






import logging
import pathlib
import re
import sys
from dataclasses import dataclass, field
from typing import Any, Callable, Dict, List, Optional, Set, Union

import datasets
import librosa
import numpy as np
import torch
from lang_trans import arabic
from packaging import version
from torch import nn

from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaProcessor,
    is_apex_available,
    trainer_utils,
)


if is_apex_available():
							from apex import amp

if version.parse(version.parse(torch.__version__).base_version) >= version.parse("""1.6"""):
							__UpperCamelCase   :						Union[str, Any]							=  True
							from torch.cuda.amp import autocast


__UpperCamelCase   :						Dict							=  logging.getLogger(__name__)


@dataclass
class 					__SCREAMING_SNAKE_CASE:
							_UpperCAmelCase	      =    field(
							    metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}		)
							_UpperCAmelCase	      =    field(
							    default=a_      ,					metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}      ,					)
							_UpperCAmelCase	      =    field(
							    default=a_      ,					metadata={"help": "Whether to freeze the feature extractor layers of the model."}		)
							_UpperCAmelCase	      =    field(
							    default=a_      ,					metadata={"help": "Whether to log verbose messages or not."}      ,					)


def        a_	( _A ,				_A					)     ->     Union[str, Any]:







				"""simple docstring"""
				logging.basicConfig(
				    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s' ,				datefmt='%m/%d/%Y %H:%M:%S' ,				handlers=[logging.StreamHandler(sys.stdout					)] ,				)
				snake_case__													=       logging.WARNING
				if model_args.verbose_logging:
								snake_case__													=       logging.DEBUG
				elif trainer_utils.is_main_process(training_args.local_rank					):
								snake_case__													=       logging.INFO
				logger.setLevel(_A					)


@dataclass
class 					__SCREAMING_SNAKE_CASE:
							_UpperCAmelCase	      =    field(
							    default=a_      ,					metadata={"help": "The name of the dataset to use (via the datasets library)."}		)
							_UpperCAmelCase	      =    field(
							    default=a_      ,					metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}		)
							_UpperCAmelCase	      =    field(
							    default="train"      ,					metadata={
							        "help": "The name of the training data set split to use (via the datasets library). Defaults to 'train'"
							    }      ,					)
							_UpperCAmelCase	      =    field(
							    default="validation"      ,					metadata={
							        "help": (
							            "The name of the validation data set split to use (via the datasets library). Defaults to 'validation'"
							        )
							    }      ,					)
							_UpperCAmelCase	      =    field(
							    default="text"      ,					metadata={"help": "Column in the dataset that contains label (target text). Defaults to 'text'"}      ,					)
							_UpperCAmelCase	      =    field(
							    default="file"      ,					metadata={"help": "Column in the dataset that contains speech file path. Defaults to 'file'"}      ,					)
							_UpperCAmelCase	      =    field(
							    default=a_      ,					metadata={"help": "Resample loaded audio to target feature extractor's sampling rate or not."}      ,					)
							_UpperCAmelCase	      =    field(
							    default=a_      ,					metadata={"help": "Filters out examples longer than specified. Defaults to no filtering."}      ,					)
							_UpperCAmelCase	      =    field(
							    default="librispeech"      ,					metadata={
							        "help": (
							            "Orthography used for normalization and tokenization: 'librispeech' (default), 'timit', or"
							            " 'buckwalter'."
							        )
							    }      ,					)
							_UpperCAmelCase	      =    field(
							    default=a_      ,					metadata={"help": "Overwrite the cached preprocessed datasets or not."}		)
							_UpperCAmelCase	      =    field(
							    default=a_      ,					metadata={"help": "The number of processes to use for the preprocessing."}      ,					)


