




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,
)


_lowerCamelCase					:     Tuple					  =  logging.getLogger(__name__)
logging.basicConfig(stream=sys.stdout, level=logging.INFO)


_lowerCamelCase					:     int					  =  namedtuple("Batch", ["document_names", "batch_size", "src", "segs", "mask_src", "tgt_str"])




def 							_UpperCAmelCase     (UpperCamelCase_       :       int				):
				'''simple docstring'''
				_lowerCAmelCase			:			Optional[int]        =					BertTokenizer.from_pretrained("""bert-base-uncased"""					,			do_lower_case=UpperCamelCase_				)
				_lowerCAmelCase			:			Dict        =					BertAbs.from_pretrained("""remi/bertabs-finetuned-extractive-abstractive-summarization"""				)
				model.to(args.device				)
				model.eval()

				_lowerCAmelCase			:			List[Any]        =					{
				    """BOS""": tokenizer.vocab["""[unused0]"""],
				    """EOS""": tokenizer.vocab["""[unused1]"""],
				    """PAD""": tokenizer.vocab["""[PAD]"""],
				}

				if args.compute_rouge:
								_lowerCAmelCase			:			str        =					[]
								_lowerCAmelCase			:			str        =					[]

								import nltk
								import rouge

								nltk.download("""punkt"""				)
								_lowerCAmelCase			:			Union[str, Any]        =					rouge.Rouge(
								    metrics=["""rouge-n""", """rouge-l"""]					,			max_n=2					,			limit_length=UpperCamelCase_					,			length_limit=args.beam_size					,			length_limit_type="""words"""					,			apply_avg=UpperCamelCase_					,			apply_best=UpperCamelCase_					,			alpha=0.5					,			weight_factor=1.2					,			stemming=UpperCamelCase_					,			)

				# these (unused) arguments are defined to keep the compatibility
				# with the legacy code and will be deleted in a next iteration.
				_lowerCAmelCase			:			Tuple        =					""""""
				_lowerCAmelCase			:			Dict        =					""""""

				_lowerCAmelCase			:			Tuple        =					build_data_iterator(UpperCamelCase_					,			UpperCamelCase_				)
				_lowerCAmelCase			:			Dict        =					build_predictor(UpperCamelCase_					,			UpperCamelCase_					,			UpperCamelCase_					,			UpperCamelCase_				)

				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(UpperCamelCase_				):
								_lowerCAmelCase			:			Any        =					predictor.translate_batch(UpperCamelCase_				)
								_lowerCAmelCase			:			int        =					predictor.from_batch(UpperCamelCase_				)
								_lowerCAmelCase			:			Tuple        =					[format_summary(UpperCamelCase_				) for t in translations]
								save_summaries(UpperCamelCase_					,			args.summaries_output_dir					,			batch.document_names				)

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

				if args.compute_rouge:
								_lowerCAmelCase			:			Any        =					rouge_evaluator.get_scores(UpperCamelCase_					,			UpperCamelCase_				)
								_lowerCAmelCase			:			int        =					format_rouge_scores(UpperCamelCase_				)
								save_rouge_scores(UpperCamelCase_				)
								print(UpperCamelCase_				)




def 							_UpperCAmelCase     (UpperCamelCase_       :       Tuple					,			UpperCamelCase_       :       List[str]					,			UpperCamelCase_       :       List[Any]				):
				'''simple docstring'''
				for summary, document_name in zip(UpperCamelCase_					,			UpperCamelCase_				):
								# Prepare the summary file's name
								if "." in document_name:
												_lowerCAmelCase			:			List[str]        =					""".""".join(document_name.split("""."""				)[:-1]				)
												_lowerCAmelCase			:			Union[str, Any]        =					document_name.split("""."""				)[-1]
												_lowerCAmelCase			:			int        =					bare_document_name + """_summary.""" + extension
								else:
												_lowerCAmelCase			:			Any        =					document_name + """_summary"""

								_lowerCAmelCase			:			List[str]        =					os.path.join(UpperCamelCase_					,			UpperCamelCase_				)
								with open(UpperCamelCase_					,			"""w"""				) as output:
												output.write(UpperCamelCase_				)




def 							_UpperCAmelCase     (UpperCamelCase_       :       Optional[Any]				):
				'''simple docstring'''
				_lowerCAmelCase    ,			_lowerCAmelCase    ,			_lowerCAmelCase			:			Union[str, Any]        =					translation
				_lowerCAmelCase			:			Any        =					(
				    raw_summary.replace("""[unused0]"""					,			""""""				)
				    .replace("""[unused3]"""					,			""""""				)
				    .replace("""[PAD]"""					,			""""""				)
				    .replace("""[unused1]"""					,			""""""				)
				    .replace(R""" +"""					,			""" """				)
				    .replace(""" [unused2] """					,			""". """				)
				    .replace("""[unused2]"""					,			""""""				)
				    .strip()
				)

