


import math





def     _lowerCamelCase		(				SCREAMING_SNAKE_CASE_					:			int						,						SCREAMING_SNAKE_CASE_					:			Union[str, Any]   ):






							"""simple docstring"""



							a_							: Tuple								=						len(SCREAMING_SNAKE_CASE_   )
							a_							: Tuple								=						int(math.floor(math.sqrt(SCREAMING_SNAKE_CASE_   )   )   )
							a_							: Tuple								=						0
							while arr[min(SCREAMING_SNAKE_CASE_						,						SCREAMING_SNAKE_CASE_   ) - 1] < x:
														a_							: Any								=						step
														step += int(math.floor(math.sqrt(SCREAMING_SNAKE_CASE_   )   )   )
														if prev >= n:
																					return -1

							while arr[prev] < x:
														a_							: Optional[Any]								=						prev + 1
														if prev == min(SCREAMING_SNAKE_CASE_						,						SCREAMING_SNAKE_CASE_   ):
																					return -1
							if arr[prev] == x:
														return prev
							return -1


if __name__ == "__main__":
	SCREAMING_SNAKE_CASE			:			List[Any]        =     input("Enter numbers separated by a comma:\n").strip()
	SCREAMING_SNAKE_CASE			:			Optional[int]        =     [int(item) for item in user_input.split(",")]
	SCREAMING_SNAKE_CASE			:			Tuple        =     int(input("Enter the number to be searched:\n"))
	SCREAMING_SNAKE_CASE			:			int        =     jump_search(arr, x)
	if res == -1:
		print("Number not found!")
	else:
		print(F"""Number {x} is at index {res}""")



from typing import TYPE_CHECKING

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


_lowercase:	Optional[Any]       			=		{
    '''configuration_rag''': ['''RagConfig'''],
    '''retrieval_rag''': ['''RagRetriever'''],
    '''tokenization_rag''': ['''RagTokenizer'''],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowercase:	Optional[Any]       			=		[
				    '''RagModel''',
				    '''RagPreTrainedModel''',
				    '''RagSequenceForGeneration''',
				    '''RagTokenForGeneration''',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowercase:	Optional[Any]       			=		[
				    '''TFRagModel''',
				    '''TFRagPreTrainedModel''',
				    '''TFRagSequenceForGeneration''',
				    '''TFRagTokenForGeneration''',
				]


if TYPE_CHECKING:
				from .configuration_rag import RagConfig
				from .retrieval_rag import RagRetriever
				from .tokenization_rag import RagTokenizer

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_rag import (
								    TFRagModel,
								    TFRagPreTrainedModel,
								    TFRagSequenceForGeneration,
								    TFRagTokenForGeneration,
								)

else:
				import sys

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






