



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_		:  Optional[Any]										=  logging.get_logger(__name__)



class 		_lowercase		(							UpperCAmelCase__						):
   _UpperCAmelCase  						=      ['''input_values''', '''attention_mask''']

   def __init__(   self      :							Union[str, Any]				, __lowerCAmelCase      :							Any=1				, __lowerCAmelCase      :							List[Any]=1_6000				, __lowerCAmelCase      :							List[str]=0.0				, __lowerCAmelCase      :							Optional[Any]=False				, __lowerCAmelCase      :							Tuple=True				, **__lowerCAmelCase      :							int				, )							->					Union[str, Any]:


    """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      :							List[np.ndarray]				, __lowerCAmelCase      :							List[np.ndarray]				, __lowerCAmelCase      :							float = 0.0				)							->					List[np.ndarray]:


    """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      :							Dict				, __lowerCAmelCase      :							Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]				, __lowerCAmelCase      :							Union[bool, str, PaddingStrategy] = False				, __lowerCAmelCase      :							Optional[int] = None				, __lowerCAmelCase      :							bool = False				, __lowerCAmelCase      :							Optional[int] = None				, __lowerCAmelCase      :							Optional[bool] = None				, __lowerCAmelCase      :							Optional[Union[str, TensorType]] = None				, __lowerCAmelCase      :							Optional[int] = None				, **__lowerCAmelCase      :							Tuple				, )							->					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."				)

    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 collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowercase_  							=			logging.get_logger(__name__)

lowercase_  							=			{
    "google/mobilebert-uncased": "https://huggingface.co/google/mobilebert-uncased/resolve/main/config.json"
}







class      SCREAMING_SNAKE_CASE__					(  __UpperCamelCase    ):
       A							:	List[Any]          =	MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
       A							:	int          =	"mobilebert"

       def __init__(							self					:     Any			,			_lowerCAmelCase					:     Union[str, Any]=3_05_22			,			_lowerCAmelCase					:     Tuple=5_12			,			_lowerCAmelCase					:     Dict=24			,			_lowerCAmelCase					:     int=4			,			_lowerCAmelCase					:     Optional[Any]=5_12			,			_lowerCAmelCase					:     Union[str, Any]="relu"			,			_lowerCAmelCase					:     List[str]=0.0			,			_lowerCAmelCase					:     str=0.1			,			_lowerCAmelCase					:     Tuple=5_12			,			_lowerCAmelCase					:     Optional[Any]=2			,			_lowerCAmelCase					:     Optional[int]=0.02			,			_lowerCAmelCase					:     List[str]=1e-12			,			_lowerCAmelCase					:     int=0			,			_lowerCAmelCase					:     Union[str, Any]=1_28			,			_lowerCAmelCase					:     Optional[Any]=True			,			_lowerCAmelCase					:     Dict=True			,			_lowerCAmelCase					:     Any=1_28			,			_lowerCAmelCase					:     Optional[int]=False			,			_lowerCAmelCase					:     str=True			,			_lowerCAmelCase					:     Tuple=4			,			_lowerCAmelCase					:     List[str]="no_norm"			,			_lowerCAmelCase					:     Union[str, Any]=True			,			_lowerCAmelCase					:     Optional[int]=None			,			**_lowerCAmelCase					:     int			,			):
           super().__init__(pad_token_id=_lowerCAmelCase			,			**_lowerCAmelCase			)

           __snake_case       :				Any 			=    vocab_size
           __snake_case       :				Tuple 			=    hidden_size
           __snake_case       :				int 			=    num_hidden_layers
           __snake_case       :				Dict 			=    num_attention_heads
           __snake_case       :				List[Any] 			=    hidden_act
           __snake_case       :				List[Any] 			=    intermediate_size
           __snake_case       :				Union[str, Any] 			=    hidden_dropout_prob
           __snake_case       :				Any 			=    attention_probs_dropout_prob
           __snake_case       :				Any 			=    max_position_embeddings
           __snake_case       :				List[Any] 			=    type_vocab_size
           __snake_case       :				List[Any] 			=    initializer_range
           __snake_case       :				Dict 			=    layer_norm_eps
           __snake_case       :				Any 			=    embedding_size
           __snake_case       :				List[str] 			=    trigram_input
           __snake_case       :				Union[str, Any] 			=    use_bottleneck
           __snake_case       :				List[Any] 			=    intra_bottleneck_size
           __snake_case       :				Tuple 			=    use_bottleneck_attention
           __snake_case       :				Optional[Any] 			=    key_query_shared_bottleneck
           __snake_case       :				Tuple 			=    num_feedforward_networks
           __snake_case       :				int 			=    normalization_type
           __snake_case       :				List[Any] 			=    classifier_activation

           if self.use_bottleneck:
               __snake_case       :				List[str] 			=    intra_bottleneck_size
           else:
               __snake_case       :				int 			=    hidden_size

           __snake_case       :				Optional[Any] 			=    classifier_dropout







class      SCREAMING_SNAKE_CASE__					(  __UpperCamelCase    ):

       @property
       def 			snake_case__    (							self					:     Optional[Any]			):
           if self.task == "multiple-choice":
               __snake_case       :				Any 			=    {0: """batch""", 1: """choice""", 2: """sequence"""}
           else:
               __snake_case       :				Optional[Any] 			=    {0: """batch""", 1: """sequence"""}
           return OrderedDict(
               [
                   ("""input_ids""", dynamic_axis),
                   ("""attention_mask""", dynamic_axis),
                   ("""token_type_ids""", dynamic_axis),
               ]			)



