






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


a_		:						Dict             =    logging.get_logger(__name__)

class 		_snake_case  (  A__					):
			_lowercase		:    Any			     =							['''input_features''', '''attention_mask''']






			def __init__(       self		,					a=80		,					a=1_6000		,					a=80		,					a=0.0		,					a=True		,					a=True		,					a=True		,					**a		,					)      ->       int:
						super().__init__(feature_size=a		,					sampling_rate=a		,					padding_value=a		,					**a)
						SCREAMING_SNAKE_CASE						= num_mel_bins
						SCREAMING_SNAKE_CASE						= do_ceptral_normalize
						SCREAMING_SNAKE_CASE						= normalize_means
						SCREAMING_SNAKE_CASE						= normalize_vars
						SCREAMING_SNAKE_CASE						= True






			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					)      ->       np.ndarray:
						SCREAMING_SNAKE_CASE						= waveform * (2**15)  # Kaldi compliance: 16-bit signed integers
						SCREAMING_SNAKE_CASE						= torch.from_numpy(a).unsqueeze(0)
						SCREAMING_SNAKE_CASE						= ta_kaldi.fbank(a		,					num_mel_bins=self.num_mel_bins		,					sample_frequency=self.sampling_rate)
						return features.numpy()






			@staticmethod
			def    SCREAMING_SNAKE_CASE__    (       a		,					a		,					a = True		,					a = True		,					a = 0.0		,					)      ->       np.ndarray:
						# make sure we normalize float32 arrays
						if normalize_means:
									SCREAMING_SNAKE_CASE						= x[:input_length].mean(axis=0)
									SCREAMING_SNAKE_CASE						= np.subtract(a		,					a)
						if normalize_vars:
									SCREAMING_SNAKE_CASE						= x[:input_length].std(axis=0)
									SCREAMING_SNAKE_CASE						= np.divide(a		,					a)

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

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

						return x






			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a = None)      ->       List[np.ndarray]:
						SCREAMING_SNAKE_CASE						= attention_mask.sum(-1) if attention_mask is not None else [x.shape[0] for x in input_features]
						return [
						    self.utterance_cmvn(a		,					a		,					self.normalize_means		,					self.normalize_vars		,					self.padding_value)
						    for x, n in zip(a		,					a)
						]







			def __call__(       self		,					a		,					a = False		,					a = None		,					a = False		,					a = None		,					a = None		,					a = None		,					a = None		,					**a		,					)      ->       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.')

						SCREAMING_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}''')
						SCREAMING_SNAKE_CASE						= is_batched_numpy or (
						    isinstance(a		,					(list, tuple)) and (isinstance(raw_speech[0]		,					(np.ndarray, tuple, list)))
						)

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

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

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

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

						SCREAMING_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
						SCREAMING_SNAKE_CASE						= padded_inputs.get('input_features')
						if isinstance(input_features[0]		,					a):
									SCREAMING_SNAKE_CASE						= [np.asarray(a		,					dtype=np.floataa) for feature in input_features]

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

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

						if return_tensors is not None:
									SCREAMING_SNAKE_CASE						= padded_inputs.convert_to_tensors(a)

						return padded_inputs












import os
from collections.abc import Iterator






def 				lowerCamelCase__				(_UpperCAmelCase = "."):
			for dir_path, dir_names, filenames in os.walk(_UpperCAmelCase):
						SCREAMING_SNAKE_CASE						= [d for d in dir_names if d != 'scripts' and d[0] not in '._']
						for filename in filenames:
									if filename == "__init__.py":
												continue
									if os.path.splitext(_UpperCAmelCase)[1] in (".py", ".ipynb"):
												yield os.path.join(_UpperCAmelCase							,       _UpperCAmelCase).lstrip('./')






def 				lowerCamelCase__				(_UpperCAmelCase):
			return F'''{i * '  '}*''' if i else "\n##"






def 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase):
			SCREAMING_SNAKE_CASE						= old_path.split(os.sep)
			for i, new_part in enumerate(new_path.split(os.sep)):
						if (i + 1 > len(_UpperCAmelCase) or old_parts[i] != new_part) and new_part:
									print(F'''{md_prefix(_UpperCAmelCase)} {new_part.replace('_'							,       ' ').title()}''')
			return new_path






def 				lowerCamelCase__				(_UpperCAmelCase = "."):
			SCREAMING_SNAKE_CASE						= ''
			for filepath in sorted(good_file_paths(_UpperCAmelCase)):
						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= os.path.split(_UpperCAmelCase)
						if filepath != old_path:
									SCREAMING_SNAKE_CASE						= print_path(_UpperCAmelCase							,       _UpperCAmelCase)
						SCREAMING_SNAKE_CASE						= (filepath.count(os.sep) + 1) if filepath else 0
						SCREAMING_SNAKE_CASE						= F'''{filepath}/{filename}'''.replace(' '							,       '%20')
						SCREAMING_SNAKE_CASE						= os.path.splitext(filename.replace('_'							,       ' ').title())[0]
						print(F'''{md_prefix(_UpperCAmelCase)} [{filename}]({url})''')


if __name__ == "__main__":
				print_directory_md('.')




