
'''simple docstring'''


__lowercase							:				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 lowercase_		(				_lowercase			)       ->							Optional[int]:
					'''simple docstring'''

					lowerCamelCase_   : Union[str, Any]       =						set()
					for row in edge_array:
										for item in row:
															distinct_edge.add(item[0]			)
					return list(_lowercase			)




def lowercase_		(				_lowercase     ,    _lowercase			)       ->							int:
					'''simple docstring'''

					lowerCamelCase_   : List[Any]       =						['''0'''] * len(_lowercase			)
					for i, row in enumerate(_lowercase			):
										for item in row:
															if distinct_edge in item[0]:
																				lowerCamelCase_   : Union[str, Any]       =						'''1'''
																				break
					return "".join(_lowercase			)




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

					lowerCamelCase_   : int       =						get_distinct_edge(_lowercase			)
					lowerCamelCase_   : Any       =						{}

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




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

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




def lowercase_		(				_lowercase			)       ->							str:
					'''simple docstring'''

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




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

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




def lowercase_		(				)       ->							None:
					'''simple docstring'''

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




def lowercase_		(				_lowercase     ,    _lowercase     ,    _lowercase     ,    _lowercase			)       ->							Tuple:
					'''simple docstring'''

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




def lowercase_		(				_lowercase     ,    _lowercase			)       ->							Dict:
					'''simple docstring'''

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




def lowercase_		(				_lowercase     ,    _lowercase     ,    _lowercase     ,    _lowercase=None			)       ->							Dict:
					'''simple docstring'''

					lowerCamelCase_   : List[str]       =						(path or []) + [start]
					if start == end:
										paths.append(_lowercase			)
					for node in graph[start]:
										if tuple(_lowercase			) not in path:
															my_dfs(_lowercase     ,    tuple(_lowercase			)     ,    _lowercase     ,    _lowercase			)




def lowercase_		(				_lowercase     ,    _lowercase     ,    _lowercase			)       ->							int:
					'''simple docstring'''

					lowerCamelCase_   : Dict       =						int(s / 100 * (len(_lowercase			) - 1)			)
					for i in cluster[k]:
										my_dfs(_lowercase     ,    tuple(cluster[k][i]			)     ,    (['''Header'''],)			)




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

					lowerCamelCase_   : Tuple       =						[]
					for edges in paths:
										lowerCamelCase_   : Optional[int]       =						[]
										for j in range(len(_lowercase			) - 1			):
															lowerCamelCase_   : List[str]       =						list(edges[j]			)
															for e in temp:
																				lowerCamelCase_   : Dict       =						(e[0], e[1])
																				el.append(_lowercase			)
										freq_sub_el.append(_lowercase			)
					return freq_sub_el




def lowercase_		(				_lowercase			)       ->							int:
					'''simple docstring'''

					for i in range(len(_lowercase			)			):
										for j in range(len(edge_array[i]			)			):
															lowerCamelCase_   : Any       =						edge_array[i][j].split('''-'''			)
															lowerCamelCase_   : Optional[Any]       =						t


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




'''simple docstring'''


import argparse
import logging
import math
import time

import torch

from transformers import TransfoXLCorpus, TransfoXLLMHeadModel


logging.basicConfig(
    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
__lowercase							:				Optional[Any]					=       logging.getLogger(__name__)




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

					lowerCamelCase_   : Optional[int]       =						argparse.ArgumentParser(description='''PyTorch Transformer Language Model'''			)
					parser.add_argument('''--model_name'''     ,    type=_lowercase     ,    default='''transfo-xl-wt103'''     ,    help='''pretrained model name'''			)
					parser.add_argument(
					    '''--split'''     ,    type=_lowercase     ,    default='''test'''     ,    choices=['''all''', '''valid''', '''test''']     ,    help='''which split to evaluate'''			)
					parser.add_argument('''--batch_size'''     ,    type=_lowercase     ,    default=10     ,    help='''batch size'''			)
					parser.add_argument('''--tgt_len'''     ,    type=_lowercase     ,    default=128     ,    help='''number of tokens to predict'''			)
					parser.add_argument('''--ext_len'''     ,    type=_lowercase     ,    default=0     ,    help='''length of the extended context'''			)
					parser.add_argument('''--mem_len'''     ,    type=_lowercase     ,    default=1_600     ,    help='''length of the retained previous heads'''			)
					parser.add_argument('''--clamp_len'''     ,    type=_lowercase     ,    default=1_000     ,    help='''max positional embedding index'''			)
					parser.add_argument('''--no_cuda'''     ,    action='''store_true'''     ,    help='''Do not use CUDA even though CUA is available'''			)
					parser.add_argument('''--work_dir'''     ,    type=_lowercase     ,    required=_lowercase     ,    help='''path to the work_dir'''			)
					parser.add_argument('''--no_log'''     ,    action='''store_true'''     ,    help='''do not log the eval result'''			)
					parser.add_argument('''--same_length'''     ,    action='''store_true'''     ,    help='''set same length attention with masking'''			)
					parser.add_argument('''--server_ip'''     ,    type=_lowercase     ,    default=''''''     ,    help='''Can be used for distant debugging.'''			)
					parser.add_argument('''--server_port'''     ,    type=_lowercase     ,    default=''''''     ,    help='''Can be used for distant debugging.'''			)
					lowerCamelCase_   : Dict       =						parser.parse_args()
					assert args.ext_len >= 0, "extended context length must be non-negative"

