





'''simple docstring'''



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,
)


_SCREAMING_SNAKE_CASE    =			logging.getLogger(__name__)
logging.basicConfig(stream=sys.stdout, level=logging.INFO)


_SCREAMING_SNAKE_CASE    =			namedtuple('''Batch''', ['''document_names''', '''batch_size''', '''src''', '''segs''', '''mask_src''', '''tgt_str'''])




def       _lowerCAmelCase  ( lowerCamelCase_	:     int ):
			__lowercase   =							BertTokenizer.from_pretrained('''bert-base-uncased'''						,				do_lower_case=lowerCamelCase_ )
			__lowercase   =							BertAbs.from_pretrained('''remi/bertabs-finetuned-extractive-abstractive-summarization''' )
			model.to(args.device )
			model.eval()

			__lowercase   =							{
			    '''BOS''': tokenizer.vocab['''[unused0]'''],
			    '''EOS''': tokenizer.vocab['''[unused1]'''],
			    '''PAD''': tokenizer.vocab['''[PAD]'''],
			}

			if args.compute_rouge:
						__lowercase   =							[]
						__lowercase   =							[]

						import nltk
						import rouge

						nltk.download('''punkt''' )
						__lowercase   =							rouge.Rouge(
						    metrics=['''rouge-n''', '''rouge-l''']						,				max_n=2						,				limit_length=lowerCamelCase_						,				length_limit=args.beam_size						,				length_limit_type='''words'''						,				apply_avg=lowerCamelCase_						,				apply_best=lowerCamelCase_						,				alpha=0.5						,				weight_factor=1.2						,				stemming=lowerCamelCase_						,				)

			# these (unused) arguments are defined to keep the compatibility
			# with the legacy code and will be deleted in a next iteration.
			__lowercase   =							''''''
			__lowercase   =							''''''

			__lowercase   =							build_data_iterator(lowerCamelCase_						,				lowerCamelCase_ )
			__lowercase   =							build_predictor(lowerCamelCase_						,				lowerCamelCase_						,				lowerCamelCase_						,				lowerCamelCase_ )

			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(lowerCamelCase_ ):
						__lowercase   =							predictor.translate_batch(lowerCamelCase_ )
						__lowercase   =							predictor.from_batch(lowerCamelCase_ )
						__lowercase   =							[format_summary(lowerCamelCase_ ) for t in translations]
						save_summaries(lowerCamelCase_						,				args.summaries_output_dir						,				batch.document_names )

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

			if args.compute_rouge:
						__lowercase   =							rouge_evaluator.get_scores(lowerCamelCase_						,				lowerCamelCase_ )
						__lowercase   =							format_rouge_scores(lowerCamelCase_ )
						save_rouge_scores(lowerCamelCase_ )
						print(lowerCamelCase_ )




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

						__lowercase   =							os.path.join(lowerCamelCase_						,				lowerCamelCase_ )
						with open(lowerCamelCase_						,				'''w''' ) as output:
									output.write(lowerCamelCase_ )




def       _lowerCAmelCase  ( lowerCamelCase_	:     List[Any] ):
			__lowercase  ,						__lowercase  ,						__lowercase   =							translation
			__lowercase   =							(
			    raw_summary.replace('''[unused0]'''						,				'''''' )
			    .replace('''[unused3]'''						,				'''''' )
			    .replace('''[PAD]'''						,				'''''' )
			    .replace('''[unused1]'''						,				'''''' )
			    .replace(r''' +'''						,				''' ''' )
			    .replace(''' [unused2] '''						,				'''. ''' )
			    .replace('''[unused2]'''						,				'''''' )
			    .strip()
			)

			return summary




def       _lowerCAmelCase  ( lowerCamelCase_	:     int ):
			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       _lowerCAmelCase  ( lowerCamelCase_	:     Dict ):
			with open('''rouge_scores.txt'''						,				'''w''' ) as output:
						output.write(lowerCamelCase_ )




def       _lowerCAmelCase  ( lowerCamelCase_	:     List[Any]						,				lowerCamelCase_	:     List[Any] ):
			__lowercase   =							load_and_cache_examples(lowerCamelCase_						,				lowerCamelCase_ )
			__lowercase   =							SequentialSampler(lowerCamelCase_ )

			def collate_fn(lowerCamelCase_	:     List[str] ):
						return collate(lowerCamelCase_						,				lowerCamelCase_						,				block_size=5_1_2						,				device=args.device )

