
from __future__ import annotations


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__		):
      UpperCamelCase__    :   Optional[int]					=				list(range(len(UpperCamelCase__		)		)		)
      UpperCamelCase__    :   Optional[int]					=				[v / w for v, w in zip(UpperCamelCase__ ,		UpperCamelCase__		)]
      index.sort(key=lambda UpperCamelCase__		: ratio[i] ,		reverse=UpperCamelCase__		)

      UpperCamelCase__    :   float					=				0
      UpperCamelCase__    :   list[float]					=				[0] * len(UpperCamelCase__		)
      for i in index:
            if weight[i] <= capacity:
                  UpperCamelCase__    :   int					=				1
                  max_value += value[i]
                  capacity -= weight[i]
            else:
                  UpperCamelCase__    :   Any					=				capacity / weight[i]
                  max_value += value[i] * capacity / weight[i]
                  break

      return max_value, fractions


if __name__ == "__main__":
    import doctest

    doctest.testmod()




from typing import TYPE_CHECKING

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


lowerCamelCase      ={
    "configuration_mobilebert": [
        "MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "MobileBertConfig",
        "MobileBertOnnxConfig",
    ],
    "tokenization_mobilebert": ["MobileBertTokenizer"],
}

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

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase      =[
        "MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
        "MobileBertForMaskedLM",
        "MobileBertForMultipleChoice",
        "MobileBertForNextSentencePrediction",
        "MobileBertForPreTraining",
        "MobileBertForQuestionAnswering",
        "MobileBertForSequenceClassification",
        "MobileBertForTokenClassification",
        "MobileBertLayer",
        "MobileBertModel",
        "MobileBertPreTrainedModel",
        "load_tf_weights_in_mobilebert",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase      =[
        "TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
        "TFMobileBertForMaskedLM",
        "TFMobileBertForMultipleChoice",
        "TFMobileBertForNextSentencePrediction",
        "TFMobileBertForPreTraining",
        "TFMobileBertForQuestionAnswering",
        "TFMobileBertForSequenceClassification",
        "TFMobileBertForTokenClassification",
        "TFMobileBertMainLayer",
        "TFMobileBertModel",
        "TFMobileBertPreTrainedModel",
    ]


if TYPE_CHECKING:
    from .configuration_mobilebert import (
        MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
        MobileBertConfig,
        MobileBertOnnxConfig,
    )
    from .tokenization_mobilebert import MobileBertTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_mobilebert_fast import MobileBertTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_mobilebert import (
            MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
            MobileBertForMaskedLM,
            MobileBertForMultipleChoice,
            MobileBertForNextSentencePrediction,
            MobileBertForPreTraining,
            MobileBertForQuestionAnswering,
            MobileBertForSequenceClassification,
            MobileBertForTokenClassification,
            MobileBertLayer,
            MobileBertModel,
            MobileBertPreTrainedModel,
            load_tf_weights_in_mobilebert,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_mobilebert import (
            TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFMobileBertForMaskedLM,
            TFMobileBertForMultipleChoice,
            TFMobileBertForNextSentencePrediction,
            TFMobileBertForPreTraining,
            TFMobileBertForQuestionAnswering,
            TFMobileBertForSequenceClassification,
            TFMobileBertForTokenClassification,
            TFMobileBertMainLayer,
            TFMobileBertModel,
            TFMobileBertPreTrainedModel,
        )

else:
    import sys

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


