


def 				UpperCamelCase      (			__magic_name__  :							list					,					__magic_name__  :							int  )     ->						int:
		"""simple docstring"""




		for index, item in enumerate(__magic_name__  ):
				if item == target:
						return index
		return -1






def 				UpperCamelCase      (			__magic_name__  :							list					,					__magic_name__  :							int					,					__magic_name__  :							int					,					__magic_name__  :							int  )     ->						int:
		"""simple docstring"""




		if not (0 <= high < len(__magic_name__  ) and 0 <= low < len(__magic_name__  )):
				raise Exception("""Invalid upper or lower bound!"""  )
		if high < low:
				return -1
		if sequence[low] == target:
				return low
		if sequence[high] == target:
				return high
		return rec_linear_search(__magic_name__					,					low + 1					,					high - 1					,					__magic_name__  )


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

	A :	List[Any]												=     int(input('Enter a single number to be found in the list:\n').strip())
	A :	Tuple												=     linear_search(sequence, target)
	if result != -1:
		print(F'linear_search({sequence}, {target}) = {result}')
	else:
		print(F'{target} was not found in {sequence}')





from typing import TYPE_CHECKING

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


A :	str												=     {
    'configuration_nystromformer': ['NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'NystromformerConfig'],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A :	Optional[int]												=     [
	    'NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'NystromformerForMaskedLM',
	    'NystromformerForMultipleChoice',
	    'NystromformerForQuestionAnswering',
	    'NystromformerForSequenceClassification',
	    'NystromformerForTokenClassification',
	    'NystromformerLayer',
	    'NystromformerModel',
	    'NystromformerPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_nystromformer import NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, NystromformerConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_nystromformer import (
		    NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    NystromformerForMaskedLM,
		    NystromformerForMultipleChoice,
		    NystromformerForQuestionAnswering,
		    NystromformerForSequenceClassification,
		    NystromformerForTokenClassification,
		    NystromformerLayer,
		    NystromformerModel,
		    NystromformerPreTrainedModel,
		)


else:
	import sys

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

