



import unittest

from transformers import MraConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_torch_available():
  import torch

  from transformers import (
      MraForMaskedLM,
      MraForMultipleChoice,
      MraForQuestionAnswering,
      MraForSequenceClassification,
      MraForTokenClassification,
      MraModel,
  )
  from transformers.models.mra.modeling_mra import MRA_PRETRAINED_MODEL_ARCHIVE_LIST







class 						snake_case  :



   '''simple docstring'''






   def __init__(    self							:	List[str] ,						lowerCAmelCase							:	Any ,						lowerCAmelCase							:	int=2 ,						lowerCAmelCase							:	Any=8 ,						lowerCAmelCase							:	List[str]=True ,						lowerCAmelCase							:	str=True ,						lowerCAmelCase							:	Tuple=True ,						lowerCAmelCase							:	Tuple=True ,						lowerCAmelCase							:	Tuple=99 ,						lowerCAmelCase							:	Tuple=16 ,						lowerCAmelCase							:	Dict=5 ,						lowerCAmelCase							:	Any=2 ,						lowerCAmelCase							:	List[str]=36 ,						lowerCAmelCase							:	str="gelu" ,						lowerCAmelCase							:	str=0.0 ,						lowerCAmelCase							:	Optional[int]=0.0 ,						lowerCAmelCase							:	Optional[int]=512 ,						lowerCAmelCase							:	List[Any]=16 ,						lowerCAmelCase							:	Tuple=2 ,						lowerCAmelCase							:	List[Any]=0.02 ,						lowerCAmelCase							:	List[Any]=3 ,						lowerCAmelCase							:	Dict=4 ,						lowerCAmelCase							:	Optional[int]=None ,						) -> Tuple:

    """simple docstring"""







    _snake_case			:     int 		=    parent
    _snake_case			:     int 		=    batch_size
    _snake_case			:     List[str] 		=    seq_length
    _snake_case			:     Any 		=    is_training
    _snake_case			:     List[str] 		=    use_input_mask
    _snake_case			:     Tuple 		=    use_token_type_ids
    _snake_case			:     Optional[Any] 		=    use_labels
    _snake_case			:     Any 		=    vocab_size
    _snake_case			:     List[Any] 		=    hidden_size
    _snake_case			:     Dict 		=    num_hidden_layers
    _snake_case			:     List[str] 		=    num_attention_heads
    _snake_case			:     Optional[Any] 		=    intermediate_size
    _snake_case			:     List[str] 		=    hidden_act
    _snake_case			:     Dict 		=    hidden_dropout_prob
    _snake_case			:     Tuple 		=    attention_probs_dropout_prob
    _snake_case			:     str 		=    max_position_embeddings
    _snake_case			:     List[Any] 		=    type_vocab_size
    _snake_case			:     Any 		=    type_sequence_label_size
    _snake_case			:     Any 		=    initializer_range
    _snake_case			:     Union[str, Any] 		=    num_labels
    _snake_case			:     str 		=    num_choices
    _snake_case			:     Dict 		=    scope

   def    UpperCamelCase_     (    self							:	Any) -> Union[str, Any]:

    """simple docstring"""







    _snake_case			:     Union[str, Any] 		=    ids_tensor([self.batch_size, self.seq_length] ,						self.vocab_size)

    _snake_case			:     Dict 		=    None
    if self.use_input_mask:
     _snake_case			:     Union[str, Any] 		=    random_attention_mask([self.batch_size, self.seq_length])

    _snake_case			:     Union[str, Any] 		=    None
    if self.use_token_type_ids:
     _snake_case			:     List[str] 		=    ids_tensor([self.batch_size, self.seq_length] ,						self.type_vocab_size)

    _snake_case			:     Dict 		=    None
    _snake_case			:     Optional[int] 		=    None
    _snake_case			:     str 		=    None
    if self.use_labels:
     _snake_case			:     Optional[int] 		=    ids_tensor([self.batch_size] ,						self.type_sequence_label_size)
     _snake_case			:     Optional[Any] 		=    ids_tensor([self.batch_size, self.seq_length] ,						self.num_labels)
     _snake_case			:     Any 		=    ids_tensor([self.batch_size] ,						self.num_choices)

    _snake_case			:     Optional[Any] 		=    self.get_config()

    return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels

   def    UpperCamelCase_     (    self							:	Tuple) -> Union[str, Any]:

