

from __future__ import annotations

import unittest

from transformers import RoFormerConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
					import tensorflow as tf

					from transformers import (
					    TFRoFormerForCausalLM,
					    TFRoFormerForMaskedLM,
					    TFRoFormerForMultipleChoice,
					    TFRoFormerForQuestionAnswering,
					    TFRoFormerForSequenceClassification,
					    TFRoFormerForTokenClassification,
					    TFRoFormerModel,
					)
					from transformers.models.roformer.modeling_tf_roformer import (
					    TFRoFormerSelfAttention,
					    TFRoFormerSinusoidalPositionalEmbedding,
					)







class  lowercase				:




				def __init__(					self      ,   snake_case      ,   snake_case=13      ,   snake_case=7      ,   snake_case=True      ,   snake_case=True      ,   snake_case=True      ,   snake_case=True      ,   snake_case=99      ,   snake_case=32      ,   snake_case=2      ,   snake_case=4      ,   snake_case=37      ,   snake_case="gelu"      ,   snake_case=0.1      ,   snake_case=0.1      ,   snake_case=512      ,   snake_case=16      ,   snake_case=2      ,   snake_case=0.02      ,   snake_case=3      ,   snake_case=4      ,   snake_case=None      ,   ):
											snake_case_							=    parent
											snake_case_							=    13
											snake_case_							=    7
											snake_case_							=    True
											snake_case_							=    True
											snake_case_							=    True
											snake_case_							=    True
											snake_case_							=    99
											snake_case_							=    32
											snake_case_							=    2
											snake_case_							=    4
											snake_case_							=    37
											snake_case_							=    'gelu'
											snake_case_							=    0.1
											snake_case_							=    0.1
											snake_case_							=    512
											snake_case_							=    16
											snake_case_							=    2
											snake_case_							=    0.02
											snake_case_							=    3
											snake_case_							=    4
											snake_case_							=    None




				def     a							(					self       ):
											snake_case_							=    ids_tensor([self.batch_size, self.seq_length]      ,   self.vocab_size       )

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

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

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

											snake_case_							=    RoFormerConfig(
											    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      ,   initializer_range=self.initializer_range      ,   return_dict=snake_case      ,   )

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




				def     a							(					self      ,   snake_case      ,   snake_case      ,   snake_case      ,   snake_case      ,   snake_case      ,   snake_case      ,   snake_case       ):
											snake_case_							=    TFRoFormerModel(config=snake_case       )
											snake_case_							=    {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}

											snake_case_							=    [input_ids, input_mask]
											snake_case_							=    model(snake_case       )

											snake_case_							=    model(snake_case       )

											self.parent.assertEqual(result.last_hidden_state.shape      ,   (self.batch_size, self.seq_length, self.hidden_size)       )




				def     a							(					self      ,   snake_case      ,   snake_case      ,   snake_case      ,   snake_case      ,   snake_case      ,   snake_case      ,   snake_case       ):
											snake_case_							=    True
											snake_case_							=    TFRoFormerForCausalLM(config=snake_case       )
											snake_case_							=    {
											    'input_ids': input_ids,
											    'attention_mask': input_mask,
											    'token_type_ids': token_type_ids,
											}
											snake_case_							=    model(snake_case       )['logits']
											self.parent.assertListEqual(
											    list(prediction_scores.numpy().shape       )      ,   [self.batch_size, self.seq_length, self.vocab_size]       )




				def     a							(					self      ,   snake_case      ,   snake_case      ,   snake_case      ,   snake_case      ,   snake_case      ,   snake_case      ,   snake_case       ):
											snake_case_							=    TFRoFormerForMaskedLM(config=snake_case       )
											snake_case_							=    {
											    'input_ids': input_ids,
											    'attention_mask': input_mask,
											    'token_type_ids': token_type_ids,
											}
											snake_case_							=    model(snake_case       )
											self.parent.assertEqual(result.logits.shape      ,   (self.batch_size, self.seq_length, self.vocab_size)       )




				def     a							(					self      ,   snake_case      ,   snake_case      ,   snake_case      ,   snake_case      ,   snake_case      ,   snake_case      ,   snake_case       ):
											snake_case_							=    self.num_labels
											snake_case_							=    TFRoFormerForSequenceClassification(config=snake_case       )
											snake_case_							=    {
											    'input_ids': input_ids,
											    'attention_mask': input_mask,
											    'token_type_ids': token_type_ids,
											}

											snake_case_							=    model(snake_case       )
											self.parent.assertEqual(result.logits.shape      ,   (self.batch_size, self.num_labels)       )




