



from __future__ import annotations

from typing import Generic, TypeVar

A__     				=	TypeVar("""T""")





class  __lowerCAmelCase			(					Generic[T]	):




				def __init__(							self						,    _snake_case		):



									"""simple docstring"""





									_lowerCAmelCase      =   data  # Assign data
									_lowerCAmelCase      =   None  # Initialize next as null
									_lowerCAmelCase      =   None  # Initialize prev as null





class  __lowerCAmelCase			(					Generic[T]	):




				def __init__(							self		):



									"""simple docstring"""





									_lowerCAmelCase      =   None




				def 							snake_case       (							self						,    _snake_case		):



									"""simple docstring"""





									if self.head is None:
														_lowerCAmelCase      =   Node(_snake_case		)
									else:
														_lowerCAmelCase      =   Node(_snake_case		)
														_lowerCAmelCase      =   new_node
														_lowerCAmelCase      =   self.head
														_lowerCAmelCase      =   None
														_lowerCAmelCase      =   new_node




				def 							snake_case       (							self		):



									"""simple docstring"""





									if self.head is None:
														return None
									else:
														assert self.head is not None
														_lowerCAmelCase      =   self.head.data
														_lowerCAmelCase      =   self.head.next
														if self.head is not None:
																			_lowerCAmelCase      =   None
														return temp




				def 							snake_case       (							self		):



									"""simple docstring"""





									return self.head.data if self.head is not None else None




				def __len__(							self		):



									"""simple docstring"""





									_lowerCAmelCase      =   self.head
									_lowerCAmelCase      =   0
									while temp is not None:
														count += 1
														_lowerCAmelCase      =   temp.next
									return count




				def 							snake_case       (							self		):



									"""simple docstring"""





									return self.head is None




				def 							snake_case       (							self		):



									"""simple docstring"""





									print("""stack elements are:"""		)
									_lowerCAmelCase      =   self.head
									while temp is not None:
														print(temp.data						,    end="""->"""		)
														_lowerCAmelCase      =   temp.next


# Code execution starts here
if __name__ == "__main__":
	# Start with the empty stack
	A__     				=	Stack()

	# Insert 4 at the beginning. So stack becomes 4->None
	print("""Stack operations using Doubly LinkedList""")
	stack.push(4)

	# Insert 5 at the beginning. So stack becomes 4->5->None
	stack.push(5)

	# Insert 6 at the beginning. So stack becomes 4->5->6->None
	stack.push(6)

	# Insert 7 at the beginning. So stack becomes 4->5->6->7->None
	stack.push(7)

	# Print the stack
	stack.print_stack()

	# Print the top element
	print("""\nTop element is """, stack.top())

	# Print the stack size
	print("""Size of the stack is """, len(stack))

	# pop the top element
	stack.pop()

	# pop the top element
	stack.pop()

	# two elements have now been popped off
	stack.print_stack()

	# Print True if the stack is empty else False
	print("""\nstack is empty:""", stack.is_empty())








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 TensorType, logging


A__     				=	logging.get_logger(__name__)





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




				def __init__(							self						,    _snake_case=1						,    _snake_case=16000						,    _snake_case=128						,    _snake_case=1024						,    _snake_case=0.0						,    _snake_case=True						,    _snake_case=-4.267_7393						,    _snake_case=4.568_9974						,    _snake_case=False						,    **_snake_case						,    ):



									"""simple docstring"""





									super().__init__(feature_size=_snake_case						,    sampling_rate=_snake_case						,    padding_value=_snake_case						,    **_snake_case		)
									_lowerCAmelCase      =   num_mel_bins
									_lowerCAmelCase      =   max_length
									_lowerCAmelCase      =   do_normalize
									_lowerCAmelCase      =   mean
									_lowerCAmelCase      =   std
									_lowerCAmelCase      =   return_attention_mask




				def 							snake_case       (							self						,    _snake_case						,    _snake_case						,    ):



									"""simple docstring"""





									_lowerCAmelCase      =   torch.from_numpy(_snake_case		).unsqueeze(0		)
									_lowerCAmelCase      =   ta_kaldi.fbank(
									    _snake_case						,    htk_compat=_snake_case						,    sample_frequency=self.sampling_rate						,    use_energy=_snake_case						,    window_type="""hanning"""						,    num_mel_bins=self.num_mel_bins						,    dither=0.0						,    frame_shift=10						,    )

									_lowerCAmelCase      =   fbank.shape[0]
									_lowerCAmelCase      =   max_length - n_frames

									# pad or truncate, depending on difference
									if difference > 0:
														_lowerCAmelCase      =   torch.nn.ZeroPadad((0, 0, 0, difference)		)
														_lowerCAmelCase      =   pad_module(_snake_case		)
									elif difference < 0:
														_lowerCAmelCase      =   fbank[0:max_length, :]

									_lowerCAmelCase      =   fbank.numpy()

									return fbank




				def 							snake_case       (							self						,    _snake_case		):



									"""simple docstring"""





									return (input_values - (self.mean)) / (self.std * 2)




				def __call__(							self						,    _snake_case						,    _snake_case = None						,    _snake_case = None						,    **_snake_case						,    ):



									"""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      =   isinstance(_snake_case						,    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      =   is_batched_numpy or (
									    isinstance(_snake_case						,    (list, tuple)		) and (isinstance(raw_speech[0]						,    (np.ndarray, tuple, list)		))
									)

									if is_batched:
														_lowerCAmelCase      =   [np.asarray(_snake_case						,    dtype=np.floataa		) for speech in raw_speech]
									elif not is_batched and not isinstance(_snake_case						,    np.ndarray		):
														_lowerCAmelCase      =   np.asarray(_snake_case						,    dtype=np.floataa		)
									elif isinstance(_snake_case						,    np.ndarray		) and raw_speech.dtype is np.dtype(np.floataa		):
														_lowerCAmelCase      =   raw_speech.astype(np.floataa		)

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

									# extract fbank features and pad/truncate to max_length
									_lowerCAmelCase      =   [self._extract_fbank_features(_snake_case						,    max_length=self.max_length		) for waveform in raw_speech]

									# convert into BatchFeature
									_lowerCAmelCase      =   BatchFeature({"""input_values""": features}		)

									# make sure list is in array format
									_lowerCAmelCase      =   padded_inputs.get("""input_values"""		)
									if isinstance(input_values[0]						,    _snake_case		):
														_lowerCAmelCase      =   [np.asarray(_snake_case						,    dtype=np.floataa		) for feature in input_values]

									# normalization
									if self.do_normalize:
														_lowerCAmelCase      =   [self.normalize(_snake_case		) for feature in input_values]

									if return_tensors is not None:
														_lowerCAmelCase      =   padded_inputs.convert_to_tensors(_snake_case		)

									return padded_inputs



