

from collections import namedtuple

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

SCREAMING_SNAKE_CASE_						=					{
    "atm": from_to(1, 1),
    "pascal": from_to(0.000_0098, 101325),
    "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 							__SCREAMING_SNAKE_CASE							( lowerCAmelCase:     Tuple     ,					lowerCAmelCase:     Tuple     ,					lowerCAmelCase:     str       )      ->				Tuple:
				if from_type not in PRESSURE_CONVERSION:
								raise ValueError(
								    F'Invalid \'from_type\' value: {from_type!r}  Supported values are:\n'
								    + ", ".join(_lowerCamelCase       )       )
				if to_type not in PRESSURE_CONVERSION:
								raise ValueError(
								    F'Invalid \'to_type\' value: {to_type!r}.  Supported values are:\n'
								    + ", ".join(_lowerCamelCase       )       )
				return (
				    value * PRESSURE_CONVERSION[from_type].from_ * PRESSURE_CONVERSION[to_type].to
				)


if __name__ == "__main__":
					import doctest

					doctest.testmod()










"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A_			: Dict				=			{"configuration_qdqbert": ["QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "QDQBertConfig"]}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A_			: Tuple				=			[
          "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

      A_			: Tuple				=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

