




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__a							     =					{
    'configuration_rembert': ['REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RemBertConfig', 'RemBertOnnxConfig']
}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __a							     =					['RemBertTokenizer']

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __a							     =					['RemBertTokenizerFast']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __a							     =					[
         'REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
         'RemBertForCausalLM',
         'RemBertForMaskedLM',
         'RemBertForMultipleChoice',
         'RemBertForQuestionAnswering',
         'RemBertForSequenceClassification',
         'RemBertForTokenClassification',
         'RemBertLayer',
         'RemBertModel',
         'RemBertPreTrainedModel',
         'load_tf_weights_in_rembert',
     ]


try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __a							     =					[
         'TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
         'TFRemBertForCausalLM',
         'TFRemBertForMaskedLM',
         'TFRemBertForMultipleChoice',
         'TFRemBertForQuestionAnswering',
         'TFRemBertForSequenceClassification',
         'TFRemBertForTokenClassification',
         'TFRemBertLayer',
         'TFRemBertModel',
         'TFRemBertPreTrainedModel',
     ]


if TYPE_CHECKING:
     from .configuration_rembert import REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RemBertConfig, RemBertOnnxConfig

     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_rembert import RemBertTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_rembert_fast import RemBertTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_rembert import (
              REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
              RemBertForCausalLM,
              RemBertForMaskedLM,
              RemBertForMultipleChoice,
              RemBertForQuestionAnswering,
              RemBertForSequenceClassification,
              RemBertForTokenClassification,
              RemBertLayer,
              RemBertModel,
              RemBertPreTrainedModel,
              load_tf_weights_in_rembert,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_rembert import (
              TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFRemBertForCausalLM,
              TFRemBertForMaskedLM,
              TFRemBertForMultipleChoice,
              TFRemBertForQuestionAnswering,
              TFRemBertForSequenceClassification,
              TFRemBertForTokenClassification,
              TFRemBertLayer,
              TFRemBertModel,
              TFRemBertPreTrainedModel,
          )


else:
     import sys

     __a							     =					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





import warnings

from ...utils import logging
from .image_processing_clip import CLIPImageProcessor


__a							     =					logging.get_logger(__name__)

class     __a(	_a  ):






    """simple docstring"""





    def __init__( self  ,*_SCREAMING_SNAKE_CASE  ,**_SCREAMING_SNAKE_CASE    )					->						None:
         warnings.warn(
             '''The class CLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
             ''' use CLIPImageProcessor instead.'''  ,_SCREAMING_SNAKE_CASE  ,)
         super().__init__(*_SCREAMING_SNAKE_CASE  ,**_SCREAMING_SNAKE_CASE    )