






"""simple docstring"""

from typing import List, Optional, Union

import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


lowercase__          = logging.get_logger(__name__)


class 		__lowerCamelCase				( A__			):





		'''simple docstring'''






		a_	:				int            =							['''input_features''', '''attention_mask''']





		def __init__(   self							:	int						,			a_							:	List[Any]=80						,			a_							:	Union[str, Any]=1_60_00						,			a_							:	List[str]=80						,			a_							:	str=0.0						,			a_							:	Union[str, Any]=True						,			a_							:	List[Any]=True						,			a_							:	Union[str, Any]=True						,			**a_							:	Tuple						,			):
				super().__init__(feature_size=lowerCAmelCase__						,			sampling_rate=lowerCAmelCase__						,			padding_value=lowerCAmelCase__						,			**lowerCAmelCase__	)
				lowerCAmelCase_   :							Any									=			num_mel_bins
				lowerCAmelCase_   :							List[str]									=			do_ceptral_normalize
				lowerCAmelCase_   :							Any									=			normalize_means
				lowerCAmelCase_   :							Dict									=			normalize_vars
				lowerCAmelCase_   :							Any									=			True





		def    lowerCamelCase       (   self							:	str						,			a_							:	np.ndarray						,			):
				lowerCAmelCase_   :							Any									=			waveform * (2**15)  # Kaldi compliance: 16-bit signed integers
				lowerCAmelCase_   :							str									=			torch.from_numpy(lowerCAmelCase__	).unsqueeze(0	)
				lowerCAmelCase_   :							Union[str, Any]									=			ta_kaldi.fbank(lowerCAmelCase__						,			num_mel_bins=self.num_mel_bins						,			sample_frequency=self.sampling_rate	)
				return features.numpy()





		@staticmethod
		def    lowerCamelCase       (   a_							:	np.ndarray						,			a_							:	int						,			a_							:	Optional[bool] = True						,			a_							:	Optional[bool] = True						,			a_							:	float = 0.0						,			):
				# make sure we normalize float32 arrays
				if normalize_means:
						lowerCAmelCase_   :							int									=			x[:input_length].mean(axis=0	)
						lowerCAmelCase_   :							List[str]									=			np.subtract(lowerCAmelCase__						,			lowerCAmelCase__	)
				if normalize_vars:
						lowerCAmelCase_   :							Optional[int]									=			x[:input_length].std(axis=0	)
						lowerCAmelCase_   :							Union[str, Any]									=			np.divide(lowerCAmelCase__						,			lowerCAmelCase__	)

				if input_length < x.shape[0]:
						lowerCAmelCase_   :							Tuple									=			padding_value

				# make sure array is in float32
				lowerCAmelCase_   :							Union[str, Any]									=			x.astype(np.floataa	)

				return x





		def    lowerCamelCase       (   self							:	str						,			a_							:	List[np.ndarray]						,			a_							:	Optional[np.ndarray] = None	):
				lowerCAmelCase_   :							Optional[Any]									=			attention_mask.sum(-1	) if attention_mask is not None else [x.shape[0] for x in input_features]
				return [
				    self.utterance_cmvn(lowerCAmelCase__						,			lowerCAmelCase__						,			self.normalize_means						,			self.normalize_vars						,			self.padding_value	)
				    for x, n in zip(lowerCAmelCase__						,			lowerCAmelCase__	)
				]





