'''simple docstring'''

from operator import delitem, getitem, setitem

import pytest

from data_structures.hashing.hash_map import HashMap
def 			__UpperCamelCase       (   UpperCAmelCase     ):
 return getitem, k
def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase     ):
 return setitem, k, v
def 			__UpperCamelCase       (   UpperCAmelCase     ):
 return delitem, k
def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase		,     *UpperCAmelCase     ):
 try:
  return fun(snake_case__		,     *snake_case__     ), None
 except Exception as e:
  return None, e


__a: List[str]          =   (
    _set("""key_a""", """val_a"""),
    _set("""key_b""", """val_b"""),
)

__a: Dict          =   [
    _set("""key_a""", """val_a"""),
    _set("""key_a""", """val_b"""),
]

__a: 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"""),
]

__a: Tuple          =   [
    _get("""key_a"""),
    _del("""key_a"""),
    _set("""key_a""", """val_a"""),
    _del("""key_a"""),
    _del("""key_a"""),
    _get("""key_a"""),
]

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

__a: 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 			__UpperCamelCase       (   UpperCAmelCase     ):
 lowercase__				:    Any  =					HashMap(initial_block_size=4     )
 lowercase__				:    Optional[int]  =					{}
 for _, (fun, *args) in enumerate(snake_case__     ):
  lowercase__				:    Optional[Any]  =					_run_operation(snake_case__		,     snake_case__		,     *snake_case__     )
  lowercase__				:    Any  =					_run_operation(snake_case__		,     snake_case__		,     *snake_case__     )
  assert my_res == py_res
  assert str(snake_case__     ) == str(snake_case__     )
  assert set(snake_case__     ) == set(snake_case__     )
  assert len(snake_case__     ) == len(snake_case__     )
  assert set(my.items()     ) == set(py.items()     )





def 			__UpperCamelCase       (   ):
 def is_public(UpperCAmelCase     ) -> bool:
  return not name.startswith('''_'''     )

 lowercase__				:    Optional[int]  =					{name for name in dir({}     ) if is_public(snake_case__     )}
 lowercase__				:    Optional[Any]  =					{name for name in dir(HashMap()     ) if is_public(snake_case__     )}

 assert dict_public_names > hash_public_names






from typing import TYPE_CHECKING

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


__UpperCAmelCase          =       {
    '''configuration_transfo_xl''': ['''TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TransfoXLConfig'''],
    '''tokenization_transfo_xl''': ['''TransfoXLCorpus''', '''TransfoXLTokenizer'''],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __UpperCAmelCase          =       [
        '''TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''AdaptiveEmbedding''',
        '''TransfoXLForSequenceClassification''',
        '''TransfoXLLMHeadModel''',
        '''TransfoXLModel''',
        '''TransfoXLPreTrainedModel''',
        '''load_tf_weights_in_transfo_xl''',
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __UpperCAmelCase          =       [
        '''TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''TFAdaptiveEmbedding''',
        '''TFTransfoXLForSequenceClassification''',
        '''TFTransfoXLLMHeadModel''',
        '''TFTransfoXLMainLayer''',
        '''TFTransfoXLModel''',
        '''TFTransfoXLPreTrainedModel''',
    ]


if TYPE_CHECKING:
    from .configuration_transfo_xl import TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, TransfoXLConfig
    from .tokenization_transfo_xl import TransfoXLCorpus, TransfoXLTokenizer

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_transfo_xl import (
            TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
            AdaptiveEmbedding,
            TransfoXLForSequenceClassification,
            TransfoXLLMHeadModel,
            TransfoXLModel,
            TransfoXLPreTrainedModel,
            load_tf_weights_in_transfo_xl,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_transfo_xl import (
            TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFAdaptiveEmbedding,
            TFTransfoXLForSequenceClassification,
            TFTransfoXLLMHeadModel,
            TFTransfoXLMainLayer,
            TFTransfoXLModel,
            TFTransfoXLPreTrainedModel,
        )

else:
    import sys

    __UpperCAmelCase          =       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



