






from collections import namedtuple

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

_snake_case					   =     {
    """atm""": from_to(1, 1),
    """pascal""": from_to(0.0_00_00_98, 10_1325),
    """bar""": from_to(0.98_69_23, 1.0_13_25),
    """kilopascal""": from_to(0.00_98_69_23, 101.325),
    """megapascal""": from_to(9.8_69_23, 0.10_13_25),
    """psi""": from_to(0.06_80_46, 14.69_59),
    """inHg""": from_to(0.0_33_42_11, 29.92_13),
    """torr""": from_to(0.00_13_15_79, 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()









"""simple docstring"""




# this script builds a small sample spm file tests/fixtures/test_sentencepiece_no_bos.model, with features needed by pegasus

# 1. pip install sentencepiece
#
# 2. wget https://raw.githubusercontent.com/google/sentencepiece/master/data/botchan.txt

# 3. build
import sentencepiece as spm

# pegasus:
# 1. no bos
# 2. eos_id is 1
# 3. unk_id is 2
# build a sample spm file accordingly
spm.SentencePieceTrainer.train(
    '--input=botchan.txt --model_prefix=test_sentencepiece_no_bos --bos_id=-1 --unk_id=2  --eos_id=1  --vocab_size=1000'
)

# 4. now update the fixture
# mv test_sentencepiece_no_bos.model ../../tests/fixtures/

