




'''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 TensorType, logging


_lowerCAmelCase       =   logging.get_logger(__name__)



class    A						(					SCREAMING_SNAKE_CASE__						):




				'''simple docstring'''





				A       =							["input_values", "attention_mask"]
				def __init__(self					,    _UpperCAmelCase=1					,    _UpperCAmelCase=1_6_0_0_0					,    _UpperCAmelCase=1_2_8					,    _UpperCAmelCase=1_0_2_4					,    _UpperCAmelCase=0.0					,    _UpperCAmelCase=True					,    _UpperCAmelCase=-4.2_677_393					,    _UpperCAmelCase=4.5_689_974					,    _UpperCAmelCase=False					,    **_UpperCAmelCase					,    )      ->			Tuple:
							super().__init__(feature_size=_UpperCAmelCase					,    sampling_rate=_UpperCAmelCase					,    padding_value=_UpperCAmelCase					,    **_UpperCAmelCase     )
							__UpperCamelCase :     Any										=  num_mel_bins
							__UpperCamelCase :     int										=  max_length
							__UpperCamelCase :     str										=  do_normalize
							__UpperCamelCase :     List[str]										=  mean
							__UpperCamelCase :     List[str]										=  std
							__UpperCamelCase :     List[Any]										=  return_attention_mask
				def 			a_			(self					,    _UpperCAmelCase					,    _UpperCAmelCase					,    )      ->			np.ndarray:
							__UpperCamelCase :     Optional[int]										=  torch.from_numpy(_UpperCAmelCase     ).unsqueeze(0     )
							__UpperCamelCase :     Tuple										=  ta_kaldi.fbank(
							    _UpperCAmelCase					,    htk_compat=_UpperCAmelCase					,    sample_frequency=self.sampling_rate					,    use_energy=_UpperCAmelCase					,    window_type="hanning"					,    num_mel_bins=self.num_mel_bins					,    dither=0.0					,    frame_shift=1_0					,    )

							__UpperCamelCase :     int										=  fbank.shape[0]
							__UpperCamelCase :     Optional[int]										=  max_length - n_frames

							# pad or truncate, depending on difference
							if difference > 0:
										__UpperCamelCase :     Dict										=  torch.nn.ZeroPadad((0, 0, 0, difference)     )
										__UpperCamelCase :     Any										=  pad_module(_UpperCAmelCase     )
							elif difference < 0:
										__UpperCamelCase :     Union[str, Any]										=  fbank[0:max_length, :]

							__UpperCamelCase :     Any										=  fbank.numpy()

							return fbank
				def 			a_			(self					,    _UpperCAmelCase     )      ->			np.ndarray:
							return (input_values - (self.mean)) / (self.std * 2)





				def __call__(self					,    _UpperCAmelCase					,    _UpperCAmelCase = None					,    _UpperCAmelCase = None					,    **_UpperCAmelCase					,    )      ->			BatchFeature:

							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 :     Union[str, Any]										=  isinstance(_UpperCAmelCase					,    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 :     Any										=  is_batched_numpy or (
							    isinstance(_UpperCAmelCase					,    (list, tuple)     ) and (isinstance(raw_speech[0]					,    (np.ndarray, tuple, list)     ))
							)

							if is_batched:
										__UpperCamelCase :     Tuple										=  [np.asarray(_UpperCAmelCase					,    dtype=np.floataa     ) for speech in raw_speech]
							elif not is_batched and not isinstance(_UpperCAmelCase					,    np.ndarray     ):
										__UpperCamelCase :     Tuple										=  np.asarray(_UpperCAmelCase					,    dtype=np.floataa     )
							elif isinstance(_UpperCAmelCase					,    np.ndarray     ) and raw_speech.dtype is np.dtype(np.floataa     ):
										__UpperCamelCase :     Optional[int]										=  raw_speech.astype(np.floataa     )

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

							# extract fbank features and pad/truncate to max_length
							__UpperCamelCase :     Tuple										=  [self._extract_fbank_features(_UpperCAmelCase					,    max_length=self.max_length     ) for waveform in raw_speech]

							# convert into BatchFeature
							__UpperCamelCase :     Any										=  BatchFeature({"input_values": features}     )

							# make sure list is in array format
							__UpperCamelCase :     Dict										=  padded_inputs.get("input_values"     )
							if isinstance(input_values[0]					,    _UpperCAmelCase     ):
										__UpperCamelCase :     List[Any]										=  [np.asarray(_UpperCAmelCase					,    dtype=np.floataa     ) for feature in input_values]

							# normalization
							if self.do_normalize:
										__UpperCamelCase :     Optional[int]										=  [self.normalize(_UpperCAmelCase     ) for feature in input_values]

							if return_tensors is not None:
										__UpperCamelCase :     Tuple										=  padded_inputs.convert_to_tensors(_UpperCAmelCase     )

							return padded_inputs









'''simple docstring'''






def      __lowerCAmelCase   (       snake_case__		):
			__UpperCamelCase :     int										=  word.upper()
			__UpperCamelCase :     List[str]										=  0
			__UpperCamelCase :     int										=  {}
			__UpperCamelCase :     Tuple										=  []

			for letter in word:
						if letter not in letter_nums:
									__UpperCamelCase :     Tuple										=  str(snake_case__		)
									next_num += 1
						word_pattern.append(letter_nums[letter]		)
			return ".".join(snake_case__		)


if __name__ == "__main__":
				import pprint
				import time

				_lowerCAmelCase       =   time.time()
				with open('''dictionary.txt''') as in_file:
								_lowerCAmelCase       =   in_file.read().splitlines()

				_lowerCAmelCase       =   {}
				for word in word_list:
								_lowerCAmelCase       =   get_word_pattern(word)
								if pattern in all_patterns:
												all_patterns[pattern].append(word)
								else:
												_lowerCAmelCase       =   [word]

				with open('''word_patterns.txt''', '''w''') as out_file:
								out_file.write(pprint.pformat(all_patterns))

				_lowerCAmelCase       =   round(time.time() - start_time, 2)
				print(f'Done!  {len(all_patterns):,} word patterns found in {total_time} seconds.')
				# Done!  9,581 word patterns found in 0.58 seconds.



