"""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,
)


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


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





def 			lowerCamelCase_ (					__lowerCAmelCase				)							->					Tuple:




						'''simple docstring'''



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

						# 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(__lowerCAmelCase   ,    __lowerCAmelCase				)
						lowerCamelCase__					=build_predictor(__lowerCAmelCase   ,    __lowerCAmelCase   ,    __lowerCAmelCase   ,    __lowerCAmelCase				)

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





def 			lowerCamelCase_ (					__lowerCAmelCase   ,    __lowerCAmelCase   ,    __lowerCAmelCase				)							->					Optional[Any]:




						'''simple docstring'''



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





def 			lowerCamelCase_ (					__lowerCAmelCase				)							->					List[Any]:




						'''simple docstring'''



						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 			lowerCamelCase_ (					__lowerCAmelCase				)							->					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 			lowerCamelCase_ (					__lowerCAmelCase				)							->					List[Any]:




						'''simple docstring'''



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





def 			lowerCamelCase_ (					__lowerCAmelCase   ,    __lowerCAmelCase				)							->					Union[str, Any]:




						'''simple docstring'''



						lowerCamelCase__					=load_and_cache_examples(__lowerCAmelCase   ,    __lowerCAmelCase				)
						lowerCamelCase__					=SequentialSampler(__lowerCAmelCase				)

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

						lowerCamelCase__					=DataLoader(
						    __lowerCAmelCase   ,    sampler=__lowerCAmelCase   ,    batch_size=args.batch_size   ,    collate_fn=__lowerCAmelCase   ,    )

						return iterator





def 			lowerCamelCase_ (					__lowerCAmelCase   ,    __lowerCAmelCase				)							->					Tuple:




						'''simple docstring'''



						lowerCamelCase__					=CNNDMDataset(args.documents_dir				)
						return dataset





def 			lowerCamelCase_ (					__lowerCAmelCase   ,    __lowerCAmelCase   ,    __lowerCAmelCase   ,    __lowerCAmelCase				)							->					str:




						'''simple docstring'''



						lowerCamelCase__					=[x for x in data if not len(x[1]				) == 0]  # remove empty_files
						lowerCamelCase__					=[name for name, _, _ in data]
						lowerCamelCase__					=[" ".join(__lowerCAmelCase				) for _, _, summary_list in data]

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

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

						return batch





def 			lowerCamelCase_ (					__lowerCAmelCase   ,    __lowerCAmelCase				)							->					Optional[Any]:




						'''simple docstring'''



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





def 			lowerCamelCase_ (					)							->					List[Any]:




						'''simple docstring'''



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

						evaluate(__lowerCAmelCase				)





def 			lowerCamelCase_ (					__lowerCAmelCase				)							->					List[str]:




						'''simple docstring'''



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

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

						return True


if __name__ == "__main__":
	main()



"""simple docstring"""


import unittest

import pytest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    MBartaaTokenizerFast,
    MBartConfig,
    MBartForConditionalGeneration,
    TranslationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch, slow

from .test_pipelines_common import ANY






@is_pipeline_test
class    __UpperCAmelCase						(	unittest.TestCase	):
				A__      :   Optional[Any]  							=       MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
				A__      :   int  							=       TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

				def 		_a	(      self	, _lowerCamelCase	, _lowerCamelCase	, _lowerCamelCase      ):
										if isinstance(model.config	, _lowerCamelCase      ):
																lowerCamelCase__					,      lowerCamelCase__					=list(tokenizer.lang_code_to_id.keys()      )[:2]
																lowerCamelCase__					=TranslationPipeline(model=_lowerCamelCase	, tokenizer=_lowerCamelCase	, src_lang=_lowerCamelCase	, tgt_lang=_lowerCamelCase      )
										else:
																lowerCamelCase__					=TranslationPipeline(model=_lowerCamelCase	, tokenizer=_lowerCamelCase      )
										return translator, ["Some string", "Some other text"]

				def 		_a	(      self	, _lowerCamelCase	, _lowerCamelCase      ):
										lowerCamelCase__					=translator("Some string"      )
										self.assertEqual(_lowerCamelCase	, [{"translation_text": ANY(_lowerCamelCase      )}]      )

										lowerCamelCase__					=translator(["Some string"]      )
										self.assertEqual(_lowerCamelCase	, [{"translation_text": ANY(_lowerCamelCase      )}]      )

										lowerCamelCase__					=translator(["Some string", "other string"]      )
										self.assertEqual(_lowerCamelCase	, [{"translation_text": ANY(_lowerCamelCase      )}, {"translation_text": ANY(_lowerCamelCase      )}]      )

				@require_torch
				def 		_a	(      self      ):
										lowerCamelCase__					=pipeline("translation_en_to_ro"	, model="patrickvonplaten/t5-tiny-random"	, framework="pt"      )
										lowerCamelCase__					=translator("This is a test string"	, max_length=20      )
										self.assertEqual(
										    _lowerCamelCase	, [
										        {
										            "translation_text": (
										                "Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide"
										                " Beide Beide"
										            )
										        }
										    ]	, )

