






import argparse
import logging
import os
import sys
from collections import namedtuple

import torch
from modeling_bertabs import BertAbs, build_predictor
from torch.utils.data import DataLoader, SequentialSampler
from tqdm import tqdm

from transformers import BertTokenizer

from .utils_summarization import (
    CNNDMDataset,
    build_mask,
    compute_token_type_ids,
    encode_for_summarization,
    truncate_or_pad,
)


__magic_name__:  Dict  	=			logging.getLogger(__name__)
logging.basicConfig(stream=sys.stdout, level=logging.INFO)


__magic_name__:  List[str]  	=			namedtuple("Batch", ["document_names", "batch_size", "src", "segs", "mask_src", "tgt_str"])

def 					UpperCamelCase		( _A				):






						"""simple docstring"""







						__magic_name__    :     Union[str, Any]     		=      BertTokenizer.from_pretrained("""bert-base-uncased""",					do_lower_case=_A				)
						__magic_name__    :     List[Any]     		=      BertAbs.from_pretrained("""remi/bertabs-finetuned-extractive-abstractive-summarization"""				)
						model.to(args.device				)
						model.eval()

						__magic_name__    :     str     		=      {
						    """BOS""": tokenizer.vocab["""[unused0]"""],
						    """EOS""": tokenizer.vocab["""[unused1]"""],
						    """PAD""": tokenizer.vocab["""[PAD]"""],
						}

						if args.compute_rouge:
												__magic_name__    :     Dict     		=      []
												__magic_name__    :     Optional[int]     		=      []

												import nltk
												import rouge

												nltk.download("""punkt"""				)
												__magic_name__    :     List[str]     		=      rouge.Rouge(
												    metrics=["""rouge-n""", """rouge-l"""],					max_n=2,					limit_length=_A,					length_limit=args.beam_size,					length_limit_type="""words""",					apply_avg=_A,					apply_best=_A,					alpha=0.5,					weight_factor=1.2,					stemming=_A,					)

						# these (unused) arguments are defined to keep the compatibility
						# with the legacy code and will be deleted in a next iteration.
						__magic_name__    :     Any     		=      """"""
						__magic_name__    :     int     		=      """"""

						__magic_name__    :     List[Any]     		=      build_data_iterator(_A,					_A				)
						__magic_name__    :     Dict     		=      build_predictor(_A,					_A,					_A,					_A				)

						logger.info("""***** Running evaluation *****"""				)
						logger.info("""  Number examples = %d""",					len(data_iterator.dataset				)				)
						logger.info("""  Batch size = %d""",					args.batch_size				)
						logger.info(""""""				)
						logger.info("""***** Beam Search parameters *****"""				)
						logger.info("""  Beam size = %d""",					args.beam_size				)
						logger.info("""  Minimum length = %d""",					args.min_length				)
						logger.info("""  Maximum length = %d""",					args.max_length				)
						logger.info("""  Alpha (length penalty) = %.2f""",					args.alpha				)
						logger.info("""  Trigrams %s be blocked""",					("""will""" if args.block_trigram else """will NOT""")				)

						for batch in tqdm(_A				):
												__magic_name__    :     Dict     		=      predictor.translate_batch(_A				)
												__magic_name__    :     List[str]     		=      predictor.from_batch(_A				)
												__magic_name__    :     Any     		=      [format_summary(_A				) for t in translations]
												save_summaries(_A,					args.summaries_output_dir,					batch.document_names				)

												if args.compute_rouge:
																		reference_summaries += batch.tgt_str
																		generated_summaries += summaries

						if args.compute_rouge:
												__magic_name__    :     str     		=      rouge_evaluator.get_scores(_A,					_A				)
												__magic_name__    :     Optional[int]     		=      format_rouge_scores(_A				)
												save_rouge_scores(_A				)
												print(_A				)

def 					UpperCamelCase		( _A,					_A,					_A				):






						"""simple docstring"""







						for summary, document_name in zip(_A,					_A				):
												# Prepare the summary file's name
												if "." in document_name:
																		__magic_name__    :     int     		=      """.""".join(document_name.split("""."""				)[:-1]				)
																		__magic_name__    :     Optional[int]     		=      document_name.split("""."""				)[-1]
																		__magic_name__    :     Any     		=      bare_document_name + """_summary.""" + extension
												else:
																		__magic_name__    :     Dict     		=      document_name + """_summary"""

												__magic_name__    :     Any     		=      os.path.join(_A,					_A				)
												with open(_A,					"""w"""				) as output:
																		output.write(_A				)

def 					UpperCamelCase		( _A				):






						"""simple docstring"""







						__magic_name__    :     List[Any]     		=      translation
						__magic_name__    :     List[str]     		=      (
						    raw_summary.replace("""[unused0]""",					""""""				)
						    .replace("""[unused3]""",					""""""				)
						    .replace("""[PAD]""",					""""""				)
						    .replace("""[unused1]""",					""""""				)
						    .replace(R""" +""",					""" """				)
						    .replace(""" [unused2] """,					""". """				)
						    .replace("""[unused2]""",					""""""				)
						    .strip()
						)

						return summary

def 					UpperCamelCase		( _A				):






						"""simple docstring"""







						return """\n
****** ROUGE SCORES ******

** ROUGE 1
F1        >> {:.3f}
Precision >> {:.3f}
Recall    >> {:.3f}

** ROUGE 2
F1        >> {:.3f}
Precision >> {:.3f}
Recall    >> {:.3f}

** ROUGE L
F1        >> {:.3f}
Precision >> {:.3f}
Recall    >> {:.3f}""".format(
						    scores["""rouge-1"""]["""f"""],					scores["""rouge-1"""]["""p"""],					scores["""rouge-1"""]["""r"""],					scores["""rouge-2"""]["""f"""],					scores["""rouge-2"""]["""p"""],					scores["""rouge-2"""]["""r"""],					scores["""rouge-l"""]["""f"""],					scores["""rouge-l"""]["""p"""],					scores["""rouge-l"""]["""r"""],					)

def 					UpperCamelCase		( _A				):






