


"""simple docstring"""


import os
from pathlib import Path

import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader

from transformers import AutoTokenizer
from transformers.models.mbart.modeling_mbart import shift_tokens_right
from transformers.testing_utils import TestCasePlus, slow
from utils import FAIRSEQ_AVAILABLE, DistributedSortishSampler, LegacySeqaSeqDataset, SeqaSeqDataset


UpperCamelCase						:				int             =      "bert-base-cased"
UpperCamelCase						:				List[str]             =      "google/pegasus-xsum"
UpperCamelCase						:				List[Any]             =      [" Sam ate lunch today.", "Sams lunch ingredients."]
UpperCamelCase						:				Any             =      ["A very interesting story about what I ate for lunch.", "Avocado, celery, turkey, coffee"]
UpperCamelCase						:				Optional[Any]             =      "patrickvonplaten/t5-tiny-random"
UpperCamelCase						:				Optional[int]             =      "sshleifer/bart-tiny-random"
UpperCamelCase						:				List[str]             =      "sshleifer/tiny-mbart"
UpperCamelCase						:				Any             =      "sshleifer/tiny-marian-en-de"







def  __snake_case       (   UpperCamelCase__ ,			UpperCamelCase__	)      ->		Tuple:







		"""simple docstring"""





		A					       = '\n'.join(UpperCamelCase__	)
		Path(UpperCamelCase__	).open('w'	).writelines(UpperCamelCase__	)







def  __snake_case       (   UpperCamelCase__	)      ->		str:







		"""simple docstring"""





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



class 			lowerCamelCase__						(   UpperCAmelCase_			):







						@parameterized.expand(
						    [
						        MBART_TINY,
						        MARIAN_TINY,
						        T5_TINY,
						        BART_TINY,
						        PEGASUS_XSUM,
						    ]			,			)
						@slow
						def __a						(							self   :    Optional[Any]			,			_lowercase   :    Any		):
								A					       = AutoTokenizer.from_pretrained(_lowercase		)
								A					       = make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()		)
								A					       = max(len(tokenizer.encode(_lowercase		)		) for a in ARTICLES		)
								A					       = max(len(tokenizer.encode(_lowercase		)		) for a in SUMMARIES		)
								A					       = 4
								A					       = 8
								assert max_len_target > max_src_len  # Will be truncated
								assert max_len_source > max_src_len  # Will be truncated
								A       , A					       = 'ro_RO', 'de_DE'  # ignored for all but mbart, but never causes error.
								A					       = SeqaSeqDataset(
								    _lowercase			,			data_dir=_lowercase			,			type_path='train'			,			max_source_length=_lowercase			,			max_target_length=_lowercase			,			src_lang=_lowercase			,			tgt_lang=_lowercase			,			)
								A					       = DataLoader(_lowercase			,			batch_size=2			,			collate_fn=train_dataset.collate_fn		)
								for batch in dataloader:
										assert isinstance(_lowercase			,			_lowercase		)
										assert batch["attention_mask"].shape == batch["input_ids"].shape
										# show that articles were trimmed.
										assert batch["input_ids"].shape[1] == max_src_len
										# show that targets are the same len
										assert batch["labels"].shape[1] == max_tgt_len
										if tok_name != MBART_TINY:
												continue
										# check language codes in correct place
										A					       = shift_tokens_right(batch['labels']			,			tokenizer.pad_token_id		)
										assert batch["decoder_input_ids"][0, 0].item() == tokenizer.lang_code_to_id[tgt_lang]
										assert batch["decoder_input_ids"][0, -1].item() == tokenizer.eos_token_id
										assert batch["input_ids"][0, -2].item() == tokenizer.eos_token_id
										assert batch["input_ids"][0, -1].item() == tokenizer.lang_code_to_id[src_lang]

