



"""simple docstring"""





from operator import delitem, getitem, setitem

import pytest

from data_structures.hashing.hash_map import HashMap




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

		'''simple docstring'''


		return getitem, k




def 	UpperCAmelCase     (		A	:						int     ,				A	:						Optional[Any]       ):

		'''simple docstring'''


		return setitem, k, v




def 	UpperCAmelCase     (		A	:						List[Any]       ):

		'''simple docstring'''


		return delitem, k




def 	UpperCAmelCase     (		A	:						Optional[int]     ,				A	:						Tuple     ,				*A	:						List[Any]       ):

		'''simple docstring'''


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


lowercase  							=					(
    _set('''key_a''', '''val_a'''),
    _set('''key_b''', '''val_b'''),
)

lowercase  							=					[
    _set('''key_a''', '''val_a'''),
    _set('''key_a''', '''val_b'''),
]

lowercase  							=					[
    _set('''key_a''', '''val_a'''),
    _set('''key_b''', '''val_b'''),
    _del('''key_a'''),
    _del('''key_b'''),
    _set('''key_a''', '''val_a'''),
    _del('''key_a'''),
]

lowercase  							=					[
    _get('''key_a'''),
    _del('''key_a'''),
    _set('''key_a''', '''val_a'''),
    _del('''key_a'''),
    _del('''key_a'''),
    _get('''key_a'''),
]

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

lowercase  							=					[
    *[_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     (		A	:						Tuple       ):

		'''simple docstring'''


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






def 	UpperCAmelCase     (		):

		'''simple docstring'''


		def is_public(A	:						str       ) -> bool:
				return not name.startswith('_'       )

		_UpperCAmelCase							 =		{name for name in dir({}       ) if is_public(A       )}
		_UpperCAmelCase							 =		{name for name in dir(HashMap()       ) if is_public(A       )}

		assert dict_public_names > hash_public_names







"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowercase  							=					{
    '''configuration_roberta''': ['''ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RobertaConfig''', '''RobertaOnnxConfig'''],
    '''tokenization_roberta''': ['''RobertaTokenizer'''],
}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase  							=					['''RobertaTokenizerFast''']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase  							=					[
					    '''ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''RobertaForCausalLM''',
					    '''RobertaForMaskedLM''',
					    '''RobertaForMultipleChoice''',
					    '''RobertaForQuestionAnswering''',
					    '''RobertaForSequenceClassification''',
					    '''RobertaForTokenClassification''',
					    '''RobertaModel''',
					    '''RobertaPreTrainedModel''',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase  							=					[
					    '''TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''TFRobertaForCausalLM''',
					    '''TFRobertaForMaskedLM''',
					    '''TFRobertaForMultipleChoice''',
					    '''TFRobertaForQuestionAnswering''',
					    '''TFRobertaForSequenceClassification''',
					    '''TFRobertaForTokenClassification''',
					    '''TFRobertaMainLayer''',
					    '''TFRobertaModel''',
					    '''TFRobertaPreTrainedModel''',
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase  							=					[
					    '''FlaxRobertaForCausalLM''',
					    '''FlaxRobertaForMaskedLM''',
					    '''FlaxRobertaForMultipleChoice''',
					    '''FlaxRobertaForQuestionAnswering''',
					    '''FlaxRobertaForSequenceClassification''',
					    '''FlaxRobertaForTokenClassification''',
					    '''FlaxRobertaModel''',
					    '''FlaxRobertaPreTrainedModel''',
					]


if TYPE_CHECKING:
					from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
					from .tokenization_roberta import RobertaTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_roberta_fast import RobertaTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_roberta import (
										    ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
										    RobertaForCausalLM,
										    RobertaForMaskedLM,
										    RobertaForMultipleChoice,
										    RobertaForQuestionAnswering,
										    RobertaForSequenceClassification,
										    RobertaForTokenClassification,
										    RobertaModel,
										    RobertaPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_roberta import (
										    TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFRobertaForCausalLM,
										    TFRobertaForMaskedLM,
										    TFRobertaForMultipleChoice,
										    TFRobertaForQuestionAnswering,
										    TFRobertaForSequenceClassification,
										    TFRobertaForTokenClassification,
										    TFRobertaMainLayer,
										    TFRobertaModel,
										    TFRobertaPreTrainedModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_roberta import (
										    FlaxRobertaForCausalLM,
										    FlaxRobertaForMaskedLM,
										    FlaxRobertaForMultipleChoice,
										    FlaxRobertaForQuestionAnswering,
										    FlaxRobertaForSequenceClassification,
										    FlaxRobertaForTokenClassification,
										    FlaxRobertaModel,
										    FlaxRobertaPreTrainedModel,
										)

else:
					import sys

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


