import warnings

from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor


A_:      Optional[Any]        =			logging.get_logger(__name__)


class _lowercase					(       _UpperCAmelCase						):





							"""simple docstring"""




							def __init__(    self     ,   *UpperCAmelCase     ,   **UpperCAmelCase  ):







														'''simple docstring'''






														warnings.warn(
														    """The class LayoutLMv3FeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
														    """ Please use LayoutLMv3ImageProcessor instead."""     ,   UpperCAmelCase     ,   )
														super().__init__(*UpperCAmelCase     ,   **UpperCAmelCase  )



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 _lowercase					(       _UpperCAmelCase						):





							"""simple docstring"""




							lowerCAmelCase__       =					c_float



							@property
							def     _UpperCAmelCase       (    self  ):







														'''simple docstring'''






														return sizeof(self.value  )






def 					__lowerCAmelCase			(  _A ,_A ,_A = 0					):



							"""simple docstring"""


							if dimension <= 0:
														_lowercase				=	fixed_dimension

							dimension -= num_token_to_add
							return dimension


def 					__lowerCAmelCase			(  _A ,_A					):



							"""simple docstring"""


							return num_parameters * dtype.size







def 					__lowerCAmelCase			(  _A					):



							"""simple docstring"""


							from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore

							try:
														return AutoProcessor.from_pretrained(_A					)
							except (ValueError, OSError, KeyError):
														_lowercase				,       _lowercase				=	None, None
														try:
																					_lowercase				=	AutoTokenizer.from_pretrained(_A					)
														except (OSError, KeyError):
																					pass
														try:
																					_lowercase				=	AutoFeatureExtractor.from_pretrained(_A					)
														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