			__lowercase   =							DataLoader(
			    lowerCamelCase_						,				sampler=lowerCamelCase_						,				batch_size=args.batch_size						,				collate_fn=lowerCamelCase_						,				)

			return iterator




def       _lowerCAmelCase  ( lowerCamelCase_	:     Optional[int]						,				lowerCamelCase_	:     List[Any] ):
			__lowercase   =							CNNDMDataset(args.documents_dir )
			return dataset




def       _lowerCAmelCase  ( lowerCamelCase_	:     Optional[int]						,				lowerCamelCase_	:     List[Any]						,				lowerCamelCase_	:     Any						,				lowerCamelCase_	:     Optional[Any] ):
			__lowercase   =							[x for x in data if not len(x[1] ) == 0]  # remove empty_files
			__lowercase   =							[name for name, _, _ in data]
			__lowercase   =							[''' '''.join(lowerCamelCase_ ) for _, _, summary_list in data]

			__lowercase   =							[encode_for_summarization(lowerCamelCase_						,				lowerCamelCase_						,				lowerCamelCase_ ) for _, story, summary in data]
			__lowercase   =							torch.tensor(
			    [truncate_or_pad(lowerCamelCase_						,				lowerCamelCase_						,				tokenizer.pad_token_id ) for story, _ in encoded_text] )
			__lowercase   =							compute_token_type_ids(lowerCamelCase_						,				tokenizer.cls_token_id )
			__lowercase   =							build_mask(lowerCamelCase_						,				tokenizer.pad_token_id )

			__lowercase   =							Batch(
			    document_names=lowerCamelCase_						,				batch_size=len(lowerCamelCase_ )						,				src=encoded_stories.to(lowerCamelCase_ )						,				segs=encoder_token_type_ids.to(lowerCamelCase_ )						,				mask_src=encoder_mask.to(lowerCamelCase_ )						,				tgt_str=lowerCamelCase_						,				)

			return batch




def       _lowerCAmelCase  ( lowerCamelCase_	:     List[Any]						,				lowerCamelCase_	:     Union[str, Any] ):
			__lowercase   =							summary_tokens.to('''cpu''' ).numpy()
			__lowercase   =							tokenizer.decode(lowerCamelCase_ )
			__lowercase   =							summary.split('''.''' )
			__lowercase   =							[s + '''.''' for s in sentences]
			return sentences




def       _lowerCAmelCase  ( ):
			__lowercase   =							argparse.ArgumentParser()
			parser.add_argument(
			    '''--documents_dir'''						,				default=lowerCamelCase_						,				type=lowerCamelCase_						,				required=lowerCamelCase_						,				help='''The folder where the documents to summarize are located.'''						,				)
			parser.add_argument(
			    '''--summaries_output_dir'''						,				default=lowerCamelCase_						,				type=lowerCamelCase_						,				required=lowerCamelCase_						,				help='''The folder in wich the summaries should be written. Defaults to the folder where the documents are'''						,				)
			parser.add_argument(
			    '''--compute_rouge'''						,				default=lowerCamelCase_						,				type=lowerCamelCase_						,				required=lowerCamelCase_						,				help='''Compute the ROUGE metrics during evaluation. Only available for the CNN/DailyMail dataset.'''						,				)
			# EVALUATION options
			parser.add_argument(
			    '''--no_cuda'''						,				default=lowerCamelCase_						,				type=lowerCamelCase_						,				help='''Whether to force the execution on CPU.'''						,				)
			parser.add_argument(
			    '''--batch_size'''						,				default=4						,				type=lowerCamelCase_						,				help='''Batch size per GPU/CPU for training.'''						,				)
			# BEAM SEARCH arguments
			parser.add_argument(
			    '''--min_length'''						,				default=5_0						,				type=lowerCamelCase_						,				help='''Minimum number of tokens for the summaries.'''						,				)
			parser.add_argument(
			    '''--max_length'''						,				default=2_0_0						,				type=lowerCamelCase_						,				help='''Maixmum number of tokens for the summaries.'''						,				)
			parser.add_argument(
			    '''--beam_size'''						,				default=5						,				type=lowerCamelCase_						,				help='''The number of beams to start with for each example.'''						,				)
			parser.add_argument(
			    '''--alpha'''						,				default=0.95						,				type=lowerCamelCase_						,				help='''The value of alpha for the length penalty in the beam search.'''						,				)
			parser.add_argument(
			    '''--block_trigram'''						,				default=lowerCamelCase_						,				type=lowerCamelCase_						,				help='''Whether to block the existence of repeating trigrams in the text generated by beam search.'''						,				)
			__lowercase   =							parser.parse_args()

