





"""simple docstring"""




from typing import TYPE_CHECKING

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


UpperCamelCase_	 ={
    """configuration_deberta_v2""": ["""DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DebertaV2Config""", """DebertaV2OnnxConfig"""],
    """tokenization_deberta_v2""": ["""DebertaV2Tokenizer"""],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCamelCase_	 =["""DebertaV2TokenizerFast"""]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCamelCase_	 =[
						    """TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """TFDebertaV2ForMaskedLM""",
						    """TFDebertaV2ForQuestionAnswering""",
						    """TFDebertaV2ForSequenceClassification""",
						    """TFDebertaV2ForTokenClassification""",
						    """TFDebertaV2Model""",
						    """TFDebertaV2PreTrainedModel""",
						]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCamelCase_	 =[
						    """DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """DebertaV2ForMaskedLM""",
						    """DebertaV2ForMultipleChoice""",
						    """DebertaV2ForQuestionAnswering""",
						    """DebertaV2ForSequenceClassification""",
						    """DebertaV2ForTokenClassification""",
						    """DebertaV2Model""",
						    """DebertaV2PreTrainedModel""",
						]


if TYPE_CHECKING:
						from .configuration_deberta_va import (
						    DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    DebertaVaConfig,
						    DebertaVaOnnxConfig,
						)
						from .tokenization_deberta_va import DebertaVaTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_deberta_va_fast import DebertaVaTokenizerFast

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_deberta_va import (
												    TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFDebertaVaForMaskedLM,
												    TFDebertaVaForQuestionAnswering,
												    TFDebertaVaForSequenceClassification,
												    TFDebertaVaForTokenClassification,
												    TFDebertaVaModel,
												    TFDebertaVaPreTrainedModel,
												)

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_deberta_va import (
												    DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
												    DebertaVaForMaskedLM,
												    DebertaVaForMultipleChoice,
												    DebertaVaForQuestionAnswering,
												    DebertaVaForSequenceClassification,
												    DebertaVaForTokenClassification,
												    DebertaVaModel,
												    DebertaVaPreTrainedModel,
												)

else:
						import sys

						UpperCamelCase_	 =_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)










"""simple docstring"""




def 			a_							(				_lowercase  ):
						_UpperCamelCase     :   List[str]      =       []
						_UpperCamelCase     :   Union[str, Any]      =       1

						for i in range(len(_lowercase  )  ):
												if i + 1 < len(_lowercase  ) and text[i] == text[i + 1]:
																		count += 1
												else:
																		encoded.append((text[i], count)  )
																		_UpperCamelCase     :   int      =       1

						return encoded





def 			a_							(				_lowercase  ):
						return "".join(char * length for char, length in encoded  )


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

						testmod(name="""run_length_encode""", verbose=True)
						testmod(name="""run_length_decode""", verbose=True)



