




from typing import TYPE_CHECKING

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


a					  =     {
    'configuration_electra': ['ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ElectraConfig', 'ElectraOnnxConfig'],
    'tokenization_electra': ['ElectraTokenizer'],
}

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

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a					  =     [
							    'ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'ElectraForCausalLM',
							    'ElectraForMaskedLM',
							    'ElectraForMultipleChoice',
							    'ElectraForPreTraining',
							    'ElectraForQuestionAnswering',
							    'ElectraForSequenceClassification',
							    'ElectraForTokenClassification',
							    'ElectraModel',
							    'ElectraPreTrainedModel',
							    'load_tf_weights_in_electra',
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a					  =     [
							    'TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'TFElectraForMaskedLM',
							    'TFElectraForMultipleChoice',
							    'TFElectraForPreTraining',
							    'TFElectraForQuestionAnswering',
							    'TFElectraForSequenceClassification',
							    'TFElectraForTokenClassification',
							    'TFElectraModel',
							    'TFElectraPreTrainedModel',
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a					  =     [
							    'FlaxElectraForCausalLM',
							    'FlaxElectraForMaskedLM',
							    'FlaxElectraForMultipleChoice',
							    'FlaxElectraForPreTraining',
							    'FlaxElectraForQuestionAnswering',
							    'FlaxElectraForSequenceClassification',
							    'FlaxElectraForTokenClassification',
							    'FlaxElectraModel',
							    'FlaxElectraPreTrainedModel',
							]


if TYPE_CHECKING:
							from .configuration_electra import ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP, ElectraConfig, ElectraOnnxConfig
							from .tokenization_electra import ElectraTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_electra_fast import ElectraTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_electra import (
														    ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
														    ElectraForCausalLM,
														    ElectraForMaskedLM,
														    ElectraForMultipleChoice,
														    ElectraForPreTraining,
														    ElectraForQuestionAnswering,
														    ElectraForSequenceClassification,
														    ElectraForTokenClassification,
														    ElectraModel,
														    ElectraPreTrainedModel,
														    load_tf_weights_in_electra,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_electra import (
														    TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFElectraForMaskedLM,
														    TFElectraForMultipleChoice,
														    TFElectraForPreTraining,
														    TFElectraForQuestionAnswering,
														    TFElectraForSequenceClassification,
														    TFElectraForTokenClassification,
														    TFElectraModel,
														    TFElectraPreTrainedModel,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_electra import (
														    FlaxElectraForCausalLM,
														    FlaxElectraForMaskedLM,
														    FlaxElectraForMultipleChoice,
														    FlaxElectraForPreTraining,
														    FlaxElectraForQuestionAnswering,
														    FlaxElectraForSequenceClassification,
														    FlaxElectraForTokenClassification,
														    FlaxElectraModel,
														    FlaxElectraPreTrainedModel,
														)

else:
							import sys

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






def 			UpperCAmelCase_				(      UpperCAmelCase__  ):
					return " ".join(input_str.split()[::-1]  )


if __name__ == "__main__":
							import doctest

							doctest.testmod()


