from typing import TYPE_CHECKING

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


lowercase_  							=			{
    "configuration_camembert": ["CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "CamembertConfig", "CamembertOnnxConfig"],
}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowercase_  							=			["CamembertTokenizer"]

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

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowercase_  							=			[
         "CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
         "CamembertForCausalLM",
         "CamembertForMaskedLM",
         "CamembertForMultipleChoice",
         "CamembertForQuestionAnswering",
         "CamembertForSequenceClassification",
         "CamembertForTokenClassification",
         "CamembertModel",
         "CamembertPreTrainedModel",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowercase_  							=			[
         "TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
         "TFCamembertForCausalLM",
         "TFCamembertForMaskedLM",
         "TFCamembertForMultipleChoice",
         "TFCamembertForQuestionAnswering",
         "TFCamembertForSequenceClassification",
         "TFCamembertForTokenClassification",
         "TFCamembertModel",
         "TFCamembertPreTrainedModel",
     ]


if TYPE_CHECKING:
     from .configuration_camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CamembertConfig, CamembertOnnxConfig

     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_camembert import CamembertTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_camembert_fast import CamembertTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_camembert import (
              CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
              CamembertForCausalLM,
              CamembertForMaskedLM,
              CamembertForMultipleChoice,
              CamembertForQuestionAnswering,
              CamembertForSequenceClassification,
              CamembertForTokenClassification,
              CamembertModel,
              CamembertPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_camembert import (
              TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFCamembertForCausalLM,
              TFCamembertForMaskedLM,
              TFCamembertForMultipleChoice,
              TFCamembertForQuestionAnswering,
              TFCamembertForSequenceClassification,
              TFCamembertForTokenClassification,
              TFCamembertModel,
              TFCamembertPreTrainedModel,
          )

else:
     import sys

     lowercase_  							=			_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)



