
"""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 		a__						(				a_						):
			__lowerCAmelCase						=    c_float
			@property
			def       __magic_name__      (      self    ):
										return sizeof(self.value    )


def   __magic_name__     (				__snake_case    :	int					,			__snake_case    :	int					,			__snake_case    :	int = 0  )			->    int:
							# < 0 is possible if using a dynamic axis
							if dimension <= 0:
														lowercase   :    int			=							fixed_dimension

							dimension -= num_token_to_add
							return dimension


def   __magic_name__     (				__snake_case    :	int					,			__snake_case    :	ParameterFormat  )			->    int:
							return num_parameters * dtype.size


def   __magic_name__     (				__snake_case    :	str  )			->    Optional[Union["AutoTokenizer", "AutoFeatureExtractor", "AutoProcessor"]]:
							from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore

							try:
														return AutoProcessor.from_pretrained(__snake_case  )
							except (ValueError, OSError, KeyError):
														lowercase   :    Union[str, Any]			=							None, None
														try:
																					lowercase   :    Dict			=							AutoTokenizer.from_pretrained(__snake_case  )
														except (OSError, KeyError):
																					pass
														try:
																					lowercase   :    Optional[int]			=							AutoFeatureExtractor.from_pretrained(__snake_case  )
														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"""




import warnings

from ...utils import logging
from .image_processing_convnext import ConvNextImageProcessor


_A      :	List[str]       		=	logging.get_logger(__name__)

class 		a__						(				a_						):
   def __init__(      self     ,   *_a     ,   **_a    ):
          warnings.warn(
              "The class ConvNextFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
              " Please use ConvNextImageProcessor instead."     ,   _a     ,   )
          super().__init__(*_a     ,   **_a    )


