code: 
  
  
  '''simple docstring'''
  
  
  
  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
  
  
  A__	:      int				=						logging.get_logger(__name__)
  
  
  
  
  
  class      UpperCAmelCase_						(_UpperCAmelCase						):
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
      lowerCamelCase						:      Union[str, Any]							  =    ['input_values', 'attention_mask']
  
  
  
  
  
  
  
      def __init__(							self    ,  SCREAMING_SNAKE_CASE_=1    ,  SCREAMING_SNAKE_CASE_=1_60_00    ,  SCREAMING_SNAKE_CASE_=0.0    ,  SCREAMING_SNAKE_CASE_=False    ,  SCREAMING_SNAKE_CASE_=True    ,  **SCREAMING_SNAKE_CASE_    ,  )       ->  Dict:
           super().__init__(feature_size=SCREAMING_SNAKE_CASE_    ,  sampling_rate=SCREAMING_SNAKE_CASE_    ,  padding_value=SCREAMING_SNAKE_CASE_    ,  **SCREAMING_SNAKE_CASE_			)
           __lowerCamelCase     :			Union[str, Any]              =	return_attention_mask
           __lowerCamelCase     :			Optional[Any]              =	do_normalize
  
  
  
  
  
  
  
      @staticmethod
      def    lowercase_       (							SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_ = 0.0			)       ->  List[np.ndarray]:
           if attention_mask is not None:
                __lowerCamelCase     :			Dict              =	np.array(SCREAMING_SNAKE_CASE_    ,  np.intaa			)
                __lowerCamelCase     :			Union[str, Any]              =	[]
  
                for vector, length in zip(SCREAMING_SNAKE_CASE_    ,  attention_mask.sum(-1			)			):
                     __lowerCamelCase     :			int              =	(vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1E-7			)
                     if length < normed_slice.shape[0]:
                          __lowerCamelCase     :			Optional[Any]              =	padding_value
  
                     normed_input_values.append(SCREAMING_SNAKE_CASE_			)
           else:
                __lowerCamelCase     :			List[Any]              =	[(x - x.mean()) / np.sqrt(x.var() + 1E-7			) for x in input_values]
  
           return normed_input_values
  
  
  
  
  
  
  
      def __call__(							self    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_ = False    ,  SCREAMING_SNAKE_CASE_ = None    ,  SCREAMING_SNAKE_CASE_ = False    ,  SCREAMING_SNAKE_CASE_ = None    ,  SCREAMING_SNAKE_CASE_ = None    ,  SCREAMING_SNAKE_CASE_ = None    ,  SCREAMING_SNAKE_CASE_ = None    ,  **SCREAMING_SNAKE_CASE_    ,  )       ->  BatchFeature:
  
           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.'			)
  
           __lowerCamelCase     :			str              =	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     :			List[str]              =	is_batched_numpy or (
               isinstance(SCREAMING_SNAKE_CASE_    ,  (list, tuple)			) and (isinstance(raw_speech[0]    ,  (np.ndarray, tuple, list)			))
           )
  
           # always return batch
           if not is_batched:
                __lowerCamelCase     :			Tuple              =	[raw_speech]
  
           # convert into correct format for padding
           __lowerCamelCase     :			Tuple              =	BatchFeature({'input_values': raw_speech}			)
  
           __lowerCamelCase     :			Any              =	self.pad(
               SCREAMING_SNAKE_CASE_    ,  padding=SCREAMING_SNAKE_CASE_    ,  max_length=SCREAMING_SNAKE_CASE_    ,  truncation=SCREAMING_SNAKE_CASE_    ,  pad_to_multiple_of=SCREAMING_SNAKE_CASE_    ,  return_attention_mask=SCREAMING_SNAKE_CASE_    ,  )
  
           # convert input values to correct format
           __lowerCamelCase     :			Optional[int]              =	padded_inputs['input_values']
           if not isinstance(input_values[0]    ,  np.ndarray			):
                __lowerCamelCase     :			Optional[int]              =	[np.asarray(SCREAMING_SNAKE_CASE_    ,  dtype=np.floataa			) for array in input_values]
           elif (
               not isinstance(SCREAMING_SNAKE_CASE_    ,  np.ndarray			)
               and isinstance(input_values[0]    ,  np.ndarray			)
               and input_values[0].dtype is np.dtype(np.floataa			)
           ):
                __lowerCamelCase     :			Optional[Any]              =	[array.astype(np.floataa			) for array in input_values]
           elif isinstance(SCREAMING_SNAKE_CASE_    ,  np.ndarray			) and input_values.dtype is np.dtype(np.floataa			):
                __lowerCamelCase     :			Optional[Any]              =	input_values.astype(np.floataa			)
  
           # convert attention_mask to correct format
           __lowerCamelCase     :			int              =	padded_inputs.get('attention_mask'			)
           if attention_mask is not None:
                __lowerCamelCase     :			Union[str, Any]              =	[np.asarray(SCREAMING_SNAKE_CASE_    ,  dtype=np.intaa			) for array in attention_mask]
  
           # zero-mean and unit-variance normalization
           if self.do_normalize:
                __lowerCamelCase     :			List[Any]              =	(
                    attention_mask
                    if self._get_padding_strategies(SCREAMING_SNAKE_CASE_    ,  max_length=SCREAMING_SNAKE_CASE_			) is not PaddingStrategy.DO_NOT_PAD
                    else None
                )
                __lowerCamelCase     :			str              =	self.zero_mean_unit_var_norm(
                    padded_inputs['input_values']    ,  attention_mask=SCREAMING_SNAKE_CASE_    ,  padding_value=self.padding_value			)
  
           if return_tensors is not None:
                __lowerCamelCase     :			Optional[int]              =	padded_inputs.convert_to_tensors(SCREAMING_SNAKE_CASE_			)
  
           return padded_inputs
  
  
  
  
  
  
  
style_context: 
  
  
  '''simple docstring'''
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_torch_available
  
  
  A__	:      Optional[Any]				=						{
      """configuration_xlm_prophetnet""": ["""XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLMProphetNetConfig"""],
  }
  
  try:
        if not is_sentencepiece_available():
              raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
        pass
  else:
        A__	:      List[Any]				=						["""XLMProphetNetTokenizer"""]
  
  try:
        if not is_torch_available():
              raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
        pass
  else:
        A__	:      Optional[Any]				=						[
            """XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
            """XLMProphetNetDecoder""",
            """XLMProphetNetEncoder""",
            """XLMProphetNetForCausalLM""",
            """XLMProphetNetForConditionalGeneration""",
            """XLMProphetNetModel""",
            """XLMProphetNetPreTrainedModel""",
        ]
  
  
  if TYPE_CHECKING:
        from .configuration_xlm_prophetnet import XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMProphetNetConfig
  
        try:
              if not is_sentencepiece_available():
                    raise OptionalDependencyNotAvailable()
        except OptionalDependencyNotAvailable:
              pass
        else:
              from .tokenization_xlm_prophetnet import XLMProphetNetTokenizer
  
        try:
              if not is_torch_available():
                    raise OptionalDependencyNotAvailable()
        except OptionalDependencyNotAvailable:
              pass
        else:
              from .modeling_xlm_prophetnet import (
                  XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                  XLMProphetNetDecoder,
                  XLMProphetNetEncoder,
                  XLMProphetNetForCausalLM,
                  XLMProphetNetForConditionalGeneration,
                  XLMProphetNetModel,
                  XLMProphetNetPreTrainedModel,
              )
  
  else:
        import sys
  
        A__	:      List[str]				=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
  
  
  
  
  
  
  