

'''simple docstring'''
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 							A__    :





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





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =parent
       _SCREAMING_SNAKE_CASE							    =batch_size
       _SCREAMING_SNAKE_CASE							    =seq_length
       _SCREAMING_SNAKE_CASE							    =is_training
       _SCREAMING_SNAKE_CASE							    =use_input_mask
       _SCREAMING_SNAKE_CASE							    =use_token_type_ids
       _SCREAMING_SNAKE_CASE							    =use_labels
       _SCREAMING_SNAKE_CASE							    =vocab_size
       _SCREAMING_SNAKE_CASE							    =hidden_size
       _SCREAMING_SNAKE_CASE							    =num_hidden_layers
       _SCREAMING_SNAKE_CASE							    =num_attention_heads
       _SCREAMING_SNAKE_CASE							    =intermediate_size
       _SCREAMING_SNAKE_CASE							    =hidden_act
       _SCREAMING_SNAKE_CASE							    =hidden_dropout_prob
       _SCREAMING_SNAKE_CASE							    =attention_probs_dropout_prob
       _SCREAMING_SNAKE_CASE							    =max_position_embeddings
       _SCREAMING_SNAKE_CASE							    =type_vocab_size
       _SCREAMING_SNAKE_CASE							    =type_sequence_label_size
       _SCREAMING_SNAKE_CASE							    =initializer_range
       _SCREAMING_SNAKE_CASE							    =num_labels
       _SCREAMING_SNAKE_CASE							    =num_choices
       _SCREAMING_SNAKE_CASE							    =scope





  def 						A  (		self      :	Any )	->    str:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =ids_tensor([self.batch_size, self.seq_length]							,					self.vocab_size )

       _SCREAMING_SNAKE_CASE							    =None
       if self.use_input_mask:
            _SCREAMING_SNAKE_CASE							    =random_attention_mask([self.batch_size, self.seq_length] )

       _SCREAMING_SNAKE_CASE							    =None
       if self.use_token_type_ids:
            _SCREAMING_SNAKE_CASE							    =ids_tensor([self.batch_size, self.seq_length]							,					self.type_vocab_size )

       _SCREAMING_SNAKE_CASE							    =None
       _SCREAMING_SNAKE_CASE							    =None
       _SCREAMING_SNAKE_CASE							    =None
       if self.use_labels:
            _SCREAMING_SNAKE_CASE							    =ids_tensor([self.batch_size]							,					self.type_sequence_label_size )
            _SCREAMING_SNAKE_CASE							    =ids_tensor([self.batch_size, self.seq_length]							,					self.num_labels )
            _SCREAMING_SNAKE_CASE							    =ids_tensor([self.batch_size]							,					self.num_choices )

       _SCREAMING_SNAKE_CASE							    =self.get_config()

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





  def 						A  (		self      :	int )	->    Tuple:





       '''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 						A  (		self      :	Dict )	->    str:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =self.get_config()
       _SCREAMING_SNAKE_CASE							    =300
       return config





  def 						A  (		self      :	str )	->    List[str]:





       '''simple docstring'''






       (
           (
           _SCREAMING_SNAKE_CASE
       )    ,						(
           _SCREAMING_SNAKE_CASE
       )    ,						(
           _SCREAMING_SNAKE_CASE
       )    ,						(
           _SCREAMING_SNAKE_CASE
       )    ,						(
           _SCREAMING_SNAKE_CASE
       )    ,						(
           _SCREAMING_SNAKE_CASE
       )    ,						(
           _SCREAMING_SNAKE_CASE
       )    ,						
       )							    =self.prepare_config_and_inputs()

       _SCREAMING_SNAKE_CASE							    =True
       _SCREAMING_SNAKE_CASE							    =floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
       _SCREAMING_SNAKE_CASE							    =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 						A  (		self      :	Any							,					_a      :	List[Any]							,					_a      :	List[Any]							,					_a      :	Union[str, Any]							,					_a      :	str							,					_a      :	List[Any]							,					_a      :	Optional[Any]							,					_a      :	List[str] )	->    Optional[Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =MraModel(config=_a )
       model.to(_a )
       model.eval()
       _SCREAMING_SNAKE_CASE							    =model(_a							,					attention_mask=_a							,					token_type_ids=_a )
       _SCREAMING_SNAKE_CASE							    =model(_a							,					token_type_ids=_a )
       _SCREAMING_SNAKE_CASE							    =model(_a )
       self.parent.assertEqual(result.last_hidden_state.shape							,					(self.batch_size, self.seq_length, self.hidden_size) )