    """simple docstring"""







    return MraConfig(
        vocab_size=self.vocab_size ,						hidden_size=self.hidden_size ,						num_hidden_layers=self.num_hidden_layers ,						num_attention_heads=self.num_attention_heads ,						intermediate_size=self.intermediate_size ,						hidden_act=self.hidden_act ,						hidden_dropout_prob=self.hidden_dropout_prob ,						attention_probs_dropout_prob=self.attention_probs_dropout_prob ,						max_position_embeddings=self.max_position_embeddings ,						type_vocab_size=self.type_vocab_size ,						is_decoder=_a ,						initializer_range=self.initializer_range ,						)

   def    UpperCamelCase_     (    self							:	List[Any]) -> Union[str, Any]:

    """simple docstring"""







    _snake_case			:     List[str] 		=    self.get_config()
    _snake_case			:     List[str] 		=    300
    return config

   def    UpperCamelCase_     (    self							:	Union[str, Any]) -> Tuple:

    """simple docstring"""







    (
        (
        _snake_case   
    )		,			(
        _snake_case   
    )		,			(
        _snake_case   
    )		,			(
        _snake_case   
    )		,			(
        _snake_case   
    )		,			(
        _snake_case   
    )		,			(
        _snake_case   
    )		,			   
    )			:     List[str] 		=    self.prepare_config_and_inputs()

    _snake_case			:     Dict 		=    True
    _snake_case			:     List[Any] 		=    floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
    _snake_case			:     Optional[int] 		=    ids_tensor([self.batch_size, self.seq_length] ,						vocab_size=2)

    return (
        config,
        input_ids,
        token_type_ids,
        input_mask,
        sequence_labels,
        token_labels,
        choice_labels,
        encoder_hidden_states,
        encoder_attention_mask,
    )

   def    UpperCamelCase_     (    self							:	List[str] ,						lowerCAmelCase							:	Optional[int] ,						lowerCAmelCase							:	int ,						lowerCAmelCase							:	Any ,						lowerCAmelCase							:	Optional[int] ,						lowerCAmelCase							:	Any ,						lowerCAmelCase							:	List[str] ,						lowerCAmelCase							:	Optional[int]) -> Optional[int]:

    """simple docstring"""







    _snake_case			:     Union[str, Any] 		=    MraModel(config=_a)
    model.to(_a)
    model.eval()
    _snake_case			:     Tuple 		=    model(_a ,						attention_mask=_a ,						token_type_ids=_a)
    _snake_case			:     List[str] 		=    model(_a ,						token_type_ids=_a)
    _snake_case			:     List[Any] 		=    model(_a)
    self.parent.assertEqual(result.last_hidden_state.shape ,						(self.batch_size, self.seq_length, self.hidden_size))

   def    UpperCamelCase_     (    self							:	Any ,						lowerCAmelCase							:	List[str] ,						lowerCAmelCase							:	Optional[Any] ,						lowerCAmelCase							:	Tuple ,						lowerCAmelCase							:	Dict ,						lowerCAmelCase							:	List[str] ,						lowerCAmelCase							:	Any ,						lowerCAmelCase							:	str ,						lowerCAmelCase							:	Any ,						lowerCAmelCase							:	Any ,						) -> Dict:

    """simple docstring"""







    _snake_case			:     Optional[Any] 		=    True
    _snake_case			:     str 		=    MraModel(_a)
    model.to(_a)
    model.eval()
    _snake_case			:     Dict 		=    model(
        _a ,						attention_mask=_a ,						token_type_ids=_a ,						encoder_hidden_states=_a ,						encoder_attention_mask=_a ,						)
    _snake_case			:     int 		=    model(
        _a ,						attention_mask=_a ,						token_type_ids=_a ,						encoder_hidden_states=_a ,						)
    _snake_case			:     List[str] 		=    model(_a ,						attention_mask=_a ,						token_type_ids=_a)
    self.parent.assertEqual(result.last_hidden_state.shape ,						(self.batch_size, self.seq_length, self.hidden_size))

   def    UpperCamelCase_     (    self							:	int ,						lowerCAmelCase							:	List[str] ,						lowerCAmelCase							:	Dict ,						lowerCAmelCase							:	Union[str, Any] ,						lowerCAmelCase							:	str ,						lowerCAmelCase							:	str ,						lowerCAmelCase							:	List[str] ,						lowerCAmelCase							:	Union[str, Any]) -> str:

    """simple docstring"""