@dataclass
class 					__SCREAMING_SNAKE_CASE:
							_UpperCAmelCase	      =    False
							_UpperCAmelCase	      =    None
							_UpperCAmelCase	      =    "|"
							_UpperCAmelCase	      =    field(default_factory=a_		)
							_UpperCAmelCase	      =    field(default_factory=a_		)
							_UpperCAmelCase	      =    None
							@classmethod
							def lowerCAmelCase_	(       cls: Optional[Any]    ,		UpperCamelCase: str  )						->						int:
											if name == "librispeech":
															return cls()
											if name == "timit":
															return cls(
															    do_lower_case=UpperCamelCase    ,		translation_table=str.maketrans({'-': ' '}  )    ,		)
											if name == "buckwalter":
															snake_case__													=       {
															    '-': ' ',  # sometimes used to represent pauses
															    '^': 'v',  # fixing "tha" in arabic_speech_corpus dataset
															}
															return cls(
															    vocab_file=pathlib.Path(UpperCamelCase  ).parent.joinpath('vocab/buckwalter.json'  )    ,		word_delimiter_token='/'    ,		translation_table=str.maketrans(UpperCamelCase  )    ,		words_to_remove={'sil'}    ,		untransliterator=arabic.buckwalter.untransliterate    ,		)
											raise ValueError(F'''Unsupported orthography: \'{name}\'.'''  )
							def lowerCAmelCase_	(       self: List[str]    ,		UpperCamelCase: str  )						->						str:
											# TODO(elgeish) return a pipeline (e.g., from jiwer) instead? Or rely on branch predictor as is
											if len(self.translation_table  ) > 0:
															snake_case__													=       text.translate(self.translation_table  )
											if len(self.words_to_remove  ) == 0:
															snake_case__													=       ' '.join(text.split()  )  # clean up whitespaces
											else:
															snake_case__													=       ' '.join(w for w in text.split() if w not in self.words_to_remove  )  # and clean up whilespaces
											return text


							def lowerCAmelCase_	(       self: Optional[int]    ,		UpperCamelCase: ModelArguments  )						->						WavaVecaProcessor:
											snake_case__													=       WavaVecaFeatureExtractor.from_pretrained(
											    model_args.model_name_or_path    ,		cache_dir=model_args.cache_dir  )
											if self.vocab_file:
															snake_case__													=       WavaVecaCTCTokenizer(
															    self.vocab_file    ,		cache_dir=model_args.cache_dir    ,		do_lower_case=self.do_lower_case    ,		word_delimiter_token=self.word_delimiter_token    ,		)
											else:
															snake_case__													=       WavaVecaCTCTokenizer.from_pretrained(
															    model_args.model_name_or_path    ,		cache_dir=model_args.cache_dir    ,		do_lower_case=self.do_lower_case    ,		word_delimiter_token=self.word_delimiter_token    ,		)
											return WavaVecaProcessor(UpperCamelCase    ,		UpperCamelCase  )


@dataclass
class 					__SCREAMING_SNAKE_CASE:
							_UpperCAmelCase	      =    42
							_UpperCAmelCase	      =    True
							_UpperCAmelCase	      =    None
							_UpperCAmelCase	      =    None
							_UpperCAmelCase	      =    None
							_UpperCAmelCase	      =    None
							def __call__(       self: Tuple    ,		UpperCamelCase: List[Dict[str, Union[List[int], torch.Tensor]]]  )						->						Dict[str, torch.Tensor]:
											# split inputs and labels since they have to be of different lenghts and need
											# different padding methods
											snake_case__													=       [{'input_values': feature['input_values']} for feature in features]
											snake_case__													=       [{'input_ids': feature['labels']} for feature in features]

											snake_case__													=       self.processor.pad(
											    UpperCamelCase    ,		padding=self.padding    ,		max_length=self.max_length    ,		pad_to_multiple_of=self.pad_to_multiple_of    ,		return_tensors='pt'    ,		)
											snake_case__													=       self.processor.pad(
											    labels=UpperCamelCase    ,		padding=self.padding    ,		max_length=self.max_length_labels    ,		pad_to_multiple_of=self.pad_to_multiple_of_labels    ,		return_tensors='pt'    ,		)

