






from typing import TYPE_CHECKING

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


lowerCamelCase : str						   =       {
    '''configuration_convbert''': ['''CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ConvBertConfig''', '''ConvBertOnnxConfig'''],
    '''tokenization_convbert''': ['''ConvBertTokenizer'''],
}

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

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase : 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:
 lowerCamelCase : List[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

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







from .imports import is_tqdm_available


if is_tqdm_available():
	from tqdm.auto import tqdm as _tqdm

from ..state import PartialState
def       snake_case_		(     lowerCAmelCase_       :						bool = True       ,       *lowerCAmelCase_       :						int       ,       **lowerCAmelCase_       :						List[str]      ):
							if not is_tqdm_available():
														raise ImportError("""Accelerate's `tqdm` module requires `tqdm` to be installed. Please run `pip install tqdm`."""      )
							__lowercase      :							List[str]				=     False
							if main_process_only:
														__lowercase      :							Optional[int]				=     PartialState().local_process_index == 0
							return _tqdm(*lowerCAmelCase_       ,       **lowerCAmelCase_       ,       disable=lowerCAmelCase_      )