code: 
  
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  
  
  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      :				Dict											=					logging.get_logger(__name__)
  
  
  
  
  
  class 					_UpperCAmelCase							(		UpperCAmelCase__     ):
    a     :     Tuple     						=['input_values', 'attention_mask']
    def __init__(						self,__SCREAMING_SNAKE_CASE=1,__SCREAMING_SNAKE_CASE=1_60_00,__SCREAMING_SNAKE_CASE=1_28,__SCREAMING_SNAKE_CASE=10_24,__SCREAMING_SNAKE_CASE=0.0,__SCREAMING_SNAKE_CASE=True,__SCREAMING_SNAKE_CASE=-4.267_7393,__SCREAMING_SNAKE_CASE=4.568_9974,__SCREAMING_SNAKE_CASE=False,**__SCREAMING_SNAKE_CASE,):
  
  
  
       '''simple docstring'''
  
       super().__init__(feature_size=lowerCamelCase__,sampling_rate=lowerCamelCase__,padding_value=lowerCamelCase__,**lowerCamelCase__    )
       __lowerCAmelCase      		=       num_mel_bins
       __lowerCAmelCase      		=       max_length
       __lowerCAmelCase      		=       do_normalize
       __lowerCAmelCase      		=       mean
       __lowerCAmelCase      		=       std
       __lowerCAmelCase      		=       return_attention_mask
    def 						lowerCamelCase__    (						self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,):
  
  
  
       '''simple docstring'''
  
       __lowerCAmelCase      		=       torch.from_numpy(lowerCamelCase__    ).unsqueeze(0    )
       __lowerCAmelCase      		=       ta_kaldi.fbank(
           lowerCamelCase__,htk_compat=lowerCamelCase__,sample_frequency=self.sampling_rate,use_energy=lowerCamelCase__,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(lowerCamelCase__    )
       elif difference < 0:
          __lowerCAmelCase      		=       fbank[0:max_length, :]
  
       __lowerCAmelCase      		=       fbank.numpy()
  
       return fbank
    def 						lowerCamelCase__    (						self,__SCREAMING_SNAKE_CASE    ):
  
  
  
       '''simple docstring'''
  
       return (input_values - (self.mean)) / (self.std * 2)
  
  
  
  
  
  
  
    def __call__(						self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE = None,__SCREAMING_SNAKE_CASE = None,**__SCREAMING_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(lowerCamelCase__,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(lowerCamelCase__,(list, tuple)    ) and (isinstance(raw_speech[0],(np.ndarray, tuple, list)    ))
       )
  
       if is_batched:
          __lowerCAmelCase      		=       [np.asarray(lowerCamelCase__,dtype=np.floataa    ) for speech in raw_speech]
       elif not is_batched and not isinstance(lowerCamelCase__,np.ndarray    ):
          __lowerCAmelCase      		=       np.asarray(lowerCamelCase__,dtype=np.floataa    )
       elif isinstance(lowerCamelCase__,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(lowerCamelCase__,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],lowerCamelCase__    ):
          __lowerCAmelCase      		=       [np.asarray(lowerCamelCase__,dtype=np.floataa    ) for feature in input_values]
  
       # normalization
       if self.do_normalize:
          __lowerCAmelCase      		=       [self.normalize(lowerCamelCase__    ) for feature in input_values]
  
       if return_tensors is not None:
          __lowerCAmelCase      		=       padded_inputs.convert_to_tensors(lowerCamelCase__    )
  
       return padded_inputs
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  
  
  import math
  import sys
  
  
  
  
  
  class 					_UpperCAmelCase							:
  		def __init__(						self    ):
  
  
  
  					'''simple docstring'''
  
  					__lowerCAmelCase      		=       0
  					__lowerCAmelCase      		=       []
  					__lowerCAmelCase      		=       {}  # To store the pos of node in array
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					return self.cur_size == 0
  		def 						lowerCamelCase__    (						self,__SCREAMING_SNAKE_CASE    ):
  
  
  
  					'''simple docstring'''
  
