"""simple docstring"""




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        __SCREAMING_SNAKE_CASE     (   _lowerCAmelCase							):
				UpperCAmelCase	=      c_float







				@property
				def   lowercase_		(    self    :Tuple  )      ->					List[str]:




									"""simple docstring"""



									return sizeof(self.value  )





def 				__a       (			_lowercase							, _lowercase							, _lowercase = 0						):







					"""simple docstring"""





					if dimension <= 0:
										lowerCamelCase__		:   List[str]  				=					fixed_dimension

					dimension -= num_token_to_add
					return dimension



def 				__a       (			_lowercase							, _lowercase						):







					"""simple docstring"""





					return num_parameters * dtype.size





def 				__a       (			_lowercase						):







					"""simple docstring"""





					from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore

					try:
										return AutoProcessor.from_pretrained(lowerCamelCase_						)
					except (ValueError, OSError, KeyError):
										lowerCamelCase__				,						lowerCamelCase__		:   Union[str, Any]  				=					None, None
										try:
															lowerCamelCase__		:   Union[str, Any]  				=					AutoTokenizer.from_pretrained(lowerCamelCase_						)
										except (OSError, KeyError):
															pass
										try:
															lowerCamelCase__		:   Optional[Any]  				=					AutoFeatureExtractor.from_pretrained(lowerCamelCase_						)
										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










"""simple docstring"""






def __lowercase  (   lowerCamelCase_	:   str	):
				SCREAMING_SNAKE_CASE__  				=					str(lowerCamelCase_	).strip()
				if not oct_string:
								raise ValueError("Empty string was passed to the function"	)
				SCREAMING_SNAKE_CASE__  				=					oct_string[0] == "-"
				if is_negative:
								SCREAMING_SNAKE_CASE__  				=					oct_string[1:]
				if not oct_string.isdigit() or not all(0 <= int(lowerCamelCase_	) <= 7 for char in oct_string	):
								raise ValueError("Non-octal value was passed to the function"	)
				SCREAMING_SNAKE_CASE__  				=					0
				for char in oct_string:
								SCREAMING_SNAKE_CASE__  				=					8 * decimal_number + int(lowerCamelCase_	)
				if is_negative:
								SCREAMING_SNAKE_CASE__  				=					-decimal_number
				return decimal_number


if __name__ == "__main__":
	from doctest import testmod

	testmod()
