



'''simple docstring'''





from typing import List, Optional, Union

import numpy as np

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


lowercase_		       =  logging.get_logger(__name__)






class __A			(  A						):



 '''simple docstring'''







 __lowerCamelCase    :       Dict          =       ['input_values', 'padding_mask']



 def __init__(self ,   A = 1 ,   A = 24_000 ,   A = 0.0 ,   A = None ,   A = None ,   **A ,   )    ->     int:


      """simple docstring"""



      super().__init__(feature_size=A ,   sampling_rate=A ,   padding_value=A ,   **A       )
      _a      =  chunk_length_s
      _a      =  overlap



 @property
 def 					a__  (self       )    ->     Optional[int]:


      """simple docstring"""



      if self.chunk_length_s is None:
           return None
      else:
           return int(self.chunk_length_s * self.sampling_rate       )



 @property
 def 					a__  (self       )    ->     Optional[int]:


      """simple docstring"""



      if self.chunk_length_s is None or self.overlap is None:
           return None
      else:
           return max(1 ,   int((1.0 - self.overlap) * self.chunk_length       )       )



 def __call__(self ,   A ,   A = None ,   A = False ,   A = None ,   A = None ,   A = None ,   )    ->     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} 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 padding and truncation:
           raise ValueError('''Both padding and truncation were set. Make sure you only set one.'''       )
      elif padding is None:
           # by default let's pad the inputs
           _a      =  True

      _a      =  bool(
          isinstance(A ,   (list, tuple)       ) and (isinstance(raw_audio[0] ,   (np.ndarray, tuple, list)       ))       )

      if is_batched:
           _a      =  [np.asarray(A ,   dtype=np.floataa       ).T for audio in raw_audio]
      elif not is_batched and not isinstance(A ,   np.ndarray       ):
           _a      =  np.asarray(A ,   dtype=np.floataa       )
      elif isinstance(A ,   np.ndarray       ) and raw_audio.dtype is np.dtype(np.floataa       ):
           _a      =  raw_audio.astype(np.floataa       )

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

      # verify inputs are valid
      for idx, example in enumerate(A       ):
           if example.ndim > 2:
                raise ValueError(f'''Expected input shape (channels, length) but got shape {example.shape}'''       )
           if self.feature_size == 1 and example.ndim != 1:
                raise ValueError(f'''Expected mono audio but example has {example.shape[-1]} channels'''       )
           if self.feature_size == 2 and example.shape[-1] != 2:
                raise ValueError(f'''Expected stereo audio but example has {example.shape[-1]} channels'''       )

      _a      =  None
      _a      =  BatchFeature({'''input_values''': raw_audio}       )
      if self.chunk_stride is not None and self.chunk_length is not None and max_length is None:
           if truncation:
                _a      =  min(array.shape[0] for array in raw_audio       )
                _a      =  int(np.floor(max_length / self.chunk_stride       )       )
                _a      =  (nb_step - 1) * self.chunk_stride + self.chunk_length
           elif padding:
                _a      =  max(array.shape[0] for array in raw_audio       )
                _a      =  int(np.ceil(max_length / self.chunk_stride       )       )
                _a      =  (nb_step - 1) * self.chunk_stride + self.chunk_length
                _a      =  '''max_length'''
           else:
                _a      =  input_values

        # normal padding on batch
      if padded_inputs is None:
           _a      =  self.pad(
               A ,   max_length=A ,   truncation=A ,   padding=A ,   return_attention_mask=A ,   )
           if padding:
                _a      =  padded_inputs.pop('''attention_mask'''       )

      _a      =  []
      for example in padded_inputs.pop('''input_values'''       ):
           if self.feature_size == 1:
                _a      =  example[..., None]
           input_values.append(example.T       )

      _a      =  input_values
      if return_tensors is not None:
           _a      =  padded_inputs.convert_to_tensors(A       )

      return padded_inputs








'''simple docstring'''





from collections import OrderedDict
from typing import Mapping

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


lowercase_		       =  logging.get_logger(__name__)

lowercase_		       =  {
    "facebook/xmod-base": "https://huggingface.co/facebook/xmod-base/resolve/main/config.json",
    "facebook/xmod-large-prenorm": "https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json",
    "facebook/xmod-base-13-125k": "https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json",
    "facebook/xmod-base-30-125k": "https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json",
    "facebook/xmod-base-30-195k": "https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json",
    "facebook/xmod-base-60-125k": "https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json",
    "facebook/xmod-base-60-265k": "https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json",
    "facebook/xmod-base-75-125k": "https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json",
    "facebook/xmod-base-75-269k": "https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json",
}






class __A			(  A						):



 '''simple docstring'''







 __lowerCamelCase    :       Any          =       'xmod'



 def __init__(self ,   A=30_522 ,   A=768 ,   A=12 ,   A=12 ,   A=3_072 ,   A="gelu" ,   A=0.1 ,   A=0.1 ,   A=512 ,   A=2 ,   A=0.02 ,   A=1E-12 ,   A=1 ,   A=0 ,   A=2 ,   A="absolute" ,   A=True ,   A=None ,   A=False ,   A=2 ,   A=False ,   A=True ,   A=True ,   A=("en_XX",) ,   A=None ,   **A ,   )    ->     List[str]:


      """simple docstring"""



      super().__init__(pad_token_id=A ,   bos_token_id=A ,   eos_token_id=A ,   **A       )

      _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      =  use_cache
      _a      =  classifier_dropout
      _a      =  pre_norm
      _a      =  adapter_reduction_factor
      _a      =  adapter_layer_norm
      _a      =  adapter_reuse_layer_norm
      _a      =  ln_before_adapter
      _a      =  list(A       )
      _a      =  default_language






class __A			(  A						):



 '''simple docstring'''







 @property
 def 					a__  (self       )    ->     Mapping[str, Mapping[int, str]]:


      """simple docstring"""



      if self.task == "multiple-choice":
           _a      =  {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
      else:
           _a      =  {0: '''batch''', 1: '''sequence'''}
      return OrderedDict(
          [
              ('''input_ids''', dynamic_axis),
              ('''attention_mask''', dynamic_axis),
          ]       )



