


import unittest

from transformers import SqueezeBertConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
			import torch

			from transformers import (
			    SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
			    SqueezeBertForMaskedLM,
			    SqueezeBertForMultipleChoice,
			    SqueezeBertForQuestionAnswering,
			    SqueezeBertForSequenceClassification,
			    SqueezeBertForTokenClassification,
			    SqueezeBertModel,
			)

class     a		(_lowerCAmelCase				):




							"""simple docstring"""


							def __init__(							self	:  Tuple   ,     lowerCamelCase	:  Optional[Any]   ,     lowerCamelCase	:  Dict=13   ,     lowerCamelCase	:  List[Any]=7   ,     lowerCamelCase	:  str=True   ,     lowerCamelCase	:  Union[str, Any]=True   ,     lowerCamelCase	:  int=False   ,     lowerCamelCase	:  int=True   ,     lowerCamelCase	:  Optional[int]=99   ,     lowerCamelCase	:  Any=32   ,     lowerCamelCase	:  List[str]=5   ,     lowerCamelCase	:  int=4   ,     lowerCamelCase	:  Optional[Any]=64   ,     lowerCamelCase	:  Any="gelu"   ,     lowerCamelCase	:  Union[str, Any]=0.1   ,     lowerCamelCase	:  Dict=0.1   ,     lowerCamelCase	:  Optional[Any]=512   ,     lowerCamelCase	:  Any=16   ,     lowerCamelCase	:  str=2   ,     lowerCamelCase	:  int=0.02   ,     lowerCamelCase	:  List[Any]=3   ,     lowerCamelCase	:  Optional[Any]=4   ,     lowerCamelCase	:  Union[str, Any]=None   ,     lowerCamelCase	:  Any=2   ,     lowerCamelCase	:  str=2   ,     lowerCamelCase	:  List[Any]=2   ,     lowerCamelCase	:  Tuple=2   ,     lowerCamelCase	:  Optional[int]=4   ,     lowerCamelCase	:  str=1   ,     )     ->   Optional[Any]:
														__snake_case		:      int										=       parent
														__snake_case		:      Any										=       batch_size
														__snake_case		:      int										=       seq_length
														__snake_case		:      Union[str, Any]										=       is_training
														__snake_case		:      Any										=       use_input_mask
														__snake_case		:      Any										=       use_token_type_ids
														__snake_case		:      Dict										=       use_labels
														__snake_case		:      Optional[Any]										=       vocab_size
														__snake_case		:      List[str]										=       hidden_size
														__snake_case		:      str										=       num_hidden_layers
														__snake_case		:      str										=       num_attention_heads
														__snake_case		:      Union[str, Any]										=       intermediate_size
														__snake_case		:      str										=       hidden_act
														__snake_case		:      Union[str, Any]										=       hidden_dropout_prob
														__snake_case		:      Any										=       attention_probs_dropout_prob
														__snake_case		:      int										=       max_position_embeddings
														__snake_case		:      Any										=       type_vocab_size
														__snake_case		:      List[Any]										=       type_sequence_label_size
														__snake_case		:      Dict										=       initializer_range
														__snake_case		:      List[str]										=       num_labels
														__snake_case		:      Optional[Any]										=       num_choices
														__snake_case		:      Tuple										=       scope
														__snake_case		:      Optional[Any]										=       q_groups
														__snake_case		:      Any										=       k_groups
														__snake_case		:      Tuple										=       v_groups
														__snake_case		:      Dict										=       post_attention_groups
														__snake_case		:      Optional[Any]										=       intermediate_groups
														__snake_case		:      Optional[int]										=       output_groups

							def    __snake_case (							self	:  Optional[Any]    )     ->   List[str]:
														__snake_case		:      Dict										=       ids_tensor([self.batch_size, self.seq_length]   ,     self.vocab_size    )

														__snake_case		:      Any										=       None
														if self.use_input_mask:
																					__snake_case		:      Optional[int]										=       random_attention_mask([self.batch_size, self.seq_length]    )

