






import argparse
import logging
import os
import sys
import tempfile
from pathlib import Path

import lightning_base
import pytest
import pytorch_lightning as pl
import torch
from convert_pl_checkpoint_to_hf import convert_pl_to_hf
from distillation import distill_main
from finetune import SummarizationModule, main
from huggingface_hub import list_models
from parameterized import parameterized
from run_eval import generate_summaries_or_translations
from torch import nn

from transformers import AutoConfig, AutoModelForSeqaSeqLM
from transformers.testing_utils import CaptureStderr, CaptureStdout, TestCasePlus, require_torch_gpu, slow
from utils import label_smoothed_nll_loss, lmap, load_json


logging.basicConfig(level=logging.DEBUG)

__UpperCamelCase   :						str							=  logging.getLogger()
__UpperCamelCase   :						Union[str, Any]							=  torch.cuda.is_available()
__UpperCamelCase   :						Tuple							=  {
    """max_tokens_per_batch""": None,
    """supervise_forward""": True,
    """normalize_hidden""": True,
    """label_smoothing""": 0.2,
    """eval_max_gen_length""": None,
    """eval_beams""": 1,
    """val_metric""": """loss""",
    """save_top_k""": 1,
    """adafactor""": True,
    """early_stopping_patience""": 2,
    """logger_name""": """default""",
    """length_penalty""": 0.5,
    """cache_dir""": """""",
    """task""": """summarization""",
    """num_workers""": 2,
    """alpha_hid""": 0,
    """freeze_embeds""": True,
    """enc_only""": False,
    """tgt_suffix""": """""",
    """resume_from_checkpoint""": None,
    """sortish_sampler""": True,
    """student_decoder_layers""": 1,
    """val_check_interval""": 1.0,
    """output_dir""": """""",
    """fp16""": False,  # TODO(SS): set this to CUDA_AVAILABLE if ci installs apex or start using native amp
    """no_teacher""": False,
    """fp16_opt_level""": """O1""",
    """gpus""": 1 if CUDA_AVAILABLE else 0,
    """n_tpu_cores""": 0,
    """max_grad_norm""": 1.0,
    """do_train""": True,
    """do_predict""": True,
    """accumulate_grad_batches""": 1,
    """server_ip""": """""",
    """server_port""": """""",
    """seed""": 42,
    """model_name_or_path""": """sshleifer/bart-tiny-random""",
    """config_name""": """""",
    """tokenizer_name""": """facebook/bart-large""",
    """do_lower_case""": False,
    """learning_rate""": 0.3,
    """lr_scheduler""": """linear""",
    """weight_decay""": 0.0,
    """adam_epsilon""": 1E-08,
    """warmup_steps""": 0,
    """max_epochs""": 1,
    """train_batch_size""": 2,
    """eval_batch_size""": 2,
    """max_source_length""": 12,
    """max_target_length""": 12,
    """val_max_target_length""": 12,
    """test_max_target_length""": 12,
    """fast_dev_run""": False,
    """no_cache""": False,
    """n_train""": -1,
    """n_val""": -1,
    """n_test""": -1,
    """student_encoder_layers""": 1,
    """freeze_encoder""": False,
    """auto_scale_batch_size""": False,
    """overwrite_output_dir""": False,
    """student""": None,
}
def        a_	( _A ,				_A					)     ->     List[Any]:







				"""simple docstring"""
				snake_case__													=       '\n'.join(snake_case__					)
				Path(snake_case__					).open('w'					).writelines(snake_case__					)


__UpperCamelCase   :						List[Any]							=  [""" Sam ate lunch today.""", """Sams lunch ingredients."""]
__UpperCamelCase   :						int							=  ["""A very interesting story about what I ate for lunch.""", """Avocado, celery, turkey, coffee"""]
__UpperCamelCase   :						str							=  """patrickvonplaten/t5-tiny-random"""
__UpperCamelCase   :						Tuple							=  """sshleifer/t5-tinier-random"""
__UpperCamelCase   :						Optional[int]							=  """sshleifer/bart-tiny-random"""
__UpperCamelCase   :						Dict							=  """sshleifer/tiny-mbart"""
__UpperCamelCase   :						List[Any]							=  """sshleifer/tiny-marian-en-de"""
__UpperCamelCase   :						str							=  """stas/tiny-wmt19-en-de"""


