






"""simple docstring"""



from __future__ import annotations

import bisect



def 						lowercase    (				__snake_case				: list[int]		,					__snake_case				: int		,					__snake_case				: int = 0		,					__snake_case				: int = -1	):
		if hi < 0:
				lowercase_ : List[Any]							       =   len(__snake_case	)

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

		return lo



def 						lowercase    (				__snake_case				: list[int]		,					__snake_case				: int		,					__snake_case				: int = 0		,					__snake_case				: int = -1	):
		if hi < 0:
				lowercase_ : Any							       =   len(__snake_case	)

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

		return lo



def 						lowercase    (				__snake_case				: list[int]		,					__snake_case				: int		,					__snake_case				: int = 0		,					__snake_case				: int = -1	):
		sorted_collection.insert(bisect_left(__snake_case		,					__snake_case		,					__snake_case		,					__snake_case	)		,					__snake_case	)



def 						lowercase    (				__snake_case				: list[int]		,					__snake_case				: int		,					__snake_case				: int = 0		,					__snake_case				: int = -1	):
		sorted_collection.insert(bisect_right(__snake_case		,					__snake_case		,					__snake_case		,					__snake_case	)		,					__snake_case	)



def 						lowercase    (				__snake_case				: list[int]		,					__snake_case				: int	):
		lowercase_ : Optional[int]							       =   0
		lowercase_ : Tuple							       =   len(__snake_case	) - 1

		while left <= right:
				lowercase_ : str							       =   left + (right - left) // 2
				lowercase_ : Dict							       =   sorted_collection[midpoint]
				if current_item == item:
						return midpoint
				elif item < current_item:
						lowercase_ : Dict							       =   midpoint - 1
				else:
						lowercase_ : Tuple							       =   midpoint + 1
		return None



def 						lowercase    (				__snake_case				: list[int]		,					__snake_case				: int	):
		lowercase_ : int							       =   bisect.bisect_left(__snake_case		,					__snake_case	)
		if index != len(__snake_case	) and sorted_collection[index] == item:
				return index
		return None



def 						lowercase    (				__snake_case				: list[int]		,					__snake_case				: int		,					__snake_case				: int		,					__snake_case				: int	):
		if right < left:
				return None

		lowercase_ : List[str]							       =   left + (right - left) // 2

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


if __name__ == "__main__":
						__A		:       Dict  				=		input('''Enter numbers separated by comma:\n''').strip()
						__A		:       List[str]  				=		sorted(int(item) for item in user_input.split(''','''))
						__A		:       Optional[int]  				=		int(input('''Enter a single number to be found in the list:\n'''))
						__A		:       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 collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__a  :     Optional[int]       =  logging.get_logger(__name__)

__a  :     Tuple       =  {
    """facebook/data2vec-text-base""": """https://huggingface.co/data2vec/resolve/main/config.json""",
}




class    _UpperCamelCase      (						_UpperCAmelCase ):


			"""simple docstring"""






			__a  : Dict			 =						'''data2vec-text'''





			def __init__(   self      ,   lowerCAmelCase__=3_05_22      ,   lowerCAmelCase__=7_68      ,   lowerCAmelCase__=12      ,   lowerCAmelCase__=12      ,   lowerCAmelCase__=30_72      ,   lowerCAmelCase__="gelu"      ,   lowerCAmelCase__=0.1      ,   lowerCAmelCase__=0.1      ,   lowerCAmelCase__=5_12      ,   lowerCAmelCase__=2      ,   lowerCAmelCase__=0.02      ,   lowerCAmelCase__=1E-12      ,   lowerCAmelCase__=1      ,   lowerCAmelCase__=0      ,   lowerCAmelCase__=2      ,   lowerCAmelCase__="absolute"      ,   lowerCAmelCase__=True      ,   lowerCAmelCase__=None      ,   **lowerCAmelCase__      ,   )     ->					Any:

							'''simple docstring'''


							super().__init__(pad_token_id=lowerCAmelCase__      ,   bos_token_id=lowerCAmelCase__      ,   eos_token_id=lowerCAmelCase__      ,   **lowerCAmelCase__						)

							__lowercase 			=							vocab_size
							__lowercase 			=							hidden_size
							__lowercase 			=							num_hidden_layers
							__lowercase 			=							num_attention_heads
							__lowercase 			=							hidden_act
							__lowercase 			=							intermediate_size
							__lowercase 			=							hidden_dropout_prob
							__lowercase 			=							attention_probs_dropout_prob
							__lowercase 			=							max_position_embeddings
							__lowercase 			=							type_vocab_size
							__lowercase 			=							initializer_range
							__lowercase 			=							layer_norm_eps
							__lowercase 			=							position_embedding_type
							__lowercase 			=							use_cache
							__lowercase 			=							classifier_dropout




class    _UpperCamelCase      (						_UpperCAmelCase ):


			"""simple docstring"""






			@property
			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Mapping[str, Mapping[int, str]]:

							'''simple docstring'''


							if self.task == "multiple-choice":
											__lowercase 			=							{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
							else:
											__lowercase 			=							{0: '''batch''', 1: '''sequence'''}
							return OrderedDict(
							    [
							        ('''input_ids''', dynamic_axis),
							        ('''attention_mask''', dynamic_axis),
							    ]						)