




from typing import TYPE_CHECKING

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


lowercase__				:     Optional[int] 		=						{
    "configuration_convbert": ["CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ConvBertConfig", "ConvBertOnnxConfig"],
    "tokenization_convbert": ["ConvBertTokenizer"],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase__				:     Tuple 		=						["ConvBertTokenizerFast"]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase__				:     Tuple 		=						[
		    "CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "ConvBertForMaskedLM",
		    "ConvBertForMultipleChoice",
		    "ConvBertForQuestionAnswering",
		    "ConvBertForSequenceClassification",
		    "ConvBertForTokenClassification",
		    "ConvBertLayer",
		    "ConvBertModel",
		    "ConvBertPreTrainedModel",
		    "load_tf_weights_in_convbert",
		]


try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase__				:     Dict 		=						[
		    "TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "TFConvBertForMaskedLM",
		    "TFConvBertForMultipleChoice",
		    "TFConvBertForQuestionAnswering",
		    "TFConvBertForSequenceClassification",
		    "TFConvBertForTokenClassification",
		    "TFConvBertLayer",
		    "TFConvBertModel",
		    "TFConvBertPreTrainedModel",
		]


if TYPE_CHECKING:
		from .configuration_convbert import CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvBertConfig, ConvBertOnnxConfig
		from .tokenization_convbert import ConvBertTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_convbert_fast import ConvBertTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_convbert import (
				    CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ConvBertForMaskedLM,
				    ConvBertForMultipleChoice,
				    ConvBertForQuestionAnswering,
				    ConvBertForSequenceClassification,
				    ConvBertForTokenClassification,
				    ConvBertLayer,
				    ConvBertModel,
				    ConvBertPreTrainedModel,
				    load_tf_weights_in_convbert,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_convbert import (
				    TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFConvBertForMaskedLM,
				    TFConvBertForMultipleChoice,
				    TFConvBertForQuestionAnswering,
				    TFConvBertForSequenceClassification,
				    TFConvBertForTokenClassification,
				    TFConvBertLayer,
				    TFConvBertModel,
				    TFConvBertPreTrainedModel,
				)


else:
		import sys

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






def 						lowerCamelCase__    (		_A							):







	'''simple docstring'''





	snake_case_		    =     len(_A							)
	for i in range(_A							):
		for j in range(i + 1     ,	_A							):
			if numbers[j] < numbers[i]:
				snake_case_    ,      snake_case_		    =     numbers[j], numbers[i]
	return numbers


if __name__ == "__main__":
		lowercase__				:     int 		=						input("Enter numbers separated by a comma:\n").strip()
		lowercase__				:     Any 		=						[int(item) for item in user_input.split(",")]
		print(exchange_sort(unsorted))
