



'''simple docstring'''

from math import ceil
from typing import List, Optional, Union

import numpy as np

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


__lowerCAmelCase									=   logging.get_logger(__name__)

class 	__magic_name__						(    _UpperCamelCase   ):
							lowerCAmelCase       :    str   				=					['audio_values', 'audio_mask']







							def __init__(							self					:       Tuple	,_UpperCAmelCase					:       str=2048	,_UpperCAmelCase					:       List[str]=1	,_UpperCAmelCase					:       Any=[16, 16]	,_UpperCAmelCase					:       Dict=128	,_UpperCAmelCase					:       Tuple=44100	,_UpperCAmelCase					:       Optional[Any]=86	,_UpperCAmelCase					:       str=2048	,_UpperCAmelCase					:       Union[str, Any]=0.0	,**_UpperCAmelCase					:       List[str]	,):
									super().__init__(
									    feature_size=_UpperCAmelCase	,sampling_rate=_UpperCAmelCase	,padding_value=_UpperCAmelCase	,**_UpperCAmelCase	,)

									_a     :      List[Any]        =							spectrogram_length
									_a     :      Tuple        =							num_channels
									_a     :      Dict        =							patch_size
									_a     :      Optional[Any]        =							feature_size // self.patch_size[1]
									_a     :      Tuple        =							n_fft
									_a     :      List[Any]        =							sampling_rate // hop_length_to_sampling_rate
									_a     :      List[str]        =							sampling_rate
									_a     :      Union[str, Any]        =							padding_value
									_a     :      Any        =							mel_filter_bank(
									    num_frequency_bins=1 + n_fft // 2	,num_mel_filters=_UpperCAmelCase	,min_frequency=0.0	,max_frequency=2_20_50.0	,sampling_rate=_UpperCAmelCase	,norm='slaney'	,mel_scale='slaney'	,).T







							def 							__lowercase			(							self					:       Tuple	,_UpperCAmelCase					:       np.array		):
									_a     :      List[str]        =							spectrogram(
									    _UpperCAmelCase	,window_function(self.n_fft	,'hann'		)	,frame_length=self.n_fft	,hop_length=self.hop_length	,power=2.0	,mel_filters=self.mel_filters.T	,log_mel='dB'	,db_range=80.0	,)
									_a     :      List[Any]        =							log_spec[:, :-1]
									_a     :      Optional[Any]        =							log_spec - 20.0
									_a     :      Dict        =							np.clip(log_spec / 40.0	,-2.0	,0.0		) + 1.0
									return log_spec







							def __call__(							self					:       int	,_UpperCAmelCase					:       Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]	,_UpperCAmelCase					:       Optional[Union[str, TensorType]] = None	,_UpperCAmelCase					:       Optional[bool] = True	,_UpperCAmelCase					:       Optional[int] = None	,_UpperCAmelCase					:       bool = False	,_UpperCAmelCase					:       bool = False	,**_UpperCAmelCase					:       Any	,):

									if sampling_rate is not None:
											if sampling_rate != self.sampling_rate:
													raise ValueError(
													    'This feature extractor is set to support sampling rate'
													    F""" of {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled"""
													    F""" 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.'		)

									_a     :      Optional[Any]        =							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}"""		)
									_a     :      Optional[int]        =							is_batched_numpy or (
									    isinstance(_UpperCAmelCase	,(list, tuple)		) and (isinstance(raw_speech[0]	,(np.ndarray, tuple, list)		))
									)
									if is_batched:
											_a     :      Union[str, Any]        =							[np.asarray([speech]	,dtype=np.floataa		).T for speech in raw_speech]
									elif not is_batched and not isinstance(_UpperCAmelCase	,np.ndarray		):
											_a     :      Optional[int]        =							np.asarray(_UpperCAmelCase	,dtype=np.floataa		)
									elif isinstance(_UpperCAmelCase	,np.ndarray		) and raw_speech.dtype is np.dtype(np.floataa		):
											_a     :      Optional[Any]        =							raw_speech.astype(np.floataa		)
									# always return batch
									if not is_batched:
											_a     :      Dict        =							[np.asarray([raw_speech]		).T]

