
from typing import TYPE_CHECKING

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


__magic_name__            =  {
    'configuration_lxmert': ['LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LxmertConfig'],
    'tokenization_lxmert': ['LxmertTokenizer'],
}

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

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __magic_name__            =  [
         'LxmertEncoder',
         'LxmertForPreTraining',
         'LxmertForQuestionAnswering',
         'LxmertModel',
         'LxmertPreTrainedModel',
         'LxmertVisualFeatureEncoder',
         'LxmertXLayer',
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __magic_name__            =  [
         'TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST',
         'TFLxmertForPreTraining',
         'TFLxmertMainLayer',
         'TFLxmertModel',
         'TFLxmertPreTrainedModel',
         'TFLxmertVisualFeatureEncoder',
     ]


if TYPE_CHECKING:
     from .configuration_lxmert import LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP, LxmertConfig
     from .tokenization_lxmert import LxmertTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_lxmert_fast import LxmertTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_lxmert import (
              LxmertEncoder,
              LxmertForPreTraining,
              LxmertForQuestionAnswering,
              LxmertModel,
              LxmertPreTrainedModel,
              LxmertVisualFeatureEncoder,
              LxmertXLayer,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_lxmert import (
              TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFLxmertForPreTraining,
              TFLxmertMainLayer,
              TFLxmertModel,
              TFLxmertPreTrainedModel,
              TFLxmertVisualFeatureEncoder,
          )

else:
     import sys

     __magic_name__            =  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





from __future__ import annotations





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase							):
    if (voltage, current, resistance).count(0							) != 1:
        raise ValueError("One and only one argument must be 0"							)
    if resistance < 0:
        raise ValueError("Resistance cannot be negative"							)
    if voltage == 0:
        return {"voltage": float(current * resistance							)}
    elif current == 0:
        return {"current": voltage / resistance}
    elif resistance == 0:
        return {"resistance": voltage / current}
    else:
        raise ValueError("Exactly one argument must be 0"							)


if __name__ == "__main__":
     import doctest

     doctest.testmod()



