


from __future__ import annotations

def _UpperCAmelCase   (	a						:	Any  ,					a						:	Union[str, Any]  ,					a						:	Tuple  ,					a						:	List[Any] ):
							snake_case__	  =       []
							snake_case__  , snake_case__	  =       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 ) )
							snake_case__	  =       result + left + right
							return input_list

def _UpperCAmelCase   (	a						:	Tuple ):
							if len(lowercase__ ) <= 1:
														return input_list
							snake_case__	  =       list(lowercase__ )

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


a__       =     {
    """configuration_clip""": [
        """CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """CLIPConfig""",
        """CLIPOnnxConfig""",
        """CLIPTextConfig""",
        """CLIPVisionConfig""",
    ],
    """processing_clip""": ["""CLIPProcessor"""],
    """tokenization_clip""": ["""CLIPTokenizer"""],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a__       =     ["""CLIPTokenizerFast"""]

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a__       =     ["""CLIPFeatureExtractor"""]
	a__       =     ["""CLIPImageProcessor"""]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a__       =     [
	    """CLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """CLIPModel""",
	    """CLIPPreTrainedModel""",
	    """CLIPTextModel""",
	    """CLIPTextModelWithProjection""",
	    """CLIPVisionModel""",
	    """CLIPVisionModelWithProjection""",
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a__       =     [
	    """TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """TFCLIPModel""",
	    """TFCLIPPreTrainedModel""",
	    """TFCLIPTextModel""",
	    """TFCLIPVisionModel""",
	]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a__       =     [
	    """FlaxCLIPModel""",
	    """FlaxCLIPPreTrainedModel""",
	    """FlaxCLIPTextModel""",
	    """FlaxCLIPTextPreTrainedModel""",
	    """FlaxCLIPVisionModel""",
	    """FlaxCLIPVisionPreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_clip import (
	    CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    CLIPConfig,
	    CLIPOnnxConfig,
	    CLIPTextConfig,
	    CLIPVisionConfig,
	)
	from .processing_clip import CLIPProcessor
	from .tokenization_clip import CLIPTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_clip_fast import CLIPTokenizerFast

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .feature_extraction_clip import CLIPFeatureExtractor
		from .image_processing_clip import CLIPImageProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_clip import (
		    CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
		    CLIPModel,
		    CLIPPreTrainedModel,
		    CLIPTextModel,
		    CLIPTextModelWithProjection,
		    CLIPVisionModel,
		    CLIPVisionModelWithProjection,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_clip import (
		    TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFCLIPModel,
		    TFCLIPPreTrainedModel,
		    TFCLIPTextModel,
		    TFCLIPVisionModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_clip import (
		    FlaxCLIPModel,
		    FlaxCLIPPreTrainedModel,
		    FlaxCLIPTextModel,
		    FlaxCLIPTextPreTrainedModel,
		    FlaxCLIPVisionModel,
		    FlaxCLIPVisionPreTrainedModel,
		)


else:
	import sys

	a__       =     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
