
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__ ):



      """simple docstring"""







      A__		:     Union[str, Any]       				=      c_float




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






def       UpperCAmelCase_					(  __lowerCAmelCase		,   __lowerCAmelCase		,   __lowerCAmelCase = 0		)	->  List[Any]:







    if dimension <= 0:
        __lowercase						:     str		      =    fixed_dimension

    dimension -= num_token_to_add
    return dimension


def       UpperCAmelCase_					(  __lowerCAmelCase		,   __lowerCAmelCase		)	->  List[str]:
    return num_parameters * dtype.size





def       UpperCAmelCase_					(  __lowerCAmelCase		)	->  Optional[int]:
    from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore

    try:
        return AutoProcessor.from_pretrained(__lowerCAmelCase		)
    except (ValueError, OSError, KeyError):
        __lowercase						:     str		      =    None, None
        try:
            __lowercase						:     str		      =    AutoTokenizer.from_pretrained(__lowerCAmelCase		)
        except (OSError, KeyError):
            pass
        try:
            __lowercase						:     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




__lowerCAmelCase   :     List[Any]      =						"\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"

__lowerCAmelCase   :     Optional[Any]      =						[{"type": "code", "content": INSTALL_CONTENT}]
__lowerCAmelCase   :     List[str]      =						{
    "{processor_class}": "FakeProcessorClass",
    "{model_class}": "FakeModelClass",
    "{object_class}": "FakeObjectClass",
}


