




'''simple docstring'''







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							:						Optional[int]							     =  logging.getLogger(__name__)
logging.basicConfig(stream=sys.stdout, level=logging.INFO)


_lowerCamelCase							:						List[Any]							     =  namedtuple('Batch', ['document_names', 'batch_size', 'src', 'segs', 'mask_src', 'tgt_str'])






def 		_lowercase		(     lowercase_  ):



		'''simple docstring'''







		_snake_case     	=	BertTokenizer.from_pretrained("bert-base-uncased"						,							do_lower_case=lowercase_  )
		_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=lowercase_						,							length_limit=args.beam_size						,							length_limit_type="words"						,							apply_avg=lowercase_						,							apply_best=lowercase_						,							alpha=0.5						,							weight_factor=1.2						,							stemming=lowercase_						,							)

		# 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(lowercase_						,							lowercase_  )
		_snake_case     	=	build_predictor(lowercase_						,							lowercase_						,							lowercase_						,							lowercase_  )

		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(lowercase_  ):
				_snake_case     	=	predictor.translate_batch(lowercase_  )
				_snake_case     	=	predictor.from_batch(lowercase_  )
				_snake_case     	=	[format_summary(lowercase_  ) for t in translations]
				save_summaries(lowercase_						,							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(lowercase_						,							lowercase_  )
				_snake_case     	=	format_rouge_scores(lowercase_  )
				save_rouge_scores(lowercase_  )
				print(lowercase_  )






def 		_lowercase		(     lowercase_						,							lowercase_						,							lowercase_  ):



		'''simple docstring'''







		for summary, document_name in zip(lowercase_						,							lowercase_  ):
				# 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(lowercase_						,							lowercase_  )
				with open(lowercase_						,							"w"  ) as output:
						output.write(lowercase_  )






def 		_lowercase		(     lowercase_  ):



		'''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 		_lowercase		(     lowercase_  ):



		'''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 		_lowercase		(     lowercase_  ):



		'''simple docstring'''







		with open("rouge_scores.txt"						,							"w"  ) as output:
				output.write(lowercase_  )






def 		_lowercase		(     lowercase_						,							lowercase_  ):



		'''simple docstring'''







		_snake_case     	=	load_and_cache_examples(lowercase_						,							lowercase_  )
		_snake_case     	=	SequentialSampler(lowercase_  )

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

		_snake_case     	=	DataLoader(
		    lowercase_						,							sampler=lowercase_						,							batch_size=args.batch_size						,							collate_fn=lowercase_						,							)

		return iterator






def 		_lowercase		(     lowercase_						,							lowercase_  ):



		'''simple docstring'''







		_snake_case     	=	CNNDMDataset(args.documents_dir  )
		return dataset






def 		_lowercase		(     lowercase_						,							lowercase_						,							lowercase_						,							lowercase_  ):



		'''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(lowercase_  ) for _, _, summary_list in data]

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

		_snake_case     	=	Batch(
		    document_names=lowercase_						,							batch_size=len(lowercase_  )						,							src=encoded_stories.to(lowercase_  )						,							segs=encoder_token_type_ids.to(lowercase_  )						,							mask_src=encoder_mask.to(lowercase_  )						,							tgt_str=lowercase_						,							)

		return batch






def 		_lowercase		(     lowercase_						,							lowercase_  ):



		'''simple docstring'''







		_snake_case     	=	summary_tokens.to("cpu"  ).numpy()
		_snake_case     	=	tokenizer.decode(lowercase_  )
		_snake_case     	=	summary.split("."  )
		_snake_case     	=	[s + "." for s in sentences]
		return sentences






def 		_lowercase		(     ):



		'''simple docstring'''







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

		evaluate(lowercase_  )






def 		_lowercase		(     lowercase_  ):



		'''simple docstring'''







		if not os.path.exists(lowercase_  ):
				return False

		_snake_case     	=	os.listdir(lowercase_  )
		if len(lowercase_  ) == 0:
				return False

		return True


if __name__ == "__main__":
						main()











'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_lowerCamelCase							:						List[Any]							     =  {
    'configuration_mobilenet_v1': [
        'MOBILENET_V1_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'MobileNetV1Config',
        'MobileNetV1OnnxConfig',
    ],
}

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_lowerCamelCase							:						str							     =  ['MobileNetV1FeatureExtractor']
						_lowerCamelCase							:						Tuple							     =  ['MobileNetV1ImageProcessor']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_lowerCamelCase							:						List[Any]							     =  [
						    'MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'MobileNetV1ForImageClassification',
						    'MobileNetV1Model',
						    'MobileNetV1PreTrainedModel',
						    'load_tf_weights_in_mobilenet_v1',
						]


if TYPE_CHECKING:
						from .configuration_mobilenet_va import (
						    MOBILENET_V1_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    MobileNetVaConfig,
						    MobileNetVaOnnxConfig,
						)

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .feature_extraction_mobilenet_va import MobileNetVaFeatureExtractor
												from .image_processing_mobilenet_va import MobileNetVaImageProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_mobilenet_va import (
												    MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST,
												    MobileNetVaForImageClassification,
												    MobileNetVaModel,
												    MobileNetVaPreTrainedModel,
												    load_tf_weights_in_mobilenet_va,
												)


else:
						import sys

						_lowerCamelCase							:						Union[str, Any]							     =  _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





