






import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict

import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info

from transformers import (
    AdamW,
    AutoConfig,
    AutoModel,
    AutoModelForPreTraining,
    AutoModelForQuestionAnswering,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoModelForTokenClassification,
    AutoModelWithLMHead,
    AutoTokenizer,
    PretrainedConfig,
    PreTrainedTokenizer,
)
from transformers.optimization import (
    Adafactor,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.utils.versions import require_version


__magic_name__:  Any  	=			logging.getLogger(__name__)

require_version("pytorch_lightning>=1.0.4")

__magic_name__:  Optional[Any]  	=			{
    "base": AutoModel,
    "sequence-classification": AutoModelForSequenceClassification,
    "question-answering": AutoModelForQuestionAnswering,
    "pretraining": AutoModelForPreTraining,
    "token-classification": AutoModelForTokenClassification,
    "language-modeling": AutoModelWithLMHead,
    "summarization": AutoModelForSeqaSeqLM,
    "translation": AutoModelForSeqaSeqLM,
}


# update this and the import above to support new schedulers from transformers.optimization
__magic_name__:  Tuple  	=			{
    "linear": get_linear_schedule_with_warmup,
    "cosine": get_cosine_schedule_with_warmup,
    "cosine_w_restarts": get_cosine_with_hard_restarts_schedule_with_warmup,
    "polynomial": get_polynomial_decay_schedule_with_warmup,
    # '': get_constant_schedule,             # not supported for now
    # '': get_constant_schedule_with_warmup, # not supported for now
}
__magic_name__:  str  	=			sorted(arg_to_scheduler.keys())
__magic_name__:  List[str]  	=			"{" + ", ".join(arg_to_scheduler_choices) + "}"





class  snake_case__      (							pl.LightningModule   ):
	def __init__(       self    ,     lowerCAmelCase__    ,     lowerCAmelCase__=None    ,     lowerCAmelCase__="base"    ,     lowerCAmelCase__=None    ,     lowerCAmelCase__=None    ,     lowerCAmelCase__=None    ,     **lowerCAmelCase__    ,     )							->   Union[str, Any]:
							super().__init__()
							# TODO: move to self.save_hyperparameters()
							# self.save_hyperparameters()
							# can also expand arguments into trainer signature for easier reading

							self.save_hyperparameters(lowerCAmelCase__				)
							__magic_name__    :     List[Any]     		=      0
							__magic_name__    :     Any     		=      Path(self.hparams.output_dir				)
							__magic_name__    :     List[Any]     		=      self.hparams.cache_dir if self.hparams.cache_dir else None
							if config is None:
													__magic_name__    :     Tuple     		=      AutoConfig.from_pretrained(
													    self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path    ,     **({"""num_labels""": num_labels} if num_labels is not None else {})    ,     cache_dir=lowerCAmelCase__    ,     **lowerCAmelCase__    ,     )
							else:
													__magic_name__    :     PretrainedConfig     		=      config

							__magic_name__    :     List[str]     		=      ("""encoder_layerdrop""", """decoder_layerdrop""", """dropout""", """attention_dropout""")
							for p in extra_model_params:
													if getattr(self.hparams    ,     lowerCAmelCase__    ,     lowerCAmelCase__				):
																			assert hasattr(self.config    ,     lowerCAmelCase__				), F'model config doesn\'t have a `{p}` attribute'
																			setattr(self.config    ,     lowerCAmelCase__    ,     getattr(self.hparams    ,     lowerCAmelCase__				)				)