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

														__snake_case		:      Any										=       self.get_config()

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

							def    __snake_case (							self	:  Tuple    )     ->   List[str]:
														return SqueezeBertConfig(
														    embedding_size=self.hidden_size   ,     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   ,     attention_probs_dropout_prob=self.hidden_dropout_prob   ,     attention_dropout=self.attention_probs_dropout_prob   ,     max_position_embeddings=self.max_position_embeddings   ,     initializer_range=self.initializer_range   ,     q_groups=self.q_groups   ,     k_groups=self.k_groups   ,     v_groups=self.v_groups   ,     post_attention_groups=self.post_attention_groups   ,     intermediate_groups=self.intermediate_groups   ,     output_groups=self.output_groups   ,     )

							def    __snake_case (							self	:  List[Any]   ,     lowerCamelCase	:  Optional[int]   ,     lowerCamelCase	:  List[str]   ,     lowerCamelCase	:  int   ,     lowerCamelCase	:  Optional[Any]   ,     lowerCamelCase	:  Dict   ,     lowerCamelCase	:  Tuple    )     ->   Union[str, Any]:
														__snake_case		:      Optional[int]										=       SqueezeBertModel(config=lowerCamelCase    )
														model.to(lowerCamelCase    )
														model.eval()
														__snake_case		:      Union[str, Any]										=       model(lowerCamelCase   ,     lowerCamelCase    )
														__snake_case		:      Optional[int]										=       model(lowerCamelCase    )
														self.parent.assertEqual(result.last_hidden_state.shape   ,     (self.batch_size, self.seq_length, self.hidden_size)    )

							def    __snake_case (							self	:  Optional[int]   ,     lowerCamelCase	:  Optional[Any]   ,     lowerCamelCase	:  Any   ,     lowerCamelCase	:  Union[str, Any]   ,     lowerCamelCase	:  Tuple   ,     lowerCamelCase	:  Optional[Any]   ,     lowerCamelCase	:  List[str]    )     ->   str:
														__snake_case		:      str										=       SqueezeBertForMaskedLM(config=lowerCamelCase    )
														model.to(lowerCamelCase    )
														model.eval()
														__snake_case		:      Optional[int]										=       model(lowerCamelCase   ,     attention_mask=lowerCamelCase   ,     labels=lowerCamelCase    )
														self.parent.assertEqual(result.logits.shape   ,     (self.batch_size, self.seq_length, self.vocab_size)    )

							def    __snake_case (							self	:  str   ,     lowerCamelCase	:  Tuple   ,     lowerCamelCase	:  List[str]   ,     lowerCamelCase	:  List[Any]   ,     lowerCamelCase	:  Any   ,     lowerCamelCase	:  Any   ,     lowerCamelCase	:  Optional[Any]    )     ->   Tuple:
														__snake_case		:      List[Any]										=       SqueezeBertForQuestionAnswering(config=lowerCamelCase    )
														model.to(lowerCamelCase    )
														model.eval()
														__snake_case		:      Union[str, Any]										=       model(
														    lowerCamelCase   ,     attention_mask=lowerCamelCase   ,     start_positions=lowerCamelCase   ,     end_positions=lowerCamelCase    )
														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    __snake_case (							self	:  List[str]   ,     lowerCamelCase	:  Optional[int]   ,     lowerCamelCase	:  Any   ,     lowerCamelCase	:  Union[str, Any]   ,     lowerCamelCase	:  int   ,     lowerCamelCase	:  List[str]   ,     lowerCamelCase	:  Any    )     ->   Any:
														__snake_case		:      Optional[int]										=       self.num_labels
														__snake_case		:      Union[str, Any]										=       SqueezeBertForSequenceClassification(lowerCamelCase    )
														model.to(lowerCamelCase    )
														model.eval()
														__snake_case		:      Union[str, Any]										=       model(lowerCamelCase   ,     attention_mask=lowerCamelCase   ,     labels=lowerCamelCase    )
														self.parent.assertEqual(result.logits.shape   ,     (self.batch_size, self.num_labels)    )

							def    __snake_case (							self	:  Dict   ,     lowerCamelCase	:  Union[str, Any]   ,     lowerCamelCase	:  Dict   ,     lowerCamelCase	:  List[str]   ,     lowerCamelCase	:  Any   ,     lowerCamelCase	:  Optional[Any]   ,     lowerCamelCase	:  str    )     ->   Optional[int]:
														__snake_case		:      str										=       self.num_labels
														__snake_case		:      Optional[int]										=       SqueezeBertForTokenClassification(config=lowerCamelCase    )
														model.to(lowerCamelCase    )
														model.eval()

