




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__      :			Union[str, Any]           =					logging.get_logger(__name__)




class 			a__       (					__a      ):
     a		:			List[Any]							=	["""input_values""", """attention_mask"""]







     def __init__(	self  ,		A=1  ,		A=16000  ,		A=0.0  ,		A=False  ,		A=True  ,		**A  ,		)  ->    Any:



          '''simple docstring'''



          super().__init__(feature_size=lowerCAmelCase__  ,		sampling_rate=lowerCAmelCase__  ,		padding_value=lowerCAmelCase__  ,		**lowerCAmelCase__ )
          a      =      return_attention_mask
          a      =      do_normalize







     @staticmethod
     def 			lowerCAmelCase_				(	A  ,		A  ,		A = 0.0 )  ->    List[str]:



          '''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  ,		A  ,		A = False  ,		A = None  ,		A = False  ,		A = None  ,		A = None  ,		A = None  ,		A = None  ,		**A  ,		)  ->    Optional[Any]:



          '''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



'''simple docstring'''

import unittest

from transformers import is_torch_available, load_tool

from .test_tools_common import ToolTesterMixin


if is_torch_available():
   import torch







class 	__lowerCAmelCase    (  unittest.TestCase       ,					__a				):



     def     snake_case_ (self	):
          _UpperCAmelCase					:  Tuple         =    load_tool("""speech-to-text"""	)
          self.tool.setup()



     def     snake_case_ (self	):
          _UpperCAmelCase					:  str         =    self.tool(torch.ones(3_0_0_0	)	)
          self.assertEqual(lowerCAmelCase__						,	""" you"""	)



     def     snake_case_ (self	):
          _UpperCAmelCase					:  Union[str, Any]         =    self.tool(audio=torch.ones(3_0_0_0	)	)
          self.assertEqual(lowerCAmelCase__						,	""" you"""	)


