
"""simple docstring"""




from random import randint
from tempfile import TemporaryFile

import numpy as np

def        _lowerCamelCase ( __a,							__a,							__a    ):
		SCREAMING_SNAKE_CASE_						 = 0
		if start < end:
				SCREAMING_SNAKE_CASE_						 = randint(_lowercase,							_lowercase    )
				SCREAMING_SNAKE_CASE_						 = a[end]
				SCREAMING_SNAKE_CASE_						 = a[pivot]
				SCREAMING_SNAKE_CASE_						 = temp

				SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = _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        _lowerCamelCase ( __a,							__a,							__a    ):
		SCREAMING_SNAKE_CASE_						 = 0
		SCREAMING_SNAKE_CASE_						 = randint(_lowercase,							_lowercase    )
		SCREAMING_SNAKE_CASE_						 = a[end]
		SCREAMING_SNAKE_CASE_						 = a[pivot]
		SCREAMING_SNAKE_CASE_						 = temp
		SCREAMING_SNAKE_CASE_						 = 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_						 = new_pivot_index + 1
						SCREAMING_SNAKE_CASE_						 = a[new_pivot_index]
						SCREAMING_SNAKE_CASE_						 = a[index]
						SCREAMING_SNAKE_CASE_						 = temp

		SCREAMING_SNAKE_CASE_						 = a[new_pivot_index + 1]
		SCREAMING_SNAKE_CASE_						 = a[end]
		SCREAMING_SNAKE_CASE_						 = 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 json
import os
import unittest

from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin





class  snake_case     (							__lowercase       ,			unittest.TestCase   ):
							UpperCAmelCase__						     =      CTRLTokenizer
							UpperCAmelCase__						     =      False
							UpperCAmelCase__						     =      False

							def _lowercase					(self    ):

									"""simple docstring"""







									super().setUp()

									# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
									SCREAMING_SNAKE_CASE_						 = ['''adapt''', '''re@@''', '''a@@''', '''apt''', '''c@@''', '''t''', '''<unk>''']
									SCREAMING_SNAKE_CASE_						 = dict(zip(SCREAMING_SNAKE_CASE_       ,       range(len(SCREAMING_SNAKE_CASE_    )    )    )    )
									SCREAMING_SNAKE_CASE_						 = ['''#version: 0.2''', '''a p''', '''ap t</w>''', '''r e''', '''a d''', '''ad apt</w>''', '''''']
									SCREAMING_SNAKE_CASE_						 = {'''unk_token''': '''<unk>'''}

									SCREAMING_SNAKE_CASE_						 = os.path.join(self.tmpdirname       ,       VOCAB_FILES_NAMES['''vocab_file''']    )
									SCREAMING_SNAKE_CASE_						 = os.path.join(self.tmpdirname       ,       VOCAB_FILES_NAMES['''merges_file''']    )
									with open(self.vocab_file       ,       '''w'''       ,       encoding='''utf-8'''    ) as fp:
											fp.write(json.dumps(SCREAMING_SNAKE_CASE_    ) + '''\n'''    )
									with open(self.merges_file       ,       '''w'''       ,       encoding='''utf-8'''    ) as fp:
											fp.write('''\n'''.join(SCREAMING_SNAKE_CASE_    )    )

							def _lowercase					(self       ,       **SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									kwargs.update(self.special_tokens_map    )
									return CTRLTokenizer.from_pretrained(self.tmpdirname       ,       **SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = '''adapt react readapt apt'''
									SCREAMING_SNAKE_CASE_						 = '''adapt react readapt apt'''
									return input_text, output_text







							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = CTRLTokenizer(self.vocab_file       ,       self.merges_file       ,       **self.special_tokens_map    )
									SCREAMING_SNAKE_CASE_						 = '''adapt react readapt apt'''
									SCREAMING_SNAKE_CASE_						 = '''adapt re@@ a@@ c@@ t re@@ adapt apt'''.split()
									SCREAMING_SNAKE_CASE_						 = tokenizer.tokenize(SCREAMING_SNAKE_CASE_    )
									self.assertListEqual(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )

									SCREAMING_SNAKE_CASE_						 = tokens + [tokenizer.unk_token]

									SCREAMING_SNAKE_CASE_						 = [0, 1, 2, 4, 5, 1, 0, 3, 6]
									self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_    )       ,       SCREAMING_SNAKE_CASE_    )