										break  # No need to test every batch







						@parameterized.expand([BART_TINY, BERT_BASE_CASED]		)
						def __a						(							self   :    Any			,			_lowercase   :    int		):
								A					       = AutoTokenizer.from_pretrained(_lowercase		)
								A					       = make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()		)
								A					       = max(len(tokenizer.encode(_lowercase		)		) for a in ARTICLES		)
								A					       = max(len(tokenizer.encode(_lowercase		)		) for a in SUMMARIES		)
								A					       = 4
								A					       = LegacySeqaSeqDataset(
								    _lowercase			,			data_dir=_lowercase			,			type_path='train'			,			max_source_length=20			,			max_target_length=_lowercase			,			)
								A					       = DataLoader(_lowercase			,			batch_size=2			,			collate_fn=train_dataset.collate_fn		)
								for batch in dataloader:
										assert batch["attention_mask"].shape == batch["input_ids"].shape
										# show that articles were trimmed.
										assert batch["input_ids"].shape[1] == max_len_source
										assert 20 >= batch["input_ids"].shape[1]  # trimmed significantly
										# show that targets were truncated
										assert batch["labels"].shape[1] == trunc_target  # Truncated
										assert max_len_target > trunc_target  # Truncated
										break  # No need to test every batch







						def __a						(							self   :    Optional[int]		):
								A					       = AutoTokenizer.from_pretrained('facebook/mbart-large-cc25'		)

								A					       = Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()		)		)
								A					       = tmp_dir.joinpath('train.source'		).open().readlines()
								A					       = Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()		)		)
								pack_data_dir(_lowercase			,			_lowercase			,			128			,			_lowercase		)
								A					       = {x.name for x in tmp_dir.iterdir()}
								A					       = {x.name for x in save_dir.iterdir()}
								A					       = save_dir.joinpath('train.source'		).open().readlines()
								# orig: [' Sam ate lunch today.\n', 'Sams lunch ingredients.']
								# desired_packed: [' Sam ate lunch today.\n Sams lunch ingredients.']
								assert len(_lowercase		) < len(_lowercase		)
								assert len(_lowercase		) == 1
								assert len(packed_examples[0]		) == sum(len(_lowercase		) for x in orig_examples		)
								assert orig_paths == new_paths







						@pytest.mark.skipif(not FAIRSEQ_AVAILABLE			,			reason='This test requires fairseq'		)
						def __a						(							self   :    Any		):
								if not FAIRSEQ_AVAILABLE:
										return
								A       , A       , A					       = self._get_dataset(max_len=64		)
								A					       = 64
								A					       = ds.make_dynamic_sampler(_lowercase			,			required_batch_size_multiple=_lowercase		)
								A					       = [len(_lowercase		) for x in batch_sampler]
								assert len(set(_lowercase		)		) > 1  # it's not dynamic batch size if every batch is the same length
								assert sum(_lowercase		) == len(_lowercase		)  # no dropped or added examples
								A					       = DataLoader(_lowercase			,			batch_sampler=_lowercase			,			collate_fn=ds.collate_fn			,			num_workers=2		)
								A					       = []
								A					       = []
								for batch in data_loader:
										A					       = batch['input_ids'].shape
										A					       = src_shape[0]
										assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
										A					       = np.product(batch['input_ids'].shape		)
										num_src_per_batch.append(_lowercase		)
										if num_src_tokens > (max_tokens * 1.1):
												failures.append(_lowercase		)
								assert num_src_per_batch[0] == max(_lowercase		)
								if failures:
										raise AssertionError(f'too many tokens in {len(_lowercase		)} batches'		)







						def __a						(							self   :    int		):
								A       , A       , A					       = self._get_dataset(max_len=512		)
								A					       = 2
								A					       = ds.make_sortish_sampler(_lowercase			,			shuffle=_lowercase		)

								A					       = DataLoader(_lowercase			,			batch_size=_lowercase			,			collate_fn=ds.collate_fn			,			num_workers=2		)
								A					       = DataLoader(_lowercase			,			batch_size=_lowercase			,			collate_fn=ds.collate_fn			,			num_workers=2			,			sampler=_lowercase		)

								A					       = tokenizer.pad_token_id

								def count_pad_tokens(_lowercase   :    Any			,			_lowercase   :    str="input_ids"		):
										return [batch[k].eq(_lowercase		).sum().item() for batch in data_loader]

								assert sum(count_pad_tokens(_lowercase			,			k='labels'		)		) < sum(count_pad_tokens(_lowercase			,			k='labels'		)		)
								assert sum(count_pad_tokens(_lowercase		)		) < sum(count_pad_tokens(_lowercase		)		)
								assert len(_lowercase		) == len(_lowercase		)







						def __a						(							self   :    Any			,			_lowercase   :    List[Any]=1_000			,			_lowercase   :    Any=128		):
								if os.getenv('USE_REAL_DATA'			,			_lowercase		):
										A					       = 'examples/seq2seq/wmt_en_ro'
										A					       = max_len * 2 * 64
										if not Path(_lowercase		).joinpath('train.len'		).exists():
												save_len_file(_lowercase			,			_lowercase		)
								else:
										A					       = 'examples/seq2seq/test_data/wmt_en_ro'
										A					       = max_len * 4
										save_len_file(_lowercase			,			_lowercase		)