						"""simple docstring"""







						with open("""rouge_scores.txt""",					"""w"""				) as output:
												output.write(_A				)

def 					UpperCamelCase		( _A,					_A				):






						"""simple docstring"""







						__magic_name__    :     List[Any]     		=      load_and_cache_examples(_A,					_A				)
						__magic_name__    :     Tuple     		=      SequentialSampler(_A				)

						def collate_fn(_A				):
												return collate(_A,					_A,					block_size=512,					device=args.device				)

						__magic_name__    :     Any     		=      DataLoader(
						    _A,					sampler=_A,					batch_size=args.batch_size,					collate_fn=_A,					)

						return iterator

def 					UpperCamelCase		( _A,					_A				):






						"""simple docstring"""







						__magic_name__    :     Tuple     		=      CNNDMDataset(args.documents_dir				)
						return dataset

def 					UpperCamelCase		( _A,					_A,					_A,					_A				):






						"""simple docstring"""







						__magic_name__    :     Dict     		=      [x for x in data if not len(x[1]				) == 0]  # remove empty_files
						__magic_name__    :     str     		=      [name for name, _, _ in data]
						__magic_name__    :     Optional[int]     		=      [""" """.join(_A				) for _, _, summary_list in data]

						__magic_name__    :     List[Any]     		=      [encode_for_summarization(_A,					_A,					_A				) for _, story, summary in data]
						__magic_name__    :     Optional[Any]     		=      torch.tensor(
						    [truncate_or_pad(_A,					_A,					tokenizer.pad_token_id				) for story, _ in encoded_text]				)
						__magic_name__    :     Tuple     		=      compute_token_type_ids(_A,					tokenizer.cls_token_id				)
						__magic_name__    :     str     		=      build_mask(_A,					tokenizer.pad_token_id				)

						__magic_name__    :     str     		=      Batch(
						    document_names=_A,					batch_size=len(_A				),					src=encoded_stories.to(_A				),					segs=encoder_token_type_ids.to(_A				),					mask_src=encoder_mask.to(_A				),					tgt_str=_A,					)

						return batch

def 					UpperCamelCase		( _A,					_A				):






						"""simple docstring"""







						__magic_name__    :     Optional[Any]     		=      summary_tokens.to("""cpu"""				).numpy()
						__magic_name__    :     List[str]     		=      tokenizer.decode(_A				)
						__magic_name__    :     int     		=      summary.split("""."""				)
						__magic_name__    :     Dict     		=      [s + """.""" for s in sentences]
						return sentences

def 					UpperCamelCase		( ):






						"""simple docstring"""







						__magic_name__    :     List[str]     		=      argparse.ArgumentParser()
						parser.add_argument(
						    """--documents_dir""",					default=_A,					type=_A,					required=_A,					help="""The folder where the documents to summarize are located.""",					)
						parser.add_argument(
						    """--summaries_output_dir""",					default=_A,					type=_A,					required=_A,					help="""The folder in wich the summaries should be written. Defaults to the folder where the documents are""",					)
						parser.add_argument(
						    """--compute_rouge""",					default=_A,					type=_A,					required=_A,					help="""Compute the ROUGE metrics during evaluation. Only available for the CNN/DailyMail dataset.""",					)
						# EVALUATION options
						parser.add_argument(
						    """--no_cuda""",					default=_A,					type=_A,					help="""Whether to force the execution on CPU.""",					)
						parser.add_argument(
						    """--batch_size""",					default=4,					type=_A,					help="""Batch size per GPU/CPU for training.""",					)
						# BEAM SEARCH arguments
						parser.add_argument(
						    """--min_length""",					default=50,					type=_A,					help="""Minimum number of tokens for the summaries.""",					)
						parser.add_argument(
						    """--max_length""",					default=200,					type=_A,					help="""Maixmum number of tokens for the summaries.""",					)
						parser.add_argument(
						    """--beam_size""",					default=5,					type=_A,					help="""The number of beams to start with for each example.""",					)
						parser.add_argument(
						    """--alpha""",					default=0.95,					type=_A,					help="""The value of alpha for the length penalty in the beam search.""",					)
						parser.add_argument(
						    """--block_trigram""",					default=_A,					type=_A,					help="""Whether to block the existence of repeating trigrams in the text generated by beam search.""",					)
						__magic_name__    :     int     		=      parser.parse_args()

						# Select device (distibuted not available)
						__magic_name__    :     Optional[Any]     		=      torch.device("""cuda""" if torch.cuda.is_available() and not args.no_cuda else """cpu"""				)

						# Check the existence of directories
						if not args.summaries_output_dir:
												__magic_name__    :     Any     		=      args.documents_dir

						if not documents_dir_is_valid(args.documents_dir				):
												raise FileNotFoundError(
												    """We could not find the directory you specified for the documents to summarize, or it was empty. Please"""
												    """ specify a valid path."""				)
						os.makedirs(args.summaries_output_dir,					exist_ok=_A				)

						evaluate(_A				)

def 					UpperCamelCase		( _A				):






						"""simple docstring"""







						if not os.path.exists(_A				):
												return False

						__magic_name__    :     List[str]     		=      os.listdir(_A				)
						if len(_A				) == 0:
												return False

						return True


if __name__ == "__main__":
						main()








__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()
