"""simple docstring"""







import pickle
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


__SCREAMING_SNAKE_CASE								=get_tests_dir("fixtures/test_sentencepiece.model")







@require_sentencepiece
@require_tokenizers
class  UpperCamelCase	(    lowercase_		,   unittest.TestCase						):
   lowercase							=						XLMRobertaTokenizer
   lowercase							=						XLMRobertaTokenizerFast
   lowercase							=						True
   lowercase							=						True
   def     _UpperCAmelCase     (    self    )			->   int:


        '''simple docstring'''


        super().setUp()

        # We have a SentencePiece fixture for testing
        lowercase_			:       Optional[Any]									=			XLMRobertaTokenizer(__UpperCamelCase  ,keep_accents=__UpperCamelCase    )
        tokenizer.save_pretrained(self.tmpdirname    )
   def     _UpperCAmelCase     (    self    )			->   Union[str, Any]:


        '''simple docstring'''


        lowercase_			:       Tuple									=			'<pad>'
        lowercase_			:       Tuple									=			1

        self.assertEqual(self.get_tokenizer()._convert_token_to_id(__UpperCamelCase    )  ,__UpperCamelCase    )
        self.assertEqual(self.get_tokenizer()._convert_id_to_token(__UpperCamelCase    )  ,__UpperCamelCase    )
   def     _UpperCAmelCase     (    self    )			->   Tuple:


        '''simple docstring'''


        lowercase_			:       List[str]									=			list(self.get_tokenizer().get_vocab().keys()    )

        self.assertEqual(vocab_keys[0]  ,'<s>'    )
        self.assertEqual(vocab_keys[1]  ,'<pad>'    )
        self.assertEqual(vocab_keys[-1]  ,'<mask>'    )
        self.assertEqual(len(__UpperCamelCase    )  ,1002    )
   def     _UpperCAmelCase     (    self    )			->   List[Any]:


        '''simple docstring'''


        self.assertEqual(self.get_tokenizer().vocab_size  ,1002    )
   def     _UpperCAmelCase     (    self    )			->   Dict:


        '''simple docstring'''


        lowercase_			:       Dict									=			XLMRobertaTokenizer(__UpperCamelCase  ,keep_accents=__UpperCamelCase    )

        lowercase_			:       Dict									=			tokenizer.tokenize('This is a test'    )
        self.assertListEqual(__UpperCamelCase  ,['▁This', '▁is', '▁a', '▁t', 'est']    )

        self.assertListEqual(
            tokenizer.convert_tokens_to_ids(__UpperCamelCase    )  ,[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]  ,)

        lowercase_			:       Optional[Any]									=			tokenizer.tokenize('I was born in 92000, and this is falsé.'    )
        self.assertListEqual(
            __UpperCamelCase  ,[
                SPIECE_UNDERLINE + 'I',
                SPIECE_UNDERLINE + 'was',
                SPIECE_UNDERLINE + 'b',
                'or',
                'n',
                SPIECE_UNDERLINE + 'in',
                SPIECE_UNDERLINE + '',
                '9',
                '2',
                '0',
                '0',
                '0',
                ',',
                SPIECE_UNDERLINE + 'and',
                SPIECE_UNDERLINE + 'this',
                SPIECE_UNDERLINE + 'is',
                SPIECE_UNDERLINE + 'f',
                'al',
                's',
                'é',
                '.',
            ]  ,)
        lowercase_			:       Any									=			tokenizer.convert_tokens_to_ids(__UpperCamelCase    )
        self.assertListEqual(
            __UpperCamelCase  ,[
                value + tokenizer.fairseq_offset
                for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
                #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
            ]  ,)

