
'''simple docstring'''





from typing import TYPE_CHECKING

from ..utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_tf_available, is_torch_available


a__		: Optional[int]  							=							{
    "configuration_utils": ["GenerationConfig"],
    "streamers": ["TextIteratorStreamer", "TextStreamer"],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a__		: List[Any]  							=							[
     "Constraint",
     "ConstraintListState",
     "DisjunctiveConstraint",
     "PhrasalConstraint",
 ]
 a__		: List[Any]  							=							[
     "BeamHypotheses",
     "BeamScorer",
     "BeamSearchScorer",
     "ConstrainedBeamSearchScorer",
 ]
 a__		: int  							=							[
     "EpsilonLogitsWarper",
     "EtaLogitsWarper",
     "ForcedBOSTokenLogitsProcessor",
     "ForcedEOSTokenLogitsProcessor",
     "HammingDiversityLogitsProcessor",
     "InfNanRemoveLogitsProcessor",
     "LogitsProcessor",
     "LogitsProcessorList",
     "LogitsWarper",
     "MinLengthLogitsProcessor",
     "MinNewTokensLengthLogitsProcessor",
     "NoBadWordsLogitsProcessor",
     "NoRepeatNGramLogitsProcessor",
     "PrefixConstrainedLogitsProcessor",
     "RepetitionPenaltyLogitsProcessor",
     "SequenceBiasLogitsProcessor",
     "EncoderRepetitionPenaltyLogitsProcessor",
     "TemperatureLogitsWarper",
     "TopKLogitsWarper",
     "TopPLogitsWarper",
     "TypicalLogitsWarper",
     "EncoderNoRepeatNGramLogitsProcessor",
     "ExponentialDecayLengthPenalty",
     "LogitNormalization",
 ]
 a__		: Tuple  							=							[
     "MaxNewTokensCriteria",
     "MaxLengthCriteria",
     "MaxTimeCriteria",
     "StoppingCriteria",
     "StoppingCriteriaList",
     "validate_stopping_criteria",
 ]
 a__		: Optional[int]  							=							[
     "GenerationMixin",
     "top_k_top_p_filtering",
     "GreedySearchEncoderDecoderOutput",
     "GreedySearchDecoderOnlyOutput",
     "SampleEncoderDecoderOutput",
     "SampleDecoderOnlyOutput",
     "BeamSearchEncoderDecoderOutput",
     "BeamSearchDecoderOnlyOutput",
     "BeamSampleEncoderDecoderOutput",
     "BeamSampleDecoderOnlyOutput",
     "ContrastiveSearchEncoderDecoderOutput",
     "ContrastiveSearchDecoderOnlyOutput",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a__		: Any  							=							[
     "TFForcedBOSTokenLogitsProcessor",
     "TFForcedEOSTokenLogitsProcessor",
     "TFLogitsProcessor",
     "TFLogitsProcessorList",
     "TFLogitsWarper",
     "TFMinLengthLogitsProcessor",
     "TFNoBadWordsLogitsProcessor",
     "TFNoRepeatNGramLogitsProcessor",
     "TFRepetitionPenaltyLogitsProcessor",
     "TFTemperatureLogitsWarper",
     "TFTopKLogitsWarper",
     "TFTopPLogitsWarper",
     "TFForceTokensLogitsProcessor",
     "TFSuppressTokensAtBeginLogitsProcessor",
     "TFSuppressTokensLogitsProcessor",
 ]
 a__		: List[Any]  							=							[
     "TFGenerationMixin",
     "tf_top_k_top_p_filtering",
     "TFGreedySearchDecoderOnlyOutput",
     "TFGreedySearchEncoderDecoderOutput",
     "TFSampleEncoderDecoderOutput",
     "TFSampleDecoderOnlyOutput",
     "TFBeamSearchEncoderDecoderOutput",
     "TFBeamSearchDecoderOnlyOutput",
     "TFBeamSampleEncoderDecoderOutput",
     "TFBeamSampleDecoderOnlyOutput",
     "TFContrastiveSearchEncoderDecoderOutput",
     "TFContrastiveSearchDecoderOnlyOutput",
 ]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a__		: Tuple  							=							[
     "FlaxForcedBOSTokenLogitsProcessor",
     "FlaxForcedEOSTokenLogitsProcessor",
     "FlaxLogitsProcessor",
     "FlaxLogitsProcessorList",
     "FlaxLogitsWarper",
     "FlaxMinLengthLogitsProcessor",
     "FlaxTemperatureLogitsWarper",
     "FlaxTopKLogitsWarper",
     "FlaxTopPLogitsWarper",
 ]
 a__		: List[Any]  							=							[
     "FlaxGenerationMixin",
     "FlaxGreedySearchOutput",
     "FlaxSampleOutput",
     "FlaxBeamSearchOutput",
 ]

