

import os
from tempfile import TemporaryDirectory
from unittest import TestCase

import pytest
from absl.testing import parameterized

from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
from datasets.dataset_dict import IterableDatasetDict
from datasets.iterable_dataset import IterableDataset
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.file_utils import cached_path


lowerCAmelCase__	   = [
    {"""dataset""": """wikipedia""", """config_name""": """20220301.de"""},
    {"""dataset""": """wikipedia""", """config_name""": """20220301.en"""},
    {"""dataset""": """wikipedia""", """config_name""": """20220301.fr"""},
    {"""dataset""": """wikipedia""", """config_name""": """20220301.frr"""},
    {"""dataset""": """wikipedia""", """config_name""": """20220301.it"""},
    {"""dataset""": """wikipedia""", """config_name""": """20220301.simple"""},
    {"""dataset""": """snli""", """config_name""": """plain_text"""},
    {"""dataset""": """eli5""", """config_name""": """LFQA_reddit"""},
    {"""dataset""": """wiki40b""", """config_name""": """en"""},
    {"""dataset""": """wiki_dpr""", """config_name""": """psgs_w100.nq.compressed"""},
    {"""dataset""": """wiki_dpr""", """config_name""": """psgs_w100.nq.no_index"""},
    {"""dataset""": """wiki_dpr""", """config_name""": """psgs_w100.multiset.no_index"""},
    {"""dataset""": """natural_questions""", """config_name""": """default"""},
]



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							str=True      )			->      Union[str, Any]:




				'''simple docstring'''





				if with_config:
								return [
								    {
								        "testcase_name": d["dataset"] + "/" + d["config_name"],
								        "dataset": d["dataset"],
								        "config_name": d["config_name"],
								    }
								    for d in DATASETS_ON_HF_GCP
								]
				else:
								return [
								    {"testcase_name": dataset, "dataset": dataset} for dataset in {d["dataset"] for d in DATASETS_ON_HF_GCP}
								]

@parameterized.named_parameters(list_datasets_on_hf_gcp_parameters(with_config=snake_case			)			)
class 					a__						(					snake_case			):
			"""simple docstring"""







			__lowerCamelCase        =       None
			__lowerCamelCase        =       None


			def      UpperCamelCase		(    self      ,      lowercase      ,      lowercase       )  ->						Dict:
							'''simple docstring'''






							with TemporaryDirectory() as tmp_dir:
											A__        =			dataset_module_factory(lowercase      ,      cache_dir=lowercase       )

											A__        =			import_main_class(dataset_module.module_path      ,      dataset=lowercase       )

											A__        =			builder_cls(
											    cache_dir=lowercase      ,      config_name=lowercase      ,      hash=dataset_module.hash      ,      )

											A__        =			"/".join(
											    [
											        HF_GCP_BASE_URL,
											        builder_instance._relative_data_dir(with_hash=lowercase       ).replace(os.sep      ,      "/"       ),
											        config.DATASET_INFO_FILENAME,
											    ]       )
											A__        =			cached_path(lowercase      ,      cache_dir=lowercase       )
											self.assertTrue(os.path.exists(lowercase       )       )



@pytest.mark.integration
def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							Optional[int]      )			->      Any:




				'''simple docstring'''





				A__        =			tmp_path_factory.mktemp("test_hf_gcp"      ) / "test_wikipedia_simple"
				A__        =			dataset_module_factory("wikipedia"	,       cache_dir=SCREAMING_SNAKE_CASE_      )
				A__        =			import_main_class(dataset_module.module_path      )
				A__        =			builder_cls(
				    cache_dir=SCREAMING_SNAKE_CASE_	,       config_name="20220301.frr"	,       hash=dataset_module.hash	,       )
				# use the HF cloud storage, not the original download_and_prepare that uses apache-beam
				A__        =			None
				builder_instance.download_and_prepare()
				A__        =			builder_instance.as_dataset()
				assert ds





@pytest.mark.integration
def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							List[Any]      )			->      Optional[Any]:




				'''simple docstring'''





				A__        =			dataset_module_factory("wikipedia"	,       cache_dir=SCREAMING_SNAKE_CASE_      )
				A__        =			import_main_class(dataset_module.module_path	,       dataset=SCREAMING_SNAKE_CASE_      )
				A__        =			builder_cls(
				    cache_dir=SCREAMING_SNAKE_CASE_	,       config_name="20220301.frr"	,       hash=dataset_module.hash	,       )
				A__        =			builder_instance.as_streaming_dataset()
				assert ds
				assert isinstance(SCREAMING_SNAKE_CASE_	,       SCREAMING_SNAKE_CASE_      )
				assert "train" in ds
				assert isinstance(ds["train"]	,       SCREAMING_SNAKE_CASE_      )
				assert next(iter(ds["train"]      )      )




import argparse
import shutil
from pathlib import Path

from tqdm import tqdm

from transformers import AutoTokenizer



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							List[str]	,       SCREAMING_SNAKE_CASE_:							List[str]	,       SCREAMING_SNAKE_CASE_:							List[str]	,       SCREAMING_SNAKE_CASE_:							str=1_0_2_4      )			->      Any:




				'''simple docstring'''





				A__    ,				A__        =			[], []

				A__        =			list(zip(SCREAMING_SNAKE_CASE_	,       SCREAMING_SNAKE_CASE_      )      )
				A__    ,				A__        =			sorted_examples[0]

				def is_too_big(SCREAMING_SNAKE_CASE_:							List[str]      ):
								return tok(SCREAMING_SNAKE_CASE_	,       return_tensors="pt"      ).input_ids.shape[1] > max_tokens

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

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



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							Optional[int]	,       SCREAMING_SNAKE_CASE_:							Path	,       SCREAMING_SNAKE_CASE_:							str	,       SCREAMING_SNAKE_CASE_:							Tuple      )			->      Union[str, Any]:




				'''simple docstring'''





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



def 					lowerCAmelCase__							(	)			->      Optional[int]:




				'''simple docstring'''





				A__        =			argparse.ArgumentParser()
				parser.add_argument("--tok_name"	,       type=SCREAMING_SNAKE_CASE_	,       help="like facebook/bart-large-cnn,t5-base, etc."      )
				parser.add_argument("--max_seq_len"	,       type=SCREAMING_SNAKE_CASE_	,       default=1_2_8      )
				parser.add_argument("--data_dir"	,       type=SCREAMING_SNAKE_CASE_      )
				parser.add_argument("--save_path"	,       type=SCREAMING_SNAKE_CASE_      )
				A__        =			parser.parse_args()
				A__        =			AutoTokenizer.from_pretrained(args.tok_name      )
				return pack_data_dir(SCREAMING_SNAKE_CASE_	,       Path(args.data_dir      )	,       args.max_seq_len	,       args.save_path      )


if __name__ == "__main__":
	packer_cli()