							if tokenizer is None:
													__magic_name__    :     str     		=      AutoTokenizer.from_pretrained(
													    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path    ,     cache_dir=lowerCAmelCase__    ,     )
							else:
													__magic_name__    :     PreTrainedTokenizer     		=      tokenizer
							__magic_name__    :     Dict     		=      MODEL_MODES[mode]
							if model is None:
													__magic_name__    :     List[str]     		=      self.model_type.from_pretrained(
													    self.hparams.model_name_or_path    ,     from_tf=bool(""".ckpt""" in self.hparams.model_name_or_path				)    ,     config=self.config    ,     cache_dir=lowerCAmelCase__    ,     )
							else:
													__magic_name__    :     Optional[Any]     		=      model
	def     __magic_name__       (       self    ,     *lowerCAmelCase__    ,     **lowerCAmelCase__				)							->   Tuple:
							__magic_name__    :     Tuple     		=      self.model_type.from_pretrained(*lowerCAmelCase__    ,     **lowerCAmelCase__				)
	def     __magic_name__       (       self				)							->   str:
							__magic_name__    :     Union[str, Any]     		=      arg_to_scheduler[self.hparams.lr_scheduler]
							__magic_name__    :     Optional[Any]     		=      get_schedule_func(
							    self.opt    ,     num_warmup_steps=self.hparams.warmup_steps    ,     num_training_steps=self.total_steps()				)
							__magic_name__    :     Dict     		=      {"""scheduler""": scheduler, """interval""": """step""", """frequency""": 1}
							return scheduler
	def     __magic_name__       (       self				)							->   Tuple:
							__magic_name__    :     Dict     		=      self.model
							__magic_name__    :     Tuple     		=      ["""bias""", """LayerNorm.weight"""]
							__magic_name__    :     Dict     		=      [
							    {
							        """params""": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay				)],
							        """weight_decay""": self.hparams.weight_decay,
							    },
							    {
							        """params""": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay				)],
							        """weight_decay""": 0.0,
							    },
							]
							if self.hparams.adafactor:
													__magic_name__    :     int     		=      Adafactor(
													    lowerCAmelCase__    ,     lr=self.hparams.learning_rate    ,     scale_parameter=lowerCAmelCase__    ,     relative_step=lowerCAmelCase__				)

							else:
													__magic_name__    :     List[Any]     		=      AdamW(
													    lowerCAmelCase__    ,     lr=self.hparams.learning_rate    ,     eps=self.hparams.adam_epsilon				)
							__magic_name__    :     Union[str, Any]     		=      optimizer

							__magic_name__    :     List[Any]     		=      self.get_lr_scheduler()

							return [optimizer], [scheduler]
	def     __magic_name__       (       self    ,     lowerCAmelCase__    ,     lowerCAmelCase__				)							->   Union[str, Any]:
							return self.validation_step(lowerCAmelCase__    ,     lowerCAmelCase__				)
	def     __magic_name__       (       self    ,     lowerCAmelCase__				)							->   List[Any]:
							return self.validation_end(lowerCAmelCase__				)
	def     __magic_name__       (       self				)							->   int:
							__magic_name__    :     Optional[Any]     		=      max(1    ,     self.hparams.gpus				)  # TODO: consider num_tpu_cores
							__magic_name__    :     str     		=      self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
							return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs
	def     __magic_name__       (       self    ,     lowerCAmelCase__				)							->   int:
							if mode == "test":
													__magic_name__    :     Optional[int]     		=      len(self.test_dataloader().dataset				)
							else:
													__magic_name__    :     List[str]     		=      self.get_dataloader("""train"""    ,     self.hparams.train_batch_size    ,     shuffle=lowerCAmelCase__				)
													__magic_name__    :     Dict     		=      len(self.train_dataloader().dataset				)
	def     __magic_name__       (       self    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__ = False				)							->   List[Any]:
							raise NotImplementedError("""You must implement this for your task"""				)
	def     __magic_name__       (       self				)							->   List[Any]:
							return self.train_loader
	def     __magic_name__       (       self				)							->   int:
							return self.get_dataloader("""dev"""    ,     self.hparams.eval_batch_size    ,     shuffle=lowerCAmelCase__				)
	def     __magic_name__       (       self				)							->   Union[str, Any]:
							return self.get_dataloader("""test"""    ,     self.hparams.eval_batch_size    ,     shuffle=lowerCAmelCase__				)
	def     __magic_name__       (       self    ,     lowerCAmelCase__				)							->   List[Any]:
							return os.path.join(
							    self.hparams.data_dir    ,     """cached_{}_{}_{}""".format(
							        lowerCAmelCase__    ,     list(filter(lowerCAmelCase__    ,     self.hparams.model_name_or_path.split("""/"""				)				)				).pop()    ,     str(self.hparams.max_seq_length				)    ,     )    ,     )
	@pl.utilities.rank_zero_only
	def     __magic_name__       (       self    ,     lowerCAmelCase__				)							->   None:
							__magic_name__    :     Optional[Any]     		=      self.output_dir.joinpath("""best_tfmr"""				)
							__magic_name__    :     List[Any]     		=      self.step_count
							self.model.save_pretrained(lowerCAmelCase__				)
							self.tokenizer.save_pretrained(lowerCAmelCase__				)







