


'''simple docstring'''
import numpy as np  # noqa: E402

from ...configuration_utils import ConfigMixin, register_to_config
from ...schedulers.scheduling_utils import SchedulerMixin


try:
							import librosa  # noqa: E402

							__SCREAMING_SNAKE_CASE				:							List[str]		       =		True
							__SCREAMING_SNAKE_CASE				:							Dict		       =		''''''
except Exception as e:
							__SCREAMING_SNAKE_CASE				:							List[str]		       =		False
							__SCREAMING_SNAKE_CASE				:							Optional[int]		       =		(
							    F"Cannot import librosa because {e}. Make sure to correctly install librosa to be able to install it."
							)


from PIL import Image  # noqa: E402






class 				__lowerCAmelCase      (      lowercase							, lowercase	):

						"""simple docstring"""






						_UpperCAmelCase		:					Tuple      	="mel_config.json"






						@register_to_config
						def __init__(     self       :			Any   ,     lowerCAmelCase       :			int = 2_56   ,     lowerCAmelCase       :			int = 2_56   ,     lowerCAmelCase       :			int = 2_20_50   ,     lowerCAmelCase       :			int = 20_48   ,     lowerCAmelCase       :			int = 5_12   ,     lowerCAmelCase       :			int = 80   ,     lowerCAmelCase       :			int = 32   ,     ):
													A_ 						=		hop_length
													A_ 						=		sample_rate
													A_ 						=		n_fft
													A_ 						=		top_db
													A_ 						=		n_iter
													self.set_resolution(lowerCAmelCase   ,     lowerCAmelCase  )
													A_ 						=		None

													if not _librosa_can_be_imported:
																				raise ValueError(_import_error  )






						def 							_UpperCAmelCase					(     self       :			List[str]   ,     lowerCAmelCase       :			int   ,     lowerCAmelCase       :			int  ):
													A_ 						=		x_res
													A_ 						=		y_res
													A_ 						=		self.y_res
													A_ 						=		self.x_res * self.hop_length - 1






						def 							_UpperCAmelCase					(     self       :			Union[str, Any]   ,     lowerCAmelCase       :			str = None   ,     lowerCAmelCase       :			np.ndarray = None  ):
													if audio_file is not None:
																				A_						,				A_ 						=		librosa.load(lowerCAmelCase   ,     mono=lowerCAmelCase   ,     sr=self.sr  )
													else:
																				A_ 						=		raw_audio

													# Pad with silence if necessary.
													if len(self.audio  ) < self.x_res * self.hop_length:
																				A_ 						=		np.concatenate([self.audio, np.zeros((self.x_res * self.hop_length - len(self.audio  ),)  )]  )






						def 							_UpperCAmelCase					(     self       :			int  ):
													return len(self.audio  ) // self.slice_size






						def 							_UpperCAmelCase					(     self       :			Union[str, Any]   ,     lowerCAmelCase       :			int = 0  ):
													return self.audio[self.slice_size * slice : self.slice_size * (slice + 1)]






						def 							_UpperCAmelCase					(     self       :			int  ):
													return self.sr






						def 							_UpperCAmelCase					(     self       :			Optional[Any]   ,     lowerCAmelCase       :			int  ):
													A_ 						=		librosa.feature.melspectrogram(
													    y=self.get_audio_slice(lowerCAmelCase  )   ,     sr=self.sr   ,     n_fft=self.n_fft   ,     hop_length=self.hop_length   ,     n_mels=self.n_mels  )
													A_ 						=		librosa.power_to_db(lowerCAmelCase   ,     ref=np.max   ,     top_db=self.top_db  )
													A_ 						=		(((log_S + self.top_db) * 2_55 / self.top_db).clip(0   ,     2_55  ) + 0.5).astype(np.uinta  )
													A_ 						=		Image.fromarray(lowerCAmelCase  )
													return image






						def 							_UpperCAmelCase					(     self       :			Any   ,     lowerCAmelCase       :			Image.Image  ):
													A_ 						=		np.frombuffer(image.tobytes()   ,     dtype="uint8"  ).reshape((image.height, image.width)  )
													A_ 						=		bytedata.astype("float"  ) * self.top_db / 2_55 - self.top_db
													A_ 						=		librosa.db_to_power(lowerCAmelCase  )
													A_ 						=		librosa.feature.inverse.mel_to_audio(
													    lowerCAmelCase   ,     sr=self.sr   ,     n_fft=self.n_fft   ,     hop_length=self.hop_length   ,     n_iter=self.n_iter  )
													return audio









'''simple docstring'''
import shutil
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import SpeechaTextTokenizer, is_speech_available
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, save_json
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, require_torchaudio
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_speech_to_text import floats_list


if is_speech_available():
							from transformers import SpeechaTextFeatureExtractor, SpeechaTextProcessor


__SCREAMING_SNAKE_CASE				:							int		       =		get_tests_dir('''fixtures/test_sentencepiece.model''')






@require_torch
@require_torchaudio
@require_sentencepiece
class 				__lowerCAmelCase      (      unittest.TestCase	):

						"""simple docstring"""






						def 							_UpperCAmelCase					(     self       :			List[str]  ):
													A_ 						=		tempfile.mkdtemp()

													A_ 						=		["<s>", "<pad>", "</s>", "<unk>", "▁This", "▁is", "▁a", "▁t", "est"]
													A_ 						=		dict(zip(lowerCAmelCase   ,     range(len(lowerCAmelCase  )  )  )  )
													A_ 						=		Path(self.tmpdirname  )
													save_json(lowerCAmelCase   ,     save_dir / VOCAB_FILES_NAMES["vocab_file"]  )
													if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
																				copyfile(lowerCAmelCase   ,     save_dir / VOCAB_FILES_NAMES["spm_file"]  )

