from random import randint
from tempfile import TemporaryFile

import numpy as np
def    A			(					_lowercase	,						_lowercase	,						_lowercase		):
						SCREAMING_SNAKE_CASE							:					Any           =							0
						if start < end:
												SCREAMING_SNAKE_CASE							:					Any           =							randint(_lowercase	,						_lowercase		)
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							a[end]
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							a[pivot]
												SCREAMING_SNAKE_CASE							:					List[Any]           =							temp

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

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


__UpperCamelCase	: Dict    		=  TemporaryFile()
__UpperCamelCase	: List[str]    		=  100  # 1000 elements are to be sorted


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


outfile.seek(0)  # using the same array
__UpperCamelCase	: List[str]    		=  np.load(outfile)
__UpperCamelCase	: str    		=  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)






import os
import unittest

from transformers import FunnelTokenizer, FunnelTokenizerFast
from transformers.models.funnel.tokenization_funnel import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
class       lowercase__		(     UpperCamelCase_			,							unittest.TestCase):
						UpperCamelCase_  =	FunnelTokenizer
						UpperCamelCase_  =	FunnelTokenizerFast
						UpperCamelCase_  =	True
						UpperCamelCase_  =	True


						def  __A ( self			:       Union[str, Any]		):





												'''simple docstring'''





												super().setUp()

												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							[
												    '''<unk>''',
												    '''<cls>''',
												    '''<sep>''',
												    '''want''',
												    '''##want''',
												    '''##ed''',
												    '''wa''',
												    '''un''',
												    '''runn''',
												    '''##ing''',
												    ''',''',
												    '''low''',
												    '''lowest''',
												]
												SCREAMING_SNAKE_CASE							:					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  __A ( self			:       int					,	**UpperCamelCase__			:       List[Any]		):





												'''simple docstring'''





												return FunnelTokenizer.from_pretrained(self.tmpdirname					,	**UpperCamelCase__		)


						def  __A ( self			:       int					,	**UpperCamelCase__			:       List[Any]		):





												'''simple docstring'''





												return FunnelTokenizerFast.from_pretrained(self.tmpdirname					,	**UpperCamelCase__		)


						def  __A ( self			:       Any					,	UpperCamelCase__			:       List[str]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							'''UNwant\u00E9d,running'''
												SCREAMING_SNAKE_CASE							:					int           =							'''unwanted, running'''
												return input_text, output_text


						def  __A ( self			:       Any		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					List[Any]           =							self.tokenizer_class(self.vocab_file		)

												SCREAMING_SNAKE_CASE							:					int           =							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  __A ( self			:       str		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Dict           =							self.get_tokenizers(do_lower_case=UpperCamelCase__		)
												for tokenizer in tokenizers:
																		SCREAMING_SNAKE_CASE							:					int           =							tokenizer('''UNwant\u00E9d,running'''		)
																		SCREAMING_SNAKE_CASE							:					Optional[Any]           =							len(inputs['''input_ids''']		) - 1
																		self.assertListEqual(inputs['''token_type_ids''']					,	[2] + [0] * sentence_len		)

																		SCREAMING_SNAKE_CASE							:					List[Any]           =							tokenizer('''UNwant\u00E9d,running'''					,	'''UNwant\u00E9d,running'''		)
																		self.assertListEqual(inputs['''token_type_ids''']					,	[2] + [0] * sentence_len + [1] * sentence_len		)