	@staticmethod
	def     __magic_name__       (       lowerCAmelCase__    ,     lowerCAmelCase__				)							->   Optional[int]:
							parser.add_argument(
							    """--model_name_or_path"""    ,     default=lowerCAmelCase__    ,     type=lowerCAmelCase__    ,     required=lowerCAmelCase__    ,     help="""Path to pretrained model or model identifier from huggingface.co/models"""    ,     )
							parser.add_argument(
							    """--config_name"""    ,     default=""""""    ,     type=lowerCAmelCase__    ,     help="""Pretrained config name or path if not the same as model_name"""				)
							parser.add_argument(
							    """--tokenizer_name"""    ,     default=lowerCAmelCase__    ,     type=lowerCAmelCase__    ,     help="""Pretrained tokenizer name or path if not the same as model_name"""    ,     )
							parser.add_argument(
							    """--cache_dir"""    ,     default=""""""    ,     type=lowerCAmelCase__    ,     help="""Where do you want to store the pre-trained models downloaded from huggingface.co"""    ,     )
							parser.add_argument(
							    """--encoder_layerdrop"""    ,     type=lowerCAmelCase__    ,     help="""Encoder layer dropout probability (Optional). Goes into model.config"""    ,     )
							parser.add_argument(
							    """--decoder_layerdrop"""    ,     type=lowerCAmelCase__    ,     help="""Decoder layer dropout probability (Optional). Goes into model.config"""    ,     )
							parser.add_argument(
							    """--dropout"""    ,     type=lowerCAmelCase__    ,     help="""Dropout probability (Optional). Goes into model.config"""    ,     )
							parser.add_argument(
							    """--attention_dropout"""    ,     type=lowerCAmelCase__    ,     help="""Attention dropout probability (Optional). Goes into model.config"""    ,     )
							parser.add_argument("""--learning_rate"""    ,     default=5e-5    ,     type=lowerCAmelCase__    ,     help="""The initial learning rate for Adam."""				)
							parser.add_argument(
							    """--lr_scheduler"""    ,     default="""linear"""    ,     choices=lowerCAmelCase__    ,     metavar=lowerCAmelCase__    ,     type=lowerCAmelCase__    ,     help="""Learning rate scheduler"""    ,     )
							parser.add_argument("""--weight_decay"""    ,     default=0.0    ,     type=lowerCAmelCase__    ,     help="""Weight decay if we apply some."""				)
							parser.add_argument("""--adam_epsilon"""    ,     default=1e-8    ,     type=lowerCAmelCase__    ,     help="""Epsilon for Adam optimizer."""				)
							parser.add_argument("""--warmup_steps"""    ,     default=0    ,     type=lowerCAmelCase__    ,     help="""Linear warmup over warmup_steps."""				)
							parser.add_argument("""--num_workers"""    ,     default=4    ,     type=lowerCAmelCase__    ,     help="""kwarg passed to DataLoader"""				)
							parser.add_argument("""--num_train_epochs"""    ,     dest="""max_epochs"""    ,     default=3    ,     type=lowerCAmelCase__				)
							parser.add_argument("""--train_batch_size"""    ,     default=32    ,     type=lowerCAmelCase__				)
							parser.add_argument("""--eval_batch_size"""    ,     default=32    ,     type=lowerCAmelCase__				)
							parser.add_argument("""--adafactor"""    ,     action="""store_true"""				)





class  snake_case__      (							pl.Callback   ):
	def     __magic_name__       (       self    ,     lowerCAmelCase__    ,     lowerCAmelCase__				)							->   int:
							__magic_name__    :     Dict     		=      trainer.lr_schedulers[0]["""scheduler"""]
							__magic_name__    :     List[str]     		=      {F'lr_group_{i}': lr for i, lr in enumerate(lr_scheduler.get_lr()				)}
							pl_module.logger.log_metrics(lowerCAmelCase__				)
	def     __magic_name__       (       self    ,     lowerCAmelCase__    ,     lowerCAmelCase__				)							->   str:
							rank_zero_info("""***** Validation results *****"""				)
							__magic_name__    :     Union[str, Any]     		=      trainer.callback_metrics
							# Log results
							for key in sorted(lowerCAmelCase__				):
													if key not in ["log", "progress_bar"]:
																			rank_zero_info("""{} = {}\n""".format(lowerCAmelCase__    ,     str(metrics[key]				)				)				)







