
from typing import TYPE_CHECKING

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


_A  =	{
    "configuration_xlm": ["XLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMConfig", "XLMOnnxConfig"],
    "tokenization_xlm": ["XLMTokenizer"],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A  =	[
			    "XLM_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "XLMForMultipleChoice",
			    "XLMForQuestionAnswering",
			    "XLMForQuestionAnsweringSimple",
			    "XLMForSequenceClassification",
			    "XLMForTokenClassification",
			    "XLMModel",
			    "XLMPreTrainedModel",
			    "XLMWithLMHeadModel",
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A  =	[
			    "TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "TFXLMForMultipleChoice",
			    "TFXLMForQuestionAnsweringSimple",
			    "TFXLMForSequenceClassification",
			    "TFXLMForTokenClassification",
			    "TFXLMMainLayer",
			    "TFXLMModel",
			    "TFXLMPreTrainedModel",
			    "TFXLMWithLMHeadModel",
			]


if TYPE_CHECKING:
			from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
			from .tokenization_xlm import XLMTokenizer

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_xlm import (
						    XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
						    XLMForMultipleChoice,
						    XLMForQuestionAnswering,
						    XLMForQuestionAnsweringSimple,
						    XLMForSequenceClassification,
						    XLMForTokenClassification,
						    XLMModel,
						    XLMPreTrainedModel,
						    XLMWithLMHeadModel,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_xlm import (
						    TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFXLMForMultipleChoice,
						    TFXLMForQuestionAnsweringSimple,
						    TFXLMForSequenceClassification,
						    TFXLMForTokenClassification,
						    TFXLMMainLayer,
						    TFXLMModel,
						    TFXLMPreTrainedModel,
						    TFXLMWithLMHeadModel,
						)

else:
			import sys

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





def   lowerCamelCase__							(							__lowerCAmelCase    :    list							):




			"""simple docstring"""


			for i in range(len(__lowerCAmelCase							) - 1       ,				0       ,				-1							):
						lowerCAmelCase_         =      False

						for j in range(__lowerCAmelCase       ,				0       ,				-1							):
									if unsorted[j] < unsorted[j - 1]:
												lowerCAmelCase_      ,    lowerCAmelCase_         =      unsorted[j - 1], unsorted[j]
												lowerCAmelCase_         =      True

						for j in range(__lowerCAmelCase							):
									if unsorted[j] > unsorted[j + 1]:
												lowerCAmelCase_      ,    lowerCAmelCase_         =      unsorted[j + 1], unsorted[j]
												lowerCAmelCase_         =      True

						if not swapped:
									break
			return unsorted


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			_A  =	input("Enter numbers separated by a comma:\n").strip()
			_A  =	[int(item) for item in user_input.split(",")]
			print(f"""{cocktail_shaker_sort(unsorted) = }""")



