
from typing import List, Optional, Union

import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


lowercase  :					Union[str, Any]													=       logging.get_logger(__name__)




class  lowerCamelCase__							(				__lowercase):




	'''simple docstring'''




	_A								=     ['input_features', 'attention_mask']







	def __init__(					self		:List[Any]     ,   a		:int=8_0     ,   a		:int=1_6_0_0_0     ,   a		:Any=8_0     ,   a		:str=0.0     ,   a		:Any=True     ,   a		:List[Any]=True     ,   a		:Optional[int]=True     ,   **a		:int     ,   )		-> List[Any]:
								super().__init__(feature_size=a     ,   sampling_rate=a     ,   padding_value=a     ,   **a						)
								__UpperCamelCase	:   Union[str, Any]			   =					num_mel_bins
								__UpperCamelCase	:   str			   =					do_ceptral_normalize
								__UpperCamelCase	:   List[str]			   =					normalize_means
								__UpperCamelCase	:   str			   =					normalize_vars
								__UpperCamelCase	:   Any			   =					True







	def    _lowerCamelCase				(					self		:List[Any]     ,   a		:np.ndarray     ,   )		-> np.ndarray:
								__UpperCamelCase	:   Optional[int]			   =					waveform * (2**1_5)  # Kaldi compliance: 16-bit signed integers
								__UpperCamelCase	:   Union[str, Any]			   =					torch.from_numpy(a						).unsqueeze(0						)
								__UpperCamelCase	:   Union[str, Any]			   =					ta_kaldi.fbank(a     ,   num_mel_bins=self.num_mel_bins     ,   sample_frequency=self.sampling_rate						)
								return features.numpy()







	@staticmethod
	def    _lowerCamelCase				(					a		:np.ndarray     ,   a		:int     ,   a		:Optional[bool] = True     ,   a		:Optional[bool] = True     ,   a		:float = 0.0     ,   )		-> np.ndarray:
								# make sure we normalize float32 arrays
								if normalize_means:
															__UpperCamelCase	:   Any			   =					x[:input_length].mean(axis=0						)
															__UpperCamelCase	:   Tuple			   =					np.subtract(a     ,   a						)
								if normalize_vars:
															__UpperCamelCase	:   Optional[int]			   =					x[:input_length].std(axis=0						)
															__UpperCamelCase	:   Union[str, Any]			   =					np.divide(a     ,   a						)

								if input_length < x.shape[0]:
															__UpperCamelCase	:   str			   =					padding_value

								# make sure array is in float32
								__UpperCamelCase	:   List[Any]			   =					x.astype(np.floataa						)

								return x







	def    _lowerCamelCase				(					self		:Any     ,   a		:List[np.ndarray]     ,   a		:Optional[np.ndarray] = None						)		-> List[np.ndarray]:
								__UpperCamelCase	:   List[str]			   =					attention_mask.sum(-1						) if attention_mask is not None else [x.shape[0] for x in input_features]
								return [
								    self.utterance_cmvn(a     ,   a     ,   self.normalize_means     ,   self.normalize_vars     ,   self.padding_value						)
								    for x, n in zip(a     ,   a						)
								]







	def __call__(					self		:List[str]     ,   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[Union[str, TensorType]] = None     ,   a		:Optional[int] = None     ,   a		:Optional[bool] = None     ,   **a		:List[str]     ,   )		-> 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."						)

								__UpperCamelCase	:   str			   =					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}'						)
								__UpperCamelCase	:   str			   =					is_batched_numpy or (
								    isinstance(a     ,   (list, tuple)						) and (isinstance(raw_speech[0]     ,   (np.ndarray, tuple, list)						))
								)

								if is_batched:
															__UpperCamelCase	:   Any			   =					[np.asarray(a     ,   dtype=np.floataa						) for speech in raw_speech]
								elif not is_batched and not isinstance(a     ,   np.ndarray						):
															__UpperCamelCase	:   Optional[int]			   =					np.asarray(a     ,   dtype=np.floataa						)
								elif isinstance(a     ,   np.ndarray						) and raw_speech.dtype is np.dtype(np.floataa						):
															__UpperCamelCase	:   int			   =					raw_speech.astype(np.floataa						)

