


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


_snake_case					=					logging.get_logger(__name__)
class      UpperCAmelCase_ ( UpperCamelCase   ):

   '''simple docstring'''




   __A			:				Union[str, Any]							=		["input_values", "attention_mask"]






   def __init__(		self						,    __A=1						,    __A=1_6000						,    __A=0.0						,    __A=False						,    __A=True						,    **__A						,    ):






     """simple docstring"""





     super().__init__(feature_size=__A						,    sampling_rate=__A						,    padding_value=__A						,    **__A			)
     lowerCamelCase			: List[str]   =    return_attention_mask
     lowerCamelCase			: Optional[int]   =    do_normalize






   @staticmethod
   def _snake_case   (		__A						,    __A						,    __A = 0.0			):






     """simple docstring"""





     if attention_mask is not None:
       lowerCamelCase			: Union[str, Any]   =    np.array(__A						,    np.intaa			)
       lowerCamelCase			: Dict   =    []

       for vector, length in zip(__A						,    attention_mask.sum(-1			)			):
         lowerCamelCase			: Union[str, Any]   =    (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7			)
         if length < normed_slice.shape[0]:
           lowerCamelCase			: List[Any]   =    padding_value

         normed_input_values.append(__A			)
     else:
       lowerCamelCase			: int   =    [(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						,    ):






     """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."			)

     lowerCamelCase			: Optional[int]   =    isinstance(__A						,    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			: Any   =    is_batched_numpy or (
         isinstance(__A						,    (list, tuple)			) and (isinstance(raw_speech[0]						,    (np.ndarray, tuple, list)			))
     )

     # always return batch
     if not is_batched:
       lowerCamelCase			: List[str]   =    [raw_speech]

     # convert into correct format for padding
     lowerCamelCase			: int   =    BatchFeature({"input_values": raw_speech}			)

     lowerCamelCase			: int   =    self.pad(
         __A						,    padding=__A						,    max_length=__A						,    truncation=__A						,    pad_to_multiple_of=__A						,    return_attention_mask=__A						,    )

     # convert input values to correct format
     lowerCamelCase			: List[str]   =    padded_inputs["input_values"]
     if not isinstance(input_values[0]						,    np.ndarray			):
       lowerCamelCase			: Union[str, Any]   =    [np.asarray(__A						,    dtype=np.floataa			) for array in input_values]
     elif (
         not isinstance(__A						,    np.ndarray			)
         and isinstance(input_values[0]						,    np.ndarray			)
         and input_values[0].dtype is np.dtype(np.floataa			)
     ):
       lowerCamelCase			: Union[str, Any]   =    [array.astype(np.floataa			) for array in input_values]
     elif isinstance(__A						,    np.ndarray			) and input_values.dtype is np.dtype(np.floataa			):
       lowerCamelCase			: int   =    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(__A						,    dtype=np.intaa			) for array in attention_mask]

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

     if return_tensors is not None:
       lowerCamelCase			: int   =    padded_inputs.convert_to_tensors(__A			)

     return padded_inputs







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


_snake_case					=					{
    '''configuration_rwkv''': ['''RWKV_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RwkvConfig''', '''RwkvOnnxConfig'''],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _snake_case					=					[
       '''RWKV_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''RwkvForCausalLM''',
       '''RwkvModel''',
       '''RwkvPreTrainedModel''',
   ]


if TYPE_CHECKING:
   from .configuration_rwkv import RWKV_PRETRAINED_CONFIG_ARCHIVE_MAP, RwkvConfig, RwkvOnnxConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_rwkv import (
          RWKV_PRETRAINED_MODEL_ARCHIVE_LIST,
          RwkvForCausalLM,
          RwkvModel,
          RwkvPreTrainedModel,
      )
else:
   import sys

   _snake_case					=					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



