





"""simple docstring"""





import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np
import torch

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


_UpperCamelCase							=      logging.get_logger(__name__)
class 		SCREAMING_SNAKE_CASE_							(    snake_case__   ):
				"""simple docstring"""
				__snake_case   :       Any	 =		["""input_features""", """is_longer"""]




				def __init__(     self  :Dict						,    __lowercase  :Dict=64						,    __lowercase  :Any=4_8000						,    __lowercase  :Optional[Any]=480						,    __lowercase  :Tuple=10						,    __lowercase  :Optional[Any]=1024						,    __lowercase  :Any=0.0						,    __lowercase  :Dict=False						,    __lowercase  :float = 0						,    __lowercase  :float = 1_4000						,    __lowercase  :int = None						,    __lowercase  :str = "fusion"						,    __lowercase  :str = "repeatpad"						,    **__lowercase  :Dict						,    ):
											super().__init__(
											    feature_size=__lowercase						,    sampling_rate=__lowercase						,    padding_value=__lowercase						,    return_attention_mask=__lowercase						,    **__lowercase						,    )
											__lowerCamelCase      :      Tuple         =top_db
											__lowerCamelCase      :      str         =truncation
											__lowerCamelCase      :      List[Any]         =padding
											__lowerCamelCase      :      int         =fft_window_size
											__lowerCamelCase      :      List[str]         =(fft_window_size >> 1) + 1
											__lowerCamelCase      :      Dict         =hop_length
											__lowerCamelCase      :      Union[str, Any]         =max_length_s
											__lowerCamelCase      :      int         =max_length_s * sampling_rate
											__lowerCamelCase      :      Optional[int]         =sampling_rate
											__lowerCamelCase      :      str         =frequency_min
											__lowerCamelCase      :      List[Any]         =frequency_max
											__lowerCamelCase      :      Optional[Any]         =mel_filter_bank(
											    num_frequency_bins=self.nb_frequency_bins						,    num_mel_filters=__lowercase						,    min_frequency=__lowercase						,    max_frequency=__lowercase						,    sampling_rate=__lowercase						,    norm=__lowercase						,    mel_scale='''htk'''						,    )
											__lowerCamelCase      :      Dict         =mel_filter_bank(
											    num_frequency_bins=self.nb_frequency_bins						,    num_mel_filters=__lowercase						,    min_frequency=__lowercase						,    max_frequency=__lowercase						,    sampling_rate=__lowercase						,    norm='''slaney'''						,    mel_scale='''slaney'''						,    )




				def 					__lowercase  (     self  :List[Any]			):
											__lowerCamelCase      :      List[Any]         =copy.deepcopy(self.__dict__			)
											__lowerCamelCase      :      int         =self.__class__.__name__
											if "mel_filters" in output:
																		del output["mel_filters"]
											if "mel_filters_slaney" in output:
																		del output["mel_filters_slaney"]
											return output




				def 					__lowercase  (     self  :Any						,    __lowercase  :np.array						,    __lowercase  :Optional[np.array] = None			):
											__lowerCamelCase      :      Union[str, Any]         =spectrogram(
											    __lowercase						,    window_function(self.fft_window_size						,    '''hann'''			)						,    frame_length=self.fft_window_size						,    hop_length=self.hop_length						,    power=2.0						,    mel_filters=__lowercase						,    log_mel='''dB'''						,    )
											return log_mel_spectrogram.T




				def 					__lowercase  (     self  :Dict						,    __lowercase  :Dict						,    __lowercase  :Dict						,    __lowercase  :Union[str, Any]			):
											__lowerCamelCase      :      Optional[Any]         =np.array_split(list(range(0						,    total_frames - chunk_frames + 1			)			)						,    3			)
											if len(ranges[1]			) == 0:
																		# if the audio is too short, we just use the first chunk
																		__lowerCamelCase      :      Any         =[0]
											if len(ranges[2]			) == 0:
																		# if the audio is too short, we just use the first chunk
																		__lowerCamelCase      :      List[str]         =[0]
											# randomly choose index for each part
											__lowerCamelCase      :      str         =np.random.choice(ranges[0]			)
											__lowerCamelCase      :      Any         =np.random.choice(ranges[1]			)
											__lowerCamelCase      :      List[str]         =np.random.choice(ranges[2]			)

