






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












from __future__ import annotations





def 				snake_case  (     lowerCamelCase					):




	'''simple docstring'''







	if len(lowerCamelCase					) < 2:
		raise ValueError("""Monogons and Digons are not polygons in the Euclidean space"""					)
	if any(i <= 0 for i in nums					):
		raise ValueError("""All values must be greater than 0"""					)
	__lowercase             =  nums.copy()
	copy_nums.sort()
	return copy_nums[-1] < sum(copy_nums[:-1]					)


if __name__ == "__main__":
						import doctest

						doctest.testmod()

