
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 		__magic_name__   (						snake_case    ):
  _lowerCAmelCase		=	c_float





  @property
  def       _A							(							self    :	int				):
       return sizeof(self.value				)



def     UpperCAmelCase__							(       __magic_name__						:     int							,  __magic_name__						:     int							,  __magic_name__						:     int = 0  ):


     '''simple docstring'''

     if dimension <= 0:
          lowerCAmelCase		:					Tuple  	=     fixed_dimension

     dimension -= num_token_to_add
     return dimension
def     UpperCAmelCase__							(       __magic_name__						:     int							,  __magic_name__						:     ParameterFormat  ):


     '''simple docstring'''

     return num_parameters * dtype.size
def     UpperCAmelCase__							(       __magic_name__						:     str  ):


     '''simple docstring'''

     from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore

     try:
          return AutoProcessor.from_pretrained(__magic_name__  )
     except (ValueError, OSError, KeyError):
          lowerCAmelCase		:					Any  	=     None, None
          try:
               lowerCAmelCase		:					Union[str, Any]  	=     AutoTokenizer.from_pretrained(__magic_name__  )
          except (OSError, KeyError):
               pass
          try:
               lowerCAmelCase		:					List[Any]  	=     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


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 		__magic_name__   (						snake_case    ):
		_lowerCAmelCase		=	c_float





		@property
		def       _A							(							self    :	int				):
							return sizeof(self.value				)



def     UpperCAmelCase__							(       __magic_name__						:     int							,  __magic_name__						:     int							,  __magic_name__						:     int = 0  ):


					'''simple docstring'''

					if dimension <= 0:
										lowerCAmelCase		:					Tuple  	=     fixed_dimension

					dimension -= num_token_to_add
					return dimension
def     UpperCAmelCase__							(       __magic_name__						:     int							,  __magic_name__						:     ParameterFormat  ):


					'''simple docstring'''

					return num_parameters * dtype.size
def     UpperCAmelCase__							(       __magic_name__						:     str  ):


					'''simple docstring'''

					from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore

					try:
										return AutoProcessor.from_pretrained(__magic_name__  )
					except (ValueError, OSError, KeyError):
										lowerCAmelCase	,							lowerCAmelCase		:					Any  	=     None, None
										try:
															lowerCAmelCase		:					Union[str, Any]  	=     AutoTokenizer.from_pretrained(__magic_name__  )
										except (OSError, KeyError):
															pass
										try:
															lowerCAmelCase		:					List[Any]  	=     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
