
"""simple docstring"""

from __future__ import annotations






def 							A		(       snake_case   :list[int | str]	)   ->			None:
 create_state_space_tree(snake_case       ,      []       ,      0       ,      [0 for i in range(len(snake_case	)	)]	)






def 							A		(       snake_case   :list[int | str]       ,      snake_case   :list[int | str]       ,      snake_case   :int       ,      snake_case   :list[int]       ,      )   ->			None:

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

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

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





generate_all_permutations(sequence_a)





"""simple docstring"""

from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCamelCase			:       List[str]									=				{
    "configuration_autoformer": [
        "AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "AutoformerConfig",
    ],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase			:       Dict									=				[
          "AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
          "AutoformerForPrediction",
          "AutoformerModel",
          "AutoformerPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_autoformer import (
          AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
          AutoformerConfig,
      )

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_autoformer import (
                AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                AutoformerForPrediction,
                AutoformerModel,
                AutoformerPreTrainedModel,
            )

else:
      import sys

      UpperCamelCase			:       int									=				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



