
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,
)


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


__snake_case					:							Optional[Any]           =							namedtuple('Batch', ['document_names', 'batch_size', 'src', 'segs', 'mask_src', 'tgt_str'])




def 		A						(  SCREAMING_SNAKE_CASE							):




			"""simple docstring"""


			UpperCAmelCase__		:str 		=    BertTokenizer.from_pretrained('bert-base-uncased'     ,						do_lower_case=__SCREAMING_SNAKE_CASE							)
			UpperCAmelCase__		:Optional[Any] 		=    BertAbs.from_pretrained('remi/bertabs-finetuned-extractive-abstractive-summarization'							)
			model.to(args.device							)
			model.eval()

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

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

						import nltk
						import rouge

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

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

			UpperCAmelCase__		:str 		=    build_data_iterator(__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE							)
			UpperCAmelCase__		:Tuple 		=    build_predictor(__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE							)

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

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

			if args.compute_rouge:
						UpperCAmelCase__		:Optional[Any] 		=    rouge_evaluator.get_scores(__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE							)
						UpperCAmelCase__		:List[str] 		=    format_rouge_scores(__SCREAMING_SNAKE_CASE							)
						save_rouge_scores(__SCREAMING_SNAKE_CASE							)
						print(__SCREAMING_SNAKE_CASE							)




def 		A						(  SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE							):




			"""simple docstring"""


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

						UpperCAmelCase__		:Tuple 		=    os.path.join(__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE							)
						with open(__SCREAMING_SNAKE_CASE     ,						'w'							) as output:
									output.write(__SCREAMING_SNAKE_CASE							)




def 		A						(  SCREAMING_SNAKE_CASE							):




			"""simple docstring"""


			UpperCAmelCase__		:Any 		=    translation
			UpperCAmelCase__		:Optional[Any] 		=    (
			    raw_summary.replace('[unused0]'     ,						''							)
			    .replace('[unused3]'     ,						''							)
			    .replace('[PAD]'     ,						''							)
			    .replace('[unused1]'     ,						''							)
			    .replace(r' +'     ,						' '							)
			    .replace(' [unused2] '     ,						'. '							)
			    .replace('[unused2]'     ,						''							)
			    .strip()
			)

			return summary




def 		A						(  SCREAMING_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 		A						(  SCREAMING_SNAKE_CASE							):




			"""simple docstring"""


			with open('rouge_scores.txt'     ,						'w'							) as output:
						output.write(__SCREAMING_SNAKE_CASE							)




def 		A						(  SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE							):




			"""simple docstring"""


			UpperCAmelCase__		:str 		=    load_and_cache_examples(__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE							)
			UpperCAmelCase__		:Union[str, Any] 		=    SequentialSampler(__SCREAMING_SNAKE_CASE							)

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

			UpperCAmelCase__		:int 		=    DataLoader(
			    __SCREAMING_SNAKE_CASE     ,						sampler=__SCREAMING_SNAKE_CASE     ,						batch_size=args.batch_size     ,						collate_fn=__SCREAMING_SNAKE_CASE     ,						)

			return iterator




def 		A						(  SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE							):




			"""simple docstring"""


			UpperCAmelCase__		:List[str] 		=    CNNDMDataset(args.documents_dir							)
			return dataset




def 		A						(  SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE							):




			"""simple docstring"""


			UpperCAmelCase__		:List[Any] 		=    [x for x in data if not len(x[1]							) == 0]  # remove empty_files
			UpperCAmelCase__		:str 		=    [name for name, _, _ in data]
			UpperCAmelCase__		:int 		=    [" ".join(__SCREAMING_SNAKE_CASE							) for _, _, summary_list in data]

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

			UpperCAmelCase__		:Optional[int] 		=    Batch(
			    document_names=__SCREAMING_SNAKE_CASE     ,						batch_size=len(__SCREAMING_SNAKE_CASE							)     ,						src=encoded_stories.to(__SCREAMING_SNAKE_CASE							)     ,						segs=encoder_token_type_ids.to(__SCREAMING_SNAKE_CASE							)     ,						mask_src=encoder_mask.to(__SCREAMING_SNAKE_CASE							)     ,						tgt_str=__SCREAMING_SNAKE_CASE     ,						)

			return batch




def 		A						(  SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE							):




			"""simple docstring"""


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




def 		A						(  ):




			"""simple docstring"""


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

			# Select device (distibuted not available)
			UpperCAmelCase__		:int 		=    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__		:Dict 		=    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=__SCREAMING_SNAKE_CASE							)

			evaluate(__SCREAMING_SNAKE_CASE							)




def 		A						(  SCREAMING_SNAKE_CASE							):




			"""simple docstring"""


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

			UpperCAmelCase__		:str 		=    os.listdir(__SCREAMING_SNAKE_CASE							)
			if len(__SCREAMING_SNAKE_CASE							) == 0:
						return False

			return True


if __name__ == "__main__":
						main()







'''simple docstring'''
def     UpperCamelCase_  (						__SCREAMING_SNAKE_CASE							):
    """simple docstring"""






    if number != int(__SCREAMING_SNAKE_CASE							):
        raise ValueError("factorial() only accepts integral values"							)
    if number < 0:
        raise ValueError("factorial() not defined for negative values"							)
    snake_case_			:     int        =    1
    for i in range(1,		number + 1							):
        value *= i
    return value

def     UpperCamelCase_  (						__SCREAMING_SNAKE_CASE							):
    """simple docstring"""






    if not isinstance(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							):
        raise ValueError("factorial() only accepts integral values"							)
    if n < 0:
        raise ValueError("factorial() not defined for negative values"							)
    return 1 if n == 0 or n == 1 else n * factorial(n - 1							)


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  a_           =    int(input("Enter a positive integer: ").strip() or 0)
  print(f"""factorial{n} is {factorial(n)}""")