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

				if sampling_rate is not None:
						if sampling_rate != self.sampling_rate:
								raise ValueError(
								    f'''The model corresponding to this feature extractor: {self} was trained using a sampling rate of'''
								    f''' {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with'''
								    f''' {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_   :							Tuple									=			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_   :							List[str]									=			is_batched_numpy or (
				    isinstance(lowerCAmelCase__						,			(list, tuple)	) and (isinstance(raw_speech[0]						,			(np.ndarray, tuple, list)	))
				)

				if is_batched:
						lowerCAmelCase_   :							List[Any]									=			[np.asarray(lowerCAmelCase__						,			dtype=np.floataa	) for speech in raw_speech]
				elif not is_batched and not isinstance(lowerCAmelCase__						,			np.ndarray	):
						lowerCAmelCase_   :							Optional[Any]									=			np.asarray(lowerCAmelCase__						,			dtype=np.floataa	)
				elif isinstance(lowerCAmelCase__						,			np.ndarray	) and raw_speech.dtype is np.dtype(np.floataa	):
						lowerCAmelCase_   :							List[str]									=			raw_speech.astype(np.floataa	)

				# always return batch
				if not is_batched:
						lowerCAmelCase_   :							Optional[Any]									=			[raw_speech]

				# extract fbank features
				lowerCAmelCase_   :							Dict									=			[self._extract_fbank_features(lowerCAmelCase__	) for waveform in raw_speech]

				# convert into correct format for padding
				lowerCAmelCase_   :							Tuple									=			BatchFeature({"input_features": features}	)

				lowerCAmelCase_   :							Tuple									=			self.pad(
				    lowerCAmelCase__						,			padding=lowerCAmelCase__						,			max_length=lowerCAmelCase__						,			truncation=lowerCAmelCase__						,			pad_to_multiple_of=lowerCAmelCase__						,			return_attention_mask=lowerCAmelCase__						,			**lowerCAmelCase__						,			)

				# make sure list is in array format
				lowerCAmelCase_   :							str									=			padded_inputs.get("input_features"	)
				if isinstance(input_features[0]						,			lowerCAmelCase__	):
						lowerCAmelCase_   :							int									=			[np.asarray(lowerCAmelCase__						,			dtype=np.floataa	) for feature in input_features]

				lowerCAmelCase_   :							Dict									=			padded_inputs.get("attention_mask"	)
				if attention_mask is not None:
						lowerCAmelCase_   :							Tuple									=			[np.asarray(lowerCAmelCase__						,			dtype=np.intaa	) for array in attention_mask]

				# Utterance-level cepstral mean and variance normalization
				if self.do_ceptral_normalize:
						lowerCAmelCase_   :							Tuple									=			(
						    np.array(lowerCAmelCase__						,			dtype=np.intaa	)
						    if self._get_padding_strategies(lowerCAmelCase__						,			max_length=lowerCAmelCase__	) is not PaddingStrategy.DO_NOT_PAD
						    else None
						)
						lowerCAmelCase_   :							List[str]									=			self.normalize(
						    padded_inputs["input_features"]						,			attention_mask=lowerCAmelCase__	)

				if return_tensors is not None:
						lowerCAmelCase_   :							Dict									=			padded_inputs.convert_to_tensors(lowerCAmelCase__	)

				return padded_inputs




import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow
@require_torch
class        _lowercase      (			unittest.TestCase       ):






				'''simple docstring'''



				def 		__magic_name__(       self			:Union[str, Any]	)		->			Tuple:
										__SCREAMING_SNAKE_CASE						:     str					=		'''ylacombe/bark-small'''
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		tempfile.mkdtemp()
										__SCREAMING_SNAKE_CASE						:     str					=		'''en_speaker_1'''
										__SCREAMING_SNAKE_CASE						:     Any					=		'''This is a test string'''
										__SCREAMING_SNAKE_CASE						:     int					=		'''speaker_embeddings_path.json'''
										__SCREAMING_SNAKE_CASE						:     int					=		'''speaker_embeddings'''


				def 		__magic_name__(       self			:List[str]			,			**lowerCAmelCase__			:Union[str, Any]	)		->			Any:
										return AutoTokenizer.from_pretrained(self.checkpoint			,			**lowerCAmelCase__	)


				def 		__magic_name__(       self			:List[str]	)		->			int:
										shutil.rmtree(self.tmpdirname	)


				def 		__magic_name__(       self			:Dict	)		->			str:
										__SCREAMING_SNAKE_CASE						:     Dict					=		self.get_tokenizer()