									# Convert audio signals to log mel spectrograms, truncate by time axis
									_a     :      Any        =							[
									    self._np_extract_fbank_features(waveform.squeeze()		).T[: self.spectrogram_length] for waveform in raw_speech
									]
									if isinstance(audio_features[0]	,_UpperCAmelCase		):
											_a     :      Tuple        =							[np.asarray(_UpperCAmelCase	,dtype=np.floataa		) for feature in audio_features]

									# Create audio attention mask
									_a     :      List[Any]        =							max(
									    [ceil(feature.shape[0] / self.patch_size[0]		) * self.freq_len for feature in audio_features]		)  # The maximum number of audio patches in a batch
									if return_attention_mask:
											_a     :      Dict        =							[
											    (ceil(feature.shape[0] / self.patch_size[0]		) * self.freq_len) * [1]
											    + (max_patch_len - ceil(feature.shape[0] / self.patch_size[0]		) * self.freq_len) * [0]
											    for feature in audio_features
											]
											_a     :      Union[str, Any]        =							np.array(_UpperCAmelCase		).astype(np.floataa		)

									# convert into correct format for padding
									_a     :      Dict        =							max_patch_len // self.freq_len * self.patch_size[0]  # The maximum audio size in a batch
									_a     :      Union[str, Any]        =							np.ones([len(_UpperCAmelCase		), 1, max_time_len, self.feature_size]		).astype(np.floataa		)
									_a     :      List[Any]        =							padded_audio_features * self.padding_value
									for i in range(len(_UpperCAmelCase		)		):
											_a     :      Any        =							audio_features[i]
											_a     :      List[Any]        =							feature

									# return as BatchFeature
									if return_attention_mask:
											_a     :      Tuple        =							{'audio_values': padded_audio_features, 'audio_mask': audio_mask}
									else:
											_a     :      Tuple        =							{'audio_values': padded_audio_features}

									_a     :      Optional[Any]        =							BatchFeature(data=_UpperCAmelCase	,tensor_type=_UpperCAmelCase		)
									return encoded_inputs









'''simple docstring'''




import unittest

from transformers import AlbertTokenizer, AlbertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


a   :			Any					  =    get_tests_dir("""fixtures/spiece.model""")

@require_sentencepiece
@require_tokenizers
class 			UpperCamelCase_ (							__magic_name__      ,     unittest.TestCase			):
	lowercase          = AlbertTokenizer
	lowercase          = AlbertTokenizerFast
	lowercase          = True
	lowercase          = True
	lowercase          = True





	def  _lowercase(  self    )			->			str:
								super().setUp()

								# We have a SentencePiece fixture for testing
								UpperCAmelCase     :		Optional[int]							     =		AlbertTokenizer(A    )
								tokenizer.save_pretrained(self.tmpdirname    )





	def  _lowercase(  self		,		A    )			->			int:
								UpperCAmelCase     :		Optional[int]							     =		"""this is a test"""
								UpperCAmelCase     :		Dict							     =		"""this is a test"""
								return input_text, output_text





	def  _lowercase(  self    )			->			int:
								UpperCAmelCase     :		Tuple							     =		"""<pad>"""
								UpperCAmelCase     :		Optional[int]							     =		0

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





	def  _lowercase(  self    )			->			Any:
								UpperCAmelCase     :		str							     =		list(self.get_tokenizer().get_vocab().keys()    )

								self.assertEqual(vocab_keys[0]		,		"""<pad>"""    )
								self.assertEqual(vocab_keys[1]		,		"""<unk>"""    )
								self.assertEqual(vocab_keys[-1]		,		"""▁eloquent"""    )
								self.assertEqual(len(A    )		,		30000    )





	def  _lowercase(  self    )			->			Tuple:
								self.assertEqual(self.get_tokenizer().vocab_size		,		30000    )





	def  _lowercase(  self    )			->			Union[str, Any]:
								if not self.test_rust_tokenizer:
															return

								UpperCAmelCase     :		int							     =		self.get_tokenizer()
								UpperCAmelCase     :		List[str]							     =		self.get_rust_tokenizer()

								UpperCAmelCase     :		Optional[Any]							     =		"""I was born in 92000, and this is falsé."""

