






'''simple docstring'''





lowerCamelCase_		  =						[
    ["""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      (		a_			)			->    Tuple:
							lowerCAmelCase_       		=  set()
							for row in edge_array:
														for item in row:
																					distinct_edge.add(item[0]			)
							return list(a_			)
def   lowerCamelCase      (		a_				,		a_			)			->    Tuple:
							lowerCAmelCase_       		=  ['0'] * len(a_			)
							for i, row in enumerate(a_			):
														for item in row:
																					if distinct_edge in item[0]:
																												lowerCAmelCase_       		=  '1'
																												break
							return "".join(a_			)
def   lowerCamelCase      (		a_			)			->    Tuple:
							lowerCAmelCase_       		=  get_distinct_edge(a_			)
							lowerCAmelCase_       		=  {}

							for item in distinct_edge:
														lowerCAmelCase_       		=  get_bitcode(a_				,		a_			)
														# print('bit',bit)
														# bt=''.join(bit)
														lowerCAmelCase_       		=  bit.count('1'			)
														lowerCAmelCase_       		=  [s, bit]
							# Store [Distinct edge, WT(Bitcode), Bitcode] in descending order
							lowerCAmelCase_       		=  [
							    [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   lowerCamelCase      (		a_			)			->    List[Any]:
							lowerCAmelCase_       		=  {}
							for _, item in enumerate(a_			):
														nodes.setdefault(item[2]				,		[]			).append(item[0]			)
							return nodes
def   lowerCamelCase      (		a_			)			->    Optional[int]:
							lowerCAmelCase_       		=  {}
							for key, value in nodes.items():
														lowerCAmelCase_       		=  value
							return cluster
def   lowerCamelCase      (		a_			)			->    List[Any]:
							return [i * 100 / len(a_			) for i in cluster]
def   lowerCamelCase      (		)			->    None:
							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   lowerCamelCase      (		a_				,		a_				,		a_				,		a_			)			->    List[Any]:
							for i in cluster[ca]:
														lowerCAmelCase_       		=  0
														lowerCAmelCase_       		=  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:
																																										lowerCAmelCase_       		=  [nodes[j]]
																																			count += 1
																					if count == 0:
																												lowerCAmelCase_       		=  ca + 1
																					else:
																												break
def   lowerCamelCase      (		a_				,		a_			)			->    Any:
							lowerCAmelCase_       		=  cluster[max(cluster.keys()			)]
							lowerCAmelCase_       		=  'Header'
							lowerCAmelCase_       		=  {}
							for i in x:
														if (["Header"],) in graph:
																					graph[(["Header"],)].append(x[i]			)
														else:
																					lowerCAmelCase_       		=  [x[i]]
							for i in x:
														lowerCAmelCase_       		=  [['Header']]
							lowerCAmelCase_       		=  1
							while i < max(a_			) - 1:
														create_edge(a_				,		a_				,		a_				,		a_			)
														lowerCAmelCase_       		=  i + 1
							return graph
def   lowerCamelCase      (		a_				,		a_				,		a_				,		a_=None			)			->    Union[str, Any]:
							lowerCAmelCase_       		=  (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   lowerCamelCase      (		a_				,		a_				,		a_			)			->    Optional[Any]:
							lowerCAmelCase_       		=  int(s / 100 * (len(a_			) - 1)			)
							for i in cluster[k]:
														my_dfs(a_				,		tuple(cluster[k][i]			)				,		(['Header'],)			)
def   lowerCamelCase      (		a_			)			->    Union[str, Any]:
							lowerCAmelCase_       		=  []
							for edges in paths:
														lowerCAmelCase_       		=  []
														for j in range(len(a_			) - 1			):
																					lowerCAmelCase_       		=  list(edges[j]			)
																					for e in temp:
																												lowerCAmelCase_       		=  (e[0], e[1])
																												el.append(a_			)
														freq_sub_el.append(a_			)
							return freq_sub_el
def   lowerCamelCase      (		a_			)			->    List[Any]:
							for i in range(len(a_			)			):
														for j in range(len(edge_array[i]			)			):
																					lowerCAmelCase_       		=  edge_array[i][j].split('-'			)
																					lowerCAmelCase_       		=  t


