






"""simple docstring"""


from collections import namedtuple

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

lowercase_								=	{
    'atm': from_to(1, 1),
    'pascal': from_to(0.000_0098, 10_1325),
    'bar': from_to(0.98_6923, 1.0_1325),
    'kilopascal': from_to(0.0098_6923, 101.325),
    'megapascal': from_to(9.8_6923, 0.10_1325),
    'psi': from_to(0.06_8046, 14.6959),
    'inHg': from_to(0.033_4211, 29.9213),
    'torr': from_to(0.0013_1579, 760),
}



def    lowerCAmelCase							(       __UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase					):




  """simple docstring"""







  if from_type not in PRESSURE_CONVERSION:
    raise ValueError(
        f'Invalid \'from_type\' value: {from_type!r}  Supported values are:\n'
        + ''', '''.join(__UpperCamelCase					)					)
  if to_type not in PRESSURE_CONVERSION:
    raise ValueError(
        f'Invalid \'to_type\' value: {to_type!r}.  Supported values are:\n'
        + ''', '''.join(__UpperCamelCase					)					)
  return (
      value * PRESSURE_CONVERSION[from_type].from_ * PRESSURE_CONVERSION[to_type].to
  )


if __name__ == "__main__":
       import doctest

       doctest.testmod()








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


snake_case__  				=			{'configuration_qdqbert': ['QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'QDQBertConfig']}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			snake_case__  				=			[
			    'QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'QDQBertForMaskedLM',
			    'QDQBertForMultipleChoice',
			    'QDQBertForNextSentencePrediction',
			    'QDQBertForQuestionAnswering',
			    'QDQBertForSequenceClassification',
			    'QDQBertForTokenClassification',
			    'QDQBertLayer',
			    'QDQBertLMHeadModel',
			    'QDQBertModel',
			    'QDQBertPreTrainedModel',
			    'load_tf_weights_in_qdqbert',
			]


if TYPE_CHECKING:
			from .configuration_qdqbert import QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, QDQBertConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_qdqbert import (
						    QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    QDQBertForMaskedLM,
						    QDQBertForMultipleChoice,
						    QDQBertForNextSentencePrediction,
						    QDQBertForQuestionAnswering,
						    QDQBertForSequenceClassification,
						    QDQBertForTokenClassification,
						    QDQBertLayer,
						    QDQBertLMHeadModel,
						    QDQBertModel,
						    QDQBertPreTrainedModel,
						    load_tf_weights_in_qdqbert,
						)


else:
			import sys

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


