





from typing import List, Optional, Union

import numpy as np

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


snake_case_           =      logging.get_logger(__name__)

class 		SCREAMING_SNAKE_CASE__    (	_UpperCAmelCase  ):
      A_						:    List[Any]							=   ['input_features', 'attention_mask']


      def __init__(self	:				int    ,      a__	:				Union[str, Any]=80    ,      a__	:				int=1_6000    ,      a__	:				Optional[int]=0.0    ,      a__	:				List[Any]=10    ,      a__	:				Union[str, Any]=25    ,      a__	:				Union[str, Any]="hamming_window"    ,      a__	:				Dict=3_2_7_6_8.0    ,      a__	:				Dict=0.9_7    ,      a__	:				Union[str, Any]=1.0    ,      a__	:				Union[str, Any]=True    ,      a__	:				List[str]=True    ,      a__	:				Tuple=False    ,      **a__	:				str    ,      ):





         """simple docstring"""



         super().__init__(feature_size=a__    ,      sampling_rate=a__    ,      padding_value=a__    ,      **a__				)

         __snake_case								=       feature_size
         __snake_case								=       sampling_rate
         __snake_case								=       padding_value
         __snake_case								=       hop_length
         __snake_case								=       win_length
         __snake_case								=       frame_signal_scale
         __snake_case								=       preemphasis_coeff
         __snake_case								=       mel_floor
         __snake_case								=       normalize_means
         __snake_case								=       normalize_vars
         __snake_case								=       win_function
         __snake_case								=       return_attention_mask

         __snake_case								=       win_length * sampling_rate // 1000
         __snake_case								=       hop_length * sampling_rate // 1000

         __snake_case								=       optimal_fft_length(self.sample_size				)
         __snake_case								=       (self.n_fft // 2) + 1


      def        a			(self	:				int    ,      a__	:				np.array				):





         """simple docstring"""



         if self.win_function == "hamming_window":
            __snake_case								=       window_function(window_length=self.sample_size    ,      name=self.win_function    ,      periodic=a__				)
         else:
            __snake_case								=       window_function(window_length=self.sample_size    ,      name=self.win_function				)

         __snake_case								=       mel_filter_bank(
             num_frequency_bins=self.n_freqs    ,      num_mel_filters=self.feature_size    ,      min_frequency=0.0    ,      max_frequency=self.sampling_rate / 2.0    ,      sampling_rate=self.sampling_rate    ,      )

         __snake_case								=       spectrogram(
             one_waveform * self.frame_signal_scale    ,      window=a__    ,      frame_length=self.sample_size    ,      hop_length=self.sample_stride    ,      fft_length=self.n_fft    ,      center=a__    ,      preemphasis=self.preemphasis_coeff    ,      mel_filters=a__    ,      mel_floor=self.mel_floor    ,      log_mel='''log'''    ,      )
         return msfc_features.T


      def        a			(self	:				Any    ,      a__	:				List[str]    ,      a__	:				Optional[int]    ,      a__	:				Optional[Any]				):





         """simple docstring"""



         if self.normalize_means:
            __snake_case								=       x[:input_length].mean(axis=0				)
            __snake_case								=       np.subtract(a__    ,      a__				)
         if self.normalize_vars:
            __snake_case								=       x[:input_length].std(axis=0				)
            __snake_case								=       np.divide(a__    ,      a__				)

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

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

         return x


      def        a			(self	:				Union[str, Any]    ,      a__	:				List[np.ndarray]    ,      a__	:				Optional[np.ndarray] = None				):





         """simple docstring"""



         __snake_case								=       attention_mask.sum(-1				) if attention_mask is not None else [x.shape[0] for x in input_features]
         return [self._normalize_one(a__    ,      a__    ,      self.padding_value				) for x, n in zip(a__    ,      a__				)]


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





         """simple docstring"""



         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.'''				)

         __snake_case								=       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}"""				)
         __snake_case								=       is_batched_numpy or (
             isinstance(a__    ,      (list, tuple)				) and (isinstance(raw_speech[0]    ,      (np.ndarray, tuple, list)				))
         )

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

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

         # extract fbank features
         __snake_case								=       [self._extract_mfsc_features(a__				) for one_waveform in raw_speech]

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

         __snake_case								=       self.pad(
             a__    ,      padding=a__    ,      max_length=a__    ,      truncation=a__    ,      pad_to_multiple_of=a__    ,      return_attention_mask=a__    ,      **a__    ,      )
         # make sure list is in array format
         __snake_case								=       padded_inputs.get('''input_features'''				)
         if isinstance(input_features[0]    ,      a__				):
            __snake_case								=       [np.asarray(a__    ,      dtype=np.floataa				) for feature in input_features]

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

         if self.normalize_means or self.normalize_vars:
            __snake_case								=       (
                np.array(a__    ,      dtype=np.intaa				)
                if self._get_padding_strategies(a__    ,      max_length=a__				) is not PaddingStrategy.DO_NOT_PAD
                and padding
                else None
            )
            __snake_case								=       self.normalize(
                padded_inputs['''input_features''']    ,      attention_mask=a__				)

         if return_tensors is not None:
            __snake_case								=       padded_inputs.convert_to_tensors(a__				)

         return padded_inputs








from __future__ import annotations

import collections
import pprint
from pathlib import Path






def lowerCamelCase__   (							snake_case_     :  str	)				->    str:
   return "".join(sorted(snake_case_	)	)






def lowerCamelCase__   (							snake_case_     :  str	)				->    list[str]:
   return word_by_signature[signature(snake_case_	)]


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

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

if __name__ == "__main__":
    snake_case_           =      {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))