        lowercase_			:       Optional[Any]									=			tokenizer.convert_ids_to_tokens(__UpperCamelCase    )
        self.assertListEqual(
            __UpperCamelCase  ,[
                SPIECE_UNDERLINE + 'I',
                SPIECE_UNDERLINE + 'was',
                SPIECE_UNDERLINE + 'b',
                'or',
                'n',
                SPIECE_UNDERLINE + 'in',
                SPIECE_UNDERLINE + '',
                '<unk>',
                '2',
                '0',
                '0',
                '0',
                ',',
                SPIECE_UNDERLINE + 'and',
                SPIECE_UNDERLINE + 'this',
                SPIECE_UNDERLINE + 'is',
                SPIECE_UNDERLINE + 'f',
                'al',
                's',
                '<unk>',
                '.',
            ]  ,)
   def     _UpperCAmelCase     (    self    )			->   Tuple:


        '''simple docstring'''


        if not self.test_slow_tokenizer:
             # as we don't have a slow version, we can't compare the outputs between slow and fast versions
             return

        lowercase_			:       str									=			(self.rust_tokenizer_class, 'hf-internal-testing/tiny-xlm-roberta', {})
        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
             with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''    ):
                  lowercase_			:       Union[str, Any]									=			self.rust_tokenizer_class.from_pretrained(__UpperCamelCase  ,**__UpperCamelCase    )
                  lowercase_			:       Union[str, Any]									=			self.tokenizer_class.from_pretrained(__UpperCamelCase  ,**__UpperCamelCase    )

                  lowercase_			:       str									=			tempfile.mkdtemp()

                  lowercase_			:       Dict									=			tokenizer_r.save_pretrained(__UpperCamelCase    )
                  lowercase_			:       List[Any]									=			tokenizer_p.save_pretrained(__UpperCamelCase    )

                  # Checks it save with the same files + the tokenizer.json file for the fast one
                  self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files    )    )
                  lowercase_			:       List[str]									=			tuple(f for f in tokenizer_r_files if 'tokenizer.json' not in f    )
                  self.assertSequenceEqual(__UpperCamelCase  ,__UpperCamelCase    )

                  # Checks everything loads correctly in the same way
                  lowercase_			:       List[Any]									=			tokenizer_r.from_pretrained(__UpperCamelCase    )
                  lowercase_			:       str									=			tokenizer_p.from_pretrained(__UpperCamelCase    )

                  # Check special tokens are set accordingly on Rust and Python
                  for key in tokenizer_pp.special_tokens_map:
                       self.assertTrue(hasattr(__UpperCamelCase  ,__UpperCamelCase    )    )
                       # self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
                       # self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

                  shutil.rmtree(__UpperCamelCase    )

                  # Save tokenizer rust, legacy_format=True
                  lowercase_			:       Any									=			tempfile.mkdtemp()

                  lowercase_			:       List[str]									=			tokenizer_r.save_pretrained(__UpperCamelCase  ,legacy_format=__UpperCamelCase    )
                  lowercase_			:       Union[str, Any]									=			tokenizer_p.save_pretrained(__UpperCamelCase    )

                  # Checks it save with the same files
                  self.assertSequenceEqual(__UpperCamelCase  ,__UpperCamelCase    )

                  # Checks everything loads correctly in the same way
                  lowercase_			:       Union[str, Any]									=			tokenizer_r.from_pretrained(__UpperCamelCase    )
                  lowercase_			:       List[Any]									=			tokenizer_p.from_pretrained(__UpperCamelCase    )

                  # Check special tokens are set accordingly on Rust and Python
                  for key in tokenizer_pp.special_tokens_map:
                       self.assertTrue(hasattr(__UpperCamelCase  ,__UpperCamelCase    )    )

                  shutil.rmtree(__UpperCamelCase    )

                  # Save tokenizer rust, legacy_format=False
                  lowercase_			:       Dict									=			tempfile.mkdtemp()

                  lowercase_			:       int									=			tokenizer_r.save_pretrained(__UpperCamelCase  ,legacy_format=__UpperCamelCase    )
                  lowercase_			:       Union[str, Any]									=			tokenizer_p.save_pretrained(__UpperCamelCase    )

