

import warnings

from ...utils import logging
from .image_processing_mobilenet_va import MobileNetVaImageProcessor


lowerCamelCase     =      logging.get_logger(__name__)







class 		__magic_name__    (							lowerCamelCase__							):

   '''simple docstring'''




   def __init__(       self,					*lowercase_,					**lowercase_ )							->					None:






     """simple docstring"""






     warnings.warn(
         '''The class MobileNetV2FeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
         ''' Please use MobileNetV2ImageProcessor instead.''',					lowercase_,					)
     super().__init__(*lowercase_,					**lowercase_ )



from typing import TYPE_CHECKING

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


lowerCamelCase     =      {
    '''configuration_deberta_v2''': ['''DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DebertaV2Config''', '''DebertaV2OnnxConfig'''],
    '''tokenization_deberta_v2''': ['''DebertaV2Tokenizer'''],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase     =      ['''DebertaV2TokenizerFast''']

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase     =      [
           '''TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''TFDebertaV2ForMaskedLM''',
           '''TFDebertaV2ForQuestionAnswering''',
           '''TFDebertaV2ForSequenceClassification''',
           '''TFDebertaV2ForTokenClassification''',
           '''TFDebertaV2Model''',
           '''TFDebertaV2PreTrainedModel''',
       ]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase     =      [
           '''DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''DebertaV2ForMaskedLM''',
           '''DebertaV2ForMultipleChoice''',
           '''DebertaV2ForQuestionAnswering''',
           '''DebertaV2ForSequenceClassification''',
           '''DebertaV2ForTokenClassification''',
           '''DebertaV2Model''',
           '''DebertaV2PreTrainedModel''',
       ]


if TYPE_CHECKING:
       from .configuration_deberta_va import (
           DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
           DebertaVaConfig,
           DebertaVaOnnxConfig,
       )
       from .tokenization_deberta_va import DebertaVaTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_deberta_va_fast import DebertaVaTokenizerFast

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_deberta_va import (
                  TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFDebertaVaForMaskedLM,
                  TFDebertaVaForQuestionAnswering,
                  TFDebertaVaForSequenceClassification,
                  TFDebertaVaForTokenClassification,
                  TFDebertaVaModel,
                  TFDebertaVaPreTrainedModel,
              )

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_deberta_va import (
                  DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
                  DebertaVaForMaskedLM,
                  DebertaVaForMultipleChoice,
                  DebertaVaForQuestionAnswering,
                  DebertaVaForSequenceClassification,
                  DebertaVaForTokenClassification,
                  DebertaVaModel,
                  DebertaVaPreTrainedModel,
              )

else:
       import sys

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