__UpperCamelCase   :						List[Any]							=  logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks
def        a_	( _A					)     ->     Optional[Any]:







				"""simple docstring"""






				for split in ["train", "val", "test"]:
								_dump_articles(os.path.join(snake_case__ ,				f'''{split}.source'''					) ,				snake_case__					)
								_dump_articles(os.path.join(snake_case__ ,				f'''{split}.target'''					) ,				snake_case__					)
				return tmp_dir


class 					__SCREAMING_SNAKE_CASE(		_a		):
			@classmethod
			def lowerCAmelCase_	(       cls: Optional[int]  )						->						Union[str, Any]:
							logging.disable(logging.CRITICAL  )  # remove noisy download output from tracebacks
							return cls
			@slow
			@require_torch_gpu
			def lowerCAmelCase_	(       self: List[Any]  )						->						List[str]:
							snake_case__													=       list_models()
							snake_case__													=       'sshleifer'
							snake_case__													=       [x.modelId for x in model_list if x.modelId.startswith(UpperCamelCase  )]
							snake_case__													=       ['sshleifer/blenderbot-3B', 'sshleifer/blenderbot-90M']
							snake_case__													=       []
							for m in model_ids:
											if m in allowed_to_be_broken:
															continue
											try:
															AutoConfig.from_pretrained(UpperCamelCase  )
											except Exception:
															failures.append(UpperCamelCase  )
							assert not failures, F'''The following models could not be loaded through AutoConfig: {failures}'''
			def lowerCAmelCase_	(       self: List[str]  )						->						str:
							snake_case__													=       {'student_encoder_layers': 2, 'student_decoder_layers': 1, 'no_teacher': True}
							self._test_distiller_cli(UpperCamelCase  )
			def lowerCAmelCase_	(       self: List[Any]  )						->						Dict:
							snake_case__													=       {
							    'student_encoder_layers': 2,
							    'student_decoder_layers': 1,
							    'max_epochs': 4,
							    'val_check_interval': 0.25,
							    'alpha_hid': 2.0,
							    'model_name_or_path': 'IGNORE_THIS_IT_DOESNT_GET_USED',
							}
							snake_case__													=       self._test_distiller_cli(UpperCamelCase    ,		check_contents=UpperCamelCase  )

							snake_case__													=       list(Path(model.output_dir  ).glob('*.ckpt'  )  )
							self.assertEqual(1    ,		len(UpperCamelCase  )  )
							snake_case__													=       list(Path(model.output_dir  ).glob('**/*.bin'  )  )
							self.assertEqual(len(UpperCamelCase  )    ,		2  )
							snake_case__													=       lmap(str.strip    ,		Path(model.hparams.data_dir  ).joinpath('test.source'  ).open().readlines()  )
							snake_case__													=       tempfile.mktemp()  # XXX: not being cleaned up
							generate_summaries_or_translations(UpperCamelCase    ,		UpperCamelCase    ,		str(model.output_dir / 'best_tfmr'  )  )
							self.assertTrue(Path(UpperCamelCase  ).exists()  )

							snake_case__													=       self.get_auto_remove_tmp_dir()
							convert_pl_to_hf(ckpts[0]    ,		transformer_ckpts[0].parent    ,		UpperCamelCase  )
							assert os.path.exists(os.path.join(UpperCamelCase    ,		'pytorch_model.bin'  )  )
			def lowerCAmelCase_	(       self: List[Any]  )						->						Tuple:
							snake_case__													=       AutoModelForSeqaSeqLM.from_pretrained(UpperCamelCase  )
							snake_case__							,  snake_case__													=       model.dummy_inputs['input_ids'], model.dummy_inputs['attention_mask']
							snake_case__													=       torch.tensor([[0, 4, 8, 2], [0, 8, 2, 1]]    ,		dtype=torch.long    ,		device=model.device  )
							snake_case__													=       target_ids[:, :-1].contiguous()  # Why this line?
							snake_case__													=       target_ids[:, 1:].clone()  # why clone?
							snake_case__													=       model(
							    UpperCamelCase    ,		attention_mask=UpperCamelCase    ,		decoder_input_ids=UpperCamelCase    ,		labels=UpperCamelCase    ,		use_cache=UpperCamelCase  ).loss

