



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


A_		:     Optional[Any]        =     logging.get_logger(__name__)



class 		_lowercase		(       UpperCAmelCase__	):
   _UpperCAmelCase     	=			['''input_features''']

   def __init__(      self			:       int							,   __lowerCAmelCase			:       Dict=80							,   __lowerCAmelCase			:       int=1_6000							,   __lowerCAmelCase			:       Optional[Any]=160							,   __lowerCAmelCase			:       Union[str, Any]=30							,   __lowerCAmelCase			:       Union[str, Any]=400							,   __lowerCAmelCase			:       Optional[int]=0.0							,   __lowerCAmelCase			:       Union[str, Any]=False							,   **__lowerCAmelCase			:       Union[str, Any]							,   )       ->				str:


    """simple docstring"""






    super().__init__(
        feature_size=__lowerCAmelCase							,   sampling_rate=__lowerCAmelCase							,   padding_value=__lowerCAmelCase							,   return_attention_mask=__lowerCAmelCase							,   **__lowerCAmelCase							,   )
    a						=		n_fft
    a						=		hop_length
    a						=		chunk_length
    a						=		chunk_length * sampling_rate
    a						=		self.n_samples // hop_length
    a						=		sampling_rate
    a						=		mel_filter_bank(
        num_frequency_bins=1 + n_fft // 2							,   num_mel_filters=__lowerCAmelCase							,   min_frequency=0.0							,   max_frequency=8000.0							,   sampling_rate=__lowerCAmelCase							,   norm="slaney"							,   mel_scale="slaney"							,   )

   def 		A							(      self			:       Optional[Any]							,   __lowerCAmelCase			:       np.array							)       ->				np.ndarray:


    """simple docstring"""






    a						=		spectrogram(
        __lowerCAmelCase							,   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"							,   )
    a						=		log_spec[:, :-1]
    a						=		np.maximum(__lowerCAmelCase							,   log_spec.max() - 8.0							)
    a						=		(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							(      __lowerCAmelCase			:       List[np.ndarray]							,   __lowerCAmelCase			:       List[np.ndarray]							,   __lowerCAmelCase			:       float = 0.0							)       ->				List[np.ndarray]:


    """simple docstring"""






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

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

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

    return normed_input_values

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

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

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

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

    a						=		BatchFeature({"input_features": raw_speech}							)

    # convert into correct format for padding

    a						=		self.pad(
        __lowerCAmelCase							,   padding=__lowerCAmelCase							,   max_length=max_length if max_length else self.n_samples							,   truncation=__lowerCAmelCase							,   pad_to_multiple_of=__lowerCAmelCase							,   return_attention_mask=return_attention_mask or do_normalize							,   )

    # zero-mean and unit-variance normalization
    if do_normalize:
     a						=		self.zero_mean_unit_var_norm(
         padded_inputs["input_features"]							,   attention_mask=padded_inputs["attention_mask"]							,   padding_value=self.padding_value							,   )
     a						=		np.stack(padded_inputs["input_features"]							,   axis=0							)

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

    a						=		[self._np_extract_fbank_features(__lowerCAmelCase							) for waveform in input_features[0]]

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

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

    if return_tensors is not None:
     a						=		padded_inputs.convert_to_tensors(__lowerCAmelCase							)

    return padded_inputs




   def 		A							(      self			:       str							)       ->				Dict[str, Any]:


    """simple docstring"""






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









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


A_		:     Any        =     logging.get_logger(__name__)

A_		:     Optional[int]        =     {
    '''SCUT-DLVCLab/lilt-roberta-en-base''': (
        '''https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/config.json'''
    ),
}



class 		_lowercase		(       UpperCAmelCase__	):
   _UpperCAmelCase     	=			'''lilt'''

   def __init__(      self			:       Union[str, Any]							,   __lowerCAmelCase			:       Optional[Any]=3_0522							,   __lowerCAmelCase			:       str=768							,   __lowerCAmelCase			:       Any=12							,   __lowerCAmelCase			:       Optional[Any]=12							,   __lowerCAmelCase			:       List[Any]=3072							,   __lowerCAmelCase			:       Dict="gelu"							,   __lowerCAmelCase			:       int=0.1							,   __lowerCAmelCase			:       Union[str, Any]=0.1							,   __lowerCAmelCase			:       List[str]=512							,   __lowerCAmelCase			:       Optional[int]=2							,   __lowerCAmelCase			:       int=0.0_2							,   __lowerCAmelCase			:       Union[str, Any]=1E-12							,   __lowerCAmelCase			:       Tuple=0							,   __lowerCAmelCase			:       List[Any]="absolute"							,   __lowerCAmelCase			:       int=None							,   __lowerCAmelCase			:       Dict=4							,   __lowerCAmelCase			:       Dict=1024							,   **__lowerCAmelCase			:       Dict							,   )       ->				int:


    """simple docstring"""






    super().__init__(pad_token_id=__lowerCAmelCase							,   **__lowerCAmelCase							)

    a						=		vocab_size
    a						=		hidden_size
    a						=		num_hidden_layers
    a						=		num_attention_heads
    a						=		hidden_act
    a						=		intermediate_size
    a						=		hidden_dropout_prob
    a						=		attention_probs_dropout_prob
    a						=		max_position_embeddings
    a						=		type_vocab_size
    a						=		initializer_range
    a						=		layer_norm_eps
    a						=		position_embedding_type
    a						=		classifier_dropout
    a						=		channel_shrink_ratio
    a						=		max_ad_position_embeddings