											__lowerCamelCase      :      int         =mel[idx_front : idx_front + chunk_frames, :]
											__lowerCamelCase      :      Tuple         =mel[idx_middle : idx_middle + chunk_frames, :]
											__lowerCamelCase      :      List[Any]         =mel[idx_back : idx_back + chunk_frames, :]

											__lowerCamelCase      :      int         =torch.tensor(mel[None, None, :]			)
											__lowerCamelCase      :      Union[str, Any]         =torch.nn.functional.interpolate(
											    __lowercase						,    size=[chunk_frames, 64]						,    mode='''bilinear'''						,    align_corners=__lowercase			)
											__lowerCamelCase      :      Dict         =mel_shrink[0][0].numpy()
											__lowerCamelCase      :      Tuple         =np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back]						,    axis=0			)
											return mel_fusion




				def 					__lowercase  (     self  :Tuple						,    __lowercase  :np.array						,    __lowercase  :Optional[Any]						,    __lowercase  :Any						,    __lowercase  :List[Any]			):
											if waveform.shape[0] > max_length:
																		if truncation == "rand_trunc":
																									__lowerCamelCase      :      Union[str, Any]         =True
																									# random crop to max_length (for compatibility) -> this should be handled by self.pad
																									__lowerCamelCase      :      Union[str, Any]         =len(__lowercase			) - max_length
																									__lowerCamelCase      :      int         =np.random.randint(0						,    overflow + 1			)
																									__lowerCamelCase      :      Dict         =waveform[idx : idx + max_length]
																									__lowerCamelCase      :      Union[str, Any]         =self._np_extract_fbank_features(__lowercase						,    self.mel_filters_slaney			)[None, :]
																		elif truncation == "fusion":
																									__lowerCamelCase      :      Any         =self._np_extract_fbank_features(__lowercase						,    self.mel_filters			)
																									__lowerCamelCase      :      Optional[int]         =max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
																									__lowerCamelCase      :      Tuple         =mel.shape[0]
																									if chunk_frames == total_frames:
																																# there is a corner case where the audio length is larger than max_length but smaller than max_length+hop_length.
																																# In this case, we just use the whole audio.
																																__lowerCamelCase      :      Dict         =np.stack([mel, mel, mel, mel]						,    axis=0			)
																																__lowerCamelCase      :      int         =False
																									else:
																																__lowerCamelCase      :      Tuple         =self._random_mel_fusion(__lowercase						,    __lowercase						,    __lowercase			)
																																__lowerCamelCase      :      Dict         =True
																		else:
																									raise NotImplementedError(f'data_truncating {truncation} not implemented'			)

											else:
																		__lowerCamelCase      :      List[str]         =False
																		# only use repeat as a new possible value for padding. you repeat the audio before applying the usual max_length padding
																		if waveform.shape[0] < max_length:
																									if padding == "repeat":
																																__lowerCamelCase      :      Optional[Any]         =int(max_length / len(__lowercase			)			)
																																__lowerCamelCase      :      int         =np.stack(np.tile(__lowercase						,    n_repeat + 1			)			)[:max_length]
																									if padding == "repeatpad":
																																__lowerCamelCase      :      List[Any]         =int(max_length / len(__lowercase			)			)
																																__lowerCamelCase      :      Tuple         =np.stack(np.tile(__lowercase						,    __lowercase			)			)
																									__lowerCamelCase      :      Optional[int]         =np.pad(__lowercase						,    (0, max_length - waveform.shape[0])						,    mode='''constant'''						,    constant_values=0			)

																		if truncation == "fusion":
																									__lowerCamelCase      :      Optional[int]         =self._np_extract_fbank_features(__lowercase						,    self.mel_filters			)
																									__lowerCamelCase      :      Tuple         =np.stack([input_mel, input_mel, input_mel, input_mel]						,    axis=0			)
																		else:
																									__lowerCamelCase      :      Optional[int]         =self._np_extract_fbank_features(__lowercase						,    self.mel_filters_slaney			)[None, :]

											return input_mel, longer




				def __call__(     self  :List[str]						,    __lowercase  :Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]						,    __lowercase  :str = None						,    __lowercase  :Optional[str] = None						,    __lowercase  :Optional[int] = None						,    __lowercase  :Optional[int] = None						,    __lowercase  :Optional[Union[str, TensorType]] = None						,    **__lowercase  :Optional[Any]						,    ):
											__lowerCamelCase      :      List[str]         =truncation if truncation is not None else self.truncation
											__lowerCamelCase      :      Optional[int]         =padding if padding else self.padding

											if sampling_rate is not None:
																		if sampling_rate != self.sampling_rate:
																									raise ValueError(
																									    f'The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a'
																									    f' sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input'
																									    f' was sampled with {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      :      List[Any]         =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}'			)
											__lowerCamelCase      :      Union[str, Any]         =is_batched_numpy or (
											    isinstance(__lowercase						,    (list, tuple)			) and (isinstance(raw_speech[0]						,    (np.ndarray, tuple, list)			))
											)

