
snake_case__  							=							[
    ["""ab-e1""", """ac-e3""", """ad-e5""", """bc-e4""", """bd-e2""", """be-e6""", """bh-e12""", """cd-e2""", """ce-e4""",
     """de-e1""", """df-e8""", """dg-e5""", """dh-e10""", """ef-e3""", """eg-e2""", """fg-e6""", """gh-e6""", """hi-e3"""],
    ["""ab-e1""", """ac-e3""", """ad-e5""", """bc-e4""", """bd-e2""", """be-e6""", """cd-e2""", """de-e1""", """df-e8""",
     """ef-e3""", """eg-e2""", """fg-e6"""],
    ["""ab-e1""", """ac-e3""", """bc-e4""", """bd-e2""", """de-e1""", """df-e8""", """dg-e5""", """ef-e3""", """eg-e2""",
     """eh-e12""", """fg-e6""", """fh-e10""", """gh-e6"""],
    ["""ab-e1""", """ac-e3""", """bc-e4""", """bd-e2""", """bh-e12""", """cd-e2""", """df-e8""", """dh-e10"""],
    ["""ab-e1""", """ac-e3""", """ad-e5""", """bc-e4""", """bd-e2""", """cd-e2""", """ce-e4""", """de-e1""", """df-e8""",
     """dg-e5""", """ef-e3""", """eg-e2""", """fg-e6"""]
]

def        lowerCamelCase_      (			UpperCAmelCase_							:				Optional[Any]       ):
						lowercase       :    Optional[int]											=  set()
						for row in edge_array:
												for item in row:
																		distinct_edge.add(item[0]       )
						return list(UpperCAmelCase_       )

def        lowerCamelCase_      (			UpperCAmelCase_							:				Any							,			UpperCAmelCase_							:				Dict       ):
						lowercase       :    int											=  ['''0'''] * len(UpperCAmelCase_       )
						for i, row in enumerate(UpperCAmelCase_       ):
												for item in row:
																		if distinct_edge in item[0]:
																								lowercase       :    Tuple											=  '''1'''
																								break
						return "".join(UpperCAmelCase_       )

def        lowerCamelCase_      (			UpperCAmelCase_							:				List[str]       ):
						lowercase       :    List[str]											=  get_distinct_edge(UpperCAmelCase_       )
						lowercase       :    List[Any]											=  {}

						for item in distinct_edge:
												lowercase       :    Tuple											=  get_bitcode(UpperCAmelCase_							,			UpperCAmelCase_       )
												# print('bit',bit)
												# bt=''.join(bit)
												lowercase       :    int											=  bit.count('''1'''       )
												lowercase       :    List[str]											=  [s, bit]
						# Store [Distinct edge, WT(Bitcode), Bitcode] in descending order
						lowercase       :    Tuple											=  [
						    [k, v[0], v[1]]
						    for k, v in sorted(frequency_table.items()							,			key=lambda UpperCAmelCase_       : v[1][0]							,			reverse=UpperCAmelCase_       )
						]
						return sorted_frequency_table

def        lowerCamelCase_      (			UpperCAmelCase_							:				List[str]       ):
						lowercase       :    Tuple											=  {}
						for _, item in enumerate(UpperCAmelCase_       ):
												nodes.setdefault(item[2]							,			[]       ).append(item[0]       )
						return nodes

def        lowerCamelCase_      (			UpperCAmelCase_							:				Union[str, Any]       ):
						lowercase       :    Any											=  {}
						for key, value in nodes.items():
												lowercase       :    Dict											=  value
						return cluster

def        lowerCamelCase_      (			UpperCAmelCase_							:				Any       ):
						return [i * 1_00 / len(UpperCAmelCase_       ) for i in cluster]

def        lowerCamelCase_      (			):
						print('''\nNodes\n'''       )
						for key, value in nodes.items():
												print(UpperCAmelCase_							,			UpperCAmelCase_       )
						print('''\nSupport\n'''       )
						print(UpperCAmelCase_       )
						print('''\n Cluster \n'''       )
						for key, value in sorted(cluster.items()							,			reverse=UpperCAmelCase_       ):
												print(UpperCAmelCase_							,			UpperCAmelCase_       )
						print('''\n Graph\n'''       )
						for key, value in graph.items():
												print(UpperCAmelCase_							,			UpperCAmelCase_       )
						print('''\n Edge List of Frequent subgraphs \n'''       )
						for edge_list in freq_subgraph_edge_list:
												print(UpperCAmelCase_       )

