



'''simple docstring'''

from random import randint
from tempfile import TemporaryFile

import numpy as np



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_  )			->      Union[str, Any]:
		_a     :      List[Any]        =							0
		if start < end:
				_a     :      Union[str, Any]        =							randint(lowerCAmelCase_						,			lowerCAmelCase_  )
				_a     :      Dict        =							a[end]
				_a     :      Optional[int]        =							a[pivot]
				_a     :      int        =							temp

				_a       ,					_a     :      Union[str, Any]        =							_in_place_partition(lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_  )
				count += _in_place_quick_sort(lowerCAmelCase_						,			lowerCAmelCase_						,			p - 1  )
				count += _in_place_quick_sort(lowerCAmelCase_						,			p + 1						,			lowerCAmelCase_  )
		return count



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

		_a     :      str        =							a[new_pivot_index + 1]
		_a     :      Tuple        =							a[end]
		_a     :      List[str]        =							temp
		return new_pivot_index + 1, count


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


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


outfile.seek(0)  # using the same array
__lowerCAmelCase									=   np.load(outfile)
__lowerCAmelCase									=   len(M) - 1
__lowerCAmelCase									=   _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 os
import unittest

from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class 	__magic_name__						(    _UpperCamelCase			,						unittest.TestCase   ):
							lowerCAmelCase       :    str   				=					LayoutLMTokenizer
							lowerCAmelCase       :    Tuple   				=					LayoutLMTokenizerFast
							lowerCAmelCase       :    List[Any]   				=					True
							lowerCAmelCase       :    int   				=					True







							def 							__lowercase			(							self					:       Dict		):
									super().setUp()

									_a     :      int        =							[
									    '[UNK]',
									    '[CLS]',
									    '[SEP]',
									    'want',
									    '##want',
									    '##ed',
									    'wa',
									    'un',
									    'runn',
									    '##ing',
									    ',',
									    'low',
									    'lowest',
									]
									_a     :      List[str]        =							os.path.join(self.tmpdirname	,VOCAB_FILES_NAMES['vocab_file']		)
									with open(self.vocab_file	,'w'	,encoding='utf-8'		) as vocab_writer:
											vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]		)		)







							def 							__lowercase			(							self					:       Dict	,**_UpperCAmelCase					:       List[str]		):
									return LayoutLMTokenizer.from_pretrained(self.tmpdirname	,**_UpperCAmelCase		)







							def 							__lowercase			(							self					:       Optional[Any]	,_UpperCAmelCase					:       Tuple		):
									_a     :      Optional[int]        =							'UNwant\u00E9d,running'
									_a     :      List[Any]        =							'unwanted, running'
									return input_text, output_text







							def 							__lowercase			(							self					:       Optional[int]		):
									_a     :      Optional[Any]        =							self.tokenizer_class(self.vocab_file		)

									_a     :      Optional[Any]        =							tokenizer.tokenize('UNwant\u00E9d,running'		)
									self.assertListEqual(_UpperCAmelCase	,['un', '##want', '##ed', ',', 'runn', '##ing']		)
									self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCAmelCase		)	,[7, 4, 5, 10, 8, 9]		)







							def 							__lowercase			(							self					:       Optional[int]		):
									pass