    _snake_case			:     List[str] 		=    MraForMaskedLM(config=_a)
    model.to(_a)
    model.eval()
    _snake_case			:     Any 		=    model(_a ,						attention_mask=_a ,						token_type_ids=_a ,						labels=_a)
    self.parent.assertEqual(result.logits.shape ,						(self.batch_size, self.seq_length, self.vocab_size))

   def    UpperCamelCase_     (    self							:	Tuple ,						lowerCAmelCase							:	Optional[Any] ,						lowerCAmelCase							:	List[str] ,						lowerCAmelCase							:	Tuple ,						lowerCAmelCase							:	Optional[int] ,						lowerCAmelCase							:	Optional[Any] ,						lowerCAmelCase							:	int ,						lowerCAmelCase							:	str) -> Any:

    """simple docstring"""







    _snake_case			:     List[Any] 		=    MraForQuestionAnswering(config=_a)
    model.to(_a)
    model.eval()
    _snake_case			:     Optional[Any] 		=    model(
        _a ,						attention_mask=_a ,						token_type_ids=_a ,						start_positions=_a ,						end_positions=_a ,						)
    self.parent.assertEqual(result.start_logits.shape ,						(self.batch_size, self.seq_length))
    self.parent.assertEqual(result.end_logits.shape ,						(self.batch_size, self.seq_length))

   def    UpperCamelCase_     (    self							:	List[str] ,						lowerCAmelCase							:	Union[str, Any] ,						lowerCAmelCase							:	Any ,						lowerCAmelCase							:	str ,						lowerCAmelCase							:	Optional[int] ,						lowerCAmelCase							:	List[Any] ,						lowerCAmelCase							:	List[str] ,						lowerCAmelCase							:	str) -> Dict:

    """simple docstring"""







    _snake_case			:     int 		=    self.num_labels
    _snake_case			:     Optional[Any] 		=    MraForSequenceClassification(_a)
    model.to(_a)
    model.eval()
    _snake_case			:     List[Any] 		=    model(_a ,						attention_mask=_a ,						token_type_ids=_a ,						labels=_a)
    self.parent.assertEqual(result.logits.shape ,						(self.batch_size, self.num_labels))

   def    UpperCamelCase_     (    self							:	List[str] ,						lowerCAmelCase							:	str ,						lowerCAmelCase							:	int ,						lowerCAmelCase							:	Any ,						lowerCAmelCase							:	Dict ,						lowerCAmelCase							:	Any ,						lowerCAmelCase							:	Dict ,						lowerCAmelCase							:	List[str]) -> Tuple:

    """simple docstring"""







    _snake_case			:     Any 		=    self.num_labels
    _snake_case			:     int 		=    MraForTokenClassification(config=_a)
    model.to(_a)
    model.eval()
    _snake_case			:     Optional[int] 		=    model(_a ,						attention_mask=_a ,						token_type_ids=_a ,						labels=_a)
    self.parent.assertEqual(result.logits.shape ,						(self.batch_size, self.seq_length, self.num_labels))

   def    UpperCamelCase_     (    self							:	Any ,						lowerCAmelCase							:	int ,						lowerCAmelCase							:	Optional[int] ,						lowerCAmelCase							:	Union[str, Any] ,						lowerCAmelCase							:	List[Any] ,						lowerCAmelCase							:	int ,						lowerCAmelCase							:	int ,						lowerCAmelCase							:	str) -> List[Any]:

    """simple docstring"""







    _snake_case			:     Any 		=    self.num_choices
    _snake_case			:     List[Any] 		=    MraForMultipleChoice(config=_a)
    model.to(_a)
    model.eval()
    _snake_case			:     Dict 		=    input_ids.unsqueeze(1).expand(-1 ,						self.num_choices ,						-1).contiguous()
    _snake_case			:     List[str] 		=    token_type_ids.unsqueeze(1).expand(-1 ,						self.num_choices ,						-1).contiguous()
    _snake_case			:     int 		=    input_mask.unsqueeze(1).expand(-1 ,						self.num_choices ,						-1).contiguous()
    _snake_case			:     int 		=    model(
        _a ,						attention_mask=_a ,						token_type_ids=_a ,						labels=_a ,						)
    self.parent.assertEqual(result.logits.shape ,						(self.batch_size, self.num_choices))


   def    UpperCamelCase_     (    self							:	Optional[int]) -> Dict:

    """simple docstring"""







