



from ctypes import c_float, sizeof
from enum import Enum
from typing import TYPE_CHECKING, Optional, Union


if TYPE_CHECKING:
		from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore


class 	lowerCamelCase__ (		_a   ):
						_lowerCAmelCase								= c_float


						@property
						def   _lowerCamelCase			( self						:  Tuple			):
													return sizeof(self.value			)





def 						__lowerCamelCase       (      __magic_name__	:  int     , __magic_name__	:  int     , __magic_name__	:  int = 0						):
							# < 0 is possible if using a dynamic axis
							if dimension <= 0:
														a__:       Any											=fixed_dimension

							dimension -= num_token_to_add
							return dimension





def 						__lowerCamelCase       (      __magic_name__	:  int     , __magic_name__	:  ParameterFormat						):
							return num_parameters * dtype.size







def 						__lowerCamelCase       (      __magic_name__	:  str						):
							from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore

							try:
														return AutoProcessor.from_pretrained(__magic_name__						)
							except (ValueError, OSError, KeyError):
														a__							,			a__:       Dict											=None, None
														try:
																					a__:       Dict											=AutoTokenizer.from_pretrained(__magic_name__						)
														except (OSError, KeyError):
																					pass
														try:
																					a__:       str											=AutoFeatureExtractor.from_pretrained(__magic_name__						)
														except (OSError, KeyError):
																					pass

														if tokenizer is not None and feature_extractor is not None:
																					raise ValueError(
																					    F"Couldn't auto-detect preprocessor for {model_name}. Found both a tokenizer and a feature extractor."						)
														elif tokenizer is None and feature_extractor is None:
																					return None
														elif tokenizer is not None:
																					return tokenizer
														else:
																					return feature_extractor










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





def 						__lowerCamelCase       (      __magic_name__	:  list     , __magic_name__	:  int     , __magic_name__	:  int     , __magic_name__	:  int						):
							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__":
		__UpperCAmelCase						  =     input('''Enter numbers separated by comma:\n''').strip()
		__UpperCAmelCase						  =     [int(item.strip()) for item in user_input.split(''',''')]

		__UpperCAmelCase						  =     int(input('''Enter a single number to be found in the list:\n''').strip())
		__UpperCAmelCase						  =     linear_search(sequence, target)
		if result != -1:
				print(f"""linear_search({sequence}, {target}) = {result}""")
		else:
				print(f"""{target} was not found in {sequence}""")