			# Select device (distibuted not available)
			__lowercase   =							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:
						__lowercase   =							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=lowerCamelCase_ )

			evaluate(lowerCamelCase_ )




def       _lowerCAmelCase  ( lowerCamelCase_	:     Optional[Any] ):
			if not os.path.exists(lowerCamelCase_ ):
						return False

			__lowercase   =							os.listdir(lowerCamelCase_ )
			if len(lowerCamelCase_ ) == 0:
						return False

			return True


if __name__ == "__main__":
	main()








from __future__ import annotations

from collections.abc import Iterable

class  a     :


						def __init__(				self  ,    A_  ,    A_		):







										'''simple docstring'''


										_UpperCAmelCase						,							_UpperCAmelCase				:						Optional[Any]       						=			float(A_		), float(A_		)


						def __eq__(				self  ,    A_		):







										'''simple docstring'''


										return self.x == other.x and self.y == other.y


						def __ne__(				self  ,    A_		):







										'''simple docstring'''


										return not self == other


						def __gt__(				self  ,    A_		):







										'''simple docstring'''


										if self.x > other.x:
														return True
										elif self.x == other.x:
														return self.y > other.y
										return False


						def __lt__(				self  ,    A_		):







										'''simple docstring'''


										return not self > other


						def __ge__(				self  ,    A_		):







										'''simple docstring'''


										if self.x > other.x:
														return True
										elif self.x == other.x:
														return self.y >= other.y
										return False


						def __le__(				self  ,    A_		):







										'''simple docstring'''


										if self.x < other.x:
														return True
										elif self.x == other.x:
														return self.y <= other.y
										return False


						def __repr__(				self		):







										'''simple docstring'''


										return f'({self.x}, {self.y})'





						def __hash__(				self		):







										'''simple docstring'''


										return hash(self.x		)







def 							__SCREAMING_SNAKE_CASE							( lowerCAmelCase:     list[Point] | list[list[float]] | Iterable[list[float]]     ,					)      ->				list[Point]:
				_UpperCAmelCase				:						list[Point]       						=			[]
				if list_of_tuples:
								for p in list_of_tuples:
												if isinstance(lowerCAmelCase     ,					lowerCAmelCase       ):
																points.append(lowerCAmelCase       )
												else:
																try:
																				points.append(Point(p[0]     ,					p[1]       )       )
																except (IndexError, TypeError):
																				print(
																				    F'Ignoring deformed point {p}. All points'
																				    " must have at least 2 coordinates."       )
				return points

def 							__SCREAMING_SNAKE_CASE							( lowerCAmelCase:     list[Point] | list[list[float]]       )      ->				list[Point]:

				if not hasattr(lowerCAmelCase     ,					"__iter__"       ):
								_UpperCAmelCase				:						Optional[Any]       						=			F'Expecting an iterable object but got an non-iterable type {points}'
								raise ValueError(lowerCAmelCase       )

