
import json
import os
import re
import warnings
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


a__				:      Union[str, Any]    		=     logging.get_logger(__name__)

a__				:      Optional[int]    		=     {
    '''source_spm''': '''source.spm''',
    '''target_spm''': '''target.spm''',
    '''vocab''': '''vocab.json''',
    '''target_vocab_file''': '''target_vocab.json''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
}

a__				:      int    		=     {
    '''source_spm''': {
        '''Helsinki-NLP/opus-mt-en-de''': '''https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/source.spm'''
    },
    '''target_spm''': {
        '''Helsinki-NLP/opus-mt-en-de''': '''https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/target.spm'''
    },
    '''vocab''': {
        '''Helsinki-NLP/opus-mt-en-de''': '''https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/vocab.json'''
    },
    '''tokenizer_config_file''': {
        '''Helsinki-NLP/opus-mt-en-de''': (
            '''https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/tokenizer_config.json'''
        )
    },
}

a__				:      Dict    		=     {'''Helsinki-NLP/opus-mt-en-de''': 512}
a__				:      List[str]    		=     {}






class     a_       (			a__							):



       """simple docstring"""
       __SCREAMING_SNAKE_CASE       :					List[Any]						=  VOCAB_FILES_NAMES
       __SCREAMING_SNAKE_CASE       :					int						=  PRETRAINED_VOCAB_FILES_MAP
       __SCREAMING_SNAKE_CASE       :					str						=  PRETRAINED_INIT_CONFIGURATION
       __SCREAMING_SNAKE_CASE       :					Optional[Any]						=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       __SCREAMING_SNAKE_CASE       :					Dict						=  ['input_ids', 'attention_mask']
       __SCREAMING_SNAKE_CASE       :					Optional[int]						=  re.compile('>>.+<<'							)  # type: re.Pattern






       def __init__(		self				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase=None				,       _lowerCamelCase=None				,       _lowerCamelCase=None				,       _lowerCamelCase="<unk>"				,       _lowerCamelCase="</s>"				,       _lowerCamelCase="<pad>"				,       _lowerCamelCase=512				,       _lowerCamelCase = None				,       _lowerCamelCase=False				,       **_lowerCamelCase				,       )					->None:
           SCREAMING_SNAKE_CASE				:				List[str]				      = {} if sp_model_kwargs is None else sp_model_kwargs

           super().__init__(
               # bos_token=bos_token,  unused. Start decoding with config.decoder_start_token_id
               source_lang=_lowerCamelCase				,       target_lang=_lowerCamelCase				,       unk_token=_lowerCamelCase				,       eos_token=_lowerCamelCase				,       pad_token=_lowerCamelCase				,       model_max_length=_lowerCamelCase				,       sp_model_kwargs=self.sp_model_kwargs				,       target_vocab_file=_lowerCamelCase				,       separate_vocabs=_lowerCamelCase				,       **_lowerCamelCase				,       )
           assert Path(_lowerCamelCase   ).exists(), F"""cannot find spm source {source_spm}"""

           SCREAMING_SNAKE_CASE				:				int				      = separate_vocabs
           SCREAMING_SNAKE_CASE				:				Optional[int]				      = load_json(_lowerCamelCase   )
           if self.unk_token not in self.encoder:
               raise KeyError('''<unk> token must be in vocab'''   )
           assert self.pad_token in self.encoder

           if separate_vocabs:
               SCREAMING_SNAKE_CASE				:				Any				      = load_json(_lowerCamelCase   )
               SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = {v: k for k, v in self.target_encoder.items()}
               SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = []
           else:
               SCREAMING_SNAKE_CASE				:				int				      = {v: k for k, v in self.encoder.items()}
               SCREAMING_SNAKE_CASE				:				list				      = [k for k in self.encoder if k.startswith('''>>'''   ) and k.endswith('''<<'''   )]

           SCREAMING_SNAKE_CASE				:				int				      = source_lang
           SCREAMING_SNAKE_CASE				:				str				      = target_lang
           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = [source_spm, target_spm]