                  # Checks it saved the tokenizer.json file
                  self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files    )    )

                  # Checks everything loads correctly in the same way
                  lowercase_			:       List[Any]									=			tokenizer_r.from_pretrained(__UpperCamelCase    )
                  lowercase_			:       Dict									=			tokenizer_p.from_pretrained(__UpperCamelCase    )

                  # Check special tokens are set accordingly on Rust and Python
                  for key in tokenizer_pp.special_tokens_map:
                       self.assertTrue(hasattr(__UpperCamelCase  ,__UpperCamelCase    )    )

                  shutil.rmtree(__UpperCamelCase    )
   @cached_property
   def     _UpperCAmelCase     (    self    )			->   List[str]:


        '''simple docstring'''


        return XLMRobertaTokenizer.from_pretrained('xlm-roberta-base'    )
   def     _UpperCAmelCase     (    self    )			->   Any:


        '''simple docstring'''


        with tempfile.NamedTemporaryFile() as f:
             shutil.copyfile(__UpperCamelCase  ,f.name    )
             lowercase_			:       List[str]									=			XLMRobertaTokenizer(f.name  ,keep_accents=__UpperCamelCase    )
             lowercase_			:       List[Any]									=			pickle.dumps(__UpperCamelCase    )
        pickle.loads(__UpperCamelCase    )
   def     _UpperCAmelCase     (    self    )			->   List[str]:


        '''simple docstring'''


        if not self.test_rust_tokenizer:
             return

        lowercase_			:       str									=			self.get_tokenizer()
        lowercase_			:       Tuple									=			self.get_rust_tokenizer()

        lowercase_			:       int									=			'I was born in 92000, and this is falsé.'

        lowercase_			:       Union[str, Any]									=			tokenizer.tokenize(__UpperCamelCase    )
        lowercase_			:       Any									=			rust_tokenizer.tokenize(__UpperCamelCase    )
        self.assertListEqual(__UpperCamelCase  ,__UpperCamelCase    )

        lowercase_			:       Optional[int]									=			tokenizer.encode(__UpperCamelCase  ,add_special_tokens=__UpperCamelCase    )
        lowercase_			:       Any									=			rust_tokenizer.encode(__UpperCamelCase  ,add_special_tokens=__UpperCamelCase    )
        self.assertListEqual(__UpperCamelCase  ,__UpperCamelCase    )

        lowercase_			:       Tuple									=			self.get_rust_tokenizer()
        lowercase_			:       str									=			tokenizer.encode(__UpperCamelCase    )
        lowercase_			:       Dict									=			rust_tokenizer.encode(__UpperCamelCase    )
        self.assertListEqual(__UpperCamelCase  ,__UpperCamelCase    )
   @slow
   def     _UpperCAmelCase     (    self    )			->   Dict:


        '''simple docstring'''


        lowercase_			:       Union[str, Any]									=			'Hello World!'
        lowercase_			:       List[Any]									=			[0, 3_5378, 6661, 38, 2]
        # xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
        # xlmr.eval()
        # xlmr.encode(symbols)

        self.assertListEqual(__UpperCamelCase  ,self.big_tokenizer.encode(__UpperCamelCase    )    )
   @slow
   def     _UpperCAmelCase     (    self    )			->   Optional[Any]:


        '''simple docstring'''


        lowercase_			:       Dict									=			(
            'This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'
            ' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'
        )
        lowercase_			:       List[str]									=			[
            0,
            3293,
            83,
            10,
            4552,
            4989,
            7986,
            678,
            10,
            5915,
            111,
            17_9459,
            12_4850,
            4,
            6044,
            237,
            12,
            6,
            5,
            6,
            4,
            6780,
            705,
            15,
            1388,
            44,
            378,
            1_0114,
            711,
            152,
            20,
            6,
            5,
            2_2376,
            642,
            1221,
            1_5190,
            3_4153,
            450,
            5608,
            959,
            1119,
            5_7702,
            136,
            186,
            47,
            1098,
            2_9367,
            47,
            # 4426, # What fairseq tokenizes from "<unk>": "_<"
            # 3678, # What fairseq tokenizes from "<unk>": "unk"
            # 2740, # What fairseq tokenizes from "<unk>": ">"
            3,  # What we tokenize from "<unk>": "<unk>"
            6,  # Residue from the tokenization: an extra sentencepiece underline
            4,
            6044,
            237,
            6284,
            5_0901,
            528,
            31,
            90,
            34,
            927,
            2,
        ]
        # xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
        # xlmr.eval()
        # xlmr.encode(symbols)

        self.assertListEqual(__UpperCamelCase  ,self.big_tokenizer.encode(__UpperCamelCase    )    )







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


        '''simple docstring'''


        lowercase_			:       int									=			{'input_ids': [[0, 1_1062, 8_2772, 7, 15, 8_2772, 538, 5_1529, 237, 1_7198, 1290, 206, 9, 21_5175, 1314, 136, 1_7198, 1290, 206, 9, 5_6359, 42, 12_2009, 9, 1_6466, 16, 8_7344, 4537, 9, 4717, 7_8381, 6, 15_9958, 7, 15, 2_4480, 618, 4, 527, 2_2693, 5428, 4, 2777, 2_4480, 9874, 4, 4_3523, 594, 4, 803, 1_8392, 3_3189, 18, 4, 4_3523, 2_4447, 1_2399, 100, 2_4955, 8_3658, 9626, 14_4057, 15, 839, 2_2335, 16, 136, 2_4955, 8_3658, 8_3479, 15, 3_9102, 724, 16, 678, 645, 2789, 1328, 4589, 42, 12_2009, 11_5774, 23, 805, 1328, 4_6876, 7, 136, 5_3894, 1940, 4_2227, 4_1159, 1_7721, 823, 425, 4, 2_7512, 9_8722, 206, 136, 5531, 4970, 919, 1_7336, 5, 2], [0, 2_0080, 618, 83, 8_2775, 47, 479, 9, 1517, 73, 5_3894, 333, 8_0581, 11_0117, 1_8811, 5256, 1295, 51, 15_2526, 297, 7986, 390, 12_4416, 538, 3_5431, 214, 98, 1_5044, 2_5737, 136, 7108, 4_3701, 23, 756, 13_5355, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 581, 6_3773, 11_9455, 6, 14_7797, 8_8203, 7, 645, 70, 21, 3285, 1_0269, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
        # fmt: on

        self.tokenizer_integration_test_util(
            expected_encoding=__UpperCamelCase  ,model_name='xlm-roberta-base'  ,revision='d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3'  ,)


"""simple docstring"""







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


__SCREAMING_SNAKE_CASE								=logging.get_logger(__name__)







class  UpperCamelCase	(    lowercase_						):
   lowercase							=						['input_features', 'is_longer']
   def __init__(    self  ,__UpperCamelCase=64  ,__UpperCamelCase=4_8000  ,__UpperCamelCase=480  ,__UpperCamelCase=10  ,__UpperCamelCase=1024  ,__UpperCamelCase=0.0  ,__UpperCamelCase=False  ,__UpperCamelCase = 0  ,__UpperCamelCase = 1_4000  ,__UpperCamelCase = None  ,__UpperCamelCase = "fusion"  ,__UpperCamelCase = "repeatpad"  ,**__UpperCamelCase  ,)			->   Optional[int]:


        '''simple docstring'''


        super().__init__(
            feature_size=__UpperCamelCase  ,sampling_rate=__UpperCamelCase  ,padding_value=__UpperCamelCase  ,return_attention_mask=__UpperCamelCase  ,**__UpperCamelCase  ,)
        lowercase_			:       Union[str, Any]									=			top_db
        lowercase_			:       Any									=			truncation
        lowercase_			:       str									=			padding
        lowercase_			:       Optional[Any]									=			fft_window_size
        lowercase_			:       List[Any]									=			(fft_window_size >> 1) + 1
        lowercase_			:       Any									=			hop_length
        lowercase_			:       List[Any]									=			max_length_s
        lowercase_			:       Any									=			max_length_s * sampling_rate
        lowercase_			:       Optional[int]									=			sampling_rate
        lowercase_			:       List[Any]									=			frequency_min
        lowercase_			:       str									=			frequency_max
        lowercase_			:       Any									=			mel_filter_bank(
            num_frequency_bins=self.nb_frequency_bins  ,num_mel_filters=__UpperCamelCase  ,min_frequency=__UpperCamelCase  ,max_frequency=__UpperCamelCase  ,sampling_rate=__UpperCamelCase  ,norm=__UpperCamelCase  ,mel_scale='htk'  ,)
        lowercase_			:       Optional[Any]									=			mel_filter_bank(
            num_frequency_bins=self.nb_frequency_bins  ,num_mel_filters=__UpperCamelCase  ,min_frequency=__UpperCamelCase  ,max_frequency=__UpperCamelCase  ,sampling_rate=__UpperCamelCase  ,norm='slaney'  ,mel_scale='slaney'  ,)
   def     _UpperCAmelCase     (    self    )			->   Dict[str, Any]:


        '''simple docstring'''


        lowercase_			:       int									=			copy.deepcopy(self.__dict__    )
        lowercase_			:       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     _UpperCAmelCase     (    self  ,__UpperCamelCase  ,__UpperCamelCase = None    )			->   np.ndarray:


        '''simple docstring'''


        lowercase_			:       Union[str, Any]									=			spectrogram(
            __UpperCamelCase  ,window_function(self.fft_window_size  ,'hann'    )  ,frame_length=self.fft_window_size  ,hop_length=self.hop_length  ,power=2.0  ,mel_filters=__UpperCamelCase  ,log_mel='dB'  ,)
        return log_mel_spectrogram.T
   def     _UpperCAmelCase     (    self  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase    )			->   Any:


        '''simple docstring'''


        lowercase_			:       Dict									=			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
             lowercase_			:       Optional[int]									=			[0]
        if len(ranges[2]    ) == 0:
             # if the audio is too short, we just use the first chunk
             lowercase_			:       List[str]									=			[0]
        # randomly choose index for each part
        lowercase_			:       List[str]									=			np.random.choice(ranges[0]    )
        lowercase_			:       Optional[Any]									=			np.random.choice(ranges[1]    )
        lowercase_			:       Union[str, Any]									=			np.random.choice(ranges[2]    )

        lowercase_			:       Tuple									=			mel[idx_front : idx_front + chunk_frames, :]
        lowercase_			:       str									=			mel[idx_middle : idx_middle + chunk_frames, :]
        lowercase_			:       Optional[Any]									=			mel[idx_back : idx_back + chunk_frames, :]

        lowercase_			:       Tuple									=			torch.tensor(mel[None, None, :]    )
        lowercase_			:       Optional[Any]									=			torch.nn.functional.interpolate(
            __UpperCamelCase  ,size=[chunk_frames, 64]  ,mode='bilinear'  ,align_corners=__UpperCamelCase    )
        lowercase_			:       str									=			mel_shrink[0][0].numpy()
        lowercase_			:       Optional[int]									=			np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back]  ,axis=0    )
        return mel_fusion
   def     _UpperCAmelCase     (    self  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase    )			->   np.array:


        '''simple docstring'''