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

								# extract fbank features
								__UpperCamelCase	:   Optional[int]			   =					[self._extract_fbank_features(a						) for waveform in raw_speech]

								# convert into correct format for padding
								__UpperCamelCase	:   Optional[int]			   =					BatchFeature({"input_features": features}						)

								__UpperCamelCase	:   Union[str, Any]			   =					self.pad(
								    a     ,   padding=a     ,   max_length=a     ,   truncation=a     ,   pad_to_multiple_of=a     ,   return_attention_mask=a     ,   **a     ,   )

								# make sure list is in array format
								__UpperCamelCase	:   List[Any]			   =					padded_inputs.get("input_features"						)
								if isinstance(input_features[0]     ,   a						):
															__UpperCamelCase	:   List[str]			   =					[np.asarray(a     ,   dtype=np.floataa						) for feature in input_features]

								__UpperCamelCase	:   Optional[Any]			   =					padded_inputs.get("attention_mask"						)
								if attention_mask is not None:
															__UpperCamelCase	:   List[str]			   =					[np.asarray(a     ,   dtype=np.intaa						) for array in attention_mask]

								# Utterance-level cepstral mean and variance normalization
								if self.do_ceptral_normalize:
															__UpperCamelCase	:   str			   =					(
															    np.array(a     ,   dtype=np.intaa						)
															    if self._get_padding_strategies(a     ,   max_length=a						) is not PaddingStrategy.DO_NOT_PAD
															    else None
															)
															__UpperCamelCase	:   List[Any]			   =					self.normalize(
															    padded_inputs["input_features"]     ,   attention_mask=a						)

								if return_tensors is not None:
															__UpperCamelCase	:   Union[str, Any]			   =					padded_inputs.convert_to_tensors(a						)

								return padded_inputs

from __future__ import annotations

import sys
from collections import deque
from typing import Generic, TypeVar

lowercase  :					Optional[Any]													=       TypeVar('T')




class  lowerCamelCase__							(				Generic[T]):




	'''simple docstring'''




	_A								=     42  # Cache store of keys
	_A								=     42  # References of the keys in cache
	_A								=     1_0  # Maximum capacity of cache







	def __init__(					self		:Optional[Any]     ,   a		:int						)		-> None:
								__UpperCamelCase	:   Union[str, Any]			   =					deque()
								__UpperCamelCase	:   str			   =					set()
								if not n:
															__UpperCamelCase	:   Union[str, Any]			   =					sys.maxsize
								elif n < 0:
															raise ValueError("n should be an integer greater than 0."						)
								else:
															__UpperCamelCase	:   Any			   =					n







	def    _lowerCamelCase				(					self		:Tuple     ,   a		:T						)		-> None:
								if x not in self.key_reference:
															if len(self.dq_store						) == LRUCache._MAX_CAPACITY:
																						__UpperCamelCase	:   int			   =					self.dq_store.pop()
																						self.key_reference.remove(a						)
								else:
															self.dq_store.remove(a						)

								self.dq_store.appendleft(a						)
								self.key_reference.add(a						)







	def    _lowerCamelCase				(					self		:Any						)		-> None:
								for k in self.dq_store:
															print(a						)







	def __repr__(					self		:Tuple						)		-> str:
								return f'LRUCache({self._MAX_CAPACITY}) => {list(self.dq_store						)}'


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		lowercase  :					LRUCache[str | int]													=       LRUCache(4)
		lru_cache.refer('A')
		lru_cache.refer(2)
		lru_cache.refer(3)
		lru_cache.refer('A')
		lru_cache.refer(4)
		lru_cache.refer(5)
		lru_cache.display()

		print(lru_cache)
		assert str(lru_cache) == "LRUCache(4) => [5, 4, 'A', 3]"