				def     a							(					self      ,   snake_case      ,   snake_case      ,   snake_case      ,   snake_case      ,   snake_case      ,   snake_case      ,   snake_case       ):
											snake_case_							=    self.num_choices
											snake_case_							=    TFRoFormerForMultipleChoice(config=snake_case       )
											snake_case_							=    tf.tile(tf.expand_dims(snake_case      ,   1       )      ,   (1, self.num_choices, 1)       )
											snake_case_							=    tf.tile(tf.expand_dims(snake_case      ,   1       )      ,   (1, self.num_choices, 1)       )
											snake_case_							=    tf.tile(tf.expand_dims(snake_case      ,   1       )      ,   (1, self.num_choices, 1)       )
											snake_case_							=    {
											    'input_ids': multiple_choice_inputs_ids,
											    'attention_mask': multiple_choice_input_mask,
											    'token_type_ids': multiple_choice_token_type_ids,
											}
											snake_case_							=    model(snake_case       )
											self.parent.assertEqual(result.logits.shape      ,   (self.batch_size, self.num_choices)       )




				def     a							(					self      ,   snake_case      ,   snake_case      ,   snake_case      ,   snake_case      ,   snake_case      ,   snake_case      ,   snake_case       ):
											snake_case_							=    self.num_labels
											snake_case_							=    TFRoFormerForTokenClassification(config=snake_case       )
											snake_case_							=    {
											    'input_ids': input_ids,
											    'attention_mask': input_mask,
											    'token_type_ids': token_type_ids,
											}
											snake_case_							=    model(snake_case       )
											self.parent.assertEqual(result.logits.shape      ,   (self.batch_size, self.seq_length, self.num_labels)       )




				def     a							(					self      ,   snake_case      ,   snake_case      ,   snake_case      ,   snake_case      ,   snake_case      ,   snake_case      ,   snake_case       ):
											snake_case_							=    TFRoFormerForQuestionAnswering(config=snake_case       )
											snake_case_							=    {
											    'input_ids': input_ids,
											    'attention_mask': input_mask,
											    'token_type_ids': token_type_ids,
											}

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







@require_tf
class  lowercase				(       lowercase_	,  lowercase_	,  unittest.TestCase			):
				__SCREAMING_SNAKE_CASE					: Union[str, Any]       			=			(
				    (
				        TFRoFormerModel,
				        TFRoFormerForCausalLM,
				        TFRoFormerForMaskedLM,
				        TFRoFormerForQuestionAnswering,
				        TFRoFormerForSequenceClassification,
				        TFRoFormerForTokenClassification,
				        TFRoFormerForMultipleChoice,
				    )
				    if is_tf_available()
				    else ()
				)
				__SCREAMING_SNAKE_CASE					: Any       			=			(
				    {
				        '''feature-extraction''': TFRoFormerModel,
				        '''fill-mask''': TFRoFormerForMaskedLM,
				        '''question-answering''': TFRoFormerForQuestionAnswering,
				        '''text-classification''': TFRoFormerForSequenceClassification,
				        '''text-generation''': TFRoFormerForCausalLM,
				        '''token-classification''': TFRoFormerForTokenClassification,
				        '''zero-shot''': TFRoFormerForSequenceClassification,
				    }
				    if is_tf_available()
				    else {}
				)

				__SCREAMING_SNAKE_CASE					: Optional[Any]       			=			False
				__SCREAMING_SNAKE_CASE					: int       			=			False




				def     a							(					self      ,   snake_case      ,   snake_case      ,   snake_case      ,   snake_case      ,   snake_case       ):
											if pipeline_test_casse_name == "TextGenerationPipelineTests":
																		return True

											return False




				def     a							(					self       ):
											snake_case_							=    TFRoFormerModelTester(self       )
											snake_case_							=    ConfigTester(self      ,   config_class=snake_case      ,   hidden_size=37       )




				def     a							(					self       ):
											self.config_tester.run_common_tests()




				def     a							(					self       ):
											snake_case_							=    self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*snake_case       )




				def     a							(					self       ):
											snake_case_							=    self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_masked_lm(*snake_case       )




				def     a							(					self       ):
											snake_case_							=    self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_lm_head(*snake_case       )




				def     a							(					self       ):
											snake_case_							=    self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_multiple_choice(*snake_case       )




				def     a							(					self       ):
											snake_case_							=    self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_question_answering(*snake_case       )




				def     a							(					self       ):
											snake_case_							=    self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_sequence_classification(*snake_case       )




				def     a							(					self       ):
											snake_case_							=    self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_token_classification(*snake_case       )




				@slow
				def     a							(					self       ):
											snake_case_							=    TFRoFormerModel.from_pretrained('junnyu/roformer_chinese_base'       )
											self.assertIsNotNone(snake_case       )







@require_tf
class  lowercase				(       unittest.TestCase			):




				@slow
				def     a							(					self       ):
											snake_case_							=    TFRoFormerForMaskedLM.from_pretrained('junnyu/roformer_chinese_base'       )
											snake_case_							=    tf.constant([[0, 1, 2, 3, 4, 5]]       )
											snake_case_							=    model(snake_case       )[0]

											# TODO Replace vocab size
											snake_case_							=    5_0000

											snake_case_							=    [1, 6, vocab_size]
											self.assertEqual(output.shape      ,   snake_case       )

											print(output[:, :3, :3]       )

