





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


UpperCAmelCase_					:   str 							=						logging.get_logger(__name__)





class      lowercase__    (							__A  ):
  __UpperCamelCase	     =  ["""input_features""", """attention_mask"""]







  def __init__(	self    ,						_lowercase=80    ,						_lowercase=16_000    ,						_lowercase=0.0    ,						_lowercase=10    ,						_lowercase=25    ,						_lowercase="hamming_window"    ,						_lowercase=3_2768.0    ,						_lowercase=0.97    ,						_lowercase=1.0    ,						_lowercase=True    ,						_lowercase=True    ,						_lowercase=False    ,						**_lowercase    ,						):
       super().__init__(feature_size=_lowercase    ,						sampling_rate=_lowercase    ,						padding_value=_lowercase    ,						**_lowercase						)

       lowerCAmelCase_					:       Dict    =		feature_size
       lowerCAmelCase_					:       List[str]    =		sampling_rate
       lowerCAmelCase_					:       str    =		padding_value
       lowerCAmelCase_					:       List[str]    =		hop_length
       lowerCAmelCase_					:       List[Any]    =		win_length
       lowerCAmelCase_					:       List[Any]    =		frame_signal_scale
       lowerCAmelCase_					:       Any    =		preemphasis_coeff
       lowerCAmelCase_					:       List[Any]    =		mel_floor
       lowerCAmelCase_					:       str    =		normalize_means
       lowerCAmelCase_					:       Optional[Any]    =		normalize_vars
       lowerCAmelCase_					:       Dict    =		win_function
       lowerCAmelCase_					:       Union[str, Any]    =		return_attention_mask

       lowerCAmelCase_					:       str    =		win_length * sampling_rate // 1_000
       lowerCAmelCase_					:       Any    =		hop_length * sampling_rate // 1_000

       lowerCAmelCase_					:       int    =		optimal_fft_length(self.sample_size						)
       lowerCAmelCase_					:       int    =		(self.n_fft // 2) + 1







  def 	UpperCAmelCase__		(	self    ,						_lowercase						):
       if self.win_function == "hamming_window":
            lowerCAmelCase_					:       Dict    =		window_function(window_length=self.sample_size    ,						name=self.win_function    ,						periodic=_lowercase						)
       else:
            lowerCAmelCase_					:       int    =		window_function(window_length=self.sample_size    ,						name=self.win_function						)

       lowerCAmelCase_					:       str    =		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    ,						)

       lowerCAmelCase_					:       List[Any]    =		spectrogram(
           one_waveform * self.frame_signal_scale    ,						window=_lowercase    ,						frame_length=self.sample_size    ,						hop_length=self.sample_stride    ,						fft_length=self.n_fft    ,						center=_lowercase    ,						preemphasis=self.preemphasis_coeff    ,						mel_filters=_lowercase    ,						mel_floor=self.mel_floor    ,						log_mel="""log"""    ,						)
       return msfc_features.T







  def 	UpperCAmelCase__		(	self    ,						_lowercase    ,						_lowercase    ,						_lowercase						):
       # make sure we normalize float32 arrays
       if self.normalize_means:
            lowerCAmelCase_					:       Tuple    =		x[:input_length].mean(axis=0						)
            lowerCAmelCase_					:       Optional[Any]    =		np.subtract(_lowercase    ,						_lowercase						)
       if self.normalize_vars:
            lowerCAmelCase_					:       Dict    =		x[:input_length].std(axis=0						)
            lowerCAmelCase_					:       Dict    =		np.divide(_lowercase    ,						_lowercase						)

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

       # make sure array is in float32
       lowerCAmelCase_					:       List[str]    =		x.astype(np.floataa						)

       return x







  def 	UpperCAmelCase__		(	self    ,						_lowercase    ,						_lowercase = None						):
       lowerCAmelCase_					:       int    =		attention_mask.sum(-1						) if attention_mask is not None else [x.shape[0] for x in input_features]
       return [self._normalize_one(_lowercase    ,						_lowercase    ,						self.padding_value						) for x, n in zip(_lowercase    ,						_lowercase						)]







  def __call__(	self    ,						_lowercase    ,						_lowercase = False    ,						_lowercase = None    ,						_lowercase = False    ,						_lowercase = None    ,						_lowercase = None    ,						_lowercase = None    ,						_lowercase = None    ,						**_lowercase    ,						):