if TYPE_CHECKING:
 from .configuration_utils import GenerationConfig
 from .streamers import TextIteratorStreamer, TextStreamer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .beam_constraints import Constraint, ConstraintListState, DisjunctiveConstraint, PhrasalConstraint
  from .beam_search import BeamHypotheses, BeamScorer, BeamSearchScorer, ConstrainedBeamSearchScorer
  from .logits_process import (
      EncoderNoRepeatNGramLogitsProcessor,
      EncoderRepetitionPenaltyLogitsProcessor,
      EpsilonLogitsWarper,
      EtaLogitsWarper,
      ExponentialDecayLengthPenalty,
      ForcedBOSTokenLogitsProcessor,
      ForcedEOSTokenLogitsProcessor,
      HammingDiversityLogitsProcessor,
      InfNanRemoveLogitsProcessor,
      LogitNormalization,
      LogitsProcessor,
      LogitsProcessorList,
      LogitsWarper,
      MinLengthLogitsProcessor,
      MinNewTokensLengthLogitsProcessor,
      NoBadWordsLogitsProcessor,
      NoRepeatNGramLogitsProcessor,
      PrefixConstrainedLogitsProcessor,
      RepetitionPenaltyLogitsProcessor,
      SequenceBiasLogitsProcessor,
      TemperatureLogitsWarper,
      TopKLogitsWarper,
      TopPLogitsWarper,
      TypicalLogitsWarper,
  )
  from .stopping_criteria import (
      MaxLengthCriteria,
      MaxNewTokensCriteria,
      MaxTimeCriteria,
      StoppingCriteria,
      StoppingCriteriaList,
      validate_stopping_criteria,
  )
  from .utils import (
      BeamSampleDecoderOnlyOutput,
      BeamSampleEncoderDecoderOutput,
      BeamSearchDecoderOnlyOutput,
      BeamSearchEncoderDecoderOutput,
      ContrastiveSearchDecoderOnlyOutput,
      ContrastiveSearchEncoderDecoderOutput,
      GenerationMixin,
      GreedySearchDecoderOnlyOutput,
      GreedySearchEncoderDecoderOutput,
      SampleDecoderOnlyOutput,
      SampleEncoderDecoderOutput,
      top_k_top_p_filtering,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tf_logits_process import (
      TFForcedBOSTokenLogitsProcessor,
      TFForcedEOSTokenLogitsProcessor,
      TFForceTokensLogitsProcessor,
      TFLogitsProcessor,
      TFLogitsProcessorList,
      TFLogitsWarper,
      TFMinLengthLogitsProcessor,
      TFNoBadWordsLogitsProcessor,
      TFNoRepeatNGramLogitsProcessor,
      TFRepetitionPenaltyLogitsProcessor,
      TFSuppressTokensAtBeginLogitsProcessor,
      TFSuppressTokensLogitsProcessor,
      TFTemperatureLogitsWarper,
      TFTopKLogitsWarper,
      TFTopPLogitsWarper,
  )
  from .tf_utils import (
      TFBeamSampleDecoderOnlyOutput,
      TFBeamSampleEncoderDecoderOutput,
      TFBeamSearchDecoderOnlyOutput,
      TFBeamSearchEncoderDecoderOutput,
      TFContrastiveSearchDecoderOnlyOutput,
      TFContrastiveSearchEncoderDecoderOutput,
      TFGenerationMixin,
      TFGreedySearchDecoderOnlyOutput,
      TFGreedySearchEncoderDecoderOutput,
      TFSampleDecoderOnlyOutput,
      TFSampleEncoderDecoderOutput,
      tf_top_k_top_p_filtering,
  )

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .flax_logits_process import (
      FlaxForcedBOSTokenLogitsProcessor,
      FlaxForcedEOSTokenLogitsProcessor,
      FlaxLogitsProcessor,
      FlaxLogitsProcessorList,
      FlaxLogitsWarper,
      FlaxMinLengthLogitsProcessor,
      FlaxTemperatureLogitsWarper,
      FlaxTopKLogitsWarper,
      FlaxTopPLogitsWarper,
  )
  from .flax_utils import FlaxBeamSearchOutput, FlaxGenerationMixin, FlaxGreedySearchOutput, FlaxSampleOutput
else:
 import sys

 a__		: str  							=							_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)








'''simple docstring'''





def 							snake_case				(		UpperCAmelCase						)->      list:







      """simple docstring"""
      __A													=      1.3
      __A													=      len(UpperCAmelCase						)
      __A													=      False

      while not completed:
            # Update the gap value for a next comb
            __A													=      int(gap / shrink_factor						)
            if gap <= 1:
                  __A													=      True

            __A													=      0
            while index + gap < len(UpperCAmelCase						):
                  if data[index] > data[index + gap]:
                        # Swap values
                        __A						,   __A													=      data[index + gap], data[index]
                        __A													=      False
                  index += 1

      return data


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 a__		: Optional[Any]  							=							input("Enter numbers separated by a comma:\n").strip()
 a__		: Union[str, Any]  							=							[int(item) for item in user_input.split(",")]
 print(comb_sort(unsorted))