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

											# always return batch
											if not is_batched:
																		__lowerCamelCase      :      Tuple         =[np.asarray(__lowercase			)]

											# convert to mel spectrogram, truncate and pad if needed.
											__lowerCamelCase      :      Tuple         =[
											    self._get_input_mel(__lowercase						,    max_length if max_length else self.nb_max_samples						,    __lowercase						,    __lowercase			)
											    for waveform in raw_speech
											]

											__lowerCamelCase      :      Any         =[]
											__lowerCamelCase      :      Dict         =[]
											for mel, longer in padded_inputs:
																		input_mel.append(__lowercase			)
																		is_longer.append(__lowercase			)

											if truncation == "fusion" and sum(__lowercase			) == 0:
																		# if no audio is longer than 10s, then randomly select one audio to be longer
																		__lowerCamelCase      :      Union[str, Any]         =np.random.randint(0						,    len(__lowercase			)			)
																		__lowerCamelCase      :      str         =True

											if isinstance(input_mel[0]						,    __lowercase			):
																		__lowerCamelCase      :      List[str]         =[np.asarray(__lowercase						,    dtype=np.floataa			) for feature in input_mel]

											# is_longer is a list of bool
											__lowerCamelCase      :      str         =[[longer] for longer in is_longer]

											__lowerCamelCase      :      Optional[Any]         ={'''input_features''': input_mel, '''is_longer''': is_longer}
											__lowerCamelCase      :      List[str]         =BatchFeature(__lowercase			)

											if return_tensors is not None:
																		__lowerCamelCase      :      Optional[int]         =input_features.convert_to_tensors(__lowercase			)

											return input_features











"""simple docstring"""





from typing import Any
class 		SCREAMING_SNAKE_CASE_							:
				"""simple docstring"""




				def __init__(     self  :Tuple						,    __lowercase  :Any			):
											__lowerCamelCase      :      str         =data
											__lowerCamelCase      :      Any         =None




				def __repr__(     self  :Optional[Any]			):
											return f'Node({self.data})'

class 		SCREAMING_SNAKE_CASE_							:
				"""simple docstring"""




				def __init__(     self  :Optional[int]			):
											__lowerCamelCase      :      Union[str, Any]         =None




				def __iter__(     self  :Optional[int]			):
											__lowerCamelCase      :      Optional[Any]         =self.head
											while node:
																		yield node.data
																		__lowerCamelCase      :      str         =node.next




				def __len__(     self  :Any			):
											return sum(1 for _ in self			)




				def __repr__(     self  :Any			):
											return "->".join([str(__lowercase			) for item in self]			)




				def __getitem__(     self  :Dict						,    __lowercase  :int			):
											if not 0 <= index < len(self			):
																		raise ValueError('''list index out of range.'''			)
											for i, node in enumerate(self			):
																		if i == index:
																									return node
											return None




				def __setitem__(     self  :Dict						,    __lowercase  :int						,    __lowercase  :Any			):
											if not 0 <= index < len(self			):
																		raise ValueError('''list index out of range.'''			)
											__lowerCamelCase      :      Optional[Any]         =self.head
											for _ in range(__lowercase			):
																		__lowerCamelCase      :      List[str]         =current.next
											__lowerCamelCase      :      Optional[int]         =data




				def 					__lowercase  (     self  :Tuple						,    __lowercase  :Any			):
											self.insert_nth(len(self			)						,    __lowercase			)




				def 					__lowercase  (     self  :int						,    __lowercase  :Any			):
											self.insert_nth(0						,    __lowercase			)




