



"""simple docstring"""
A:   Tuple								=		0  # The first color of the flag.
A:   Any								=		1  # The second color of the flag.
A:   List[Any]								=		2  # The third color of the flag.
A:   Optional[int]								=		(red, white, blue)

def       _snake_case						(						UpperCamelCase						:		list						):
 if not sequence:
  return []
 if len(UpperCamelCase						) == 1:
  return list(UpperCamelCase						)
 UpperCAmelCase	:					Optional[int]	=	0
 UpperCAmelCase	:					List[str]	=	len(UpperCamelCase						) - 1
 UpperCAmelCase	:					List[Any]	=	0
 while mid <= high:
  if sequence[mid] == colors[0]:
   UpperCAmelCase	,		UpperCAmelCase	:					str	=	sequence[mid], sequence[low]
   low += 1
   mid += 1
  elif sequence[mid] == colors[1]:
   mid += 1
  elif sequence[mid] == colors[2]:
   UpperCAmelCase	,		UpperCAmelCase	:					str	=	sequence[high], sequence[mid]
   high -= 1
  else:
   UpperCAmelCase	:					Union[str, Any]	=	F"The elements inside the sequence must contains only {colors} values"
   raise ValueError(UpperCamelCase						)
 return sequence


if __name__ == "__main__":
      import doctest

      doctest.testmod()

      A:   Tuple								=		input("Enter numbers separated by commas:\n").strip()
      A:   List[str]								=		[int(item.strip()) for item in user_input.split(",")]
      print(f"""{dutch_national_flag_sort(unsorted)}""")





"""simple docstring"""
from typing import TYPE_CHECKING

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


A:   Optional[Any]								=		{
    "configuration_tapas": ["TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP", "TapasConfig"],
    "tokenization_tapas": ["TapasTokenizer"],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A:   Optional[int]								=		[
          "TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TapasForMaskedLM",
          "TapasForQuestionAnswering",
          "TapasForSequenceClassification",
          "TapasModel",
          "TapasPreTrainedModel",
          "load_tf_weights_in_tapas",
      ]
try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A:   List[str]								=		[
          "TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFTapasForMaskedLM",
          "TFTapasForQuestionAnswering",
          "TFTapasForSequenceClassification",
          "TFTapasModel",
          "TFTapasPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
      from .tokenization_tapas import TapasTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tapas import (
                TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
                TapasForMaskedLM,
                TapasForQuestionAnswering,
                TapasForSequenceClassification,
                TapasModel,
                TapasPreTrainedModel,
                load_tf_weights_in_tapas,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_tapas import (
                TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFTapasForMaskedLM,
                TFTapasForQuestionAnswering,
                TFTapasForSequenceClassification,
                TFTapasModel,
                TFTapasPreTrainedModel,
            )


else:
      import sys

      A:   Optional[Any]								=		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
