'''simple docstring'''


import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np

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


__lowerCAmelCase	:       Dict						  =	logging.get_logger(__name__)






class 						A   (   UpperCAmelCase   ):
							a_        =      ['''input_features''']





							def __init__(      self				:							List[str]     ,		__a				:							Union[str, Any]=8_0     ,		__a				:							List[Any]=1_6_0_0_0     ,		__a				:							Any=1_6_0     ,		__a				:							Any=3_0     ,		__a				:							Union[str, Any]=4_0_0     ,		__a				:							Tuple=0.0     ,		__a				:							Tuple=False     ,		**__a				:							Any     ,		)							->	Union[str, Any]:
								super().__init__(
								    feature_size=__a     ,		sampling_rate=__a     ,		padding_value=__a     ,		return_attention_mask=__a     ,		**__a     ,		)
								__UpperCAmelCase							=			n_fft
								__UpperCAmelCase							=			hop_length
								__UpperCAmelCase							=			chunk_length
								__UpperCAmelCase							=			chunk_length * sampling_rate
								__UpperCAmelCase							=			self.n_samples // hop_length
								__UpperCAmelCase							=			sampling_rate
								__UpperCAmelCase							=			mel_filter_bank(
								    num_frequency_bins=1 + n_fft // 2     ,		num_mel_filters=__a     ,		min_frequency=0.0     ,		max_frequency=8_0_0_0.0     ,		sampling_rate=__a     ,		norm='''slaney'''     ,		mel_scale='''slaney'''     ,		)





							def       snake_case__						(      self				:							Optional[int]     ,		__a				:							np.array	)							->	np.ndarray:
								__UpperCAmelCase							=			spectrogram(
								    __a     ,		window_function(self.n_fft     ,		'''hann'''	)     ,		frame_length=self.n_fft     ,		hop_length=self.hop_length     ,		power=2.0     ,		mel_filters=self.mel_filters     ,		log_mel='''log10'''     ,		)
								__UpperCAmelCase							=			log_spec[:, :-1]
								__UpperCAmelCase							=			np.maximum(__a     ,		log_spec.max() - 8.0	)
								__UpperCAmelCase							=			(log_spec + 4.0) / 4.0
								return log_spec





							@staticmethod
							# Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
							def       snake_case__						(      __a				:							List[np.ndarray]     ,		__a				:							List[np.ndarray]     ,		__a				:							float = 0.0	)							->	List[np.ndarray]:
								if attention_mask is not None:
									__UpperCAmelCase							=			np.array(__a     ,		np.intaa	)
									__UpperCAmelCase							=			[]

									for vector, length in zip(__a     ,		attention_mask.sum(-1	)	):
										__UpperCAmelCase							=			(vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7	)
										if length < normed_slice.shape[0]:
											__UpperCAmelCase							=			padding_value

										normed_input_values.append(__a	)
								else:
									__UpperCAmelCase							=			[(x - x.mean()) / np.sqrt(x.var() + 1e-7	) for x in input_values]

								return normed_input_values





							def __call__(      self				:							Tuple     ,		__a				:							Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]     ,		__a				:							bool = True     ,		__a				:							Optional[int] = None     ,		__a				:							Optional[Union[str, TensorType]] = None     ,		__a				:							Optional[bool] = None     ,		__a				:							Optional[str] = "max_length"     ,		__a				:							Optional[int] = None     ,		__a				:							Optional[int] = None     ,		__a				:							Optional[bool] = None     ,		**__a				:							Optional[Any]     ,		)							->	BatchFeature:

								if sampling_rate is not None:
									if sampling_rate != self.sampling_rate:
										raise ValueError(
										    f"""The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a"""
										    f""" sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input"""
										    f""" was sampled with {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(__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}"""	)
								__UpperCAmelCase							=			is_batched_numpy or (
								    isinstance(__a     ,		(list, tuple)	) and (isinstance(raw_speech[0]     ,		(np.ndarray, tuple, list)	))
								)

								if is_batched:
									__UpperCAmelCase							=			[np.asarray([speech]     ,		dtype=np.floataa	).T for speech in raw_speech]
								elif not is_batched and not isinstance(__a     ,		np.ndarray	):
									__UpperCAmelCase							=			np.asarray(__a     ,		dtype=np.floataa	)
								elif isinstance(__a     ,		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							=			[np.asarray([raw_speech]	).T]

								__UpperCAmelCase							=			BatchFeature({'''input_features''': raw_speech}	)

								# convert into correct format for padding

								__UpperCAmelCase							=			self.pad(
								    __a     ,		padding=__a     ,		max_length=max_length if max_length else self.n_samples     ,		truncation=__a     ,		pad_to_multiple_of=__a     ,		return_attention_mask=return_attention_mask or do_normalize     ,		)

								# zero-mean and unit-variance normalization
								if do_normalize:
									__UpperCAmelCase							=			self.zero_mean_unit_var_norm(
									    padded_inputs['''input_features''']     ,		attention_mask=padded_inputs['''attention_mask''']     ,		padding_value=self.padding_value     ,		)
									__UpperCAmelCase							=			np.stack(padded_inputs['''input_features''']     ,		axis=0	)

								# make sure list is in array format
								__UpperCAmelCase							=			padded_inputs.get('''input_features'''	).transpose(2     ,		0     ,		1	)

								__UpperCAmelCase							=			[self._np_extract_fbank_features(__a	) for waveform in input_features[0]]

								if isinstance(input_features[0]     ,		__a	):
									__UpperCAmelCase							=			[np.asarray(__a     ,		dtype=np.floataa	) for feature in input_features]
								else:
									__UpperCAmelCase							=			input_features

								if return_attention_mask:
									# rescale from sample (48000) to feature (3000)
									__UpperCAmelCase							=			padded_inputs['''attention_mask'''][:, :: self.hop_length]

								if return_tensors is not None:
									__UpperCAmelCase							=			padded_inputs.convert_to_tensors(__a	)

								return padded_inputs






							def       snake_case__						(      self				:							List[str]	)							->	Dict[str, Any]:
								__UpperCAmelCase							=			copy.deepcopy(self.__dict__	)
								__UpperCAmelCase							=			self.__class__.__name__
								if "mel_filters" in output:
									del output["mel_filters"]
								return output

'''simple docstring'''


from pathlib import Path

import fire





def       lowerCAmelCase					(		UpperCamelCase__				:   str    ,					UpperCamelCase__				:   str    ,					UpperCamelCase__				:   int       ):


	"""simple docstring"""
	__UpperCAmelCase							=			Path(UpperCamelCase__       )
	__UpperCAmelCase							=			Path(UpperCamelCase__       )
	dest_dir.mkdir(exist_ok=UpperCamelCase__       )
	for path in src_dir.iterdir():
		__UpperCAmelCase							=			[x.rstrip() for x in list(path.open().readlines()       )][:n]
		__UpperCAmelCase							=			dest_dir.joinpath(path.name       )
		print(UpperCamelCase__       )
		dest_path.open('''w'''       ).write('''\n'''.join(UpperCamelCase__       )       )


if __name__ == "__main__":
					fire.Fire(minify)