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





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =True
       _SCREAMING_SNAKE_CASE							    =MraModel(_a )
       model.to(_a )
       model.eval()
       _SCREAMING_SNAKE_CASE							    =model(
           _a							,					attention_mask=_a							,					token_type_ids=_a							,					encoder_hidden_states=_a							,					encoder_attention_mask=_a							,					)
       _SCREAMING_SNAKE_CASE							    =model(
           _a							,					attention_mask=_a							,					token_type_ids=_a							,					encoder_hidden_states=_a							,					)
       _SCREAMING_SNAKE_CASE							    =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 						A  (		self      :	List[str]							,					_a      :	Optional[Any]							,					_a      :	Dict							,					_a      :	Optional[int]							,					_a      :	str							,					_a      :	Optional[int]							,					_a      :	Optional[Any]							,					_a      :	Tuple )	->    int:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =MraForMaskedLM(config=_a )
       model.to(_a )
       model.eval()
       _SCREAMING_SNAKE_CASE							    =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 						A  (		self      :	List[str]							,					_a      :	Optional[int]							,					_a      :	List[Any]							,					_a      :	Union[str, Any]							,					_a      :	Union[str, Any]							,					_a      :	Optional[int]							,					_a      :	Dict							,					_a      :	List[str] )	->    List[str]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =MraForQuestionAnswering(config=_a )
       model.to(_a )
       model.eval()
       _SCREAMING_SNAKE_CASE							    =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 						A  (		self      :	List[str]							,					_a      :	Optional[int]							,					_a      :	int							,					_a      :	Any							,					_a      :	str							,					_a      :	str							,					_a      :	Optional[int]							,					_a      :	Dict )	->    Optional[Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =self.num_labels
       _SCREAMING_SNAKE_CASE							    =MraForSequenceClassification(_a )
       model.to(_a )
       model.eval()
       _SCREAMING_SNAKE_CASE							    =model(_a							,					attention_mask=_a							,					token_type_ids=_a							,					labels=_a )
       self.parent.assertEqual(result.logits.shape							,					(self.batch_size, self.num_labels) )





  def 						A  (		self      :	Optional[Any]							,					_a      :	List[Any]							,					_a      :	List[str]							,					_a      :	Any							,					_a      :	List[Any]							,					_a      :	List[str]							,					_a      :	int							,					_a      :	Dict )	->    Dict:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =self.num_labels
       _SCREAMING_SNAKE_CASE							    =MraForTokenClassification(config=_a )
       model.to(_a )
       model.eval()
       _SCREAMING_SNAKE_CASE							    =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 						A  (		self      :	List[str]							,					_a      :	int							,					_a      :	Optional[int]							,					_a      :	int							,					_a      :	Dict							,					_a      :	Union[str, Any]							,					_a      :	Optional[int]							,					_a      :	Any )	->    Tuple:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =self.num_choices
       _SCREAMING_SNAKE_CASE							    =MraForMultipleChoice(config=_a )
       model.to(_a )
       model.eval()
       _SCREAMING_SNAKE_CASE							    =input_ids.unsqueeze(1 ).expand(-1							,					self.num_choices							,					-1 ).contiguous()
       _SCREAMING_SNAKE_CASE							    =token_type_ids.unsqueeze(1 ).expand(-1							,					self.num_choices							,					-1 ).contiguous()
       _SCREAMING_SNAKE_CASE							    =input_mask.unsqueeze(1 ).expand(-1							,					self.num_choices							,					-1 ).contiguous()
       _SCREAMING_SNAKE_CASE							    =model(
           _a							,					attention_mask=_a							,					token_type_ids=_a							,					labels=_a							,					)
       self.parent.assertEqual(result.logits.shape							,					(self.batch_size, self.num_choices) )





  def 						A  (		self      :	Tuple )	->    str:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =self.prepare_config_and_inputs()
       (
           (
           _SCREAMING_SNAKE_CASE
       )    ,						(
           _SCREAMING_SNAKE_CASE
       )    ,						(
           _SCREAMING_SNAKE_CASE
       )    ,						(
           _SCREAMING_SNAKE_CASE
       )    ,						(
           _SCREAMING_SNAKE_CASE
       )    ,						(
           _SCREAMING_SNAKE_CASE
       )    ,						(
           _SCREAMING_SNAKE_CASE
       )    ,						
       )							    =config_and_inputs
       _SCREAMING_SNAKE_CASE							    ={'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
       return config, inputs_dict







