






from typing import TYPE_CHECKING

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


UpperCamelCase								=		{
    "configuration_mobilebert": [
        "MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "MobileBertConfig",
        "MobileBertOnnxConfig",
    ],
    "tokenization_mobilebert": ["MobileBertTokenizer"],
}

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

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase								=		[
							    "MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "MobileBertForMaskedLM",
							    "MobileBertForMultipleChoice",
							    "MobileBertForNextSentencePrediction",
							    "MobileBertForPreTraining",
							    "MobileBertForQuestionAnswering",
							    "MobileBertForSequenceClassification",
							    "MobileBertForTokenClassification",
							    "MobileBertLayer",
							    "MobileBertModel",
							    "MobileBertPreTrainedModel",
							    "load_tf_weights_in_mobilebert",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase								=		[
							    "TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "TFMobileBertForMaskedLM",
							    "TFMobileBertForMultipleChoice",
							    "TFMobileBertForNextSentencePrediction",
							    "TFMobileBertForPreTraining",
							    "TFMobileBertForQuestionAnswering",
							    "TFMobileBertForSequenceClassification",
							    "TFMobileBertForTokenClassification",
							    "TFMobileBertMainLayer",
							    "TFMobileBertModel",
							    "TFMobileBertPreTrainedModel",
							]


if TYPE_CHECKING:
							from .configuration_mobilebert import (
							    MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    MobileBertConfig,
							    MobileBertOnnxConfig,
							)
							from .tokenization_mobilebert import MobileBertTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_mobilebert_fast import MobileBertTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_mobilebert import (
														    MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    MobileBertForMaskedLM,
														    MobileBertForMultipleChoice,
														    MobileBertForNextSentencePrediction,
														    MobileBertForPreTraining,
														    MobileBertForQuestionAnswering,
														    MobileBertForSequenceClassification,
														    MobileBertForTokenClassification,
														    MobileBertLayer,
														    MobileBertModel,
														    MobileBertPreTrainedModel,
														    load_tf_weights_in_mobilebert,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_mobilebert import (
														    TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFMobileBertForMaskedLM,
														    TFMobileBertForMultipleChoice,
														    TFMobileBertForNextSentencePrediction,
														    TFMobileBertForPreTraining,
														    TFMobileBertForQuestionAnswering,
														    TFMobileBertForSequenceClassification,
														    TFMobileBertForTokenClassification,
														    TFMobileBertMainLayer,
														    TFMobileBertModel,
														    TFMobileBertPreTrainedModel,
														)

else:
							import sys

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












'''simple docstring'''

def 				snake_case       (  a_				:			list						)							->					list:





      """simple docstring"""

      UpperCamelCase_					: List[str]            =				False
      while is_sorted is False:  # Until all the indices are traversed keep looping
            UpperCamelCase_					: Tuple            =				True
            for i in range(0     , len(a_						) - 1     , 2						):  # iterating over all even indices
                  if input_list[i] > input_list[i + 1]:
                        UpperCamelCase_     ,      UpperCamelCase_					: Optional[Any]            =				input_list[i + 1], input_list[i]
                        # swapping if elements not in order
                        UpperCamelCase_					: Any            =				False

            for i in range(1     , len(a_						) - 1     , 2						):  # iterating over all odd indices
                  if input_list[i] > input_list[i + 1]:
                        UpperCamelCase_     ,      UpperCamelCase_					: List[str]            =				input_list[i + 1], input_list[i]
                        # swapping if elements not in order
                        UpperCamelCase_					: Dict            =				False
      return input_list


if __name__ == "__main__":
      print("Enter list to be sorted")
      UpperCamelCase      					=[int(x) for x in input().split()]
      # inputing elements of the list in one line
      UpperCamelCase      					=odd_even_sort(input_list)
      print("The sorted list is")
      print(sorted_list)






