





'''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


_lowerCamelCase :		str 			=				get_tests_dir('fixtures/test_sentencepiece.model')



@require_sentencepiece
@require_tokenizers
class 							__UpperCAmelCase     (				lowerCAmelCase__						,    unittest.TestCase  ):







		'''simple docstring'''







		__lowerCAmelCase        =			XLMRobertaTokenizer
		__lowerCAmelCase        =			XLMRobertaTokenizerFast
		__lowerCAmelCase        =			True
		__lowerCAmelCase        =			True







		def 				A   (self  :					int					):
			super().setUp()

			# We have a SentencePiece fixture for testing
			A							     =					XLMRobertaTokenizer(__lowerCAmelCase					,					keep_accents=__lowerCAmelCase					)
			tokenizer.save_pretrained(self.tmpdirname					)







		def 				A   (self  :					Dict					):
			A							     =					"""<pad>"""
			A							     =					1

			self.assertEqual(self.get_tokenizer()._convert_token_to_id(__lowerCAmelCase					)					,					__lowerCAmelCase					)
			self.assertEqual(self.get_tokenizer()._convert_id_to_token(__lowerCAmelCase					)					,					__lowerCAmelCase					)







		def 				A   (self  :					Optional[int]					):
			A							     =					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(__lowerCAmelCase					)					,					1002					)







		def 				A   (self  :					Dict					):
			self.assertEqual(self.get_tokenizer().vocab_size					,					1002					)







		def 				A   (self  :					str					):
			A							     =					XLMRobertaTokenizer(__lowerCAmelCase					,					keep_accents=__lowerCAmelCase					)

			A							     =					tokenizer.tokenize("""This is a test"""					)
			self.assertListEqual(__lowerCAmelCase					,					["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]					)

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

			A							     =					tokenizer.tokenize("""I was born in 92000, and this is falsé."""					)
			self.assertListEqual(
			    __lowerCAmelCase					,					[
			        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""",
			        """.""",
			    ]					,					)
			A							     =					tokenizer.convert_tokens_to_ids(__lowerCAmelCase					)
			self.assertListEqual(
			    __lowerCAmelCase					,					[
			        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 ^
			    ]					,					)

			A							     =					tokenizer.convert_ids_to_tokens(__lowerCAmelCase					)
			self.assertListEqual(
			    __lowerCAmelCase					,					[
			        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 				A   (self  :					Dict					):


			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

			A							     =					(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})"""					):
					A							     =					self.rust_tokenizer_class.from_pretrained(__lowerCAmelCase					,					**__lowerCAmelCase					)
					A							     =					self.tokenizer_class.from_pretrained(__lowerCAmelCase					,					**__lowerCAmelCase					)

					A							     =					tempfile.mkdtemp()

					A							     =					tokenizer_r.save_pretrained(__lowerCAmelCase					)
					A							     =					tokenizer_p.save_pretrained(__lowerCAmelCase					)

					# 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					)					)
					A							     =					tuple(f for f in tokenizer_r_files if """tokenizer.json""" not in f					)
					self.assertSequenceEqual(__lowerCAmelCase					,					__lowerCAmelCase					)

					# Checks everything loads correctly in the same way
					A							     =					tokenizer_r.from_pretrained(__lowerCAmelCase					)
					A							     =					tokenizer_p.from_pretrained(__lowerCAmelCase					)

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

					shutil.rmtree(__lowerCAmelCase					)

					# Save tokenizer rust, legacy_format=True
					A							     =					tempfile.mkdtemp()

					A							     =					tokenizer_r.save_pretrained(__lowerCAmelCase					,					legacy_format=__lowerCAmelCase					)
					A							     =					tokenizer_p.save_pretrained(__lowerCAmelCase					)

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

					# Checks everything loads correctly in the same way
					A							     =					tokenizer_r.from_pretrained(__lowerCAmelCase					)
					A							     =					tokenizer_p.from_pretrained(__lowerCAmelCase					)

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

					shutil.rmtree(__lowerCAmelCase					)

					# Save tokenizer rust, legacy_format=False
					A							     =					tempfile.mkdtemp()

					A							     =					tokenizer_r.save_pretrained(__lowerCAmelCase					,					legacy_format=__lowerCAmelCase					)
					A							     =					tokenizer_p.save_pretrained(__lowerCAmelCase					)

