






from typing import TYPE_CHECKING

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


__UpperCamelCase					:		int	  =						{"""configuration_xlnet""": ["""XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLNetConfig"""]}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase					:		Tuple	  =						["""XLNetTokenizer"""]

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase					:		str	  =						["""XLNetTokenizerFast"""]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase					:		int	  =						[
							    """XLNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """XLNetForMultipleChoice""",
							    """XLNetForQuestionAnswering""",
							    """XLNetForQuestionAnsweringSimple""",
							    """XLNetForSequenceClassification""",
							    """XLNetForTokenClassification""",
							    """XLNetLMHeadModel""",
							    """XLNetModel""",
							    """XLNetPreTrainedModel""",
							    """load_tf_weights_in_xlnet""",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase					:		Dict	  =						[
							    """TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TFXLNetForMultipleChoice""",
							    """TFXLNetForQuestionAnsweringSimple""",
							    """TFXLNetForSequenceClassification""",
							    """TFXLNetForTokenClassification""",
							    """TFXLNetLMHeadModel""",
							    """TFXLNetMainLayer""",
							    """TFXLNetModel""",
							    """TFXLNetPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNetConfig

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_xlnet import XLNetTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_xlnet_fast import XLNetTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_xlnet import (
														    XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
														    XLNetForMultipleChoice,
														    XLNetForQuestionAnswering,
														    XLNetForQuestionAnsweringSimple,
														    XLNetForSequenceClassification,
														    XLNetForTokenClassification,
														    XLNetLMHeadModel,
														    XLNetModel,
														    XLNetPreTrainedModel,
														    load_tf_weights_in_xlnet,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_xlnet import (
														    TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFXLNetForMultipleChoice,
														    TFXLNetForQuestionAnsweringSimple,
														    TFXLNetForSequenceClassification,
														    TFXLNetForTokenClassification,
														    TFXLNetLMHeadModel,
														    TFXLNetMainLayer,
														    TFXLNetModel,
														    TFXLNetPreTrainedModel,
														)

else:
							import sys

							__UpperCamelCase					:		Dict	  =						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)












from __future__ import annotations
def      a_ (      _A      ,   _A  )			->			str:







				"""simple docstring"""
				# Checks if the entire collection has been sorted
				if len(_A  ) <= 1 or n <= 1:
								return

				insert_next(_A      ,   n - 1  )
				rec_insertion_sort(_A      ,   n - 1  )
def      a_ (      _A      ,   _A  )			->			Tuple:







				"""simple docstring"""
				# Checks order between adjacent elements
				if index >= len(_A  ) or collection[index - 1] <= collection[index]:
								return

				# Swaps adjacent elements since they are not in ascending order
				snake_case__							,						snake_case__									=     (
				    collection[index],
				    collection[index - 1],
				)

				insert_next(_A      ,   index + 1  )


if __name__ == "__main__":
							__UpperCamelCase					:		Optional[int]	  =						input("""Enter integers separated by spaces: """)
							__UpperCamelCase					:		list[int]	  =						[int(num) for num in numbers.split()]
							rec_insertion_sort(number_list, len(number_list))
							print(number_list)




