





'''simple docstring'''

import warnings

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


lowercase__		:    Optional[int] =       logging.get_logger(__name__)
class        __lowerCAmelCase  (					__magic_name__				):






       """simple docstring"""





       def __init__(							self     :	List[Any] ,				*lowerCAmelCase__     :	Optional[int] ,				**lowerCAmelCase__     :	str  )					->  None:

            '''simple docstring'''




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










'''simple docstring'''

from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowercase__		:    Union[str, Any] =       {
    'configuration_retribert': ['RETRIBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RetriBertConfig'],
    'tokenization_retribert': ['RetriBertTokenizer'],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowercase__		:    Dict =       ['RetriBertTokenizerFast']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowercase__		:    Tuple =       [
       'RETRIBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'RetriBertModel',
       'RetriBertPreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_retribert import RETRIBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RetriBertConfig
   from .tokenization_retribert import RetriBertTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_retribert_fast import RetriBertTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_retribert import (
          RETRIBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          RetriBertModel,
          RetriBertPreTrainedModel,
      )

else:
   import sys

   lowercase__		:    Optional[Any] =       _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



