






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


UpperCAmelCase__            =	{
    "configuration_flaubert": ["FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "FlaubertConfig", "FlaubertOnnxConfig"],
    "tokenization_flaubert": ["FlaubertTokenizer"],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase__            =	[
         "FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
         "FlaubertForMultipleChoice",
         "FlaubertForQuestionAnswering",
         "FlaubertForQuestionAnsweringSimple",
         "FlaubertForSequenceClassification",
         "FlaubertForTokenClassification",
         "FlaubertModel",
         "FlaubertWithLMHeadModel",
         "FlaubertPreTrainedModel",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase__            =	[
         "TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
         "TFFlaubertForMultipleChoice",
         "TFFlaubertForQuestionAnsweringSimple",
         "TFFlaubertForSequenceClassification",
         "TFFlaubertForTokenClassification",
         "TFFlaubertModel",
         "TFFlaubertPreTrainedModel",
         "TFFlaubertWithLMHeadModel",
     ]


if TYPE_CHECKING:
     from .configuration_flaubert import FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, FlaubertConfig, FlaubertOnnxConfig
     from .tokenization_flaubert import FlaubertTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flaubert import (
              FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
              FlaubertForMultipleChoice,
              FlaubertForQuestionAnswering,
              FlaubertForQuestionAnsweringSimple,
              FlaubertForSequenceClassification,
              FlaubertForTokenClassification,
              FlaubertModel,
              FlaubertPreTrainedModel,
              FlaubertWithLMHeadModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_flaubert import (
              TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFFlaubertForMultipleChoice,
              TFFlaubertForQuestionAnsweringSimple,
              TFFlaubertForSequenceClassification,
              TFFlaubertForTokenClassification,
              TFFlaubertModel,
              TFFlaubertPreTrainedModel,
              TFFlaubertWithLMHeadModel,
          )

else:
     import sys

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










from collections import namedtuple

UpperCAmelCase__            =	namedtuple("from_to", "from_ to")

UpperCAmelCase__            =	{
    "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 		_a					(   a				:float			,							a				:str			,							a				:str				)							->    float:
 if from_type not in PRESSURE_CONVERSION:
  raise ValueError(
      F"""Invalid 'from_type' value: {from_type!r}  Supported values are:\n"""
      + ''', '''.join(a				)				)
 if to_type not in PRESSURE_CONVERSION:
  raise ValueError(
      F"""Invalid 'to_type' value: {to_type!r}.  Supported values are:\n"""
      + ''', '''.join(a				)				)
 return (
     value * PRESSURE_CONVERSION[from_type].from_ * PRESSURE_CONVERSION[to_type].to
 )


if __name__ == "__main__":
     import doctest

     doctest.testmod()


