


def     UpperCAmelCase__		(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_       ):



     """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(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_       )
def     UpperCAmelCase__		(UpperCamelCase_       ):



     """simple docstring"""






     snake_case					=       len(UpperCamelCase_       )
     for i in range(n // 2 - 1							,-1							,-1       ):
          heapify(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_       )
     for i in range(n - 1							,0							,-1       ):
          snake_case				,						snake_case					=       unsorted[i], unsorted[0]
          heapify(UpperCamelCase_							,0							,UpperCamelCase_       )
     return unsorted


if __name__ == "__main__":
 _SCREAMING_SNAKE_CASE    :				Tuple       =					input("Enter numbers separated by a comma:\n").strip()
 _SCREAMING_SNAKE_CASE    :				Optional[int]       =					[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,
)


_SCREAMING_SNAKE_CASE    :				Optional[int]       =					{
    "configuration_layoutlm": ["LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "LayoutLMConfig", "LayoutLMOnnxConfig"],
    "tokenization_layoutlm": ["LayoutLMTokenizer"],
}

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

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _SCREAMING_SNAKE_CASE    :				List[str]       =					[
     "LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST",
     "LayoutLMForMaskedLM",
     "LayoutLMForSequenceClassification",
     "LayoutLMForTokenClassification",
     "LayoutLMForQuestionAnswering",
     "LayoutLMModel",
     "LayoutLMPreTrainedModel",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _SCREAMING_SNAKE_CASE    :				Optional[int]       =					[
     "TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST",
     "TFLayoutLMForMaskedLM",
     "TFLayoutLMForSequenceClassification",
     "TFLayoutLMForTokenClassification",
     "TFLayoutLMForQuestionAnswering",
     "TFLayoutLMMainLayer",
     "TFLayoutLMModel",
     "TFLayoutLMPreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_layoutlm import LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMConfig, LayoutLMOnnxConfig
 from .tokenization_layoutlm import LayoutLMTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_layoutlm_fast import LayoutLMTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_layoutlm import (
      LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
      LayoutLMForMaskedLM,
      LayoutLMForQuestionAnswering,
      LayoutLMForSequenceClassification,
      LayoutLMForTokenClassification,
      LayoutLMModel,
      LayoutLMPreTrainedModel,
  )
 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_layoutlm import (
      TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFLayoutLMForMaskedLM,
      TFLayoutLMForQuestionAnswering,
      TFLayoutLMForSequenceClassification,
      TFLayoutLMForTokenClassification,
      TFLayoutLMMainLayer,
      TFLayoutLMModel,
      TFLayoutLMPreTrainedModel,
  )

else:
 import sys

 _SCREAMING_SNAKE_CASE    :				Optional[int]       =					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




