






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     (  _lowerCAmelCase ):







	@slow
	@require_torch
	def 			_a (	self   :				Union[str, Any]      )		->			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   							=	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   :				List[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   :				List[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()




import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


lowercase_   							=  5_0_0_0_0_0

lowercase_							,		lowercase_   							=  os.path.split(__file__)
lowercase_   							=  os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))
@get_duration
def 	a (     A__      :  datasets.Dataset ,  **A__      :  List[str]	)       -> Optional[Any]:





      """simple docstring"""



      _lowercase      			=dataset.map(**A__	)
@get_duration
def 	a (     A__      :  datasets.Dataset ,  **A__      :  List[Any]	)       -> List[str]:





      """simple docstring"""



      _lowercase      			=dataset.filter(**A__	)
def 	a (     )       -> Union[str, Any]:





      """simple docstring"""



      _lowercase      			={'num examples': SPEED_TEST_N_EXAMPLES}
      with tempfile.TemporaryDirectory() as tmp_dir:
            _lowercase      			=datasets.Features({'text': datasets.Value('string'	), 'numbers': datasets.Value('float32'	)}	)
            _lowercase      			=generate_example_dataset(
                os.path.join(A__ ,  'dataset.arrow'	) ,  A__ ,  num_examples=A__	)

            _lowercase      			=transformers.AutoTokenizer.from_pretrained('bert-base-cased' ,  use_fast=A__	)

            def tokenize(A__      :  int	):
                  return tokenizer(examples['text']	)

            _lowercase      			=map(A__	)

            _lowercase      			=map(A__ ,  batched=A__	)

            _lowercase      			=map(A__ ,  function=lambda A__	: None ,  batched=A__	)

            with dataset.formatted_as(type='numpy'	):
                  _lowercase      			=map(A__ ,  function=lambda A__	: None ,  batched=A__	)

            with dataset.formatted_as(type='pandas'	):
                  _lowercase      			=map(A__ ,  function=lambda A__	: None ,  batched=A__	)

            with dataset.formatted_as(type='torch' ,  columns='numbers'	):
                  _lowercase      			=map(A__ ,  function=lambda A__	: None ,  batched=A__	)

            with dataset.formatted_as(type='tensorflow' ,  columns='numbers'	):
                  _lowercase      			=map(A__ ,  function=lambda A__	: None ,  batched=A__	)

            _lowercase      			=map(A__ ,  function=A__ ,  batched=A__	)

            _lowercase      			=filter(A__	)

            # Activate later when tokenizer support batched inputs
            # with dataset.formatted_as(type='numpy'):
            #     times[func.__name__ + " fast-tokenizer batched numpy"] = func(dataset, function=tokenize, batched=True)

      with open(A__ ,  'wb'	) as f:
            f.write(json.dumps(A__	).encode('utf-8'	)	)


if __name__ == "__main__":  # useful to run the profiler
  benchmark_map_filter()