				return summary




def 							_UpperCAmelCase     (UpperCamelCase_       :       Optional[int]				):
				'''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     (UpperCamelCase_       :       Dict				):
				'''simple docstring'''
				with open("""rouge_scores.txt"""					,			"""w"""				) as output:
								output.write(UpperCamelCase_				)




def 							_UpperCAmelCase     (UpperCamelCase_       :       str					,			UpperCamelCase_       :       Dict				):
				'''simple docstring'''
				_lowerCAmelCase			:			Any        =					load_and_cache_examples(UpperCamelCase_					,			UpperCamelCase_				)
				_lowerCAmelCase			:			Union[str, Any]        =					SequentialSampler(UpperCamelCase_				)

				def collate_fn(UpperCamelCase_       :       Optional[int]				):
								return collate(UpperCamelCase_					,			UpperCamelCase_					,			block_size=512					,			device=args.device				)

				_lowerCAmelCase			:			Optional[int]        =					DataLoader(
				    UpperCamelCase_					,			sampler=UpperCamelCase_					,			batch_size=args.batch_size					,			collate_fn=UpperCamelCase_					,			)

				return iterator




def 							_UpperCAmelCase     (UpperCamelCase_       :       Tuple					,			UpperCamelCase_       :       str				):
				'''simple docstring'''
				_lowerCAmelCase			:			Union[str, Any]        =					CNNDMDataset(args.documents_dir				)
				return dataset




def 							_UpperCAmelCase     (UpperCamelCase_       :       Optional[int]					,			UpperCamelCase_       :       str					,			UpperCamelCase_       :       Optional[Any]					,			UpperCamelCase_       :       Union[str, Any]				):
				'''simple docstring'''
				_lowerCAmelCase			:			int        =					[x for x in data if not len(x[1]				) == 0]  # remove empty_files
				_lowerCAmelCase			:			Any        =					[name for name, _, _ in data]
				_lowerCAmelCase			:			Tuple        =					[""" """.join(UpperCamelCase_				) for _, _, summary_list in data]

				_lowerCAmelCase			:			int        =					[encode_for_summarization(UpperCamelCase_					,			UpperCamelCase_					,			UpperCamelCase_				) for _, story, summary in data]
				_lowerCAmelCase			:			str        =					torch.tensor(
				    [truncate_or_pad(UpperCamelCase_					,			UpperCamelCase_					,			tokenizer.pad_token_id				) for story, _ in encoded_text]				)
				_lowerCAmelCase			:			str        =					compute_token_type_ids(UpperCamelCase_					,			tokenizer.cls_token_id				)
				_lowerCAmelCase			:			Dict        =					build_mask(UpperCamelCase_					,			tokenizer.pad_token_id				)

				_lowerCAmelCase			:			Dict        =					Batch(
				    document_names=UpperCamelCase_					,			batch_size=len(UpperCamelCase_				)					,			src=encoded_stories.to(UpperCamelCase_				)					,			segs=encoder_token_type_ids.to(UpperCamelCase_				)					,			mask_src=encoder_mask.to(UpperCamelCase_				)					,			tgt_str=UpperCamelCase_					,			)

				return batch




def 							_UpperCAmelCase     (UpperCamelCase_       :       str					,			UpperCamelCase_       :       Tuple				):
				'''simple docstring'''
				_lowerCAmelCase			:			Union[str, Any]        =					summary_tokens.to("""cpu"""				).numpy()
				_lowerCAmelCase			:			List[str]        =					tokenizer.decode(UpperCamelCase_				)
				_lowerCAmelCase			:			Optional[Any]        =					summary.split("""."""				)
				_lowerCAmelCase			:			Optional[int]        =					[s + """.""" for s in sentences]
				return sentences




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

				# Select device (distibuted not available)
				_lowerCAmelCase			:			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:
								_lowerCAmelCase			:			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=UpperCamelCase_				)

				evaluate(UpperCamelCase_				)




def 							_UpperCAmelCase     (UpperCamelCase_       :       Tuple				):
				'''simple docstring'''
				if not os.path.exists(UpperCamelCase_				):
								return False

				_lowerCAmelCase			:			Optional[int]        =					os.listdir(UpperCamelCase_				)
				if len(UpperCamelCase_				) == 0:
								return False

				return True


if __name__ == "__main__":
	main()







import numpy as np  # noqa: E402

from ...configuration_utils import ConfigMixin, register_to_config
from ...schedulers.scheduling_utils import SchedulerMixin


try:
	import librosa  # noqa: E402

	_lowerCamelCase					:     Union[str, Any]					  =  True
	_lowerCamelCase					:     Optional[Any]					  =  ""
except Exception as e:
	_lowerCamelCase					:     int					  =  False
	_lowerCamelCase					:     List[str]					  =  (
	    F'''Cannot import librosa because {e}. Make sure to correctly install librosa to be able to install it.'''
	)


from PIL import Image  # noqa: E402
class 				__snake_case     (_a  ,		_a						):
				lowerCAmelCase__			=   "mel_config.json"



