





'''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


A :			Dict							   =      logging.get_logger(__name__)







class       lowerCamelCase       (    SCREAMING_SNAKE_CASE__	):
					_SCREAMING_SNAKE_CASE				 =     ["input_values", "attention_mask"]


					def __init__(       self						:	List[str]							,  __snake_case						:	Union[str, Any]=1							,  __snake_case						:	Optional[Any]=1_60_00							,  __snake_case						:	Union[str, Any]=1_28							,  __snake_case						:	Any=10_24							,  __snake_case						:	Dict=0.0							,  __snake_case						:	Tuple=True							,  __snake_case						:	Dict=-4.2_677_393							,  __snake_case						:	int=4.5_689_974							,  __snake_case						:	List[str]=False							,  **__snake_case						:	Dict							,  ):





											'''simple docstring'''





											super().__init__(feature_size=_UpperCAmelCase							,  sampling_rate=_UpperCAmelCase							,  padding_value=_UpperCAmelCase							,  **_UpperCAmelCase							)
											_snake_case: Any	       =		num_mel_bins
											_snake_case: int	       =		max_length
											_snake_case: str	       =		do_normalize
											_snake_case: List[str]	       =		mean
											_snake_case: List[str]	       =		std
											_snake_case: List[Any]	       =		return_attention_mask


					def  SCREAMING_SNAKE_CASE_		(       self						:	Dict							,  __snake_case						:	Tuple							,  __snake_case						:	Tuple							,  ):





											'''simple docstring'''





											_snake_case: Optional[int]	       =		torch.from_numpy(_UpperCAmelCase							).unsqueeze(0							)
											_snake_case: 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=10							,  )

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

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

											_snake_case: Any	       =		fbank.numpy()

											return fbank


					def  SCREAMING_SNAKE_CASE_		(       self						:	Optional[Any]							,  __snake_case						:	Dict							):





											'''simple docstring'''





											return (input_values - (self.mean)) / (self.std * 2)




					def __call__(       self						:	Union[str, Any]							,  __snake_case						:	Tuple							,  __snake_case						:	Any = None							,  __snake_case						:	List[str] = None							,  **__snake_case						:	int							,  ):





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

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

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

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

											# convert into BatchFeature
											_snake_case: Any	       =		BatchFeature({'input_values': features}							)

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

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

											if return_tensors is not None:
																	_snake_case: 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.



