



"""simple docstring"""





from operator import delitem, getitem, setitem

import pytest

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


lowerCamelCase  			=		(
    _set("""key_a""", """val_a"""),
    _set("""key_b""", """val_b"""),
)

lowerCamelCase  			=		[
    _set("""key_a""", """val_a"""),
    _set("""key_a""", """val_b"""),
]

lowerCamelCase  			=		[
    _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  			=		[
    _get("""key_a"""),
    _del("""key_a"""),
    _set("""key_a""", """val_a"""),
    _del("""key_a"""),
    _del("""key_a"""),
    _get("""key_a"""),
]

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

lowerCamelCase  			=		[
    *[_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__      ( lowerCAmelCase__ ):
      UpperCAmelCase_     					=				HashMap(initial_block_size=4 )
      UpperCAmelCase_     					=				{}
      for _, (fun, *args) in enumerate(lowerCAmelCase__ ):
            UpperCAmelCase_					,       UpperCAmelCase_     					=				_run_operation(lowerCAmelCase__      ,     lowerCAmelCase__      ,     *lowerCAmelCase__ )
            UpperCAmelCase_					,       UpperCAmelCase_     					=				_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   a__      ( ):
      def is_public(lowerCAmelCase__ ) -> bool:
            return not name.startswith("_" )

      UpperCAmelCase_     					=				{name for name in dir({} ) if is_public(lowerCAmelCase__ )}
      UpperCAmelCase_     					=				{name for name in dir(HashMap() ) if is_public(lowerCAmelCase__ )}

      assert dict_public_names > hash_public_names










"""simple docstring"""






from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
			if not (is_transformers_available() and is_torch_available() and is_transformers_version(""">=""", """4.25.0""")):
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			from ...utils.dummy_torch_and_transformers_objects import (
			    VersatileDiffusionDualGuidedPipeline,
			    VersatileDiffusionImageVariationPipeline,
			    VersatileDiffusionPipeline,
			    VersatileDiffusionTextToImagePipeline,
			)
else:
			from .modeling_text_unet import UNetFlatConditionModel
			from .pipeline_versatile_diffusion import VersatileDiffusionPipeline
			from .pipeline_versatile_diffusion_dual_guided import VersatileDiffusionDualGuidedPipeline
			from .pipeline_versatile_diffusion_image_variation import VersatileDiffusionImageVariationPipeline
			from .pipeline_versatile_diffusion_text_to_image import VersatileDiffusionTextToImagePipeline