@require_torch
class 							A__    (       A__     ,   unittest.TestCase  ):
  A__		 =				(
      (
          MraModel,
          MraForMaskedLM,
          MraForMultipleChoice,
          MraForQuestionAnswering,
          MraForSequenceClassification,
          MraForTokenClassification,
      )
      if is_torch_available()
      else ()
  )
  A__		 =				False
  A__		 =				False
  A__		 =				False
  A__		 =				False

  A__		 =				()





  def 						A  (		self      :	Dict )	->    Dict:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =MraModelTester(self )
       _SCREAMING_SNAKE_CASE							    =ConfigTester(self							,					config_class=_a							,					hidden_size=37 )





  def 						A  (		self      :	Tuple )	->    Optional[int]:





       '''simple docstring'''






       self.config_tester.run_common_tests()





  def 						A  (		self      :	Dict )	->    Tuple:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*_a )





  def 						A  (		self      :	List[str] )	->    Any:





       '''simple docstring'''






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





  def 						A  (		self      :	str )	->    Any:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_lm(*_a )





  def 						A  (		self      :	List[str] )	->    Optional[int]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_multiple_choice(*_a )





  def 						A  (		self      :	Optional[int] )	->    Any:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_question_answering(*_a )





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





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_sequence_classification(*_a )





  def 						A  (		self      :	List[str] )	->    str:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_token_classification(*_a )





  @slow
  def 						A  (		self      :	int )	->    List[Any]:





       '''simple docstring'''






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





  @unittest.skip(reason='MRA does not output attentions' )
  def 						A  (		self      :	Optional[int] )	->    Tuple:





       '''simple docstring'''






       return







@require_torch
class 							A__    (       unittest.TestCase  ):





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





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =MraModel.from_pretrained('uw-madison/mra-base-512-4' )
       _SCREAMING_SNAKE_CASE							    =torch.arange(256 ).unsqueeze(0 )

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

       _SCREAMING_SNAKE_CASE							    =torch.Size((1, 256, 768) )
       self.assertEqual(output.shape							,					_a )

       _SCREAMING_SNAKE_CASE							    =torch.tensor(
           [[[-0.01_40, 0.08_30, -0.03_81], [0.15_46, 0.14_02, 0.02_20], [0.11_62, 0.08_51, 0.01_65]]] )

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





  @slow
  def 						A  (		self      :	Dict )	->    Union[str, Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =MraForMaskedLM.from_pretrained('uw-madison/mra-base-512-4' )
       _SCREAMING_SNAKE_CASE							    =torch.arange(256 ).unsqueeze(0 )

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

       _SCREAMING_SNAKE_CASE							    =5_0265

       _SCREAMING_SNAKE_CASE							    =torch.Size((1, 256, vocab_size) )
       self.assertEqual(output.shape							,					_a )

       _SCREAMING_SNAKE_CASE							    =torch.tensor(
           [[[9.25_95, -3.60_38, 11.88_19], [9.38_69, -3.26_93, 11.09_56], [11.85_24, -3.49_38, 13.12_10]]] )

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





  @slow
  def 						A  (		self      :	List[Any] )	->    str:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =MraForMaskedLM.from_pretrained('uw-madison/mra-base-4096-8-d3' )
       _SCREAMING_SNAKE_CASE							    =torch.arange(4096 ).unsqueeze(0 )

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

       _SCREAMING_SNAKE_CASE							    =5_0265

       _SCREAMING_SNAKE_CASE							    =torch.Size((1, 4096, vocab_size) )
       self.assertEqual(output.shape							,					_a )

       _SCREAMING_SNAKE_CASE							    =torch.tensor(
           [[[5.47_89, -2.35_64, 7.50_64], [7.90_67, -1.33_69, 9.96_68], [9.07_12, -1.81_06, 7.03_80]]] )

