'''simple docstring'''







from typing import TYPE_CHECKING

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


_lowercase			:  str   							=	{
    "configuration_distilbert": [
        "DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "DistilBertConfig",
        "DistilBertOnnxConfig",
    ],
    "tokenization_distilbert": ["DistilBertTokenizer"],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_lowercase			:  Optional[Any]   							=	["DistilBertTokenizerFast"]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_lowercase			:  Dict   							=	[
							    "DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "DistilBertForMaskedLM",
							    "DistilBertForMultipleChoice",
							    "DistilBertForQuestionAnswering",
							    "DistilBertForSequenceClassification",
							    "DistilBertForTokenClassification",
							    "DistilBertModel",
							    "DistilBertPreTrainedModel",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_lowercase			:  List[Any]   							=	[
							    "TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "TFDistilBertForMaskedLM",
							    "TFDistilBertForMultipleChoice",
							    "TFDistilBertForQuestionAnswering",
							    "TFDistilBertForSequenceClassification",
							    "TFDistilBertForTokenClassification",
							    "TFDistilBertMainLayer",
							    "TFDistilBertModel",
							    "TFDistilBertPreTrainedModel",
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_lowercase			:  List[str]   							=	[
							    "FlaxDistilBertForMaskedLM",
							    "FlaxDistilBertForMultipleChoice",
							    "FlaxDistilBertForQuestionAnswering",
							    "FlaxDistilBertForSequenceClassification",
							    "FlaxDistilBertForTokenClassification",
							    "FlaxDistilBertModel",
							    "FlaxDistilBertPreTrainedModel",
							]


if TYPE_CHECKING:
							from .configuration_distilbert import (
							    DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    DistilBertConfig,
							    DistilBertOnnxConfig,
							)
							from .tokenization_distilbert import DistilBertTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_distilbert_fast import DistilBertTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_distilbert import (
														    DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    DistilBertForMaskedLM,
														    DistilBertForMultipleChoice,
														    DistilBertForQuestionAnswering,
														    DistilBertForSequenceClassification,
														    DistilBertForTokenClassification,
														    DistilBertModel,
														    DistilBertPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_distilbert import (
														    TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFDistilBertForMaskedLM,
														    TFDistilBertForMultipleChoice,
														    TFDistilBertForQuestionAnswering,
														    TFDistilBertForSequenceClassification,
														    TFDistilBertForTokenClassification,
														    TFDistilBertMainLayer,
														    TFDistilBertModel,
														    TFDistilBertPreTrainedModel,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_distilbert import (
														    FlaxDistilBertForMaskedLM,
														    FlaxDistilBertForMultipleChoice,
														    FlaxDistilBertForQuestionAnswering,
														    FlaxDistilBertForSequenceClassification,
														    FlaxDistilBertForTokenClassification,
														    FlaxDistilBertModel,
														    FlaxDistilBertPreTrainedModel,
														)

else:
							import sys

							_lowercase			:  Tuple   							=	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





'''simple docstring'''







def  snake_case_		(    __SCREAMING_SNAKE_CASE :			int				,				__SCREAMING_SNAKE_CASE :			float				,				__SCREAMING_SNAKE_CASE :			float							):



						"""simple docstring"""





						return round(float(moles / volume							) * nfactor							)
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			float				,				__SCREAMING_SNAKE_CASE :			float				,				__SCREAMING_SNAKE_CASE :			float							):



						"""simple docstring"""





						return round(float((moles * 0.0821 * temperature) / (volume)							)							)
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			float				,				__SCREAMING_SNAKE_CASE :			float				,				__SCREAMING_SNAKE_CASE :			float							):



						"""simple docstring"""





						return round(float((moles * 0.0821 * temperature) / (pressure)							)							)
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			float				,				__SCREAMING_SNAKE_CASE :			float				,				__SCREAMING_SNAKE_CASE :			float							):



						"""simple docstring"""





						return round(float((pressure * volume) / (0.0821 * moles)							)							)


if __name__ == "__main__":
							import doctest

							doctest.testmod()