				@register_to_config
				def __init__(					self					:						Dict						,		_UpperCAmelCase					:						int = 256						,		_UpperCAmelCase					:						int = 256						,		_UpperCAmelCase					:						int = 2_2050						,		_UpperCAmelCase					:						int = 2048						,		_UpperCAmelCase					:						int = 512						,		_UpperCAmelCase					:						int = 80						,		_UpperCAmelCase					:						int = 32						,		)     ->			List[Any]:





								'''simple docstring'''




								_lowerCAmelCase			:			List[str]        =					hop_length
								_lowerCAmelCase			:			Optional[Any]        =					sample_rate
								_lowerCAmelCase			:			Optional[Any]        =					n_fft
								_lowerCAmelCase			:			Dict        =					top_db
								_lowerCAmelCase			:			List[Any]        =					n_iter
								self.set_resolution(_UpperCAmelCase						,		_UpperCAmelCase					)
								_lowerCAmelCase			:			Optional[int]        =					None

								if not _librosa_can_be_imported:
												raise ValueError(_import_error					)



				def 					SCREAMING_SNAKE_CASE				(					self					:						Optional[int]						,		_UpperCAmelCase					:						int						,		_UpperCAmelCase					:						int					)     ->			List[Any]:





								'''simple docstring'''




								_lowerCAmelCase			:			Dict        =					x_res
								_lowerCAmelCase			:			Union[str, Any]        =					y_res
								_lowerCAmelCase			:			Any        =					self.y_res
								_lowerCAmelCase			:			Optional[Any]        =					self.x_res * self.hop_length - 1



				def 					SCREAMING_SNAKE_CASE				(					self					:						Dict						,		_UpperCAmelCase					:						str = None						,		_UpperCAmelCase					:						np.ndarray = None					)     ->			List[str]:





								'''simple docstring'''




								if audio_file is not None:
												_lowerCAmelCase    ,			_lowerCAmelCase			:			Dict        =					librosa.load(_UpperCAmelCase						,		mono=_UpperCAmelCase						,		sr=self.sr					)
								else:
												_lowerCAmelCase			:			Any        =					raw_audio

								# Pad with silence if necessary.
								if len(self.audio					) < self.x_res * self.hop_length:
												_lowerCAmelCase			:			Optional[Any]        =					np.concatenate([self.audio, np.zeros((self.x_res * self.hop_length - len(self.audio					),)					)]					)



				def 					SCREAMING_SNAKE_CASE				(					self					:						Any					)     ->			int:





								'''simple docstring'''




								return len(self.audio					) // self.slice_size



				def 					SCREAMING_SNAKE_CASE				(					self					:						List[str]						,		_UpperCAmelCase					:						int = 0					)     ->			np.ndarray:





								'''simple docstring'''




								return self.audio[self.slice_size * slice : self.slice_size * (slice + 1)]



				def 					SCREAMING_SNAKE_CASE				(					self					:						List[Any]					)     ->			int:





								'''simple docstring'''




								return self.sr



				def 					SCREAMING_SNAKE_CASE				(					self					:						Dict						,		_UpperCAmelCase					:						int					)     ->			Image.Image:





								'''simple docstring'''




								_lowerCAmelCase			:			Union[str, Any]        =					librosa.feature.melspectrogram(
								    y=self.get_audio_slice(_UpperCAmelCase					)						,		sr=self.sr						,		n_fft=self.n_fft						,		hop_length=self.hop_length						,		n_mels=self.n_mels					)
								_lowerCAmelCase			:			List[Any]        =					librosa.power_to_db(_UpperCAmelCase						,		ref=np.max						,		top_db=self.top_db					)
								_lowerCAmelCase			:			List[Any]        =					(((log_S + self.top_db) * 255 / self.top_db).clip(0						,		255					) + 0.5).astype(np.uinta					)
								_lowerCAmelCase			:			Optional[Any]        =					Image.fromarray(_UpperCAmelCase					)
								return image



				def 					SCREAMING_SNAKE_CASE				(					self					:						Optional[int]						,		_UpperCAmelCase					:						Image.Image					)     ->			np.ndarray:





								'''simple docstring'''




								_lowerCAmelCase			:			Optional[int]        =					np.frombuffer(image.tobytes()						,		dtype="""uint8"""					).reshape((image.height, image.width)					)
								_lowerCAmelCase			:			Optional[int]        =					bytedata.astype("""float"""					) * self.top_db / 255 - self.top_db
								_lowerCAmelCase			:			List[Any]        =					librosa.db_to_power(_UpperCAmelCase					)
								_lowerCAmelCase			:			Optional[int]        =					librosa.feature.inverse.mel_to_audio(
								    _UpperCAmelCase						,		sr=self.sr						,		n_fft=self.n_fft						,		hop_length=self.hop_length						,		n_iter=self.n_iter					)
								return audio