				def 					__lowercase  (     self  :Dict						,    __lowercase  :int						,    __lowercase  :Any			):
											if not 0 <= index <= len(self			):
																		raise IndexError('''list index out of range'''			)
											__lowerCamelCase      :      List[Any]         =Node(__lowercase			)
											if self.head is None:
																		__lowerCamelCase      :      List[Any]         =new_node
											elif index == 0:
																		__lowerCamelCase      :      Optional[Any]         =self.head  # link new_node to head
																		__lowerCamelCase      :      Optional[Any]         =new_node
											else:
																		__lowerCamelCase      :      str         =self.head
																		for _ in range(index - 1			):
																									__lowerCamelCase      :      Any         =temp.next
																		__lowerCamelCase      :      List[Any]         =temp.next
																		__lowerCamelCase      :      List[Any]         =new_node




				def 					__lowercase  (     self  :Tuple			):  # print every node data
											print(self			)




				def 					__lowercase  (     self  :List[str]			):
											return self.delete_nth(0			)




				def 					__lowercase  (     self  :Any			):  # delete from tail
											return self.delete_nth(len(self			) - 1			)




				def 					__lowercase  (     self  :Dict						,    __lowercase  :int = 0			):
											if not 0 <= index <= len(self			) - 1:  # test if index is valid
																		raise IndexError('''List index out of range.'''			)
											__lowerCamelCase      :      List[Any]         =self.head  # default first node
											if index == 0:
																		__lowerCamelCase      :      int         =self.head.next
											else:
																		__lowerCamelCase      :      int         =self.head
																		for _ in range(index - 1			):
																									__lowerCamelCase      :      str         =temp.next
																		__lowerCamelCase      :      int         =temp.next
																		__lowerCamelCase      :      Optional[Any]         =temp.next.next
											return delete_node.data




				def 					__lowercase  (     self  :Any			):
											return self.head is None




				def 					__lowercase  (     self  :Tuple			):
											__lowerCamelCase      :      Any         =None
											__lowerCamelCase      :      Optional[int]         =self.head

											while current:
																		# Store the current node's next node.
																		__lowerCamelCase      :      Any         =current.next
																		# Make the current node's next point backwards
																		__lowerCamelCase      :      Optional[Any]         =prev
																		# Make the previous node be the current node
																		__lowerCamelCase      :      List[str]         =current
																		# Make the current node the next node (to progress iteration)
																		__lowerCamelCase      :      Tuple         =next_node
											# Return prev in order to put the head at the end
											__lowerCamelCase      :      List[str]         =prev



def 				lowerCAmelCase_			(      ):


							'''simple docstring'''







							__lowerCamelCase      :      Tuple         =LinkedList()
							assert linked_list.is_empty() is True
							assert str(SCREAMING_SNAKE_CASE   ) == ""

							try:
														linked_list.delete_head()
														raise AssertionError  # This should not happen.
							except IndexError:
														assert True  # This should happen.

							try:
														linked_list.delete_tail()
														raise AssertionError  # This should not happen.
							except IndexError:
														assert True  # This should happen.

							for i in range(10   ):
														assert len(SCREAMING_SNAKE_CASE   ) == i
														linked_list.insert_nth(SCREAMING_SNAKE_CASE    ,						i + 1   )
							assert str(SCREAMING_SNAKE_CASE   ) == "->".join(str(SCREAMING_SNAKE_CASE   ) for i in range(1    ,						11   )   )

							linked_list.insert_head(0   )
							linked_list.insert_tail(11   )
							assert str(SCREAMING_SNAKE_CASE   ) == "->".join(str(SCREAMING_SNAKE_CASE   ) for i in range(0    ,						12   )   )

							assert linked_list.delete_head() == 0
							assert linked_list.delete_nth(9   ) == 10
							assert linked_list.delete_tail() == 11
							assert len(SCREAMING_SNAKE_CASE   ) == 9
							assert str(SCREAMING_SNAKE_CASE   ) == "->".join(str(SCREAMING_SNAKE_CASE   ) for i in range(1    ,						10   )   )

							assert all(linked_list[i] == i + 1 for i in range(0    ,						9   )   ) is True

							for i in range(0    ,						9   ):
														__lowerCamelCase      :      List[Any]         =-i
							assert all(linked_list[i] == -i for i in range(0    ,						9   )   ) is True

							linked_list.reverse()
							assert str(SCREAMING_SNAKE_CASE   ) == "->".join(str(SCREAMING_SNAKE_CASE   ) for i in range(-8    ,						1   )   )



def 				lowerCAmelCase_			(      ):


