
"""simple docstring"""





from typing import TYPE_CHECKING

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


_a									=				{
    'configuration_bart': ['BART_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BartConfig', 'BartOnnxConfig'],
    'tokenization_bart': ['BartTokenizer'],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _a									=				['BartTokenizerFast']

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _a									=				[
        'BART_PRETRAINED_MODEL_ARCHIVE_LIST',
        'BartForCausalLM',
        'BartForConditionalGeneration',
        'BartForQuestionAnswering',
        'BartForSequenceClassification',
        'BartModel',
        'BartPretrainedModel',
        'PretrainedBartModel',
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _a									=				[
        'TFBartForConditionalGeneration',
        'TFBartForSequenceClassification',
        'TFBartModel',
        'TFBartPretrainedModel',
    ]

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _a									=				[
        'FlaxBartDecoderPreTrainedModel',
        'FlaxBartForCausalLM',
        'FlaxBartForConditionalGeneration',
        'FlaxBartForQuestionAnswering',
        'FlaxBartForSequenceClassification',
        'FlaxBartModel',
        'FlaxBartPreTrainedModel',
    ]

if TYPE_CHECKING:
    from .configuration_bart import BART_PRETRAINED_CONFIG_ARCHIVE_MAP, BartConfig, BartOnnxConfig
    from .tokenization_bart import BartTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_bart_fast import BartTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_bart import (
            BART_PRETRAINED_MODEL_ARCHIVE_LIST,
            BartForCausalLM,
            BartForConditionalGeneration,
            BartForQuestionAnswering,
            BartForSequenceClassification,
            BartModel,
            BartPretrainedModel,
            PretrainedBartModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_bart import (
            TFBartForConditionalGeneration,
            TFBartForSequenceClassification,
            TFBartModel,
            TFBartPretrainedModel,
        )

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_bart import (
            FlaxBartDecoderPreTrainedModel,
            FlaxBartForCausalLM,
            FlaxBartForConditionalGeneration,
            FlaxBartForQuestionAnswering,
            FlaxBartForSequenceClassification,
            FlaxBartModel,
            FlaxBartPreTrainedModel,
        )

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)



