

'''simple docstring'''




from typing import TYPE_CHECKING

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


_a			:	Dict								=  {
    'configuration_bart': ['BART_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BartConfig', 'BartOnnxConfig'],
    'tokenization_bart': ['BartTokenizer'],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _a			:	str								=  ['BartTokenizerFast']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _a			:	Any								=  [
      'BART_PRETRAINED_MODEL_ARCHIVE_LIST',
      'BartForCausalLM',
      'BartForConditionalGeneration',
      'BartForQuestionAnswering',
      'BartForSequenceClassification',
      'BartModel',
      'BartPretrainedModel',
      'PretrainedBartModel',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _a			:	Any								=  [
      'TFBartForConditionalGeneration',
      'TFBartForSequenceClassification',
      'TFBartModel',
      'TFBartPretrainedModel',
  ]

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _a			:	List[Any]								=  [
      'FlaxBartDecoderPreTrainedModel',
      'FlaxBartForCausalLM',
      'FlaxBartForConditionalGeneration',
      'FlaxBartForQuestionAnswering',
      'FlaxBartForSequenceClassification',
      'FlaxBartModel',
      'FlaxBartPreTrainedModel',
  ]

if TYPE_CHECKING:
  from .configuration_bart import BART_PRETRAINED_CONFIG_ARCHIVE_MAP, BartConfig, BartOnnxConfig
  from .tokenization_bart import BartTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_bart_fast import BartTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_bart import (
        BART_PRETRAINED_MODEL_ARCHIVE_LIST,
        BartForCausalLM,
        BartForConditionalGeneration,
        BartForQuestionAnswering,
        BartForSequenceClassification,
        BartModel,
        BartPretrainedModel,
        PretrainedBartModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_bart import (
        TFBartForConditionalGeneration,
        TFBartForSequenceClassification,
        TFBartModel,
        TFBartPretrainedModel,
    )

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_bart import (
        FlaxBartDecoderPreTrainedModel,
        FlaxBartForCausalLM,
        FlaxBartForConditionalGeneration,
        FlaxBartForQuestionAnswering,
        FlaxBartForSequenceClassification,
        FlaxBartModel,
        FlaxBartPreTrainedModel,
    )

else:
  import sys

  _a			:	Tuple								=  _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





'''simple docstring'''




from collections import namedtuple

_a			:	int								=  namedtuple('from_to', 'from_ to')

_a			:	Dict								=  {
    'atm': from_to(1, 1),
    'pascal': from_to(0.0000098, 101_325),
    'bar': from_to(0.986923, 1.01325),
    'kilopascal': from_to(0.00986923, 101.325),
    'megapascal': from_to(9.86923, 0.101325),
    'psi': from_to(0.068046, 14.6959),
    'inHg': from_to(0.0334211, 29.9213),
    'torr': from_to(0.00131579, 760),
}


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


if __name__ == "__main__":
  import doctest

  doctest.testmod()