													A_ 						=		SpeechaTextTokenizer.from_pretrained(self.tmpdirname  )
													tokenizer.save_pretrained(self.tmpdirname  )

													A_ 						=		{
													    "feature_size": 24,
													    "num_mel_bins": 24,
													    "padding_value": 0.0,
													    "sampling_rate": 1_60_00,
													    "return_attention_mask": False,
													    "do_normalize": True,
													}
													save_json(lowerCAmelCase   ,     save_dir / FEATURE_EXTRACTOR_NAME  )






						def 							_UpperCAmelCase					(     self       :			Union[str, Any]   ,     **lowerCAmelCase       :			str  ):
													return SpeechaTextTokenizer.from_pretrained(self.tmpdirname   ,     **lowerCAmelCase  )






						def 							_UpperCAmelCase					(     self       :			List[Any]   ,     **lowerCAmelCase       :			Optional[Any]  ):
													return SpeechaTextFeatureExtractor.from_pretrained(self.tmpdirname   ,     **lowerCAmelCase  )






						def 							_UpperCAmelCase					(     self       :			Any  ):
													shutil.rmtree(self.tmpdirname  )






						def 							_UpperCAmelCase					(     self       :			Union[str, Any]  ):
													A_ 						=		self.get_tokenizer()
													A_ 						=		self.get_feature_extractor()

													A_ 						=		SpeechaTextProcessor(tokenizer=lowerCAmelCase   ,     feature_extractor=lowerCAmelCase  )

													processor.save_pretrained(self.tmpdirname  )
													A_ 						=		SpeechaTextProcessor.from_pretrained(self.tmpdirname  )

													self.assertEqual(processor.tokenizer.get_vocab()   ,     tokenizer.get_vocab()  )
													self.assertIsInstance(processor.tokenizer   ,     lowerCAmelCase  )

													self.assertEqual(processor.feature_extractor.to_json_string()   ,     feature_extractor.to_json_string()  )
													self.assertIsInstance(processor.feature_extractor   ,     lowerCAmelCase  )






						def 							_UpperCAmelCase					(     self       :			Dict  ):
													A_ 						=		SpeechaTextProcessor(
													    tokenizer=self.get_tokenizer()   ,     feature_extractor=self.get_feature_extractor()  )
													processor.save_pretrained(self.tmpdirname  )

													A_ 						=		self.get_tokenizer(bos_token="(BOS)"   ,     eos_token="(EOS)"  )
													A_ 						=		self.get_feature_extractor(do_normalize=lowerCAmelCase   ,     padding_value=1.0  )

													A_ 						=		SpeechaTextProcessor.from_pretrained(
													    self.tmpdirname   ,     bos_token="(BOS)"   ,     eos_token="(EOS)"   ,     do_normalize=lowerCAmelCase   ,     padding_value=1.0  )

													self.assertEqual(processor.tokenizer.get_vocab()   ,     tokenizer_add_kwargs.get_vocab()  )
													self.assertIsInstance(processor.tokenizer   ,     lowerCAmelCase  )

													self.assertEqual(processor.feature_extractor.to_json_string()   ,     feature_extractor_add_kwargs.to_json_string()  )
													self.assertIsInstance(processor.feature_extractor   ,     lowerCAmelCase  )






						def 							_UpperCAmelCase					(     self       :			str  ):
													A_ 						=		self.get_feature_extractor()
													A_ 						=		self.get_tokenizer()

													A_ 						=		SpeechaTextProcessor(tokenizer=lowerCAmelCase   ,     feature_extractor=lowerCAmelCase  )

													A_ 						=		floats_list((3, 10_00)  )

													A_ 						=		feature_extractor(lowerCAmelCase   ,     return_tensors="np"  )
													A_ 						=		processor(lowerCAmelCase   ,     return_tensors="np"  )

													for key in input_feat_extract.keys():
																				self.assertAlmostEqual(input_feat_extract[key].sum()   ,     input_processor[key].sum()   ,     delta=1e-2  )






						def 							_UpperCAmelCase					(     self       :			Optional[int]  ):
													A_ 						=		self.get_feature_extractor()
													A_ 						=		self.get_tokenizer()

													A_ 						=		SpeechaTextProcessor(tokenizer=lowerCAmelCase   ,     feature_extractor=lowerCAmelCase  )

													A_ 						=		"This is a test string"

													A_ 						=		processor(text=lowerCAmelCase  )

													A_ 						=		tokenizer(lowerCAmelCase  )

													for key in encoded_tok.keys():
																				self.assertListEqual(encoded_tok[key]   ,     encoded_processor[key]  )






						def 							_UpperCAmelCase					(     self       :			int  ):
													A_ 						=		self.get_feature_extractor()
													A_ 						=		self.get_tokenizer()

													A_ 						=		SpeechaTextProcessor(tokenizer=lowerCAmelCase   ,     feature_extractor=lowerCAmelCase  )

													A_ 						=		[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

													A_ 						=		processor.batch_decode(lowerCAmelCase  )
													A_ 						=		tokenizer.batch_decode(lowerCAmelCase  )

													self.assertListEqual(lowerCAmelCase   ,     lowerCAmelCase  )






						def 							_UpperCAmelCase					(     self       :			Tuple  ):
													A_ 						=		self.get_feature_extractor()
													A_ 						=		self.get_tokenizer()

													A_ 						=		SpeechaTextProcessor(tokenizer=lowerCAmelCase   ,     feature_extractor=lowerCAmelCase  )

													self.assertListEqual(
													    processor.model_input_names   ,     feature_extractor.model_input_names   ,     msg="`processor` and `feature_extractor` model input names do not match"   ,     )





