from math import ceil
from typing import List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import BatchFeature, SequenceFeatureExtractor
from ...utils import TensorType, logging


lowercase_     :   Dict 				=				logging.get_logger(__name__)






class 		_lowerCamelCase		(   UpperCamelCase_    ):
    __a  =      ["audio_values", "audio_mask"]
    def __init__(  self , lowerCAmelCase=2048 , lowerCAmelCase=1 , lowerCAmelCase=[16, 16] , lowerCAmelCase=128 , lowerCAmelCase=44100 , lowerCAmelCase=86 , lowerCAmelCase=2048 , lowerCAmelCase=0.0 , **lowerCAmelCase , )       ->					Any:
     super().__init__(
         feature_size=lowerCAmelCase , sampling_rate=lowerCAmelCase , padding_value=lowerCAmelCase , **lowerCAmelCase , )

     SCREAMING_SNAKE_CASE__:   Optional[int]=      spectrogram_length
     SCREAMING_SNAKE_CASE__:   Optional[int]=      num_channels
     SCREAMING_SNAKE_CASE__:   Any=      patch_size
     SCREAMING_SNAKE_CASE__:   Any=      feature_size // self.patch_size[1]
     SCREAMING_SNAKE_CASE__:   List[str]=      n_fft
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      sampling_rate // hop_length_to_sampling_rate
     SCREAMING_SNAKE_CASE__:   List[str]=      sampling_rate
     SCREAMING_SNAKE_CASE__:   Any=      padding_value
     SCREAMING_SNAKE_CASE__:   Dict=      mel_filter_bank(
         num_frequency_bins=1 + n_fft // 2 , num_mel_filters=lowerCAmelCase , min_frequency=0.0 , max_frequency=22050.0 , sampling_rate=lowerCAmelCase , norm='''slaney''' , mel_scale='''slaney''' , ).T
    def 						UpperCamelCase_				(  self , lowerCAmelCase   )       ->					np.ndarray:
     SCREAMING_SNAKE_CASE__:   str=      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.T , log_mel='''dB''' , db_range=80.0 , )
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      log_spec[:, :-1]
     SCREAMING_SNAKE_CASE__:   Any=      log_spec - 20.0
     SCREAMING_SNAKE_CASE__:   Any=      np.clip(log_spec / 40.0 , -2.0 , 0.0   ) + 1.0
     return log_spec



    def __call__(  self , lowerCAmelCase , lowerCAmelCase = None , lowerCAmelCase = True , lowerCAmelCase = None , lowerCAmelCase = False , lowerCAmelCase = False , **lowerCAmelCase , )       ->					BatchFeature:

     if sampling_rate is not None:
      if sampling_rate != self.sampling_rate:
       raise ValueError(
           '''This feature extractor is set to support sampling rate'''
           f' of {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled'
           f' 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.'''   )

     SCREAMING_SNAKE_CASE__:   int=      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}'   )
     SCREAMING_SNAKE_CASE__:   Optional[int]=      is_batched_numpy or (
         isinstance(lowerCAmelCase , (list, tuple)   ) and (isinstance(raw_speech[0] , (np.ndarray, tuple, list)   ))
     )
     if is_batched:
      SCREAMING_SNAKE_CASE__:   List[str]=      [np.asarray([speech] , dtype=np.floataa   ).T for speech in raw_speech]
     elif not is_batched and not isinstance(lowerCAmelCase , np.ndarray   ):
      SCREAMING_SNAKE_CASE__:   List[Any]=      np.asarray(lowerCAmelCase , dtype=np.floataa   )
     elif isinstance(lowerCAmelCase , np.ndarray   ) and raw_speech.dtype is np.dtype(np.floataa   ):
      SCREAMING_SNAKE_CASE__:   str=      raw_speech.astype(np.floataa   )
     # always return batch
     if not is_batched:
      SCREAMING_SNAKE_CASE__:   List[Any]=      [np.asarray([raw_speech]   ).T]

     # Convert audio signals to log mel spectrograms, truncate by time axis
     SCREAMING_SNAKE_CASE__:   Optional[int]=      [
         self._np_extract_fbank_features(waveform.squeeze()   ).T[: self.spectrogram_length] for waveform in raw_speech
     ]
     if isinstance(audio_features[0] , lowerCAmelCase   ):
      SCREAMING_SNAKE_CASE__:   Optional[Any]=      [np.asarray(lowerCAmelCase , dtype=np.floataa   ) for feature in audio_features]