								A					       = AutoTokenizer.from_pretrained(_lowercase		)
								A					       = SeqaSeqDataset(
								    _lowercase			,			data_dir=_lowercase			,			type_path='train'			,			max_source_length=_lowercase			,			max_target_length=_lowercase			,			n_obs=_lowercase			,			)
								return ds, max_tokens, tokenizer







						def __a						(							self   :    List[Any]		):
								A       , A       , A					       = self._get_dataset()
								A					       = set(DistributedSortishSampler(_lowercase			,			256			,			num_replicas=2			,			rank=0			,			add_extra_examples=_lowercase		)		)
								A					       = set(DistributedSortishSampler(_lowercase			,			256			,			num_replicas=2			,			rank=1			,			add_extra_examples=_lowercase		)		)
								assert idsa.intersection(_lowercase		) == set()







						@parameterized.expand(
						    [
						        MBART_TINY,
						        MARIAN_TINY,
						        T5_TINY,
						        BART_TINY,
						        PEGASUS_XSUM,
						    ]			,			)
						def __a						(							self   :    Optional[int]			,			_lowercase   :    List[str]		):
								A					       = AutoTokenizer.from_pretrained(_lowercase			,			use_fast=_lowercase		)
								if tok_name == MBART_TINY:
										A					       = SeqaSeqDataset(
										    _lowercase			,			data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()		)			,			type_path='train'			,			max_source_length=4			,			max_target_length=8			,			src_lang='EN'			,			tgt_lang='FR'			,			)
										A					       = train_dataset.dataset_kwargs
										assert "src_lang" in kwargs and "tgt_lang" in kwargs
								else:
										A					       = SeqaSeqDataset(
										    _lowercase			,			data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()		)			,			type_path='train'			,			max_source_length=4			,			max_target_length=8			,			)
										A					       = train_dataset.dataset_kwargs
										assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
										assert len(_lowercase		) == 1 if tok_name == BART_TINY else len(_lowercase		) == 0









"""simple docstring"""


def  __snake_case       (   UpperCamelCase__ ,			UpperCamelCase__	)      ->		int:







		"""simple docstring"""





		while b:
				A       , A					       = b, a % b
		return a







def  __snake_case       (   UpperCamelCase__ ,			UpperCamelCase__	)      ->		int:







		"""simple docstring"""





		return a if b == 0 else euclidean_gcd_recursive(UpperCamelCase__ ,			a % b	)







def  __snake_case       (   )      ->		List[Any]:







		"""simple docstring"""





		print(f'euclidean_gcd(3, 5) = {euclidean_gcd(3 ,			5	)}'	)
		print(f'euclidean_gcd(5, 3) = {euclidean_gcd(5 ,			3	)}'	)
		print(f'euclidean_gcd(1, 3) = {euclidean_gcd(1 ,			3	)}'	)
		print(f'euclidean_gcd(3, 6) = {euclidean_gcd(3 ,			6	)}'	)
		print(f'euclidean_gcd(6, 3) = {euclidean_gcd(6 ,			3	)}'	)

		print(f'euclidean_gcd_recursive(3, 5) = {euclidean_gcd_recursive(3 ,			5	)}'	)
		print(f'euclidean_gcd_recursive(5, 3) = {euclidean_gcd_recursive(5 ,			3	)}'	)
		print(f'euclidean_gcd_recursive(1, 3) = {euclidean_gcd_recursive(1 ,			3	)}'	)
		print(f'euclidean_gcd_recursive(3, 6) = {euclidean_gcd_recursive(3 ,			6	)}'	)
		print(f'euclidean_gcd_recursive(6, 3) = {euclidean_gcd_recursive(6 ,			3	)}'	)


if __name__ == "__main__":
	main()





