



"""simple docstring"""


from typing import TYPE_CHECKING

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


snake_case_								=			{
    """configuration_camembert""": ["""CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CamembertConfig""", """CamembertOnnxConfig"""],
}

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

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

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				snake_case_								=			[
				    """CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """CamembertForCausalLM""",
				    """CamembertForMaskedLM""",
				    """CamembertForMultipleChoice""",
				    """CamembertForQuestionAnswering""",
				    """CamembertForSequenceClassification""",
				    """CamembertForTokenClassification""",
				    """CamembertModel""",
				    """CamembertPreTrainedModel""",
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				snake_case_								=			[
				    """TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """TFCamembertForCausalLM""",
				    """TFCamembertForMaskedLM""",
				    """TFCamembertForMultipleChoice""",
				    """TFCamembertForQuestionAnswering""",
				    """TFCamembertForSequenceClassification""",
				    """TFCamembertForTokenClassification""",
				    """TFCamembertModel""",
				    """TFCamembertPreTrainedModel""",
				]


if TYPE_CHECKING:
				from .configuration_camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CamembertConfig, CamembertOnnxConfig

				try:
								if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_camembert import CamembertTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_camembert_fast import CamembertTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_camembert import (
								    CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    CamembertForCausalLM,
								    CamembertForMaskedLM,
								    CamembertForMultipleChoice,
								    CamembertForQuestionAnswering,
								    CamembertForSequenceClassification,
								    CamembertForTokenClassification,
								    CamembertModel,
								    CamembertPreTrainedModel,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_camembert import (
								    TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFCamembertForCausalLM,
								    TFCamembertForMaskedLM,
								    TFCamembertForMultipleChoice,
								    TFCamembertForQuestionAnswering,
								    TFCamembertForSequenceClassification,
								    TFCamembertForTokenClassification,
								    TFCamembertModel,
								    TFCamembertPreTrainedModel,
								)

else:
				import sys

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










'''simple docstring'''







_UpperCamelCase  						=			{
    "kilogram": 1,
    "gram": pow(10, 3),
    "milligram": pow(10, 6),
    "metric-ton": pow(10, -3),
    "long-ton": 0.0009842073,
    "short-ton": 0.0011023122,
    "pound": 2.2046244202,
    "stone": 0.1574731728,
    "ounce": 35.273990723,
    "carrat": 5000,
    "atomic-mass-unit": 6.0_2213_6652e26,
}

_UpperCamelCase  						=			{
    "kilogram": 1,
    "gram": pow(10, -3),
    "milligram": pow(10, -6),
    "metric-ton": pow(10, 3),
    "long-ton": 1016.04608,
    "short-ton": 907.184,
    "pound": 0.453592,
    "stone": 6.35029,
    "ounce": 0.0283495,
    "carrat": 0.0002,
    "atomic-mass-unit": 1.6_6054_0199e-27,
}
def 			_lowerCAmelCase(      UpperCAmelCase_      :	str					, UpperCAmelCase_      :	str					, UpperCAmelCase_      :	float						) ->							float:
							if to_type not in KILOGRAM_CHART or from_type not in WEIGHT_TYPE_CHART:
														lowerCAmelCase__  =   (
														    F'''Invalid \'from_type\' or \'to_type\' value: {from_type!r}, {to_type!r}\n'''
														    F'''Supported values are: {", ".join(UpperCAmelCase_						)}'''
														)
														raise ValueError(UpperCAmelCase_						)
							return value * KILOGRAM_CHART[to_type] * WEIGHT_TYPE_CHART[from_type]


if __name__ == "__main__":
		import doctest

		doctest.testmod()