														__snake_case		:      Optional[int]										=       model(lowerCamelCase   ,     attention_mask=lowerCamelCase   ,     labels=lowerCamelCase    )
														self.parent.assertEqual(result.logits.shape   ,     (self.batch_size, self.seq_length, self.num_labels)    )

							def    __snake_case (							self	:  int   ,     lowerCamelCase	:  Any   ,     lowerCamelCase	:  int   ,     lowerCamelCase	:  List[Any]   ,     lowerCamelCase	:  int   ,     lowerCamelCase	:  Union[str, Any]   ,     lowerCamelCase	:  Optional[Any]    )     ->   List[str]:
														__snake_case		:      Dict										=       self.num_choices
														__snake_case		:      Optional[Any]										=       SqueezeBertForMultipleChoice(config=lowerCamelCase    )
														model.to(lowerCamelCase    )
														model.eval()
														__snake_case		:      List[str]										=       input_ids.unsqueeze(1    ).expand(-1   ,     self.num_choices   ,     -1    ).contiguous()
														__snake_case		:      List[str]										=       input_mask.unsqueeze(1    ).expand(-1   ,     self.num_choices   ,     -1    ).contiguous()
														__snake_case		:      Optional[Any]										=       model(
														    lowerCamelCase   ,     attention_mask=lowerCamelCase   ,     labels=lowerCamelCase   ,     )
														self.parent.assertEqual(result.logits.shape   ,     (self.batch_size, self.num_choices)    )






							def    __snake_case (							self	:  Optional[int]    )     ->   str:
														__snake_case		:      Dict										=       self.prepare_config_and_inputs()
														((__snake_case)			,      (__snake_case)			,      (__snake_case)			,      (__snake_case)			,      (__snake_case)			,      (__snake_case))		:      Optional[int]										=       config_and_inputs
														__snake_case		:      str										=       {"input_ids": input_ids, "attention_mask": input_mask}
														return config, inputs_dict

@require_torch
class     a		(_lowerCAmelCase					,    _lowerCAmelCase					,    unittest.TestCase				):




							"""simple docstring"""


							__UpperCAmelCase   :							Optional[int]					  =  (
							    (
							        SqueezeBertModel,
							        SqueezeBertForMaskedLM,
							        SqueezeBertForMultipleChoice,
							        SqueezeBertForQuestionAnswering,
							        SqueezeBertForSequenceClassification,
							        SqueezeBertForTokenClassification,
							    )
							    if is_torch_available()
							    else None
							)
							__UpperCAmelCase   :							Union[str, Any]					  =  (
							    {
							        "feature-extraction": SqueezeBertModel,
							        "fill-mask": SqueezeBertForMaskedLM,
							        "question-answering": SqueezeBertForQuestionAnswering,
							        "text-classification": SqueezeBertForSequenceClassification,
							        "token-classification": SqueezeBertForTokenClassification,
							        "zero-shot": SqueezeBertForSequenceClassification,
							    }
							    if is_torch_available()
							    else {}
							)
							__UpperCAmelCase   :							List[Any]					  =  False
							__UpperCAmelCase   :							Any					  =  True
							__UpperCAmelCase   :							int					  =  False

							def    __snake_case (							self	:  Dict    )     ->   int:
														__snake_case		:      Any										=       SqueezeBertModelTester(self    )
														__snake_case		:      int										=       ConfigTester(self   ,     config_class=lowerCamelCase   ,     dim=37    )

							def    __snake_case (							self	:  Union[str, Any]    )     ->   Union[str, Any]:
														self.config_tester.run_common_tests()

							def    __snake_case (							self	:  Dict    )     ->   str:
														__snake_case		:      str										=       self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_squeezebert_model(*lowerCamelCase    )

							def    __snake_case (							self	:  int    )     ->   Any:
														__snake_case		:      Any										=       self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_squeezebert_for_masked_lm(*lowerCamelCase    )

							def    __snake_case (							self	:  int    )     ->   Optional[int]:
														__snake_case		:      str										=       self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_squeezebert_for_question_answering(*lowerCamelCase    )

							def    __snake_case (							self	:  List[Any]    )     ->   Union[str, Any]:
														__snake_case		:      Tuple										=       self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_squeezebert_for_sequence_classification(*lowerCamelCase    )

							def    __snake_case (							self	:  Dict    )     ->   List[str]:
														__snake_case		:      Optional[Any]										=       self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_squeezebert_for_token_classification(*lowerCamelCase    )

							def    __snake_case (							self	:  Union[str, Any]    )     ->   str:
														__snake_case		:      List[str]										=       self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_squeezebert_for_multiple_choice(*lowerCamelCase    )






