



'''simple docstring'''



from random import randint
from tempfile import TemporaryFile

import numpy as np






def   __lowerCAmelCase					(							lowerCamelCase   :						str				,		lowerCamelCase   :						str				,		lowerCamelCase   :						Union[str, Any]					):






	'''simple docstring'''




	__lowerCAmelCase												=  0
	if start < end:
		__lowerCAmelCase												=  randint(snake_case__				,		snake_case__					)
		__lowerCAmelCase												=  a[end]
		__lowerCAmelCase												=  a[pivot]
		__lowerCAmelCase												=  temp

		__lowerCAmelCase												=  _in_place_partition(snake_case__				,		snake_case__				,		snake_case__					)
		count += _in_place_quick_sort(snake_case__				,		snake_case__				,		p - 1					)
		count += _in_place_quick_sort(snake_case__				,		p + 1				,		snake_case__					)
	return count






def   __lowerCAmelCase					(							lowerCamelCase   :						Union[str, Any]				,		lowerCamelCase   :						Dict				,		lowerCamelCase   :						int					):






	'''simple docstring'''




	__lowerCAmelCase												=  0
	__lowerCAmelCase												=  randint(snake_case__				,		snake_case__					)
	__lowerCAmelCase												=  a[end]
	__lowerCAmelCase												=  a[pivot]
	__lowerCAmelCase												=  temp
	__lowerCAmelCase												=  start - 1
	for index in range(snake_case__				,		snake_case__					):
		count += 1
		if a[index] < a[end]:  # check if current val is less than pivot value
			__lowerCAmelCase												=  new_pivot_index + 1
			__lowerCAmelCase												=  a[new_pivot_index]
			__lowerCAmelCase												=  a[index]
			__lowerCAmelCase												=  temp

	__lowerCAmelCase												=  a[new_pivot_index + 1]
	__lowerCAmelCase												=  a[end]
	__lowerCAmelCase												=  temp
	return new_pivot_index + 1, count


lowerCAmelCase	:		List[str]				=   TemporaryFile()
lowerCAmelCase	:		Union[str, Any]				=   1_0_0  # 1000 elements are to be sorted


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


outfile.seek(0)  # using the same array
lowerCAmelCase	:		Dict				=   np.load(outfile)
lowerCAmelCase	:		List[str]				=   len(M) - 1
lowerCAmelCase	:		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)




'''simple docstring'''



import importlib
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Union

import torch

from ..utils import BaseOutput


lowerCAmelCase	:		Optional[Any]				=   '''scheduler_config.json'''






class 					UpperCAmelCase__	(		UpperCamelCase__ ):
					a      :     str        =							1
					a      :     Optional[int]        =							2
					a      :     int        =							3
					a      :     Union[str, Any]        =							4
					a      :     int        =							5
					a      :     Optional[int]        =							6
					a      :     str        =							7
					a      :     List[Any]        =							8
					a      :     List[str]        =							9
					a      :     List[str]        =							1_0
					a      :     int        =							1_1
					a      :     Any        =							1_2
					a      :     Any        =							1_3
					a      :     Tuple        =							1_4






@dataclass
class 					UpperCAmelCase__	(		UpperCamelCase__ ):
					a      :     torch.FloatTensor






class 					UpperCAmelCase__	:
					a      :     Tuple        =							SCHEDULER_CONFIG_NAME
					a      :     Union[str, Any]        =							[]
					a      :     str        =							True



					@classmethod
					def        UpperCAmelCase_ (    cls			,				UpperCamelCase = None			,				UpperCamelCase = None			,				UpperCamelCase=False			,				**UpperCamelCase			,				)     ->			int:
						__lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase												=  cls.load_config(
						    pretrained_model_name_or_path=UpperCamelCase			,				subfolder=UpperCamelCase			,				return_unused_kwargs=UpperCamelCase			,				return_commit_hash=UpperCamelCase			,				**UpperCamelCase			,				)
						return cls.from_config(UpperCamelCase			,				return_unused_kwargs=UpperCamelCase			,				**UpperCamelCase     )



					def        UpperCAmelCase_ (    self			,				UpperCamelCase			,				UpperCamelCase = False			,				**UpperCamelCase     )     ->			Dict:
						self.save_config(save_directory=UpperCamelCase			,				push_to_hub=UpperCamelCase			,				**UpperCamelCase     )



					@property
					def        UpperCAmelCase_ (    self     )     ->			str:
						return self._get_compatibles()



					@classmethod
					def        UpperCAmelCase_ (    cls     )     ->			Tuple:
						__lowerCAmelCase												=  list(set([cls.__name__] + cls._compatibles     )     )
						__lowerCAmelCase												=  importlib.import_module(__name__.split("."     )[0]     )
						__lowerCAmelCase												=  [
						    getattr(UpperCamelCase			,				UpperCamelCase     ) for c in compatible_classes_str if hasattr(UpperCamelCase			,				UpperCamelCase     )
						]
						return compatible_classes