
"""simple docstring"""


from typing import TYPE_CHECKING

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


A_    							=					{"""configuration_xlnet""": ["""XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLNetConfig"""]}

try:
						if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_    							=					["""XLNetTokenizer"""]

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_    							=					["""XLNetTokenizerFast"""]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_    							=					[
						    """XLNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """XLNetForMultipleChoice""",
						    """XLNetForQuestionAnswering""",
						    """XLNetForQuestionAnsweringSimple""",
						    """XLNetForSequenceClassification""",
						    """XLNetForTokenClassification""",
						    """XLNetLMHeadModel""",
						    """XLNetModel""",
						    """XLNetPreTrainedModel""",
						    """load_tf_weights_in_xlnet""",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_    							=					[
						    """TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """TFXLNetForMultipleChoice""",
						    """TFXLNetForQuestionAnsweringSimple""",
						    """TFXLNetForSequenceClassification""",
						    """TFXLNetForTokenClassification""",
						    """TFXLNetLMHeadModel""",
						    """TFXLNetMainLayer""",
						    """TFXLNetModel""",
						    """TFXLNetPreTrainedModel""",
						]


if TYPE_CHECKING:
						from .configuration_xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNetConfig

						try:
												if not is_sentencepiece_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_xlnet import XLNetTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_xlnet_fast import XLNetTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_xlnet import (
												    XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
												    XLNetForMultipleChoice,
												    XLNetForQuestionAnswering,
												    XLNetForQuestionAnsweringSimple,
												    XLNetForSequenceClassification,
												    XLNetForTokenClassification,
												    XLNetLMHeadModel,
												    XLNetModel,
												    XLNetPreTrainedModel,
												    load_tf_weights_in_xlnet,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_xlnet import (
												    TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFXLNetForMultipleChoice,
												    TFXLNetForQuestionAnsweringSimple,
												    TFXLNetForSequenceClassification,
												    TFXLNetForTokenClassification,
												    TFXLNetLMHeadModel,
												    TFXLNetMainLayer,
												    TFXLNetModel,
												    TFXLNetPreTrainedModel,
												)

else:
						import sys

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





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase     ,		__lowerCAmelCase     ,		__lowerCAmelCase				):
    return round(float(moles / volume				) * nfactor				)





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase     ,		__lowerCAmelCase     ,		__lowerCAmelCase				):
    return round(float((moles * 0.0_821 * temperature) / (volume)				)				)





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase     ,		__lowerCAmelCase     ,		__lowerCAmelCase				):
    return round(float((moles * 0.0_821 * temperature) / (pressure)				)				)





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase     ,		__lowerCAmelCase     ,		__lowerCAmelCase				):
    return round(float((pressure * volume) / (0.0_821 * moles)				)				)


if __name__ == "__main__":
     import doctest

     doctest.testmod()



