






from collections import namedtuple

UpperCamelCase												=      namedtuple('''from_to''', '''from_ to''')

UpperCamelCase												=      {
    'millimeter': 'mm',
    'centimeter': 'cm',
    'meter': 'm',
    'kilometer': 'km',
    'inch': 'in',
    'inche': 'in',  # Trailing 's' has been stripped off
    'feet': 'ft',
    'foot': 'ft',
    'yard': 'yd',
    'mile': 'mi',
}

UpperCamelCase												=      {
    'mm': from_to(0.001, 1_000),
    'cm': from_to(0.01, 100),
    'm': from_to(1, 1),
    'km': from_to(1_000, 0.001),
    'in': from_to(0.0_254, 39.3_701),
    'ft': from_to(0.3_048, 3.28_084),
    'yd': from_to(0.9_144, 1.09_361),
    'mi': from_to(1_609.34, 0.000_621_371),
}

def 			__lowerCamelCase     (						snake_case__			,snake_case__			,snake_case__						)				->    float:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE       =    from_type.lower().rstrip("""s"""						)
						_SCREAMING_SNAKE_CASE       =    TYPE_CONVERSION.get(a_			,a_						)
						_SCREAMING_SNAKE_CASE       =    to_type.lower().rstrip("""s"""						)
						_SCREAMING_SNAKE_CASE       =    TYPE_CONVERSION.get(a_			,a_						)
						if new_from not in METRIC_CONVERSION:
												_SCREAMING_SNAKE_CASE       =    (
												    F'Invalid \'from_type\' value: {from_type!r}.\n'
												    F'Conversion abbreviations are: {", ".join(a_						)}'
												)
												raise ValueError(a_						)
						if new_to not in METRIC_CONVERSION:
												_SCREAMING_SNAKE_CASE       =    (
												    F'Invalid \'to_type\' value: {to_type!r}.\n'
												    F'Conversion abbreviations are: {", ".join(a_						)}'
												)
												raise ValueError(a_						)
						return value * METRIC_CONVERSION[new_from].from_ * METRIC_CONVERSION[new_to].to


if __name__ == "__main__":
		import doctest

		doctest.testmod()






'''simple docstring'''





from typing import TYPE_CHECKING

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


UpperCamelCase__    :     List[Any]			  =   {
    'configuration_layoutlm': ['LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LayoutLMConfig', 'LayoutLMOnnxConfig'],
    'tokenization_layoutlm': ['LayoutLMTokenizer'],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCamelCase__    :     Any			  =   ['LayoutLMTokenizerFast']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCamelCase__    :     Optional[Any]			  =   [
      'LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST',
      'LayoutLMForMaskedLM',
      'LayoutLMForSequenceClassification',
      'LayoutLMForTokenClassification',
      'LayoutLMForQuestionAnswering',
      'LayoutLMModel',
      'LayoutLMPreTrainedModel',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCamelCase__    :     str			  =   [
      'TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST',
      'TFLayoutLMForMaskedLM',
      'TFLayoutLMForSequenceClassification',
      'TFLayoutLMForTokenClassification',
      'TFLayoutLMForQuestionAnswering',
      'TFLayoutLMMainLayer',
      'TFLayoutLMModel',
      'TFLayoutLMPreTrainedModel',
  ]


if TYPE_CHECKING:
  from .configuration_layoutlm import LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMConfig, LayoutLMOnnxConfig
  from .tokenization_layoutlm import LayoutLMTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_layoutlm_fast import LayoutLMTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_layoutlm import (
        LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
        LayoutLMForMaskedLM,
        LayoutLMForQuestionAnswering,
        LayoutLMForSequenceClassification,
        LayoutLMForTokenClassification,
        LayoutLMModel,
        LayoutLMPreTrainedModel,
    )
  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_layoutlm import (
        TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFLayoutLMForMaskedLM,
        TFLayoutLMForQuestionAnswering,
        TFLayoutLMForSequenceClassification,
        TFLayoutLMForTokenClassification,
        TFLayoutLMMainLayer,
        TFLayoutLMModel,
        TFLayoutLMPreTrainedModel,
    )

else:
  import sys

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