
"""simple docstring"""




from random import randint
from tempfile import TemporaryFile

import numpy as np

def lowercase_						(     _snake_case     ,_snake_case     ,_snake_case		):
							SCREAMING_SNAKE_CASE__		: Tuple     =     0
							if start < end:
														SCREAMING_SNAKE_CASE__		: str     =     randint(_snake_case     ,_snake_case		)
														SCREAMING_SNAKE_CASE__		: str     =     a[end]
														SCREAMING_SNAKE_CASE__		: List[str]     =     a[pivot]
														SCREAMING_SNAKE_CASE__		: Dict     =     temp

														SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__		: Any     =     _in_place_partition(_snake_case     ,_snake_case     ,_snake_case		)
														count += _in_place_quick_sort(_snake_case     ,_snake_case     ,p - 1		)
														count += _in_place_quick_sort(_snake_case     ,p + 1     ,_snake_case		)
							return count

def lowercase_						(     _snake_case     ,_snake_case     ,_snake_case		):
							SCREAMING_SNAKE_CASE__		: Dict     =     0
							SCREAMING_SNAKE_CASE__		: Optional[int]     =     randint(_snake_case     ,_snake_case		)
							SCREAMING_SNAKE_CASE__		: Optional[int]     =     a[end]
							SCREAMING_SNAKE_CASE__		: str     =     a[pivot]
							SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     temp
							SCREAMING_SNAKE_CASE__		: Tuple     =     start - 1
							for index in range(_snake_case     ,_snake_case		):
														count += 1
														if a[index] < a[end]:  # check if current val is less than pivot value
																					SCREAMING_SNAKE_CASE__		: List[Any]     =     new_pivot_index + 1
																					SCREAMING_SNAKE_CASE__		: Dict     =     a[new_pivot_index]
																					SCREAMING_SNAKE_CASE__		: Any     =     a[index]
																					SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     temp

							SCREAMING_SNAKE_CASE__		: Any     =     a[new_pivot_index + 1]
							SCREAMING_SNAKE_CASE__		: List[Any]     =     a[end]
							SCREAMING_SNAKE_CASE__		: Optional[Any]     =     temp
							return new_pivot_index + 1, count


UpperCAmelCase__   :					Tuple												=				TemporaryFile()
UpperCAmelCase__   :					List[Any]												=				1_0_0  # 1000 elements are to be sorted


UpperCAmelCase__							,       UpperCAmelCase__   :					List[Any]												=				0, 1  # mean and standard deviation
UpperCAmelCase__   :					Optional[Any]												=				np.random.normal(mu, sigma, p)
np.save(outfile, X)
print('The array is')
print(X)


outfile.seek(0)  # using the same array
UpperCAmelCase__   :					Optional[int]												=				np.load(outfile)
UpperCAmelCase__   :					Union[str, Any]												=				len(M) - 1
UpperCAmelCase__   :					List[str]												=				_in_place_quick_sort(M, 0, r)

print(
    'No of Comparisons for 100 elements selected from a standard normal distribution'
    'is :'
)

print(z)






"""simple docstring"""




import itertools
from dataclasses import dataclass
from typing import Optional

import pandas as pd
import pyarrow as pa

import datasets
from datasets.table import table_cast



@dataclass
class 			lowerCAmelCase_		(datasets.BuilderConfig						):


				"""simple docstring"""



				__UpperCamelCase   :		Optional[datasets.Features]       =     None






class 			lowerCAmelCase_		(datasets.ArrowBasedBuilder						):


				"""simple docstring"""



				__UpperCamelCase   :		Union[str, Any]       =     PandasConfig



				def 			__magic_name__					(self					)      ->	List[str]:



											"""simple docstring"""




											return datasets.DatasetInfo(features=self.config.features					)



				def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__					)      ->	Optional[int]:



											"""simple docstring"""




											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}'''					)
											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     dl_manager.download_and_extract(self.config.data_files					)
											if isinstance(SCREAMING_SNAKE_CASE__     , (str, list, tuple)					):
																		SCREAMING_SNAKE_CASE__		: Tuple     =     data_files
																		if isinstance(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					):
																									SCREAMING_SNAKE_CASE__		: Optional[Any]     =     [files]
																		# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
																		SCREAMING_SNAKE_CASE__		: str     =     [dl_manager.iter_files(SCREAMING_SNAKE_CASE__					) for file in files]
																		return [datasets.SplitGenerator(name=datasets.Split.TRAIN     , gen_kwargs={"""files""": files}					)]
											SCREAMING_SNAKE_CASE__		: int     =     []
											for split_name, files in data_files.items():
																		if isinstance(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					):
																									SCREAMING_SNAKE_CASE__		: List[str]     =     [files]
																		# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
																		SCREAMING_SNAKE_CASE__		: Dict     =     [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 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__					)      ->	pa.Table:



											"""simple docstring"""




											if self.config.features is not None:
																		# more expensive cast to support nested features with keys in a different order
																		# allows str <-> int/float or str to Audio for example
																		SCREAMING_SNAKE_CASE__		: Tuple     =     table_cast(SCREAMING_SNAKE_CASE__     , self.config.features.arrow_schema					)
											return pa_table



				def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__					)      ->	Optional[Any]:



											"""simple docstring"""




											for i, file in enumerate(itertools.chain.from_iterable(SCREAMING_SNAKE_CASE__					)					):
																		with open(SCREAMING_SNAKE_CASE__     , """rb"""					) as f:
																									SCREAMING_SNAKE_CASE__		: List[str]     =     pa.Table.from_pandas(pd.read_pickle(SCREAMING_SNAKE_CASE__					)					)
																									yield i, self._cast_table(SCREAMING_SNAKE_CASE__					)




