

'''simple docstring'''





import contextlib
import importlib
import io
import unittest

import transformers

# Try to import everything from transformers to ensure every object can be loaded.
from transformers import *  # noqa F406
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, require_tf, require_torch
from transformers.utils import ContextManagers, find_labels, is_flax_available, is_tf_available, is_torch_available


if is_torch_available():
  from transformers import BertForPreTraining, BertForQuestionAnswering, BertForSequenceClassification

if is_tf_available():
  from transformers import TFBertForPreTraining, TFBertForQuestionAnswering, TFBertForSequenceClassification

if is_flax_available():
  from transformers import FlaxBertForPreTraining, FlaxBertForQuestionAnswering, FlaxBertForSequenceClassification


UpperCAmelCase__ :     int				    =    DUMMY_UNKNOWN_IDENTIFIER
# An actual model hosted on huggingface.co

UpperCAmelCase__ :     Tuple				    =    'main'
# Default branch name
UpperCAmelCase__ :     Optional[Any]				    =    'f2c752cfc5c0ab6f4bdec59acea69eefbee381c2'
# One particular commit (not the top of `main`)
UpperCAmelCase__ :     List[str]				    =    'aaaaaaa'
# This commit does not exist, so we should 404.

UpperCAmelCase__ :     List[str]				    =    'd9e9f15bc825e4b2c9249e9578f884bbcb5e3684'
# Sha-1 of config.json on the top of `main`, for checking purposes
UpperCAmelCase__ :     Optional[Any]				    =    '4b243c475af8d0a7754e87d7d096c92e5199ec2fe168a2ee7998e3b8e9bcb1d3'






@contextlib.contextmanager
def 					__lowercase      (     )     ->						Tuple:
      print("""Welcome!"""			)
      yield
      print("""Bye!"""			)






@contextlib.contextmanager
def 					__lowercase      (     )     ->						int:
      print("""Bonjour!"""			)
      yield
      print("""Au revoir!"""			)







class        a__       (   unittest.TestCase       ):







  """simple docstring"""




  def  _lowercase      (			self    : Optional[int]		)					->int:
        """simple docstring"""







        assert transformers.__spec__ is not None
        assert importlib.util.find_spec("""transformers"""		) is not None







class        a__       (   unittest.TestCase       ):







  """simple docstring"""




  @unittest.mock.patch("""sys.stdout"""				,				new_callable=io.StringIO		)
  def  _lowercase      (			self    : Any				,				UpperCAmelCase__    : Dict		)					->str:
        """simple docstring"""







        with ContextManagers([]		):
              print("""Transformers are awesome!"""		)
        # The print statement adds a new line at the end of the output
        self.assertEqual(mock_stdout.getvalue()				,				"""Transformers are awesome!\n"""		)


  @unittest.mock.patch("""sys.stdout"""				,				new_callable=io.StringIO		)
  def  _lowercase      (			self    : Dict				,				UpperCAmelCase__    : int		)					->int:
        """simple docstring"""







        with ContextManagers([context_en()]		):
              print("""Transformers are awesome!"""		)
        # The output should be wrapped with an English welcome and goodbye
        self.assertEqual(mock_stdout.getvalue()				,				"""Welcome!\nTransformers are awesome!\nBye!\n"""		)


  @unittest.mock.patch("""sys.stdout"""				,				new_callable=io.StringIO		)
  def  _lowercase      (			self    : Any				,				UpperCAmelCase__    : Dict		)					->Any:
        """simple docstring"""







        with ContextManagers([context_fr(), context_en()]		):
              print("""Transformers are awesome!"""		)
        # The output should be wrapped with an English and French welcome and goodbye
        self.assertEqual(mock_stdout.getvalue()				,				"""Bonjour!\nWelcome!\nTransformers are awesome!\nBye!\nAu revoir!\n"""		)


  @require_torch
  def  _lowercase      (			self    : Optional[int]		)					->Optional[Any]:
        """simple docstring"""







        self.assertEqual(find_labels(UpperCamelCase__		)				,				["""labels"""]		)
        self.assertEqual(find_labels(UpperCamelCase__		)				,				["""labels""", """next_sentence_label"""]		)
        self.assertEqual(find_labels(UpperCamelCase__		)				,				["""start_positions""", """end_positions"""]		)







        class        a__       (   SCREAMING_SNAKE_CASE_       ):







          """simple docstring"""




          pass


        self.assertEqual(find_labels(UpperCamelCase__		)				,				["""labels"""]		)


  @require_tf
  def  _lowercase      (			self    : List[str]		)					->Any:
        """simple docstring"""







        self.assertEqual(find_labels(UpperCamelCase__		)				,				["""labels"""]		)
        self.assertEqual(find_labels(UpperCamelCase__		)				,				["""labels""", """next_sentence_label"""]		)
        self.assertEqual(find_labels(UpperCamelCase__		)				,				["""start_positions""", """end_positions"""]		)







        class        a__       (   SCREAMING_SNAKE_CASE_       ):