							snake_case__													=       model(UpperCamelCase    ,		attention_mask=UpperCamelCase    ,		decoder_input_ids=UpperCamelCase    ,		use_cache=UpperCamelCase  ).logits

							snake_case__													=       nn.functional.log_softmax(UpperCamelCase    ,		dim=-1  )
							snake_case__							,  snake_case__													=       label_smoothed_nll_loss(
							    UpperCamelCase    ,		UpperCamelCase    ,		0.1    ,		ignore_index=model.config.pad_token_id  )
							with self.assertRaises(UpperCamelCase  ):
											# TODO: understand why this breaks
											self.assertEqual(UpperCamelCase    ,		UpperCamelCase  )
			def lowerCAmelCase_	(       self: List[Any]  )						->						List[str]:
							snake_case__													=       {
							    'student_encoder_layers': 2,
							    'student_decoder_layers': 1,
							    'num_train_epochs': 4,
							    'val_check_interval': 0.25,
							    'alpha_hid': 2.0,
							    'task': 'translation',
							    'model_name_or_path': 'IGNORE_THIS_IT_DOESNT_GET_USED',
							    'tokenizer_name': MBART_TINY,
							    'teacher': MBART_TINY,
							    'src_lang': 'en_XX',
							    'tgt_lang': 'ro_RO',
							}
							snake_case__													=       self._test_distiller_cli(UpperCamelCase    ,		check_contents=UpperCamelCase  )
							assert model.model.config.model_type == "mbart"

							snake_case__													=       list(Path(model.output_dir  ).glob('*.ckpt'  )  )
							self.assertEqual(1    ,		len(UpperCamelCase  )  )
							snake_case__													=       list(Path(model.output_dir  ).glob('**/*.bin'  )  )
							snake_case__													=       list(Path(model.output_dir  ).glob('best_tfmr/*'  )  )
							assert len(UpperCamelCase  ) > 2
							self.assertEqual(len(UpperCamelCase  )    ,		2  )
			def lowerCAmelCase_	(       self: Union[str, Any]  )						->						List[str]:
							snake_case__													=       {
							    'student_encoder_layers': 1,
							    'student_decoder_layers': 1,
							    'alpha_hid': 2.0,
							    'teacher': T5_TINY,
							    'model_name_or_path': T5_TINY,
							    'tokenizer_name': T5_TINY,
							}
							self._test_distiller_cli(UpperCamelCase  )
			def lowerCAmelCase_	(       self: str  )						->						Optional[Any]:
							snake_case__													=       {
							    'teacher': T5_TINY,
							    'student': T5_TINIER,
							    'model_name_or_path': T5_TINIER,
							    'tokenizer_name': T5_TINIER,
							}
							self._test_distiller_cli(UpperCamelCase  )


			def lowerCAmelCase_	(       self: List[str]    ,		UpperCamelCase: List[str]    ,		UpperCamelCase: Optional[Any]=True  )						->						str:
							snake_case__													=       {
							    'label_smoothing': 0.0,
							    'early_stopping_patience': -1,
							    'train_batch_size': 1,
							    'eval_batch_size': 2,
							    'max_epochs': 2,
							    'alpha_mlm': 0.2,
							    'alpha_ce': 0.8,
							    'do_predict': True,
							    'model_name_or_path': 'sshleifer/tinier_bart',
							    'teacher': CHEAP_ARGS['model_name_or_path'],
							    'val_check_interval': 0.5,
							}
							default_updates.update(UpperCamelCase  )
							snake_case__													=       CHEAP_ARGS.copy()
							snake_case__													=       make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()  )
							snake_case__													=       self.get_auto_remove_tmp_dir()