           # load SentencePiece model for pre-processing
           SCREAMING_SNAKE_CASE				:				List[Any]				      = load_spm(_lowerCamelCase				,       self.sp_model_kwargs   )
           SCREAMING_SNAKE_CASE				:				Optional[int]				      = load_spm(_lowerCamelCase				,       self.sp_model_kwargs   )
           SCREAMING_SNAKE_CASE				:				int				      = self.spm_source
           SCREAMING_SNAKE_CASE				:				int				      = self.encoder

           # Multilingual target side: default to using first supported language code.

           self._setup_normalizer()






       def 	__lowerCAmelCase     (		self   )					->Optional[Any]:
           try:
               from sacremoses import MosesPunctNormalizer

               SCREAMING_SNAKE_CASE				:				List[str]				      = MosesPunctNormalizer(self.source_lang   ).normalize
           except (ImportError, FileNotFoundError):
               warnings.warn('''Recommended: pip install sacremoses.'''   )
               SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = lambda _lowerCamelCase   : x






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase   )					->str:
           return self.punc_normalizer(_lowerCamelCase   ) if x else ""






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase   )					->Optional[int]:
           return self.current_encoder.get(_lowerCamelCase				,       self.current_encoder[self.unk_token]   )






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase   )					->str:
           SCREAMING_SNAKE_CASE				:				Dict				      = self.language_code_re.match(_lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				list				      = [match.group(0   )] if match else []
           return code, self.language_code_re.sub(''''''				,       _lowerCamelCase   )






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase   )					->List[str]:
           SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE				:				int				      = self.remove_language_code(_lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				List[Any]				      = self.current_spm.encode(_lowerCamelCase				,       out_type=_lowerCamelCase   )
           return code + pieces






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase   )					->str:
           return self.decoder.get(_lowerCamelCase				,       self.unk_token   )






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase				,       **_lowerCamelCase   )					->Optional[Any]:
           return super().batch_decode(_lowerCamelCase				,       **_lowerCamelCase   )






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase				,       **_lowerCamelCase   )					->Union[str, Any]:
           return super().decode(_lowerCamelCase				,       **_lowerCamelCase   )






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase   )					->str:
           SCREAMING_SNAKE_CASE				:				Any				      = self.spm_source if self._decode_use_source_tokenizer else self.spm_target
           SCREAMING_SNAKE_CASE				:				Optional[int]				      = []
           SCREAMING_SNAKE_CASE				:				str				      = ''''''
           for token in tokens:
               # make sure that special tokens are not decoded using sentencepiece model
               if token in self.all_special_tokens:
                   out_string += sp_model.decode_pieces(_lowerCamelCase   ) + token + " "
                   SCREAMING_SNAKE_CASE				:				Optional[Any]				      = []
               else:
                   current_sub_tokens.append(_lowerCamelCase   )
           out_string += sp_model.decode_pieces(_lowerCamelCase   )
           return out_string.strip()






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase				,       _lowerCamelCase=None   )					->List[int]:
           if token_ids_a is None:
               return token_ids_a + [self.eos_token_id]
           # We don't expect to process pairs, but leave the pair logic for API consistency
           return token_ids_a + token_ids_a + [self.eos_token_id]






       def 	__lowerCAmelCase     (		self   )					->str:
           SCREAMING_SNAKE_CASE				:				Optional[int]				      = self.spm_source
           SCREAMING_SNAKE_CASE				:				Optional[int]				      = self.encoder






       def 	__lowerCAmelCase     (		self   )					->int:
           SCREAMING_SNAKE_CASE				:				Tuple				      = self.spm_target
           if self.separate_vocabs:
               SCREAMING_SNAKE_CASE				:				Optional[Any]				      = self.target_encoder






       @property
       def 	__lowerCAmelCase     (		self   )					->int:
           return len(self.encoder   )






