






from ...configuration_utils import PretrainedConfig
from ...utils import logging


__A          = logging.get_logger(__name__)

__A          = {
    'facebook/regnet-y-040': 'https://huggingface.co/facebook/regnet-y-040/blob/main/config.json',
}





class   SCREAMING_SNAKE_CASE (       snake_case    ):





							"""simple docstring"""

							A_       =    "regnet"
							A_       =    ["x", "y"]

							def __init__(     self:      Union[str, Any]						,   __A:      int=3						,   __A:      str=32						,   __A:      Optional[Any]=[1_28, 1_92, 5_12, 10_88]						,   __A:      str=[2, 6, 12, 2]						,   __A:      Optional[Any]=64						,   __A:      Tuple="y"						,   __A:      Tuple="relu"						,   **__A:      Union[str, Any]						,   )			-> Optional[int]:
												super().__init__(**__A      )
												if layer_type not in self.layer_types:
																	raise ValueError(f"""layer_type={layer_type} is not one of {",".join(self.layer_types      )}"""      )
												_A									=       num_channels
												_A									=       embedding_size
												_A									=       hidden_sizes
												_A									=       depths
												_A									=       groups_width
												_A									=       layer_type
												_A									=       hidden_act
												# always downsample in the first stage
												_A									=       True





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  					= logging.get_logger(__name__)







class      lowerCamelCase		(  A_  ):
     UpperCAmelCase__				:							int           =		["input_values", "attention_mask"]





     def __init__(self      :    Tuple				,    _A      :    Optional[Any]=1				,    _A      :    Dict=1_6_0_0_0				,    _A      :    int=0.0				,    _A      :    List[str]=False				,    _A      :    Optional[int]=True				,    **_A      :    List[Any]				,    )						->						str:
         super().__init__(feature_size=_A				,    sampling_rate=_A				,    padding_value=_A				,    **_A   )
         snake_case      =					return_attention_mask
         snake_case      =					do_normalize





     @staticmethod
     def 		UpperCAmelCase(_A      :    List[np.ndarray]				,    _A      :    List[np.ndarray]				,    _A      :    float = 0.0   )						->						List[np.ndarray]:
         if attention_mask is not None:
             snake_case      =					np.array(_A				,    np.intaa   )
             snake_case      =					[]

             for vector, length in zip(_A				,    attention_mask.sum(-1   )   ):
                 snake_case      =					(vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1E-7   )
                 if length < normed_slice.shape[0]:
                     snake_case      =					padding_value

                 normed_input_values.append(_A   )
         else:
             snake_case      =					[(x - x.mean()) / np.sqrt(x.var() + 1E-7   ) for x in input_values]

         return normed_input_values





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

         snake_case      =					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}'   )
         snake_case      =					is_batched_numpy or (
             isinstance(_A				,    (list, tuple)   ) and (isinstance(raw_speech[0]				,    (np.ndarray, tuple, list)   ))
         )

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

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

         snake_case      =					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
         snake_case      =					padded_inputs["input_values"]
         if not isinstance(input_values[0]				,    np.ndarray   ):
             snake_case      =					[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   )
         ):
             snake_case      =					[array.astype(np.floataa   ) for array in input_values]
         elif isinstance(_A				,    np.ndarray   ) and input_values.dtype is np.dtype(np.floataa   ):
             snake_case      =					input_values.astype(np.floataa   )

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

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

         if return_tensors is not None:
             snake_case      =					padded_inputs.convert_to_tensors(_A   )

         return padded_inputs

