




'''simple docstring'''







from typing import List, Optional, Union

import numpy as np

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


A									=logging.get_logger(__name__)


class     _a						(			SCREAMING_SNAKE_CASE__				):
       __a       :  List[str]            =      ["""input_features""", """attention_mask"""]
       def __init__(			self		:			Tuple     ,       lowercase		:			Tuple=80     ,       lowercase		:			Any=16_000     ,       lowercase		:			Optional[Any]=0.0     ,       lowercase		:			int=10     ,       lowercase		:			int=25     ,       lowercase		:			Optional[int]="hamming_window"     ,       lowercase		:			str=32_768.0     ,       lowercase		:			List[str]=0.97     ,       lowercase		:			Any=1.0     ,       lowercase		:			Any=True     ,       lowercase		:			Tuple=True     ,       lowercase		:			Dict=False     ,       **lowercase		:			Optional[Any]     ,       ):



           '''simple docstring'''

           super().__init__(feature_size=snake_case__     ,       sampling_rate=snake_case__     ,       padding_value=snake_case__     ,       **snake_case__       )

           UpperCAmelCase     =	feature_size
           UpperCAmelCase     =	sampling_rate
           UpperCAmelCase     =	padding_value
           UpperCAmelCase     =	hop_length
           UpperCAmelCase     =	win_length
           UpperCAmelCase     =	frame_signal_scale
           UpperCAmelCase     =	preemphasis_coeff
           UpperCAmelCase     =	mel_floor
           UpperCAmelCase     =	normalize_means
           UpperCAmelCase     =	normalize_vars
           UpperCAmelCase     =	win_function
           UpperCAmelCase     =	return_attention_mask

           UpperCAmelCase     =	win_length * sampling_rate // 1_000
           UpperCAmelCase     =	hop_length * sampling_rate // 1_000

           UpperCAmelCase     =	optimal_fft_length(self.sample_size       )
           UpperCAmelCase     =	(self.n_fft // 2) + 1
       def 					A   (			self		:			Optional[Any]     ,       lowercase		:			Tuple       ):



           '''simple docstring'''

           if self.win_function == "hamming_window":
               UpperCAmelCase     =	window_function(window_length=self.sample_size     ,       name=self.win_function     ,       periodic=snake_case__       )
           else:
               UpperCAmelCase     =	window_function(window_length=self.sample_size     ,       name=self.win_function       )

           UpperCAmelCase     =	mel_filter_bank(
               num_frequency_bins=self.n_freqs     ,       num_mel_filters=self.feature_size     ,       min_frequency=0.0     ,       max_frequency=self.sampling_rate / 2.0     ,       sampling_rate=self.sampling_rate     ,       )

           UpperCAmelCase     =	spectrogram(
               one_waveform * self.frame_signal_scale     ,       window=snake_case__     ,       frame_length=self.sample_size     ,       hop_length=self.sample_stride     ,       fft_length=self.n_fft     ,       center=snake_case__     ,       preemphasis=self.preemphasis_coeff     ,       mel_filters=snake_case__     ,       mel_floor=self.mel_floor     ,       log_mel='''log'''     ,       )
           return msfc_features.T
       def 					A   (			self		:			Optional[int]     ,       lowercase		:			Optional[int]     ,       lowercase		:			Union[str, Any]     ,       lowercase		:			List[Any]       ):



           '''simple docstring'''

           if self.normalize_means:
               UpperCAmelCase     =	x[:input_length].mean(axis=0       )
               UpperCAmelCase     =	np.subtract(snake_case__     ,       snake_case__       )
           if self.normalize_vars:
               UpperCAmelCase     =	x[:input_length].std(axis=0       )
               UpperCAmelCase     =	np.divide(snake_case__     ,       snake_case__       )

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

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

           return x
       def 					A   (			self		:			Optional[int]     ,       lowercase		:			str     ,       lowercase		:			Union[str, Any] = None       ):



           '''simple docstring'''

           UpperCAmelCase     =	attention_mask.sum(-1       ) if attention_mask is not None else [x.shape[0] for x in input_features]
           return [self._normalize_one(snake_case__     ,       snake_case__     ,       self.padding_value       ) for x, n in zip(snake_case__     ,       snake_case__       )]







       def __call__(			self		:			Tuple     ,       lowercase		:			Dict     ,       lowercase		:			int = False     ,       lowercase		:			int = None     ,       lowercase		:			int = False     ,       lowercase		:			Union[str, Any] = None     ,       lowercase		:			Optional[int] = None     ,       lowercase		:			Tuple = None     ,       lowercase		:			int = None     ,       **lowercase		:			str     ,       ):



           '''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.'''       )

           UpperCAmelCase     =	isinstance(snake_case__     ,       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(snake_case__     ,       (list, tuple)       ) and (isinstance(raw_speech[0]     ,       (np.ndarray, tuple, list)       ))
           )

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

           # extract fbank features
           UpperCAmelCase     =	[self._extract_mfsc_features(snake_case__       ) for one_waveform in raw_speech]

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

           UpperCAmelCase     =	self.pad(
               snake_case__     ,       padding=snake_case__     ,       max_length=snake_case__     ,       truncation=snake_case__     ,       pad_to_multiple_of=snake_case__     ,       return_attention_mask=snake_case__     ,       **snake_case__     ,       )
           # make sure list is in array format
           UpperCAmelCase     =	padded_inputs.get('''input_features'''       )
           if isinstance(input_features[0]     ,       snake_case__       ):
               UpperCAmelCase     =	[np.asarray(snake_case__     ,       dtype=np.floataa       ) for feature in input_features]

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

           if self.normalize_means or self.normalize_vars:
               UpperCAmelCase     =	(
                   np.array(snake_case__     ,       dtype=np.intaa       )
                   if self._get_padding_strategies(snake_case__     ,       max_length=snake_case__       ) is not PaddingStrategy.DO_NOT_PAD
                   and padding
                   else None
               )
               UpperCAmelCase     =	self.normalize(
                   padded_inputs['''input_features''']     ,       attention_mask=snake_case__       )

           if return_tensors is not None:
               UpperCAmelCase     =	padded_inputs.convert_to_tensors(snake_case__       )

           return padded_inputs










'''simple docstring'''







from datetime import datetime

import matplotlib.pyplot as plt
import torch





def 		snake_case_							(_a			:							List[str]  ):
    for param in module.parameters():
        UpperCAmelCase     =	False





def 		snake_case_							():
    UpperCAmelCase     =	'''cuda''' if torch.cuda.is_available() else '''cpu'''
    if torch.backends.mps.is_available() and torch.backends.mps.is_built():
        UpperCAmelCase     =	'''mps'''
    if device == "mps":
        print(
            '''WARNING: MPS currently doesn\'t seem to work, and messes up backpropagation without any visible torch'''
            ''' errors. I recommend using CUDA on a colab notebook or CPU instead if you\'re facing inexplicable issues'''
            ''' with generations.'''  )
    return device





def 		snake_case_							(_a			:							Optional[Any]  ):
    UpperCAmelCase     =	plt.imshow(_a  )
    fig.axes.get_xaxis().set_visible(_a  )
    fig.axes.get_yaxis().set_visible(_a  )
    plt.show()







def 		snake_case_							():
    UpperCAmelCase     =	datetime.now()
    UpperCAmelCase     =	current_time.strftime('''%H:%M:%S'''  )
    return timestamp




