






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


__UpperCamelCase:  Any   =   logging.get_logger(__name__)


class 			__lowerCAmelCase				(					_UpperCamelCase			):


	'''simple docstring'''





	_A       	=							["input_values", "attention_mask"]

	def __init__(	self:	str,				lowerCamelCase_:	str=1,				lowerCamelCase_:	List[str]=16000,				lowerCamelCase_:	Dict=0.0,				lowerCamelCase_:	Union[str, Any]=False,				lowerCamelCase_:	int=True,				**lowerCamelCase_:	Any,				):
			super().__init__(feature_size=UpperCAmelCase_,				sampling_rate=UpperCAmelCase_,				padding_value=UpperCAmelCase_,				**UpperCAmelCase_ )
			lowercase__				:			Optional[Any]            =		return_attention_mask
			lowercase__				:			List[str]            =		do_normalize

	@staticmethod
	def       snake_case__(	lowerCamelCase_:	List[np.ndarray],				lowerCamelCase_:	List[np.ndarray],				lowerCamelCase_:	float = 0.0 ):
			if attention_mask is not None:
					lowercase__				:			int            =		np.array(UpperCAmelCase_,				np.intaa )
					lowercase__				:			List[Any]            =		[]

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

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

			return normed_input_values





	def __call__(	self:	Optional[Any],				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_:	int,				):

			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.' )

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

			# always return batch
			if not is_batched:
					lowercase__				:			Union[str, Any]            =		[raw_speech]

			# convert into correct format for padding
			lowercase__				:			List[str]            =		BatchFeature({'input_values': raw_speech} )

			lowercase__				:			Tuple            =		self.pad(
			    UpperCAmelCase_,				padding=UpperCAmelCase_,				max_length=UpperCAmelCase_,				truncation=UpperCAmelCase_,				pad_to_multiple_of=UpperCAmelCase_,				return_attention_mask=UpperCAmelCase_,				)

			# convert input values to correct format
			lowercase__				:			Optional[int]            =		padded_inputs['input_values']
			if not isinstance(input_values[0],				np.ndarray ):
					lowercase__				:			Dict            =		[np.asarray(UpperCAmelCase_,				dtype=np.floataa ) for array in input_values]
			elif (
			    not isinstance(UpperCAmelCase_,				np.ndarray )
			    and isinstance(input_values[0],				np.ndarray )
			    and input_values[0].dtype is np.dtype(np.floataa )
			):
					lowercase__				:			int            =		[array.astype(np.floataa ) for array in input_values]
			elif isinstance(UpperCAmelCase_,				np.ndarray ) and input_values.dtype is np.dtype(np.floataa ):
					lowercase__				:			int            =		input_values.astype(np.floataa )

			# convert attention_mask to correct format
			lowercase__				:			List[Any]            =		padded_inputs.get('attention_mask' )
			if attention_mask is not None:
					lowercase__				:			Tuple            =		[np.asarray(UpperCAmelCase_,				dtype=np.intaa ) for array in attention_mask]

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

			if return_tensors is not None:
					lowercase__				:			str            =		padded_inputs.convert_to_tensors(UpperCAmelCase_ )

			return padded_inputs









import enum
import inspect
from typing import Iterable, List, Optional, Tuple, Union


class 			__lowerCAmelCase				(					enum.Enum			):


	'''simple docstring'''





	_A       	=							"timm"
	_A       	=							"transformers"


def 			SCREAMING_SNAKE_CASE__     (					_lowercase    :   Optional[Iterable[str]]      ,						_lowercase    :   Optional[Iterable[int]]      ,						_lowercase    :   Optional[Iterable[str]] )      ->       Optional[int]:




		'''simple docstring'''
		if stage_names is None:
				raise ValueError('Stage_names must be set for transformers backbones' )

		if out_features is not None:
				if not isinstance(_lowercase      ,						(list,) ):
						raise ValueError(f"""out_features must be a list {type(_lowercase )}""" )
				if any(feat not in stage_names for feat in out_features ):
						raise ValueError(f"""out_features must be a subset of stage_names: {stage_names} got {out_features}""" )

		if out_indices is not None:
				if not isinstance(_lowercase      ,						(list, tuple) ):
						raise ValueError(f"""out_indices must be a list or tuple, got {type(_lowercase )}""" )
				if any(idx >= len(_lowercase ) for idx in out_indices ):
						raise ValueError('out_indices must be valid indices for stage_names {stage_names}, got {out_indices}' )

		if out_features is not None and out_indices is not None:
				if len(_lowercase ) != len(_lowercase ):
						raise ValueError('out_features and out_indices should have the same length if both are set' )
				if out_features != [stage_names[idx] for idx in out_indices]:
						raise ValueError('out_features and out_indices should correspond to the same stages if both are set' )


def 			SCREAMING_SNAKE_CASE__     (					_lowercase    :   Optional[List[str]]      ,						_lowercase    :   Optional[Union[List[int], Tuple[int]]]      ,						_lowercase    :   List[str]      ,						)      ->       Dict:




		'''simple docstring'''
		if out_indices is None and out_features is None:
				lowercase__				:			str            =		[len(_lowercase ) - 1]
				lowercase__				:			Union[str, Any]            =		[stage_names[-1]]
		elif out_indices is None and out_features is not None:
				lowercase__				:			Optional[int]            =		[stage_names.index(_lowercase ) for layer in out_features]
		elif out_features is None and out_indices is not None:
				lowercase__				:			str            =		[stage_names[idx] for idx in out_indices]
		return out_features, out_indices


def 			SCREAMING_SNAKE_CASE__     (					_lowercase    :   Optional[List[str]]      ,						_lowercase    :   Optional[Union[List[int], Tuple[int]]]      ,						_lowercase    :   List[str]      ,						)      ->       Tuple[List[str], List[int]]:




		'''simple docstring'''
		verify_out_features_out_indices(out_features=_lowercase      ,						out_indices=_lowercase      ,						stage_names=_lowercase )
		lowercase__      ,							lowercase__				:			List[str]            =		_align_output_features_output_indices(
		    out_features=_lowercase      ,						out_indices=_lowercase      ,						stage_names=_lowercase )
		# Verify that the aligned out_features and out_indices are valid
		verify_out_features_out_indices(out_features=_lowercase      ,						out_indices=_lowercase      ,						stage_names=_lowercase )
		return output_features, output_indices


class 			__lowerCAmelCase				:


	'''simple docstring'''





	_A       	=							None

	def       snake_case__(	self:	Dict,				lowerCamelCase_:	Optional[Any] ):
			if getattr(self,				'_backbone',				lowerCamelCase_ ) is None:
					raise ValueError('self._backbone must be set before calling _init_timm_backbone' )

			# These will diagree with the defaults for the transformers models e.g. for resnet50
			# the transformer model has out_features = ['stem', 'stage1', 'stage2', 'stage3', 'stage4']
			# the timm model has out_features = ['act', 'layer1', 'layer2', 'layer3', 'layer4']
			lowercase__				:			Tuple            =		[stage['module'] for stage in self._backbone.feature_info.info]
			lowercase__				:			Union[str, Any]            =		[stage['num_chs'] for stage in self._backbone.feature_info.info]
			lowercase__				:			Dict            =		self._backbone.feature_info.out_indices
			lowercase__				:			Tuple            =		self._backbone.feature_info.module_name()

			# We verify the out indices and out features are valid
			verify_out_features_out_indices(
			    out_features=lowerCamelCase_,				out_indices=lowerCamelCase_,				stage_names=self.stage_names )
			lowercase__      ,							lowercase__				:			List[Any]            =		out_features, out_indices

	def       snake_case__(	self:	Any,				lowerCamelCase_:	List[str] ):
			lowercase__				:			Dict            =		getattr(lowerCamelCase_,				'stage_names' )
			lowercase__				:			List[str]            =		getattr(lowerCamelCase_,				'out_features',				lowerCamelCase_ )
			lowercase__				:			Optional[int]            =		getattr(lowerCamelCase_,				'out_indices',				lowerCamelCase_ )

			lowercase__				:			Optional[int]            =		stage_names
			lowercase__      ,							lowercase__				:			Union[str, Any]            =		get_aligned_output_features_output_indices(
			    out_features=lowerCamelCase_,				out_indices=lowerCamelCase_,				stage_names=lowerCamelCase_ )
			# Number of channels for each stage. This is set in the transformer backbone model init
			lowercase__				:			Dict            =		None