							@slow
							def    __snake_case (							self	:  str    )     ->   Dict:
														for model_name in SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																					__snake_case		:      str										=       SqueezeBertModel.from_pretrained(lowerCamelCase    )
																					self.assertIsNotNone(lowerCamelCase    )

@require_sentencepiece
@require_tokenizers
@require_torch
class     a		(unittest.TestCase				):




							"""simple docstring"""


							@slow
							def    __snake_case (							self	:  str    )     ->   Tuple:
														__snake_case		:      Dict										=       SqueezeBertForSequenceClassification.from_pretrained("squeezebert/squeezebert-mnli"    )

														__snake_case		:      Tuple										=       torch.tensor([[1, 29414, 232, 328, 740, 1140, 12695, 69, 13, 1588, 2]]    )
														__snake_case		:      Any										=       model(lowerCamelCase    )[0]
														__snake_case		:      List[Any]										=       torch.Size((1, 3)    )
														self.assertEqual(output.shape   ,     lowerCamelCase    )
														__snake_case		:      Optional[int]										=       torch.tensor([[0.64_01, -0.03_49, -0.60_41]]    )
														self.assertTrue(torch.allclose(lowerCamelCase   ,     lowerCamelCase   ,     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


_snake_case					:		List[Any]      =    logging.get_logger(__name__)

class     a		(_lowerCAmelCase				):




							"""simple docstring"""


							__UpperCAmelCase   :							Union[str, Any]					  =  ["input_features", "is_longer"]

							def __init__(							self	:  Optional[int]   ,     lowerCamelCase	:  Any=64   ,     lowerCamelCase	:  Dict=48000   ,     lowerCamelCase	:  Dict=480   ,     lowerCamelCase	:  Tuple=10   ,     lowerCamelCase	:  Optional[int]=1024   ,     lowerCamelCase	:  int=0.0   ,     lowerCamelCase	:  Any=False   ,     lowerCamelCase	:  float = 0   ,     lowerCamelCase	:  float = 14000   ,     lowerCamelCase	:  int = None   ,     lowerCamelCase	:  str = "fusion"   ,     lowerCamelCase	:  str = "repeatpad"   ,     **lowerCamelCase	:  Optional[int]   ,     )     ->   Dict:
														super().__init__(
														    feature_size=lowerCamelCase   ,     sampling_rate=lowerCamelCase   ,     padding_value=lowerCamelCase   ,     return_attention_mask=lowerCamelCase   ,     **lowerCamelCase   ,     )
														__snake_case		:      Optional[Any]										=       top_db
														__snake_case		:      Dict										=       truncation
														__snake_case		:      Dict										=       padding
														__snake_case		:      Optional[Any]										=       fft_window_size
														__snake_case		:      Optional[Any]										=       (fft_window_size >> 1) + 1
														__snake_case		:      Dict										=       hop_length
														__snake_case		:      Optional[int]										=       max_length_s
														__snake_case		:      Optional[int]										=       max_length_s * sampling_rate
														__snake_case		:      Dict										=       sampling_rate
														__snake_case		:      Optional[int]										=       frequency_min
														__snake_case		:      Any										=       frequency_max
														__snake_case		:      Optional[int]										=       mel_filter_bank(
														    num_frequency_bins=self.nb_frequency_bins   ,     num_mel_filters=lowerCamelCase   ,     min_frequency=lowerCamelCase   ,     max_frequency=lowerCamelCase   ,     sampling_rate=lowerCamelCase   ,     norm=lowerCamelCase   ,     mel_scale="htk"   ,     )
														__snake_case		:      Tuple										=       mel_filter_bank(
														    num_frequency_bins=self.nb_frequency_bins   ,     num_mel_filters=lowerCamelCase   ,     min_frequency=lowerCamelCase   ,     max_frequency=lowerCamelCase   ,     sampling_rate=lowerCamelCase   ,     norm="slaney"   ,     mel_scale="slaney"   ,     )