							args_d.update(data_dir=UpperCamelCase    ,		output_dir=UpperCamelCase    ,		**UpperCamelCase  )
							snake_case__													=       distill_main(argparse.Namespace(**UpperCamelCase  )  )
							if not check_contents:
											return model
							snake_case__													=       os.listdir(UpperCamelCase  )
							snake_case__													=       {os.path.basename(UpperCamelCase  ) for p in contents}
							snake_case__													=       [p for p in contents if p.endswith('ckpt'  )]
							assert len(UpperCamelCase  ) > 0

							self.assertIn('test_generations.txt'    ,		UpperCamelCase  )
							self.assertIn('test_results.txt'    ,		UpperCamelCase  )

							snake_case__													=       load_json(model.metrics_save_path  )
							snake_case__													=       metrics['val'][-1]
							self.assertGreaterEqual(last_step_stats['val_avg_gen_time']    ,		0.01  )
							self.assertGreaterEqual(1.0    ,		last_step_stats['val_avg_gen_time']  )
							self.assertIsInstance(last_step_stats[F'''val_avg_{model.val_metric}''']    ,		UpperCamelCase  )
							snake_case__													=       int(args_d['max_epochs'] * (1 / args_d['val_check_interval']) + 1  )
							self.assertEqual(len(metrics['val']  )    ,		UpperCamelCase  )
							self.assertEqual(len(metrics['test']  )    ,		1  )
							return model


class 					__SCREAMING_SNAKE_CASE(		_a		):
			@parameterized.expand(
			    [T5_TINY, BART_TINY, MBART_TINY, MARIAN_TINY, FSMT_TINY]    ,		)
			def lowerCAmelCase_	(       self: int    ,		UpperCamelCase: Dict  )						->						Any:
							snake_case__													=       CHEAP_ARGS.copy()
							snake_case__													=       'translation' if model in [MBART_TINY, MARIAN_TINY, FSMT_TINY] else 'summarization'
							snake_case__													=       0.1 if task == 'translation' else 0

							snake_case__													=       make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()  )
							snake_case__													=       self.get_auto_remove_tmp_dir()
							args_d.update(
							    data_dir=UpperCamelCase    ,		model_name_or_path=UpperCamelCase    ,		tokenizer_name=UpperCamelCase    ,		train_batch_size=2    ,		eval_batch_size=2    ,		output_dir=UpperCamelCase    ,		do_predict=UpperCamelCase    ,		task=UpperCamelCase    ,		src_lang='en_XX'    ,		tgt_lang='ro_RO'    ,		freeze_encoder=UpperCamelCase    ,		freeze_embeds=UpperCamelCase    ,		)
							assert "n_train" in args_d
							snake_case__													=       argparse.Namespace(**UpperCamelCase  )
							snake_case__													=       main(UpperCamelCase  )

							snake_case__													=       module.model.get_input_embeddings()
							assert not input_embeds.weight.requires_grad
							if model == T5_TINY:
											snake_case__													=       module.model.lm_head
											assert not lm_head.weight.requires_grad
											assert (lm_head.weight == input_embeds.weight).all().item()
							elif model == FSMT_TINY:
											snake_case__													=       module.model.model
											snake_case__													=       fsmt.decoder.embed_positions
											assert not embed_pos.weight.requires_grad
											assert not fsmt.decoder.embed_tokens.weight.requires_grad
											# check that embeds are not the same
											assert fsmt.decoder.embed_tokens != fsmt.encoder.embed_tokens
							else:
											snake_case__													=       module.model.model
											snake_case__													=       bart.decoder.embed_positions
											assert not embed_pos.weight.requires_grad
											assert not bart.shared.weight.requires_grad
											# check that embeds are the same
											assert bart.decoder.embed_tokens == bart.encoder.embed_tokens
											assert bart.decoder.embed_tokens == bart.shared

