






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__			 =	logging.getLogger(__name__)
logging.basicConfig(stream=sys.stdout, level=logging.INFO)


UpperCAmelCase__			 =	namedtuple('''Batch''', ['''document_names''', '''batch_size''', '''src''', '''segs''', '''mask_src''', '''tgt_str'''])







def 						a_      (__A			)							->	str:



					"""simple docstring"""
					__a				:	Dict			=				BertTokenizer.from_pretrained("bert-base-uncased"							,					do_lower_case=__A			)
					__a				:	List[str]			=				BertAbs.from_pretrained("remi/bertabs-finetuned-extractive-abstractive-summarization"			)
					model.to(args.device			)
					model.eval()

					__a				:	Union[str, Any]			=				{
					    "BOS": tokenizer.vocab["[unused0]"],
					    "EOS": tokenizer.vocab["[unused1]"],
					    "PAD": tokenizer.vocab["[PAD]"],
					}

					if args.compute_rouge:
										__a				:	str			=				[]
										__a				:	List[str]			=				[]

										import nltk
										import rouge

										nltk.download("punkt"			)
										__a				:	Optional[Any]			=				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.
					__a				:	Union[str, Any]			=				""
					__a				:	Tuple			=				""

					__a				:	Union[str, Any]			=				build_data_iterator(__A							,					__A			)
					__a				:	Union[str, Any]			=				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			):
										__a				:	Tuple			=				predictor.translate_batch(__A			)
										__a				:	Tuple			=				predictor.from_batch(__A			)
										__a				:	Any			=				[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:
										__a				:	Dict			=				rouge_evaluator.get_scores(__A							,					__A			)
										__a				:	int			=				format_rouge_scores(__A			)
										save_rouge_scores(__A			)
										print(__A			)







def 						a_      (__A							,					__A							,					__A			)							->	Dict:



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

										__a				:	Union[str, Any]			=				os.path.join(__A							,					__A			)
										with open(__A							,					"w"			) as output:
															output.write(__A			)







def 						a_      (__A			)							->	Any:



					"""simple docstring"""
					__a			, __a			, __a				:	Union[str, Any]			=				translation
					__a				:	List[str]			=				(
					    raw_summary.replace("[unused0]"							,					""			)
					    .replace("[unused3]"							,					""			)
					    .replace("[PAD]"							,					""			)
					    .replace("[unused1]"							,					""			)
					    .replace(r" +"							,					" "			)
					    .replace(" [unused2] "							,					". "			)
					    .replace("[unused2]"							,					""			)
					    .strip()
					)

					return summary







def 						a_      (__A			)							->	Optional[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			)							->	List[str]:



					"""simple docstring"""
					with open("rouge_scores.txt"							,					"w"			) as output:
										output.write(__A			)







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



					"""simple docstring"""
					__a				:	Any			=				load_and_cache_examples(__A							,					__A			)
					__a				:	Dict			=				SequentialSampler(__A			)

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

					__a				:	str			=				DataLoader(
					    __A							,					sampler=__A							,					batch_size=args.batch_size							,					collate_fn=__A							,					)

					return iterator







def 						a_      (__A							,					__A			)							->	List[Any]:



					"""simple docstring"""
					__a				:	Tuple			=				CNNDMDataset(args.documents_dir			)
					return dataset







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



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

					__a				:	str			=				[encode_for_summarization(__A							,					__A							,					__A			) for _, story, summary in data]
					__a				:	Optional[Any]			=				torch.tensor(
					    [truncate_or_pad(__A							,					__A							,					tokenizer.pad_token_id			) for story, _ in encoded_text]			)
					__a				:	Tuple			=				compute_token_type_ids(__A							,					tokenizer.cls_token_id			)
					__a				:	Union[str, Any]			=				build_mask(__A							,					tokenizer.pad_token_id			)

					__a				:	List[Any]			=				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			)							->	Union[str, Any]:



					"""simple docstring"""
					__a				:	str			=				summary_tokens.to("cpu"			).numpy()
					__a				:	Tuple			=				tokenizer.decode(__A			)
					__a				:	Optional[int]			=				summary.split("."			)
					__a				:	Union[str, Any]			=				[s + "." for s in sentences]
					return sentences







def 						a_      ()							->	Any:



					"""simple docstring"""
					__a				:	Tuple			=				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."							,					)
					__a				:	Any			=				parser.parse_args()

					# Select device (distibuted not available)
					__a				:	str			=				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:
										__a				:	List[Any]			=				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

					__a				:	Dict			=				os.listdir(__A			)
					if len(__A			) == 0:
										return False

					return True


if __name__ == "__main__":
			main()













from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase__			 =	{'''configuration_pegasus''': ['''PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PegasusConfig''']}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			UpperCAmelCase__			 =	['''PegasusTokenizer''']

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			UpperCAmelCase__			 =	['''PegasusTokenizerFast''']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			UpperCAmelCase__			 =	[
			    '''PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''PegasusForCausalLM''',
			    '''PegasusForConditionalGeneration''',
			    '''PegasusModel''',
			    '''PegasusPreTrainedModel''',
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			UpperCAmelCase__			 =	[
			    '''TFPegasusForConditionalGeneration''',
			    '''TFPegasusModel''',
			    '''TFPegasusPreTrainedModel''',
			]

try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			UpperCAmelCase__			 =	[
			    '''FlaxPegasusForConditionalGeneration''',
			    '''FlaxPegasusModel''',
			    '''FlaxPegasusPreTrainedModel''',
			]


if TYPE_CHECKING:
			from .configuration_pegasus import PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP, PegasusConfig

			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_pegasus import PegasusTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_pegasus_fast import PegasusTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_pegasus import (
						    PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
						    PegasusForCausalLM,
						    PegasusForConditionalGeneration,
						    PegasusModel,
						    PegasusPreTrainedModel,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_pegasus import TFPegasusForConditionalGeneration, TFPegasusModel, TFPegasusPreTrainedModel

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_flax_pegasus import (
						    FlaxPegasusForConditionalGeneration,
						    FlaxPegasusModel,
						    FlaxPegasusPreTrainedModel,
						)

else:
			import sys

			UpperCAmelCase__			 =	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