  					__lowerCAmelCase      		=       self.left(__SCREAMING_SNAKE_CASE    )
  					__lowerCAmelCase      		=       self.right(__SCREAMING_SNAKE_CASE    )
  					if lc < self.cur_size and self.array(__SCREAMING_SNAKE_CASE    )[0] < self.array(__SCREAMING_SNAKE_CASE    )[0]:
  								__lowerCAmelCase      		=       lc
  					else:
  								__lowerCAmelCase      		=       idx
  					if rc < self.cur_size and self.array(__SCREAMING_SNAKE_CASE    )[0] < self.array(__SCREAMING_SNAKE_CASE    )[0]:
  								__lowerCAmelCase      		=       rc
  					if smallest != idx:
  								self.swap(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE    )
  								self.min_heapify(__SCREAMING_SNAKE_CASE    )
  		def 						lowerCamelCase__    (						self,__SCREAMING_SNAKE_CASE    ):
  
  
  
  					'''simple docstring'''
  
  					__lowerCAmelCase      		=       self.cur_size
  					self.cur_size += 1
  					self.array.append((sys.maxsize, tup[1])    )
  					self.decrease_key((sys.maxsize, tup[1]),tup[0]    )
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					__lowerCAmelCase      		=       self.array[0][1]
  					__lowerCAmelCase      		=       self.array[self.cur_size - 1]
  					self.cur_size -= 1
  					self.min_heapify(1    )
  					del self.pos[min_node]
  					return min_node
  		def 						lowerCamelCase__    (						self,__SCREAMING_SNAKE_CASE    ):
  
  
  
  					'''simple docstring'''
  
  					return 2 * i + 1
  		def 						lowerCamelCase__    (						self,__SCREAMING_SNAKE_CASE    ):
  
  
  
  					'''simple docstring'''
  
  					return 2 * i + 2
  		def 						lowerCamelCase__    (						self,__SCREAMING_SNAKE_CASE    ):
  
  
  
  					'''simple docstring'''
  
  					return math.floor(i / 2    )
  		def 						lowerCamelCase__    (						self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE    ):
  
  
  
  					'''simple docstring'''
  
  					__lowerCAmelCase      		=       j
  					__lowerCAmelCase      		=       i
  					__lowerCAmelCase      		=       self.array[i]
  					__lowerCAmelCase      		=       self.array[j]
  					__lowerCAmelCase      		=       temp
  
  
  
  
  
  
  
  		def 						lowerCamelCase__    (						self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE    ):
  
  
  
  					'''simple docstring'''
  
  					__lowerCAmelCase      		=       self.pos[tup[1]]
  					# assuming the new_d is atmost old_d
  					__lowerCAmelCase      		=       (new_d, tup[1])
  					while idx > 0 and self.array[self.par(__SCREAMING_SNAKE_CASE    )][0] > self.array[idx][0]:
  								self.swap(__SCREAMING_SNAKE_CASE,self.par(__SCREAMING_SNAKE_CASE    )    )
  								__lowerCAmelCase      		=       self.par(__SCREAMING_SNAKE_CASE    )
  
  
  
  
  
  class 					_UpperCAmelCase							:
  		def __init__(						self,__SCREAMING_SNAKE_CASE    ):
  
  
  
  					'''simple docstring'''
  
  					__lowerCAmelCase      		=       {}  # To store graph: u -> (v,w)
  					__lowerCAmelCase      		=       num  # Number of nodes in graph
  					# To store the distance from source vertex
  					__lowerCAmelCase      		=       [0] * self.num_nodes
  					__lowerCAmelCase      		=       [-1] * self.num_nodes  # To store the path
  		def 						lowerCamelCase__    (						self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE    ):
  
  
  
  					'''simple docstring'''
  
  					if u in self.adjList:
  								self.adjList[u].append((v, w)    )
  					else:
  								__lowerCAmelCase      		=       [(v, w)]
  
  					# Assuming undirected graph
  					if v in self.adjList:
  								self.adjList[v].append((u, w)    )
  					else:
  								__lowerCAmelCase      		=       [(u, w)]
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					for u in self.adjList:
  								print(__SCREAMING_SNAKE_CASE,"""->""",""" -> """.join(str(f'{v}({w})'    ) for v, w in self.adjList[u]    )    )
  		def 						lowerCamelCase__    (						self,__SCREAMING_SNAKE_CASE    ):
  
  
  
  					'''simple docstring'''
  
