


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 	lowerCamelCase_       (				lowerCAmelCase__				):



					'''simple docstring'''







					@slow
					@require_torch
					def A      (				self						)   ->			Optional[Any]:







								'''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 					=   1_2_8

								__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(3_2						)						)
								__lowercase 					=   val_dataset.select(range(1_6						)						)

								__lowercase 					=   4

								def _map_to_encoder_decoder_inputs(snake_case_						):
											# Tokenizer will automatically set [BOS] <text> [EOS]
											__lowercase 					=   tokenizer(batch['''article'''] ,       padding='''max_length''' ,       truncation=snake_case_ ,       max_length=5_1_2						)
											__lowercase 					=   tokenizer(batch['''highlights'''] ,       padding='''max_length''' ,       truncation=snake_case_ ,       max_length=1_2_8						)
											__lowercase 					=   inputs.input_ids
											__lowercase 					=   inputs.attention_mask

											__lowercase 					=   outputs.input_ids
											__lowercase 					=   outputs.input_ids.copy()
											__lowercase 					=   [
											    [-1_0_0 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch['''labels''']
											]
											__lowercase 					=   outputs.attention_mask

											assert all(len(snake_case_						) == 5_1_2 for x in inputs.input_ids						)
											assert all(len(snake_case_						) == 1_2_8 for x in outputs.input_ids						)

											return batch

								def _compute_metrics(snake_case_						):
											__lowercase 					=   pred.label_ids
											__lowercase 					=   pred.predictions

											# all unnecessary tokens are removed
											__lowercase 					=   tokenizer.batch_decode(snake_case_ ,       skip_special_tokens=snake_case_						)
											__lowercase 					=   tokenizer.batch_decode(snake_case_ ,       skip_special_tokens=snake_case_						)

											__lowercase 					=   sum([int(pred_str[i] == label_str[i]						) for i in range(len(snake_case_						)						)]						) / len(snake_case_						)

											return {"accuracy": accuracy}

								# map train dataset
								__lowercase 					=   train_dataset.map(
								    _map_to_encoder_decoder_inputs ,       batched=snake_case_ ,       batch_size=snake_case_ ,       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=snake_case_ ,       batch_size=snake_case_ ,       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=snake_case_ ,       per_device_train_batch_size=snake_case_ ,       per_device_eval_batch_size=snake_case_ ,       predict_with_generate=snake_case_ ,       evaluation_strategy='''steps''' ,       do_train=snake_case_ ,       do_eval=snake_case_ ,       warmup_steps=0 ,       eval_steps=2 ,       logging_steps=2 ,       )

								# instantiate trainer
								__lowercase 					=   SeqaSeqTrainer(
								    model=snake_case_ ,       args=snake_case_ ,       compute_metrics=_compute_metrics ,       train_dataset=snake_case_ ,       eval_dataset=snake_case_ ,       tokenizer=snake_case_ ,       )

								# start training
								trainer.train()






a : Any		=				'''
# Transformers 설치 방법
! pip install transformers datasets
# 마지막 릴리스 대신 소스에서 설치하려면, 위 명령을 주석으로 바꾸고 아래 명령을 해제하세요.
# ! pip install git+https://github.com/huggingface/transformers.git
'''

a : Union[str, Any]		=				[{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
a : Union[str, Any]		=				{
    '''{processor_class}''': '''FakeProcessorClass''',
    '''{model_class}''': '''FakeModelClass''',
    '''{object_class}''': '''FakeObjectClass''',
}


