



import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


snake_case    :						int  =						logging.get_logger(__name__)




class 							lowerCAmelCase__  (	UpperCamelCase						):
					__A     :    Dict           =						['input_features']







					def __init__(       self      :     Optional[int]			,		_A      :     Optional[Any]=80			,		_A      :     str=1_6000			,		_A      :     Optional[int]=160			,		_A      :     Any=30			,		_A      :     int=400			,		_A      :     Dict=0.0			,		_A      :     List[str]=False			,		**_A      :     int			,		):
											super().__init__(
											    feature_size=_A			,		sampling_rate=_A			,		padding_value=_A			,		return_attention_mask=_A			,		**_A			,		)
											A__	:							Any						    = n_fft
											A__	:							Optional[int]						    = hop_length
											A__	:							List[Any]						    = chunk_length
											A__	:							Optional[Any]						    = chunk_length * sampling_rate
											A__	:							List[str]						    = self.n_samples // hop_length
											A__	:							Dict						    = sampling_rate
											A__	:							int						    = mel_filter_bank(
											    num_frequency_bins=1 + n_fft // 2			,		num_mel_filters=_A			,		min_frequency=0.0			,		max_frequency=80_00.0			,		sampling_rate=_A			,		norm="slaney"			,		mel_scale="slaney"			,		)







					def  _lowercase			(       self      :     Dict			,		_A      :     np.array):
											A__	:							Any						    = spectrogram(
											    _A			,		window_function(self.n_fft			,		"hann")			,		frame_length=self.n_fft			,		hop_length=self.hop_length			,		power=2.0			,		mel_filters=self.mel_filters			,		log_mel="log10"			,		)
											A__	:							Tuple						    = log_spec[:, :-1]
											A__	:							Optional[Any]						    = np.maximum(_A			,		log_spec.max() - 8.0)
											A__	:							Optional[Any]						    = (log_spec + 4.0) / 4.0
											return log_spec







					@staticmethod
					# Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
					def  _lowercase			(       _A      :     List[np.ndarray]			,		_A      :     List[np.ndarray]			,		_A      :     float = 0.0):
											if attention_mask is not None:
																	A__	:							int						    = np.array(_A			,		np.intaa)
																	A__	:							Dict						    = []

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

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

											return normed_input_values







					def __call__(       self      :     List[str]			,		_A      :     Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]			,		_A      :     bool = True			,		_A      :     Optional[int] = None			,		_A      :     Optional[Union[str, TensorType]] = None			,		_A      :     Optional[bool] = None			,		_A      :     Optional[str] = "max_length"			,		_A      :     Optional[int] = None			,		_A      :     Optional[int] = None			,		_A      :     Optional[bool] = None			,		**_A      :     Optional[Any]			,		):

											if sampling_rate is not None:
																	if sampling_rate != self.sampling_rate:
																							raise ValueError(
																							    F'The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a'
																							    F' sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input'
																							    F' was sampled with {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__	:							int						    = isinstance(_A			,		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__	:							List[Any]						    = is_batched_numpy or (
											    isinstance(_A			,		(list, tuple)) and (isinstance(raw_speech[0]			,		(np.ndarray, tuple, list)))
											)

											if is_batched:
																	A__	:							int						    = [np.asarray([speech]			,		dtype=np.floataa).T for speech in raw_speech]
											elif not is_batched and not isinstance(_A			,		np.ndarray):
																	A__	:							int						    = np.asarray(_A			,		dtype=np.floataa)
											elif isinstance(_A			,		np.ndarray) and raw_speech.dtype is np.dtype(np.floataa):
																	A__	:							Union[str, Any]						    = raw_speech.astype(np.floataa)

											# always return batch
											if not is_batched:
																	A__	:							List[Any]						    = [np.asarray([raw_speech]).T]

											A__	:							List[str]						    = BatchFeature({"input_features": raw_speech})

