
import warnings
from typing import Any, Dict, 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 ...utils import PaddingStrategy, TensorType, logging


lowerCamelCase_    :			List[str]    =	logging.get_logger(__name__)

class 							_UpperCamelCase					(       a_	):






			'''simple docstring'''

			__UpperCamelCase   :    Optional[int]          =			["""input_values""", """attention_mask"""]

			def __init__(							self			:							List[Any]    ,						snake_case_			:							Tuple = 1    ,						snake_case_			:							str = 1_6000    ,						snake_case_			:							int = 0.0    ,						snake_case_			:							Any = False    ,						snake_case_			:							List[Any] = 80    ,						snake_case_			:							List[str] = 16    ,						snake_case_			:							List[Any] = 64    ,						snake_case_			:							Tuple = "hann_window"    ,						snake_case_			:							List[str] = 1.0    ,						snake_case_			:							Optional[int] = 80    ,						snake_case_			:							str = 7600    ,						snake_case_			:							List[Any] = 1e-10    ,						snake_case_			:							Tuple = 2    ,						snake_case_			:							str = True    ,						**snake_case_			:							List[Any]    ,						):
							super().__init__(feature_size=__UpperCamelCase    ,						sampling_rate=__UpperCamelCase    ,						padding_value=__UpperCamelCase    ,						**__UpperCamelCase   )
							UpperCamelCase_:				Union[str, Any]						=    do_normalize
							UpperCamelCase_:				Tuple						=    return_attention_mask

							UpperCamelCase_:				Optional[int]						=    num_mel_bins
							UpperCamelCase_:				List[str]						=    hop_length
							UpperCamelCase_:				List[Any]						=    win_length
							UpperCamelCase_:				Any						=    win_function
							UpperCamelCase_:				Dict						=    frame_signal_scale
							UpperCamelCase_:				Dict						=    fmin
							UpperCamelCase_:				List[Any]						=    fmax
							UpperCamelCase_:				Optional[int]						=    mel_floor
							UpperCamelCase_:				str						=    reduction_factor

							UpperCamelCase_:				Optional[int]						=    win_length * sampling_rate // 1000
							UpperCamelCase_:				Optional[int]						=    hop_length * sampling_rate // 1000
							UpperCamelCase_:				Dict						=    optimal_fft_length(self.sample_size   )
							UpperCamelCase_:				Optional[Any]						=    (self.n_fft // 2) + 1

							UpperCamelCase_:				List[Any]						=    window_function(window_length=self.sample_size    ,						name=self.win_function    ,						periodic=__UpperCamelCase   )

							UpperCamelCase_:				Tuple						=    mel_filter_bank(
							    num_frequency_bins=self.n_freqs    ,						num_mel_filters=self.num_mel_bins    ,						min_frequency=self.fmin    ,						max_frequency=self.fmax    ,						sampling_rate=self.sampling_rate    ,						norm="""slaney"""    ,						mel_scale="""slaney"""    ,						)

							if frame_signal_scale != 1.0:
											warnings.warn(
											    """The argument `frame_signal_scale` is deprecated and will be removed in version 4.30.0 of Transformers"""    ,						__UpperCamelCase    ,						)
							if reduction_factor != 2.0:
											warnings.warn(
											    """The argument `reduction_factor` is deprecated and will be removed in version 4.30.0 of Transformers"""    ,						__UpperCamelCase    ,						)

			@staticmethod
			# Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
			def    lowerCAmelCase__						(							snake_case_			:							str    ,						snake_case_			:							int    ,						snake_case_			:							Dict = 0.0   ):
							if attention_mask is not None:
											UpperCamelCase_:				Any						=    np.array(__UpperCamelCase    ,						np.intaa   )
											UpperCamelCase_:				Dict						=    []

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

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

							return normed_input_values

			def    lowerCAmelCase__						(							self			:							List[Any]    ,						snake_case_			:							List[str]    ,						):
							UpperCamelCase_:				Optional[Any]						=    spectrogram(
							    __UpperCamelCase    ,						window=self.window    ,						frame_length=self.sample_size    ,						hop_length=self.sample_stride    ,						fft_length=self.n_fft    ,						mel_filters=self.mel_filters    ,						mel_floor=self.mel_floor    ,						log_mel="""log10"""    ,						)
							return log_mel_spec.T

			def __call__(							self			:							str    ,						snake_case_			:							Optional[int] = None    ,						snake_case_			:							int = None    ,						snake_case_			:							Union[str, Any] = False    ,						snake_case_			:							str = None    ,						snake_case_			:							str = False    ,						snake_case_			:							Any = None    ,						snake_case_			:							str = None    ,						snake_case_			:							Union[str, Any] = None    ,						snake_case_			:							Optional[int] = None    ,						**snake_case_			:							str    ,						):
							if audio is None and audio_target is None:
											raise ValueError("""You must provide either `audio` or `audio_target` values."""   )

