



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






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						Path							,		_SCREAMING_SNAKE_CASE					:						list							)      ->						Optional[Any]:







	"""simple docstring"""
	lowerCAmelCase   =     """\n""".join(_SCREAMING_SNAKE_CASE							)
	Path(_SCREAMING_SNAKE_CASE							).open("""w"""							).writelines(_SCREAMING_SNAKE_CASE							)






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						Optional[Any]							)      ->						Optional[int]:







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





class __snake_case(				_lowerCAmelCase ):


						'''simple docstring'''



						@parameterized.expand(
						    [
						        MBART_TINY,
						        MARIAN_TINY,
						        T5_TINY,
						        BART_TINY,
						        PEGASUS_XSUM,
						    ]     ,	)
						@slow
						def     __snake_case   ( self     ,	A_				)     ->     int:
							lowerCAmelCase   =     AutoTokenizer.from_pretrained(A_				)
							lowerCAmelCase   =     make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()				)
							lowerCAmelCase   =     max(len(tokenizer.encode(A_				)				) for a in ARTICLES				)
							lowerCAmelCase   =     max(len(tokenizer.encode(A_				)				) for a in SUMMARIES				)
							lowerCAmelCase   =     4
							lowerCAmelCase   =     8
							assert max_len_target > max_src_len  # Will be truncated
							assert max_len_source > max_src_len  # Will be truncated
							lowerCAmelCase,       lowerCAmelCase   =     """ro_RO""", """de_DE"""  # ignored for all but mbart, but never causes error.
							lowerCAmelCase   =     SeqaSeqDataset(
							    A_     ,	data_dir=A_     ,	type_path="""train"""     ,	max_source_length=A_     ,	max_target_length=A_     ,	src_lang=A_     ,	tgt_lang=A_     ,	)
							lowerCAmelCase   =     DataLoader(A_     ,	batch_size=2     ,	collate_fn=train_dataset.collate_fn				)
							for batch in dataloader:
								assert isinstance(A_     ,	A_				)
								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
								lowerCAmelCase   =     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     __snake_case   ( self     ,	A_				)     ->     Optional[int]:
							lowerCAmelCase   =     AutoTokenizer.from_pretrained(A_				)
							lowerCAmelCase   =     make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()				)
							lowerCAmelCase   =     max(len(tokenizer.encode(A_				)				) for a in ARTICLES				)
							lowerCAmelCase   =     max(len(tokenizer.encode(A_				)				) for a in SUMMARIES				)
							lowerCAmelCase   =     4
							lowerCAmelCase   =     LegacySeqaSeqDataset(
							    A_     ,	data_dir=A_     ,	type_path="""train"""     ,	max_source_length=20     ,	max_target_length=A_     ,	)
							lowerCAmelCase   =     DataLoader(A_     ,	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     __snake_case   ( self				)     ->     List[str]:
							lowerCAmelCase   =     AutoTokenizer.from_pretrained("""facebook/mbart-large-cc25"""				)

							lowerCAmelCase   =     Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()				)				)
							lowerCAmelCase   =     tmp_dir.joinpath("""train.source"""				).open().readlines()
							lowerCAmelCase   =     Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()				)				)
							pack_data_dir(A_     ,	A_     ,	128     ,	A_				)
							lowerCAmelCase   =     {x.name for x in tmp_dir.iterdir()}
							lowerCAmelCase   =     {x.name for x in save_dir.iterdir()}
							lowerCAmelCase   =     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(A_				) < len(A_				)
							assert len(A_				) == 1
							assert len(packed_examples[0]				) == sum(len(A_				) for x in orig_examples				)
							assert orig_paths == new_paths


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


						def     __snake_case   ( self				)     ->     Dict:
							lowerCAmelCase,       lowerCAmelCase,       lowerCAmelCase   =     self._get_dataset(max_len=512				)
							lowerCAmelCase   =     2
							lowerCAmelCase   =     ds.make_sortish_sampler(A_     ,	shuffle=A_				)

							lowerCAmelCase   =     DataLoader(A_     ,	batch_size=A_     ,	collate_fn=ds.collate_fn     ,	num_workers=2				)
							lowerCAmelCase   =     DataLoader(A_     ,	batch_size=A_     ,	collate_fn=ds.collate_fn     ,	num_workers=2     ,	sampler=A_				)

							lowerCAmelCase   =     tokenizer.pad_token_id

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

							assert sum(count_pad_tokens(A_     ,	k="""labels"""				)				) < sum(count_pad_tokens(A_     ,	k="""labels"""				)				)
							assert sum(count_pad_tokens(A_				)				) < sum(count_pad_tokens(A_				)				)
							assert len(A_				) == len(A_				)


