"""simple docstring"""







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


class       _lowercase		( __lowerCamelCase	):
				lowerCamelCase__							=							['input_values', 'attention_mask']

				def __init__(  self							:     List[str]							,     _lowercase							:     List[Any]=1							,     _lowercase							:     Optional[Any]=1_6_0_0_0							,     _lowercase							:     Optional[int]=0.0							,     _lowercase							:     List[Any]=False							,     _lowercase							:     Union[str, Any]=True							,     **_lowercase							:     int							,     ):


										'''simple docstring'''
										super().__init__(feature_size=_lowercase							,     sampling_rate=_lowercase							,     padding_value=_lowercase							,     **_lowercase  )
										UpperCAmelCase__       						=		return_attention_mask
										UpperCAmelCase__       						=		do_normalize

				@staticmethod
				def 							A			(  _lowercase							:     List[np.ndarray]							,     _lowercase							:     List[np.ndarray]							,     _lowercase							:     float = 0.0  ):


										'''simple docstring'''
										if attention_mask is not None:
																UpperCAmelCase__       						=		np.array(_lowercase							,     np.intaa  )
																UpperCAmelCase__       						=		[]

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

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

										return normed_input_values

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


										'''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."""  )

										UpperCAmelCase__       						=		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__       						=		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__       						=		[raw_speech]

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

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

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

										# zero-mean and unit-variance normalization
										if self.do_normalize:
																UpperCAmelCase__       						=		(
																    attention_mask
																    if self._get_padding_strategies(_lowercase							,     max_length=_lowercase  ) is not PaddingStrategy.DO_NOT_PAD
																    else None
																)
																UpperCAmelCase__       						=		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__       						=		padded_inputs.convert_to_tensors(_lowercase  )

										return padded_inputs


"""simple docstring"""







import numpy as np


def 	a__   (		UpperCAmelCase__							:      List[str]		,      UpperCAmelCase__							:      int		,      UpperCAmelCase__							:      str      )->						Optional[int]:
						UpperCAmelCase__       						=		n_draws * class_counts / class_counts.sum()
						# floored means we don't overshoot n_samples, but probably undershoot
						UpperCAmelCase__       						=		np.floor(UpperCAmelCase__      )
						# we add samples according to how much "left over" probability
						# they had, until we arrive at n_samples
						UpperCAmelCase__       						=		int(n_draws - floored.sum()      )
						if need_to_add > 0:
												UpperCAmelCase__       						=		continuous - floored
												UpperCAmelCase__       						=		np.sort(np.unique(UpperCAmelCase__      )      )[::-1]
												# add according to remainder, but break ties
												# randomly to avoid biases
												for value in values:
																		((UpperCAmelCase__)						,   )       						=		np.where(remainder == value      )
																		# if we need_to_add less than what's in inds
																		# we draw randomly from them.
																		# if we need to add more, we add them all and
																		# go to the next value
																		UpperCAmelCase__       						=		min(len(UpperCAmelCase__      )		,      UpperCAmelCase__      )
																		UpperCAmelCase__       						=		rng.choice(UpperCAmelCase__		,      size=UpperCAmelCase__		,      replace=UpperCAmelCase__      )
																		floored[inds] += 1
																		need_to_add -= add_now
																		if need_to_add == 0:
																								break
						return floored.astype(np.intaa      )







def 	a__   (		UpperCAmelCase__							:      Optional[int]		,      UpperCAmelCase__							:      Optional[int]		,      UpperCAmelCase__							:      Dict		,      UpperCAmelCase__							:      Any		,      UpperCAmelCase__							:      Union[str, Any]=1_0      )->						Tuple:
						UpperCAmelCase__						,   UpperCAmelCase__       						=		np.unique(UpperCAmelCase__		,      return_inverse=UpperCAmelCase__      )
						UpperCAmelCase__       						=		classes.shape[0]
						UpperCAmelCase__       						=		np.bincount(UpperCAmelCase__      )
						if np.min(UpperCAmelCase__      ) < 2:
												raise ValueError("""Minimum class count error"""      )
						if n_train < n_classes:
												raise ValueError(
												    """The train_size = %d should be greater or """ """equal to the number of classes = %d""" % (n_train, n_classes)      )
						if n_test < n_classes:
												raise ValueError(
												    """The test_size = %d should be greater or """ """equal to the number of classes = %d""" % (n_test, n_classes)      )
						UpperCAmelCase__       						=		np.split(np.argsort(UpperCAmelCase__		,      kind="""mergesort"""      )		,      np.cumsum(UpperCAmelCase__      )[:-1]      )
						for _ in range(UpperCAmelCase__      ):
												UpperCAmelCase__       						=		approximate_mode(UpperCAmelCase__		,      UpperCAmelCase__		,      UpperCAmelCase__      )
												UpperCAmelCase__       						=		class_counts - n_i
												UpperCAmelCase__       						=		approximate_mode(UpperCAmelCase__		,      UpperCAmelCase__		,      UpperCAmelCase__      )

												UpperCAmelCase__       						=		[]
												UpperCAmelCase__       						=		[]

												for i in range(UpperCAmelCase__      ):
																		UpperCAmelCase__       						=		rng.permutation(class_counts[i]      )
																		UpperCAmelCase__       						=		class_indices[i].take(UpperCAmelCase__		,      mode="""clip"""      )
																		train.extend(perm_indices_class_i[: n_i[i]]      )
																		test.extend(perm_indices_class_i[n_i[i] : n_i[i] + t_i[i]]      )
												UpperCAmelCase__       						=		rng.permutation(UpperCAmelCase__      )
												UpperCAmelCase__       						=		rng.permutation(UpperCAmelCase__      )

												yield train, test

