






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


_UpperCAmelCase			:		Dict   				=    logging.get_logger(__name__)
class  lowerCAmelCase_       (				snake_case__					):
							UpperCamelCase_		:Optional[Any]		      =    ['input_features']




							def __init__(   self     :   str ,							SCREAMING_SNAKE_CASE_     :   List[str]=80 ,							SCREAMING_SNAKE_CASE_     :   Union[str, Any]=16_000 ,							SCREAMING_SNAKE_CASE_     :   str=160 ,							SCREAMING_SNAKE_CASE_     :   Union[str, Any]=30 ,							SCREAMING_SNAKE_CASE_     :   List[str]=400 ,							SCREAMING_SNAKE_CASE_     :   Optional[Any]=0.0 ,							SCREAMING_SNAKE_CASE_     :   Union[str, Any]=False ,							**SCREAMING_SNAKE_CASE_     :   Tuple ,							):
											super().__init__(
											    feature_size=SCREAMING_SNAKE_CASE_ ,							sampling_rate=SCREAMING_SNAKE_CASE_ ,							padding_value=SCREAMING_SNAKE_CASE_ ,							return_attention_mask=SCREAMING_SNAKE_CASE_ ,							**SCREAMING_SNAKE_CASE_ ,							)
											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=SCREAMING_SNAKE_CASE_ ,							min_frequency=0.0 ,							max_frequency=8_000.0 ,							sampling_rate=SCREAMING_SNAKE_CASE_ ,							norm='''slaney''' ,							mel_scale='''slaney''' ,							)




							def 						__snake_case					(   self     :   Union[str, Any] ,							SCREAMING_SNAKE_CASE_     :   np.array	):
											lowerCAmelCase__      			=						spectrogram(
											    SCREAMING_SNAKE_CASE_ ,							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(SCREAMING_SNAKE_CASE_ ,							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 						__snake_case					(   SCREAMING_SNAKE_CASE_     :   List[np.ndarray] ,							SCREAMING_SNAKE_CASE_     :   List[np.ndarray] ,							SCREAMING_SNAKE_CASE_     :   float = 0.0	):
											if attention_mask is not None:
															lowerCAmelCase__      			=						np.array(SCREAMING_SNAKE_CASE_ ,							np.intaa	)
															lowerCAmelCase__      			=						[]

															for vector, length in zip(SCREAMING_SNAKE_CASE_ ,							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(SCREAMING_SNAKE_CASE_	)
											else:
															lowerCAmelCase__      			=						[(x - x.mean()) / np.sqrt(x.var() + 1e-7	) for x in input_values]

											return normed_input_values




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

											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(SCREAMING_SNAKE_CASE_ ,							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(SCREAMING_SNAKE_CASE_ ,							(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(SCREAMING_SNAKE_CASE_ ,							np.ndarray	):
															lowerCAmelCase__      			=						np.asarray(SCREAMING_SNAKE_CASE_ ,							dtype=np.floataa	)
											elif isinstance(SCREAMING_SNAKE_CASE_ ,							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(
											    SCREAMING_SNAKE_CASE_ ,							padding=SCREAMING_SNAKE_CASE_ ,							max_length=max_length if max_length else self.n_samples ,							truncation=SCREAMING_SNAKE_CASE_ ,							pad_to_multiple_of=SCREAMING_SNAKE_CASE_ ,							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(SCREAMING_SNAKE_CASE_	) for waveform in input_features[0]]

											if isinstance(input_features[0] ,							SCREAMING_SNAKE_CASE_	):
															lowerCAmelCase__      			=						[np.asarray(SCREAMING_SNAKE_CASE_ ,							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(SCREAMING_SNAKE_CASE_	)

											return padded_inputs




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







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  lowerCAmelCase_       (				unittest.TestCase					):




							def 						__snake_case					(   self     :   Optional[int]	):
											lowerCAmelCase__      			=						'''ylacombe/bark-small'''
											lowerCAmelCase__      			=						tempfile.mkdtemp()
											lowerCAmelCase__      			=						'''en_speaker_1'''
											lowerCAmelCase__      			=						'''This is a test string'''
											lowerCAmelCase__      			=						'''speaker_embeddings_path.json'''
											lowerCAmelCase__      			=						'''speaker_embeddings'''




							def 						__snake_case					(   self     :   Optional[Any] ,							**SCREAMING_SNAKE_CASE_     :   Optional[int]	):
											return AutoTokenizer.from_pretrained(self.checkpoint ,							**SCREAMING_SNAKE_CASE_	)




							def 						__snake_case					(   self     :   List[str]	):
											shutil.rmtree(self.tmpdirname	)




							def 						__snake_case					(   self     :   Optional[Any]	):
											lowerCAmelCase__      			=						self.get_tokenizer()

											lowerCAmelCase__      			=						BarkProcessor(tokenizer=SCREAMING_SNAKE_CASE_	)

											processor.save_pretrained(self.tmpdirname	)
											lowerCAmelCase__      			=						BarkProcessor.from_pretrained(self.tmpdirname	)

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




							@slow
							def 						__snake_case					(   self     :   Any	):
											lowerCAmelCase__      			=						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 ,							)

											lowerCAmelCase__      			=						self.get_tokenizer(bos_token='''(BOS)''' ,							eos_token='''(EOS)'''	)

											lowerCAmelCase__      			=						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 						__snake_case					(   self     :   Dict	):
											lowerCAmelCase__      			=						BarkProcessor.from_pretrained(
											    pretrained_processor_name_or_path=self.checkpoint ,							speaker_embeddings_dict_path=self.speaker_embeddings_dict_path ,							)

											lowerCAmelCase__      			=						35
											lowerCAmelCase__      			=						2
											lowerCAmelCase__      			=						8

											lowerCAmelCase__      			=						{
											    '''semantic_prompt''': np.ones(SCREAMING_SNAKE_CASE_	),
											    '''coarse_prompt''': np.ones((nb_codebooks_coarse, seq_len)	),
											    '''fine_prompt''': np.ones((nb_codebooks_total, seq_len)	),
											}

											# test providing already loaded voice_preset
											lowerCAmelCase__      			=						processor(text=self.input_string ,							voice_preset=SCREAMING_SNAKE_CASE_	)

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

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

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

											# test loading voice preset from the hub
											lowerCAmelCase__      			=						processor(text=self.input_string ,							voice_preset=self.voice_preset	)




							def 						__snake_case					(   self     :   Optional[int]	):
											lowerCAmelCase__      			=						self.get_tokenizer()

											lowerCAmelCase__      			=						BarkProcessor(tokenizer=SCREAMING_SNAKE_CASE_	)

											lowerCAmelCase__      			=						processor(text=self.input_string	)

											lowerCAmelCase__      			=						tokenizer(
											    self.input_string ,							padding='''max_length''' ,							max_length=256 ,							add_special_tokens=SCREAMING_SNAKE_CASE_ ,							return_attention_mask=SCREAMING_SNAKE_CASE_ ,							return_token_type_ids=SCREAMING_SNAKE_CASE_ ,							)

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