						def     __snake_case   ( self     ,	A_=1000     ,	A_=128				)     ->     Any:
							if os.getenv("""USE_REAL_DATA"""     ,	A_				):
								lowerCAmelCase   =     """examples/seq2seq/wmt_en_ro"""
								lowerCAmelCase   =     max_len * 2 * 64
								if not Path(A_				).joinpath("""train.len"""				).exists():
									save_len_file(A_     ,	A_				)
							else:
								lowerCAmelCase   =     """examples/seq2seq/test_data/wmt_en_ro"""
								lowerCAmelCase   =     max_len * 4
								save_len_file(A_     ,	A_				)

							lowerCAmelCase   =     AutoTokenizer.from_pretrained(A_				)
							lowerCAmelCase   =     SeqaSeqDataset(
							    A_     ,	data_dir=A_     ,	type_path="""train"""     ,	max_source_length=A_     ,	max_target_length=A_     ,	n_obs=A_     ,	)
							return ds, max_tokens, tokenizer


						def     __snake_case   ( self				)     ->     Optional[int]:
							lowerCAmelCase,       lowerCAmelCase,       lowerCAmelCase   =     self._get_dataset()
							lowerCAmelCase   =     set(DistributedSortishSampler(A_     ,	256     ,	num_replicas=2     ,	rank=0     ,	add_extra_examples=A_				)				)
							lowerCAmelCase   =     set(DistributedSortishSampler(A_     ,	256     ,	num_replicas=2     ,	rank=1     ,	add_extra_examples=A_				)				)
							assert idsa.intersection(A_				) == set()




						@parameterized.expand(
						    [
						        MBART_TINY,
						        MARIAN_TINY,
						        T5_TINY,
						        BART_TINY,
						        PEGASUS_XSUM,
						    ]     ,	)
						def     __snake_case   ( self     ,	A_				)     ->     str:
							lowerCAmelCase   =     AutoTokenizer.from_pretrained(A_     ,	use_fast=A_				)
							if tok_name == MBART_TINY:
								lowerCAmelCase   =     SeqaSeqDataset(
								    A_     ,	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"""     ,	)
								lowerCAmelCase   =     train_dataset.dataset_kwargs
								assert "src_lang" in kwargs and "tgt_lang" in kwargs
							else:
								lowerCAmelCase   =     SeqaSeqDataset(
								    A_     ,	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     ,	)
								lowerCAmelCase   =     train_dataset.dataset_kwargs
								assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
								assert len(A_				) == 1 if tok_name == BART_TINY else len(A_				) == 0




'''simple docstring'''

def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						list							)      ->						list:







	"""simple docstring"""

	if len(_SCREAMING_SNAKE_CASE							) < 2:
		return collection

	def circle_sort_util(_SCREAMING_SNAKE_CASE					:						list							,		_SCREAMING_SNAKE_CASE					:						int							,		_SCREAMING_SNAKE_CASE					:						int							) -> bool:
		lowerCAmelCase   =     False

		if low == high:
			return swapped

		lowerCAmelCase   =     low
		lowerCAmelCase   =     high

		while left < right:
			if collection[left] > collection[right]:
				lowerCAmelCase,       lowerCAmelCase   =     (
				    collection[right],
				    collection[left],
				)
				lowerCAmelCase   =     True

			left += 1
			right -= 1

		if left == right and collection[left] > collection[right + 1]:
			lowerCAmelCase,       lowerCAmelCase   =     (
			    collection[right + 1],
			    collection[left],
			)

			lowerCAmelCase   =     True

		lowerCAmelCase   =     low + int((high - low) / 2							)
		lowerCAmelCase   =     circle_sort_util(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							)
		lowerCAmelCase   =     circle_sort_util(_SCREAMING_SNAKE_CASE							,		mid + 1							,		_SCREAMING_SNAKE_CASE							)

		return swapped or left_swap or right_swap

	lowerCAmelCase   =     True

	while is_not_sorted is True:
		lowerCAmelCase   =     circle_sort_util(_SCREAMING_SNAKE_CASE							,		0							,		len(_SCREAMING_SNAKE_CASE							) - 1							)

	return collection


if __name__ == "__main__":
				UpperCAmelCase     		=   input('Enter numbers separated by a comma:\n').strip()
				UpperCAmelCase     		=   [int(item) for item in user_input.split(',')]
				print(circle_sort(unsorted))