




"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


A				=							{
    """configuration_deberta""": ["""DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DebertaConfig""", """DebertaOnnxConfig"""],
    """tokenization_deberta""": ["""DebertaTokenizer"""],
}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A				=							["""DebertaTokenizerFast"""]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A				=							[
					    """DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """DebertaForMaskedLM""",
					    """DebertaForQuestionAnswering""",
					    """DebertaForSequenceClassification""",
					    """DebertaForTokenClassification""",
					    """DebertaModel""",
					    """DebertaPreTrainedModel""",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A				=							[
					    """TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """TFDebertaForMaskedLM""",
					    """TFDebertaForQuestionAnswering""",
					    """TFDebertaForSequenceClassification""",
					    """TFDebertaForTokenClassification""",
					    """TFDebertaModel""",
					    """TFDebertaPreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
					from .tokenization_deberta import DebertaTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_deberta_fast import DebertaTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_deberta import (
										    DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
										    DebertaForMaskedLM,
										    DebertaForQuestionAnswering,
										    DebertaForSequenceClassification,
										    DebertaForTokenClassification,
										    DebertaModel,
										    DebertaPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_deberta import (
										    TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFDebertaForMaskedLM,
										    TFDebertaForQuestionAnswering,
										    TFDebertaForSequenceClassification,
										    TFDebertaForTokenClassification,
										    TFDebertaModel,
										    TFDebertaPreTrainedModel,
										)


else:
					import sys

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







UpperCAmelCase_		 =					0  # The first color of the flag.
UpperCAmelCase_		 =					1  # The second color of the flag.
UpperCAmelCase_		 =					2  # The third color of the flag.
UpperCAmelCase_		 =					(red, white, blue)

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:list				)  ->     list:
   if not sequence:
      return []
   if len(_snake_case				) == 1:
      return list(_snake_case				)
   _A      =       0
   _A      =       len(_snake_case				) - 1
   _A      =       0
   while mid <= high:
      if sequence[mid] == colors[0]:
         _A  ,    _A      =       sequence[mid], sequence[low]
         low += 1
         mid += 1
      elif sequence[mid] == colors[1]:
         mid += 1
      elif sequence[mid] == colors[2]:
         _A  ,    _A      =       sequence[high], sequence[mid]
         high -= 1
      else:
         _A      =       F'''The elements inside the sequence must contains only {colors} values'''
         raise ValueError(_snake_case				)
   return sequence


if __name__ == "__main__":
   import doctest

   doctest.testmod()

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



