



from typing import List, Optional, Union

import numpy as np

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


snake_case     :    List[Any]						=    logging.get_logger(__name__)




class 				lowerCAmelCase__	(						snake_case__    ):
					__A  :     int     =    ['input_values', 'attention_mask']







					def __init__(							self   :  Tuple      ,		_A   :  List[Any]=1      ,		_A   :  str=1_6000      ,		_A   :  List[Any]=0.0      ,		_A   :  List[Any]=False      ,		_A   :  Union[str, Any]=True      ,		**_A   :  Tuple      ,		):
											super().__init__(feature_size=__SCREAMING_SNAKE_CASE      ,		sampling_rate=__SCREAMING_SNAKE_CASE      ,		padding_value=__SCREAMING_SNAKE_CASE      ,		**__SCREAMING_SNAKE_CASE)
											A__						:				List[str]         =			return_attention_mask
											A__						:				Any         =			do_normalize







					@staticmethod
					def 			_lowercase      (							_A   :  Union[str, Any]      ,		_A   :  List[str]      ,		_A   :  List[str] = 0.0):







											if attention_mask is not None:
																	A__						:				List[str]         =			np.array(__SCREAMING_SNAKE_CASE      ,		np.intaa)
																	A__						:				Any         =			[]

																	for vector, length in zip(__SCREAMING_SNAKE_CASE      ,		attention_mask.sum(-1)):
																							A__						:				Dict         =			(vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7)
																							if length < normed_slice.shape[0]:
																													A__						:				Union[str, Any]         =			padding_value

																							normed_input_values.append(__SCREAMING_SNAKE_CASE)
											else:
																	A__						:				str         =			[(x - x.mean()) / np.sqrt(x.var() + 1e-7) for x in input_values]

											return normed_input_values







					def __call__(							self   :  Optional[Any]      ,		_A   :  List[Any]      ,		_A   :  Tuple = False      ,		_A   :  Tuple = None      ,		_A   :  str = False      ,		_A   :  List[Any] = None      ,		_A   :  Dict = None      ,		_A   :  Dict = None      ,		_A   :  str = None      ,		**_A   :  str      ,		):







											if sampling_rate is not None:
																	if sampling_rate != self.sampling_rate:
																							raise ValueError(
																							    F'The model corresponding to this feature extractor: {self} was trained using a sampling rate of'
																							    F' {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with'
																							    F' {self.sampling_rate} and not {sampling_rate}.')
											else:
																	logger.warning(
																	    "It is strongly recommended to pass the ``sampling_rate`` argument to this function. "
																	    "Failing to do so can result in silent errors that might be hard to debug.")

											A__						:				Tuple         =			isinstance(__SCREAMING_SNAKE_CASE      ,		np.ndarray) and len(raw_speech.shape) > 1
											if is_batched_numpy and len(raw_speech.shape) > 2:
																	raise ValueError(F'Only mono-channel audio is supported for input to {self}')
											A__						:				str         =			is_batched_numpy or (
											    isinstance(__SCREAMING_SNAKE_CASE      ,		(list, tuple)) and (isinstance(raw_speech[0]      ,		(np.ndarray, tuple, list)))
											)

											# always return batch
											if not is_batched:
																	A__						:				Union[str, Any]         =			[raw_speech]

											# convert into correct format for padding
											A__						:				str         =			BatchFeature({"input_values": raw_speech})

											A__						:				Dict         =			self.pad(
											    __SCREAMING_SNAKE_CASE      ,		padding=__SCREAMING_SNAKE_CASE      ,		max_length=__SCREAMING_SNAKE_CASE      ,		truncation=__SCREAMING_SNAKE_CASE      ,		pad_to_multiple_of=__SCREAMING_SNAKE_CASE      ,		return_attention_mask=__SCREAMING_SNAKE_CASE      ,		)

											# convert input values to correct format
											A__						:				Any         =			padded_inputs["input_values"]
											if not isinstance(input_values[0]      ,		np.ndarray):
																	A__						:				Tuple         =			[np.asarray(__SCREAMING_SNAKE_CASE      ,		dtype=np.floataa) for array in input_values]
											elif (
											    not isinstance(__SCREAMING_SNAKE_CASE      ,		np.ndarray)
											    and isinstance(input_values[0]      ,		np.ndarray)
											    and input_values[0].dtype is np.dtype(np.floataa)
											):
																	A__						:				int         =			[array.astype(np.floataa) for array in input_values]
											elif isinstance(__SCREAMING_SNAKE_CASE      ,		np.ndarray) and input_values.dtype is np.dtype(np.floataa):
																	A__						:				Dict         =			input_values.astype(np.floataa)

											# convert attention_mask to correct format
											A__						:				Dict         =			padded_inputs.get("attention_mask")
											if attention_mask is not None:
																	A__						:				Dict         =			[np.asarray(__SCREAMING_SNAKE_CASE      ,		dtype=np.intaa) for array in attention_mask]

											# zero-mean and unit-variance normalization
											if self.do_normalize:
																	A__						:				Optional[int]         =			(
																	    attention_mask
																	    if self._get_padding_strategies(__SCREAMING_SNAKE_CASE      ,		max_length=__SCREAMING_SNAKE_CASE) is not PaddingStrategy.DO_NOT_PAD
																	    else None
																	)
																	A__						:				Optional[int]         =			self.zero_mean_unit_var_norm(
																	    padded_inputs["input_values"]      ,		attention_mask=__SCREAMING_SNAKE_CASE      ,		padding_value=self.padding_value)

											if return_tensors is not None:
																	A__						:				List[Any]         =			padded_inputs.convert_to_tensors(__SCREAMING_SNAKE_CASE)

											return padded_inputs



def 	_lowerCamelCase  (		A_  :	float							,  A_  :	float							,  A_  :	int      )			->			float:

   '''simple docstring'''






   UpperCamelCase__ :			Union[str, Any] 						=[1 / first_term]
   UpperCamelCase__ :			Tuple 						=1 / first_term
   for _ in range(number_of_terms - 1      ):
      first_term += common_difference
      arithmetic_progression.append(A_      )
   UpperCamelCase__ :			List[str] 						=[1 / step for step in arithmetic_progression]
   return sum(A_      )


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     print(sum_of_harmonic_progression(1 / 2, 2, 2))

