






from __future__ import annotations


def __A    (      __lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase	)->  list:





						"""simple docstring"""



						_UpperCAmelCase									=			[]
						_UpperCAmelCase					, _UpperCAmelCase									=			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	)	)
						_UpperCAmelCase									=			result + left + right
						return input_list


def __A    (      __lowerCAmelCase	)->  list:





						"""simple docstring"""



						if len(__lowerCAmelCase	) <= 1:
												return input_list
						_UpperCAmelCase									=			list(__lowerCAmelCase	)

						# iteration for two-way merging
						_UpperCAmelCase									=			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	):
																		_UpperCAmelCase									=			i
																		_UpperCAmelCase									=			i + p - 1
																		_UpperCAmelCase									=			(low + high + 1) // 2
																		_UpperCAmelCase									=			merge(__lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase	)
												# final merge of last two parts
												if p * 2 >= len(__lowerCAmelCase	):
																		_UpperCAmelCase									=			i
																		_UpperCAmelCase									=			merge(__lowerCAmelCase ,		0 ,		__lowerCAmelCase ,		len(__lowerCAmelCase	) - 1	)
																		break
												p *= 2

						return input_list


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








from typing import TYPE_CHECKING

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


_a      	=  {'''configuration_opt''': ['''OPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''OPTConfig''']}

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

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

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_a      	=  [
		    '''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      	=  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
