



from typing import List, Optional, Union

import numpy as np

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


_lowercase				:					Dict     					=logging.get_logger(__name__)
class lowerCAmelCase_       (   A_			):







	'''simple docstring'''




	A_       :     Union[str, Any]									=		['input_values', 'attention_mask']

	def __init__(	self   ,       lowerCamelCase=1   ,       lowerCamelCase=1_6000   ,       lowerCamelCase=0.0   ,       lowerCamelCase=False   ,       lowerCamelCase=True   ,       **lowerCamelCase   ,       ):



				'''simple docstring'''


				super().__init__(feature_size=lowerCamelCase   ,       sampling_rate=lowerCamelCase   ,       padding_value=lowerCamelCase   ,       **lowerCamelCase       )
				a__					=  return_attention_mask
				a__					=  do_normalize

	@staticmethod
	def 				_A			(	lowerCamelCase   ,       lowerCamelCase   ,       lowerCamelCase = 0.0       ):



				'''simple docstring'''


				if attention_mask is not None:
							a__					=  np.array(lowerCamelCase   ,       np.intaa       )
							a__					=  []

							for vector, length in zip(lowerCamelCase   ,       attention_mask.sum(-1       )       ):
										a__					=  (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7       )
										if length < normed_slice.shape[0]:
													a__					=  padding_value

										normed_input_values.append(lowerCamelCase       )
				else:
							a__					=  [(x - x.mean()) / np.sqrt(x.var() + 1e-7       ) for x in input_values]

				return normed_input_values







	def __call__(	self   ,       lowerCamelCase   ,       lowerCamelCase = False   ,       lowerCamelCase = None   ,       lowerCamelCase = False   ,       lowerCamelCase = None   ,       lowerCamelCase = None   ,       lowerCamelCase = None   ,       lowerCamelCase = None   ,       **lowerCamelCase   ,       ):



				'''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."""       )

				a__					=  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}'       )
				a__					=  is_batched_numpy or (
				    isinstance(lowerCamelCase   ,       (list, tuple)       ) and (isinstance(raw_speech[0]   ,       (np.ndarray, tuple, list)       ))
				)

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

				# convert into correct format for padding
				a__					=  BatchFeature({"""input_values""": raw_speech}       )

				a__					=  self.pad(
				    lowerCamelCase   ,       padding=lowerCamelCase   ,       max_length=lowerCamelCase   ,       truncation=lowerCamelCase   ,       pad_to_multiple_of=lowerCamelCase   ,       return_attention_mask=lowerCamelCase   ,       )

				# convert input values to correct format
				a__					=  padded_inputs["""input_values"""]
				if not isinstance(input_values[0]   ,       np.ndarray       ):
							a__					=  [np.asarray(lowerCamelCase   ,       dtype=np.floataa       ) for array in input_values]
				elif (
				    not isinstance(lowerCamelCase   ,       np.ndarray       )
				    and isinstance(input_values[0]   ,       np.ndarray       )
				    and input_values[0].dtype is np.dtype(np.floataa       )
				):
							a__					=  [array.astype(np.floataa       ) for array in input_values]
				elif isinstance(lowerCamelCase   ,       np.ndarray       ) and input_values.dtype is np.dtype(np.floataa       ):
							a__					=  input_values.astype(np.floataa       )

				# convert attention_mask to correct format
				a__					=  padded_inputs.get("""attention_mask"""       )
				if attention_mask is not None:
							a__					=  [np.asarray(lowerCamelCase   ,       dtype=np.intaa       ) for array in attention_mask]

				# zero-mean and unit-variance normalization
				if self.do_normalize:
							a__					=  (
							    attention_mask
							    if self._get_padding_strategies(lowerCamelCase   ,       max_length=lowerCamelCase       ) is not PaddingStrategy.DO_NOT_PAD
							    else None
							)
							a__					=  self.zero_mean_unit_var_norm(
							    padded_inputs["""input_values"""]   ,       attention_mask=lowerCamelCase   ,       padding_value=self.padding_value       )

				if return_tensors is not None:
							a__					=  padded_inputs.convert_to_tensors(lowerCamelCase       )

				return padded_inputs






from .prime_check import Test

Test()

