
'''simple docstring'''





from typing import TYPE_CHECKING

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


A							     = {
    "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:
     A							     = ["DebertaV2TokenizerFast"]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     A							     = [
         "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:
     A							     = [
         "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

     A							     = _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)