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



import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np
import torch

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


_UpperCAmelCase		:   Tuple										=       logging.get_logger(__name__)







class  lowercase				(       lowercase_			):
				__SCREAMING_SNAKE_CASE					: Optional[Any]       			=			['''input_features''', '''is_longer''']




				def __init__(					self      ,   snake_case=64      ,   snake_case=4_8000      ,   snake_case=480      ,   snake_case=10      ,   snake_case=1024      ,   snake_case=0.0      ,   snake_case=False      ,   snake_case = 0      ,   snake_case = 1_4000      ,   snake_case = None      ,   snake_case = "fusion"      ,   snake_case = "repeatpad"      ,   **snake_case      ,   ):
											super().__init__(
											    feature_size=snake_case      ,   sampling_rate=snake_case      ,   padding_value=snake_case      ,   return_attention_mask=snake_case      ,   **snake_case      ,   )
											snake_case_							=    top_db
											snake_case_							=    truncation
											snake_case_							=    padding
											snake_case_							=    fft_window_size
											snake_case_							=    (fft_window_size >> 1) + 1
											snake_case_							=    hop_length
											snake_case_							=    max_length_s
											snake_case_							=    max_length_s * sampling_rate
											snake_case_							=    sampling_rate
											snake_case_							=    frequency_min
											snake_case_							=    frequency_max
											snake_case_							=    mel_filter_bank(
											    num_frequency_bins=self.nb_frequency_bins      ,   num_mel_filters=snake_case      ,   min_frequency=snake_case      ,   max_frequency=snake_case      ,   sampling_rate=snake_case      ,   norm=snake_case      ,   mel_scale='htk'      ,   )
											snake_case_							=    mel_filter_bank(
											    num_frequency_bins=self.nb_frequency_bins      ,   num_mel_filters=snake_case      ,   min_frequency=snake_case      ,   max_frequency=snake_case      ,   sampling_rate=snake_case      ,   norm='slaney'      ,   mel_scale='slaney'      ,   )




				def     a							(					self       ):
											snake_case_							=    copy.deepcopy(self.__dict__       )
											snake_case_							=    self.__class__.__name__
											if "mel_filters" in output:
																		del output["mel_filters"]
											if "mel_filters_slaney" in output:
																		del output["mel_filters_slaney"]
											return output




				def     a							(					self      ,   snake_case      ,   snake_case = None       ):
											snake_case_							=    spectrogram(
											    snake_case      ,   window_function(self.fft_window_size      ,   'hann'       )      ,   frame_length=self.fft_window_size      ,   hop_length=self.hop_length      ,   power=2.0      ,   mel_filters=snake_case      ,   log_mel='dB'      ,   )
											return log_mel_spectrogram.T




				def     a							(					self      ,   snake_case      ,   snake_case      ,   snake_case       ):
											snake_case_							=    np.array_split(list(range(0      ,   total_frames - chunk_frames + 1       )       )      ,   3       )
											if len(ranges[1]       ) == 0:
																		# if the audio is too short, we just use the first chunk
																		snake_case_							=    [0]
											if len(ranges[2]       ) == 0:
																		# if the audio is too short, we just use the first chunk
																		snake_case_							=    [0]
											# randomly choose index for each part
											snake_case_							=    np.random.choice(ranges[0]       )
											snake_case_							=    np.random.choice(ranges[1]       )
											snake_case_							=    np.random.choice(ranges[2]       )

											snake_case_							=    mel[idx_front : idx_front + chunk_frames, :]
											snake_case_							=    mel[idx_middle : idx_middle + chunk_frames, :]
											snake_case_							=    mel[idx_back : idx_back + chunk_frames, :]

											snake_case_							=    torch.tensor(mel[None, None, :]       )
											snake_case_							=    torch.nn.functional.interpolate(
											    snake_case      ,   size=[chunk_frames, 64]      ,   mode='bilinear'      ,   align_corners=snake_case       )
											snake_case_							=    mel_shrink[0][0].numpy()
											snake_case_							=    np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back]      ,   axis=0       )
											return mel_fusion




				def     a							(					self      ,   snake_case      ,   snake_case      ,   snake_case      ,   snake_case       ):
											if waveform.shape[0] > max_length:
																		if truncation == "rand_trunc":
																									snake_case_							=    True
																									# random crop to max_length (for compatibility) -> this should be handled by self.pad
																									snake_case_							=    len(snake_case       ) - max_length
																									snake_case_							=    np.random.randint(0      ,   overflow + 1       )
																									snake_case_							=    waveform[idx : idx + max_length]
																									snake_case_							=    self._np_extract_fbank_features(snake_case      ,   self.mel_filters_slaney       )[None, :]
																		elif truncation == "fusion":
																									snake_case_							=    self._np_extract_fbank_features(snake_case      ,   self.mel_filters       )
																									snake_case_							=    max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
																									snake_case_							=    mel.shape[0]
																									if chunk_frames == total_frames:
																																# there is a corner case where the audio length is larger than max_length but smaller than max_length+hop_length.
																																# In this case, we just use the whole audio.
																																snake_case_							=    np.stack([mel, mel, mel, mel]      ,   axis=0       )
																																snake_case_							=    False
																									else:
																																snake_case_							=    self._random_mel_fusion(snake_case      ,   snake_case      ,   snake_case       )
																																snake_case_							=    True
																		else:
																									raise NotImplementedError(F'''data_truncating {truncation} not implemented'''       )

