






import multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser






def     a     (		lowerCamelCase_							):






				'''simple docstring'''
				lowercase__  					=     {}
				lowercase__  					=     tokenizer(example['''content''']						,  truncation=lowerCamelCase_							)['''input_ids''']
				lowercase__  					=     len(example['''content''']							) / len(output['''input_ids''']							)
				return output


A__      :     Optional[int]       =					HfArgumentParser(PretokenizationArguments)
A__      :     Optional[int]       =					parser.parse_args()
if args.num_workers is None:
							A__      :     Any       =					multiprocessing.cpu_count()
A__      :     Union[str, Any]       =					AutoTokenizer.from_pretrained(args.tokenizer_dir)

A__      :     List[Any]       =					time.time()
A__      :     Tuple       =					load_dataset(args.dataset_name, split='train')
print(F"Dataset loaded in {time.time()-t_start:.2f}s")

A__      :     Optional[int]       =					time.time()
A__      :     Dict       =					ds.map(
    tokenize,
    num_proc=args.num_workers,
    remove_columns=[
        'repo_name',
        'path',
        'copies',
        'size',
        'content',
        'license',
        'hash',
        'line_mean',
        'line_max',
        'alpha_frac',
        'autogenerated',
    ],
)
print(F"Dataset tokenized in {time.time()-t_start:.2f}s")

A__      :     Any       =					time.time()
ds.push_to_hub(args.tokenized_data_repo)




print(F"Data pushed to the hub in {time.time()-t_start:.2f}s")












from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available


if is_datasets_available():
							import datasets
class  _UpperCAmelCase   (			A__      ):

				"""simple docstring"""




				@slow
				@require_torch
				def 							lowercase__  (	self		:       Optional[int]    ):

								'''simple docstring'''
								lowercase__  					=     EncoderDecoderModel.from_encoder_decoder_pretrained('''prajjwal1/bert-tiny''',    '''prajjwal1/bert-tiny'''    )
								lowercase__  					=     BertTokenizer.from_pretrained('''bert-base-uncased'''    )

								lowercase__  					=     bertabert.config.encoder.vocab_size
								lowercase__  					=     tokenizer.sep_token_id
								lowercase__  					=     tokenizer.cls_token_id
								lowercase__  					=     128

								lowercase__  					=     datasets.load_dataset('''cnn_dailymail''',    '''3.0.0''',    split='''train[:1%]'''    )
								lowercase__  					=     datasets.load_dataset('''cnn_dailymail''',    '''3.0.0''',    split='''validation[:1%]'''    )

								lowercase__  					=     train_dataset.select(range(32    )    )
								lowercase__  					=     val_dataset.select(range(16    )    )

								lowercase__  					=     4

								def _map_to_encoder_decoder_inputs(lowerCamelCase		:       str    ):
												# Tokenizer will automatically set [BOS] <text> [EOS]
												lowercase__  					=     tokenizer(batch['''article'''],    padding='''max_length''',    truncation=lowerCamelCase,    max_length=512    )
												lowercase__  					=     tokenizer(batch['''highlights'''],    padding='''max_length''',    truncation=lowerCamelCase,    max_length=128    )
												lowercase__  					=     inputs.input_ids
												lowercase__  					=     inputs.attention_mask

												lowercase__  					=     outputs.input_ids
												lowercase__  					=     outputs.input_ids.copy()
												lowercase__  					=     [
												    [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch['''labels''']
												]
												lowercase__  					=     outputs.attention_mask

												assert all(len(lowerCamelCase    ) == 512 for x in inputs.input_ids    )
												assert all(len(lowerCamelCase    ) == 128 for x in outputs.input_ids    )

												return batch

								def _compute_metrics(lowerCamelCase		:       Any    ):
												lowercase__  					=     pred.label_ids
												lowercase__  					=     pred.predictions

												# all unnecessary tokens are removed
												lowercase__  					=     tokenizer.batch_decode(lowerCamelCase,    skip_special_tokens=lowerCamelCase    )
												lowercase__  					=     tokenizer.batch_decode(lowerCamelCase,    skip_special_tokens=lowerCamelCase    )

												lowercase__  					=     sum([int(pred_str[i] == label_str[i]    ) for i in range(len(lowerCamelCase    )    )]    ) / len(lowerCamelCase    )

												return {"accuracy": accuracy}

								# map train dataset
								lowercase__  					=     train_dataset.map(
								    _map_to_encoder_decoder_inputs,    batched=lowerCamelCase,    batch_size=lowerCamelCase,    remove_columns=['''article''', '''highlights'''],    )
								train_dataset.set_format(
								    type='''torch''',    columns=['''input_ids''', '''attention_mask''', '''decoder_input_ids''', '''decoder_attention_mask''', '''labels'''],    )

								# same for validation dataset
								lowercase__  					=     val_dataset.map(
								    _map_to_encoder_decoder_inputs,    batched=lowerCamelCase,    batch_size=lowerCamelCase,    remove_columns=['''article''', '''highlights'''],    )
								val_dataset.set_format(
								    type='''torch''',    columns=['''input_ids''', '''attention_mask''', '''decoder_input_ids''', '''decoder_attention_mask''', '''labels'''],    )

								lowercase__  					=     self.get_auto_remove_tmp_dir()

								lowercase__  					=     SeqaSeqTrainingArguments(
								    output_dir=lowerCamelCase,    per_device_train_batch_size=lowerCamelCase,    per_device_eval_batch_size=lowerCamelCase,    predict_with_generate=lowerCamelCase,    evaluation_strategy='''steps''',    do_train=lowerCamelCase,    do_eval=lowerCamelCase,    warmup_steps=0,    eval_steps=2,    logging_steps=2,    )

								# instantiate trainer
								lowercase__  					=     SeqaSeqTrainer(
								    model=lowerCamelCase,    args=lowerCamelCase,    compute_metrics=_compute_metrics,    train_dataset=lowerCamelCase,    eval_dataset=lowerCamelCase,    tokenizer=lowerCamelCase,    )

								# start training
								trainer.train()