							snake_case__													=       load_json(module.output_dir / 'text_batch.json'  )
							assert isinstance(UpperCamelCase    ,		UpperCamelCase  )
							assert len(UpperCamelCase  ) >= 4
			def lowerCAmelCase_	(       self: List[str]  )						->						Union[str, Any]:
							snake_case__													=       CHEAP_ARGS.copy()

							snake_case__													=       'summarization'
							snake_case__													=       make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()  )

							args_d.update(
							    data_dir=UpperCamelCase    ,		tokenizer_name=UpperCamelCase    ,		train_batch_size=2    ,		eval_batch_size=2    ,		do_predict=UpperCamelCase    ,		task=UpperCamelCase    ,		src_lang='en_XX'    ,		tgt_lang='ro_RO'    ,		freeze_encoder=UpperCamelCase    ,		freeze_embeds=UpperCamelCase    ,		)

							# test models whose config includes the extra_model_args
							snake_case__													=       BART_TINY
							snake_case__													=       self.get_auto_remove_tmp_dir()
							snake_case__													=       args_d.copy()
							args_da.update(
							    model_name_or_path=UpperCamelCase    ,		output_dir=UpperCamelCase    ,		)
							snake_case__													=       ('encoder_layerdrop', 'decoder_layerdrop', 'dropout', 'attention_dropout')
							for p in extra_model_params:
											snake_case__													=       0.5
							snake_case__													=       argparse.Namespace(**UpperCamelCase  )
							snake_case__													=       main(UpperCamelCase  )
							for p in extra_model_params:
											assert getattr(model.config    ,		UpperCamelCase  ) == 0.5, F'''failed to override the model config for param {p}'''

							# test models whose config doesn't include the extra_model_args
							snake_case__													=       T5_TINY
							snake_case__													=       self.get_auto_remove_tmp_dir()
							snake_case__													=       args_d.copy()
							args_da.update(
							    model_name_or_path=UpperCamelCase    ,		output_dir=UpperCamelCase    ,		)
							snake_case__													=       'encoder_layerdrop'
							snake_case__													=       0.5
							snake_case__													=       argparse.Namespace(**UpperCamelCase  )
							with pytest.raises(UpperCamelCase  ) as excinfo:
											snake_case__													=       main(UpperCamelCase  )
							assert str(excinfo.value  ) == F'''model config doesn\'t have a `{unsupported_param}` attribute'''


			def lowerCAmelCase_	(       self: Dict  )						->						int:
							snake_case__													=       CHEAP_ARGS.copy()

							snake_case__													=       'summarization'
							snake_case__													=       make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()  )

							snake_case__													=       BART_TINY
							snake_case__													=       self.get_auto_remove_tmp_dir()

							args_d.update(
							    data_dir=UpperCamelCase    ,		model_name_or_path=UpperCamelCase    ,		output_dir=UpperCamelCase    ,		tokenizer_name=UpperCamelCase    ,		train_batch_size=2    ,		eval_batch_size=2    ,		do_predict=UpperCamelCase    ,		task=UpperCamelCase    ,		src_lang='en_XX'    ,		tgt_lang='ro_RO'    ,		freeze_encoder=UpperCamelCase    ,		freeze_embeds=UpperCamelCase    ,		)

							# emulate finetune.py
							snake_case__													=       argparse.ArgumentParser()
							snake_case__													=       pl.Trainer.add_argparse_args(UpperCamelCase  )
							snake_case__													=       SummarizationModule.add_model_specific_args(UpperCamelCase    ,		os.getcwd()  )
							snake_case__													=       {'--help': True}

							# --help test
							with pytest.raises(UpperCamelCase  ) as excinfo:
											with CaptureStdout() as cs:
															snake_case__													=       parser.parse_args(UpperCamelCase  )
											assert False, "--help is expected to sys.exit"
							assert excinfo.type == SystemExit
							snake_case__													=       lightning_base.arg_to_scheduler_metavar
							assert expected in cs.out, "--help is expected to list the supported schedulers"