       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."""						)

       lowerCAmelCase_					:       Any    =		isinstance(_lowercase    ,						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_					:       Dict    =		is_batched_numpy or (
           isinstance(_lowercase    ,						(list, tuple)						) and (isinstance(raw_speech[0]    ,						(np.ndarray, tuple, list)						))
       )

       if is_batched:
            lowerCAmelCase_					:       Dict    =		[np.asarray(_lowercase    ,						dtype=np.floataa						) for speech in raw_speech]
       elif not is_batched and not isinstance(_lowercase    ,						np.ndarray						):
            lowerCAmelCase_					:       Optional[int]    =		np.asarray(_lowercase    ,						dtype=np.floataa						)
       elif isinstance(_lowercase    ,						np.ndarray						) and raw_speech.dtype is np.dtype(np.floataa						):
            lowerCAmelCase_					:       List[Any]    =		raw_speech.astype(np.floataa						)

       # always return batch
       if not is_batched:
            lowerCAmelCase_					:       str    =		[raw_speech]

       # extract fbank features
       lowerCAmelCase_					:       Union[str, Any]    =		[self._extract_mfsc_features(_lowercase						) for one_waveform in raw_speech]

       # convert into correct format for padding
       lowerCAmelCase_					:       List[str]    =		BatchFeature({"""input_features""": features}						)

       lowerCAmelCase_					:       Union[str, Any]    =		self.pad(
           _lowercase    ,						padding=_lowercase    ,						max_length=_lowercase    ,						truncation=_lowercase    ,						pad_to_multiple_of=_lowercase    ,						return_attention_mask=_lowercase    ,						**_lowercase    ,						)
       # make sure list is in array format
       lowerCAmelCase_					:       List[str]    =		padded_inputs.get("""input_features"""						)
       if isinstance(input_features[0]    ,						_lowercase						):
            lowerCAmelCase_					:       Optional[Any]    =		[np.asarray(_lowercase    ,						dtype=np.floataa						) for feature in input_features]

       lowerCAmelCase_					:       Dict    =		padded_inputs.get("""attention_mask"""						)
       if attention_mask is not None:
            lowerCAmelCase_					:       Tuple    =		[np.asarray(_lowercase    ,						dtype=np.intaa						) for array in attention_mask]

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

       if return_tensors is not None:
            lowerCAmelCase_					:       Union[str, Any]    =		padded_inputs.convert_to_tensors(_lowercase						)

       return padded_inputs







from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase_					:   Tuple 							=						logging.get_logger(__name__)

UpperCAmelCase_					:   List[str] 							=						{
    """google/fnet-base""": """https://huggingface.co/google/fnet-base/resolve/main/config.json""",
    """google/fnet-large""": """https://huggingface.co/google/fnet-large/resolve/main/config.json"""
    # See all FNet models at https://huggingface.co/models?filter=fnet
}





class      lowercase__    (							__A  ):
  __UpperCamelCase	     =  """fnet"""







  def __init__(	self    ,						_lowercase=32_000    ,						_lowercase=768    ,						_lowercase=12    ,						_lowercase=3_072    ,						_lowercase="gelu_new"    ,						_lowercase=0.1    ,						_lowercase=512    ,						_lowercase=4    ,						_lowercase=0.02    ,						_lowercase=1e-12    ,						_lowercase=False    ,						_lowercase=512    ,						_lowercase=3    ,						_lowercase=1    ,						_lowercase=2    ,						**_lowercase    ,						):
       super().__init__(pad_token_id=_lowercase    ,						bos_token_id=_lowercase    ,						eos_token_id=_lowercase    ,						**_lowercase						)

       lowerCAmelCase_					:       Optional[Any]    =		vocab_size
       lowerCAmelCase_					:       List[str]    =		max_position_embeddings
       lowerCAmelCase_					:       str    =		hidden_size
       lowerCAmelCase_					:       Any    =		num_hidden_layers
       lowerCAmelCase_					:       Optional[int]    =		intermediate_size
       lowerCAmelCase_					:       int    =		hidden_act
       lowerCAmelCase_					:       List[str]    =		hidden_dropout_prob
       lowerCAmelCase_					:       Tuple    =		initializer_range
       lowerCAmelCase_					:       int    =		type_vocab_size
       lowerCAmelCase_					:       str    =		layer_norm_eps
       lowerCAmelCase_					:       Optional[Any]    =		use_tpu_fourier_optimizations
       lowerCAmelCase_					:       Union[str, Any]    =		tpu_short_seq_length
