from __future__ import annotations

import bisect

def       A__(		__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase = 0	,			__lowerCAmelCase = -1 ):
				if hi < 0:
								_snake_case :			int  						=   len(__lowerCAmelCase )

				while lo < hi:
								_snake_case :			Optional[int]  						=   lo + (hi - lo) // 2
								if sorted_collection[mid] < item:
												_snake_case :			Optional[Any]  						=   mid + 1
								else:
												_snake_case :			List[str]  						=   mid

				return lo

def       A__(		__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase = 0	,			__lowerCAmelCase = -1 ):
				if hi < 0:
								_snake_case :			Dict  						=   len(__lowerCAmelCase )

				while lo < hi:
								_snake_case :			List[str]  						=   lo + (hi - lo) // 2
								if sorted_collection[mid] <= item:
												_snake_case :			List[Any]  						=   mid + 1
								else:
												_snake_case :			List[str]  						=   mid

				return lo

def       A__(		__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase = 0	,			__lowerCAmelCase = -1 ):
				sorted_collection.insert(bisect_left(__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase )	,			__lowerCAmelCase )

def       A__(		__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase = 0	,			__lowerCAmelCase = -1 ):
				sorted_collection.insert(bisect_right(__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase )	,			__lowerCAmelCase )

def       A__(		__lowerCAmelCase	,			__lowerCAmelCase ):
				_snake_case :			List[str]  						=   0
				_snake_case :			Optional[Any]  						=   len(__lowerCAmelCase ) - 1

				while left <= right:
								_snake_case :			int  						=   left + (right - left) // 2
								_snake_case :			Optional[Any]  						=   sorted_collection[midpoint]
								if current_item == item:
												return midpoint
								elif item < current_item:
												_snake_case :			List[str]  						=   midpoint - 1
								else:
												_snake_case :			Optional[int]  						=   midpoint + 1
				return None

def       A__(		__lowerCAmelCase	,			__lowerCAmelCase ):
				_snake_case :			List[Any]  						=   bisect.bisect_left(__lowerCAmelCase	,			__lowerCAmelCase )
				if index != len(__lowerCAmelCase ) and sorted_collection[index] == item:
								return index
				return None

def       A__(		__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase ):
				if right < left:
								return None

				_snake_case :			Tuple  						=   left + (right - left) // 2

				if sorted_collection[midpoint] == item:
								return midpoint
				elif sorted_collection[midpoint] > item:
								return binary_search_by_recursion(__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase	,			midpoint - 1 )
				else:
								return binary_search_by_recursion(__lowerCAmelCase	,			__lowerCAmelCase	,			midpoint + 1	,			__lowerCAmelCase )


if __name__ == "__main__":
		lowercase_		:				List[str]				=						input('''Enter numbers separated by comma:\n''').strip()
		lowercase_		:				Any				=						sorted(int(item) for item in user_input.split(''','''))
		lowercase_		:				Tuple				=						int(input('''Enter a single number to be found in the list:\n'''))
		lowercase_		:				Optional[int]				=						binary_search(collection, target)
		if result is None:
				print(F'''{target} was not found in {collection}.''')
		else:
				print(F'''{target} was found at position {result} in {collection}.''')













from typing import TYPE_CHECKING

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


lowercase_		:				Any				=						{
    '''configuration_canine''': ['''CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CanineConfig'''],
    '''tokenization_canine''': ['''CanineTokenizer'''],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase_		:				Optional[Any]				=						[
		    '''CANINE_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''CanineForMultipleChoice''',
		    '''CanineForQuestionAnswering''',
		    '''CanineForSequenceClassification''',
		    '''CanineForTokenClassification''',
		    '''CanineLayer''',
		    '''CanineModel''',
		    '''CaninePreTrainedModel''',
		    '''load_tf_weights_in_canine''',
		]


if TYPE_CHECKING:
		from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
		from .tokenization_canine import CanineTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_canine import (
				    CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
				    CanineForMultipleChoice,
				    CanineForQuestionAnswering,
				    CanineForSequenceClassification,
				    CanineForTokenClassification,
				    CanineLayer,
				    CanineModel,
				    CaninePreTrainedModel,
				    load_tf_weights_in_canine,
				)


else:
		import sys

		lowercase_		:				str				=						_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






