'''simple docstring'''




from typing import TYPE_CHECKING

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


__UpperCAmelCase												={
    "configuration_squeezebert": [
        "SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "SqueezeBertConfig",
        "SqueezeBertOnnxConfig",
    ],
    "tokenization_squeezebert": ["SqueezeBertTokenizer"],
}

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

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __UpperCAmelCase												=[
      "SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
      "SqueezeBertForMaskedLM",
      "SqueezeBertForMultipleChoice",
      "SqueezeBertForQuestionAnswering",
      "SqueezeBertForSequenceClassification",
      "SqueezeBertForTokenClassification",
      "SqueezeBertModel",
      "SqueezeBertModule",
      "SqueezeBertPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_squeezebert import (
      SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
      SqueezeBertConfig,
      SqueezeBertOnnxConfig,
  )
  from .tokenization_squeezebert import SqueezeBertTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_squeezebert_fast import SqueezeBertTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_squeezebert import (
        SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        SqueezeBertForMaskedLM,
        SqueezeBertForMultipleChoice,
        SqueezeBertForQuestionAnswering,
        SqueezeBertForSequenceClassification,
        SqueezeBertForTokenClassification,
        SqueezeBertModel,
        SqueezeBertModule,
        SqueezeBertPreTrainedModel,
    )

else:
  import sys

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

'''simple docstring'''




def __lowerCAmelCase			(							UpperCamelCase__    )     ->   str:
    __lowerCamelCase          =  []
    __lowerCamelCase          =  set({'''(''', '''[''', '''{'''}    )
    __lowerCamelCase          =  set({''')''', ''']''', '''}'''}    )
    __lowerCamelCase          =  {'''{''': '''}''', '''[''': ''']''', '''(''': ''')'''}

    for i in range(len(UpperCamelCase__    )    ):
        if s[i] in open_brackets:
            stack.append(s[i]    )

        elif s[i] in closed_brackets and (
            len(UpperCamelCase__    ) == 0 or (len(UpperCamelCase__    ) > 0 and open_to_closed[stack.pop()] != s[i])
        ):
            return False

    return len(UpperCamelCase__    ) == 0






def __lowerCAmelCase			(							)     ->   Union[str, Any]:
    __lowerCamelCase          =  input('''Enter sequence of brackets: '''    )
    if is_balanced(UpperCamelCase__    ):
        print(UpperCamelCase__    ,  '''is balanced'''    )
    else:
        print(UpperCamelCase__    ,  '''is not balanced'''    )


if __name__ == "__main__":
  main()
