

from __future__ import annotations




def     UpperCAmelCase__       (    _A   :				List[Any]			,   _A   :				List[Any]							):





  '''simple docstring'''







  a__						=0
  a__						=len(_A							) - 1
  while left <= right:
    a__						=(left + right) // 2
    if left == right:
      if sorted_list[middle] < item:
        a__						=middle + 1
      break
    elif sorted_list[middle] < item:
      a__						=middle + 1
    else:
      a__						=middle - 1
  sorted_list.insert(_A			,   _A							)
  return sorted_list




def     UpperCAmelCase__       (    _A   :				Any			,   _A   :				Tuple							):





  '''simple docstring'''







  a__						=[]
  while left and right:
    if left[0][0] < right[0][0]:
      result.append(left.pop(0							)							)
    else:
      result.append(right.pop(0							)							)
  return result + left + right




def     UpperCAmelCase__       (    _A   :				Optional[Any]							):





  '''simple docstring'''







  a__						=len(_A							)
  if length <= 1:
    return list_ad
  a__						=length // 2
  return merge(sortlist_ad(list_ad[:middle]							)			,   sortlist_ad(list_ad[middle:]							)							)




def     UpperCAmelCase__       (    _A   :				list[int]							):





  '''simple docstring'''







  if len(_A							) <= 1:
    return collection
  a__						=[]
  a__						=False
  for i in range(0			,   len(_A							)			,   2							):
    if i == len(_A							) - 1:
      a__						=True
    else:
      if collection[i] < collection[i + 1]:
        two_paired_list.append([collection[i], collection[i + 1]]							)
      else:
        two_paired_list.append([collection[i + 1], collection[i]]							)
  a__						=sortlist_ad(_A							)
  a__						=[i[0] for i in sorted_list_ad]
  result.append(sorted_list_ad[-1][1]							)
  if has_last_odd_item:
    a__						=collection[-1]
    a__						=binary_search_insertion(_A			,   _A							)
  a__						=False
  for i in range(len(_A							) - 1							):
    if result[i] == collection[-1] and has_last_odd_item:
      a__						=True
    a__						=sorted_list_ad[i][1]
    # If last_odd_item is inserted before the item's index,
    # you should forward index one more.
    if is_last_odd_item_inserted_before_this_index:
      a__						=result[: i + 2] + binary_search_insertion(result[i + 2 :]			,   _A							)
    else:
      a__						=result[: i + 1] + binary_search_insertion(result[i + 1 :]			,   _A							)

  return result


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



from typing import TYPE_CHECKING

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


lowerCamelCase     =      {
    '''configuration_deberta_v2''': ['''DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DebertaV2Config''', '''DebertaV2OnnxConfig'''],
    '''tokenization_deberta_v2''': ['''DebertaV2Tokenizer'''],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase     =      ['''DebertaV2TokenizerFast''']

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase     =      [
           '''TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''TFDebertaV2ForMaskedLM''',
           '''TFDebertaV2ForQuestionAnswering''',
           '''TFDebertaV2ForSequenceClassification''',
           '''TFDebertaV2ForTokenClassification''',
           '''TFDebertaV2Model''',
           '''TFDebertaV2PreTrainedModel''',
       ]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase     =      [
           '''DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''DebertaV2ForMaskedLM''',
           '''DebertaV2ForMultipleChoice''',
           '''DebertaV2ForQuestionAnswering''',
           '''DebertaV2ForSequenceClassification''',
           '''DebertaV2ForTokenClassification''',
           '''DebertaV2Model''',
           '''DebertaV2PreTrainedModel''',
       ]


if TYPE_CHECKING:
       from .configuration_deberta_va import (
           DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
           DebertaVaConfig,
           DebertaVaOnnxConfig,
       )
       from .tokenization_deberta_va import DebertaVaTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_deberta_va_fast import DebertaVaTokenizerFast

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_deberta_va import (
                  TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFDebertaVaForMaskedLM,
                  TFDebertaVaForQuestionAnswering,
                  TFDebertaVaForSequenceClassification,
                  TFDebertaVaForTokenClassification,
                  TFDebertaVaModel,
                  TFDebertaVaPreTrainedModel,
              )

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_deberta_va import (
                  DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
                  DebertaVaForMaskedLM,
                  DebertaVaForMultipleChoice,
                  DebertaVaForQuestionAnswering,
                  DebertaVaForSequenceClassification,
                  DebertaVaForTokenClassification,
                  DebertaVaModel,
                  DebertaVaPreTrainedModel,
              )

else:
       import sys

       lowerCamelCase     =      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