							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 audio 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."""   )

							if audio is not None:
											UpperCamelCase_:				Dict						=    self._process_audio(
											    __UpperCamelCase    ,						__UpperCamelCase    ,						__UpperCamelCase    ,						__UpperCamelCase    ,						__UpperCamelCase    ,						__UpperCamelCase    ,						__UpperCamelCase    ,						__UpperCamelCase    ,						**__UpperCamelCase    ,						)
							else:
											UpperCamelCase_:				str						=    None

							if audio_target is not None:
											UpperCamelCase_:				Union[str, Any]						=    self._process_audio(
											    __UpperCamelCase    ,						__UpperCamelCase    ,						__UpperCamelCase    ,						__UpperCamelCase    ,						__UpperCamelCase    ,						__UpperCamelCase    ,						__UpperCamelCase    ,						__UpperCamelCase    ,						**__UpperCamelCase    ,						)

											if inputs is None:
															return inputs_target
											else:
															UpperCamelCase_:				str						=    inputs_target["""input_values"""]
															UpperCamelCase_:				List[str]						=    inputs_target.get("""attention_mask"""   )
															if decoder_attention_mask is not None:
																			UpperCamelCase_:				Dict						=    decoder_attention_mask

							return inputs

			def    lowerCAmelCase__						(							self			:							str    ,						snake_case_			:							Optional[Any]    ,						snake_case_			:							Dict = False    ,						snake_case_			:							Union[str, Any] = False    ,						snake_case_			:							int = None    ,						snake_case_			:							List[Any] = False    ,						snake_case_			:							Tuple = None    ,						snake_case_			:							List[str] = None    ,						snake_case_			:							Union[str, Any] = None    ,						**snake_case_			:							Union[str, Any]    ,						):
							UpperCamelCase_:				Tuple						=    isinstance(__UpperCamelCase    ,						np.ndarray   ) and len(speech.shape   ) > 1
							if is_batched_numpy and len(speech.shape   ) > 2:
											raise ValueError(f'''Only mono-channel audio is supported for input to {self}'''   )
							UpperCamelCase_:				str						=    is_batched_numpy or (
							    isinstance(__UpperCamelCase    ,						(list, tuple)   ) and (isinstance(speech[0]    ,						(np.ndarray, tuple, list)   ))
							)

							if is_batched:
											UpperCamelCase_:				int						=    [np.asarray(__UpperCamelCase    ,						dtype=np.floataa   ) for speech in speech]
							elif not is_batched and not isinstance(__UpperCamelCase    ,						np.ndarray   ):
											UpperCamelCase_:				int						=    np.asarray(__UpperCamelCase    ,						dtype=np.floataa   )
							elif isinstance(__UpperCamelCase    ,						np.ndarray   ) and speech.dtype is np.dtype(np.floataa   ):
											UpperCamelCase_:				str						=    speech.astype(np.floataa   )

							# always return batch
							if not is_batched:
											UpperCamelCase_:				Tuple						=    [speech]

							# needed to make pad() work on spectrogram inputs
							UpperCamelCase_:				Dict						=    self.feature_size

							# convert into correct format for padding
							if is_target:
											UpperCamelCase_:				str						=    [self._extract_mel_features(__UpperCamelCase   ) for waveform in speech]
											UpperCamelCase_:				List[Any]						=    BatchFeature({"""input_values""": features}   )
											UpperCamelCase_:				Union[str, Any]						=    self.num_mel_bins
							else:
											UpperCamelCase_:				Any						=    BatchFeature({"""input_values""": speech}   )

							UpperCamelCase_:				Dict						=    self.pad(
							    __UpperCamelCase    ,						padding=__UpperCamelCase    ,						max_length=__UpperCamelCase    ,						truncation=__UpperCamelCase    ,						pad_to_multiple_of=__UpperCamelCase    ,						return_attention_mask=__UpperCamelCase    ,						**__UpperCamelCase    ,						)