				@require_tf
				def 		_a	(      self      ):
										lowerCamelCase__					=pipeline("translation_en_to_ro"	, model="patrickvonplaten/t5-tiny-random"	, framework="tf"      )
										lowerCamelCase__					=translator("This is a test string"	, max_length=20      )
										self.assertEqual(
										    _lowerCamelCase	, [
										        {
										            "translation_text": (
										                "Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide"
										                " Beide Beide"
										            )
										        }
										    ]	, )

				@require_torch
				def 		_a	(      self      ):
										lowerCamelCase__					=pipeline("translation_en_to_de"	, model="patrickvonplaten/t5-tiny-random"	, framework="pt"      )
										lowerCamelCase__					=translator("This is a test string"	, max_length=20      )
										self.assertEqual(
										    _lowerCamelCase	, [
										        {
										            "translation_text": (
										                "monoton monoton monoton monoton monoton monoton monoton monoton monoton monoton urine urine"
										                " urine urine urine urine urine urine urine"
										            )
										        }
										    ]	, )






				@require_tf
				def 		_a	(      self      ):
										lowerCamelCase__					=pipeline("translation_en_to_de"	, model="patrickvonplaten/t5-tiny-random"	, framework="tf"      )
										lowerCamelCase__					=translator("This is a test string"	, max_length=20      )
										self.assertEqual(
										    _lowerCamelCase	, [
										        {
										            "translation_text": (
										                "monoton monoton monoton monoton monoton monoton monoton monoton monoton monoton urine urine"
										                " urine urine urine urine urine urine urine"
										            )
										        }
										    ]	, )






class    __UpperCAmelCase						(	unittest.TestCase	):

				@require_torch
				@slow
				def 		_a	(      self      ):
										# We don't provide a default for this pair
										with self.assertRaises(_lowerCamelCase      ):
																pipeline(task="translation_cn_to_ar"      )

										# but we do for this one
										lowerCamelCase__					=pipeline(task="translation_en_to_de"      )
										self.assertEqual(translator._preprocess_params["src_lang"]	, "en"      )
										self.assertEqual(translator._preprocess_params["tgt_lang"]	, "de"      )

				@require_torch
				@slow
				def 		_a	(      self      ):
										lowerCamelCase__					=MBartForConditionalGeneration.from_pretrained("facebook/mbart-large-50-many-to-many-mmt"      )
										lowerCamelCase__					=MBartaaTokenizerFast.from_pretrained("facebook/mbart-large-50-many-to-many-mmt"      )

										lowerCamelCase__					=pipeline(task="translation"	, model=_lowerCamelCase	, tokenizer=_lowerCamelCase      )
										# Missing src_lang, tgt_lang
										with self.assertRaises(_lowerCamelCase      ):
																translator("This is a test"      )

										lowerCamelCase__					=translator("This is a test"	, src_lang="en_XX"	, tgt_lang="ar_AR"      )
										self.assertEqual(_lowerCamelCase	, [{"translation_text": "هذا إختبار"}]      )

										lowerCamelCase__					=translator("This is a test"	, src_lang="en_XX"	, tgt_lang="hi_IN"      )
										self.assertEqual(_lowerCamelCase	, [{"translation_text": "यह एक परीक्षण है"}]      )

										# src_lang, tgt_lang can be defined at pipeline call time
										lowerCamelCase__					=pipeline(task="translation"	, model=_lowerCamelCase	, tokenizer=_lowerCamelCase	, src_lang="en_XX"	, tgt_lang="ar_AR"      )
										lowerCamelCase__					=translator("This is a test"      )
										self.assertEqual(_lowerCamelCase	, [{"translation_text": "هذا إختبار"}]      )

				@require_torch
				def 		_a	(      self      ):
										lowerCamelCase__					="patrickvonplaten/t5-tiny-random"
										lowerCamelCase__					=pipeline(task="translation_cn_to_ar"	, model=_lowerCamelCase      )
										self.assertEqual(translator._preprocess_params["src_lang"]	, "cn"      )
										self.assertEqual(translator._preprocess_params["tgt_lang"]	, "ar"      )

				@require_torch
				def 		_a	(      self      ):
										lowerCamelCase__					="patrickvonplaten/t5-tiny-random"
										with pytest.warns(_lowerCamelCase	, match=r".*translation_en_to_de.*"      ):
																lowerCamelCase__					=pipeline(task="translation"	, model=_lowerCamelCase      )
										self.assertEqual(translator.task	, "translation_en_to_de"      )
										self.assertEqual(translator._preprocess_params["src_lang"]	, "en"      )
										self.assertEqual(translator._preprocess_params["tgt_lang"]	, "de"      )






				@require_torch
				def 		_a	(      self      ):
										with self.assertRaises(_lowerCamelCase      ):
																pipeline(task="translation"      )


