





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


lowercase_          =		logging.get_logger(__name__)
class     SCREAMING_SNAKE_CASE       (UpperCAmelCase				):
			_UpperCamelCase   :		Any			 =	["""input_features"""]


			def __init__(   self      :    int      ,					a      :    Any=80      ,					a      :    Any=16_000      ,					a      :    Union[str, Any]=160      ,					a      :    int=30      ,					a      :    str=400      ,					a      :    Any=0.0      ,					a      :    Optional[int]=False      ,					**a      :    List[str]      ,					)->       Optional[int]:





					"""simple docstring"""







					super().__init__(
					    feature_size=_A      ,					sampling_rate=_A      ,					padding_value=_A      ,					return_attention_mask=_A      ,					**_A      ,					)
					lowercase__           =  n_fft
					lowercase__           =  hop_length
					lowercase__           =  chunk_length
					lowercase__           =  chunk_length * sampling_rate
					lowercase__           =  self.n_samples // hop_length
					lowercase__           =  sampling_rate
					lowercase__           =  mel_filter_bank(
					    num_frequency_bins=1 + n_fft // 2      ,					num_mel_filters=_A      ,					min_frequency=0.0      ,					max_frequency=8_000.0      ,					sampling_rate=_A      ,					norm='slaney'      ,					mel_scale='slaney'      ,					)


			def 	SCREAMING_SNAKE_CASE_							(   self      :    str      ,					a      :    np.array	)->       np.ndarray:





					"""simple docstring"""







					lowercase__           =  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'      ,					)
					lowercase__           =  log_spec[:, :-1]
					lowercase__           =  np.maximum(_A      ,					log_spec.max() - 8.0	)
					lowercase__           =  (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 	SCREAMING_SNAKE_CASE_							(   a      :    List[np.ndarray]      ,					a      :    List[np.ndarray]      ,					a      :    float = 0.0	)->       List[np.ndarray]:





					"""simple docstring"""







					if attention_mask is not None:
							lowercase__           =  np.array(_A      ,					np.intaa	)
							lowercase__           =  []

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

									normed_input_values.append(_A	)
					else:
							lowercase__           =  [(x - x.mean()) / np.sqrt(x.var() + 1E-7	) for x in input_values]

					return normed_input_values


			def __call__(   self      :    Any      ,					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      :    Any      ,					)->       BatchFeature:





					"""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.__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.'	)

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

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

					# always return batch
					if not is_batched:
							lowercase__           =  [np.asarray([raw_speech]	).T]

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

					# convert into correct format for padding

					lowercase__           =  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:
							lowercase__           =  self.zero_mean_unit_var_norm(
							    padded_inputs['input_features']      ,					attention_mask=padded_inputs['attention_mask']      ,					padding_value=self.padding_value      ,					)
							lowercase__           =  np.stack(padded_inputs['input_features']      ,					axis=0	)

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

					lowercase__           =  [self._np_extract_fbank_features(_A	) for waveform in input_features[0]]

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

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

					if return_tensors is not None:
							lowercase__           =  padded_inputs.convert_to_tensors(_A	)

					return padded_inputs


			def 	SCREAMING_SNAKE_CASE_							(   self      :    Optional[int]	)->       Dict[str, Any]:





					"""simple docstring"""







					lowercase__           =  copy.deepcopy(self.__dict__	)
					lowercase__           =  self.__class__.__name__
					if "mel_filters" in output:
							del output["mel_filters"]
					return output









from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=UpperCAmelCase				)
class     SCREAMING_SNAKE_CASE       (UpperCAmelCase				):
   _UpperCamelCase   :		str			 =	field(default='language-modeling'      ,     metadata={'include_in_asdict_even_if_is_default': True}				)

   _UpperCamelCase   :		ClassVar[Features]			 =	Features({'text': Value('string'				)}				)
   _UpperCamelCase   :		ClassVar[Features]			 =	Features({}				)
   _UpperCamelCase   :		str			 =	"text"


   @property
   def 	SCREAMING_SNAKE_CASE_							(   self      :    List[str]	)->       Dict[str, str]:





     """simple docstring"""







     return {self.text_column: "text"}


