"""simple docstring"""






from __future__ import annotations


def     UpperCAmelCase__ (       lowerCAmelCase__  :list      ,	lowerCAmelCase__  :int      ,	lowerCAmelCase__  :int      ,	lowerCAmelCase__  :int	)				->					list:
      '''simple docstring'''


      lowercase														=			[]
      lowercase							,							lowercase														=			input_list[low:mid], input_list[mid : high + 1]
      while left and right:
            result.append((left if left[0] <= right[0] else right).pop(0	)	)
      lowercase														=			result + left + right
      return input_list


def     UpperCAmelCase__ (       lowerCAmelCase__  :list	)				->					list:
      '''simple docstring'''


      if len(lowerCAmelCase__	) <= 1:
            return input_list
      lowercase														=			list(lowerCAmelCase__	)

      # iteration for two-way merging
      lowercase														=			2
      while p <= len(lowerCAmelCase__	):
            # getting low, high and middle value for merge-sort of single list
            for i in range(0      ,	len(lowerCAmelCase__	)      ,	lowerCAmelCase__	):
                  lowercase														=			i
                  lowercase														=			i + p - 1
                  lowercase														=			(low + high + 1) // 2
                  lowercase														=			merge(lowerCAmelCase__      ,	lowerCAmelCase__      ,	lowerCAmelCase__      ,	lowerCAmelCase__	)
            # final merge of last two parts
            if p * 2 >= len(lowerCAmelCase__	):
                  lowercase														=			i
                  lowercase														=			merge(lowerCAmelCase__      ,	0      ,	lowerCAmelCase__      ,	len(lowerCAmelCase__	) - 1	)
                  break
            p *= 2

      return input_list


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




"""simple docstring"""






from typing import TYPE_CHECKING

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


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

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowerCAmelCase					: int        =[
           """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:
       __lowerCAmelCase					: Union[str, Any]        =[
           """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

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