				if not points:
								_UpperCAmelCase				:						str       						=			F'Expecting a list of points but got {points}'
								raise ValueError(lowerCAmelCase       )

				return _construct_points(lowerCAmelCase       )

def 							__SCREAMING_SNAKE_CASE							( lowerCAmelCase:     Point     ,					lowerCAmelCase:     Point     ,					lowerCAmelCase:     Point       )      ->				float:
				_UpperCAmelCase				:						Union[str, Any]       						=			(a.x * b.y + b.x * c.y + c.x * a.y) - (a.y * b.x + b.y * c.x + c.y * a.x)
				return det

def 							__SCREAMING_SNAKE_CASE							( lowerCAmelCase:     list[Point]       )      ->				list[Point]:
				_UpperCAmelCase				:						Union[str, Any]       						=			sorted(_validate_input(lowerCAmelCase       )       )
				_UpperCAmelCase				:						Any       						=			len(lowerCAmelCase       )
				_UpperCAmelCase				:						Any       						=			set()

				for i in range(n - 1       ):
								for j in range(i + 1     ,					lowerCAmelCase       ):
												_UpperCAmelCase				:						Optional[int]       						=			False
												_UpperCAmelCase				:						Union[str, Any]       						=			True
												for k in range(lowerCAmelCase       ):
																if k != i and k != j:
																				_UpperCAmelCase				:						Tuple       						=			_det(points[i]     ,					points[j]     ,					points[k]       )

																				if det_k > 0:
																								_UpperCAmelCase				:						str       						=			True
																				elif det_k < 0:
																								_UpperCAmelCase				:						Optional[Any]       						=			True
																				else:
																								# point[i], point[j], point[k] all lie on a straight line
																								# if point[k] is to the left of point[i] or it's to the
																								# right of point[j], then point[i], point[j] cannot be
																								# part of the convex hull of A
																								if points[k] < points[i] or points[k] > points[j]:
																												_UpperCAmelCase				:						Any       						=			False
																												break

																if points_left_of_ij and points_right_of_ij:
																				_UpperCAmelCase				:						int       						=			False
																				break

												if ij_part_of_convex_hull:
																convex_set.update([points[i], points[j]]       )

				return sorted(lowerCAmelCase       )

def 							__SCREAMING_SNAKE_CASE							( lowerCAmelCase:     list[Point]       )      ->				list[Point]:
				_UpperCAmelCase				:						List[Any]       						=			sorted(_validate_input(lowerCAmelCase       )       )
				_UpperCAmelCase				:						int       						=			len(lowerCAmelCase       )

				# divide all the points into an upper hull and a lower hull
				# the left most point and the right most point are definitely
				# members of the convex hull by definition.
				# use these two anchors to divide all the points into two hulls,
				# an upper hull and a lower hull.

				# all points to the left (above) the line joining the extreme points belong to the
				# upper hull
				# all points to the right (below) the line joining the extreme points below to the
				# lower hull
				# ignore all points on the line joining the extreme points since they cannot be
				# part of the convex hull

				_UpperCAmelCase				:						Tuple       						=			points[0]
				_UpperCAmelCase				:						Optional[Any]       						=			points[n - 1]

				_UpperCAmelCase				:						Optional[Any]       						=			{left_most_point, right_most_point}
				_UpperCAmelCase				:						Tuple       						=			[]
				_UpperCAmelCase				:						Optional[Any]       						=			[]

				for i in range(1     ,					n - 1       ):
								_UpperCAmelCase				:						Optional[int]       						=			_det(lowerCAmelCase     ,					lowerCAmelCase     ,					points[i]       )

								if det > 0:
												upper_hull.append(points[i]       )
								elif det < 0:
												lower_hull.append(points[i]       )