	def     __magic_name__       (       self    ,     lowerCAmelCase__    ,     lowerCAmelCase__				)							->   Optional[Any]:
							rank_zero_info("""***** Test results *****"""				)
							__magic_name__    :     Dict     		=      trainer.callback_metrics
							# Log and save results to file
							__magic_name__    :     str     		=      os.path.join(pl_module.hparams.output_dir    ,     """test_results.txt"""				)
							with open(lowerCAmelCase__    ,     """w"""				) as writer:
													for key in sorted(lowerCAmelCase__				):
																			if key not in ["log", "progress_bar"]:
																									rank_zero_info("""{} = {}\n""".format(lowerCAmelCase__    ,     str(metrics[key]				)				)				)
																									writer.write("""{} = {}\n""".format(lowerCAmelCase__    ,     str(metrics[key]				)				)				)



def 					UpperCamelCase		( _A,					_A				):






						"""simple docstring"""







						parser.add_argument(
						    """--output_dir""",					default=_A,					type=_A,					required=_A,					help="""The output directory where the model predictions and checkpoints will be written.""",					)
						parser.add_argument(
						    """--fp16""",					action="""store_true""",					help="""Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit""",					)

						parser.add_argument(
						    """--fp16_opt_level""",					type=_A,					default="""O2""",					help=(
						        """For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."""
						        """See details at https://nvidia.github.io/apex/amp.html"""
						    ),					)
						parser.add_argument("""--n_tpu_cores""",					dest="""tpu_cores""",					type=_A				)
						parser.add_argument("""--max_grad_norm""",					dest="""gradient_clip_val""",					default=1.0,					type=_A,					help="""Max gradient norm"""				)
						parser.add_argument("""--do_train""",					action="""store_true""",					help="""Whether to run training."""				)
						parser.add_argument("""--do_predict""",					action="""store_true""",					help="""Whether to run predictions on the test set."""				)
						parser.add_argument(
						    """--gradient_accumulation_steps""",					dest="""accumulate_grad_batches""",					type=_A,					default=1,					help="""Number of updates steps to accumulate before performing a backward/update pass.""",					)
						parser.add_argument("""--seed""",					type=_A,					default=42,					help="""random seed for initialization"""				)
						parser.add_argument(
						    """--data_dir""",					default=_A,					type=_A,					required=_A,					help="""The input data dir. Should contain the training files for the CoNLL-2003 NER task.""",					)

def 					UpperCamelCase		( _A,					_A,					_A=None,					_A=True,					_A=[],					_A=None,					_A=None,					**_A,					):






						"""simple docstring"""







						pl.seed_everything(args.seed				)

						# init model
						__magic_name__    :     List[str]     		=      Path(model.hparams.output_dir				)
						odir.mkdir(exist_ok=_A				)

						# add custom checkpoints
						if checkpoint_callback is None:
												__magic_name__    :     List[Any]     		=      pl.callbacks.ModelCheckpoint(
												    filepath=args.output_dir,					prefix="""checkpoint""",					monitor="""val_loss""",					mode="""min""",					save_top_k=1				)
						if early_stopping_callback:
												extra_callbacks.append(_A				)
						if logging_callback is None:
												__magic_name__    :     Union[str, Any]     		=      LoggingCallback()

						__magic_name__    :     int     		=      {}

						# TODO: remove with PyTorch 1.6 since pl uses native amp
						if args.fpaa:
												__magic_name__    :     Optional[Any]     		=      16
												__magic_name__    :     Tuple     		=      args.fpaa_opt_level

						if args.gpus > 1:
												__magic_name__    :     List[Any]     		=      """ddp"""

						__magic_name__    :     int     		=      args.accumulate_grad_batches
						__magic_name__    :     str     		=      extra_train_kwargs.get("""accelerator""",					_A				)
						__magic_name__    :     Any     		=      extra_train_kwargs.get("""profiler""",					_A				)

						__magic_name__    :     Dict     		=      pl.Trainer.from_argparse_args(
						    _A,					weights_summary=_A,					callbacks=[logging_callback] + extra_callbacks,					logger=_A,					checkpoint_callback=_A,					**_A,					)

						if args.do_train:
												trainer.fit(_A				)

						return trainer








__magic_name__:  Tuple  	=			[
    ["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				):






						"""simple docstring"""







						__magic_name__    :     str     		=      set()
						for row in edge_array:
												for item in row:
																		distinct_edge.add(item[0]				)
						return list(_A				)

def 					UpperCamelCase		( _A,					_A				):