def        lowerCamelCase_      (			UpperCAmelCase_							:				str							,			UpperCAmelCase_							:				int							,			UpperCAmelCase_							:				Optional[int]							,			UpperCAmelCase_							:				Optional[Any]       ):
						for i in cluster[ca]:
												lowercase       :    Optional[int]											=  0
												lowercase       :    Union[str, Any]											=  ca + 1
												while ca < max(cluster.keys()       ):
																		for j in cluster[ca]:
																								if int(UpperCAmelCase_							,			2       ) & int(UpperCAmelCase_							,			2       ) == int(UpperCAmelCase_							,			2       ):
																														if tuple(nodes[i]       ) in graph:
																																				graph[tuple(nodes[i]       )].append(nodes[j]       )
																														else:
																																				lowercase       :    Optional[int]											=  [nodes[j]]
																														count += 1
																		if count == 0:
																								lowercase       :    List[Any]											=  ca + 1
																		else:
																								break

def        lowerCamelCase_      (			UpperCAmelCase_							:				Any							,			UpperCAmelCase_							:				Optional[Any]       ):
						lowercase       :    List[str]											=  cluster[max(cluster.keys()       )]
						lowercase       :    int											=  '''Header'''
						lowercase       :    str											=  {}
						for i in x:
												if (["Header"],) in graph:
																		graph[(["Header"],)].append(x[i]       )
												else:
																		lowercase       :    Union[str, Any]											=  [x[i]]
						for i in x:
												lowercase       :    Union[str, Any]											=  [['''Header''']]
						lowercase       :    Optional[int]											=  1
						while i < max(UpperCAmelCase_       ) - 1:
												create_edge(UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_       )
												lowercase       :    Dict											=  i + 1
						return graph

def        lowerCamelCase_      (			UpperCAmelCase_							:				List[str]							,			UpperCAmelCase_							:				Optional[int]							,			UpperCAmelCase_							:				int							,			UpperCAmelCase_							:				int=None       ):
						lowercase       :    int											=  (path or []) + [start]
						if start == end:
												paths.append(UpperCAmelCase_       )
						for node in graph[start]:
												if tuple(UpperCAmelCase_       ) not in path:
																		my_dfs(UpperCAmelCase_							,			tuple(UpperCAmelCase_       )							,			UpperCAmelCase_							,			UpperCAmelCase_       )

def        lowerCamelCase_      (			UpperCAmelCase_							:				int							,			UpperCAmelCase_							:				Dict							,			UpperCAmelCase_							:				Union[str, Any]       ):
						lowercase       :    Dict											=  int(s / 1_00 * (len(UpperCAmelCase_       ) - 1)       )
						for i in cluster[k]:
												my_dfs(UpperCAmelCase_							,			tuple(cluster[k][i]       )							,			(['''Header'''],)       )

def        lowerCamelCase_      (			UpperCAmelCase_							:				str       ):
						lowercase       :    List[Any]											=  []
						for edges in paths:
												lowercase       :    Tuple											=  []
												for j in range(len(UpperCAmelCase_       ) - 1       ):
																		lowercase       :    Any											=  list(edges[j]       )
																		for e in temp:
																								lowercase       :    str											=  (e[0], e[1])
																								el.append(UpperCAmelCase_       )
												freq_sub_el.append(UpperCAmelCase_       )
						return freq_sub_el

def        lowerCamelCase_      (			UpperCAmelCase_							:				Optional[Any]       ):
						for i in range(len(UpperCAmelCase_       )       ):
												for j in range(len(edge_array[i]       )       ):
																		lowercase       :    Dict											=  edge_array[i][j].split('''-'''       )
																		lowercase       :    str											=  t


if __name__ == "__main__":
				preprocess(edge_array)
				snake_case__  							=							get_frequency_table(edge_array)
				snake_case__  							=							get_nodes(frequency_table)
				snake_case__  							=							get_cluster(nodes)
				snake_case__  							=							get_support(cluster)
				snake_case__  							=							construct_graph(cluster, nodes)
				find_freq_subgraph_given_support(60, cluster, graph)
				snake_case__  							=							[]
				snake_case__  							=							freq_subgraphs_edge_list(paths)
				print_all()