				_construct_hull(lowerCAmelCase     ,					lowerCAmelCase     ,					lowerCAmelCase     ,					lowerCAmelCase       )
				_construct_hull(lowerCAmelCase     ,					lowerCAmelCase     ,					lowerCAmelCase     ,					lowerCAmelCase       )

				return sorted(lowerCAmelCase       )

def 							__SCREAMING_SNAKE_CASE							( lowerCAmelCase:     list[Point]     ,					lowerCAmelCase:     Point     ,					lowerCAmelCase:     Point     ,					lowerCAmelCase:     set[Point]       )      ->				None:
				if points:
								_UpperCAmelCase				:						List[str]       						=			None
								_UpperCAmelCase				:						Optional[int]       						=			float("-inf"       )
								_UpperCAmelCase				:						Dict       						=			[]

								for p in points:
												_UpperCAmelCase				:						int       						=			_det(lowerCAmelCase     ,					lowerCAmelCase     ,					lowerCAmelCase       )

												if det > 0:
																candidate_points.append(lowerCAmelCase       )

																if det > extreme_point_distance:
																				_UpperCAmelCase				:						Dict       						=			det
																				_UpperCAmelCase				:						Union[str, Any]       						=			p

								if extreme_point:
												_construct_hull(lowerCAmelCase     ,					lowerCAmelCase     ,					lowerCAmelCase     ,					lowerCAmelCase       )
												convex_set.add(lowerCAmelCase       )
												_construct_hull(lowerCAmelCase     ,					lowerCAmelCase     ,					lowerCAmelCase     ,					lowerCAmelCase       )

def 							__SCREAMING_SNAKE_CASE							( lowerCAmelCase:     list[Point]       )      ->				list[Point]:
				_UpperCAmelCase				:						int       						=			sorted(_validate_input(lowerCAmelCase       )       )
				_UpperCAmelCase				:						Any       						=			len(lowerCAmelCase       )

				_UpperCAmelCase				:						List[Any]       						=			points[:2]
				for i in range(2     ,					lowerCAmelCase       ):
								_UpperCAmelCase				:						Tuple       						=			_det(convex_hull[1]     ,					convex_hull[0]     ,					points[i]       )
								if det > 0:
												convex_hull.insert(0     ,					points[i]       )
												break
								elif det < 0:
												convex_hull.append(points[i]       )
												break
								else:
												_UpperCAmelCase				:						Optional[int]       						=			points[i]
				i += 1

				for j in range(lowerCAmelCase     ,					lowerCAmelCase       ):
								if (
								    _det(convex_hull[0]     ,					convex_hull[-1]     ,					points[j]       ) > 0
								    and _det(convex_hull[-1]     ,					convex_hull[0]     ,					points[1]       ) < 0
								):
												# The point lies within the convex hull
												continue

								convex_hull.insert(0     ,					points[j]       )
								convex_hull.append(points[j]       )
								while _det(convex_hull[0]     ,					convex_hull[1]     ,					convex_hull[2]       ) >= 0:
												del convex_hull[1]
								while _det(convex_hull[-1]     ,					convex_hull[-2]     ,					convex_hull[-3]       ) <= 0:
												del convex_hull[-2]

    # `convex_hull` is contains the convex hull in circular order
				return sorted(convex_hull[1:] if len(lowerCAmelCase       ) > 3 else convex_hull       )

def 							__SCREAMING_SNAKE_CASE							( )      ->				List[str]:
				_UpperCAmelCase				:						Union[str, Any]       						=			[
				    (0, 3),
				    (2, 2),
				    (1, 1),
				    (2, 1),
				    (3, 0),
				    (0, 0),
				    (3, 3),
				    (2, -1),
				    (2, -4),
				    (1, -3),
				]
				# the convex set of points is
				# [(0, 0), (0, 3), (1, -3), (2, -4), (3, 0), (3, 3)]
				_UpperCAmelCase				:						int       						=			convex_hull_bf(lowerCAmelCase       )

				_UpperCAmelCase				:						Union[str, Any]       						=			convex_hull_recursive(lowerCAmelCase       )
				assert results_bf == results_recursive

				_UpperCAmelCase				:						int       						=			convex_hull_melkman(lowerCAmelCase       )
				assert results_bf == results_melkman

				print(lowerCAmelCase       )


if __name__ == "__main__":
					main()


