (			self   :      List[str]       )      ->      Optional[Any]:
							a_		:	List[Any]									=   {
							    '''sep''': self.sep,
							    '''header''': self.header,
							    '''names''': self.names,
							    '''index_col''': self.index_col,
							    '''usecols''': self.usecols,
							    '''prefix''': self.prefix,
							    '''mangle_dupe_cols''': self.mangle_dupe_cols,
							    '''engine''': self.engine,
							    '''converters''': self.converters,
							    '''true_values''': self.true_values,
							    '''false_values''': self.false_values,
							    '''skipinitialspace''': self.skipinitialspace,
							    '''skiprows''': self.skiprows,
							    '''nrows''': self.nrows,
							    '''na_values''': self.na_values,
							    '''keep_default_na''': self.keep_default_na,
							    '''na_filter''': self.na_filter,
							    '''verbose''': self.verbose,
							    '''skip_blank_lines''': self.skip_blank_lines,
							    '''thousands''': self.thousands,
							    '''decimal''': self.decimal,
							    '''lineterminator''': self.lineterminator,
							    '''quotechar''': self.quotechar,
							    '''quoting''': self.quoting,
							    '''escapechar''': self.escapechar,
							    '''comment''': self.comment,
							    '''encoding''': self.encoding,
							    '''dialect''': self.dialect,
							    '''error_bad_lines''': self.error_bad_lines,
							    '''warn_bad_lines''': self.warn_bad_lines,
							    '''skipfooter''': self.skipfooter,
							    '''doublequote''': self.doublequote,
							    '''memory_map''': self.memory_map,
							    '''float_precision''': self.float_precision,
							    '''chunksize''': self.chunksize,
							    '''encoding_errors''': self.encoding_errors,
							    '''on_bad_lines''': self.on_bad_lines,
							    '''date_format''': self.date_format,
							}

							# some kwargs must not be passed if they don't have a default value
							# some others are deprecated and we can also not pass them if they are the default value
							for pd_read_csv_parameter in _PANDAS_READ_CSV_NO_DEFAULT_PARAMETERS + _PANDAS_READ_CSV_DEPRECATED_PARAMETERS:
											if pd_read_csv_kwargs[pd_read_csv_parameter] == getattr(CsvConfig()	,    __SCREAMING_SNAKE_CASE       ):
															del pd_read_csv_kwargs[pd_read_csv_parameter]

        # Remove 2.0 new arguments
							if not (datasets.config.PANDAS_VERSION.major >= 2):
											for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_2_0_0_PARAMETERS:
															del pd_read_csv_kwargs[pd_read_csv_parameter]

        # Remove 1.3 new arguments
							if not (datasets.config.PANDAS_VERSION.major >= 1 and datasets.config.PANDAS_VERSION.minor >= 3):
											for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_1_3_0_PARAMETERS:
															del pd_read_csv_kwargs[pd_read_csv_parameter]

							return pd_read_csv_kwargs





class 						SCREAMING_SNAKE_CASE       (       datasets.ArrowBasedBuilder							):
			snake_case__	=					CsvConfig






			def       SCREAMING_SNAKE_CASE   (			self   :      List[str]       )      ->      Tuple:
							return datasets.DatasetInfo(features=self.config.features       )






			def       SCREAMING_SNAKE_CASE   (			self   :      Tuple	,    __SCREAMING_SNAKE_CASE   :      Optional[int]       )      ->      Any:
							if not self.config.data_files:
											raise ValueError(f'At least one data file must be specified, but got data_files={self.config.data_files}'       )
							a_		:	Tuple									=   dl_manager.download_and_extract(self.config.data_files       )
							if isinstance(__SCREAMING_SNAKE_CASE	,    (str, list, tuple)       ):
											a_		:	int									=   data_files
											if isinstance(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       ):
															a_		:	Optional[Any]									=   [files]
											a_		:	Optional[Any]									=   [dl_manager.iter_files(__SCREAMING_SNAKE_CASE       ) for file in files]
											return [datasets.SplitGenerator(name=datasets.Split.TRAIN	,    gen_kwargs={'''files''': files}       )]
							a_		:	List[str]									=   []
							for split_name, files in data_files.items():
											if isinstance(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       ):
															a_		:	int									=   [files]
											a_		:	Union[str, Any]									=   [dl_manager.iter_files(__SCREAMING_SNAKE_CASE       ) for file in files]
											splits.append(datasets.SplitGenerator(name=__SCREAMING_SNAKE_CASE	,    gen_kwargs={'''files''': files}       )       )
							return splits






			def       SCREAMING_SNAKE_CASE   (			self   :      Union[str, Any]	,    __SCREAMING_SNAKE_CASE   :      pa.Table       )      ->      pa.Table:
							if self.config.features is not None:
											a_		:	Any									=   self.config.features.arrow_schema
											if all(not require_storage_cast(__SCREAMING_SNAKE_CASE       ) for feature in self.config.features.values()       ):
															# cheaper cast
															a_		:	List[Any]									=   pa.Table.from_arrays([pa_table[field.name] for field in schema]	,    schema=__SCREAMING_SNAKE_CASE       )
											else:
															# more expensive cast; allows str <-> int/float or str to Audio for example
															a_		:	Optional[Any]									=   table_cast(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       )
							return pa_table







			def       SCREAMING_SNAKE_CASE   (			self   :      Optional[int]	,    __SCREAMING_SNAKE_CASE   :      Any       )      ->      Optional[int]:
							a_		:	Union[str, Any]									=   self.config.features.arrow_schema if self.config.features else None
							# dtype allows reading an int column as str
							a_		:	Optional[int]									=   (
							    {
							        name: dtype.to_pandas_dtype() if not require_storage_cast(__SCREAMING_SNAKE_CASE       ) else object
							        for name, dtype, feature in zip(schema.names	,    schema.types	,    self.config.features.values()       )
							    }
							    if schema is not None
							    else None
							)
							for file_idx, file in enumerate(itertools.chain.from_iterable(__SCREAMING_SNAKE_CASE       )       ):
											a_		:	Tuple									=   pd.read_csv(__SCREAMING_SNAKE_CASE	,    iterator=__SCREAMING_SNAKE_CASE	,    dtype=__SCREAMING_SNAKE_CASE	,    **self.config.pd_read_csv_kwargs       )
											try:
															for batch_idx, df in enumerate(__SCREAMING_SNAKE_CASE       ):
																			a_		:	Optional[int]									=   pa.Table.from_pandas(__SCREAMING_SNAKE_CASE       )
																			# Uncomment for debugging (will print the Arrow table size and elements)
																			# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
																			# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
																			yield (file_idx, batch_idx), self._cast_table(__SCREAMING_SNAKE_CASE       )
											except ValueError as e:
															logger.error(f'Failed to read file \'{file}\' with error {type(__SCREAMING_SNAKE_CASE       )}: {e}'       )
															raise




