
"""simple docstring"""



from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
 from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline

"""simple docstring"""



from __future__ import annotations


def  _UpperCamelCase	(						_A      )    ->  None:





    """simple docstring"""

    create_state_space_tree(_A					,      []					,      0					,      [0 for i in range(len(_A      )      )]      )


def  _UpperCamelCase	(						_A					,      _A					,      _A					,      _A					,      )    ->  None:





    """simple docstring"""

    if index == len(_A      ):
        print(_A      )
        return

    for i in range(len(_A      )      ):
        if not index_used[i]:
            current_sequence.append(sequence[i]      )
            _UpperCAmelCase          =   True
            create_state_space_tree(_A					,      _A					,      index + 1					,      _A      )
            current_sequence.pop()
            _UpperCAmelCase          =   False
a					:	list[int | str]							=		[3, 1, 2, 4]
generate_all_permutations(sequence)

a					:	list[int | str]							=		["A", "B", "C"]







generate_all_permutations(sequence_a)