


'''simple docstring'''






from typing import TYPE_CHECKING

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


__A	:	str     =     {'configuration_opt': ['OPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'OPTConfig']}

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

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__A	:	str     =     ['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	:	int     =     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



'''simple docstring'''






import math





def      UpperCAmelCase      (		lowerCamelCase_  :list				,      lowerCamelCase_  :int ):







		'''simple docstring'''
		snake_case_		:							Union[str, Any]			       =						len(lowerCamelCase_ )
		snake_case_		:							List[Any]			       =						int(math.floor(math.sqrt(lowerCamelCase_ ) ) )
		snake_case_		:							str			       =						0
		while arr[min(lowerCamelCase_				,      lowerCamelCase_ ) - 1] < x:
				snake_case_		:							Any			       =						step
				step += int(math.floor(math.sqrt(lowerCamelCase_ ) ) )
				if prev >= n:
						return -1

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


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