"""simple docstring"""

import re

from filelock import FileLock


try:
			import nltk

			a_			   =					True
except (ImportError, ModuleNotFoundError):
			a_			   =					False

if NLTK_AVAILABLE:
			with FileLock('''.lock''') as lock:
						nltk.download('''punkt''', quiet=True)







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  str			):



					"""simple docstring"""





					re.sub("""<n>"""				,    """"""				,    SCREAMING_SNAKE_CASE__			)  # remove pegasus newline char
					assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
					return "\n".join(nltk.sent_tokenize(SCREAMING_SNAKE_CASE__			)			)









"""simple docstring"""

from random import randint
from tempfile import TemporaryFile

import numpy as np







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  List[Any]				,    SCREAMING_SNAKE_CASE__ :  Dict				,    SCREAMING_SNAKE_CASE__ :  Optional[int]			):



					"""simple docstring"""





					snake_case_					:		Union[str, Any] 					=					0
					if start < end:
										snake_case_					:		Union[str, Any] 					=					randint(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			)
										snake_case_					:		List[Any] 					=					a[end]
										snake_case_					:		Dict 					=					a[pivot]
										snake_case_					:		Any 					=					temp

										snake_case_					,			snake_case_					:		Dict 					=					_in_place_partition(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			)
										count += _in_place_quick_sort(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__				,    p - 1			)
										count += _in_place_quick_sort(SCREAMING_SNAKE_CASE__				,    p + 1				,    SCREAMING_SNAKE_CASE__			)
					return count







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  int				,    SCREAMING_SNAKE_CASE__ :  List[Any]				,    SCREAMING_SNAKE_CASE__ :  Dict			):



					"""simple docstring"""





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

					snake_case_					:		Union[str, Any] 					=					a[new_pivot_index + 1]
					snake_case_					:		Union[str, Any] 					=					a[end]
					snake_case_					:		Union[str, Any] 					=					temp
					return new_pivot_index + 1, count


a_			   =					TemporaryFile()
a_			   =					100  # 1000 elements are to be sorted


a_   ,    a_			   =					0, 1  # mean and standard deviation
a_			   =					np.random.normal(mu, sigma, p)
np.save(outfile, X)
print('''The array is''')
print(X)


outfile.seek(0)  # using the same array
a_			   =					np.load(outfile)
a_			   =					len(M) - 1
a_			   =					_in_place_quick_sort(M, 0, r)

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


print(z)




