


'''simple docstring'''


from typing import List, Optional, TypeVar

from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interleave_iterable_datasets
from .splits import NamedSplit
from .utils import logging
from .utils.py_utils import Literal


lowerCamelCase__					=   logging.get_logger(__name__)


lowerCamelCase__					=   TypeVar('DatasetType', Dataset, IterableDataset)






def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase = None					,       __lowerCAmelCase = None					,       __lowerCAmelCase = None					,       __lowerCAmelCase = None					,       __lowerCAmelCase = "first_exhausted"					,       ):
		from .arrow_dataset import Dataset
		from .iterable_dataset import IterableDataset

		if not datasets:
				raise ValueError("Unable to interleave an empty list of datasets."      )
		for i, dataset in enumerate(UpperCamelCase__      ):
				if not isinstance(UpperCamelCase__					,       (Dataset, IterableDataset)      ):
						if isinstance(UpperCamelCase__					,       (DatasetDict, IterableDatasetDict)      ):
								if not dataset:
										raise ValueError(
										    F"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} """
										    "is an empty dataset dictionary."      )
								raise ValueError(
								    F"""Dataset at position {i} has at least one split: {list(UpperCamelCase__      )}\n"""
								    F"""Please pick one to interleave with the other datasets, for example: dataset[\'{next(iter(UpperCamelCase__      )      )}\']"""      )
						raise ValueError(
						    F"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(UpperCamelCase__      ).__name__}."""      )
				if i == 0:
						_UpperCAmelCase    ,			_UpperCAmelCase						:				Tuple	    =					(
						    (Dataset, IterableDataset) if isinstance(UpperCamelCase__					,       UpperCamelCase__      ) else (IterableDataset, Dataset)
						)
				elif not isinstance(UpperCamelCase__					,       UpperCamelCase__      ):
						raise ValueError(
						    F"""Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects."""      )
		if stopping_strategy not in ["first_exhausted", "all_exhausted"]:
				raise ValueError(F"""{stopping_strategy} is not supported. Please enter a valid stopping_strategy."""      )
		if dataset_type is Dataset:
				return _interleave_map_style_datasets(
				    UpperCamelCase__					,       UpperCamelCase__					,       UpperCamelCase__					,       info=UpperCamelCase__					,       split=UpperCamelCase__					,       stopping_strategy=UpperCamelCase__      )
		else:
				return _interleave_iterable_datasets(
				    UpperCamelCase__					,       UpperCamelCase__					,       UpperCamelCase__					,       info=UpperCamelCase__					,       split=UpperCamelCase__					,       stopping_strategy=UpperCamelCase__      )






def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase = None					,       __lowerCAmelCase = None					,       __lowerCAmelCase = 0					,       ):






		if not dsets:
				raise ValueError("Unable to concatenate an empty list of datasets."      )
		for i, dataset in enumerate(UpperCamelCase__      ):
				if not isinstance(UpperCamelCase__					,       (Dataset, IterableDataset)      ):
						if isinstance(UpperCamelCase__					,       (DatasetDict, IterableDatasetDict)      ):
								if not dataset:
										raise ValueError(
										    F"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} """
										    "is an empty dataset dictionary."      )
								raise ValueError(
								    F"""Dataset at position {i} has at least one split: {list(UpperCamelCase__      )}\n"""
								    F"""Please pick one to interleave with the other datasets, for example: dataset[\'{next(iter(UpperCamelCase__      )      )}\']"""      )
						raise ValueError(
						    F"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(UpperCamelCase__      ).__name__}."""      )
				if i == 0:
						_UpperCAmelCase    ,			_UpperCAmelCase						:				Any	    =					(
						    (Dataset, IterableDataset) if isinstance(UpperCamelCase__					,       UpperCamelCase__      ) else (IterableDataset, Dataset)
						)
				elif not isinstance(UpperCamelCase__					,       UpperCamelCase__      ):
						raise ValueError(
						    F"""Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects."""      )
		if dataset_type is Dataset:
				return _concatenate_map_style_datasets(UpperCamelCase__					,       info=UpperCamelCase__					,       split=UpperCamelCase__					,       axis=UpperCamelCase__      )
		else:
				return _concatenate_iterable_datasets(UpperCamelCase__					,       info=UpperCamelCase__					,       split=UpperCamelCase__					,       axis=UpperCamelCase__      )






import multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser





def 					__lowerCamelCase	( UpperCamelCase__      ):







							'''simple docstring'''






							snake_case_							=    {}
							snake_case_							=    tokenizer(example['content']   ,							truncation=UpperCamelCase__      )['input_ids']
							snake_case_							=    len(example['content']      ) / len(output['input_ids']      )
							return output


_UpperCAmelCase		:   Dict										=       HfArgumentParser(PretokenizationArguments)
_UpperCAmelCase		:   List[Any]										=       parser.parse_args()
if args.num_workers is None:
					_UpperCAmelCase		:   Union[str, Any]										=       multiprocessing.cpu_count()
_UpperCAmelCase		:   int										=       AutoTokenizer.from_pretrained(args.tokenizer_dir)

_UpperCAmelCase		:   Optional[int]										=       time.time()
_UpperCAmelCase		:   List[str]										=       load_dataset(args.dataset_name, split="""train""")
print(F'''Dataset loaded in {time.time()-t_start:.2f}s''')

_UpperCAmelCase		:   Tuple										=       time.time()
_UpperCAmelCase		:   Union[str, Any]										=       ds.map(
    tokenize,
    num_proc=args.num_workers,
    remove_columns=[
        """repo_name""",
        """path""",
        """copies""",
        """size""",
        """content""",
        """license""",
        """hash""",
        """line_mean""",
        """line_max""",
        """alpha_frac""",
        """autogenerated""",
    ],
)
print(F'''Dataset tokenized in {time.time()-t_start:.2f}s''')

_UpperCAmelCase		:   Dict										=       time.time()
ds.push_to_hub(args.tokenized_data_repo)


print(F'''Data pushed to the hub in {time.time()-t_start:.2f}s''')