											else:
																		snake_case_							=    False
																		# only use repeat as a new possible value for padding. you repeat the audio before applying the usual max_length padding
																		if waveform.shape[0] < max_length:
																									if padding == "repeat":
																																snake_case_							=    int(max_length / len(snake_case       )       )
																																snake_case_							=    np.stack(np.tile(snake_case      ,   n_repeat + 1       )       )[:max_length]
																									if padding == "repeatpad":
																																snake_case_							=    int(max_length / len(snake_case       )       )
																																snake_case_							=    np.stack(np.tile(snake_case      ,   snake_case       )       )
																									snake_case_							=    np.pad(snake_case      ,   (0, max_length - waveform.shape[0])      ,   mode='constant'      ,   constant_values=0       )

																		if truncation == "fusion":
																									snake_case_							=    self._np_extract_fbank_features(snake_case      ,   self.mel_filters       )
																									snake_case_							=    np.stack([input_mel, input_mel, input_mel, input_mel]      ,   axis=0       )
																		else:
																									snake_case_							=    self._np_extract_fbank_features(snake_case      ,   self.mel_filters_slaney       )[None, :]

											return input_mel, longer




				def __call__(					self      ,   snake_case      ,   snake_case = None      ,   snake_case = None      ,   snake_case = None      ,   snake_case = None      ,   snake_case = None      ,   **snake_case      ,   ):
											snake_case_							=    truncation if truncation is not None else self.truncation
											snake_case_							=    padding if padding else self.padding

											if sampling_rate is not None:
																		if sampling_rate != self.sampling_rate:
																									raise ValueError(
																									    F'''The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a'''
																									    F''' sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input'''
																									    F''' was sampled 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.'       )

											snake_case_							=    isinstance(snake_case      ,   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}'''       )
											snake_case_							=    is_batched_numpy or (
											    isinstance(snake_case      ,   (list, tuple)       ) and (isinstance(raw_speech[0]      ,   (np.ndarray, tuple, list)       ))
											)

											if is_batched:
																		snake_case_							=    [np.asarray(snake_case      ,   dtype=np.floataa       ) for speech in raw_speech]
											elif not is_batched and not isinstance(snake_case      ,   np.ndarray       ):
																		snake_case_							=    np.asarray(snake_case      ,   dtype=np.floataa       )
											elif isinstance(snake_case      ,   np.ndarray       ) and raw_speech.dtype is np.dtype(np.floataa       ):
																		snake_case_							=    raw_speech.astype(np.floataa       )

											# always return batch
											if not is_batched:
																		snake_case_							=    [np.asarray(snake_case       )]

											# convert to mel spectrogram, truncate and pad if needed.
											snake_case_							=    [
											    self._get_input_mel(snake_case      ,   max_length if max_length else self.nb_max_samples      ,   snake_case      ,   snake_case       )
											    for waveform in raw_speech
											]

											snake_case_							=    []
											snake_case_							=    []
											for mel, longer in padded_inputs:
																		input_mel.append(snake_case       )
																		is_longer.append(snake_case       )

											if truncation == "fusion" and sum(snake_case       ) == 0:
																		# if no audio is longer than 10s, then randomly select one audio to be longer
																		snake_case_							=    np.random.randint(0      ,   len(snake_case       )       )
																		snake_case_							=    True

											if isinstance(input_mel[0]      ,   snake_case       ):
																		snake_case_							=    [np.asarray(snake_case      ,   dtype=np.floataa       ) for feature in input_mel]

											# is_longer is a list of bool
											snake_case_							=    [[longer] for longer in is_longer]

											snake_case_							=    {'input_features': input_mel, 'is_longer': is_longer}
											snake_case_							=    BatchFeature(snake_case       )

											if return_tensors is not None:
																		snake_case_							=    input_features.convert_to_tensors(snake_case       )

											return input_features




