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  _lowerCamelCase					(   a_				: str						,			a_				: str):
			lowerCamelCase						:List[str]						  =     len(a_)
			lowerCamelCase						:List[str]						  =     len(a_)
			lowerCamelCase						:int						  =     [[False for _ in range(m + 1)] for _ in range(n + 1)]
			lowerCamelCase						:Optional[Any]						  =     True
			for i in range(a_):
						for j in range(m + 1):
									if dp[i][j]:
												if j < m and a[i].upper() == b[j]:
															lowerCamelCase						:Any						  =     True
												if a[i].islower():
															lowerCamelCase						:List[str]						  =     True
			return dp[n][m]


if __name__ == "__main__":
			import doctest

			doctest.testmod()



