
"""simple docstring"""




from typing import TYPE_CHECKING

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


snake_case					  =						{
    'configuration_led': ['LED_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LEDConfig'],
    'tokenization_led': ['LEDTokenizer'],
}

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

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	snake_case					  =						[
	    'LED_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'LEDForConditionalGeneration',
	    'LEDForQuestionAnswering',
	    'LEDForSequenceClassification',
	    'LEDModel',
	    'LEDPreTrainedModel',
	]


try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	snake_case					  =						['TFLEDForConditionalGeneration', 'TFLEDModel', 'TFLEDPreTrainedModel']


if TYPE_CHECKING:
	from .configuration_led import LED_PRETRAINED_CONFIG_ARCHIVE_MAP, LEDConfig
	from .tokenization_led import LEDTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_led_fast import LEDTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_led import (
		    LED_PRETRAINED_MODEL_ARCHIVE_LIST,
		    LEDForConditionalGeneration,
		    LEDForQuestionAnswering,
		    LEDForSequenceClassification,
		    LEDModel,
		    LEDPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_led import TFLEDForConditionalGeneration, TFLEDModel, TFLEDPreTrainedModel

else:
	import sys

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






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 (		SCREAMING_SNAKE_CASE_				:							int	):
					__lowerCAmelCase							  =			BertTokenizer.from_pretrained("bert-base-uncased" , do_lower_case=SCREAMING_SNAKE_CASE_	)
					__lowerCAmelCase							  =			BertAbs.from_pretrained("remi/bertabs-finetuned-extractive-abstractive-summarization"	)
					model.to(args.device	)
					model.eval()

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

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

										import nltk
										import rouge

										nltk.download("punkt"	)
										__lowerCAmelCase							  =			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.
					__lowerCAmelCase							  =			""
					__lowerCAmelCase							  =			""

					__lowerCAmelCase							  =			build_data_iterator(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_	)
					__lowerCAmelCase							  =			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_	):
										__lowerCAmelCase							  =			predictor.translate_batch(SCREAMING_SNAKE_CASE_	)
										__lowerCAmelCase							  =			predictor.from_batch(SCREAMING_SNAKE_CASE_	)
										__lowerCAmelCase							  =			[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:
										__lowerCAmelCase							  =			rouge_evaluator.get_scores(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_	)
										__lowerCAmelCase							  =			format_rouge_scores(SCREAMING_SNAKE_CASE_	)
										save_rouge_scores(SCREAMING_SNAKE_CASE_	)
										print(SCREAMING_SNAKE_CASE_	)







def       _a (		SCREAMING_SNAKE_CASE_				:							List[str] , SCREAMING_SNAKE_CASE_				:							Optional[Any] , SCREAMING_SNAKE_CASE_				:							Dict	):
					for summary, document_name in zip(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_	):
										# Prepare the summary file's name
										if "." in document_name:
															__lowerCAmelCase							  =			".".join(document_name.split("."	)[:-1]	)
															__lowerCAmelCase							  =			document_name.split("."	)[-1]
															__lowerCAmelCase							  =			bare_document_name + "_summary." + extension
										else:
															__lowerCAmelCase							  =			document_name + "_summary"

										__lowerCAmelCase							  =			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_				:							Optional[int]	):
					__lowerCAmelCase  ,				__lowerCAmelCase  ,				__lowerCAmelCase							  =			translation
					__lowerCAmelCase							  =			(
					    raw_summary.replace("[unused0]" , ""	)
					    .replace("[unused3]" , ""	)
					    .replace("[PAD]" , ""	)
					    .replace("[unused1]" , ""	)
					    .replace(R" +" , " "	)
					    .replace(" [unused2] " , ". "	)
					    .replace("[unused2]" , ""	)
					    .strip()
					)

					return summary







def       _a (		SCREAMING_SNAKE_CASE_				:							Union[str, Any]	):
					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_				:							Optional[int]	):
					with open("rouge_scores.txt" , "w"	) as output:
										output.write(SCREAMING_SNAKE_CASE_	)







def       _a (		SCREAMING_SNAKE_CASE_				:							str , SCREAMING_SNAKE_CASE_				:							Optional[int]	):
					__lowerCAmelCase							  =			load_and_cache_examples(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_	)
					__lowerCAmelCase							  =			SequentialSampler(SCREAMING_SNAKE_CASE_	)

					def collate_fn(SCREAMING_SNAKE_CASE_				:							List[Any]	):
										return collate(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , block_size=5_12 , device=args.device	)

					__lowerCAmelCase							  =			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_				:							Union[str, Any] , SCREAMING_SNAKE_CASE_				:							List[Any]	):
					__lowerCAmelCase							  =			CNNDMDataset(args.documents_dir	)
					return dataset







def       _a (		SCREAMING_SNAKE_CASE_				:							Optional[int] , SCREAMING_SNAKE_CASE_				:							Optional[Any] , SCREAMING_SNAKE_CASE_				:							str , SCREAMING_SNAKE_CASE_				:							int	):
					__lowerCAmelCase							  =			[x for x in data if not len(x[1]	) == 0]  # remove empty_files
					__lowerCAmelCase							  =			[name for name, _, _ in data]
					__lowerCAmelCase							  =			[" ".join(SCREAMING_SNAKE_CASE_	) for _, _, summary_list in data]

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

					__lowerCAmelCase							  =			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_				:							Dict , SCREAMING_SNAKE_CASE_				:							Optional[Any]	):
					__lowerCAmelCase							  =			summary_tokens.to("cpu"	).numpy()
					__lowerCAmelCase							  =			tokenizer.decode(SCREAMING_SNAKE_CASE_	)
					__lowerCAmelCase							  =			summary.split("."	)
					__lowerCAmelCase							  =			[s + "." for s in sentences]
					return sentences







def       _a (		):
					__lowerCAmelCase							  =			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=2_00 , 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." , )
					__lowerCAmelCase							  =			parser.parse_args()

					# Select device (distibuted not available)
					__lowerCAmelCase							  =			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							  =			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_				:							Optional[int]	):
					if not os.path.exists(SCREAMING_SNAKE_CASE_	):
										return False

					__lowerCAmelCase							  =			os.listdir(SCREAMING_SNAKE_CASE_	)
					if len(SCREAMING_SNAKE_CASE_	) == 0:
										return False

					return True


if __name__ == "__main__":
					main()




