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,
)


__lowerCamelCase					:	List[Any]       =  {
    '''configuration_xlm_roberta''': [
        '''XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''XLMRobertaConfig''',
        '''XLMRobertaOnnxConfig''',
    ],
}

try:
		if not is_sentencepiece_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCamelCase					:	Dict       =  ['''XLMRobertaTokenizer''']

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCamelCase					:	Union[str, Any]       =  ['''XLMRobertaTokenizerFast''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCamelCase					:	List[str]       =  [
		    '''XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''XLMRobertaForCausalLM''',
		    '''XLMRobertaForMaskedLM''',
		    '''XLMRobertaForMultipleChoice''',
		    '''XLMRobertaForQuestionAnswering''',
		    '''XLMRobertaForSequenceClassification''',
		    '''XLMRobertaForTokenClassification''',
		    '''XLMRobertaModel''',
		    '''XLMRobertaPreTrainedModel''',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCamelCase					:	Any       =  [
		    '''TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''TFXLMRobertaForCausalLM''',
		    '''TFXLMRobertaForMaskedLM''',
		    '''TFXLMRobertaForMultipleChoice''',
		    '''TFXLMRobertaForQuestionAnswering''',
		    '''TFXLMRobertaForSequenceClassification''',
		    '''TFXLMRobertaForTokenClassification''',
		    '''TFXLMRobertaModel''',
		    '''TFXLMRobertaPreTrainedModel''',
		]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCamelCase					:	str       =  [
		    '''FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''FlaxXLMRobertaForMaskedLM''',
		    '''FlaxXLMRobertaForCausalLM''',
		    '''FlaxXLMRobertaForMultipleChoice''',
		    '''FlaxXLMRobertaForQuestionAnswering''',
		    '''FlaxXLMRobertaForSequenceClassification''',
		    '''FlaxXLMRobertaForTokenClassification''',
		    '''FlaxXLMRobertaModel''',
		    '''FlaxXLMRobertaPreTrainedModel''',
		]

if TYPE_CHECKING:
		from .configuration_xlm_roberta import (
		    XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    XLMRobertaConfig,
		    XLMRobertaOnnxConfig,
		)

		try:
				if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_xlm_roberta import XLMRobertaTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_xlm_roberta_fast import XLMRobertaTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_xlm_roberta import (
				    XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
				    XLMRobertaForCausalLM,
				    XLMRobertaForMaskedLM,
				    XLMRobertaForMultipleChoice,
				    XLMRobertaForQuestionAnswering,
				    XLMRobertaForSequenceClassification,
				    XLMRobertaForTokenClassification,
				    XLMRobertaModel,
				    XLMRobertaPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_xlm_roberta import (
				    TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFXLMRobertaForCausalLM,
				    TFXLMRobertaForMaskedLM,
				    TFXLMRobertaForMultipleChoice,
				    TFXLMRobertaForQuestionAnswering,
				    TFXLMRobertaForSequenceClassification,
				    TFXLMRobertaForTokenClassification,
				    TFXLMRobertaModel,
				    TFXLMRobertaPreTrainedModel,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_xlm_roberta import (
				    FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
				    FlaxXLMRobertaForCausalLM,
				    FlaxXLMRobertaForMaskedLM,
				    FlaxXLMRobertaForMultipleChoice,
				    FlaxXLMRobertaForQuestionAnswering,
				    FlaxXLMRobertaForSequenceClassification,
				    FlaxXLMRobertaForTokenClassification,
				    FlaxXLMRobertaModel,
				    FlaxXLMRobertaPreTrainedModel,
				)

else:
		import sys

		__lowerCamelCase					:	Tuple       =  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

from __future__ import annotations

from math import pi, sqrt




def       _snake_case	(   lowerCAmelCase							:   float   , lowerCAmelCase							:   float	):
		"""simple docstring"""







		if inductance <= 0:
				raise ValueError("Inductance cannot be 0 or negative"	)

		elif capacitance <= 0:
				raise ValueError("Capacitance cannot be 0 or negative"	)

		else:
				return (
				    "Resonant frequency",
				    float(1 / (2 * pi * (sqrt(inductance * capacitance	)))	),
				)


if __name__ == "__main__":
		import doctest

		doctest.testmod()