    _snake_case			:     Union[str, Any] 		=    self.prepare_config_and_inputs()
    (
        (
        _snake_case   
    )		,			(
        _snake_case   
    )		,			(
        _snake_case   
    )		,			(
        _snake_case   
    )		,			(
        _snake_case   
    )		,			(
        _snake_case   
    )		,			(
        _snake_case   
    )		,			   
    )			:     List[Any] 		=    config_and_inputs
    _snake_case			:     int 		=    {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
    return config, inputs_dict







@require_torch
class 						snake_case  (		UpperCAmelCase__    ,unittest.TestCase     ):



   '''simple docstring'''






   snake_case_    :       str								=						(
       (
           MraModel,
           MraForMaskedLM,
           MraForMultipleChoice,
           MraForQuestionAnswering,
           MraForSequenceClassification,
           MraForTokenClassification,
       )
       if is_torch_available()
       else ()
   )
   snake_case_    :       List[Any]								=						False
   snake_case_    :       Union[str, Any]								=						False
   snake_case_    :       Optional[Any]								=						False
   snake_case_    :       Optional[Any]								=						False

   snake_case_    :       Any								=						()

   def    UpperCamelCase_     (    self							:	int) -> Optional[int]:

    """simple docstring"""







    _snake_case			:     Union[str, Any] 		=    MraModelTester(self)
    _snake_case			:     Tuple 		=    ConfigTester(self ,						config_class=_a ,						hidden_size=37)

   def    UpperCamelCase_     (    self							:	Optional[int]) -> int:

    """simple docstring"""







    self.config_tester.run_common_tests()

   def    UpperCamelCase_     (    self							:	Any) -> str:

    """simple docstring"""







    _snake_case			:     Optional[Any] 		=    self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*_a)

   def    UpperCamelCase_     (    self							:	Optional[Any]) -> Tuple:

    """simple docstring"""







    _snake_case			:     List[Any] 		=    self.model_tester.prepare_config_and_inputs()
    for type in ["absolute", "relative_key", "relative_key_query"]:
     _snake_case			:     List[Any] 		=    type
     self.model_tester.create_and_check_model(*_a)

   def    UpperCamelCase_     (    self							:	List[Any]) -> Union[str, Any]:

    """simple docstring"""







    _snake_case			:     Tuple 		=    self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_masked_lm(*_a)

   def    UpperCamelCase_     (    self							:	List[str]) -> Tuple:

    """simple docstring"""







    _snake_case			:     List[Any] 		=    self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_multiple_choice(*_a)

   def    UpperCamelCase_     (    self							:	Any) -> int:

    """simple docstring"""







    _snake_case			:     str 		=    self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_question_answering(*_a)

   def    UpperCamelCase_     (    self							:	Dict) -> List[Any]:

    """simple docstring"""







    _snake_case			:     List[str] 		=    self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_sequence_classification(*_a)

   def    UpperCamelCase_     (    self							:	Optional[Any]) -> List[Any]:

    """simple docstring"""







    _snake_case			:     int 		=    self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_token_classification(*_a)

   @slow
   def    UpperCamelCase_     (    self							:	Optional[Any]) -> Optional[Any]:

    """simple docstring"""







    for model_name in MRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     _snake_case			:     int 		=    MraModel.from_pretrained(_a)
     self.assertIsNotNone(_a)


   @unittest.skip(reason="""MRA does not output attentions""")
   def    UpperCamelCase_     (    self							:	Optional[int]) -> str:

    """simple docstring"""







    return







@require_torch
class 						snake_case  (		unittest.TestCase     ):



   '''simple docstring'''






   @slow
   def    UpperCamelCase_     (    self							:	Optional[int]) -> int:

    """simple docstring"""







    _snake_case			:     Union[str, Any] 		=    MraModel.from_pretrained("""uw-madison/mra-base-512-4""")
    _snake_case			:     Any 		=    torch.arange(256).unsqueeze(0)

    with torch.no_grad():
     _snake_case			:     str 		=    model(_a)[0]

    _snake_case			:     str 		=    torch.Size((1, 256, 768))
    self.assertEqual(output.shape ,						_a)

    _snake_case			:     Optional[Any] 		=    torch.tensor(
        [[[-0.0_140, 0.0_830, -0.0_381], [0.1_546, 0.1_402, 0.0_220], [0.1_162, 0.0_851, 0.0_165]]])

    self.assertTrue(torch.allclose(output[:, :3, :3] ,						_a ,						atol=1E-4))

   @slow
   def    UpperCamelCase_     (    self							:	Dict) -> Any:

    """simple docstring"""







