





'''simple docstring'''


from typing import TYPE_CHECKING

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


lowerCAmelCase_   :   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:
		lowerCAmelCase_   :   Optional[Any]       =					["ElectraTokenizerFast"]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCAmelCase_   :   List[Any]       =					[
		    "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:
		lowerCAmelCase_   :   Union[str, Any]       =					[
		    "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:
		lowerCAmelCase_   :   Union[str, Any]       =					[
		    "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

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










'''simple docstring'''


def    _lowerCamelCase  (__lowerCamelCase  :     str     )   ->    bool:
			a__     = 0
			for ch in input_str:
						a__     = ord(__lowerCamelCase     )
						a__     = pow(2					,  __lowerCamelCase     )

						# If we already turned on bit for current character's unicode
						if bitmap >> ch_unicode & 1 == 1:
									return False
						bitmap |= ch_bit_index_on
			return True


if __name__ == "__main__":
		import doctest

		doctest.testmod()