										__SCREAMING_SNAKE_CASE						:     Tuple					=		BarkProcessor(tokenizer=lowerCAmelCase__	)

										processor.save_pretrained(self.tmpdirname	)
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		BarkProcessor.from_pretrained(self.tmpdirname	)

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


				@slow
				def 		__magic_name__(       self			:Tuple	)		->			List[Any]:
										__SCREAMING_SNAKE_CASE						:     Dict					=		BarkProcessor.from_pretrained(
										    pretrained_processor_name_or_path=self.checkpoint			,			speaker_embeddings_dict_path=self.speaker_embeddings_dict_path			,			)
										processor.save_pretrained(
										    self.tmpdirname			,			speaker_embeddings_dict_path=self.speaker_embeddings_dict_path			,			speaker_embeddings_directory=self.speaker_embeddings_directory			,			)

										__SCREAMING_SNAKE_CASE						:     Dict					=		self.get_tokenizer(bos_token='''(BOS)'''			,			eos_token='''(EOS)'''	)

										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		BarkProcessor.from_pretrained(
										    self.tmpdirname			,			self.speaker_embeddings_dict_path			,			bos_token='''(BOS)'''			,			eos_token='''(EOS)'''			,			)

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


				def 		__magic_name__(       self			:List[str]	)		->			Tuple:
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		BarkProcessor.from_pretrained(
										    pretrained_processor_name_or_path=self.checkpoint			,			speaker_embeddings_dict_path=self.speaker_embeddings_dict_path			,			)

										__SCREAMING_SNAKE_CASE						:     str					=		35
										__SCREAMING_SNAKE_CASE						:     str					=		2
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		8

										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		{
										    '''semantic_prompt''': np.ones(lowerCAmelCase__	),
										    '''coarse_prompt''': np.ones((nb_codebooks_coarse, seq_len)	),
										    '''fine_prompt''': np.ones((nb_codebooks_total, seq_len)	),
										}

										# test providing already loaded voice_preset
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		processor(text=self.input_string			,			voice_preset=lowerCAmelCase__	)

										__SCREAMING_SNAKE_CASE						:     List[str]					=		inputs['''history_prompt''']
										for key in voice_preset:
																self.assertListEqual(voice_preset[key].tolist()			,			processed_voice_preset.get(lowerCAmelCase__			,			np.array([]	)	).tolist()	)

										# test loading voice preset from npz file
										__SCREAMING_SNAKE_CASE						:     str					=		os.path.join(self.tmpdirname			,			'''file.npz'''	)
										np.savez(lowerCAmelCase__			,			**lowerCAmelCase__	)
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		processor(text=self.input_string			,			voice_preset=lowerCAmelCase__	)
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		inputs['''history_prompt''']

										for key in voice_preset:
																self.assertListEqual(voice_preset[key].tolist()			,			processed_voice_preset.get(lowerCAmelCase__			,			np.array([]	)	).tolist()	)

										# test loading voice preset from the hub
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		processor(text=self.input_string			,			voice_preset=self.voice_preset	)



				def 		__magic_name__(       self			:Tuple	)		->			Optional[Any]:
										__SCREAMING_SNAKE_CASE						:     Tuple					=		self.get_tokenizer()

										__SCREAMING_SNAKE_CASE						:     Any					=		BarkProcessor(tokenizer=lowerCAmelCase__	)

										__SCREAMING_SNAKE_CASE						:     List[Any]					=		processor(text=self.input_string	)

										__SCREAMING_SNAKE_CASE						:     List[Any]					=		tokenizer(
										    self.input_string			,			padding='''max_length'''			,			max_length=256			,			add_special_tokens=lowerCAmelCase__			,			return_attention_mask=lowerCAmelCase__			,			return_token_type_ids=lowerCAmelCase__			,			)

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