							# --lr_scheduler=non_existing_scheduler test
							snake_case__													=       'non_existing_scheduler'
							snake_case__													=       {F'''--lr_scheduler={unsupported_param}'''}
							with pytest.raises(UpperCamelCase  ) as excinfo:
											with CaptureStderr() as cs:
															snake_case__													=       parser.parse_args(UpperCamelCase  )
											assert False, "invalid argument is expected to sys.exit"
							assert excinfo.type == SystemExit
							snake_case__													=       F'''invalid choice: \'{unsupported_param}\''''
							assert expected in cs.err, F'''should have bailed on invalid choice of scheduler {unsupported_param}'''

							# --lr_scheduler=existing_scheduler test
							snake_case__													=       'cosine'
							snake_case__													=       args_d.copy()
							snake_case__													=       supported_param
							snake_case__													=       argparse.Namespace(**UpperCamelCase  )
							snake_case__													=       main(UpperCamelCase  )
							assert (
							    getattr(model.hparams    ,		'lr_scheduler'  ) == supported_param
							), F'''lr_scheduler={supported_param} shouldn\'t fail'''












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


__UpperCamelCase   :						Tuple							=  namedtuple("""Batch""", ["""document_names""", """batch_size""", """src""", """segs""", """mask_src""", """tgt_str"""])
def        a_	( _A					)     ->     Union[str, Any]:







				"""simple docstring"""
				snake_case__													=       BertTokenizer.from_pretrained('bert-base-uncased' ,				do_lower_case=_A					)
				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=_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.
				snake_case__													=       ''
				snake_case__													=       ''

				snake_case__													=       build_data_iterator(_A ,				_A					)
				snake_case__													=       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					):
								snake_case__													=       predictor.translate_batch(_A					)
								snake_case__													=       predictor.from_batch(_A					)
								snake_case__													=       [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:
								snake_case__													=       rouge_evaluator.get_scores(_A ,				_A					)
								snake_case__													=       format_rouge_scores(_A					)
								save_rouge_scores(_A					)
								print(_A					)
def        a_	( _A ,				_A ,				_A					)     ->     Optional[Any]:







				"""simple docstring"""
				for summary, document_name in zip(_A ,				_A					):
								# 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(_A ,				_A					)
								with open(_A ,				'w'					) as output:
												output.write(_A					)
def        a_	( _A					)     ->     Union[str, Any]:







				"""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        a_	( _A					)     ->     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					)     ->     int:







				"""simple docstring"""
				with open('rouge_scores.txt' ,				'w'					) as output:
								output.write(_A					)
def        a_	( _A ,				_A					)     ->     Optional[Any]:







				"""simple docstring"""
				snake_case__													=       load_and_cache_examples(_A ,				_A					)
				snake_case__													=       SequentialSampler(_A					)

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

				snake_case__													=       DataLoader(
				    _A ,				sampler=_A ,				batch_size=args.batch_size ,				collate_fn=_A ,				)

				return iterator
def        a_	( _A ,				_A					)     ->     Any:







				"""simple docstring"""
				snake_case__													=       CNNDMDataset(args.documents_dir					)
				return dataset
def        a_	( _A ,				_A ,				_A ,				_A					)     ->     str:







				"""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(_A					) for _, _, summary_list in data]

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

				snake_case__													=       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					)     ->     Optional[Any]:







				"""simple docstring"""
				snake_case__													=       summary_tokens.to('cpu'					).numpy()
				snake_case__													=       tokenizer.decode(_A					)
				snake_case__													=       summary.split('.'					)
				snake_case__													=       [s + '.' for s in sentences]
				return sentences
def        a_	( )     ->     int:







				"""simple docstring"""
				snake_case__													=       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.' ,				)
				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=_A					)

				evaluate(_A					)
def        a_	( _A					)     ->     str:







				"""simple docstring"""
				if not os.path.exists(_A					):
								return False

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

				return True


if __name__ == "__main__":
							main()