								UpperCAmelCase     :		str							     =		tokenizer.tokenize(A    )
								UpperCAmelCase     :		Optional[int]							     =		rust_tokenizer.tokenize(A    )
								self.assertListEqual(A		,		A    )

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

								UpperCAmelCase     :		Optional[Any]							     =		self.get_rust_tokenizer()
								UpperCAmelCase     :		Union[str, Any]							     =		tokenizer.encode(A    )
								UpperCAmelCase     :		Optional[int]							     =		rust_tokenizer.encode(A    )
								self.assertListEqual(A		,		A    )





	def  _lowercase(  self    )			->			Any:
								UpperCAmelCase     :		List[Any]							     =		AlbertTokenizer(A		,		keep_accents=A    )

								UpperCAmelCase     :		Optional[int]							     =		tokenizer.tokenize("""This is a test"""    )
								self.assertListEqual(A		,		["""▁this""", """▁is""", """▁a""", """▁test"""]    )

								self.assertListEqual(tokenizer.convert_tokens_to_ids(A    )		,		[48, 25, 21, 1289]    )

								UpperCAmelCase     :		Optional[Any]							     =		tokenizer.tokenize("""I was born in 92000, and this is falsé."""    )
								self.assertListEqual(
								    A		,		["""▁i""", """▁was""", """▁born""", """▁in""", """▁9""", """2000""", """,""", """▁and""", """▁this""", """▁is""", """▁fal""", """s""", """é""", """."""]    )
								UpperCAmelCase     :		str							     =		tokenizer.convert_tokens_to_ids(A    )
								self.assertListEqual(A		,		[31, 23, 386, 19, 561, 3050, 15, 17, 48, 25, 8256, 18, 1, 9]    )

								UpperCAmelCase     :		List[str]							     =		tokenizer.convert_ids_to_tokens(A    )
								self.assertListEqual(
								    A		,		["""▁i""", """▁was""", """▁born""", """▁in""", """▁9""", """2000""", """,""", """▁and""", """▁this""", """▁is""", """▁fal""", """s""", """<unk>""", """."""]		,		)





	def  _lowercase(  self    )			->			Optional[Any]:
								UpperCAmelCase     :		str							     =		AlbertTokenizer(A    )

								UpperCAmelCase     :		Optional[int]							     =		tokenizer.encode("""sequence builders"""    )
								UpperCAmelCase     :		Any							     =		tokenizer.encode("""multi-sequence build"""    )

								UpperCAmelCase     :		Optional[Any]							     =		tokenizer.build_inputs_with_special_tokens(A    )
								UpperCAmelCase     :		List[str]							     =		tokenizer.build_inputs_with_special_tokens(A		,		A    )

								assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
								assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
								    tokenizer.sep_token_id
								]






	@slow
	def  _lowercase(  self    )			->			Dict:
								# fmt: off
								UpperCAmelCase     :		Tuple							     =		{"""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, 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], [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]], """input_ids""": [[2, 21970, 13, 5, 6092, 167, 28, 7103, 2153, 673, 8, 7028, 12051, 18, 17, 7103, 2153, 673, 8, 3515, 18684, 8, 4461, 6, 1927, 297, 8, 12060, 2607, 18, 13, 5, 4461, 15, 10538, 38, 8, 135, 15, 822, 58, 15, 993, 10363, 15, 1460, 8005, 4461, 15, 993, 255, 2328, 9, 9, 9, 6, 26, 1112, 816, 3260, 13, 5, 103, 2377, 6, 17, 1112, 816, 2782, 13, 5, 103, 10641, 6, 29, 84, 2512, 2430, 782, 18684, 2761, 19, 808, 2430, 2556, 17, 855, 1480, 9477, 4091, 128, 11712, 15, 7103, 2153, 673, 17, 24883, 9990, 9, 3], [2, 11502, 25, 1006, 20, 782, 8, 11809, 855, 1732, 19393, 18667, 37, 367, 21018, 69, 1854, 34, 11860, 19124, 27, 156, 225, 17, 193, 4141, 19, 65, 9124, 9, 3, 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], [2, 14, 2231, 886, 2385, 17659, 84, 14, 16792, 1952, 9, 3, 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]], """token_type_ids""": [[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, 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, 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, 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=A		,		model_name="""albert-base-v2"""		,		revision="""6b6560eaf5ff2e250b00c50f380c5389a9c2d82e"""		,		)

