import argparse
import shutil
from pathlib import Path

from tqdm import tqdm

from transformers import AutoTokenizer






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase=1024       )->       Tuple:



			"""simple docstring"""







			lowercase		,lowercase							=		[], []

			lowercase							=		list(zip(UpperCAmelCase,		UpperCAmelCase       )       )
			lowercase		,lowercase							=		sorted_examples[0]

			def is_too_big(UpperCAmelCase       ):
						return tok(UpperCAmelCase,		return_tensors='''pt'''       ).input_ids.shape[1] > max_tokens

			for src, tgt in tqdm(sorted_examples[1:]       ):
						lowercase							=		new_src + ''' ''' + src
						lowercase							=		new_tgt + ''' ''' + tgt
						if is_too_big(UpperCAmelCase       ) or is_too_big(UpperCAmelCase       ):  # cant fit, finalize example
									finished_src.append(UpperCAmelCase       )
									finished_tgt.append(UpperCAmelCase       )
									lowercase		,lowercase							=		src, tgt
						else:  # can fit, keep adding
									lowercase		,lowercase							=		cand_src, cand_tgt

									 # cleanup
			if new_src:
						assert new_tgt
						finished_src.append(UpperCAmelCase       )
						finished_tgt.append(UpperCAmelCase       )
			return finished_src, finished_tgt






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase       )->       Optional[Any]:



			"""simple docstring"""







			lowercase							=		Path(UpperCAmelCase       )
			save_path.mkdir(exist_ok=UpperCAmelCase       )
			for split in ["train"]:
						lowercase		,lowercase							=		data_dir / f'{split}.source', data_dir / f'{split}.target'
						lowercase							=		[x.rstrip() for x in Path(UpperCAmelCase       ).open().readlines()]
						lowercase							=		[x.rstrip() for x in Path(UpperCAmelCase       ).open().readlines()]
						lowercase		,lowercase							=		pack_examples(UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase       )
						print(f'packed {split} split from {len(UpperCAmelCase       )} examples -> {len(UpperCAmelCase       )}.'       )
						Path(save_path / f'{split}.source'       ).open('''w'''       ).write('''\n'''.join(UpperCAmelCase       )       )
						Path(save_path / f'{split}.target'       ).open('''w'''       ).write('''\n'''.join(UpperCAmelCase       )       )
			for split in ["val", "test"]:
						lowercase		,lowercase							=		data_dir / f'{split}.source', data_dir / f'{split}.target'
						shutil.copyfile(UpperCAmelCase,		save_path / f'{split}.source'       )
						shutil.copyfile(UpperCAmelCase,		save_path / f'{split}.target'       )






def        __UpperCAmelCase    (       )->       Any:



			"""simple docstring"""







			lowercase							=		argparse.ArgumentParser()
			parser.add_argument('''--tok_name''',		type=UpperCAmelCase,		help='''like facebook/bart-large-cnn,t5-base, etc.'''       )
			parser.add_argument('''--max_seq_len''',		type=UpperCAmelCase,		default=128       )
			parser.add_argument('''--data_dir''',		type=UpperCAmelCase       )
			parser.add_argument('''--save_path''',		type=UpperCAmelCase       )
			lowercase							=		parser.parse_args()
			lowercase							=		AutoTokenizer.from_pretrained(args.tok_name       )
			return pack_data_dir(UpperCAmelCase,		Path(args.data_dir       ),		args.max_seq_len,		args.save_path       )


if __name__ == "__main__":
						packer_cli()






import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch

import datasets
import datasets.config

from .utils import require_beam






class      __lowercase					(    datasets.BeamBasedBuilder    ):

						def        __a		(	self				:      Tuple   )							->		Tuple:


									'''simple docstring'''

									return datasets.DatasetInfo(
									    features=datasets.Features({'''content''': datasets.Value('''string'''   )}   )							,							supervised_keys=__lowerCamelCase							,							)

						def        __a		(	self				:      Union[str, Any]							,							__lowerCamelCase				:      Union[str, Any]							,							__lowerCamelCase				:      Tuple   )							->		int:


									'''simple docstring'''

									return [datasets.SplitGenerator(name=datasets.Split.TRAIN							,							gen_kwargs={'''examples''': get_test_dummy_examples()}   )]



						def        __a		(	self				:      List[Any]							,							__lowerCamelCase				:      Any							,							__lowerCamelCase				:      Union[str, Any]   )							->		Optional[int]:


									'''simple docstring'''

									import apache_beam as beam

									return pipeline | "Load Examples" >> beam.Create(__lowerCamelCase   )






class      __lowercase					(    datasets.BeamBasedBuilder    ):

						def        __a		(	self				:      int   )							->		Any:


									'''simple docstring'''

									return datasets.DatasetInfo(
									    features=datasets.Features({'''a''': datasets.Sequence({'''b''': datasets.Value('''string'''   )}   )}   )							,							supervised_keys=__lowerCamelCase							,							)

						def        __a		(	self				:      Optional[int]							,							__lowerCamelCase				:      List[Any]							,							__lowerCamelCase				:      Optional[int]   )							->		int:


									'''simple docstring'''

									return [
									    datasets.SplitGenerator(name=datasets.Split.TRAIN							,							gen_kwargs={'''examples''': get_test_nested_examples()}   )
									]



						def        __a		(	self				:      Union[str, Any]							,							__lowerCamelCase				:      Tuple							,							__lowerCamelCase				:      Union[str, Any]   )							->		Tuple:


									'''simple docstring'''

									import apache_beam as beam

									return pipeline | "Load Examples" >> beam.Create(__lowerCamelCase   )






def        __UpperCAmelCase    (       )->       Dict:



			"""simple docstring"""







			return [(i, {"content": content}) for i, content in enumerate(['''foo''', '''bar''', '''foobar''']       )]






def        __UpperCAmelCase    (       )->       List[str]:



			"""simple docstring"""







			return [(i, {"a": {"b": [content]}}) for i, content in enumerate(['''foo''', '''bar''', '''foobar''']       )]






class      __lowercase					(    _A    ):

						@require_beam
						def        __a		(	self				:      Optional[int]   )							->		Optional[Any]:


									'''simple docstring'''

									lowercase							=		len(get_test_dummy_examples()   )
									with tempfile.TemporaryDirectory() as tmp_cache_dir:
												lowercase							=		DummyBeamDataset(cache_dir=__lowerCamelCase							,							beam_runner='''DirectRunner'''   )
												builder.download_and_prepare()
												self.assertTrue(
												    os.path.exists(
												        os.path.join(__lowerCamelCase							,							builder.name							,							'''default'''							,							'''0.0.0'''							,							f'{builder.name}-train.arrow'   )   )   )
												self.assertDictEqual(builder.info.features							,							datasets.Features({'''content''': datasets.Value('''string'''   )}   )   )
												lowercase							=		builder.as_dataset()
												self.assertEqual(dset['''train'''].num_rows							,							__lowerCamelCase   )
												self.assertEqual(dset['''train'''].info.splits['''train'''].num_examples							,							__lowerCamelCase   )
												self.assertDictEqual(dset['''train'''][0]							,							get_test_dummy_examples()[0][1]   )
												self.assertDictEqual(
												    dset['''train'''][expected_num_examples - 1]							,							get_test_dummy_examples()[expected_num_examples - 1][1]   )
												self.assertTrue(
												    os.path.exists(os.path.join(__lowerCamelCase							,							builder.name							,							'''default'''							,							'''0.0.0'''							,							'''dataset_info.json'''   )   )   )
												del dset

						@require_beam
						def        __a		(	self				:      Union[str, Any]   )							->		List[Any]:


									'''simple docstring'''

									import apache_beam as beam

									lowercase							=		beam.io.parquetio.WriteToParquet

									lowercase							=		len(get_test_dummy_examples()   )
									with tempfile.TemporaryDirectory() as tmp_cache_dir:
												lowercase							=		DummyBeamDataset(cache_dir=__lowerCamelCase							,							beam_runner='''DirectRunner'''   )
												with patch('''apache_beam.io.parquetio.WriteToParquet'''   ) as write_parquet_mock:
															lowercase							=		partial(__lowerCamelCase							,							num_shards=2   )
															builder.download_and_prepare()
												self.assertTrue(
												    os.path.exists(
												        os.path.join(
												            __lowerCamelCase							,							builder.name							,							'''default'''							,							'''0.0.0'''							,							f'{builder.name}-train-00000-of-00002.arrow'   )   )   )
												self.assertTrue(
												    os.path.exists(
												        os.path.join(
												            __lowerCamelCase							,							builder.name							,							'''default'''							,							'''0.0.0'''							,							f'{builder.name}-train-00000-of-00002.arrow'   )   )   )
												self.assertDictEqual(builder.info.features							,							datasets.Features({'''content''': datasets.Value('''string'''   )}   )   )
												lowercase							=		builder.as_dataset()
												self.assertEqual(dset['''train'''].num_rows							,							__lowerCamelCase   )
												self.assertEqual(dset['''train'''].info.splits['''train'''].num_examples							,							__lowerCamelCase   )
												# Order is not preserved when sharding, so we just check that all the elements are there
												self.assertListEqual(sorted(dset['''train''']['''content''']   )							,							sorted(['''foo''', '''bar''', '''foobar''']   )   )
												self.assertTrue(
												    os.path.exists(os.path.join(__lowerCamelCase							,							builder.name							,							'''default'''							,							'''0.0.0'''							,							'''dataset_info.json'''   )   )   )
												del dset

						@require_beam
						def        __a		(	self				:      int   )							->		int:


									'''simple docstring'''

									with tempfile.TemporaryDirectory() as tmp_cache_dir:
												lowercase							=		DummyBeamDataset(cache_dir=__lowerCamelCase   )
												self.assertRaises(datasets.builder.MissingBeamOptions							,							builder.download_and_prepare   )



						@require_beam
						def        __a		(	self				:      str   )							->		List[Any]:


									'''simple docstring'''

									lowercase							=		len(get_test_nested_examples()   )
									with tempfile.TemporaryDirectory() as tmp_cache_dir:
												lowercase							=		NestedBeamDataset(cache_dir=__lowerCamelCase							,							beam_runner='''DirectRunner'''   )
												builder.download_and_prepare()
												self.assertTrue(
												    os.path.exists(
												        os.path.join(__lowerCamelCase							,							builder.name							,							'''default'''							,							'''0.0.0'''							,							f'{builder.name}-train.arrow'   )   )   )
												self.assertDictEqual(
												    builder.info.features							,							datasets.Features({'''a''': datasets.Sequence({'''b''': datasets.Value('''string'''   )}   )}   )   )
												lowercase							=		builder.as_dataset()
												self.assertEqual(dset['''train'''].num_rows							,							__lowerCamelCase   )
												self.assertEqual(dset['''train'''].info.splits['''train'''].num_examples							,							__lowerCamelCase   )
												self.assertDictEqual(dset['''train'''][0]							,							get_test_nested_examples()[0][1]   )
												self.assertDictEqual(
												    dset['''train'''][expected_num_examples - 1]							,							get_test_nested_examples()[expected_num_examples - 1][1]   )
												self.assertTrue(
												    os.path.exists(os.path.join(__lowerCamelCase							,							builder.name							,							'''default'''							,							'''0.0.0'''							,							'''dataset_info.json'''   )   )   )
												del dset