							def    __snake_case (							self	:  str    )     ->   Dict[str, Any]:
														__snake_case		:      List[str]										=       copy.deepcopy(self.__dict__    )
														__snake_case		:      List[Any]										=       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    __snake_case (							self	:  List[Any]   ,     lowerCamelCase	:  np.array   ,     lowerCamelCase	:  Optional[np.array] = None    )     ->   np.ndarray:
														__snake_case		:      List[Any]										=       spectrogram(
														    lowerCamelCase   ,     window_function(self.fft_window_size   ,     "hann"    )   ,     frame_length=self.fft_window_size   ,     hop_length=self.hop_length   ,     power=2.0   ,     mel_filters=lowerCamelCase   ,     log_mel="dB"   ,     )
														return log_mel_spectrogram.T

							def    __snake_case (							self	:  List[Any]   ,     lowerCamelCase	:  Any   ,     lowerCamelCase	:  Union[str, Any]   ,     lowerCamelCase	:  Any    )     ->   str:
														__snake_case		:      Tuple										=       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		:      Tuple										=       [0]
														if len(ranges[2]    ) == 0:
																					# if the audio is too short, we just use the first chunk
																					__snake_case		:      Tuple										=       [0]
														# randomly choose index for each part
														__snake_case		:      List[Any]										=       np.random.choice(ranges[0]    )
														__snake_case		:      int										=       np.random.choice(ranges[1]    )
														__snake_case		:      List[str]										=       np.random.choice(ranges[2]    )

														__snake_case		:      Dict										=       mel[idx_front : idx_front + chunk_frames, :]
														__snake_case		:      Optional[Any]										=       mel[idx_middle : idx_middle + chunk_frames, :]
														__snake_case		:      Tuple										=       mel[idx_back : idx_back + chunk_frames, :]

														__snake_case		:      Optional[Any]										=       torch.tensor(mel[None, None, :]    )
														__snake_case		:      Optional[int]										=       torch.nn.functional.interpolate(
														    lowerCamelCase   ,     size=[chunk_frames, 64]   ,     mode="bilinear"   ,     align_corners=lowerCamelCase    )
														__snake_case		:      List[Any]										=       mel_shrink[0][0].numpy()
														__snake_case		:      Union[str, Any]										=       np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back]   ,     axis=0    )
														return mel_fusion

							def    __snake_case (							self	:  Any   ,     lowerCamelCase	:  np.array   ,     lowerCamelCase	:  str   ,     lowerCamelCase	:  Tuple   ,     lowerCamelCase	:  Dict    )     ->   np.array:
														if waveform.shape[0] > max_length:
																					if truncation == "rand_trunc":
																												__snake_case		:      List[Any]										=       True
																												# random crop to max_length (for compatibility) -> this should be handled by self.pad
																												__snake_case		:      Tuple										=       len(lowerCamelCase    ) - max_length
																												__snake_case		:      List[Any]										=       np.random.randint(0   ,     overflow + 1    )
																												__snake_case		:      Dict										=       waveform[idx : idx + max_length]
																												__snake_case		:      Optional[int]										=       self._np_extract_fbank_features(lowerCamelCase   ,     self.mel_filters_slaney    )[None, :]
																					elif truncation == "fusion":
																												__snake_case		:      Any										=       self._np_extract_fbank_features(lowerCamelCase   ,     self.mel_filters    )
																												__snake_case		:      Optional[int]										=       max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
																												__snake_case		:      str										=       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		:      str										=       np.stack([mel, mel, mel, mel]   ,     axis=0    )
																																			__snake_case		:      Optional[Any]										=       False
																												else:
																																			__snake_case		:      Any										=       self._random_mel_fusion(lowerCamelCase   ,     lowerCamelCase   ,     lowerCamelCase    )
																																			__snake_case		:      Tuple										=       True
																					else:
																												raise NotImplementedError(F'data_truncating {truncation} not implemented'    )

