



def        __lowerCAmelCase		(	__lowerCamelCase						:     int					, __lowerCamelCase						:     float					, __lowerCamelCase						:     float						)	->    float:
					return round(float(moles / volume						) * nfactor						)






def        __lowerCAmelCase		(	__lowerCamelCase						:     float					, __lowerCamelCase						:     float					, __lowerCamelCase						:     float						)	->    float:
					return round(float((moles * 0.0821 * temperature) / (volume)						)						)






def        __lowerCAmelCase		(	__lowerCamelCase						:     float					, __lowerCamelCase						:     float					, __lowerCamelCase						:     float						)	->    float:
					return round(float((moles * 0.0821 * temperature) / (pressure)						)						)






def        __lowerCAmelCase		(	__lowerCamelCase						:     float					, __lowerCamelCase						:     float					, __lowerCamelCase						:     float						)	->    float:
					return round(float((pressure * volume) / (0.0821 * moles)						)						)


if __name__ == "__main__":
				import doctest

				doctest.testmod()









from typing import TYPE_CHECKING

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


lowercase_													=      {
    '''configuration_albert''': ['''ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''AlbertConfig''', '''AlbertOnnxConfig'''],
}

try:
				if not is_sentencepiece_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase_													=      ['''AlbertTokenizer''']

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase_													=      ['''AlbertTokenizerFast''']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase_													=      [
				    '''ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''AlbertForMaskedLM''',
				    '''AlbertForMultipleChoice''',
				    '''AlbertForPreTraining''',
				    '''AlbertForQuestionAnswering''',
				    '''AlbertForSequenceClassification''',
				    '''AlbertForTokenClassification''',
				    '''AlbertModel''',
				    '''AlbertPreTrainedModel''',
				    '''load_tf_weights_in_albert''',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase_													=      [
				    '''TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''TFAlbertForMaskedLM''',
				    '''TFAlbertForMultipleChoice''',
				    '''TFAlbertForPreTraining''',
				    '''TFAlbertForQuestionAnswering''',
				    '''TFAlbertForSequenceClassification''',
				    '''TFAlbertForTokenClassification''',
				    '''TFAlbertMainLayer''',
				    '''TFAlbertModel''',
				    '''TFAlbertPreTrainedModel''',
				]

try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase_													=      [
				    '''FlaxAlbertForMaskedLM''',
				    '''FlaxAlbertForMultipleChoice''',
				    '''FlaxAlbertForPreTraining''',
				    '''FlaxAlbertForQuestionAnswering''',
				    '''FlaxAlbertForSequenceClassification''',
				    '''FlaxAlbertForTokenClassification''',
				    '''FlaxAlbertModel''',
				    '''FlaxAlbertPreTrainedModel''',
				]

if TYPE_CHECKING:
				from .configuration_albert import ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, AlbertConfig, AlbertOnnxConfig

				try:
								if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_albert import AlbertTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_albert_fast import AlbertTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_albert import (
								    ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    AlbertForMaskedLM,
								    AlbertForMultipleChoice,
								    AlbertForPreTraining,
								    AlbertForQuestionAnswering,
								    AlbertForSequenceClassification,
								    AlbertForTokenClassification,
								    AlbertModel,
								    AlbertPreTrainedModel,
								    load_tf_weights_in_albert,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_albert import (
								    TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFAlbertForMaskedLM,
								    TFAlbertForMultipleChoice,
								    TFAlbertForPreTraining,
								    TFAlbertForQuestionAnswering,
								    TFAlbertForSequenceClassification,
								    TFAlbertForTokenClassification,
								    TFAlbertMainLayer,
								    TFAlbertModel,
								    TFAlbertPreTrainedModel,
								)

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_albert import (
								    FlaxAlbertForMaskedLM,
								    FlaxAlbertForMultipleChoice,
								    FlaxAlbertForPreTraining,
								    FlaxAlbertForQuestionAnswering,
								    FlaxAlbertForSequenceClassification,
								    FlaxAlbertForTokenClassification,
								    FlaxAlbertModel,
								    FlaxAlbertPreTrainedModel,
								)
else:
				import sys

				lowercase_													=      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




