






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	: Tuple      =				logging.get_logger(__name__)
class 					a					(				lowercase__     ):



      """simple docstring"""


      a						:				List[Any]							 =			['input_values', 'attention_mask']
      def __init__(   self      :    Dict	,   __lowercase      :    int=1	,   __lowercase      :    List[str]=16000	,   __lowercase      :    Union[str, Any]=0.0	,   __lowercase      :    Union[str, Any]=False	,   __lowercase      :    Dict=True	,   **__lowercase      :    str	,   )							->   Optional[Any]:
        super().__init__(feature_size=__lowercase	,   sampling_rate=__lowercase	,   padding_value=__lowercase	,   **__lowercase      )
        __UpperCAmelCase							:			Tuple     =						return_attention_mask
        __UpperCAmelCase							:			Optional[int]     =						do_normalize
      @staticmethod
      def 			UpperCAmelCase   (   __lowercase      :    List[np.ndarray]	,   __lowercase      :    List[np.ndarray]	,   __lowercase      :    float = 0.0      )							->   List[np.ndarray]:
        if attention_mask is not None:
          __UpperCAmelCase							:			Any     =						np.array(__lowercase	,   np.intaa      )
          __UpperCAmelCase							:			int     =						[]

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

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

        return normed_input_values




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

        __UpperCAmelCase							:			int     =						isinstance(__lowercase	,   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}"""      )
        __UpperCAmelCase							:			Union[str, Any]     =						is_batched_numpy or (
            isinstance(__lowercase	,   (list, tuple)      ) and (isinstance(raw_speech[0]	,   (np.ndarray, tuple, list)      ))
        )

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

        # convert into correct format for padding
        __UpperCAmelCase							:			Optional[Any]     =						BatchFeature({"""input_values""": raw_speech}      )

        __UpperCAmelCase							:			Union[str, Any]     =						self.pad(
            __lowercase	,   padding=__lowercase	,   max_length=__lowercase	,   truncation=__lowercase	,   pad_to_multiple_of=__lowercase	,   return_attention_mask=__lowercase	,   )

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

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

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

        if return_tensors is not None:
          __UpperCAmelCase							:			Optional[Any]     =						padded_inputs.convert_to_tensors(__lowercase      )

        return padded_inputs










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


a	: str      =				{"configuration_hubert": ["HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "HubertConfig"]}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a	: str      =				[
           "HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
           "HubertForCTC",
           "HubertForSequenceClassification",
           "HubertModel",
           "HubertPreTrainedModel",
       ]


try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a	: List[str]      =				[
           "TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
           "TFHubertForCTC",
           "TFHubertModel",
           "TFHubertPreTrainedModel",
       ]

if TYPE_CHECKING:
       from .configuration_hubert import HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, HubertConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_hubert import (
                  HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  HubertForCTC,
                  HubertForSequenceClassification,
                  HubertModel,
                  HubertPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_hubert import (
                  TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFHubertForCTC,
                  TFHubertModel,
                  TFHubertPreTrainedModel,
              )


else:
       import sys

       a	: List[Any]      =				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


