





"""simple docstring"""




from random import randint
from tempfile import TemporaryFile

import numpy as np





def 	a_    (							_lowercase	,						_lowercase	,						_lowercase       ):
						_UpperCamelCase      : str         =   0
						if start < end:
												_UpperCamelCase      : Tuple         =   randint(lowerCAmelCase__	,						lowerCAmelCase__       )
												_UpperCamelCase      : Tuple         =   a[end]
												_UpperCamelCase      : Tuple         =   a[pivot]
												_UpperCamelCase      : List[Any]         =   temp

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

						_UpperCamelCase      : Union[str, Any]         =   a[new_pivot_index + 1]
						_UpperCamelCase      : Union[str, Any]         =   a[end]
						_UpperCamelCase      : Dict         =   temp
						return new_pivot_index + 1, count


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


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


outfile.seek(0)  # using the same array
UpperCamelCase_									=np.load(outfile)
UpperCamelCase_									=len(M) - 1
UpperCamelCase_									=_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"""




from ...processing_utils import ProcessorMixin




class 			_a						(				_lowerCAmelCase    ):
				UpperCamelCase								=   ['''image_processor''', '''feature_extractor''']
				UpperCamelCase								=   '''TvltImageProcessor'''
				UpperCamelCase								=   '''TvltFeatureExtractor'''






				def __init__(			self		:   Union[str, Any],			lowerCAmelCase__		:   str,			lowerCAmelCase__		:   List[str]							)     ->				Optional[Any]:




										'''simple docstring'''



										super().__init__(image_processor=lowerCAmelCase__,			feature_extractor=lowerCAmelCase__							)

										_UpperCamelCase      : List[str]         =   image_processor
										_UpperCamelCase      : int         =   feature_extractor






				def __call__(			self		:   List[str],			lowerCAmelCase__		:   Optional[int]=None,			lowerCAmelCase__		:   str=None,			lowerCAmelCase__		:   Dict=None,			lowerCAmelCase__		:   str=None,			lowerCAmelCase__		:   Optional[int]=False,			lowerCAmelCase__		:   str=False,			*lowerCAmelCase__		:   List[str],			**lowerCAmelCase__		:   Optional[int],			)     ->				Dict:




										'''simple docstring'''



										if images is None and audio is None:
																raise ValueError('''You need to specify either an `images` or `audio` input to process.'''							)

										_UpperCamelCase      : Optional[int]         =   None
										if images is not None:
																_UpperCamelCase      : Optional[int]         =   self.image_processor(lowerCAmelCase__,			mask_pixel=lowerCAmelCase__,			*lowerCAmelCase__,			**lowerCAmelCase__							)
										if images_mixed is not None:
																_UpperCamelCase      : str         =   self.image_processor(lowerCAmelCase__,			is_mixed=lowerCAmelCase__,			*lowerCAmelCase__,			**lowerCAmelCase__							)
										if audio is not None:
																_UpperCamelCase      : Union[str, Any]         =   self.feature_extractor(
																    lowerCAmelCase__,			*lowerCAmelCase__,			sampling_rate=lowerCAmelCase__,			mask_audio=lowerCAmelCase__,			**lowerCAmelCase__							)

										_UpperCamelCase      : str         =   {}
										if audio is not None:
																output_dict.update(lowerCAmelCase__							)
										if images is not None:
																output_dict.update(lowerCAmelCase__							)
										if images_mixed_dict is not None:
																output_dict.update(lowerCAmelCase__							)
										return output_dict






				@property
				def     snake_case     (			self		:   List[str]							)     ->				Tuple:




										'''simple docstring'''



										_UpperCamelCase      : List[str]         =   self.image_processor.model_input_names
										_UpperCamelCase      : List[Any]         =   self.feature_extractor.model_input_names
										return list(dict.fromkeys(image_processor_input_names + feature_extractor_input_names							)							)



