






'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


__A							:							Tuple  							=		{"""configuration_reformer""": ["""REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ReformerConfig"""]}

try:
 if not is_sentencepiece_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A							:							Union[str, Any]  							=		["""ReformerTokenizer"""]

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A							:							Union[str, Any]  							=		["""ReformerTokenizerFast"""]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A							:							List[str]  							=		[
     """REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """ReformerAttention""",
     """ReformerForMaskedLM""",
     """ReformerForQuestionAnswering""",
     """ReformerForSequenceClassification""",
     """ReformerLayer""",
     """ReformerModel""",
     """ReformerModelWithLMHead""",
     """ReformerPreTrainedModel""",
 ]


if TYPE_CHECKING:
 from .configuration_reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig

 try:
  if not is_sentencepiece_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_reformer import ReformerTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_reformer_fast import ReformerTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_reformer import (
      REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      ReformerAttention,
      ReformerForMaskedLM,
      ReformerForQuestionAnswering,
      ReformerForSequenceClassification,
      ReformerLayer,
      ReformerModel,
      ReformerModelWithLMHead,
      ReformerPreTrainedModel,
  )

else:
 import sys

 __A							:							Optional[Any]  							=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)









'''simple docstring'''





def    lowerCamelCase_     (    lowercase__				,     lowercase__				,     lowercase__):
 return round(float(moles / volume) * nfactor)






def    lowerCamelCase_     (    lowercase__				,     lowercase__				,     lowercase__):
 return round(float((moles * 0.0_821 * temperature) / (volume)))






def    lowerCamelCase_     (    lowercase__				,     lowercase__				,     lowercase__):
 return round(float((moles * 0.0_821 * temperature) / (pressure)))






def    lowerCamelCase_     (    lowercase__				,     lowercase__				,     lowercase__):
 return round(float((pressure * volume) / (0.0_821 * moles)))


if __name__ == "__main__":
 import doctest

 doctest.testmod()

