
from collections import namedtuple

SCREAMING_SNAKE_CASE							:Optional[int]											=       namedtuple('from_to', 'from_ to')

SCREAMING_SNAKE_CASE							:int											=       {
    'millimeter': 'mm',
    'centimeter': 'cm',
    'meter': 'm',
    'kilometer': 'km',
    'inch': 'in',
    'inche': 'in',  # Trailing 's' has been stripped off
    'feet': 'ft',
    'foot': 'ft',
    'yard': 'yd',
    'mile': 'mi',
}

SCREAMING_SNAKE_CASE							:int											=       {
    'mm': from_to(0.001, 1000),
    'cm': from_to(0.01, 100),
    'm': from_to(1, 1),
    'km': from_to(1000, 0.001),
    'in': from_to(0.0254, 39.3701),
    'ft': from_to(0.3048, 3.28084),
    'yd': from_to(0.9144, 1.09361),
    'mi': from_to(1609.34, 0.000621371),
}


def 		UpperCAmelCase    (			a_	,     a_	,     a_		)	->			float:







    """simple docstring"""



    __A         =				from_type.lower().rstrip("s"		)
    __A         =				TYPE_CONVERSION.get(a_	,     a_		)
    __A         =				to_type.lower().rstrip("s"		)
    __A         =				TYPE_CONVERSION.get(a_	,     a_		)
    if new_from not in METRIC_CONVERSION:
        __A         =				(
            F'''Invalid \'from_type\' value: {from_type!r}.\n'''
            F'''Conversion abbreviations are: {', '.join(a_		)}'''
        )
        raise ValueError(a_		)
    if new_to not in METRIC_CONVERSION:
        __A         =				(
            F'''Invalid \'to_type\' value: {to_type!r}.\n'''
            F'''Conversion abbreviations are: {', '.join(a_		)}'''
        )
        raise ValueError(a_		)
    return value * METRIC_CONVERSION[new_from].from_ * METRIC_CONVERSION[new_to].to


if __name__ == "__main__":
     import doctest

     doctest.testmod()


from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE							:str											=       {
    'configuration_deberta_v2': ['DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DebertaV2Config', 'DebertaV2OnnxConfig'],
    'tokenization_deberta_v2': ['DebertaV2Tokenizer'],
}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE							:int											=       ['DebertaV2TokenizerFast']

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE							:Any											=       [
					    'TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'TFDebertaV2ForMaskedLM',
					    'TFDebertaV2ForQuestionAnswering',
					    'TFDebertaV2ForSequenceClassification',
					    'TFDebertaV2ForTokenClassification',
					    'TFDebertaV2Model',
					    'TFDebertaV2PreTrainedModel',
					]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE							:Any											=       [
					    'DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'DebertaV2ForMaskedLM',
					    'DebertaV2ForMultipleChoice',
					    'DebertaV2ForQuestionAnswering',
					    'DebertaV2ForSequenceClassification',
					    'DebertaV2ForTokenClassification',
					    'DebertaV2Model',
					    'DebertaV2PreTrainedModel',
					]


if TYPE_CHECKING:
					from .configuration_deberta_va import (
					    DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    DebertaVaConfig,
					    DebertaVaOnnxConfig,
					)
					from .tokenization_deberta_va import DebertaVaTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_deberta_va_fast import DebertaVaTokenizerFast

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_deberta_va import (
										    TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFDebertaVaForMaskedLM,
										    TFDebertaVaForQuestionAnswering,
										    TFDebertaVaForSequenceClassification,
										    TFDebertaVaForTokenClassification,
										    TFDebertaVaModel,
										    TFDebertaVaPreTrainedModel,
										)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_deberta_va import (
										    DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
										    DebertaVaForMaskedLM,
										    DebertaVaForMultipleChoice,
										    DebertaVaForQuestionAnswering,
										    DebertaVaForSequenceClassification,
										    DebertaVaForTokenClassification,
										    DebertaVaModel,
										    DebertaVaPreTrainedModel,
										)

else:
					import sys

					SCREAMING_SNAKE_CASE							:List[Any]											=       _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