if __name__ == "__main__":
		preprocess(edge_array)
		lowerCamelCase_		  =						get_frequency_table(edge_array)
		lowerCamelCase_		  =						get_nodes(frequency_table)
		lowerCamelCase_		  =						get_cluster(nodes)
		lowerCamelCase_		  =						get_support(cluster)
		lowerCamelCase_		  =						construct_graph(cluster, nodes)
		find_freq_subgraph_given_support(6_0, cluster, graph)
		lowerCamelCase_		  =						[]
		lowerCamelCase_		  =						freq_subgraphs_edge_list(paths)
		print_all()











import shutil
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import SpeechaTextTokenizer, is_speech_available
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, save_json
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, require_torchaudio
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_speech_to_text import floats_list


if is_speech_available():
		from transformers import SpeechaTextFeatureExtractor, SpeechaTextProcessor


lowerCamelCase_		  =						get_tests_dir("""fixtures/test_sentencepiece.model""")




@require_torch
@require_torchaudio
@require_sentencepiece
class 							a_				(		unittest.TestCase	):


							'''simple docstring'''






							def   _lowercase (	self     )      ->      Optional[int]:
														'''simple docstring'''

														lowerCAmelCase_       		=  tempfile.mkdtemp()

														lowerCAmelCase_       		=  ['<s>', '<pad>', '</s>', '<unk>', '▁This', '▁is', '▁a', '▁t', 'est']
														lowerCAmelCase_       		=  dict(zip(lowercase_    ,    range(len(lowercase_     )     )     )     )
														lowerCAmelCase_       		=  Path(self.tmpdirname     )
														save_json(lowercase_    ,    save_dir / VOCAB_FILES_NAMES['vocab_file']     )
														if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
																					copyfile(lowercase_    ,    save_dir / VOCAB_FILES_NAMES['spm_file']     )

														lowerCAmelCase_       		=  SpeechaTextTokenizer.from_pretrained(self.tmpdirname     )
														tokenizer.save_pretrained(self.tmpdirname     )

														lowerCAmelCase_       		=  {
														    'feature_size': 2_4,
														    'num_mel_bins': 2_4,
														    'padding_value': 0.0,
														    'sampling_rate': 1_6_0_0_0,
														    'return_attention_mask': False,
														    'do_normalize': True,
														}
														save_json(lowercase_    ,    save_dir / FEATURE_EXTRACTOR_NAME     )

							def   _lowercase (	self    ,    **lowercase_     )      ->      List[Any]:
														'''simple docstring'''

														return SpeechaTextTokenizer.from_pretrained(self.tmpdirname    ,    **lowercase_     )

							def   _lowercase (	self    ,    **lowercase_     )      ->      Optional[Any]:
														'''simple docstring'''

														return SpeechaTextFeatureExtractor.from_pretrained(self.tmpdirname    ,    **lowercase_     )

							def   _lowercase (	self     )      ->      List[Any]:
														'''simple docstring'''

														shutil.rmtree(self.tmpdirname     )

							def   _lowercase (	self     )      ->      List[Any]:
														'''simple docstring'''

														lowerCAmelCase_       		=  self.get_tokenizer()
														lowerCAmelCase_       		=  self.get_feature_extractor()

														lowerCAmelCase_       		=  SpeechaTextProcessor(tokenizer=lowercase_    ,    feature_extractor=lowercase_     )

														processor.save_pretrained(self.tmpdirname     )
														lowerCAmelCase_       		=  SpeechaTextProcessor.from_pretrained(self.tmpdirname     )

														self.assertEqual(processor.tokenizer.get_vocab()    ,    tokenizer.get_vocab()     )
														self.assertIsInstance(processor.tokenizer    ,    lowercase_     )

														self.assertEqual(processor.feature_extractor.to_json_string()    ,    feature_extractor.to_json_string()     )
														self.assertIsInstance(processor.feature_extractor    ,    lowercase_     )