					# 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
					A							     =					tokenizer_r.from_pretrained(__lowerCAmelCase					)
					A							     =					tokenizer_p.from_pretrained(__lowerCAmelCase					)

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

					shutil.rmtree(__lowerCAmelCase					)







		@cached_property
		def 				A   (self  :					Optional[Any]					):
			return XLMRobertaTokenizer.from_pretrained("""xlm-roberta-base"""					)







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


			with tempfile.NamedTemporaryFile() as f:
				shutil.copyfile(__lowerCAmelCase					,					f.name					)
				A							     =					XLMRobertaTokenizer(f.name					,					keep_accents=__lowerCAmelCase					)
				A							     =					pickle.dumps(__lowerCAmelCase					)
			pickle.loads(__lowerCAmelCase					)







		def 				A   (self  :					List[Any]					):


			if not self.test_rust_tokenizer:
				return

			A							     =					self.get_tokenizer()
			A							     =					self.get_rust_tokenizer()

			A							     =					"""I was born in 92000, and this is falsé."""

			A							     =					tokenizer.tokenize(__lowerCAmelCase					)
			A							     =					rust_tokenizer.tokenize(__lowerCAmelCase					)
			self.assertListEqual(__lowerCAmelCase					,					__lowerCAmelCase					)

			A							     =					tokenizer.encode(__lowerCAmelCase					,					add_special_tokens=__lowerCAmelCase					)
			A							     =					rust_tokenizer.encode(__lowerCAmelCase					,					add_special_tokens=__lowerCAmelCase					)
			self.assertListEqual(__lowerCAmelCase					,					__lowerCAmelCase					)

			A							     =					self.get_rust_tokenizer()
			A							     =					tokenizer.encode(__lowerCAmelCase					)
			A							     =					rust_tokenizer.encode(__lowerCAmelCase					)
			self.assertListEqual(__lowerCAmelCase					,					__lowerCAmelCase					)







		@slow
		def 				A   (self  :					int					):
			A							     =					"""Hello World!"""
			A							     =					[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(__lowerCAmelCase					,					self.big_tokenizer.encode(__lowerCAmelCase					)					)







		@slow
		def 				A   (self  :					Dict					):
			A							     =					(
			    """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"""
			)
			A							     =					[
			    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(__lowerCAmelCase					,					self.big_tokenizer.encode(__lowerCAmelCase					)					)







		@slow
		def 				A   (self  :					List[str]					):
			A							     =					{"""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=__lowerCAmelCase					,					model_name="""xlm-roberta-base"""					,					revision="""d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3"""					,					)


'''simple docstring'''




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

import numpy as np

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


__lowercase          =  logging.get_logger(__name__)






class 				a__(					lowerCAmelCase__	):


			'''simple docstring'''






			UpperCAmelCase_			:     Union[str, Any]      =       ['''input_features''']



			def __init__(   self       ,    __lowerCAmelCase=80       ,    __lowerCAmelCase=16000       ,    __lowerCAmelCase=160       ,    __lowerCAmelCase=30       ,    __lowerCAmelCase=400       ,    __lowerCAmelCase=0.0       ,    __lowerCAmelCase=False       ,    **__lowerCAmelCase       ,    ):




					"""simple docstring"""


					super().__init__(
					    feature_size=__lowerCAmelCase       ,    sampling_rate=__lowerCAmelCase       ,    padding_value=__lowerCAmelCase       ,    return_attention_mask=__lowerCAmelCase       ,    **__lowerCAmelCase       ,    )
					lowerCAmelCase   					=  n_fft
					lowerCAmelCase   					=  hop_length
					lowerCAmelCase   					=  chunk_length
					lowerCAmelCase   					=  chunk_length * sampling_rate
					lowerCAmelCase   					=  self.n_samples // hop_length
					lowerCAmelCase   					=  sampling_rate
					lowerCAmelCase   					=  mel_filter_bank(
					    num_frequency_bins=1 + n_fft // 2       ,    num_mel_filters=__lowerCAmelCase       ,    min_frequency=0.0       ,    max_frequency=8000.0       ,    sampling_rate=__lowerCAmelCase       ,    norm="""slaney"""       ,    mel_scale="""slaney"""       ,    )