    _snake_case			:     Tuple 		=    MraForMaskedLM.from_pretrained("""uw-madison/mra-base-512-4""")
    _snake_case			:     Tuple 		=    torch.arange(256).unsqueeze(0)

    with torch.no_grad():
     _snake_case			:     Dict 		=    model(_a)[0]

    _snake_case			:     Union[str, Any] 		=    5_0265

    _snake_case			:     Dict 		=    torch.Size((1, 256, vocab_size))
    self.assertEqual(output.shape ,						_a)

    _snake_case			:     Dict 		=    torch.tensor(
        [[[9.2_595, -3.6_038, 11.8_819], [9.3_869, -3.2_693, 11.0_956], [11.8_524, -3.4_938, 13.1_210]]])

    self.assertTrue(torch.allclose(output[:, :3, :3] ,						_a ,						atol=1E-4))


   @slow
   def    UpperCamelCase_     (    self							:	Optional[Any]) -> List[Any]:

    """simple docstring"""







    _snake_case			:     Tuple 		=    MraForMaskedLM.from_pretrained("""uw-madison/mra-base-4096-8-d3""")
    _snake_case			:     str 		=    torch.arange(4096).unsqueeze(0)

    with torch.no_grad():
     _snake_case			:     str 		=    model(_a)[0]

    _snake_case			:     Any 		=    5_0265

    _snake_case			:     Tuple 		=    torch.Size((1, 4096, vocab_size))
    self.assertEqual(output.shape ,						_a)

    _snake_case			:     Optional[int] 		=    torch.tensor(
        [[[5.4_789, -2.3_564, 7.5_064], [7.9_067, -1.3_369, 9.9_668], [9.0_712, -1.8_106, 7.0_380]]])

    self.assertTrue(torch.allclose(output[:, :3, :3] ,						_a ,						atol=1E-4))






"""simple docstring"""




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


lowerCAmelCase							:			List[str]     							=							logging.get_logger(__name__)






class     __magic_name__							(       UpperCAmelCase__				):






				'''simple docstring'''
				__UpperCamelCase			  =						["audio_values", "audio_mask"]





				def __init__(       self		, _a=2_048		, _a=1		, _a=[16, 16]		, _a=128		, _a=44_100		, _a=86		, _a=2_048		, _a=0.0		, **_a		, ):





							"""simple docstring"""
							super().__init__(
							    feature_size=_a		, sampling_rate=_a		, padding_value=_a		, **_a		, )

							lowerCamelCase			      =					spectrogram_length
							lowerCamelCase			      =					num_channels
							lowerCamelCase			      =					patch_size
							lowerCamelCase			      =					feature_size // self.patch_size[1]
							lowerCamelCase			      =					n_fft
							lowerCamelCase			      =					sampling_rate // hop_length_to_sampling_rate
							lowerCamelCase			      =					sampling_rate
							lowerCamelCase			      =					padding_value
							lowerCamelCase			      =					mel_filter_bank(
							    num_frequency_bins=1 + n_fft // 2		, num_mel_filters=_a		, min_frequency=0.0		, max_frequency=22_050.0		, sampling_rate=_a		, norm="""slaney"""		, mel_scale="""slaney"""		, ).T





				def    _lowerCAmelCase				(       self		, _a ):





							"""simple docstring"""
							lowerCamelCase			      =					spectrogram(
							    _a		, 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		, )
							lowerCamelCase			      =					log_spec[:, :-1]
							lowerCamelCase			      =					log_spec - 20.0
							lowerCamelCase			      =					np.clip(log_spec / 40.0		, -2.0		, 0.0 ) + 1.0
							return log_spec





				def __call__(       self		, _a		, _a = None		, _a = True		, _a = None		, _a = False		, _a = False		, **_a		, ):





							"""simple docstring"""

							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.""" )

							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([speech]		, dtype=np.floataa ).T 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			      =					[np.asarray([raw_speech] ).T]

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

							# Create audio attention mask
							lowerCamelCase			      =					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:
										lowerCamelCase			      =					[
										    (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
										]
										lowerCamelCase			      =					np.array(_a ).astype(np.floataa )

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

							# return as BatchFeature
							if return_attention_mask:
										lowerCamelCase			      =					{"""audio_values""": padded_audio_features, """audio_mask""": audio_mask}
							else:
										lowerCamelCase			      =					{"""audio_values""": padded_audio_features}

							lowerCamelCase			      =					BatchFeature(data=_a		, tensor_type=_a )
							return encoded_inputs