							def   _lowercase (	self     )      ->      Union[str, Any]:
														'''simple docstring'''

														lowerCAmelCase_       		=  SpeechaTextProcessor(
														    tokenizer=self.get_tokenizer()    ,    feature_extractor=self.get_feature_extractor()     )
														processor.save_pretrained(self.tmpdirname     )

														lowerCAmelCase_       		=  self.get_tokenizer(bos_token='(BOS)'    ,    eos_token='(EOS)'     )
														lowerCAmelCase_       		=  self.get_feature_extractor(do_normalize=lowercase_    ,    padding_value=1.0     )

														lowerCAmelCase_       		=  SpeechaTextProcessor.from_pretrained(
														    self.tmpdirname    ,    bos_token='(BOS)'    ,    eos_token='(EOS)'    ,    do_normalize=lowercase_    ,    padding_value=1.0     )

														self.assertEqual(processor.tokenizer.get_vocab()    ,    tokenizer_add_kwargs.get_vocab()     )
														self.assertIsInstance(processor.tokenizer    ,    lowercase_     )

														self.assertEqual(processor.feature_extractor.to_json_string()    ,    feature_extractor_add_kwargs.to_json_string()     )
														self.assertIsInstance(processor.feature_extractor    ,    lowercase_     )

							def   _lowercase (	self     )      ->      Dict:
														'''simple docstring'''

														lowerCAmelCase_       		=  self.get_feature_extractor()
														lowerCAmelCase_       		=  self.get_tokenizer()

														lowerCAmelCase_       		=  SpeechaTextProcessor(tokenizer=lowercase_    ,    feature_extractor=lowercase_     )

														lowerCAmelCase_       		=  floats_list((3, 1_0_0_0)     )

														lowerCAmelCase_       		=  feature_extractor(lowercase_    ,    return_tensors='np'     )
														lowerCAmelCase_       		=  processor(lowercase_    ,    return_tensors='np'     )

														for key in input_feat_extract.keys():
																					self.assertAlmostEqual(input_feat_extract[key].sum()    ,    input_processor[key].sum()    ,    delta=1e-2     )

							def   _lowercase (	self     )      ->      Union[str, Any]:
														'''simple docstring'''

														lowerCAmelCase_       		=  self.get_feature_extractor()
														lowerCAmelCase_       		=  self.get_tokenizer()

														lowerCAmelCase_       		=  SpeechaTextProcessor(tokenizer=lowercase_    ,    feature_extractor=lowercase_     )

														lowerCAmelCase_       		=  'This is a test string'

														lowerCAmelCase_       		=  processor(text=lowercase_     )

														lowerCAmelCase_       		=  tokenizer(lowercase_     )

														for key in encoded_tok.keys():
																					self.assertListEqual(encoded_tok[key]    ,    encoded_processor[key]     )

							def   _lowercase (	self     )      ->      Any:
														'''simple docstring'''

														lowerCAmelCase_       		=  self.get_feature_extractor()
														lowerCAmelCase_       		=  self.get_tokenizer()

														lowerCAmelCase_       		=  SpeechaTextProcessor(tokenizer=lowercase_    ,    feature_extractor=lowercase_     )

														lowerCAmelCase_       		=  [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

														lowerCAmelCase_       		=  processor.batch_decode(lowercase_     )
														lowerCAmelCase_       		=  tokenizer.batch_decode(lowercase_     )

														self.assertListEqual(lowercase_    ,    lowercase_     )






							def   _lowercase (	self     )      ->      Optional[Any]:
														'''simple docstring'''

														lowerCAmelCase_       		=  self.get_feature_extractor()
														lowerCAmelCase_       		=  self.get_tokenizer()

														lowerCAmelCase_       		=  SpeechaTextProcessor(tokenizer=lowercase_    ,    feature_extractor=lowercase_     )

														self.assertListEqual(
														    processor.model_input_names    ,    feature_extractor.model_input_names    ,    msg='`processor` and `feature_extractor` model input names do not match'    ,    )



