






def        a_	( _A ,				_A ,				_A					)     ->     Optional[int]:







				"""simple docstring"""
				snake_case__													=       index
				snake_case__													=       2 * index + 1
				snake_case__													=       2 * index + 2
				if left_index < heap_size and unsorted[left_index] > unsorted[largest]:
								snake_case__													=       left_index

				if right_index < heap_size and unsorted[right_index] > unsorted[largest]:
								snake_case__													=       right_index

				if largest != index:
								snake_case__							,  snake_case__													=       unsorted[index], unsorted[largest]
								heapify(_A ,				_A ,				_A					)
def        a_	( _A					)     ->     List[str]:







				"""simple docstring"""
				snake_case__													=       len(_A					)
				for i in range(n // 2 - 1 ,				-1 ,				-1					):
								heapify(_A ,				_A ,				_A					)
				for i in range(n - 1 ,				0 ,				-1					):
								snake_case__							,  snake_case__													=       unsorted[i], unsorted[0]
								heapify(_A ,				0 ,				_A					)
				return unsorted


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












from typing import TYPE_CHECKING

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


__UpperCamelCase   :						int							=  {
    """configuration_openai""": ["""OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """OpenAIGPTConfig"""],
    """tokenization_openai""": ["""OpenAIGPTTokenizer"""],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase   :						Optional[Any]							=  ["""OpenAIGPTTokenizerFast"""]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase   :						List[str]							=  [
							    """OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """OpenAIGPTDoubleHeadsModel""",
							    """OpenAIGPTForSequenceClassification""",
							    """OpenAIGPTLMHeadModel""",
							    """OpenAIGPTModel""",
							    """OpenAIGPTPreTrainedModel""",
							    """load_tf_weights_in_openai_gpt""",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase   :						Dict							=  [
							    """TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TFOpenAIGPTDoubleHeadsModel""",
							    """TFOpenAIGPTForSequenceClassification""",
							    """TFOpenAIGPTLMHeadModel""",
							    """TFOpenAIGPTMainLayer""",
							    """TFOpenAIGPTModel""",
							    """TFOpenAIGPTPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_openai import OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenAIGPTConfig
							from .tokenization_openai import OpenAIGPTTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_openai_fast import OpenAIGPTTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_openai import (
														    OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    OpenAIGPTDoubleHeadsModel,
														    OpenAIGPTForSequenceClassification,
														    OpenAIGPTLMHeadModel,
														    OpenAIGPTModel,
														    OpenAIGPTPreTrainedModel,
														    load_tf_weights_in_openai_gpt,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_openai import (
														    TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFOpenAIGPTDoubleHeadsModel,
														    TFOpenAIGPTForSequenceClassification,
														    TFOpenAIGPTLMHeadModel,
														    TFOpenAIGPTMainLayer,
														    TFOpenAIGPTModel,
														    TFOpenAIGPTPreTrainedModel,
														)

else:
							import sys

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




