




from operator import delitem, getitem, setitem

import pytest

from data_structures.hashing.hash_map import HashMap




def     __magic_name__       (				A	:      Union[str, Any]     ):






			'''simple docstring'''







			return getitem, k




def     __magic_name__       (				A	:      Any,						A	:      Dict     ):






			'''simple docstring'''







			return setitem, k, v




def     __magic_name__       (				A	:      Dict     ):






			'''simple docstring'''







			return delitem, k




def     __magic_name__       (				A	:      Tuple,						A	:      Optional[Any],						*A	:      Optional[Any]     ):






			'''simple docstring'''







			try:
						return fun(lowerCamelCase_,						*lowerCamelCase_     ), None
			except Exception as e:
						return None, e


__lowerCAmelCase	:       Optional[int]     =	(
    _set('key_a', 'val_a'),
    _set('key_b', 'val_b'),
)

__lowerCAmelCase	:       List[str]     =	[
    _set('key_a', 'val_a'),
    _set('key_a', 'val_b'),
]

__lowerCAmelCase	:       Union[str, Any]     =	[
    _set('key_a', 'val_a'),
    _set('key_b', 'val_b'),
    _del('key_a'),
    _del('key_b'),
    _set('key_a', 'val_a'),
    _del('key_a'),
]

__lowerCAmelCase	:       Any     =	[
    _get('key_a'),
    _del('key_a'),
    _set('key_a', 'val_a'),
    _del('key_a'),
    _del('key_a'),
    _get('key_a'),
]

__lowerCAmelCase	:       Optional[int]     =	[
    *[_set(x, x) for x in range(5)],  # guaranteed upsize
]

__lowerCAmelCase	:       Dict     =	[
    *[_set(x, x) for x in range(5)],  # guaranteed upsize
    *[_del(x) for x in range(5)],
    _set('key_a', 'val_b'),
]




@pytest.mark.parametrize(
    "operations",						(
        pytest.param(_add_items,						id="add items"     ),
        pytest.param(_overwrite_items,						id="overwrite items"     ),
        pytest.param(_delete_items,						id="delete items"     ),
        pytest.param(_access_absent_items,						id="access absent items"     ),
        pytest.param(_add_with_resize_up,						id="add with resize up"     ),
        pytest.param(_add_with_resize_down,						id="add with resize down"     ),
    ),						)
def     __magic_name__       (				A	:      int     ):






			'''simple docstring'''







			a					=    HashMap(initial_block_size=4     )
			a					=    {}
			for _, (fun, *args) in enumerate(lowerCamelCase_     ):
						a					=    _run_operation(lowerCamelCase_,						lowerCamelCase_,						*lowerCamelCase_     )
						a					=    _run_operation(lowerCamelCase_,						lowerCamelCase_,						*lowerCamelCase_     )
						assert my_res == py_res
						assert str(lowerCamelCase_     ) == str(lowerCamelCase_     )
						assert set(lowerCamelCase_     ) == set(lowerCamelCase_     )
						assert len(lowerCamelCase_     ) == len(lowerCamelCase_     )
						assert set(my.items()     ) == set(py.items()     )






def     __magic_name__       (				):






			'''simple docstring'''







			def is_public(A	:      int     ) -> bool:
						return not name.startswith("_"     )

			a					=    {name for name in dir({}     ) if is_public(lowerCamelCase_     )}
			a					=    {name for name in dir(HashMap()     ) if is_public(lowerCamelCase_     )}

			assert dict_public_names > hash_public_names












import multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser




def     __magic_name__       (				A	:      List[str]     ):






			'''simple docstring'''







			a					=    {}
			a					=    tokenizer(example["content"],						truncation=A     )["input_ids"]
			a					=    len(example["content"]     ) / len(output["input_ids"]     )
			return output


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

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

__lowerCAmelCase	:       int     =	time.time()
__lowerCAmelCase	:       Optional[int]     =	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''')

__lowerCAmelCase	:       Tuple     =	time.time()
ds.push_to_hub(args.tokenized_data_repo)






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






