



"""simple docstring"""







def     UpperCamelCase_   (   lowerCAmelCase__				:    list						)						->       list:

  """simple docstring"""




  lowerCAmelCase_       :    int								=			len(lowerCAmelCase__						)
  for i in range(1   ,       lowerCAmelCase__						):
    lowerCAmelCase_       :    List[Any]								=			collection[i]
    lowerCAmelCase_       :    str								=			0
    lowerCAmelCase_       :    Dict								=			i - 1

    while low <= high:
      lowerCAmelCase_       :    Dict								=			(low + high) // 2
      if val < collection[mid]:
        lowerCAmelCase_       :    Optional[int]								=			mid - 1
      else:
        lowerCAmelCase_       :    List[str]								=			mid + 1
    for j in range(lowerCAmelCase__   ,       lowerCAmelCase__   ,       -1						):
      lowerCAmelCase_       :    Union[str, Any]								=			collection[j - 1]
    lowerCAmelCase_       :    Optional[Any]								=			val
  return collection


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










"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


lowercase__		:    Optional[int]				   =     {
    """configuration_trocr""": ["""TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TrOCRConfig"""],
    """processing_trocr""": ["""TrOCRProcessor"""],
}


try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowercase__		:    Dict				   =     [
         """TROCR_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """TrOCRForCausalLM""",
         """TrOCRPreTrainedModel""",
     ]


if TYPE_CHECKING:
     from .configuration_trocr import TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP, TrOCRConfig
     from .processing_trocr import TrOCRProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_trocr import TROCR_PRETRAINED_MODEL_ARCHIVE_LIST, TrOCRForCausalLM, TrOCRPreTrainedModel

else:
     import sys

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