											# replace padding with -100 to ignore loss correctly
											snake_case__													=       labels_batch['input_ids'].masked_fill(labels_batch.attention_mask.ne(1  )    ,		-1_00  )

											snake_case__													=       labels

											return batch


class 					__SCREAMING_SNAKE_CASE(		a_		):
							def lowerCAmelCase_	(       self: Optional[Any]    ,		UpperCamelCase: nn.Module    ,		UpperCamelCase: Dict[str, Union[torch.Tensor, Any]]  )						->						torch.Tensor:
											model.train()
											snake_case__													=       self._prepare_inputs(UpperCamelCase  )

											if self.use_amp:
															with autocast():
																			snake_case__													=       self.compute_loss(UpperCamelCase    ,		UpperCamelCase  )
											else:
															snake_case__													=       self.compute_loss(UpperCamelCase    ,		UpperCamelCase  )

											if self.args.n_gpu > 1:
															if model.module.config.ctc_loss_reduction == "mean":
																			snake_case__													=       loss.mean()
															elif model.module.config.ctc_loss_reduction == "sum":
																			snake_case__													=       loss.sum() / (inputs['labels'] >= 0).sum()
															else:
																			raise ValueError(F'''{model.config.ctc_loss_reduction} is not valid. Choose one of [\'mean\', \'sum\']'''  )

											if self.args.gradient_accumulation_steps > 1:
															snake_case__													=       loss / self.args.gradient_accumulation_steps

											if self.use_amp:
															self.scaler.scale(UpperCamelCase  ).backward()
											elif self.use_apex:
															with amp.scale_loss(UpperCamelCase    ,		self.optimizer  ) as scaled_loss:
																			scaled_loss.backward()
											elif self.deepspeed:
															self.deepspeed.backward(UpperCamelCase  )
											else:
															loss.backward()

											return loss.detach()


def        a_	( )     ->     int:







				"""simple docstring"""
				# See all possible arguments in src/transformers/training_args.py
				# or by passing the --help flag to this script.
				# We now keep distinct sets of args, for a cleaner separation of concerns.

				snake_case__													=       HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)					)

				snake_case__							,  snake_case__							,  snake_case__													=       parser.parse_args_into_dataclasses()
				configure_logger(_A ,				_A					)

				snake_case__													=       Orthography.from_name(data_args.orthography.lower()					)
				snake_case__													=       orthography.create_processor(_A					)
				snake_case__													=       WavaVecaForCTC.from_pretrained(
				    model_args.model_name_or_path ,				cache_dir=model_args.cache_dir ,				gradient_checkpointing=training_args.gradient_checkpointing ,				vocab_size=len(processor.tokenizer					) ,				)

				snake_case__													=       datasets.load_dataset(
				    data_args.dataset_name ,				data_args.dataset_config_name ,				split=data_args.train_split_name					)
				snake_case__													=       datasets.load_dataset(
				    data_args.dataset_name ,				data_args.dataset_config_name ,				split=data_args.validation_split_name					)

				snake_case__													=       datasets.load_metric('wer'					)
				snake_case__													=       processor.feature_extractor.sampling_rate if data_args.target_feature_extractor_sampling_rate else None
				snake_case__													=       ''.join(t for t in processor.tokenizer.get_vocab().keys() if len(_A					) == 1					)
				snake_case__													=       re.compile(  # remove characters not in vocabulary
				    Rf'''[^\s{re.escape(_A					)}]''' ,				flags=re.IGNORECASE if processor.tokenizer.do_lower_case else 0 ,				)
				snake_case__													=       []

				def prepare_example(_A					):  # TODO(elgeish) make use of multiprocessing?
								snake_case__							,  snake_case__													=       librosa.load(example[data_args.speech_file_column] ,				sr=_A					)
								if data_args.max_duration_in_seconds is not None:
												snake_case__													=       len(example['speech']					) / example['sampling_rate']
								# Normalize and clean up text; order matters!
								snake_case__													=       orthography.preprocess_for_training(example[data_args.target_text_column]					)
								snake_case__													=       vocabulary_text_cleaner.sub('' ,				_A					)
								if updated_text != example[data_args.target_text_column]:
												text_updates.append((example[data_args.target_text_column], updated_text)					)
												snake_case__													=       updated_text
								return example

