


"""simple docstring"""


from typing import TYPE_CHECKING

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


UpperCamelCase						:				Dict             =      {
    "configuration_electra": ["ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP", "ElectraConfig", "ElectraOnnxConfig"],
    "tokenization_electra": ["ElectraTokenizer"],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCamelCase						:				Tuple             =      ["ElectraTokenizerFast"]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCamelCase						:				Optional[int]             =      [
	    "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:
	UpperCamelCase						:				List[str]             =      [
	    "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:
	UpperCamelCase						:				int             =      [
	    "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

	UpperCamelCase						:				List[str]             =      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)









"""simple docstring"""


def  __snake_case       (   UpperCamelCase__ ,			UpperCamelCase__ = " "	)      ->		list:







		"""simple docstring"""





		A					       = []

		A					       = 0
		for index, char in enumerate(UpperCamelCase__	):
				if char == separator:
						split_words.append(string[last_index:index]	)
						A					       = index + 1
				elif index + 1 == len(UpperCamelCase__	):
						split_words.append(string[last_index : index + 1]	)
		return split_words


if __name__ == "__main__":
	from doctest import testmod

	testmod()





