




'''simple docstring'''






# This test is meant to be run in on an instance with TPUs like this:
#
#   python examples/pytorch/xla_spawn.py --num_cores=8 tests/test_trainer_tpu.py
#
# Replace 8 with the number of TPU cores you have.
#

import sys
from typing import Dict

from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.utils import logging


A_ 			=		logging.get_logger(__name__)


if is_torch_available():
      import torch
      from torch import nn
      from torch.utils.data import Dataset

      from transformers import Trainer






      class 					lowercase_			(	lowerCAmelCase_       ):





           def __init__(    self    :				List[str]    ,       __lowerCamelCase    :				int = 101 ):
                 snake_case__					:      Optional[Any]  			=						length





           def __len__(    self    :				Optional[int] ):
                 return self.length





           def __getitem__(    self    :				Optional[int]    ,       __lowerCamelCase    :				Tuple ):
                 return i






      class 					lowercase_			:





           def __call__(    self    :				Union[str, Any]    ,       __lowerCamelCase    :				List[str] ):
                 return {"input_ids": torch.tensor(__lowerCamelCase ), "labels": torch.tensor(__lowerCamelCase )}






      class 					lowercase_			(	nn.Module       ):





           def __init__(    self    :				Dict ):
                 super().__init__()
                 # Add some (unused) params otherwise DDP will complain.
                 snake_case__					:      List[Any]  			=						nn.Linear(120    ,       80 )





           def  _lowerCAmelCase			(    self    :				Dict    ,       __lowerCamelCase    :				List[Any]    ,       __lowerCamelCase    :				str=None ):
                 if labels is not None:
                       return torch.tensor(0.0    ,       device=input_ids.device ), input_ids
                 else:
                       return input_ids






def 		UpperCamelCase__			(		)						->      Optional[int]:
      snake_case__					:      Dict  			=						HfArgumentParser((TrainingArguments,)		)
      sys.argv += ["--output_dir", "./examples"]
      snake_case__					:      Dict  			=						parser.parse_args_into_dataclasses()[0]

      logger.warning(
          f"Process rank: {training_args.local_rank}, device: {training_args.device}, "
          f"tpu_num_cores: {training_args.tpu_num_cores}"							,			)

      # Essentially, what we want to verify in the distributed case is
      # that we get all samples back, in the right order.
      # (this is crucial for prediction for instance)
      for dataset_length in [1001, 256, 15]:
            snake_case__					:      Any  			=						DummyDataset(__SCREAMING_SNAKE_CASE		)

            def compute_metrics(__SCREAMING_SNAKE_CASE		) -> Dict:
                  snake_case__					:      Dict  			=						list(range(len(__SCREAMING_SNAKE_CASE		)		)		)
                  snake_case__					:      Tuple  			=						p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
                  return {"success": success}

            snake_case__					:      List[Any]  			=						Trainer(
                model=DummyModel()							,			args=__SCREAMING_SNAKE_CASE							,			data_collator=DummyDataCollator()							,			eval_dataset=__SCREAMING_SNAKE_CASE							,			compute_metrics=__SCREAMING_SNAKE_CASE							,			)
            snake_case__					:      Union[str, Any]  			=						trainer.evaluate()
            logger.info(__SCREAMING_SNAKE_CASE		)
            if metrics["eval_success"] is not True:
                  logger.error(__SCREAMING_SNAKE_CASE		)
                  exit(1		)

            snake_case__					:      Dict  			=						trainer.predict(__SCREAMING_SNAKE_CASE		)
            logger.info(p.metrics		)
            if p.metrics["test_success"] is not True:
                  logger.error(p.metrics		)
                  exit(1		)

            snake_case__					:      Optional[int]  			=						2

            snake_case__					:      Any  			=						trainer.evaluate()
            logger.info(__SCREAMING_SNAKE_CASE		)
            if metrics["eval_success"] is not True:
                  logger.error(__SCREAMING_SNAKE_CASE		)
                  exit(1		)

            snake_case__					:      int  			=						trainer.predict(__SCREAMING_SNAKE_CASE		)
            logger.info(p.metrics		)
            if p.metrics["test_success"] is not True:
                  logger.error(p.metrics		)
                  exit(1		)

            snake_case__					:      Tuple  			=						None

      logger.info('🔥 All distributed tests successful'		)






def 		UpperCamelCase__			(		__SCREAMING_SNAKE_CASE		)						->      Any:
      # For xla_spawn (TPUs)
      main()


if __name__ == "__main__":
      main()






'''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 		UpperCamelCase__			(		__SCREAMING_SNAKE_CASE		)						->      Dict:
      snake_case__					:      Dict  			=						BertTokenizer.from_pretrained('bert-base-uncased'							,			do_lower_case=__SCREAMING_SNAKE_CASE		)
      snake_case__					:      List[Any]  			=						BertAbs.from_pretrained('remi/bertabs-finetuned-extractive-abstractive-summarization'		)
      model.to(args.device		)
      model.eval()

      snake_case__					:      Optional[int]  			=						{
          'BOS': tokenizer.vocab['[unused0]'],
          'EOS': tokenizer.vocab['[unused1]'],
          'PAD': tokenizer.vocab['[PAD]'],
      }

      if args.compute_rouge:
            snake_case__					:      Any  			=						[]
            snake_case__					:      Any  			=						[]

            import nltk
            import rouge

            nltk.download('punkt'		)
            snake_case__					:      str  			=						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.
      snake_case__					:      List[Any]  			=						''
      snake_case__					:      int  			=						''