							UpperCamelCase_:				List[Any]						=    feature_size_hack

							# convert input values to correct format
							UpperCamelCase_:				Optional[int]						=    padded_inputs["""input_values"""]
							if not isinstance(input_values[0]    ,						np.ndarray   ):
											UpperCamelCase_:				List[Any]						=    [np.asarray(__UpperCamelCase    ,						dtype=np.floataa   ) for array in input_values]
							elif (
							    not isinstance(__UpperCamelCase    ,						np.ndarray   )
							    and isinstance(input_values[0]    ,						np.ndarray   )
							    and input_values[0].dtype is np.dtype(np.floataa   )
							):
											UpperCamelCase_:				Dict						=    [array.astype(np.floataa   ) for array in input_values]
							elif isinstance(__UpperCamelCase    ,						np.ndarray   ) and input_values.dtype is np.dtype(np.floataa   ):
											UpperCamelCase_:				Tuple						=    input_values.astype(np.floataa   )

							# convert attention_mask to correct format
							UpperCamelCase_:				List[Any]						=    padded_inputs.get("""attention_mask"""   )
							if attention_mask is not None:
											UpperCamelCase_:				Any						=    [np.asarray(__UpperCamelCase    ,						dtype=np.intaa   ) for array in attention_mask]

							# zero-mean and unit-variance normalization
							if not is_target and self.do_normalize:
											UpperCamelCase_:				Optional[Any]						=    (
											    attention_mask
											    if self._get_padding_strategies(__UpperCamelCase    ,						max_length=__UpperCamelCase   ) is not PaddingStrategy.DO_NOT_PAD
											    else None
											)
											UpperCamelCase_:				Union[str, Any]						=    self.zero_mean_unit_var_norm(
											    padded_inputs["""input_values"""]    ,						attention_mask=__UpperCamelCase    ,						padding_value=self.padding_value   )

							if return_tensors is not None:
											UpperCamelCase_:				str						=    padded_inputs.convert_to_tensors(__UpperCamelCase   )

							return padded_inputs




			def    lowerCAmelCase__						(							self			:							str   ):
							UpperCamelCase_:				List[str]						=    super().to_dict()

							# Don't serialize these as they are derived from the other properties.
							UpperCamelCase_:				Union[str, Any]						=    ["""window""", """mel_filters""", """sample_size""", """sample_stride""", """n_fft""", """n_freqs"""]
							for name in names:
											if name in output:
															del output[name]

							return output














lowerCamelCase      :       List[Any]    =						{
    '''meter''': '''m''',
    '''kilometer''': '''km''',
    '''megametre''': '''Mm''',
    '''gigametre''': '''Gm''',
    '''terametre''': '''Tm''',
    '''petametre''': '''Pm''',
    '''exametre''': '''Em''',
    '''zettametre''': '''Zm''',
    '''yottametre''': '''Ym''',
}
# Exponent of the factor(meter)
lowerCamelCase      :       Union[str, Any]    =						{
    '''m''': 0,
    '''km''': 3,
    '''Mm''': 6,
    '''Gm''': 9,
    '''Tm''': 12,
    '''Pm''': 15,
    '''Em''': 18,
    '''Zm''': 21,
    '''Ym''': 24,
}





def    __lowerCAmelCase   (    __snake_case       ,			__snake_case       ,			__snake_case							):
     __lowerCAmelCase             =       from_type.lower().strip("s"							)
     __lowerCAmelCase             =       to_type.lower().strip("s"							)

     __lowerCAmelCase             =       UNIT_SYMBOL.get(__snake_case       ,			__snake_case							)
     __lowerCAmelCase             =       UNIT_SYMBOL.get(__snake_case       ,			__snake_case							)

     if from_sanitized not in METRIC_CONVERSION:
          __lowerCAmelCase             =       (
              F"""Invalid 'from_type' value: {from_type!r}.\n"""
              F"""Conversion abbreviations are: {", ".join(__snake_case							)}"""
          )
          raise ValueError(__snake_case							)
     if to_sanitized not in METRIC_CONVERSION:
          __lowerCAmelCase             =       (
              F"""Invalid 'to_type' value: {to_type!r}.\n"""
              F"""Conversion abbreviations are: {", ".join(__snake_case							)}"""
          )
          raise ValueError(__snake_case							)
     __lowerCAmelCase             =       METRIC_CONVERSION[from_sanitized]
     __lowerCAmelCase             =       METRIC_CONVERSION[to_sanitized]
     __lowerCAmelCase             =       1

     if from_exponent > to_exponent:
          __lowerCAmelCase             =       from_exponent - to_exponent
     else:
          __lowerCAmelCase             =       -(to_exponent - from_exponent)

     return value * pow(10       ,			__snake_case							)


if __name__ == "__main__":
 from doctest import testmod

 testmod()






