


'''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   : Optional[int]             =				datasets.utils.logging.get_logger(__name__)

lowerCAmelCase   : Optional[int]             =				["""names""", """prefix"""]
lowerCAmelCase   : List[Any]             =				["""warn_bad_lines""", """error_bad_lines""", """mangle_dupe_cols"""]
lowerCAmelCase   : str             =				["""encoding_errors""", """on_bad_lines"""]
lowerCAmelCase   : Tuple             =				["""date_format"""]




@dataclass
class        _UpperCamelCase	(    datasets.BuilderConfig):


				'''simple docstring'''


				_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 	a__		(     self							)    ->  str:
					if self.delimiter is not None:
						lowercase						:		int   					=		self.delimiter
					if self.column_names is not None:
						lowercase						:		List[Any]   					=		self.column_names


				@property
				def 	a__		(     self							)    ->  str:
					lowercase						:		Dict   					=		{
					    "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()							,      a_							):
							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        _UpperCamelCase	(    datasets.ArrowBasedBuilder):


				'''simple docstring'''


				_snake_case						       =				CsvConfig

				def 	a__		(     self							)    ->  Union[str, Any]:
					return datasets.DatasetInfo(features=self.config.features							)

				def 	a__		(     self							,      a_							)    ->  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}'''							)
					lowercase						:		Optional[Any]   					=		dl_manager.download_and_extract(self.config.data_files							)
					if isinstance(a_							,      (str, list, tuple)							):
						lowercase						:		Optional[int]   					=		data_files
						if isinstance(a_							,      a_							):
							lowercase						:		Tuple   					=		[files]
						lowercase						:		int   					=		[dl_manager.iter_files(a_							) for file in files]
						return [datasets.SplitGenerator(name=datasets.Split.TRAIN							,      gen_kwargs={"files": files}							)]
					lowercase						:		Optional[int]   					=		[]
					for split_name, files in data_files.items():
						if isinstance(a_							,      a_							):
							lowercase						:		Dict   					=		[files]
						lowercase						:		Dict   					=		[dl_manager.iter_files(a_							) for file in files]
						splits.append(datasets.SplitGenerator(name=a_							,      gen_kwargs={"files": files}							)							)
					return splits

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


				def 	a__		(     self							,      a_							)    ->  Union[str, Any]:
					lowercase						:		Optional[int]   					=		self.config.features.arrow_schema if self.config.features else None
					# dtype allows reading an int column as str
					lowercase						:		List[Any]   					=		(
					    {
					        name: dtype.to_pandas_dtype() if not require_storage_cast(a_							) 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(a_							)							):
						lowercase						:		Dict   					=		pd.read_csv(a_							,      iterator=a_							,      dtype=a_							,      **self.config.pd_read_csv_kwargs							)
						try:
							for batch_idx, df in enumerate(a_							):
								lowercase						:		List[Any]   					=		pa.Table.from_pandas(a_							)
								# 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(a_							)
						except ValueError as e:
							logger.error(F'''Failed to read file \'{file}\' with error {type(a_							)}: {e}'''							)
							raise






'''simple docstring'''
def 	_A  (       A							)  ->				list:

	if len(A							) <= 1:
		return [tuple(A							)]

	lowercase						:		Dict   					=		[]

	def generate(A							,A							):
		lowercase						:		List[Any]   					=		[0] * n
		res.append(tuple(A							)							)

		lowercase						:		Tuple   					=		0
		while i < n:
			if c[i] < i:
				if i % 2 == 0:
					lowercase					,		lowercase						:		List[Any]   					=		arr[i], arr[0]
				else:
					lowercase					,		lowercase						:		Optional[Any]   					=		arr[i], arr[c[i]]
				res.append(tuple(A							)							)
				c[i] += 1
				lowercase						:		Optional[Any]   					=		0
			else:
				lowercase						:		Any   					=		0
				i += 1

	generate(len(A							)							,A							)
	return res


if __name__ == "__main__":
					lowerCAmelCase   : Any             =				input("""Enter numbers separated by a comma:\n""").strip()
					lowerCAmelCase   : List[str]             =				[int(item) for item in user_input.split(""",""")]
					print(heaps(arr))