	def       snake_case__(	self:	List[Any],				lowerCamelCase_:	List[str] ):
			lowercase__				:			List[Any]            =		config

			lowercase__				:			Optional[int]            =		getattr(lowerCamelCase_,				'use_timm_backbone',				lowerCamelCase_ )
			lowercase__				:			List[Any]            =		BackboneType.TIMM if self.use_timm_backbone else BackboneType.TRANSFORMERS

			if self.backbone_type == BackboneType.TIMM:
					self._init_timm_backbone(lowerCamelCase_ )
			elif self.backbone_type == BackboneType.TRANSFORMERS:
					self._init_transformers_backbone(lowerCamelCase_ )
			else:
					raise ValueError(F"""backbone_type {self.backbone_type} not supported.""" )

	@property
	def       snake_case__(	self:	List[str] ):
			return self._out_features

	@out_features.setter
	def       snake_case__(	self:	Union[str, Any],				lowerCamelCase_:	List[str] ):
			lowercase__      ,							lowercase__				:			Optional[int]            =		get_aligned_output_features_output_indices(
			    out_features=lowerCamelCase_,				out_indices=lowerCamelCase_,				stage_names=self.stage_names )

	@property
	def       snake_case__(	self:	List[Any] ):
			return self._out_indices

	@out_indices.setter
	def       snake_case__(	self:	Tuple,				lowerCamelCase_:	Union[Tuple[int], List[int]] ):
			lowercase__      ,							lowercase__				:			List[str]            =		get_aligned_output_features_output_indices(
			    out_features=lowerCamelCase_,				out_indices=lowerCamelCase_,				stage_names=self.stage_names )

	@property
	def       snake_case__(	self:	str ):
			# the current backbones will output the number of channels for each stage
			# even if that stage is not in the out_features list.
			return {stage: self.num_features[i] for i, stage in enumerate(self.stage_names )}

	@property
	def       snake_case__(	self:	Optional[int] ):
			return [self.out_feature_channels[name] for name in self.out_features]

	def       snake_case__(	self:	List[Any],				*lowerCamelCase_:	List[Any],				**lowerCamelCase_:	Optional[Any] ):
			lowercase__				:			Any            =		dict(inspect.signature(self.forward ).parameters )
			lowercase__				:			List[Any]            =		{k: v for k, v in kwargs.items() if k in signature}
			return self(*lowerCamelCase_,				**lowerCamelCase_ )

	def       snake_case__(	self:	int,				lowerCamelCase_:	str,				lowerCamelCase_:	Optional[bool] = None,				lowerCamelCase_:	Optional[bool] = None,				lowerCamelCase_:	Optional[bool] = None,				):
			raise NotImplementedError('This method should be implemented by the derived class.' )





	def       snake_case__(	self:	Dict ):
			lowercase__				:			List[Any]            =		super().to_dict()
			lowercase__				:			Union[str, Any]            =		output.pop('_out_features' )
			lowercase__				:			Any            =		output.pop('_out_indices' )
			return output


class 			__lowerCAmelCase				:


	'''simple docstring'''





	@property
	def       snake_case__(	self:	Optional[int] ):
			return self._out_features

	@out_features.setter
	def       snake_case__(	self:	List[Any],				lowerCamelCase_:	List[str] ):
			lowercase__      ,							lowercase__				:			List[Any]            =		get_aligned_output_features_output_indices(
			    out_features=lowerCamelCase_,				out_indices=lowerCamelCase_,				stage_names=self.stage_names )

	@property
	def       snake_case__(	self:	Dict ):
			return self._out_indices

	@out_indices.setter
	def       snake_case__(	self:	Optional[int],				lowerCamelCase_:	Union[Tuple[int], List[int]] ):
			lowercase__      ,							lowercase__				:			Any            =		get_aligned_output_features_output_indices(
			    out_features=lowerCamelCase_,				out_indices=lowerCamelCase_,				stage_names=self.stage_names )





	def       snake_case__(	self:	Union[str, Any] ):
			lowercase__				:			List[str]            =		super().to_dict()
			lowercase__				:			str            =		output.pop('_out_features' )
			lowercase__				:			str            =		output.pop('_out_indices' )
			return output