							'''simple docstring'''







							__lowerCamelCase      :      Any         =[
							    -9,
							    100,
							    Node(77345112   ),
							    '''dlrow olleH''',
							    7,
							    5555,
							    0,
							    -192.55_555,
							    '''Hello, world!''',
							    77.9,
							    Node(10   ),
							    None,
							    None,
							    12.20,
							]
							__lowerCamelCase      :      str         =LinkedList()

							for i in test_input:
														linked_list.insert_tail(SCREAMING_SNAKE_CASE   )

							# Check if it's empty or not
							assert linked_list.is_empty() is False
							assert (
							    str(SCREAMING_SNAKE_CASE   ) == "-9->100->Node(77345112)->dlrow olleH->7->5555->0->"
							    "-192.55555->Hello, world!->77.9->Node(10)->None->None->12.2"
							)

							# Delete the head
							__lowerCamelCase      :      str         =linked_list.delete_head()
							assert result == -9
							assert (
							    str(SCREAMING_SNAKE_CASE   ) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
							    "Hello, world!->77.9->Node(10)->None->None->12.2"
							)

							# Delete the tail
							__lowerCamelCase      :      Union[str, Any]         =linked_list.delete_tail()
							assert result == 12.2
							assert (
							    str(SCREAMING_SNAKE_CASE   ) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
							    "Hello, world!->77.9->Node(10)->None->None"
							)

							# Delete a node in specific location in linked list
							__lowerCamelCase      :      Union[str, Any]         =linked_list.delete_nth(10   )
							assert result is None
							assert (
							    str(SCREAMING_SNAKE_CASE   ) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
							    "Hello, world!->77.9->Node(10)->None"
							)

							# Add a Node instance to its head
							linked_list.insert_head(Node('''Hello again, world!'''   )   )
							assert (
							    str(SCREAMING_SNAKE_CASE   )
							    == "Node(Hello again, world!)->100->Node(77345112)->dlrow olleH->"
							    "7->5555->0->-192.55555->Hello, world!->77.9->Node(10)->None"
							)

							# Add None to its tail
							linked_list.insert_tail(SCREAMING_SNAKE_CASE   )
							assert (
							    str(SCREAMING_SNAKE_CASE   )
							    == "Node(Hello again, world!)->100->Node(77345112)->dlrow olleH->"
							    "7->5555->0->-192.55555->Hello, world!->77.9->Node(10)->None->None"
							)

							# Reverse the linked list
							linked_list.reverse()
							assert (
							    str(SCREAMING_SNAKE_CASE   )
							    == "None->None->Node(10)->77.9->Hello, world!->-192.55555->0->5555->"
							    "7->dlrow olleH->Node(77345112)->100->Node(Hello again, world!)"
							)



def 				lowerCAmelCase_			(      ):


							'''simple docstring'''







							from doctest import testmod

							testmod()

							__lowerCamelCase      :      Optional[Any]         =LinkedList()
							linked_list.insert_head(input('''Inserting 1st at head '''   ).strip()   )
							linked_list.insert_head(input('''Inserting 2nd at head '''   ).strip()   )
							print('''\nPrint list:'''   )
							linked_list.print_list()
							linked_list.insert_tail(input('''\nInserting 1st at tail '''   ).strip()   )
							linked_list.insert_tail(input('''Inserting 2nd at tail '''   ).strip()   )
							print('''\nPrint list:'''   )
							linked_list.print_list()
							print('''\nDelete head'''   )
							linked_list.delete_head()
							print('''Delete tail'''   )
							linked_list.delete_tail()
							print('''\nPrint list:'''   )
							linked_list.print_list()
							print('''\nReverse linked list'''   )
							linked_list.reverse()
							print('''\nPrint list:'''   )
							linked_list.print_list()
							print('''\nString representation of linked list:'''   )
							print(SCREAMING_SNAKE_CASE   )
							print('''\nReading/changing Node data using indexing:'''   )
							print(F'Element at Position 1: {linked_list[1]}'   )
							__lowerCamelCase      :      Any         =input('''Enter New Value: '''   ).strip()
							print('''New list:'''   )
							print(SCREAMING_SNAKE_CASE   )
							print(F'length of linked_list is : {len(SCREAMING_SNAKE_CASE   )}'   )


if __name__ == "__main__":
							main()