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


snake_case__  							=							logging.get_logger(__name__)







class 							UpperCAmelCase	(   __lowerCamelCase					):
					a__: str			  =				["""input_values""", """attention_mask"""]

					def __init__(			self      :					Union[str, Any] ,      lowerCAmelCase      :					Tuple=1 ,      lowerCAmelCase      :					Dict=1_6000 ,      lowerCAmelCase      :					Optional[Any]=128 ,      lowerCAmelCase      :					Dict=1024 ,      lowerCAmelCase      :					List[Any]=0.0 ,      lowerCAmelCase      :					Any=True ,      lowerCAmelCase      :					Tuple=-4.2_67_73_93 ,      lowerCAmelCase      :					Dict=4.5_68_99_74 ,      lowerCAmelCase      :					int=False ,      **lowerCAmelCase      :					Optional[Any] ,      ):
											super().__init__(feature_size=lowerCAmelCase ,      sampling_rate=lowerCAmelCase ,      padding_value=lowerCAmelCase ,      **lowerCAmelCase					)
											lowercase       :    Optional[int]											=  num_mel_bins
											lowercase       :    List[Any]											=  max_length
											lowercase       :    Any											=  do_normalize
											lowercase       :    Any											=  mean
											lowercase       :    Union[str, Any]											=  std
											lowercase       :    int											=  return_attention_mask

					def        _lowerCAmelCase	(			self      :					int ,      lowerCAmelCase      :					np.ndarray ,      lowerCAmelCase      :					int ,      ):
											lowercase       :    List[Any]											=  torch.from_numpy(lowerCAmelCase					).unsqueeze(0					)
											lowercase       :    List[str]											=  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 ,      )

											lowercase       :    Tuple											=  fbank.shape[0]
											lowercase       :    Optional[Any]											=  max_length - n_frames

											# pad or truncate, depending on difference
											if difference > 0:
																	lowercase       :    int											=  torch.nn.ZeroPadad((0, 0, 0, difference)					)
																	lowercase       :    str											=  pad_module(lowerCAmelCase					)
											elif difference < 0:
																	lowercase       :    Optional[int]											=  fbank[0:max_length, :]

											lowercase       :    List[str]											=  fbank.numpy()

											return fbank

					def        _lowerCAmelCase	(			self      :					Tuple ,      lowerCAmelCase      :					np.ndarray					):
											return (input_values - (self.mean)) / (self.std * 2)




					def __call__(			self      :					Any ,      lowerCAmelCase      :					Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] ,      lowerCAmelCase      :					Optional[int] = None ,      lowerCAmelCase      :					Optional[Union[str, TensorType]] = None ,      **lowerCAmelCase      :					Tuple ,      ):

											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.'''					)

											lowercase       :    Optional[Any]											=  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}'''					)
											lowercase       :    Tuple											=  is_batched_numpy or (
											    isinstance(lowerCAmelCase ,      (list, tuple)					) and (isinstance(raw_speech[0] ,      (np.ndarray, tuple, list)					))
											)

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

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

											# extract fbank features and pad/truncate to max_length
											lowercase       :    Dict											=  [self._extract_fbank_features(lowerCAmelCase ,      max_length=self.max_length					) for waveform in raw_speech]

											# convert into BatchFeature
											lowercase       :    Any											=  BatchFeature({'''input_values''': features}					)

											# make sure list is in array format
											lowercase       :    str											=  padded_inputs.get('''input_values'''					)
											if isinstance(input_values[0] ,      lowerCAmelCase					):
																	lowercase       :    Any											=  [np.asarray(lowerCAmelCase ,      dtype=np.floataa					) for feature in input_values]

											# normalization
											if self.do_normalize:
																	lowercase       :    List[str]											=  [self.normalize(lowerCAmelCase					) for feature in input_values]

											if return_tensors is not None:
																	lowercase       :    str											=  padded_inputs.convert_to_tensors(lowerCAmelCase					)

											return padded_inputs