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

        else:
             lowercase_			:       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":
                       lowercase_			:       Optional[int]									=			int(max_length / len(__UpperCamelCase    )    )
                       lowercase_			:       Any									=			np.stack(np.tile(__UpperCamelCase  ,n_repeat + 1    )    )[:max_length]
                  if padding == "repeatpad":
                       lowercase_			:       Union[str, Any]									=			int(max_length / len(__UpperCamelCase    )    )
                       lowercase_			:       int									=			np.stack(np.tile(__UpperCamelCase  ,__UpperCamelCase    )    )
                  lowercase_			:       str									=			np.pad(__UpperCamelCase  ,(0, max_length - waveform.shape[0])  ,mode='constant'  ,constant_values=0    )

             if truncation == "fusion":
                  lowercase_			:       List[str]									=			self._np_extract_fbank_features(__UpperCamelCase  ,self.mel_filters    )
                  lowercase_			:       List[str]									=			np.stack([input_mel, input_mel, input_mel, input_mel]  ,axis=0    )
             else:
                  lowercase_			:       Any									=			self._np_extract_fbank_features(__UpperCamelCase  ,self.mel_filters_slaney    )[None, :]

        return input_mel, longer







   def __call__(    self  ,__UpperCamelCase  ,__UpperCamelCase = None  ,__UpperCamelCase = None  ,__UpperCamelCase = None  ,__UpperCamelCase = None  ,__UpperCamelCase = None  ,**__UpperCamelCase  ,)			->   BatchFeature:


        '''simple docstring'''


        lowercase_			:       Union[str, Any]									=			truncation if truncation is not None else self.truncation
        lowercase_			:       List[Any]									=			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.'    )

        lowercase_			:       Tuple									=			isinstance(__UpperCamelCase  ,np.ndarray    ) and len(raw_speech.shape    ) > 1
        if is_batched_numpy and len(raw_speech.shape    ) > 2:
             raise ValueError(f'''Only mono-channel audio is supported for input to {self}'''    )
        lowercase_			:       List[str]									=			is_batched_numpy or (
            isinstance(__UpperCamelCase  ,(list, tuple)    ) and (isinstance(raw_speech[0]  ,(np.ndarray, tuple, list)    ))
        )

        if is_batched:
             lowercase_			:       str									=			[np.asarray(__UpperCamelCase  ,dtype=np.floataa    ) for speech in raw_speech]
        elif not is_batched and not isinstance(__UpperCamelCase  ,np.ndarray    ):
             lowercase_			:       Any									=			np.asarray(__UpperCamelCase  ,dtype=np.floataa    )
        elif isinstance(__UpperCamelCase  ,np.ndarray    ) and raw_speech.dtype is np.dtype(np.floataa    ):
             lowercase_			:       Dict									=			raw_speech.astype(np.floataa    )

        # always return batch
        if not is_batched:
             lowercase_			:       int									=			[np.asarray(__UpperCamelCase    )]

        # convert to mel spectrogram, truncate and pad if needed.
        lowercase_			:       List[str]									=			[
            self._get_input_mel(__UpperCamelCase  ,max_length if max_length else self.nb_max_samples  ,__UpperCamelCase  ,__UpperCamelCase    )
            for waveform in raw_speech
        ]

        lowercase_			:       int									=			[]
        lowercase_			:       int									=			[]
        for mel, longer in padded_inputs:
             input_mel.append(__UpperCamelCase    )
             is_longer.append(__UpperCamelCase    )

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

        if isinstance(input_mel[0]  ,__UpperCamelCase    ):
             lowercase_			:       Union[str, Any]									=			[np.asarray(__UpperCamelCase  ,dtype=np.floataa    ) for feature in input_mel]

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

        lowercase_			:       List[Any]									=			{'input_features': input_mel, 'is_longer': is_longer}
        lowercase_			:       Union[str, Any]									=			BatchFeature(__UpperCamelCase    )

        if return_tensors is not None:
             lowercase_			:       Any									=			input_features.convert_to_tensors(__UpperCamelCase    )

        return input_features