      snake_case__					:      str  			=						build_data_iterator(__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		)
      snake_case__					:      Optional[Any]  			=						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		):
            snake_case__					:      Dict  			=						predictor.translate_batch(__SCREAMING_SNAKE_CASE		)
            snake_case__					:      Optional[Any]  			=						predictor.from_batch(__SCREAMING_SNAKE_CASE		)
            snake_case__					:      Union[str, Any]  			=						[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:
            snake_case__					:      int  			=						rouge_evaluator.get_scores(__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		)
            snake_case__					:      str  			=						format_rouge_scores(__SCREAMING_SNAKE_CASE		)
            save_rouge_scores(__SCREAMING_SNAKE_CASE		)
            print(__SCREAMING_SNAKE_CASE		)






def 		UpperCamelCase__			(		__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		)						->      Optional[int]:
      for summary, document_name in zip(__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		):
            # Prepare the summary file's name
            if "." in document_name:
                  snake_case__					:      str  			=						'.'.join(document_name.split('.'		)[:-1]		)
                  snake_case__					:      Optional[int]  			=						document_name.split('.'		)[-1]
                  snake_case__					:      Tuple  			=						bare_document_name + '_summary.' + extension
            else:
                  snake_case__					:      Union[str, Any]  			=						document_name + '_summary'

            snake_case__					:      Union[str, Any]  			=						os.path.join(__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		)
            with open(__SCREAMING_SNAKE_CASE							,			'w'		) as output:
                  output.write(__SCREAMING_SNAKE_CASE		)






def 		UpperCamelCase__			(		__SCREAMING_SNAKE_CASE		)						->      List[Any]:
      snake_case__			,       snake_case__			,       snake_case__					:      Any  			=						translation
      snake_case__					:      Any  			=						(
          raw_summary.replace('[unused0]'							,			''		)
          .replace('[unused3]'							,			''		)
          .replace('[PAD]'							,			''		)
          .replace('[unused1]'							,			''		)
          .replace(r' +'							,			' '		)
          .replace(' [unused2] '							,			'. '		)
          .replace('[unused2]'							,			''		)
          .strip()
      )

      return summary






def 		UpperCamelCase__			(		__SCREAMING_SNAKE_CASE		)						->      Optional[int]:
      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__			(		__SCREAMING_SNAKE_CASE		)						->      Optional[Any]:
      with open('rouge_scores.txt'							,			'w'		) as output:
            output.write(__SCREAMING_SNAKE_CASE		)






def 		UpperCamelCase__			(		__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		)						->      Any:
      snake_case__					:      Optional[Any]  			=						load_and_cache_examples(__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		)
      snake_case__					:      Union[str, Any]  			=						SequentialSampler(__SCREAMING_SNAKE_CASE		)

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

      snake_case__					:      List[Any]  			=						DataLoader(
          __SCREAMING_SNAKE_CASE							,			sampler=__SCREAMING_SNAKE_CASE							,			batch_size=args.batch_size							,			collate_fn=__SCREAMING_SNAKE_CASE							,			)

      return iterator






def 		UpperCamelCase__			(		__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		)						->      Dict:
      snake_case__					:      Any  			=						CNNDMDataset(args.documents_dir		)
      return dataset






def 		UpperCamelCase__			(		__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		)						->      List[Any]:
      snake_case__					:      Optional[int]  			=						[x for x in data if not len(x[1]		) == 0]  # remove empty_files
      snake_case__					:      List[str]  			=						[name for name, _, _ in data]
      snake_case__					:      List[str]  			=						[' '.join(__SCREAMING_SNAKE_CASE		) for _, _, summary_list in data]

      snake_case__					:      int  			=						[encode_for_summarization(__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		) for _, story, summary in data]
      snake_case__					:      str  			=						torch.tensor(
          [truncate_or_pad(__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			tokenizer.pad_token_id		) for story, _ in encoded_text]		)
      snake_case__					:      Any  			=						compute_token_type_ids(__SCREAMING_SNAKE_CASE							,			tokenizer.cls_token_id		)
      snake_case__					:      List[str]  			=						build_mask(__SCREAMING_SNAKE_CASE							,			tokenizer.pad_token_id		)

      snake_case__					:      int  			=						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 		UpperCamelCase__			(		__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		)						->      Optional[int]:
      snake_case__					:      Optional[Any]  			=						summary_tokens.to('cpu'		).numpy()
      snake_case__					:      Dict  			=						tokenizer.decode(__SCREAMING_SNAKE_CASE		)
      snake_case__					:      Optional[int]  			=						summary.split('.'		)
      snake_case__					:      Dict  			=						[s + '.' for s in sentences]
      return sentences






def 		UpperCamelCase__			(		)						->      Any:
      snake_case__					:      Optional[int]  			=						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=200							,			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.'							,			)
      snake_case__					:      str  			=						parser.parse_args()

      # Select device (distibuted not available)
      snake_case__					:      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:
            snake_case__					:      Union[str, 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=__SCREAMING_SNAKE_CASE		)

      evaluate(__SCREAMING_SNAKE_CASE		)






def 		UpperCamelCase__			(		__SCREAMING_SNAKE_CASE		)						->      Dict:
      if not os.path.exists(__SCREAMING_SNAKE_CASE		):
            return False

      snake_case__					:      int  			=						os.listdir(__SCREAMING_SNAKE_CASE		)
      if len(__SCREAMING_SNAKE_CASE		) == 0:
            return False

      return True


if __name__ == "__main__":
      main()
