




from typing import TYPE_CHECKING

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


snake_case_ : List[Any]    		=       {
    "configuration_rembert": ["REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "RemBertConfig", "RemBertOnnxConfig"]
}

try:
    if not is_sentencepiece_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    snake_case_ : int    		=       ["RemBertTokenizer"]

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    snake_case_ : List[Any]    		=       ["RemBertTokenizerFast"]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    snake_case_ : Optional[int]    		=       [
        "REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
        "RemBertForCausalLM",
        "RemBertForMaskedLM",
        "RemBertForMultipleChoice",
        "RemBertForQuestionAnswering",
        "RemBertForSequenceClassification",
        "RemBertForTokenClassification",
        "RemBertLayer",
        "RemBertModel",
        "RemBertPreTrainedModel",
        "load_tf_weights_in_rembert",
    ]


try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    snake_case_ : int    		=       [
        "TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
        "TFRemBertForCausalLM",
        "TFRemBertForMaskedLM",
        "TFRemBertForMultipleChoice",
        "TFRemBertForQuestionAnswering",
        "TFRemBertForSequenceClassification",
        "TFRemBertForTokenClassification",
        "TFRemBertLayer",
        "TFRemBertModel",
        "TFRemBertPreTrainedModel",
    ]


if TYPE_CHECKING:
    from .configuration_rembert import REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RemBertConfig, RemBertOnnxConfig

    try:
        if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_rembert import RemBertTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_rembert_fast import RemBertTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_rembert import (
            REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
            RemBertForCausalLM,
            RemBertForMaskedLM,
            RemBertForMultipleChoice,
            RemBertForQuestionAnswering,
            RemBertForSequenceClassification,
            RemBertForTokenClassification,
            RemBertLayer,
            RemBertModel,
            RemBertPreTrainedModel,
            load_tf_weights_in_rembert,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_rembert import (
            TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFRemBertForCausalLM,
            TFRemBertForMaskedLM,
            TFRemBertForMultipleChoice,
            TFRemBertForQuestionAnswering,
            TFRemBertForSequenceClassification,
            TFRemBertForTokenClassification,
            TFRemBertLayer,
            TFRemBertModel,
            TFRemBertPreTrainedModel,
        )


else:
    import sys

    snake_case_ : List[str]    		=       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)












def    A						(__A   :    list   ,				__A   :    int   ,				__A   :    int = 0   ,				__A   :    int = 0       )   ->      int:







     """simple docstring"""


     UpperCAmelCase_          =							right or len(__A       ) - 1
     if left > right:
          return -1
     elif list_data[left] == key:
          return left
     elif list_data[right] == key:
          return right
     else:
          return search(__A   ,				__A   ,				left + 1   ,				right - 1       )


if __name__ == "__main__":
    import doctest

    doctest.testmod()






