





import copy
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union







@dataclass
class 				__lowerCAmelCase       :
      _lowercase				:					Optional[Union[str, Path]]						=			None
      _lowercase				:					bool						=			False
      _lowercase				:					bool						=			False
      _lowercase				:					bool						=			False
      _lowercase				:					Optional[Dict]						=			None
      _lowercase				:					Optional[str]						=			None
      _lowercase				:					bool						=			False
      _lowercase				:					bool						=			False
      _lowercase				:					bool						=			False
      _lowercase				:					bool						=			True
      _lowercase				:					Optional[int]						=			None
      _lowercase				:					int						=			1
      _lowercase				:					Optional[Union[str, bool]]						=			None
      _lowercase				:					bool						=			False
      _lowercase				:					Optional[Dict]						=			None
      _lowercase				:					Optional[str]						=			None







      def    _lowercase  (    self							)   ->						"DownloadConfig":



        '''simple docstring'''




        return self.__class__(**{k: copy.deepcopy(lowerCAmelCase__							) for k, v in self.__dict__.items()}							)













from typing import TYPE_CHECKING

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


UpperCAmelCase	:	Any		     =       {
    """configuration_convbert""": ["""CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ConvBertConfig""", """ConvBertOnnxConfig"""],
    """tokenization_convbert""": ["""ConvBertTokenizer"""],
}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase	:	List[Any]		     =       ["""ConvBertTokenizerFast"""]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase	:	List[str]		     =       [
         """CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """ConvBertForMaskedLM""",
         """ConvBertForMultipleChoice""",
         """ConvBertForQuestionAnswering""",
         """ConvBertForSequenceClassification""",
         """ConvBertForTokenClassification""",
         """ConvBertLayer""",
         """ConvBertModel""",
         """ConvBertPreTrainedModel""",
         """load_tf_weights_in_convbert""",
     ]


try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase	:	Union[str, Any]		     =       [
         """TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """TFConvBertForMaskedLM""",
         """TFConvBertForMultipleChoice""",
         """TFConvBertForQuestionAnswering""",
         """TFConvBertForSequenceClassification""",
         """TFConvBertForTokenClassification""",
         """TFConvBertLayer""",
         """TFConvBertModel""",
         """TFConvBertPreTrainedModel""",
     ]


if TYPE_CHECKING:
     from .configuration_convbert import CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvBertConfig, ConvBertOnnxConfig
     from .tokenization_convbert import ConvBertTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_convbert_fast import ConvBertTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_convbert import (
              CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
              ConvBertForMaskedLM,
              ConvBertForMultipleChoice,
              ConvBertForQuestionAnswering,
              ConvBertForSequenceClassification,
              ConvBertForTokenClassification,
              ConvBertLayer,
              ConvBertModel,
              ConvBertPreTrainedModel,
              load_tf_weights_in_convbert,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_convbert import (
              TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFConvBertForMaskedLM,
              TFConvBertForMultipleChoice,
              TFConvBertForQuestionAnswering,
              TFConvBertForSequenceClassification,
              TFConvBertForTokenClassification,
              TFConvBertLayer,
              TFConvBertModel,
              TFConvBertPreTrainedModel,
          )


else:
     import sys

     UpperCAmelCase	:	Union[str, Any]		     =       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






