




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,
)


lowercase_     :    List[Any]      =			logging.getLogger(__name__)
logging.basicConfig(stream=sys.stdout, level=logging.INFO)


lowercase_     :    Union[str, Any]      =			namedtuple('Batch', ['document_names', 'batch_size', 'src', 'segs', 'mask_src', 'tgt_str'])


def 							__SCREAMING_SNAKE_CASE  (      snake_case_						):

						'''simple docstring'''


						_UpperCAmelCase													=					BertTokenizer.from_pretrained("bert-base-uncased"     ,   do_lower_case=A__						)
						_UpperCAmelCase													=					BertAbs.from_pretrained("remi/bertabs-finetuned-extractive-abstractive-summarization"						)
						model.to(args.device						)
						model.eval()

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

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

												import nltk
												import rouge

												nltk.download("punkt"						)
												_UpperCAmelCase													=					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.
						_UpperCAmelCase													=					''
						_UpperCAmelCase													=					''

						_UpperCAmelCase													=					build_data_iterator(A__     ,   A__						)
						_UpperCAmelCase													=					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__						):
												_UpperCAmelCase													=					predictor.translate_batch(A__						)
												_UpperCAmelCase													=					predictor.from_batch(A__						)
												_UpperCAmelCase													=					[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:
												_UpperCAmelCase													=					rouge_evaluator.get_scores(A__     ,   A__						)
												_UpperCAmelCase													=					format_rouge_scores(A__						)
												save_rouge_scores(A__						)
												print(A__						)


def 							__SCREAMING_SNAKE_CASE  (      snake_case_     ,   snake_case_     ,   snake_case_						):

						'''simple docstring'''


						for summary, document_name in zip(A__     ,   A__						):
												# Prepare the summary file's name
												if "." in document_name:
																		_UpperCAmelCase													=					'.'.join(document_name.split("."						)[:-1]						)
																		_UpperCAmelCase													=					document_name.split("."						)[-1]
																		_UpperCAmelCase													=					bare_document_name + '_summary.' + extension
												else:
																		_UpperCAmelCase													=					document_name + '_summary'

												_UpperCAmelCase													=					os.path.join(A__     ,   A__						)
												with open(A__     ,   "w"						) as output:
																		output.write(A__						)


def 							__SCREAMING_SNAKE_CASE  (      snake_case_						):

						'''simple docstring'''


						_UpperCAmelCase													=					translation
						_UpperCAmelCase													=					(
						    raw_summary.replace("[unused0]"     ,   ""						)
						    .replace("[unused3]"     ,   ""						)
						    .replace("[PAD]"     ,   ""						)
						    .replace("[unused1]"     ,   ""						)
						    .replace(R" +"     ,   " "						)
						    .replace(" [unused2] "     ,   ". "						)
						    .replace("[unused2]"     ,   ""						)
						    .strip()
						)

						return summary


def 							__SCREAMING_SNAKE_CASE  (      snake_case_						):

						'''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 							__SCREAMING_SNAKE_CASE  (      snake_case_						):

						'''simple docstring'''


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


def 							__SCREAMING_SNAKE_CASE  (      snake_case_     ,   snake_case_						):

						'''simple docstring'''


						_UpperCAmelCase													=					load_and_cache_examples(A__     ,   A__						)
						_UpperCAmelCase													=					SequentialSampler(A__						)

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

						_UpperCAmelCase													=					DataLoader(
						    A__     ,   sampler=A__     ,   batch_size=args.batch_size     ,   collate_fn=A__     ,   )

						return iterator


def 							__SCREAMING_SNAKE_CASE  (      snake_case_     ,   snake_case_						):

						'''simple docstring'''


						_UpperCAmelCase													=					CNNDMDataset(args.documents_dir						)
						return dataset


def 							__SCREAMING_SNAKE_CASE  (      snake_case_     ,   snake_case_     ,   snake_case_     ,   snake_case_						):

						'''simple docstring'''


						_UpperCAmelCase													=					[x for x in data if not len(x[1]						) == 0]  # remove empty_files
						_UpperCAmelCase													=					[name for name, _, _ in data]
						_UpperCAmelCase													=					[' '.join(A__						) for _, _, summary_list in data]

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

						_UpperCAmelCase													=					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 							__SCREAMING_SNAKE_CASE  (      snake_case_     ,   snake_case_						):

						'''simple docstring'''


						_UpperCAmelCase													=					summary_tokens.to("cpu"						).numpy()
						_UpperCAmelCase													=					tokenizer.decode(A__						)
						_UpperCAmelCase													=					summary.split("."						)
						_UpperCAmelCase													=					[s + '.' for s in sentences]
						return sentences


def 							__SCREAMING_SNAKE_CASE  (      ):

						'''simple docstring'''


						_UpperCAmelCase													=					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."     ,   )
						_UpperCAmelCase													=					parser.parse_args()

						# Select device (distibuted not available)
						_UpperCAmelCase													=					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:
												_UpperCAmelCase													=					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 							__SCREAMING_SNAKE_CASE  (      snake_case_						):

						'''simple docstring'''


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

						_UpperCAmelCase													=					os.listdir(A__						)
						if len(A__						) == 0:
												return False

						return True


if __name__ == "__main__":
						main()










class    __lowerCAmelCase			:







			def __init__(	self   :      Dict   ,   snake_case__   :      List[str]=None				):




									"""simple docstring"""


									_UpperCAmelCase													=					data
									_UpperCAmelCase													=					None







			def __repr__(	self   :      Union[str, Any]				):




									"""simple docstring"""


									_UpperCAmelCase													=					""
									_UpperCAmelCase													=					self
									while temp:
															string_rep += F"""<{temp.data}> ---> """
															_UpperCAmelCase													=					temp.next
									string_rep += "<END>"
									return string_rep




def 							__SCREAMING_SNAKE_CASE  (      snake_case_						):

						'''simple docstring'''


						if not elements_list:
												raise Exception("The Elements List is empty"						)

						# Set first element as Head
						_UpperCAmelCase													=					Node(elements_list[0]						)
						_UpperCAmelCase													=					head
						# Loop through elements from position 1
						for data in elements_list[1:]:
												_UpperCAmelCase													=					Node(snake_case_						)
												_UpperCAmelCase													=					current.next
						return head


lowercase_     :    str      =			[1, 3, 5, 32, 44, 12, 43]
print(f"""List: {list_data}""")
print('Creating Linked List from List.')
lowercase_     :    Dict      =			make_linked_list(list_data)
print('Linked List:')





print(linked_list)




