



from random import randint
from tempfile import TemporaryFile

import numpy as np

def      _A				(UpperCamelCase		:      Optional[Any]      ,			UpperCamelCase		:      Tuple      ,			UpperCamelCase		:      Tuple )  ->Dict:



			'''simple docstring'''







			lowerCamelCase__ :      List[Any]								=		0
			if start < end:
						lowerCamelCase__ :      Optional[int]								=		randint(UpperCamelCase      ,			UpperCamelCase )
						lowerCamelCase__ :      Union[str, Any]								=		a[end]
						lowerCamelCase__ :      Optional[Any]								=		a[pivot]
						lowerCamelCase__ :      Any								=		temp

						lowerCamelCase__ :      List[str]								=		_in_place_partition(UpperCamelCase      ,			UpperCamelCase      ,			UpperCamelCase )
						count += _in_place_quick_sort(UpperCamelCase      ,			UpperCamelCase      ,			p - 1 )
						count += _in_place_quick_sort(UpperCamelCase      ,			p + 1      ,			UpperCamelCase )
			return count

def      _A				(UpperCamelCase		:      int      ,			UpperCamelCase		:      str      ,			UpperCamelCase		:      Optional[Any] )  ->Dict:



			'''simple docstring'''







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

			lowerCamelCase__ :      List[str]								=		a[new_pivot_index + 1]
			lowerCamelCase__ :      str								=		a[end]
			lowerCamelCase__ :      Any								=		temp
			return new_pivot_index + 1, count


_lowercase     							=						TemporaryFile()
_lowercase     							=						1_00  # 1000 elements are to be sorted


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


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

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








from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase






def      _A				(UpperCamelCase		:      str )  ->None:



			'''simple docstring'''







			lowerCamelCase__							,lowerCamelCase__ :      List[str]								=		analyze_text(UpperCamelCase )
			lowerCamelCase__ :      Any								=		list(""" """ + ascii_lowercase )
			# what is our total sum of probabilities.
			lowerCamelCase__ :      str								=		sum(single_char_strings.values() )

			# one length string
			lowerCamelCase__ :      Union[str, Any]								=		0
			# for each alpha we go in our dict and if it is in it we calculate entropy
			for ch in my_alphas:
						if ch in single_char_strings:
									lowerCamelCase__ :      List[Any]								=		single_char_strings[ch]
									lowerCamelCase__ :      List[str]								=		my_str / all_sum
									my_fir_sum += prob * math.loga(UpperCamelCase )  # entropy formula.

    # print entropy
			print(f"{round(-1 * my_fir_sum ):.1f}" )

			# two len string
			lowerCamelCase__ :      str								=		sum(two_char_strings.values() )
			lowerCamelCase__ :      int								=		0
			# for each alpha (two in size) calculate entropy.
			for cha in my_alphas:
						for cha in my_alphas:
									lowerCamelCase__ :      str								=		cha + cha
									if sequence in two_char_strings:
												lowerCamelCase__ :      str								=		two_char_strings[sequence]
												lowerCamelCase__ :      int								=		int(UpperCamelCase ) / all_sum
												my_sec_sum += prob * math.loga(UpperCamelCase )

    # print second entropy
			print(f"{round(-1 * my_sec_sum ):.1f}" )

			# print the difference between them
			print(f"{round((-1 * my_sec_sum) - (-1 * my_fir_sum) ):.1f}" )






def      _A				(UpperCamelCase		:      str )  ->tuple[dict, dict]:



			'''simple docstring'''







			lowerCamelCase__ :      Optional[int]								=		Counter()  # type: ignore
			lowerCamelCase__ :      List[Any]								=		Counter()  # type: ignore
			single_char_strings[text[-1]] += 1

			# first case when we have space at start.
			two_char_strings[" " + text[0]] += 1
			for i in range(0      ,			len(UpperCamelCase ) - 1 ):
						single_char_strings[text[i]] += 1
						two_char_strings[text[i : i + 2]] += 1
			return single_char_strings, two_char_strings






def      _A				()  ->List[str]:



			'''simple docstring'''







			import doctest

			doctest.testmod()
			# text = (
			#     "Had repulsive dashwoods suspicion sincerity but advantage now him. Remark "
			#     "easily garret nor nay. Civil those mrs enjoy shy fat merry. You greatest "
			#     "jointure saw horrible. He private he on be imagine suppose. Fertile "
			#     "beloved evident through no service elderly is. Blind there if every no so "
			#     "at. Own neglected you preferred way sincerity delivered his attempted. To "
			#     "of message cottage windows do besides against uncivil.  Delightful "
			#     "unreserved impossible few estimating men favourable see entreaties. She "
			#     "propriety immediate was improving. He or entrance humoured likewise "
			#     "moderate. Much nor game son say feel. Fat make met can must form into "
			#     "gate. Me we offending prevailed discovery. "
			# )

			# calculate_prob(text)


if __name__ == "__main__":
				main()



