


'''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__  : Optional[int]				=	logging.get_logger(__name__)
class  lowercase_					(   a__       ):
    __UpperCAmelCase             =     ['input_values', 'attention_mask']


    def __init__(					self  , a=1  , a=1_60_00  , a=1_28  , a=10_24  , a=0.0  , a=True  , a=-4.267_7393  , a=4.568_9974  , a=False  , **a  , ):
          super().__init__(feature_size=a  , sampling_rate=a  , padding_value=a  , **a   )
          UpperCamelCase__  =    num_mel_bins
          UpperCamelCase__  =    max_length
          UpperCamelCase__  =    do_normalize
          UpperCamelCase__  =    mean
          UpperCamelCase__  =    std
          UpperCamelCase__  =    return_attention_mask


    def        __a							(					self  , a  , a  , ):
          UpperCamelCase__  =    torch.from_numpy(a   ).unsqueeze(0   )
          UpperCamelCase__  =    ta_kaldi.fbank(
              a  , htk_compat=a  , sample_frequency=self.sampling_rate  , use_energy=a  , window_type="hanning"  , num_mel_bins=self.num_mel_bins  , dither=0.0  , frame_shift=10  , )

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

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

          UpperCamelCase__  =    fbank.numpy()

          return fbank


    def        __a							(					self  , a   ):
          return (input_values - (self.mean)) / (self.std * 2)


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

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

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

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

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

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

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

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

          if return_tensors is not None:
                UpperCamelCase__  =    padded_inputs.convert_to_tensors(a   )

          return padded_inputs









'''simple docstring'''







a__  : str				=	[
    ['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 _UpperCamelCase	(					__A   )							->		Tuple:


      '''simple docstring'''



      UpperCamelCase__  =    set()
      for row in edge_array:
            for item in row:
                  distinct_edge.add(item[0]   )
      return list(__A   )







def _UpperCamelCase	(					__A				,      __A   )							->		int:


      '''simple docstring'''



      UpperCamelCase__  =    ["0"] * len(__A   )
      for i, row in enumerate(__A   ):
            for item in row:
                  if distinct_edge in item[0]:
                        UpperCamelCase__  =    "1"
                        break
      return "".join(__A   )







def _UpperCamelCase	(					__A   )							->		Union[str, Any]:


      '''simple docstring'''



      UpperCamelCase__  =    get_distinct_edge(__A   )
      UpperCamelCase__  =    {}

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







def _UpperCamelCase	(					__A   )							->		Union[str, Any]:


      '''simple docstring'''



      UpperCamelCase__  =    {}
      for _, item in enumerate(__A   ):
            nodes.setdefault(item[2]				,      []   ).append(item[0]   )
      return nodes







def _UpperCamelCase	(					__A   )							->		Tuple:


      '''simple docstring'''



      UpperCamelCase__  =    {}
      for key, value in nodes.items():
            UpperCamelCase__  =    value
      return cluster







def _UpperCamelCase	(					__A   )							->		str:


      '''simple docstring'''



      return [i * 100 / len(__A   ) for i in cluster]







def _UpperCamelCase	(					)							->		None:


      '''simple docstring'''



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







def _UpperCamelCase	(					__A				,      __A				,      __A				,      __A   )							->		Optional[int]:


      '''simple docstring'''



      for i in cluster[ca]:
            UpperCamelCase__  =    0
            UpperCamelCase__  =    ca + 1
            while ca < max(cluster.keys()   ):
                  for j in cluster[ca]:
                        if int(__A				,      2   ) & int(__A				,      2   ) == int(__A				,      2   ):
                              if tuple(nodes[i]   ) in graph:
                                    graph[tuple(nodes[i]   )].append(nodes[j]   )
                              else:
                                    UpperCamelCase__  =    [nodes[j]]
                              count += 1
                  if count == 0:
                        UpperCamelCase__  =    ca + 1
                  else:
                        break







def _UpperCamelCase	(					__A				,      __A   )							->		List[str]:


      '''simple docstring'''



      UpperCamelCase__  =    cluster[max(cluster.keys()   )]
      UpperCamelCase__  =    "Header"
      UpperCamelCase__  =    {}
      for i in x:
            if (["Header"],) in graph:
                  graph[(["Header"],)].append(x[i]   )
            else:
                  UpperCamelCase__  =    [x[i]]
      for i in x:
            UpperCamelCase__  =    [["Header"]]
      UpperCamelCase__  =    1
      while i < max(__A   ) - 1:
            create_edge(__A				,      __A				,      __A				,      __A   )
            UpperCamelCase__  =    i + 1
      return graph







def _UpperCamelCase	(					__A				,      __A				,      __A				,      __A=None   )							->		str:


      '''simple docstring'''



      UpperCamelCase__  =    (path or []) + [start]
      if start == end:
            paths.append(__A   )
      for node in graph[start]:
            if tuple(__A   ) not in path:
                  my_dfs(__A				,      tuple(__A   )				,      __A				,      __A   )







def _UpperCamelCase	(					__A				,      __A				,      __A   )							->		Union[str, Any]:


      '''simple docstring'''



      UpperCamelCase__  =    int(s / 100 * (len(__A   ) - 1)   )
      for i in cluster[k]:
            my_dfs(__A				,      tuple(cluster[k][i]   )				,      (["Header"],)   )







def _UpperCamelCase	(					__A   )							->		Optional[Any]:


      '''simple docstring'''



      UpperCamelCase__  =    []
      for edges in paths:
            UpperCamelCase__  =    []
            for j in range(len(__A   ) - 1   ):
                  UpperCamelCase__  =    list(edges[j]   )
                  for e in temp:
                        UpperCamelCase__  =    (e[0], e[1])
                        el.append(__A   )
            freq_sub_el.append(__A   )
      return freq_sub_el







def _UpperCamelCase	(					__A   )							->		Optional[int]:


      '''simple docstring'''



      for i in range(len(__A   )   ):
            for j in range(len(edge_array[i]   )   ):
                  UpperCamelCase__  =    edge_array[i][j].split("-"   )
                  UpperCamelCase__  =    t


if __name__ == "__main__":
  preprocess(edge_array)
  a__  : List[Any]				=	get_frequency_table(edge_array)
  a__  : Optional[int]				=	get_nodes(frequency_table)
  a__  : Any				=	get_cluster(nodes)
  a__  : Tuple				=	get_support(cluster)
  a__  : str				=	construct_graph(cluster, nodes)
  find_freq_subgraph_given_support(6_0, cluster, graph)
  a__  : list				=	[]
  a__  : str				=	freq_subgraphs_edge_list(paths)
  print_all()





