"""simple docstring"""






from typing import List, Optional, Union

import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi

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


lowerCAmelCase__						:						Union[str, Any]		  =			logging.get_logger(__name__)
class        snake_case (				__UpperCAmelCase       ):




					"""simple docstring"""

					snake_case__       					=				["input_features", "attention_mask"]







					def __init__(       self     :			Optional[int]					,lowerCamelCase__     :			int=80					,lowerCamelCase__     :			Any=16_000					,lowerCamelCase__     :			int=80					,lowerCamelCase__     :			str=0.0					,lowerCamelCase__     :			Union[str, Any]=True					,lowerCamelCase__     :			str=True					,lowerCamelCase__     :			Optional[Any]=True					,**lowerCamelCase__     :			str					,):
									super().__init__(feature_size=lowerCamelCase__					,sampling_rate=lowerCamelCase__					,padding_value=lowerCamelCase__					,**lowerCamelCase__    )
									UpperCAmelCase__    =							num_mel_bins
									UpperCAmelCase__    =							do_ceptral_normalize
									UpperCAmelCase__    =							normalize_means
									UpperCAmelCase__    =							normalize_vars
									UpperCAmelCase__    =							True







					def 	__lowerCAmelCase		(       self     :			List[Any]					,lowerCamelCase__     :			np.ndarray					,):
									UpperCAmelCase__    =							waveform * (2**15)  # Kaldi compliance: 16-bit signed integers
									UpperCAmelCase__    =							torch.from_numpy(lowerCamelCase__    ).unsqueeze(0    )
									UpperCAmelCase__    =							ta_kaldi.fbank(lowerCamelCase__					,num_mel_bins=self.num_mel_bins					,sample_frequency=self.sampling_rate    )
									return features.numpy()







					@staticmethod
					def 	__lowerCAmelCase		(       lowerCamelCase__     :			np.ndarray					,lowerCamelCase__     :			int					,lowerCamelCase__     :			Optional[bool] = True					,lowerCamelCase__     :			Optional[bool] = True					,lowerCamelCase__     :			float = 0.0					,):
									# make sure we normalize float32 arrays
									if normalize_means:
													UpperCAmelCase__    =							x[:input_length].mean(axis=0    )
													UpperCAmelCase__    =							np.subtract(lowerCamelCase__					,lowerCamelCase__    )
									if normalize_vars:
													UpperCAmelCase__    =							x[:input_length].std(axis=0    )
													UpperCAmelCase__    =							np.divide(lowerCamelCase__					,lowerCamelCase__    )

									if input_length < x.shape[0]:
													UpperCAmelCase__    =							padding_value

									# make sure array is in float32
									UpperCAmelCase__    =							x.astype(np.floataa    )

									return x







					def 	__lowerCAmelCase		(       self     :			int					,lowerCamelCase__     :			List[np.ndarray]					,lowerCamelCase__     :			Optional[np.ndarray] = None    ):
									UpperCAmelCase__    =							attention_mask.sum(-1    ) if attention_mask is not None else [x.shape[0] for x in input_features]
									return [
									    self.utterance_cmvn(lowerCamelCase__					,lowerCamelCase__					,self.normalize_means					,self.normalize_vars					,self.padding_value    )
									    for x, n in zip(lowerCamelCase__					,lowerCamelCase__    )
									]







					def __call__(       self     :			Dict					,lowerCamelCase__     :			Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]					,lowerCamelCase__     :			Union[bool, str, PaddingStrategy] = False					,lowerCamelCase__     :			Optional[int] = None					,lowerCamelCase__     :			bool = False					,lowerCamelCase__     :			Optional[int] = None					,lowerCamelCase__     :			Optional[Union[str, TensorType]] = None					,lowerCamelCase__     :			Optional[int] = None					,lowerCamelCase__     :			Optional[bool] = None					,**lowerCamelCase__     :			Union[str, Any]					,):

									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.'    )

									UpperCAmelCase__    =							isinstance(lowerCamelCase__					,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}'''    )
									UpperCAmelCase__    =							is_batched_numpy or (
									    isinstance(lowerCamelCase__					,(list, tuple)    ) and (isinstance(raw_speech[0]					,(np.ndarray, tuple, list)    ))
									)

									if is_batched:
													UpperCAmelCase__    =							[np.asarray(lowerCamelCase__					,dtype=np.floataa    ) for speech in raw_speech]
									elif not is_batched and not isinstance(lowerCamelCase__					,np.ndarray    ):
													UpperCAmelCase__    =							np.asarray(lowerCamelCase__					,dtype=np.floataa    )
									elif isinstance(lowerCamelCase__					,np.ndarray    ) and raw_speech.dtype is np.dtype(np.floataa    ):
													UpperCAmelCase__    =							raw_speech.astype(np.floataa    )

									# always return batch
									if not is_batched:
													UpperCAmelCase__    =							[raw_speech]

									# extract fbank features
									UpperCAmelCase__    =							[self._extract_fbank_features(lowerCamelCase__    ) for waveform in raw_speech]

									# convert into correct format for padding
									UpperCAmelCase__    =							BatchFeature({'input_features': features}    )

									UpperCAmelCase__    =							self.pad(
									    lowerCamelCase__					,padding=lowerCamelCase__					,max_length=lowerCamelCase__					,truncation=lowerCamelCase__					,pad_to_multiple_of=lowerCamelCase__					,return_attention_mask=lowerCamelCase__					,**lowerCamelCase__					,)

									# make sure list is in array format
									UpperCAmelCase__    =							padded_inputs.get('input_features'    )
									if isinstance(input_features[0]					,lowerCamelCase__    ):
													UpperCAmelCase__    =							[np.asarray(lowerCamelCase__					,dtype=np.floataa    ) for feature in input_features]

									UpperCAmelCase__    =							padded_inputs.get('attention_mask'    )
									if attention_mask is not None:
													UpperCAmelCase__    =							[np.asarray(lowerCamelCase__					,dtype=np.intaa    ) for array in attention_mask]

									# Utterance-level cepstral mean and variance normalization
									if self.do_ceptral_normalize:
													UpperCAmelCase__    =							(
													    np.array(lowerCamelCase__					,dtype=np.intaa    )
													    if self._get_padding_strategies(lowerCamelCase__					,max_length=lowerCamelCase__    ) is not PaddingStrategy.DO_NOT_PAD
													    else None
													)
													UpperCAmelCase__    =							self.normalize(
													    padded_inputs['input_features']					,attention_mask=lowerCamelCase__    )

									if return_tensors is not None:
													UpperCAmelCase__    =							padded_inputs.convert_to_tensors(lowerCamelCase__    )

									return padded_inputs




"""simple docstring"""






from __future__ import annotations

import collections
import pprint
from pathlib import Path



def 						a_		(      lowerCamelCase ):
				return "".join(sorted(lowerCamelCase ) )



def 						a_		(      lowerCamelCase ):
				return word_by_signature[signature(lowerCamelCase )]


lowerCAmelCase__						:						str		  =			Path(__file__).parent.joinpath('words.txt').read_text(encoding='utf-8')
lowerCAmelCase__						:						str		  =			sorted({word.strip().lower() for word in data.splitlines()})

lowerCAmelCase__						:						Optional[Any]		  =			collections.defaultdict(list)
for word in word_list:
						word_by_signature[signature(word)].append(word)

if __name__ == "__main__":
						lowerCAmelCase__						:						int		  =			{word: anagram(word) for word in word_list if len(anagram(word)) > 1}

						with open('anagrams.txt', 'w') as file:
												file.write('all_anagrams = \n ')
												file.write(pprint.pformat(all_anagrams))



