"""simple docstring"""






from typing import TYPE_CHECKING

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


__A       =					{
    'configuration_layoutlmv3': [
        'LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'LayoutLMv3Config',
        'LayoutLMv3OnnxConfig',
    ],
    'processing_layoutlmv3': ['LayoutLMv3Processor'],
    'tokenization_layoutlmv3': ['LayoutLMv3Tokenizer'],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __A       =					['LayoutLMv3TokenizerFast']

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __A       =					[
           'LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST',
           'LayoutLMv3ForQuestionAnswering',
           'LayoutLMv3ForSequenceClassification',
           'LayoutLMv3ForTokenClassification',
           'LayoutLMv3Model',
           'LayoutLMv3PreTrainedModel',
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __A       =					[
           'TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST',
           'TFLayoutLMv3ForQuestionAnswering',
           'TFLayoutLMv3ForSequenceClassification',
           'TFLayoutLMv3ForTokenClassification',
           'TFLayoutLMv3Model',
           'TFLayoutLMv3PreTrainedModel',
       ]

try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __A       =					['LayoutLMv3FeatureExtractor']
       __A       =					['LayoutLMv3ImageProcessor']


if TYPE_CHECKING:
       from .configuration_layoutlmva import (
           LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP,
           LayoutLMvaConfig,
           LayoutLMvaOnnxConfig,
       )
       from .processing_layoutlmva import LayoutLMvaProcessor
       from .tokenization_layoutlmva import LayoutLMvaTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_layoutlmva import (
                  LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
                  LayoutLMvaForQuestionAnswering,
                  LayoutLMvaForSequenceClassification,
                  LayoutLMvaForTokenClassification,
                  LayoutLMvaModel,
                  LayoutLMvaPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_layoutlmva import (
                  TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFLayoutLMvaForQuestionAnswering,
                  TFLayoutLMvaForSequenceClassification,
                  TFLayoutLMvaForTokenClassification,
                  TFLayoutLMvaModel,
                  TFLayoutLMvaPreTrainedModel,
              )

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor
              from .image_processing_layoutlmva import LayoutLMvaImageProcessor

else:
       import sys

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




SCREAMING_SNAKE_CASE__					:     dict[str, float]       =     {
    "joule": 1.0,
    "kilojoule": 1_000,
    "megajoule": 1_000_000,
    "gigajoule": 1_000_000_000,
    "wattsecond": 1.0,
    "watthour": 3_600,
    "kilowatthour": 3_600_000,
    "newtonmeter": 1.0,
    "calorie_nutr": 4_186.8,
    "kilocalorie_nutr": 4_186_800.00,
    "electronvolt": 1.602176634e-19,
    "britishthermalunit_it": 1_055.05_585,
    "footpound": 1.355_818,
}




def    a__    (		snake_case__					:							str						,   snake_case__					:							str						,   snake_case__					:							float     ):
  if to_type not in ENERGY_CONVERSION or from_type not in ENERGY_CONVERSION:
    _UpperCAmelCase		:							Optional[Any]							=     (
        f'''Incorrect \'from_type\' or \'to_type\' value: {from_type!r}, {to_type!r}\n'''
        f'''Valid values are: {", ".join(snake_case__     )}'''
    )
    raise ValueError(snake_case__     )
  return value * ENERGY_CONVERSION[from_type] / ENERGY_CONVERSION[to_type]


if __name__ == "__main__":
 import doctest

 doctest.testmod()





