


'''simple docstring'''







from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE_      =			{
    'configuration_roformer': ['ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RoFormerConfig', 'RoFormerOnnxConfig'],
    'tokenization_roformer': ['RoFormerTokenizer'],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE_      =			['RoFormerTokenizerFast']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE_      =			[
     'ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
     'RoFormerForCausalLM',
     'RoFormerForMaskedLM',
     'RoFormerForMultipleChoice',
     'RoFormerForQuestionAnswering',
     'RoFormerForSequenceClassification',
     'RoFormerForTokenClassification',
     'RoFormerLayer',
     'RoFormerModel',
     'RoFormerPreTrainedModel',
     'load_tf_weights_in_roformer',
 ]


try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE_      =			[
     'TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
     'TFRoFormerForCausalLM',
     'TFRoFormerForMaskedLM',
     'TFRoFormerForMultipleChoice',
     'TFRoFormerForQuestionAnswering',
     'TFRoFormerForSequenceClassification',
     'TFRoFormerForTokenClassification',
     'TFRoFormerLayer',
     'TFRoFormerModel',
     'TFRoFormerPreTrainedModel',
 ]


try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE_      =			[
     'FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
     'FlaxRoFormerForMaskedLM',
     'FlaxRoFormerForMultipleChoice',
     'FlaxRoFormerForQuestionAnswering',
     'FlaxRoFormerForSequenceClassification',
     'FlaxRoFormerForTokenClassification',
     'FlaxRoFormerModel',
     'FlaxRoFormerPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
 from .tokenization_roformer import RoFormerTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_roformer_fast import RoFormerTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_roformer import (
      ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      RoFormerForCausalLM,
      RoFormerForMaskedLM,
      RoFormerForMultipleChoice,
      RoFormerForQuestionAnswering,
      RoFormerForSequenceClassification,
      RoFormerForTokenClassification,
      RoFormerLayer,
      RoFormerModel,
      RoFormerPreTrainedModel,
      load_tf_weights_in_roformer,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_roformer import (
      TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFRoFormerForCausalLM,
      TFRoFormerForMaskedLM,
      TFRoFormerForMultipleChoice,
      TFRoFormerForQuestionAnswering,
      TFRoFormerForSequenceClassification,
      TFRoFormerForTokenClassification,
      TFRoFormerLayer,
      TFRoFormerModel,
      TFRoFormerPreTrainedModel,
  )

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_roformer import (
      FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      FlaxRoFormerForMaskedLM,
      FlaxRoFormerForMultipleChoice,
      FlaxRoFormerForQuestionAnswering,
      FlaxRoFormerForSequenceClassification,
      FlaxRoFormerForTokenClassification,
      FlaxRoFormerModel,
      FlaxRoFormerPreTrainedModel,
  )


else:
 import sys

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










'''simple docstring'''







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




def        __lowercase						(      __SCREAMING_SNAKE_CASE		)				->					list:
     """simple docstring"""




     if not sequence:
          return []
     if len(__SCREAMING_SNAKE_CASE		) == 1:
          return list(__SCREAMING_SNAKE_CASE		)
     __a       =   0
     __a       =   len(__SCREAMING_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(__SCREAMING_SNAKE_CASE		)
     return sequence


if __name__ == "__main__":
 import doctest

 doctest.testmod()

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






