






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 (      snake_case__):





	"""simple docstring"""






	@slow
	@require_torch
	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







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

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

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

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

							_UpperCAmelCase									=			4

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

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

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

													return batch

							def _compute_metrics(UpperCAmelCase    ):
													_UpperCAmelCase									=			pred.label_ids
													_UpperCAmelCase									=			pred.predictions

													# all unnecessary tokens are removed
													_UpperCAmelCase									=			tokenizer.batch_decode(UpperCAmelCase       ,    skip_special_tokens=UpperCAmelCase    )
													_UpperCAmelCase									=			tokenizer.batch_decode(UpperCAmelCase       ,    skip_special_tokens=UpperCAmelCase    )

													_UpperCAmelCase									=			sum([int(pred_str[i] == label_str[i]    ) for i in range(len(UpperCAmelCase    )    )]    ) / len(UpperCAmelCase    )

													return {"accuracy": accuracy}

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

							_UpperCAmelCase									=			self.get_auto_remove_tmp_dir()

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

							# instantiate trainer
							_UpperCAmelCase									=			SeqaSeqTrainer(
							    model=UpperCAmelCase       ,    args=UpperCAmelCase       ,    compute_metrics=_compute_metrics       ,    train_dataset=UpperCAmelCase       ,    eval_dataset=UpperCAmelCase       ,    tokenizer=UpperCAmelCase       ,    )

							# start training
							trainer.train()








from __future__ import annotations

from itertools import permutations
from random import randint
from timeit import repeat


def __A    (      )->  tuple[list[int], int]:





						"""simple docstring"""



						_UpperCAmelCase									=			[randint(-1_000 ,		1_000	) for i in range(10	)]
						_UpperCAmelCase									=			randint(-5_000 ,		5_000	)
						return (arr, r)


_a      	=  make_dataset()


def __A    (      __lowerCAmelCase ,		__lowerCAmelCase	)->  tuple[int, ...]:





						"""simple docstring"""



						for triplet in permutations(__lowerCAmelCase ,		3	):
												if sum(__lowerCAmelCase	) == target:
																		return tuple(sorted(__lowerCAmelCase	)	)
						return (0, 0, 0)


def __A    (      __lowerCAmelCase ,		__lowerCAmelCase	)->  tuple[int, int, int]:





						"""simple docstring"""



						arr.sort()
						_UpperCAmelCase									=			len(__lowerCAmelCase	)
						for i in range(n - 1	):
												_UpperCAmelCase					, _UpperCAmelCase									=			i + 1, n - 1
												while left < right:
																		if arr[i] + arr[left] + arr[right] == target:
																								return (arr[i], arr[left], arr[right])
																		elif arr[i] + arr[left] + arr[right] < target:
																								left += 1
																		elif arr[i] + arr[left] + arr[right] > target:
																								right -= 1
						return (0, 0, 0)


def __A    (      )->  tuple[float, float]:





						"""simple docstring"""



						_UpperCAmelCase									=			'\nfrom __main__ import dataset, triplet_sum1, triplet_sum2\n'
						_UpperCAmelCase									=			'\ntriplet_sum1(*dataset)\n'
						_UpperCAmelCase									=			'\ntriplet_sum2(*dataset)\n'
						_UpperCAmelCase									=			repeat(setup=__lowerCAmelCase ,		stmt=__lowerCAmelCase ,		repeat=5 ,		number=10_000	)
						_UpperCAmelCase									=			repeat(setup=__lowerCAmelCase ,		stmt=__lowerCAmelCase ,		repeat=5 ,		number=10_000	)
						return (min(__lowerCAmelCase	), min(__lowerCAmelCase	))


if __name__ == "__main__":
		from doctest import testmod

		testmod()
		_a      	=  solution_times()
		print(F'''The time for naive implementation is {times[0]}.''')
		print(F'''The time for optimized implementation is {times[1]}.''')