				snake_case__													=       train_dataset.map(_A ,				remove_columns=[data_args.speech_file_column]					)
				snake_case__													=       val_dataset.map(_A ,				remove_columns=[data_args.speech_file_column]					)

				if data_args.max_duration_in_seconds is not None:

								def filter_by_max_duration(_A					):
												return example["duration_in_seconds"] <= data_args.max_duration_in_seconds

								snake_case__													=       len(_A					)
								snake_case__													=       len(_A					)
								snake_case__													=       train_dataset.filter(_A ,				remove_columns=['duration_in_seconds']					)
								snake_case__													=       val_dataset.filter(_A ,				remove_columns=['duration_in_seconds']					)
								if len(_A					) > old_train_size:
												logger.warning(
												    f'''Filtered out {len(_A					) - old_train_size} train example(s) longer than'''
												    f''' {data_args.max_duration_in_seconds} second(s).'''					)
								if len(_A					) > old_val_size:
												logger.warning(
												    f'''Filtered out {len(_A					) - old_val_size} validation example(s) longer than'''
												    f''' {data_args.max_duration_in_seconds} second(s).'''					)
				logger.info(f'''Split sizes: {len(_A					)} train and {len(_A					)} validation.'''					)

				logger.warning(f'''Updated {len(_A					)} transcript(s) using \'{data_args.orthography}\' orthography rules.'''					)
				if logger.isEnabledFor(logging.DEBUG					):
								for original_text, updated_text in text_updates:
												logger.debug(f'''Updated text: "{original_text}" -> "{updated_text}"'''					)
				snake_case__													=       None

				def prepare_dataset(_A					):
								# check that all files have the correct sampling rate
								assert (
								    len(set(batch['sampling_rate']					)					) == 1
								), f'''Make sure all inputs have the same sampling rate of {processor.feature_extractor.sampling_rate}.'''

								snake_case__													=       processor(
								    audio=batch['speech'] ,				text=batch[data_args.target_text_column] ,				sampling_rate=batch['sampling_rate'][0]					)
								batch.update(_A					)
								return batch

				snake_case__													=       train_dataset.map(
				    _A ,				batch_size=training_args.per_device_train_batch_size ,				batched=_A ,				num_proc=data_args.preprocessing_num_workers ,				)
				snake_case__													=       val_dataset.map(
				    _A ,				batch_size=training_args.per_device_train_batch_size ,				batched=_A ,				num_proc=data_args.preprocessing_num_workers ,				)

				snake_case__													=       DataCollatorCTCWithPadding(processor=_A ,				padding=_A					)

				def compute_metrics(_A					):
								snake_case__													=       pred.predictions
								snake_case__													=       np.argmax(_A ,				axis=-1					)

								snake_case__													=       processor.tokenizer.pad_token_id

								snake_case__													=       processor.batch_decode(_A					)
								# we do not want to group tokens when computing the metrics
								snake_case__													=       processor.batch_decode(pred.label_ids ,				group_tokens=_A					)
								if logger.isEnabledFor(logging.DEBUG					):
												for reference, predicted in zip(_A ,				_A					):
																logger.debug(f'''reference: "{reference}"'''					)
																logger.debug(f'''predicted: "{predicted}"'''					)
																if orthography.untransliterator is not None:
																				logger.debug(f'''reference (untransliterated): "{orthography.untransliterator(_A					)}"'''					)
																				logger.debug(f'''predicted (untransliterated): "{orthography.untransliterator(_A					)}"'''					)

								snake_case__													=       wer_metric.compute(predictions=_A ,				references=_A					)

								return {"wer": wer}

				if model_args.freeze_feature_extractor:
								model.freeze_feature_extractor()

				snake_case__													=       CTCTrainer(
				    model=_A ,				data_collator=_A ,				args=_A ,				compute_metrics=_A ,				train_dataset=_A ,				eval_dataset=_A ,				tokenizer=processor.feature_extractor ,				)

				trainer.train()


if __name__ == "__main__":
							main()












