





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


lowerCAmelCase_    =    logging.get_logger(__name__)



class  __lowerCAmelCase	(      _a					):
			lowerCamelCase_					:					int	      =				['''input_features''', '''attention_mask''']






			def __init__(self ,							__magic_name__=80 ,							__magic_name__=1_6000 ,							__magic_name__=80 ,							__magic_name__=0.0 ,							__magic_name__=True ,							__magic_name__=True ,							__magic_name__=True ,							**__magic_name__ ,							)					->       Optional[Any]:


							'''simple docstring'''



							super().__init__(feature_size=__magic_name__ ,							sampling_rate=__magic_name__ ,							padding_value=__magic_name__ ,							**__magic_name__   )
							snake_case_						:   Dict           =  num_mel_bins
							snake_case_						:   Optional[int]           =  do_ceptral_normalize
							snake_case_						:   Optional[Any]           =  normalize_means
							snake_case_						:   Any           =  normalize_vars
							snake_case_						:   str           =  True






			def       lowerCamelCase  (self ,							__magic_name__ ,							)					->       np.ndarray:


							'''simple docstring'''



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






			@staticmethod
			def       lowerCamelCase  (__magic_name__ ,							__magic_name__ ,							__magic_name__ = True ,							__magic_name__ = True ,							__magic_name__ = 0.0 ,							)					->       np.ndarray:


							'''simple docstring'''



							if normalize_means:
											snake_case_						:   int           =  x[:input_length].mean(axis=0   )
											snake_case_						:   Any           =  np.subtract(__magic_name__ ,							__magic_name__   )
							if normalize_vars:
											snake_case_						:   List[str]           =  x[:input_length].std(axis=0   )
											snake_case_						:   Dict           =  np.divide(__magic_name__ ,							__magic_name__   )

							if input_length < x.shape[0]:
											snake_case_						:   Any           =  padding_value

							# make sure array is in float32
							snake_case_						:   Optional[Any]           =  x.astype(np.floataa   )

							return x






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__ = None   )					->       List[np.ndarray]:


							'''simple docstring'''



							snake_case_						:   List[str]           =  attention_mask.sum(-1   ) if attention_mask is not None else [x.shape[0] for x in input_features]
							return [
							    self.utterance_cmvn(__magic_name__ ,							__magic_name__ ,							self.normalize_means ,							self.normalize_vars ,							self.padding_value   )
							    for x, n in zip(__magic_name__ ,							__magic_name__   )
							]






			def __call__(self ,							__magic_name__ ,							__magic_name__ = False ,							__magic_name__ = None ,							__magic_name__ = False ,							__magic_name__ = None ,							__magic_name__ = None ,							__magic_name__ = None ,							__magic_name__ = None ,							**__magic_name__ ,							)					->       BatchFeature:


							'''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} 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.'''   )

							snake_case_						:   Union[str, Any]           =  isinstance(__magic_name__ ,							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}'''   )
							snake_case_						:   Union[str, Any]           =  is_batched_numpy or (
							    isinstance(__magic_name__ ,							(list, tuple)   ) and (isinstance(raw_speech[0] ,							(np.ndarray, tuple, list)   ))
							)

							if is_batched:
											snake_case_						:   Optional[Any]           =  [np.asarray(__magic_name__ ,							dtype=np.floataa   ) for speech in raw_speech]
							elif not is_batched and not isinstance(__magic_name__ ,							np.ndarray   ):
											snake_case_						:   Optional[Any]           =  np.asarray(__magic_name__ ,							dtype=np.floataa   )
							elif isinstance(__magic_name__ ,							np.ndarray   ) and raw_speech.dtype is np.dtype(np.floataa   ):
											snake_case_						:   Tuple           =  raw_speech.astype(np.floataa   )

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

							# extract fbank features
							snake_case_						:   Optional[int]           =  [self._extract_fbank_features(__magic_name__   ) for waveform in raw_speech]

							# convert into correct format for padding
							snake_case_						:   Union[str, Any]           =  BatchFeature({'''input_features''': features}   )

							snake_case_						:   str           =  self.pad(
							    __magic_name__ ,							padding=__magic_name__ ,							max_length=__magic_name__ ,							truncation=__magic_name__ ,							pad_to_multiple_of=__magic_name__ ,							return_attention_mask=__magic_name__ ,							**__magic_name__ ,							)

							# make sure list is in array format
							snake_case_						:   List[str]           =  padded_inputs.get('''input_features'''   )
							if isinstance(input_features[0] ,							__magic_name__   ):
											snake_case_						:   Any           =  [np.asarray(__magic_name__ ,							dtype=np.floataa   ) for feature in input_features]

							snake_case_						:   Tuple           =  padded_inputs.get('''attention_mask'''   )
							if attention_mask is not None:
											snake_case_						:   Union[str, Any]           =  [np.asarray(__magic_name__ ,							dtype=np.intaa   ) for array in attention_mask]

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

							if return_tensors is not None:
											snake_case_						:   Optional[int]           =  padded_inputs.convert_to_tensors(__magic_name__   )

							return padded_inputs







import unittest

from transformers import load_tool
from transformers.utils import is_torch_available


if is_torch_available():
	import torch

from transformers.testing_utils import require_torch

from .test_tools_common import ToolTesterMixin



@require_torch
class  __lowerCAmelCase	(      unittest.TestCase,					_a					):






			def       lowerCamelCase  (self   )					->       int:


							'''simple docstring'''



							snake_case_						:   Dict           =  load_tool('''text-to-speech'''   )
							self.tool.setup()






			def       lowerCamelCase  (self   )					->       List[str]:


							'''simple docstring'''



							torch.manual_seed(0   )
							snake_case_						:   Optional[int]           =  self.tool('''hey'''   )
							snake_case_						:   Union[str, Any]           =  result.to_raw()
							self.assertTrue(
							    torch.allclose(
							        resulting_tensor[:3] ,							torch.tensor([-0.0_005_966_668_832_115_829, -0.0_003_657_640_190_795_064, -0.00_013_439_502_799_883_485]   ) ,							)   )






			def       lowerCamelCase  (self   )					->       Optional[Any]:


							'''simple docstring'''



							torch.manual_seed(0   )
							snake_case_						:   Any           =  self.tool('''hey'''   )
							snake_case_						:   str           =  result.to_raw()
							self.assertTrue(
							    torch.allclose(
							        resulting_tensor[:3] ,							torch.tensor([-0.0_005_966_668_832_115_829, -0.0_003_657_640_190_795_064, -0.00_013_439_502_799_883_485]   ) ,							)   )
