





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


_UpperCAmelCase							:			List[Any]        =							logging.get_logger(__name__)

class   lowerCAmelCase				(   UpperCAmelCase_     ):
 UpperCAmelCase__						=						["input_features"]
 def __init__( self    : Dict     ,	UpperCAmelCase    : str=80     ,	UpperCAmelCase    : List[Any]=16000     ,	UpperCAmelCase    : str=160     ,	UpperCAmelCase    : Any=30     ,	UpperCAmelCase    : Dict=400     ,	UpperCAmelCase    : int=0.0     ,	UpperCAmelCase    : Tuple=False     ,	**UpperCAmelCase    : Optional[Any]     ,	)     ->      Optional[Any]:
     super().__init__(
         feature_size=_snake_case     ,	sampling_rate=_snake_case     ,	padding_value=_snake_case     ,	return_attention_mask=_snake_case     ,	**_snake_case     ,	)
     lowerCamelCase__       : Tuple   					=						n_fft
     lowerCamelCase__       : Union[str, Any]   					=						hop_length
     lowerCamelCase__       : Tuple   					=						chunk_length
     lowerCamelCase__       : int   					=						chunk_length * sampling_rate
     lowerCamelCase__       : Dict   					=						self.n_samples // hop_length
     lowerCamelCase__       : Any   					=						sampling_rate
     lowerCamelCase__       : int   					=						mel_filter_bank(
         num_frequency_bins=1 + n_fft // 2     ,	num_mel_filters=_snake_case     ,	min_frequency=0.0     ,	max_frequency=8_0_0_0.0     ,	sampling_rate=_snake_case     ,	norm='slaney'     ,	mel_scale='slaney'     ,	)
 def 	A_      ( self    : List[str]     ,	UpperCAmelCase    : np.array      )     ->      Any:
     lowerCamelCase__       : int   					=						spectrogram(
         _snake_case     ,	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'     ,	)
     lowerCamelCase__       : Any   					=						log_spec[:, :-1]
     lowerCamelCase__       : Optional[Any]   					=						np.maximum(_snake_case     ,	log_spec.max() - 8.0      )
     lowerCamelCase__       : Any   					=						(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 	A_      ( UpperCAmelCase    : List[np.ndarray]     ,	UpperCAmelCase    : List[np.ndarray]     ,	UpperCAmelCase    : float = 0.0      )     ->      Tuple:


     if attention_mask is not None:
         lowerCamelCase__       : List[str]   					=						np.array(_snake_case     ,	np.intaa      )
         lowerCamelCase__       : List[Any]   					=						[]

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

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

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


     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.'      )

     lowerCamelCase__       : Optional[Any]   					=						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}"""      )
     lowerCamelCase__       : Optional[int]   					=						is_batched_numpy or (
         isinstance(_snake_case     ,	(list, tuple)      ) and (isinstance(raw_speech[0]     ,	(np.ndarray, tuple, list)      ))
     )

     if is_batched:
         lowerCamelCase__       : List[Any]   					=						[np.asarray([speech]     ,	dtype=np.floataa      ).T for speech in raw_speech]
     elif not is_batched and not isinstance(_snake_case     ,	np.ndarray      ):
         lowerCamelCase__       : str   					=						np.asarray(_snake_case     ,	dtype=np.floataa      )
     elif isinstance(_snake_case     ,	np.ndarray      ) and raw_speech.dtype is np.dtype(np.floataa      ):
         lowerCamelCase__       : Optional[Any]   					=						raw_speech.astype(np.floataa      )

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

     lowerCamelCase__       : str   					=						BatchFeature({'input_features': raw_speech}      )

     # convert into correct format for padding

     lowerCamelCase__       : List[str]   					=						self.pad(
         _snake_case     ,	padding=_snake_case     ,	max_length=max_length if max_length else self.n_samples     ,	truncation=_snake_case     ,	pad_to_multiple_of=_snake_case     ,	return_attention_mask=return_attention_mask or do_normalize     ,	)

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

     # make sure list is in array format
     lowerCamelCase__       : Optional[Any]   					=						padded_inputs.get('input_features'      ).transpose(2     ,	0     ,	1      )

     lowerCamelCase__       : Tuple   					=						[self._np_extract_fbank_features(_snake_case      ) for waveform in input_features[0]]

     if isinstance(input_features[0]     ,	_snake_case      ):
         lowerCamelCase__       : Union[str, Any]   					=						[np.asarray(_snake_case     ,	dtype=np.floataa      ) for feature in input_features]
     else:
         lowerCamelCase__       : Any   					=						input_features

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

     if return_tensors is not None:
         lowerCamelCase__       : List[Any]   					=						padded_inputs.convert_to_tensors(_snake_case      )

     return padded_inputs







 def 	A_      ( self    : Optional[Any]      )     ->      List[Any]:
     lowerCamelCase__       : Tuple   					=						copy.deepcopy(self.__dict__      )
     lowerCamelCase__       : List[Any]   					=						self.__class__.__name__
     if "mel_filters" in output:
         del output["mel_filters"]
     return output










import warnings

from ...utils import logging
from .image_processing_clip import CLIPImageProcessor


SCREAMING_SNAKE_CASE__   			=   logging.get_logger(__name__)


class   __lowerCAmelCase  (		UpperCAmelCase_   ):
					"""simple docstring"""






					def __init__(  self       :							Dict					,       *_snake_case       :							int					,       **_snake_case       :							Optional[int]       ):






								"""simple docstring"""


								warnings.warn(
								    'The class CLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
								    ' use CLIPImageProcessor instead.'					,       _snake_case					,       )
								super().__init__(*_snake_case					,       **_snake_case       )




