





from __future__ import annotations

from typing import Any

def   a_    ( lowerCAmelCase_      :			Optional[Any]	):
   create_state_space_tree(lowerCAmelCase_,	[],	0	)

def   a_    ( lowerCAmelCase_      :			Dict,	lowerCAmelCase_      :			Optional[int],	lowerCAmelCase_      :			Optional[int]	):

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

   create_state_space_tree(lowerCAmelCase_,	lowerCAmelCase_,	index + 1	)
   current_subsequence.append(sequence[index]	)
   create_state_space_tree(lowerCAmelCase_,	lowerCAmelCase_,	index + 1	)
   current_subsequence.pop()


if __name__ == "__main__":
     _snake_case  :    str						    =  [3, 1, 2, 4]
     generate_all_subsequences(seq)

     seq.clear()
     seq.extend(['A', 'B', 'C'])
     generate_all_subsequences(seq)





from typing import TYPE_CHECKING

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


__lowerCAmelCase		       =							{
    '''configuration_canine''': ['''CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CanineConfig'''],
    '''tokenization_canine''': ['''CanineTokenizer'''],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __lowerCAmelCase		       =							[
     '''CANINE_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''CanineForMultipleChoice''',
     '''CanineForQuestionAnswering''',
     '''CanineForSequenceClassification''',
     '''CanineForTokenClassification''',
     '''CanineLayer''',
     '''CanineModel''',
     '''CaninePreTrainedModel''',
     '''load_tf_weights_in_canine''',
 ]


if TYPE_CHECKING:
 from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
 from .tokenization_canine import CanineTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_canine import (
      CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
      CanineForMultipleChoice,
      CanineForQuestionAnswering,
      CanineForSequenceClassification,
      CanineForTokenClassification,
      CanineLayer,
      CanineModel,
      CaninePreTrainedModel,
      load_tf_weights_in_canine,
  )


else:
 import sys

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