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase				,       _lowerCamelCase = None   )					->Tuple[str]:
           if not os.path.isdir(_lowerCamelCase   ):
               logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""   )
               return
           SCREAMING_SNAKE_CASE				:				Optional[int]				      = []

           if self.separate_vocabs:
               SCREAMING_SNAKE_CASE				:				Any				      = os.path.join(
                   _lowerCamelCase				,       (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab''']				,       )
               SCREAMING_SNAKE_CASE				:				List[str]				      = os.path.join(
                   _lowerCamelCase				,       (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''target_vocab_file''']				,       )
               save_json(self.encoder				,       _lowerCamelCase   )
               save_json(self.target_encoder				,       _lowerCamelCase   )
               saved_files.append(_lowerCamelCase   )
               saved_files.append(_lowerCamelCase   )
           else:
               SCREAMING_SNAKE_CASE				:				Optional[Any]				      = os.path.join(
                   _lowerCamelCase				,       (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab''']   )
               save_json(self.encoder				,       _lowerCamelCase   )
               saved_files.append(_lowerCamelCase   )

           for spm_save_filename, spm_orig_path, spm_model in zip(
               [VOCAB_FILES_NAMES['''source_spm'''], VOCAB_FILES_NAMES['''target_spm''']]				,       self.spm_files				,       [self.spm_source, self.spm_target]				,       ):
               SCREAMING_SNAKE_CASE				:				List[Any]				      = os.path.join(
                   _lowerCamelCase				,       (filename_prefix + '''-''' if filename_prefix else '''''') + spm_save_filename   )
               if os.path.abspath(_lowerCamelCase   ) != os.path.abspath(_lowerCamelCase   ) and os.path.isfile(_lowerCamelCase   ):
                   copyfile(_lowerCamelCase				,       _lowerCamelCase   )
                   saved_files.append(_lowerCamelCase   )
               elif not os.path.isfile(_lowerCamelCase   ):
                   with open(_lowerCamelCase				,       '''wb'''   ) as fi:
                       SCREAMING_SNAKE_CASE				:				Tuple				      = spm_model.serialized_model_proto()
                       fi.write(_lowerCamelCase   )
                   saved_files.append(_lowerCamelCase   )

           return tuple(_lowerCamelCase   )






       def 	__lowerCAmelCase     (		self   )					->Dict:
           return self.get_src_vocab()






       def 	__lowerCAmelCase     (		self   )					->Tuple:
           return dict(self.encoder				,       **self.added_tokens_encoder   )






       def 	__lowerCAmelCase     (		self   )					->str:
           return dict(self.target_encoder				,       **self.added_tokens_decoder   )






       def __getstate__(		self   )					->Dict:
           SCREAMING_SNAKE_CASE				:				Tuple				      = self.__dict__.copy()
           state.update(
               {k: None for k in ['''spm_source''', '''spm_target''', '''current_spm''', '''punc_normalizer''', '''target_vocab_file''']}   )
           return state






       def __setstate__(		self				,       _lowerCamelCase   )					->None:
           SCREAMING_SNAKE_CASE				:				Any				      = d

           # for backward compatibility
           if not hasattr(self				,       '''sp_model_kwargs'''   ):
               SCREAMING_SNAKE_CASE				:				Dict				      = {}

           SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE				:				Any				      = (load_spm(_lowerCamelCase				,       self.sp_model_kwargs   ) for f in self.spm_files)
           SCREAMING_SNAKE_CASE				:				int				      = self.spm_source
           self._setup_normalizer()






       def 	__lowerCAmelCase     (		self				,       *_lowerCamelCase				,       **_lowerCamelCase   )					->Union[str, Any]:
           return 1






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase   )					->Tuple:
           SCREAMING_SNAKE_CASE				:				int				      = set(self.all_special_ids   )  # call it once instead of inside list comp
           all_special_ids.remove(self.unk_token_id   )  # <unk> is only sometimes special
           return [1 if x in all_special_ids else 0 for x in seq]






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase				,       _lowerCamelCase = None				,       _lowerCamelCase = False   )					->List[int]:
           if already_has_special_tokens:
               return self._special_token_mask(_lowerCamelCase   )
           elif token_ids_a is None:
               return self._special_token_mask(_lowerCamelCase   ) + [1]
           else:
               return self._special_token_mask(token_ids_a + token_ids_a   ) + [1]







def 				UpperCAmelCase_(    a__ ,						a__   ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				Any				      = sentencepiece.SentencePieceProcessor(**a__   )
    spm.Load(a__   )
    return spm







def 				UpperCAmelCase_(    a__ ,						a__   ):