					if args.server_ip and args.server_port:
										# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
										import ptvsd

										print('''Waiting for debugger attach'''			)
										ptvsd.enable_attach(address=(args.server_ip, args.server_port)     ,    redirect_output=_lowercase			)
										ptvsd.wait_for_attach()

					lowerCamelCase_   : List[str]       =						torch.device('''cuda''' if torch.cuda.is_available() and not args.no_cuda else '''cpu'''			)
					logger.info('''device: {}'''.format(_lowercase			)			)

					# Load a pre-processed dataset
					# You can also build the corpus yourself using TransfoXLCorpus methods
					# The pre-processing involve computing word frequencies to prepare the Adaptive input and SoftMax
					# and tokenizing the dataset
					# The pre-processed corpus is a convertion (using the conversion script )
					lowerCamelCase_   : str       =						TransfoXLCorpus.from_pretrained(args.model_name			)

					lowerCamelCase_   : Optional[int]       =						corpus.get_iterator('''valid'''     ,    args.batch_size     ,    args.tgt_len     ,    device=_lowercase     ,    ext_len=args.ext_len			)
					lowerCamelCase_   : str       =						corpus.get_iterator('''test'''     ,    args.batch_size     ,    args.tgt_len     ,    device=_lowercase     ,    ext_len=args.ext_len			)

					# Load a pre-trained model
					lowerCamelCase_   : str       =						TransfoXLLMHeadModel.from_pretrained(args.model_name			)
					model.to(_lowercase			)

					logger.info(
					    '''Evaluating with bsz {} tgt_len {} ext_len {} mem_len {} clamp_len {}'''.format(
					        args.batch_size     ,    args.tgt_len     ,    args.ext_len     ,    args.mem_len     ,    args.clamp_len			)			)

					model.reset_memory_length(args.mem_len			)
					if args.clamp_len > 0:
										lowerCamelCase_   : Union[str, Any]       =						args.clamp_len
					if args.same_length:
										lowerCamelCase_   : int       =						True

					###############################################################################
					# Evaluation code
					###############################################################################
					def evaluate(_lowercase			):
										# Turn on evaluation mode which disables dropout.
										model.eval()
										lowerCamelCase_, lowerCamelCase_   : Union[str, Any]       =						0, 0.0
										lowerCamelCase_   : Optional[int]       =						time.time()
										with torch.no_grad():
															lowerCamelCase_   : Dict       =						None
															for idx, (data, target, seq_len) in enumerate(_lowercase			):
																				lowerCamelCase_   : str       =						model(_lowercase     ,    lm_labels=_lowercase     ,    mems=_lowercase			)
																				lowerCamelCase_, lowerCamelCase_, lowerCamelCase_   : int       =						ret
																				lowerCamelCase_   : List[str]       =						loss.mean()
																				total_loss += seq_len * loss.item()
																				total_len += seq_len
															lowerCamelCase_   : List[str]       =						time.time() - start_time
										logger.info('''Time : {:.2f}s, {:.2f}ms/segment'''.format(_lowercase     ,    1_000 * total_time / (idx + 1)			)			)
										return total_loss / total_len

					# Run on test data.
					if args.split == "all":
										lowerCamelCase_   : Tuple       =						evaluate(_lowercase			)
										lowerCamelCase_   : List[Any]       =						evaluate(_lowercase			)
					elif args.split == "valid":
										lowerCamelCase_   : Union[str, Any]       =						evaluate(_lowercase			)
										lowerCamelCase_   : str       =						None
					elif args.split == "test":
										lowerCamelCase_   : Any       =						evaluate(_lowercase			)
										lowerCamelCase_   : int       =						None

					def format_log(_lowercase     ,    _lowercase			):
										lowerCamelCase_   : Optional[Any]       =						'''| {0} loss {1:5.2f} | {0} ppl {2:9.3f} '''.format(_lowercase     ,    _lowercase     ,    math.exp(_lowercase			)			)
										return log_str

					lowerCamelCase_   : List[Any]       =						''''''
					if valid_loss is not None:
										log_str += format_log(_lowercase     ,    '''valid'''			)
					if test_loss is not None:
										log_str += format_log(_lowercase     ,    '''test'''			)

					logger.info('''=''' * 100			)
					logger.info(_lowercase			)
					logger.info('''=''' * 100			)


if __name__ == "__main__":
					main()


