






"""simple docstring"""





from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding



class       __A		(   SCREAMING_SNAKE_CASE_			):
					UpperCAmelCase__								=				"ClapFeatureExtractor"
					UpperCAmelCase__								=				("RobertaTokenizer", "RobertaTokenizerFast")

					def __init__(			self  :      Any     ,		__snake_case  :      Any     ,		__snake_case  :      List[Any]					)			->							str:
											super().__init__(__snake_case     ,		__snake_case					)

					def __call__(			self  :      List[Any]     ,		__snake_case  :      Optional[int]=None     ,		__snake_case  :      List[Any]=None     ,		__snake_case  :      Union[str, Any]=None     ,		**__snake_case  :      int					)			->							Tuple:
											__magic_name__:	Any							=       kwargs.pop("""sampling_rate"""     ,		__snake_case					)

											if text is None and audios is None:
																	raise ValueError("""You have to specify either text or audios. Both cannot be none."""					)

											if text is not None:
																	__magic_name__:	Tuple							=       self.tokenizer(__snake_case     ,		return_tensors=__snake_case     ,		**__snake_case					)

											if audios is not None:
																	__magic_name__:	Dict							=       self.feature_extractor(
																	    __snake_case     ,		sampling_rate=__snake_case     ,		return_tensors=__snake_case     ,		**__snake_case					)

											if text is not None and audios is not None:
																	__magic_name__:	List[str]							=       audio_features.input_features
																	return encoding
											elif text is not None:
																	return encoding
											else:
																	return BatchEncoding(data=dict(**__snake_case					)     ,		tensor_type=__snake_case					)

					def   lowerCamelCase__   (			self  :      Tuple     ,		*__snake_case  :      Union[str, Any]     ,		**__snake_case  :      List[str]					)			->							List[str]:
											return self.tokenizer.batch_decode(*__snake_case     ,		**__snake_case					)

					def   lowerCamelCase__   (			self  :      Optional[Any]     ,		*__snake_case  :      int     ,		**__snake_case  :      str					)			->							Any:
											return self.tokenizer.decode(*__snake_case     ,		**__snake_case					)






					@property
					def   lowerCamelCase__   (			self  :      Dict					)			->							Tuple:
											__magic_name__:	Any							=       self.tokenizer.model_input_names
											__magic_name__:	str							=       self.feature_extractor.model_input_names
											return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names					)					)











"""simple docstring"""





from random import randint
from tempfile import TemporaryFile

import numpy as np



def   a    (     __UpperCAmelCase      :    Optional[Any]      ,   __UpperCAmelCase      :    Optional[int]      ,   __UpperCAmelCase      :    Optional[int]  )    ->							int:
						__magic_name__:	Optional[int]							=       0
						if start < end:
												__magic_name__:	Union[str, Any]							=       randint(__UpperCAmelCase      ,   __UpperCAmelCase  )
												__magic_name__:	int							=       a[end]
												__magic_name__:	Optional[int]							=       a[pivot]
												__magic_name__:	Tuple							=       temp

												__magic_name__,			__magic_name__:	int							=       _in_place_partition(__UpperCAmelCase      ,   __UpperCAmelCase      ,   __UpperCAmelCase  )
												count += _in_place_quick_sort(__UpperCAmelCase      ,   __UpperCAmelCase      ,   p - 1  )
												count += _in_place_quick_sort(__UpperCAmelCase      ,   p + 1      ,   __UpperCAmelCase  )
						return count



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

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


__lowerCamelCase							=  TemporaryFile()
__lowerCamelCase							=  1_00  # 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)