    """simple docstring"""



    with open(a__ ,						'''w'''   ) as f:
        json.dump(a__ ,						a__ ,						indent=2   )







def 				UpperCAmelCase_(    a__   ):



    """simple docstring"""



    with open(a__ ,						'''r'''   ) as f:
        return json.load(a__   )


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,
)


a__				:      Any    		=     logging.getLogger(__name__)
logging.basicConfig(stream=sys.stdout, level=logging.INFO)


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







def 				UpperCAmelCase_(    a__   ):



    """simple docstring"""



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

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

    if args.compute_rouge:
        SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = []
        SCREAMING_SNAKE_CASE				:				Dict				      = []

        import nltk
        import rouge

        nltk.download('''punkt'''   )
        SCREAMING_SNAKE_CASE				:				Tuple				      = 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.
    SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = ''''''
    SCREAMING_SNAKE_CASE				:				Tuple				      = ''''''

    SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = build_data_iterator(a__ ,						a__   )
    SCREAMING_SNAKE_CASE				:				str				      = 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__   ):
        SCREAMING_SNAKE_CASE				:				Tuple				      = predictor.translate_batch(a__   )
        SCREAMING_SNAKE_CASE				:				Optional[Any]				      = predictor.from_batch(a__   )
        SCREAMING_SNAKE_CASE				:				List[str]				      = [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:
        SCREAMING_SNAKE_CASE				:				List[Any]				      = rouge_evaluator.get_scores(a__ ,						a__   )
        SCREAMING_SNAKE_CASE				:				Dict				      = 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:
            SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = '''.'''.join(document_name.split('''.'''   )[:-1]   )
            SCREAMING_SNAKE_CASE				:				int				      = document_name.split('''.'''   )[-1]
            SCREAMING_SNAKE_CASE				:				Tuple				      = bare_document_name + '''_summary.''' + extension
        else:
            SCREAMING_SNAKE_CASE				:				Optional[int]				      = document_name + '''_summary'''

        SCREAMING_SNAKE_CASE				:				List[Any]				      = os.path.join(a__ ,						a__   )
        with open(a__ ,						'''w'''   ) as output:
            output.write(a__   )







def 				UpperCAmelCase_(    a__   ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE				:				Any				      = translation
    SCREAMING_SNAKE_CASE				:				int				      = (
        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"""



    SCREAMING_SNAKE_CASE				:				List[str]				      = load_and_cache_examples(a__ ,						a__   )
    SCREAMING_SNAKE_CASE				:				List[str]				      = SequentialSampler(a__   )

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

    SCREAMING_SNAKE_CASE				:				Dict				      = DataLoader(
        a__ ,						sampler=a__ ,						batch_size=args.batch_size ,						collate_fn=a__ ,						)

    return iterator







def 				UpperCAmelCase_(    a__ ,						a__   ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				Optional[int]				      = CNNDMDataset(args.documents_dir   )
    return dataset







def 				UpperCAmelCase_(    a__ ,						a__ ,						a__ ,						a__   ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				str				      = [x for x in data if not len(x[1]   ) == 0]  # remove empty_files
    SCREAMING_SNAKE_CASE				:				int				      = [name for name, _, _ in data]
    SCREAMING_SNAKE_CASE				:				List[str]				      = [''' '''.join(a__   ) for _, _, summary_list in data]

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

    SCREAMING_SNAKE_CASE				:				Optional[int]				      = 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"""



    SCREAMING_SNAKE_CASE				:				Optional[Any]				      = summary_tokens.to('''cpu'''   ).numpy()
    SCREAMING_SNAKE_CASE				:				str				      = tokenizer.decode(a__   )
    SCREAMING_SNAKE_CASE				:				int				      = summary.split('''.'''   )
    SCREAMING_SNAKE_CASE				:				Dict				      = [s + '''.''' for s in sentences]
    return sentences







def 				UpperCAmelCase_(    ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				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.''' ,						)
    SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = parser.parse_args()

    # Select device (distibuted not available)
    SCREAMING_SNAKE_CASE				:				List[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:
        SCREAMING_SNAKE_CASE				:				Tuple				      = 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

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

    return True


if __name__ == "__main__":
      main()