											# convert into correct format for padding

											A__	:							List[str]						    = self.pad(
											    _A			,		padding=_A			,		max_length=max_length if max_length else self.n_samples			,		truncation=_A			,		pad_to_multiple_of=_A			,		return_attention_mask=return_attention_mask or do_normalize			,		)

											# zero-mean and unit-variance normalization
											if do_normalize:
																	A__	:							Union[str, Any]						    = self.zero_mean_unit_var_norm(
																	    padded_inputs["input_features"]			,		attention_mask=padded_inputs["attention_mask"]			,		padding_value=self.padding_value			,		)
																	A__	:							int						    = np.stack(padded_inputs["input_features"]			,		axis=0)

											# make sure list is in array format
											A__	:							Any						    = padded_inputs.get("input_features").transpose(2			,		0			,		1)

											A__	:							Optional[Any]						    = [self._np_extract_fbank_features(_A) for waveform in input_features[0]]

											if isinstance(input_features[0]			,		_A):
																	A__	:							Optional[Any]						    = [np.asarray(_A			,		dtype=np.floataa) for feature in input_features]
											else:
																	A__	:							Union[str, Any]						    = input_features

											if return_attention_mask:
																	# rescale from sample (48000) to feature (3000)
																	A__	:							Tuple						    = padded_inputs["attention_mask"][:, :: self.hop_length]

											if return_tensors is not None:
																	A__	:							Tuple						    = padded_inputs.convert_to_tensors(_A)

											return padded_inputs







					def  _lowercase			(       self      :     List[Any]):
											A__	:							Optional[int]						    = copy.deepcopy(self.__dict__)
											A__	:							str						    = self.__class__.__name__
											if "mel_filters" in output:
																	del output["mel_filters"]
											return output




from collections import Counter
from timeit import timeit
def 	snake_case__       (					__lowercase = ""							,     )		->		bool:







						"""simple docstring"""

						return sum(c % 2 for c in Counter(input_str.replace(" "							,     ""       ).lower()       ).values()       ) < 2
def 	snake_case__       (					__lowercase = ""       )		->		bool:







						"""simple docstring"""

						if len(__lowercase       ) == 0:
												return True
						A__	:							Any						    = input_str.replace(" "							,     ""       ).lower()
						# character_freq_dict: Stores the frequency of every character in the input string
						A__	:							dict[str, int]						    = {}

						for character in lower_case_input_str:
												A__	:							Optional[int]						    = character_freq_dict.get(__lowercase							,     0       ) + 1
						A__	:							List[Any]						    = 0

						for character_count in character_freq_dict.values():
												if character_count % 2:
																		odd_char += 1
						if odd_char > 1:
												return False
						return True
def 	snake_case__       (					__lowercase = ""       )		->		None:







						"""simple docstring"""

						print("\nFor string = "							,     __lowercase							,     ":"       )
						print(
						    "> can_string_be_rearranged_as_palindrome_counter()"							,     "\tans ="							,     can_string_be_rearranged_as_palindrome_counter(__lowercase       )							,     "\ttime ="							,     timeit(
						        "z.can_string_be_rearranged_as_palindrome_counter(z.check_str)"							,     setup="import __main__ as z"							,     )							,     "seconds"							,     )
						print(
						    "> can_string_be_rearranged_as_palindrome()"							,     "\tans ="							,     can_string_be_rearranged_as_palindrome(__lowercase       )							,     "\ttime ="							,     timeit(
						        "z.can_string_be_rearranged_as_palindrome(z.check_str)"							,     setup="import __main__ as z"							,     )							,     "seconds"							,     )


if __name__ == "__main__":
		snake_case    :						Dict  =						input(
		    'Enter string to determine if it can be rearranged as a palindrome or not: '
		).strip()
		benchmark(check_str)
		snake_case    :						Dict  =						can_string_be_rearranged_as_palindrome_counter(check_str)
		print(f"""{check_str} can {'' if status else 'not '}be rearranged as a palindrome""")