  					__lowerCAmelCase      		=       [-1] * self.num_nodes
  					# src is the source node
  					__lowerCAmelCase      		=       0
  					__lowerCAmelCase      		=       PriorityQueue()
  					q.insert((0, src)    )  # (dist from src, node)
  					for u in self.adjList:
  								if u != src:
  											__lowerCAmelCase      		=       sys.maxsize  # Infinity
  											__lowerCAmelCase      		=       -1
  
  					while not q.is_empty():
  								__lowerCAmelCase      		=       q.extract_min()  # Returns node with the min dist from source
  								# Update the distance of all the neighbours of u and
  								# if their prev dist was INFINITY then push them in Q
  								for v, w in self.adjList[u]:
  											__lowerCAmelCase      		=       self.dist[u] + w
  											if self.dist[v] > new_dist:
  														if self.dist[v] == sys.maxsize:
  																	q.insert((new_dist, v)    )
  														else:
  																	q.decrease_key((self.dist[v], v),__SCREAMING_SNAKE_CASE    )
  														__lowerCAmelCase      		=       new_dist
  														__lowerCAmelCase      		=       u
  
          # Show the shortest distances from src
  					self.show_distances(__SCREAMING_SNAKE_CASE    )
  		def 						lowerCamelCase__    (						self,__SCREAMING_SNAKE_CASE    ):
  
  
  
  					'''simple docstring'''
  
  					print(f'Distance from node: {src}'    )
  					for u in range(self.num_nodes    ):
  								print(f'Node {u} has distance: {self.dist[u]}'    )
  
  
  
  
  
  
  
  		def 						lowerCamelCase__    (						self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE    ):
  
  
  
  					'''simple docstring'''
  
  					__lowerCAmelCase      		=       []
  					__lowerCAmelCase      		=       0
  					__lowerCAmelCase      		=       dest
  					# Backtracking from dest to src
  					while self.par[temp] != -1:
  								path.append(__SCREAMING_SNAKE_CASE    )
  								if temp != src:
  											for v, w in self.adjList[temp]:
  														if v == self.par[temp]:
  																	cost += w
  																	break
  								__lowerCAmelCase      		=       self.par[temp]
  					path.append(__SCREAMING_SNAKE_CASE    )
  					path.reverse()
  
  					print(f'----Path to reach {dest} from {src}----'    )
  					for u in path:
  								print(f'{u}',end=""" """    )
  								if u != dest:
  											print("""-> """,end=""""""    )
  
  					print("""\nTotal cost of path: """,__SCREAMING_SNAKE_CASE    )
  
  
  if __name__ == "__main__":
  	_a      :				str											=					Graph(9)
  	graph.add_edge(0, 1, 4)
  	graph.add_edge(0, 7, 8)
  	graph.add_edge(1, 2, 8)
  	graph.add_edge(1, 7, 1_1)
  	graph.add_edge(2, 3, 7)
  	graph.add_edge(2, 8, 2)
  	graph.add_edge(2, 5, 4)
  	graph.add_edge(3, 4, 9)
  	graph.add_edge(3, 5, 1_4)
  	graph.add_edge(4, 5, 1_0)
  	graph.add_edge(5, 6, 2)
  	graph.add_edge(6, 7, 1)
  	graph.add_edge(6, 8, 6)
  	graph.add_edge(7, 8, 7)
  	graph.show_graph()
  	graph.dijkstra(0)
  	graph.show_path(0, 4)
  
  # OUTPUT
  # 0 -> 1(4) -> 7(8)
  # 1 -> 0(4) -> 2(8) -> 7(11)
  # 7 -> 0(8) -> 1(11) -> 6(1) -> 8(7)
  # 2 -> 1(8) -> 3(7) -> 8(2) -> 5(4)
  # 3 -> 2(7) -> 4(9) -> 5(14)
  # 8 -> 2(2) -> 6(6) -> 7(7)
  # 5 -> 2(4) -> 3(14) -> 4(10) -> 6(2)
  # 4 -> 3(9) -> 5(10)
  # 6 -> 5(2) -> 7(1) -> 8(6)
  # Distance from node: 0
  # Node 0 has distance: 0
  # Node 1 has distance: 4
  # Node 2 has distance: 12
  # Node 3 has distance: 19
  # Node 4 has distance: 21
  # Node 5 has distance: 11
  # Node 6 has distance: 9
  # Node 7 has distance: 8
  # Node 8 has distance: 14
  # ----Path to reach 4 from 0----
  # 0 -> 7 -> 6 -> 5 -> 4
  # Total cost of path:  21
  
  