											# TODO Replace values below with what was printed above.
											snake_case_							=    tf.constant(
											    [
											        [
											            [-0.12_05_33_41, -1.0_26_49_01, 0.29_22_19_46],
											            [-1.5_13_37_83, 0.19_74_33, 0.15_19_06_07],
											            [-5.0_13_54_03, -3.90_02_56, -0.84_03_87_64],
											        ]
											    ]       )
											tf.debugging.assert_near(output[:, :3, :3]      ,   snake_case      ,   atol=1e-4       )







@require_tf
class  lowercase				(       unittest.TestCase			):
				__SCREAMING_SNAKE_CASE					: Dict       			=			1e-4




				def     a							(					self       ):
											snake_case_							=    tf.constant([[4, 10]]       )
											snake_case_							=    TFRoFormerSinusoidalPositionalEmbedding(num_positions=6      ,   embedding_dim=6       )

											snake_case_							=    emba(input_ids.shape       )
											snake_case_							=    tf.constant(
											    [[0.00_00, 0.00_00, 0.00_00, 1.00_00, 1.00_00, 1.00_00], [0.84_15, 0.04_64, 0.00_22, 0.54_03, 0.99_89, 1.00_00]]       )

											tf.debugging.assert_near(snake_case      ,   snake_case      ,   atol=self.tolerance       )




				def     a							(					self       ):
											snake_case_							=    tf.constant(
											    [
											        [0.00_00, 0.00_00, 0.00_00, 0.00_00, 0.00_00],
											        [0.84_15, 0.82_19, 0.80_20, 0.78_19, 0.76_17],
											        [0.90_93, 0.93_64, 0.95_81, 0.97_49, 0.98_70],
											    ]       )
											snake_case_							=    TFRoFormerSinusoidalPositionalEmbedding(num_positions=512      ,   embedding_dim=512       )
											emba([2, 16, 512]       )
											snake_case_							=    emba.weight[:3, :5]

											tf.debugging.assert_near(snake_case      ,   snake_case      ,   atol=self.tolerance       )







@require_tf
class  lowercase				(       unittest.TestCase			):
				__SCREAMING_SNAKE_CASE					: str       			=			1e-4




				def     a							(					self       ):
											# 2,12,16,64
											snake_case_							=    tf.reshape(tf.range(2 * 12 * 16 * 64      ,   dtype=tf.floataa       )      ,   shape=(2, 12, 16, 64)       ) / 100

											snake_case_							=    -tf.reshape(tf.range(2 * 12 * 16 * 64      ,   dtype=tf.floataa       )      ,   shape=(2, 12, 16, 64)       ) / 100

											snake_case_							=    TFRoFormerSinusoidalPositionalEmbedding(num_positions=32      ,   embedding_dim=64       )
											snake_case_							=    embed_positions([2, 16, 768]       )[None, None, :, :]

											snake_case_				,    snake_case_							=    TFRoFormerSelfAttention.apply_rotary_position_embeddings(
											    snake_case      ,   snake_case      ,   snake_case       )

											snake_case_							=    tf.constant(
											    [
											        [0.00_00, 0.01_00, 0.02_00, 0.03_00, 0.04_00, 0.05_00, 0.06_00, 0.07_00],
											        [-0.20_12, 0.88_97, 0.02_63, 0.94_01, 0.20_74, 0.94_63, 0.34_81, 0.93_43],
											        [-1.70_57, 0.62_71, -1.21_45, 1.38_97, -0.63_03, 1.76_47, -0.11_73, 1.89_85],
											        [-2.17_31, -1.63_97, -2.73_58, 0.28_54, -2.18_40, 1.71_83, -1.30_18, 2.48_71],
											        [0.27_17, -3.61_73, -2.92_06, -2.19_88, -3.66_38, 0.38_58, -2.91_55, 2.29_80],
											        [3.98_59, -2.15_80, -0.79_84, -4.49_04, -4.11_81, -2.02_52, -4.47_82, 1.12_53],
											    ]       )
											snake_case_							=    tf.constant(
											    [
											        [0.00_00, -0.01_00, -0.02_00, -0.03_00, -0.04_00, -0.05_00, -0.06_00, -0.07_00],
											        [0.20_12, -0.88_97, -0.02_63, -0.94_01, -0.20_74, -0.94_63, -0.34_81, -0.93_43],
											        [1.70_57, -0.62_71, 1.21_45, -1.38_97, 0.63_03, -1.76_47, 0.11_73, -1.89_85],
											        [2.17_31, 1.63_97, 2.73_58, -0.28_54, 2.18_40, -1.71_83, 1.30_18, -2.48_71],
											        [-0.27_17, 3.61_73, 2.92_06, 2.19_88, 3.66_38, -0.38_58, 2.91_55, -2.29_80],
											        [-3.98_59, 2.15_80, 0.79_84, 4.49_04, 4.11_81, 2.02_52, 4.47_82, -1.12_53],
											    ]       )

											tf.debugging.assert_near(query_layer[0, 0, :6, :8]      ,   snake_case      ,   atol=self.tolerance       )
											tf.debugging.assert_near(key_layer[0, 0, :6, :8]      ,   snake_case      ,   atol=self.tolerance       )







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