     # Create audio attention mask
     SCREAMING_SNAKE_CASE__:   Optional[int]=      max(
         [ceil(feature.shape[0] / self.patch_size[0]   ) * self.freq_len for feature in audio_features]   )  # The maximum number of audio patches in a batch
     if return_attention_mask:
      SCREAMING_SNAKE_CASE__:   Union[str, Any]=      [
          (ceil(feature.shape[0] / self.patch_size[0]   ) * self.freq_len) * [1]
          + (max_patch_len - ceil(feature.shape[0] / self.patch_size[0]   ) * self.freq_len) * [0]
          for feature in audio_features
      ]
      SCREAMING_SNAKE_CASE__:   List[Any]=      np.array(lowerCAmelCase   ).astype(np.floataa   )

     # convert into correct format for padding
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      max_patch_len // self.freq_len * self.patch_size[0]  # The maximum audio size in a batch
     SCREAMING_SNAKE_CASE__:   int=      np.ones([len(lowerCAmelCase   ), 1, max_time_len, self.feature_size]   ).astype(np.floataa   )
     SCREAMING_SNAKE_CASE__:   List[str]=      padded_audio_features * self.padding_value
     for i in range(len(lowerCAmelCase   )   ):
      SCREAMING_SNAKE_CASE__:   str=      audio_features[i]
      SCREAMING_SNAKE_CASE__:   str=      feature

     # return as BatchFeature
     if return_attention_mask:
      SCREAMING_SNAKE_CASE__:   List[Any]=      {'''audio_values''': padded_audio_features, '''audio_mask''': audio_mask}
     else:
      SCREAMING_SNAKE_CASE__:   Union[str, Any]=      {'''audio_values''': padded_audio_features}

     SCREAMING_SNAKE_CASE__:   Dict=      BatchFeature(data=lowerCAmelCase , tensor_type=lowerCAmelCase   )
     return encoded_inputs


from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


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

lowercase_     :   List[Any] 				=				{
    'facebook/xlm-roberta-xl': 'https://huggingface.co/facebook/xlm-roberta-xl/resolve/main/config.json',
    'facebook/xlm-roberta-xxl': 'https://huggingface.co/facebook/xlm-roberta-xxl/resolve/main/config.json',
    # See all XLM-RoBERTa-XL models at https://huggingface.co/models?filter=xlm-roberta-xl
}






class 		_lowerCamelCase		(   UpperCamelCase_    ):
    __a  =      "xlm-roberta-xl"
    def __init__(  self , lowerCAmelCase=250880 , lowerCAmelCase=2560 , lowerCAmelCase=36 , lowerCAmelCase=32 , lowerCAmelCase=10240 , lowerCAmelCase="gelu" , lowerCAmelCase=0.1 , lowerCAmelCase=0.1 , lowerCAmelCase=514 , lowerCAmelCase=1 , lowerCAmelCase=0.02 , lowerCAmelCase=1e-05 , lowerCAmelCase=1 , lowerCAmelCase=0 , lowerCAmelCase=2 , lowerCAmelCase="absolute" , lowerCAmelCase=True , lowerCAmelCase=None , **lowerCAmelCase , )       ->					Union[str, Any]:
     super().__init__(pad_token_id=lowerCAmelCase , bos_token_id=lowerCAmelCase , eos_token_id=lowerCAmelCase , **lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   List[Any]=      vocab_size
     SCREAMING_SNAKE_CASE__:   Dict=      hidden_size
     SCREAMING_SNAKE_CASE__:   Tuple=      num_hidden_layers
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      num_attention_heads
     SCREAMING_SNAKE_CASE__:   int=      hidden_act
     SCREAMING_SNAKE_CASE__:   Any=      intermediate_size
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      hidden_dropout_prob
     SCREAMING_SNAKE_CASE__:   str=      attention_probs_dropout_prob
     SCREAMING_SNAKE_CASE__:   List[Any]=      max_position_embeddings
     SCREAMING_SNAKE_CASE__:   Dict=      type_vocab_size
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      initializer_range
     SCREAMING_SNAKE_CASE__:   Dict=      layer_norm_eps
     SCREAMING_SNAKE_CASE__:   Dict=      position_embedding_type
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      use_cache
     SCREAMING_SNAKE_CASE__:   str=      classifier_dropout






class 		_lowerCamelCase		(   UpperCamelCase_    ):
    @property
    def 						UpperCamelCase_				(  self   )       ->					Mapping[str, Mapping[int, str]]:
     if self.task == "multiple-choice":
      SCREAMING_SNAKE_CASE__:   Dict=      {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
     else:
      SCREAMING_SNAKE_CASE__:   List[str]=      {0: '''batch''', 1: '''sequence'''}
     return OrderedDict(
         [
             ('''input_ids''', dynamic_axis),
             ('''attention_mask''', dynamic_axis),
         ]   )

