


"""simple docstring"""
from operator import delitem, getitem, setitem

import pytest

from data_structures.hashing.hash_map import HashMap







def 						_a				(      _SCREAMING_SNAKE_CASE				)    ->				str:
							return getitem, k







def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)    ->				Optional[Any]:
							return setitem, k, v







def 						_a				(      _SCREAMING_SNAKE_CASE				)    ->				Optional[int]:
							return delitem, k







def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, *_SCREAMING_SNAKE_CASE				)    ->				Any:
							try:
														return fun(_SCREAMING_SNAKE_CASE				, *_SCREAMING_SNAKE_CASE				), None
							except Exception as e:
														return None, e


__SCREAMING_SNAKE_CASE							:    str		=    (
    _set('key_a', 'val_a'),
    _set('key_b', 'val_b'),
)

__SCREAMING_SNAKE_CASE							:    int		=    [
    _set('key_a', 'val_a'),
    _set('key_a', 'val_b'),
]

__SCREAMING_SNAKE_CASE							:    int		=    [
    _set('key_a', 'val_a'),
    _set('key_b', 'val_b'),
    _del('key_a'),
    _del('key_b'),
    _set('key_a', 'val_a'),
    _del('key_a'),
]

__SCREAMING_SNAKE_CASE							:    Union[str, Any]		=    [
    _get('key_a'),
    _del('key_a'),
    _set('key_a', 'val_a'),
    _del('key_a'),
    _del('key_a'),
    _get('key_a'),
]

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

__SCREAMING_SNAKE_CASE							:    List[str]		=    [
    *[_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 						_a				(      _SCREAMING_SNAKE_CASE				)    ->				Optional[int]:
							snake_case_           =		HashMap(initial_block_size=4				)
							snake_case_           =		{}
							for _, (fun, *args) in enumerate(_SCREAMING_SNAKE_CASE				):
														snake_case_       ,						snake_case_           =		_run_operation(_SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, *_SCREAMING_SNAKE_CASE				)
														snake_case_       ,						snake_case_           =		_run_operation(_SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, *_SCREAMING_SNAKE_CASE				)
														assert my_res == py_res
														assert str(_SCREAMING_SNAKE_CASE				) == str(_SCREAMING_SNAKE_CASE				)
														assert set(_SCREAMING_SNAKE_CASE				) == set(_SCREAMING_SNAKE_CASE				)
														assert len(_SCREAMING_SNAKE_CASE				) == len(_SCREAMING_SNAKE_CASE				)
														assert set(my.items()				) == set(py.items()				)







def 						_a				(      )    ->				Dict:
							def is_public(_SCREAMING_SNAKE_CASE				) -> bool:
														return not name.startswith("""_"""				)

							snake_case_           =		{name for name in dir({}				) if is_public(_SCREAMING_SNAKE_CASE				)}
							snake_case_           =		{name for name in dir(HashMap()				) if is_public(_SCREAMING_SNAKE_CASE				)}

							assert dict_public_names > hash_public_names







"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__SCREAMING_SNAKE_CASE							:    Optional[Any]		=    {
    'configuration_biogpt': ['BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BioGptConfig'],
    'tokenization_biogpt': ['BioGptTokenizer'],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__SCREAMING_SNAKE_CASE							:    Optional[Any]		=    [
				    'BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'BioGptForCausalLM',
				    'BioGptForTokenClassification',
				    'BioGptForSequenceClassification',
				    'BioGptModel',
				    'BioGptPreTrainedModel',
				]


if TYPE_CHECKING:
				from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
				from .tokenization_biogpt import BioGptTokenizer

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_biogpt import (
								    BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    BioGptForCausalLM,
								    BioGptForSequenceClassification,
								    BioGptForTokenClassification,
								    BioGptModel,
								    BioGptPreTrainedModel,
								)


else:
				import sys

				__SCREAMING_SNAKE_CASE							:    Dict		=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



