
import unittest

import numpy as np

from transformers import is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
				import tensorflow as tf

				from transformers.activations_tf import get_tf_activation







@require_tf
class 							UpperCAmelCase	(   unittest.TestCase					):

					def        _lowerCAmelCase	(			self      :					Union[str, Any]					):
											lowercase       :    Optional[Any]											=  tf.constant([-100, -1.0, -0.1, 0, 0.1, 1.0, 1_00.0]					)
											lowercase       :    Optional[int]											=  get_tf_activation('''gelu'''					)
											lowercase       :    Optional[Any]											=  get_tf_activation('''gelu_10'''					)

											lowercase       :    Union[str, Any]											=  gelu(lowerCAmelCase					)
											lowercase       :    Union[str, Any]											=  geluaa(lowerCAmelCase					)

											lowercase       :    Optional[Any]											=  tf.where(y_gelu_aa < 10.0 ,      1.0 ,      0.0					)

											self.assertEqual(tf.math.reduce_max(lowerCAmelCase					).numpy().item() ,      10.0					)
											self.assertTrue(np.allclose(y_gelu * clipped_mask ,      y_gelu_aa * clipped_mask					)					)




					def        _lowerCAmelCase	(			self      :					List[str]					):
											get_tf_activation('''gelu'''					)
											get_tf_activation('''gelu_10'''					)
											get_tf_activation('''gelu_fast'''					)
											get_tf_activation('''gelu_new'''					)
											get_tf_activation('''glu'''					)
											get_tf_activation('''mish'''					)
											get_tf_activation('''quick_gelu'''					)
											get_tf_activation('''relu'''					)
											get_tf_activation('''sigmoid'''					)
											get_tf_activation('''silu'''					)
											get_tf_activation('''swish'''					)
											get_tf_activation('''tanh'''					)
											with self.assertRaises(lowerCAmelCase					):
																	get_tf_activation('''bogus'''					)
											with self.assertRaises(lowerCAmelCase					):
																	get_tf_activation(lowerCAmelCase					)




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


snake_case__  							=							logging.get_logger(__name__)







class 							UpperCAmelCase	(   __lowerCamelCase					):
					a__: str			  =				["""input_values""", """attention_mask"""]

					def __init__(			self      :					Union[str, Any] ,      lowerCAmelCase      :					Tuple=1 ,      lowerCAmelCase      :					Dict=1_6000 ,      lowerCAmelCase      :					Optional[Any]=128 ,      lowerCAmelCase      :					Dict=1024 ,      lowerCAmelCase      :					List[Any]=0.0 ,      lowerCAmelCase      :					Any=True ,      lowerCAmelCase      :					Tuple=-4.2_67_73_93 ,      lowerCAmelCase      :					Dict=4.5_68_99_74 ,      lowerCAmelCase      :					int=False ,      **lowerCAmelCase      :					Optional[Any] ,      ):
											super().__init__(feature_size=lowerCAmelCase ,      sampling_rate=lowerCAmelCase ,      padding_value=lowerCAmelCase ,      **lowerCAmelCase					)
											lowercase       :    Optional[int]											=  num_mel_bins
											lowercase       :    List[Any]											=  max_length
											lowercase       :    Any											=  do_normalize
											lowercase       :    Any											=  mean
											lowercase       :    Union[str, Any]											=  std
											lowercase       :    int											=  return_attention_mask

					def        _lowerCAmelCase	(			self      :					int ,      lowerCAmelCase      :					np.ndarray ,      lowerCAmelCase      :					int ,      ):
											lowercase       :    List[Any]											=  torch.from_numpy(lowerCAmelCase					).unsqueeze(0					)
											lowercase       :    List[str]											=  ta_kaldi.fbank(
											    lowerCAmelCase ,      htk_compat=lowerCAmelCase ,      sample_frequency=self.sampling_rate ,      use_energy=lowerCAmelCase ,      window_type='''hanning''' ,      num_mel_bins=self.num_mel_bins ,      dither=0.0 ,      frame_shift=10 ,      )

											lowercase       :    Tuple											=  fbank.shape[0]
											lowercase       :    Optional[Any]											=  max_length - n_frames

											# pad or truncate, depending on difference
											if difference > 0:
																	lowercase       :    int											=  torch.nn.ZeroPadad((0, 0, 0, difference)					)
																	lowercase       :    str											=  pad_module(lowerCAmelCase					)
											elif difference < 0:
																	lowercase       :    Optional[int]											=  fbank[0:max_length, :]

											lowercase       :    List[str]											=  fbank.numpy()

											return fbank

					def        _lowerCAmelCase	(			self      :					Tuple ,      lowerCAmelCase      :					np.ndarray					):
											return (input_values - (self.mean)) / (self.std * 2)




					def __call__(			self      :					Any ,      lowerCAmelCase      :					Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] ,      lowerCAmelCase      :					Optional[int] = None ,      lowerCAmelCase      :					Optional[Union[str, TensorType]] = None ,      **lowerCAmelCase      :					Tuple ,      ):

											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.'''					)

											lowercase       :    Optional[Any]											=  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}'''					)
											lowercase       :    Tuple											=  is_batched_numpy or (
											    isinstance(lowerCAmelCase ,      (list, tuple)					) and (isinstance(raw_speech[0] ,      (np.ndarray, tuple, list)					))
											)

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

											# always return batch
											if not is_batched:
																	lowercase       :    Dict											=  [raw_speech]

											# extract fbank features and pad/truncate to max_length
											lowercase       :    Dict											=  [self._extract_fbank_features(lowerCAmelCase ,      max_length=self.max_length					) for waveform in raw_speech]

											# convert into BatchFeature
											lowercase       :    Any											=  BatchFeature({'''input_values''': features}					)

											# make sure list is in array format
											lowercase       :    str											=  padded_inputs.get('''input_values'''					)
											if isinstance(input_values[0] ,      lowerCAmelCase					):
																	lowercase       :    Any											=  [np.asarray(lowerCAmelCase ,      dtype=np.floataa					) for feature in input_values]

											# normalization
											if self.do_normalize:
																	lowercase       :    List[str]											=  [self.normalize(lowerCAmelCase					) for feature in input_values]

											if return_tensors is not None:
																	lowercase       :    str											=  padded_inputs.convert_to_tensors(lowerCAmelCase					)

											return padded_inputs


