"""simple docstring"""
import math





def 				lowerCAmelCase__      (     _UpperCamelCase    :			list	,     _UpperCamelCase    :			int = 0	,     _UpperCamelCase    :			int = 0   )   ->			list:





						"""simple docstring"""






						snake_case        =     end or len(_UpperCamelCase   )
						for i in range(_UpperCamelCase	,     _UpperCamelCase   ):
												snake_case        =     i
												snake_case        =     array[i]
												while temp_index != start and temp_index_value < array[temp_index - 1]:
																		snake_case        =     array[temp_index - 1]
																		temp_index -= 1
												snake_case        =     temp_index_value
						return array





def 				lowerCAmelCase__      (     _UpperCamelCase    :			list	,     _UpperCamelCase    :			int	,     _UpperCamelCase    :			int   )   ->			None:  # Max Heap





						"""simple docstring"""






						snake_case        =     index
						snake_case        =     2 * index + 1  # Left Node
						snake_case        =     2 * index + 2  # Right Node

						if left_index < heap_size and array[largest] < array[left_index]:
												snake_case        =     left_index

						if right_index < heap_size and array[largest] < array[right_index]:
												snake_case        =     right_index

						if largest != index:
												snake_case   ,snake_case        =     array[largest], array[index]
												heapify(_UpperCamelCase	,     _UpperCamelCase	,     _UpperCamelCase   )





def 				lowerCAmelCase__      (     _UpperCamelCase    :			list   )   ->			list:





						"""simple docstring"""






						snake_case        =     len(_UpperCamelCase   )

						for i in range(n // 2	,     -1	,     -1   ):
												heapify(_UpperCamelCase	,     _UpperCamelCase	,     _UpperCamelCase   )

						for i in range(n - 1	,     0	,     -1   ):
												snake_case   ,snake_case        =     array[0], array[i]
												heapify(_UpperCamelCase	,     0	,     _UpperCamelCase   )

						return array





def 				lowerCAmelCase__      (     _UpperCamelCase    :			list	,     _UpperCamelCase    :			int	,     _UpperCamelCase    :			int	,     _UpperCamelCase    :			int   )   ->			int:





						"""simple docstring"""






						if (array[first_index] > array[middle_index]) != (
						    array[first_index] > array[last_index]
						):
												return array[first_index]
						elif (array[middle_index] > array[first_index]) != (
						    array[middle_index] > array[last_index]
						):
												return array[middle_index]
						else:
												return array[last_index]





def 				lowerCAmelCase__      (     _UpperCamelCase    :			list	,     _UpperCamelCase    :			int	,     _UpperCamelCase    :			int	,     _UpperCamelCase    :			int   )   ->			int:





						"""simple docstring"""






						snake_case        =     low
						snake_case        =     high
						while True:
												while array[i] < pivot:
																		i += 1
												j -= 1
												while pivot < array[j]:
																		j -= 1
												if i >= j:
																		return i
												snake_case   ,snake_case        =     array[j], array[i]
												i += 1





def 				lowerCAmelCase__      (     _UpperCamelCase    :			list   )   ->			list:





						"""simple docstring"""






						if len(_UpperCamelCase   ) == 0:
												return array
						snake_case        =     2 * math.ceil(math.loga(len(_UpperCamelCase   )   )   )
						snake_case        =     1_6
						return intro_sort(_UpperCamelCase	,     0	,     len(_UpperCamelCase   )	,     _UpperCamelCase	,     _UpperCamelCase   )





def 				lowerCAmelCase__      (     _UpperCamelCase    :			list	,     _UpperCamelCase    :			int	,     _UpperCamelCase    :			int	,     _UpperCamelCase    :			int	,     _UpperCamelCase    :			int   )   ->			list:





						"""simple docstring"""






						while end - start > size_threshold:
												if max_depth == 0:
																		return heap_sort(_UpperCamelCase   )
												max_depth -= 1
												snake_case        =     median_of_a(_UpperCamelCase	,     _UpperCamelCase	,     start + ((end - start) // 2) + 1	,     end - 1   )
												snake_case        =     partition(_UpperCamelCase	,     _UpperCamelCase	,     _UpperCamelCase	,     _UpperCamelCase   )
												intro_sort(_UpperCamelCase	,     _UpperCamelCase	,     _UpperCamelCase	,     _UpperCamelCase	,     _UpperCamelCase   )
												snake_case        =     p
						return insertion_sort(_UpperCamelCase	,     _UpperCamelCase	,     _UpperCamelCase   )


if __name__ == "__main__":
					import doctest

					doctest.testmod()

					SCREAMING_SNAKE_CASE__   =					input("Enter numbers separated by a comma : ").strip()
					SCREAMING_SNAKE_CASE__   =					[float(item) for item in user_input.split(",")]
					print(sort(unsorted))




"""simple docstring"""
from typing import TYPE_CHECKING

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


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

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__   =					[
					    "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:
					SCREAMING_SNAKE_CASE__   =					[
					    "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

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


