code: 
  
  
  """simple docstring"""
  
  
  
  
  from typing import List, Optional, Union
  
  import numpy as np
  import torch
  import torchaudio.compliance.kaldi as ta_kaldi
  
  from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
  from ...feature_extraction_utils import BatchFeature
  from ...utils import TensorType, logging
  
  
  __magic_name__	:   int          =							logging.get_logger(__name__)
  
  
  
  
  class  __snake_case      (lowerCamelCase     ):
  							__a				   =					['''input_values''', '''attention_mask''']
  
  
  
  
  							def __init__(    self:						Union[str, Any]    ,       A_:						int=1    ,       A_:						List[Any]=1_60_00    ,       A_:						Union[str, Any]=1_28    ,       A_:						Dict=10_24    ,       A_:						Optional[Any]=0.0    ,       A_:						List[Any]=True    ,       A_:						Optional[Any]=-4.2_677_393    ,       A_:						Dict=4.5_689_974    ,       A_:						Dict=False    ,       **A_:						Optional[int]    ,       ):
  										super().__init__(feature_size=A_    ,       sampling_rate=A_    ,       padding_value=A_    ,       **A_      )
  										__lowerCamelCase        =			num_mel_bins
  										__lowerCamelCase        =			max_length
  										__lowerCamelCase        =			do_normalize
  										__lowerCamelCase        =			mean
  										__lowerCamelCase        =			std
  										__lowerCamelCase        =			return_attention_mask
  
  
  
  
  							def      __a      (    self:						Dict    ,       A_:						np.ndarray    ,       A_:						int    ,       ):
  										__lowerCamelCase        =			torch.from_numpy(A_      ).unsqueeze(0      )
  										__lowerCamelCase        =			ta_kaldi.fbank(
  										    A_    ,       htk_compat=A_    ,       sample_frequency=self.sampling_rate    ,       use_energy=A_    ,       window_type="""hanning"""    ,       num_mel_bins=self.num_mel_bins    ,       dither=0.0    ,       frame_shift=10    ,       )
  
  										__lowerCamelCase        =			fbank.shape[0]
  										__lowerCamelCase        =			max_length - n_frames
  
  										# pad or truncate, depending on difference
  										if difference > 0:
  													__lowerCamelCase        =			torch.nn.ZeroPadad((0, 0, 0, difference)      )
  													__lowerCamelCase        =			pad_module(A_      )
  										elif difference < 0:
  													__lowerCamelCase        =			fbank[0:max_length, :]
  
  										__lowerCamelCase        =			fbank.numpy()
  
  										return fbank
  
  
  
  
  							def      __a      (    self:						Tuple    ,       A_:						np.ndarray      ):
  										return (input_values - (self.mean)) / (self.std * 2)
  
  
  
  
  							def __call__(    self:						List[str]    ,       A_:						Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]    ,       A_:						Optional[int] = None    ,       A_:						Optional[Union[str, TensorType]] = None    ,       **A_:						str    ,       ):
  
  										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        =			isinstance(A_    ,       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        =			is_batched_numpy or (
  										    isinstance(A_    ,       (list, tuple)      ) and (isinstance(raw_speech[0]    ,       (np.ndarray, tuple, list)      ))
  										)
  
  										if is_batched:
  													__lowerCamelCase        =			[np.asarray(A_    ,       dtype=np.floataa      ) for speech in raw_speech]
  										elif not is_batched and not isinstance(A_    ,       np.ndarray      ):
  													__lowerCamelCase        =			np.asarray(A_    ,       dtype=np.floataa      )
  										elif isinstance(A_    ,       np.ndarray      ) and raw_speech.dtype is np.dtype(np.floataa      ):
  													__lowerCamelCase        =			raw_speech.astype(np.floataa      )
  
  										# always return batch
  										if not is_batched:
  													__lowerCamelCase        =			[raw_speech]
  
  										# extract fbank features and pad/truncate to max_length
  										__lowerCamelCase        =			[self._extract_fbank_features(A_    ,       max_length=self.max_length      ) for waveform in raw_speech]
  
  										# convert into BatchFeature
  										__lowerCamelCase        =			BatchFeature({"""input_values""": features}      )
  
  										# make sure list is in array format
  										__lowerCamelCase        =			padded_inputs.get("""input_values"""      )
  										if isinstance(input_values[0]    ,       A_      ):
  													__lowerCamelCase        =			[np.asarray(A_    ,       dtype=np.floataa      ) for feature in input_values]
  
  										# normalization
  										if self.do_normalize:
  													__lowerCamelCase        =			[self.normalize(A_      ) for feature in input_values]
  
  										if return_tensors is not None:
  													__lowerCamelCase        =			padded_inputs.convert_to_tensors(A_      )
  
  										return padded_inputs
  
  
  
  
  
style_context: 
  
  
  """simple docstring"""
  
  
  
  
  import argparse
  import logging
  import sys
  from unittest.mock import patch
  
  import run_glue_with_pabee
  
  from transformers.testing_utils import TestCasePlus
  
  
  logging.basicConfig(level=logging.DEBUG)
  
  __magic_name__	:   Optional[int]          =							logging.getLogger()
  
  
  
  
  
  def 			a_   (  ):
  			__lowerCamelCase        =			argparse.ArgumentParser()
  			parser.add_argument("""-f"""    )
  			__lowerCamelCase        =			parser.parse_args()
  			return args.f
  
  
  
  
  class  __snake_case      (lowerCamelCase     ):
  
  
  
  
  							def      __a      (    self:						Optional[int]      ):
  										__lowerCamelCase        =			logging.StreamHandler(sys.stdout      )
  										logger.addHandler(A_      )
  
  										__lowerCamelCase        =			self.get_auto_remove_tmp_dir()
  										__lowerCamelCase        =			f'\n            run_glue_with_pabee.py\n            --model_type albert\n            --model_name_or_path albert-base-v2\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --task_name mrpc\n            --do_train\n            --do_eval\n            --per_gpu_train_batch_size=2\n            --per_gpu_eval_batch_size=1\n            --learning_rate=2e-5\n            --max_steps=50\n            --warmup_steps=2\n            --seed=42\n            --max_seq_length=128\n            '.split()
  
  										with patch.object(A_    ,       """argv"""    ,       A_      ):
  													__lowerCamelCase        =			run_glue_with_pabee.main()
  													for value in result.values():
  																self.assertGreaterEqual(A_    ,       0.75      )
  
  
  
  
  