          """simple docstring"""




          pass


        self.assertEqual(find_labels(UpperCamelCase__		)				,				["""labels"""]		)




  @require_flax
  def  _lowercase      (			self    : Tuple		)					->Tuple:
        """simple docstring"""







        self.assertEqual(find_labels(UpperCamelCase__		)				,				[]		)
        self.assertEqual(find_labels(UpperCamelCase__		)				,				[]		)
        self.assertEqual(find_labels(UpperCamelCase__		)				,				[]		)







        class        a__       (   SCREAMING_SNAKE_CASE_       ):







          """simple docstring"""




          pass


        self.assertEqual(find_labels(UpperCamelCase__		)				,				[]		)







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


UpperCAmelCase__ :     Tuple				    =    logging.get_logger(__name__)







class        a__       (   UpperCAmelCase       ):







  """simple docstring"""




  UpperCAmelCase__      : Optional[int]			       =["""input_features""", """attention_mask"""]


  def __init__(			self    : List[str]				,				UpperCAmelCase__    : Optional[Any]=8_0				,				UpperCAmelCase__    : List[str]=1_6_0_0_0				,				UpperCAmelCase__    : Optional[int]=8_0				,				UpperCAmelCase__    : List[str]=0.0				,				UpperCAmelCase__    : List[Any]=True				,				UpperCAmelCase__    : Tuple=True				,				UpperCAmelCase__    : Tuple=True				,				**UpperCAmelCase__    : Optional[int]				,				)					->Optional[int]:
        """simple docstring"""







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


  def  _lowercase      (			self    : Tuple				,				UpperCAmelCase__    : np.ndarray				,				)					->np.ndarray:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      List[str]       =   waveform * (2**1_5)  # Kaldi compliance: 16-bit signed integers
        SCREAMING_SNAKE_CASE :      Dict       =   torch.from_numpy(UpperCAmelCase__		).unsqueeze(0		)
        SCREAMING_SNAKE_CASE :      List[Any]       =   ta_kaldi.fbank(UpperCAmelCase__				,				num_mel_bins=self.num_mel_bins				,				sample_frequency=self.sampling_rate		)
        return features.numpy()


  @staticmethod
  def  _lowercase      (			UpperCAmelCase__    : np.ndarray				,				UpperCAmelCase__    : int				,				UpperCAmelCase__    : Optional[bool] = True				,				UpperCAmelCase__    : Optional[bool] = True				,				UpperCAmelCase__    : float = 0.0				,				)					->np.ndarray:
        """simple docstring"""







        if normalize_means:
              SCREAMING_SNAKE_CASE :      Tuple       =   x[:input_length].mean(axis=0		)
              SCREAMING_SNAKE_CASE :      List[Any]       =   np.subtract(UpperCAmelCase__				,				UpperCAmelCase__		)
        if normalize_vars:
              SCREAMING_SNAKE_CASE :      Optional[Any]       =   x[:input_length].std(axis=0		)
              SCREAMING_SNAKE_CASE :      Union[str, Any]       =   np.divide(UpperCAmelCase__				,				UpperCAmelCase__		)

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

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

        return x


  def  _lowercase      (			self    : List[str]				,				UpperCAmelCase__    : List[np.ndarray]				,				UpperCAmelCase__    : Optional[np.ndarray] = None		)					->List[np.ndarray]:
        """simple docstring"""







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




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

        SCREAMING_SNAKE_CASE :      Tuple       =   isinstance(UpperCAmelCase__				,				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}"		)
        SCREAMING_SNAKE_CASE :      Optional[Any]       =   is_batched_numpy or (
            isinstance(UpperCAmelCase__				,				(list, tuple)		) and (isinstance(raw_speech[0]				,				(np.ndarray, tuple, list)		))
        )

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

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

        # extract fbank features
        SCREAMING_SNAKE_CASE :      Tuple       =   [self._extract_fbank_features(UpperCAmelCase__		) for waveform in raw_speech]

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

        SCREAMING_SNAKE_CASE :      str       =   self.pad(
            UpperCAmelCase__				,				padding=UpperCAmelCase__				,				max_length=UpperCAmelCase__				,				truncation=UpperCAmelCase__				,				pad_to_multiple_of=UpperCAmelCase__				,				return_attention_mask=UpperCAmelCase__				,				**UpperCAmelCase__				,				)

        # make sure list is in array format
        SCREAMING_SNAKE_CASE :      Dict       =   padded_inputs.get("""input_features"""		)
        if isinstance(input_features[0]				,				UpperCAmelCase__		):
              SCREAMING_SNAKE_CASE :      Union[str, Any]       =   [np.asarray(UpperCAmelCase__				,				dtype=np.floataa		) for feature in input_features]

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

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

        if return_tensors is not None:
              SCREAMING_SNAKE_CASE :      Any       =   padded_inputs.convert_to_tensors(UpperCAmelCase__		)

        return padded_inputs