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,
)


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


__UpperCamelCase   :						Tuple							=  namedtuple("""Batch""", ["""document_names""", """batch_size""", """src""", """segs""", """mask_src""", """tgt_str"""])
def        a_	( _A					)     ->     Union[str, Any]:







				"""simple docstring"""
				snake_case__													=       BertTokenizer.from_pretrained('bert-base-uncased' ,				do_lower_case=_A					)
				snake_case__													=       BertAbs.from_pretrained('remi/bertabs-finetuned-extractive-abstractive-summarization'					)
				model.to(args.device					)
				model.eval()

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

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

								import nltk
								import rouge

								nltk.download('punkt'					)
								snake_case__													=       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.
				snake_case__													=       ''
				snake_case__													=       ''

				snake_case__													=       build_data_iterator(_A ,				_A					)
				snake_case__													=       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					):
								snake_case__													=       predictor.translate_batch(_A					)
								snake_case__													=       predictor.from_batch(_A					)
								snake_case__													=       [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:
								snake_case__													=       rouge_evaluator.get_scores(_A ,				_A					)
								snake_case__													=       format_rouge_scores(_A					)
								save_rouge_scores(_A					)
								print(_A					)
def        a_	( _A ,				_A ,				_A					)     ->     Optional[Any]:







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

								snake_case__													=       os.path.join(_A ,				_A					)
								with open(_A ,				'w'					) as output:
												output.write(_A					)
def        a_	( _A					)     ->     Union[str, Any]:







				"""simple docstring"""
				snake_case__							,  snake_case__							,  snake_case__													=       translation
				snake_case__													=       (
				    raw_summary.replace('[unused0]' ,				''					)
				    .replace('[unused3]' ,				''					)
				    .replace('[PAD]' ,				''					)
				    .replace('[unused1]' ,				''					)
				    .replace(R' +' ,				' '					)
				    .replace(' [unused2] ' ,				'. '					)
				    .replace('[unused2]' ,				''					)
				    .strip()
				)

				return summary
def        a_	( _A					)     ->     Any:







				"""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_	( _A					)     ->     int:







				"""simple docstring"""
				with open('rouge_scores.txt' ,				'w'					) as output:
								output.write(_A					)
def        a_	( _A ,				_A					)     ->     Optional[Any]:







				"""simple docstring"""
				snake_case__													=       load_and_cache_examples(_A ,				_A					)
				snake_case__													=       SequentialSampler(_A					)

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

				snake_case__													=       DataLoader(
				    _A ,				sampler=_A ,				batch_size=args.batch_size ,				collate_fn=_A ,				)

				return iterator
def        a_	( _A ,				_A					)     ->     Any:







				"""simple docstring"""
				snake_case__													=       CNNDMDataset(args.documents_dir					)
				return dataset
def        a_	( _A ,				_A ,				_A ,				_A					)     ->     str:







				"""simple docstring"""
				snake_case__													=       [x for x in data if not len(x[1]					) == 0]  # remove empty_files
				snake_case__													=       [name for name, _, _ in data]
				snake_case__													=       [' '.join(_A					) for _, _, summary_list in data]

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

				snake_case__													=       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        a_	( _A ,				_A					)     ->     Optional[Any]:







				"""simple docstring"""
				snake_case__													=       summary_tokens.to('cpu'					).numpy()
				snake_case__													=       tokenizer.decode(_A					)
				snake_case__													=       summary.split('.'					)
				snake_case__													=       [s + '.' for s in sentences]
				return sentences
def        a_	( )     ->     int:







				"""simple docstring"""
				snake_case__													=       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.' ,				)
				snake_case__													=       parser.parse_args()

				# Select device (distibuted not available)
				snake_case__													=       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:
								snake_case__													=       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        a_	( _A					)     ->     str:







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

				snake_case__													=       os.listdir(_A					)
				if len(_A					) == 0:
								return False

				return True


if __name__ == "__main__":
							main()