			def 							a_       (   self       ,    __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase   					=  spectrogram(
					    __lowerCAmelCase       ,    window_function(self.n_fft       ,    """hann""")       ,    frame_length=self.n_fft       ,    hop_length=self.hop_length       ,    power=2.0       ,    mel_filters=self.mel_filters       ,    log_mel="""log10"""       ,    )
					lowerCAmelCase   					=  log_spec[:, :-1]
					lowerCAmelCase   					=  np.maximum(__lowerCAmelCase       ,    log_spec.max() - 8.0)
					lowerCAmelCase   					=  (log_spec + 4.0) / 4.0
					return log_spec



			@staticmethod
			# Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
			def 							a_       (   __lowerCAmelCase       ,    __lowerCAmelCase       ,    __lowerCAmelCase = 0.0):




					"""simple docstring"""


					if attention_mask is not None:
							lowerCAmelCase   					=  np.array(__lowerCAmelCase       ,    np.intaa)
							lowerCAmelCase   					=  []

							for vector, length in zip(__lowerCAmelCase       ,    attention_mask.sum(-1)):
									lowerCAmelCase   					=  (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1E-7)
									if length < normed_slice.shape[0]:
											lowerCAmelCase   					=  padding_value

									normed_input_values.append(__lowerCAmelCase)
					else:
							lowerCAmelCase   					=  [(x - x.mean()) / np.sqrt(x.var() + 1E-7) for x in input_values]

					return normed_input_values



			def __call__(   self       ,    __lowerCAmelCase       ,    __lowerCAmelCase = True       ,    __lowerCAmelCase = None       ,    __lowerCAmelCase = None       ,    __lowerCAmelCase = None       ,    __lowerCAmelCase = "max_length"       ,    __lowerCAmelCase = None       ,    __lowerCAmelCase = None       ,    __lowerCAmelCase = None       ,    **__lowerCAmelCase       ,    ):




					"""simple docstring"""


					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.""")

					lowerCAmelCase   					=  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}")
					lowerCAmelCase   					=  is_batched_numpy or (
					    isinstance(__lowerCAmelCase       ,    (list, tuple)) and (isinstance(raw_speech[0]       ,    (np.ndarray, tuple, list)))
					)

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

					# always return batch
					if not is_batched:
							lowerCAmelCase   					=  [np.asarray([raw_speech]).T]

					lowerCAmelCase   					=  BatchFeature({"""input_features""": raw_speech})

					# convert into correct format for padding

					lowerCAmelCase   					=  self.pad(
					    __lowerCAmelCase       ,    padding=__lowerCAmelCase       ,    max_length=max_length if max_length else self.n_samples       ,    truncation=__lowerCAmelCase       ,    pad_to_multiple_of=__lowerCAmelCase       ,    return_attention_mask=return_attention_mask or do_normalize       ,    )

					# zero-mean and unit-variance normalization
					if do_normalize:
							lowerCAmelCase   					=  self.zero_mean_unit_var_norm(
							    padded_inputs["""input_features"""]       ,    attention_mask=padded_inputs["""attention_mask"""]       ,    padding_value=self.padding_value       ,    )
							lowerCAmelCase   					=  np.stack(padded_inputs["""input_features"""]       ,    axis=0)

					# make sure list is in array format
					lowerCAmelCase   					=  padded_inputs.get("""input_features""").transpose(2       ,    0       ,    1)

					lowerCAmelCase   					=  [self._np_extract_fbank_features(__lowerCAmelCase) for waveform in input_features[0]]

					if isinstance(input_features[0]       ,    __lowerCAmelCase):
							lowerCAmelCase   					=  [np.asarray(__lowerCAmelCase       ,    dtype=np.floataa) for feature in input_features]
					else:
							lowerCAmelCase   					=  input_features

					if return_attention_mask:
							# rescale from sample (48000) to feature (3000)
							lowerCAmelCase   					=  padded_inputs["""attention_mask"""][:, :: self.hop_length]

					if return_tensors is not None:
							lowerCAmelCase   					=  padded_inputs.convert_to_tensors(__lowerCAmelCase)

					return padded_inputs







			def 							a_       (   self):




					"""simple docstring"""


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




