





from typing import TYPE_CHECKING

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


snake_case								=							{
    """configuration_albert""": ["""ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """AlbertConfig""", """AlbertOnnxConfig"""],
}

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

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

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      snake_case								=							[
          """ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """AlbertForMaskedLM""",
          """AlbertForMultipleChoice""",
          """AlbertForPreTraining""",
          """AlbertForQuestionAnswering""",
          """AlbertForSequenceClassification""",
          """AlbertForTokenClassification""",
          """AlbertModel""",
          """AlbertPreTrainedModel""",
          """load_tf_weights_in_albert""",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      snake_case								=							[
          """TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """TFAlbertForMaskedLM""",
          """TFAlbertForMultipleChoice""",
          """TFAlbertForPreTraining""",
          """TFAlbertForQuestionAnswering""",
          """TFAlbertForSequenceClassification""",
          """TFAlbertForTokenClassification""",
          """TFAlbertMainLayer""",
          """TFAlbertModel""",
          """TFAlbertPreTrainedModel""",
      ]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      snake_case								=							[
          """FlaxAlbertForMaskedLM""",
          """FlaxAlbertForMultipleChoice""",
          """FlaxAlbertForPreTraining""",
          """FlaxAlbertForQuestionAnswering""",
          """FlaxAlbertForSequenceClassification""",
          """FlaxAlbertForTokenClassification""",
          """FlaxAlbertModel""",
          """FlaxAlbertPreTrainedModel""",
      ]

if TYPE_CHECKING:
      from .configuration_albert import ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, AlbertConfig, AlbertOnnxConfig

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_albert import AlbertTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_albert_fast import AlbertTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_albert import (
                ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                AlbertForMaskedLM,
                AlbertForMultipleChoice,
                AlbertForPreTraining,
                AlbertForQuestionAnswering,
                AlbertForSequenceClassification,
                AlbertForTokenClassification,
                AlbertModel,
                AlbertPreTrainedModel,
                load_tf_weights_in_albert,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_albert import (
                TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFAlbertForMaskedLM,
                TFAlbertForMultipleChoice,
                TFAlbertForPreTraining,
                TFAlbertForQuestionAnswering,
                TFAlbertForSequenceClassification,
                TFAlbertForTokenClassification,
                TFAlbertMainLayer,
                TFAlbertModel,
                TFAlbertPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_albert import (
                FlaxAlbertForMaskedLM,
                FlaxAlbertForMultipleChoice,
                FlaxAlbertForPreTraining,
                FlaxAlbertForQuestionAnswering,
                FlaxAlbertForSequenceClassification,
                FlaxAlbertForTokenClassification,
                FlaxAlbertModel,
                FlaxAlbertPreTrainedModel,
            )
else:
      import sys

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






'''simple docstring'''




from __future__ import annotations






def 			_lowerCAmelCase     (    __snake_case    :					list[int]					,				__snake_case    :					list[int]					,				__snake_case    :					int     )	->	tuple[float, list[float]]:
     __A		:					int						=			list(range(len(__snake_case     )     )     )
     __A		:					Optional[Any]						=			[v / w for v, w in zip(__snake_case					,				__snake_case     )]
     index.sort(key=lambda __snake_case     : ratio[i]					,				reverse=__snake_case     )

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

     return max_value, fractions


if __name__ == "__main__":
  import doctest

  doctest.testmod()