						"""simple docstring"""







						__magic_name__    :     Union[str, Any]     		=      ["""0"""] * len(_A				)
						for i, row in enumerate(_A				):
												for item in row:
																		if distinct_edge in item[0]:
																								__magic_name__    :     Optional[Any]     		=      """1"""
																								break
						return "".join(_A				)

def 					UpperCamelCase		( _A				):






						"""simple docstring"""







						__magic_name__    :     List[str]     		=      get_distinct_edge(_A				)
						__magic_name__    :     List[str]     		=      {}

						for item in distinct_edge:
												__magic_name__    :     Optional[Any]     		=      get_bitcode(_A,					_A				)
												# print('bit',bit)
												# bt=''.join(bit)
												__magic_name__    :     Union[str, Any]     		=      bit.count("""1"""				)
												__magic_name__    :     Dict     		=      [s, bit]
						# Store [Distinct edge, WT(Bitcode), Bitcode] in descending order
						__magic_name__    :     List[Any]     		=      [
						    [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				):






						"""simple docstring"""







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

def 					UpperCamelCase		( _A				):






						"""simple docstring"""







						__magic_name__    :     Optional[int]     		=      {}
						for key, value in nodes.items():
												__magic_name__    :     Tuple     		=      value
						return cluster

def 					UpperCamelCase		( _A				):






						"""simple docstring"""







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

def 					UpperCamelCase		( ):






						"""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				):






						"""simple docstring"""







						for i in cluster[ca]:
												__magic_name__    :     Any     		=      0
												__magic_name__    :     Any     		=      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:
																																				__magic_name__    :     int     		=      [nodes[j]]
																														count += 1
																		if count == 0:
																								__magic_name__    :     Optional[Any]     		=      ca + 1
																		else:
																								break

def 					UpperCamelCase		( _A,					_A				):






						"""simple docstring"""







						__magic_name__    :     List[str]     		=      cluster[max(cluster.keys()				)]
						__magic_name__    :     List[Any]     		=      """Header"""
						__magic_name__    :     Tuple     		=      {}
						for i in x:
												if (["Header"],) in graph:
																		graph[(["Header"],)].append(x[i]				)
												else:
																		__magic_name__    :     List[str]     		=      [x[i]]
						for i in x:
												__magic_name__    :     Tuple     		=      [["""Header"""]]
						__magic_name__    :     Union[str, Any]     		=      1
						while i < max(_A				) - 1:
												create_edge(_A,					_A,					_A,					_A				)
												__magic_name__    :     Optional[Any]     		=      i + 1
						return graph

def 					UpperCamelCase		( _A,					_A,					_A,					_A=None				):






						"""simple docstring"""







						__magic_name__    :     str     		=      (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				):






						"""simple docstring"""







						__magic_name__    :     Union[str, Any]     		=      int(s / 100 * (len(_A				) - 1)				)
						for i in cluster[k]:
												my_dfs(_A,					tuple(cluster[k][i]				),					(["""Header"""],)				)

def 					UpperCamelCase		( _A				):






						"""simple docstring"""







						__magic_name__    :     str     		=      []
						for edges in paths:
												__magic_name__    :     List[Any]     		=      []
												for j in range(len(_A				) - 1				):
																		__magic_name__    :     Any     		=      list(edges[j]				)
																		for e in temp:
																								__magic_name__    :     Optional[Any]     		=      (e[0], e[1])
																								el.append(_A				)
												freq_sub_el.append(_A				)
						return freq_sub_el

def 					UpperCamelCase		( _A				):






						"""simple docstring"""







						for i in range(len(_A				)				):
												for j in range(len(edge_array[i]				)				):
																		__magic_name__    :     Any     		=      edge_array[i][j].split("""-"""				)
																		__magic_name__    :     Optional[int]     		=      t


if __name__ == "__main__":
		preprocess(edge_array)
		__magic_name__:  Optional[int]  	=			get_frequency_table(edge_array)
		__magic_name__:  str  	=			get_nodes(frequency_table)
		__magic_name__:  List[str]  	=			get_cluster(nodes)
		__magic_name__:  Optional[Any]  	=			get_support(cluster)
		__magic_name__:  Dict  	=			construct_graph(cluster, nodes)
		find_freq_subgraph_given_support(60, cluster, graph)
		__magic_name__:  list  	=			[]
		__magic_name__:  Dict  	=			freq_subgraphs_edge_list(paths)
		print_all()