														else:
																					__snake_case		:      Dict										=       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		:      List[str]										=       int(max_length / len(lowerCamelCase    )    )
																																			__snake_case		:      Any										=       np.stack(np.tile(lowerCamelCase   ,     n_repeat + 1    )    )[:max_length]
																												if padding == "repeatpad":
																																			__snake_case		:      str										=       int(max_length / len(lowerCamelCase    )    )
																																			__snake_case		:      List[str]										=       np.stack(np.tile(lowerCamelCase   ,     lowerCamelCase    )    )
																												__snake_case		:      str										=       np.pad(lowerCamelCase   ,     (0, max_length - waveform.shape[0])   ,     mode="constant"   ,     constant_values=0    )

																					if truncation == "fusion":
																												__snake_case		:      List[str]										=       self._np_extract_fbank_features(lowerCamelCase   ,     self.mel_filters    )
																												__snake_case		:      List[str]										=       np.stack([input_mel, input_mel, input_mel, input_mel]   ,     axis=0    )
																					else:
																												__snake_case		:      Optional[int]										=       self._np_extract_fbank_features(lowerCamelCase   ,     self.mel_filters_slaney    )[None, :]

														return input_mel, longer






							def __call__(							self	:  List[str]   ,     lowerCamelCase	:  Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]   ,     lowerCamelCase	:  str = None   ,     lowerCamelCase	:  Optional[str] = None   ,     lowerCamelCase	:  Optional[int] = None   ,     lowerCamelCase	:  Optional[int] = None   ,     lowerCamelCase	:  Optional[Union[str, TensorType]] = None   ,     **lowerCamelCase	:  Any   ,     )     ->   BatchFeature:
														__snake_case		:      Union[str, Any]										=       truncation if truncation is not None else self.truncation
														__snake_case		:      int										=       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		:      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}'    )
														__snake_case		:      str										=       is_batched_numpy or (
														    isinstance(lowerCamelCase   ,     (list, tuple)    ) and (isinstance(raw_speech[0]   ,     (np.ndarray, tuple, list)    ))
														)

														if is_batched:
																					__snake_case		:      Tuple										=       [np.asarray(lowerCamelCase   ,     dtype=np.floataa    ) for speech in raw_speech]
														elif not is_batched and not isinstance(lowerCamelCase   ,     np.ndarray    ):
																					__snake_case		:      str										=       np.asarray(lowerCamelCase   ,     dtype=np.floataa    )
														elif isinstance(lowerCamelCase   ,     np.ndarray    ) and raw_speech.dtype is np.dtype(np.floataa    ):
																					__snake_case		:      int										=       raw_speech.astype(np.floataa    )

														# always return batch
														if not is_batched:
																					__snake_case		:      Union[str, Any]										=       [np.asarray(lowerCamelCase    )]

														# convert to mel spectrogram, truncate and pad if needed.
														__snake_case		:      Optional[int]										=       [
														    self._get_input_mel(lowerCamelCase   ,     max_length if max_length else self.nb_max_samples   ,     lowerCamelCase   ,     lowerCamelCase    )
														    for waveform in raw_speech
														]

														__snake_case		:      Optional[int]										=       []
														__snake_case		:      Any										=       []
														for mel, longer in padded_inputs:
																					input_mel.append(lowerCamelCase    )
																					is_longer.append(lowerCamelCase    )

														if truncation == "fusion" and sum(lowerCamelCase    ) == 0:
																					# if no audio is longer than 10s, then randomly select one audio to be longer
																					__snake_case		:      Optional[Any]										=       np.random.randint(0   ,     len(lowerCamelCase    )    )
																					__snake_case		:      Union[str, Any]										=       True

														if isinstance(input_mel[0]   ,     lowerCamelCase    ):
																					__snake_case		:      List[str]										=       [np.asarray(lowerCamelCase   ,     dtype=np.floataa    ) for feature in input_mel]

														# is_longer is a list of bool
														__snake_case		:      Any										=       [[longer] for longer in is_longer]

														__snake_case		:      Tuple										=       {"input_features": input_mel, "is_longer": is_longer}
														__snake_case		:      List[str]										=       BatchFeature(lowerCamelCase    )

														if return_tensors is not None:
																					__snake_case		:      Any										=       input_features.convert_to_tensors(lowerCamelCase    )

														return input_features

