





from __future__ import annotations



def 				lowerCAmelCase_ (							A_    ,A_    ,A_    ,A_):
    if (direction == 1 and array[indexa] > array[indexa]) or (
        direction == 0 and array[indexa] < array[indexa]
    ):
        UpperCamelCase__				, UpperCamelCase__:	Any					=     array[indexa], array[indexa]



def 				lowerCAmelCase_ (							A_    ,A_    ,A_    ,A_):
    if length > 1:
        UpperCamelCase__:	List[Any]					=     int(length / 2)
        for i in range(A_    ,low + middle):
            comp_and_swap(A_    ,A_    ,i + middle    ,A_)
        bitonic_merge(A_    ,A_    ,A_    ,A_)
        bitonic_merge(A_    ,low + middle    ,A_    ,A_)



def 				lowerCAmelCase_ (							A_    ,A_    ,A_    ,A_):
    if length > 1:
        UpperCamelCase__:	int					=     int(length / 2)
        bitonic_sort(A_    ,A_    ,A_    ,1)
        bitonic_sort(A_    ,low + middle    ,A_    ,0)
        bitonic_merge(A_    ,A_    ,A_    ,A_)


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

   bitonic_sort(unsorted, 0, len(unsorted), 1)
   print('''\nSorted array in ascending order is: ''', end='''''')
   print(*unsorted, sep=''', ''')

   bitonic_merge(unsorted, 0, len(unsorted), 0)
   print('''Sorted array in descending order is: ''', end='''''')
   print(*unsorted, sep=''', ''')








from typing import TYPE_CHECKING

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


A__:    List[Any]  	=	{'''configuration_opt''': ['''OPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''OPTConfig''']}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A__:    Dict  	=	[
       '''OPT_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''OPTForCausalLM''',
       '''OPTModel''',
       '''OPTPreTrainedModel''',
       '''OPTForSequenceClassification''',
       '''OPTForQuestionAnswering''',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A__:    Optional[int]  	=	['''TFOPTForCausalLM''', '''TFOPTModel''', '''TFOPTPreTrainedModel''']

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A__:    str  	=	[
       '''FlaxOPTForCausalLM''',
       '''FlaxOPTModel''',
       '''FlaxOPTPreTrainedModel''',
   ]


if TYPE_CHECKING:
   from .configuration_opt import OPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OPTConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_opt import (
          OPT_PRETRAINED_MODEL_ARCHIVE_LIST,
          OPTForCausalLM,
          OPTForQuestionAnswering,
          OPTForSequenceClassification,
          OPTModel,
          OPTPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_opt import TFOPTForCausalLM, TFOPTModel, TFOPTPreTrainedModel

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_opt import FlaxOPTForCausalLM, FlaxOPTModel, FlaxOPTPreTrainedModel

else:
   import sys

   A__:    Optional[Any]  	=	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

